categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2405.07994 | null | null | http://arxiv.org/pdf/2405.07994v1 | 2024-03-20T05:17:43Z | 2024-03-20T05:17:43Z | BubbleID: A Deep Learning Framework for Bubble Interface Dynamics
Analysis | This paper presents BubbleID, a sophisticated deep learning architecture designed to comprehensively identify both static and dynamic attributes of bubbles within sequences of boiling images. By amalgamating segmentation powered by Mask R-CNN with SORT-based tracking techniques, the framework is capable of analyzing each bubble's location, dimensions, interface shape, and velocity over its lifetime, and capturing dynamic events such as bubble departure. BubbleID is trained and tested on boiling images across diverse heater surfaces and operational settings. This paper also offers a comparative analysis of bubble interface dynamics prior to and post-critical heat flux (CHF) conditions. | [
"['Christy Dunlap' 'Changgen Li' 'Hari Pandey' 'Ngan Le' 'Han Hu']"
]
|
null | null | 2405.08005 | null | null | http://arxiv.org/pdf/2405.08005v2 | 2024-06-05T02:51:23Z | 2024-05-08T04:44:16Z | Graphon Mean Field Games with a Representative Player: Analysis and
Learning Algorithm | We propose a discrete time graphon game formulation on continuous state and action spaces using a representative player to study stochastic games with heterogeneous interaction among agents. This formulation admits both philosophical and mathematical advantages, compared to a widely adopted formulation using a continuum of players. We prove the existence and uniqueness of the graphon equilibrium with mild assumptions, and show that this equilibrium can be used to construct an approximate solution for finite player game on networks, which is challenging to analyze and solve due to curse of dimensionality. An online oracle-free learning algorithm is developed to solve the equilibrium numerically, and sample complexity analysis is provided for its convergence. | [
"['Fuzhong Zhou' 'Chenyu Zhang' 'Xu Chen' 'Xuan Di']"
]
|
null | null | 2405.08011 | null | null | http://arxiv.org/abs/2405.08011v2 | 2024-06-24T10:25:19Z | 2024-05-10T18:05:37Z | A Survey of Large Language Models for Graphs | Graphs are an essential data structure utilized to represent relationships in real-world scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver impressive outcomes in graph-centric tasks, such as link prediction and node classification. Despite these advancements, challenges like data sparsity and limited generalization capabilities continue to persist. Recently, Large Language Models (LLMs) have gained attention in natural language processing. They excel in language comprehension and summarization. Integrating LLMs with graph learning techniques has attracted interest as a way to enhance performance in graph learning tasks. In this survey, we conduct an in-depth review of the latest state-of-the-art LLMs applied in graph learning and introduce a novel taxonomy to categorize existing methods based on their framework design. We detail four unique designs: i) GNNs as Prefix, ii) LLMs as Prefix, iii) LLMs-Graphs Integration, and iv) LLMs-Only, highlighting key methodologies within each category. We explore the strengths and limitations of each framework, and emphasize potential avenues for future research, including overcoming current integration challenges between LLMs and graph learning techniques, and venturing into new application areas. This survey aims to serve as a valuable resource for researchers and practitioners eager to leverage large language models in graph learning, and to inspire continued progress in this dynamic field. We consistently maintain the related open-source materials at url{https://github.com/HKUDS/Awesome-LLM4Graph-Papers}. | [
"['Xubin Ren' 'Jiabin Tang' 'Dawei Yin' 'Nitesh Chawla' 'Chao Huang']"
]
|
null | null | 2405.08013 | null | null | http://arxiv.org/pdf/2405.08013v1 | 2024-05-11T03:39:22Z | 2024-05-11T03:39:22Z | CTRL: Continuous-Time Representation Learning on Temporal Heterogeneous
Information Network | Inductive representation learning on temporal heterogeneous graphs is crucial for scalable deep learning on heterogeneous information networks (HINs) which are time-varying, such as citation networks. However, most existing approaches are not inductive and thus cannot handle new nodes or edges. Moreover, previous temporal graph embedding methods are often trained with the temporal link prediction task to simulate the link formation process of temporal graphs, while ignoring the evolution of high-order topological structures on temporal graphs. To fill these gaps, we propose a Continuous-Time Representation Learning (CTRL) model on temporal HINs. To preserve heterogeneous node features and temporal structures, CTRL integrates three parts in a single layer, they are 1) a emph{heterogeneous attention} unit that measures the semantic correlation between nodes, 2) a emph{edge-based Hawkes process} to capture temporal influence between heterogeneous nodes, and 3) emph{dynamic centrality} that indicates the dynamic importance of a node. We train the CTRL model with a future event (a subgraph) prediction task to capture the evolution of the high-order network structure. Extensive experiments have been conducted on three benchmark datasets. The results demonstrate that our model significantly boosts performance and outperforms various state-of-the-art approaches. Ablation studies are conducted to demonstrate the effectiveness of the model design. | [
"['Chenglin Li' 'Yuanzhen Xie' 'Chenyun Yu' 'Lei Cheng' 'Bo Hu' 'Zang Li'\n 'Di Niu']"
]
|
null | null | 2405.08015 | null | null | http://arxiv.org/pdf/2405.08015v1 | 2024-05-11T05:10:07Z | 2024-05-11T05:10:07Z | A Methodology-Oriented Study of Catastrophic Forgetting in Incremental
Deep Neural Networks | Human being and different species of animals having the skills to gather, transferring knowledge, processing, fine-tune and generating information throughout their lifetime. The ability of learning throughout their lifespan is referred as continuous learning which is using neurocognition mechanism. Consequently, in real world computational system of incremental learning autonomous agents also needs such continuous learning mechanism which provide retrieval of information and long-term memory consolidation. However, the main challenge in artificial intelligence is that the incremental learning of the autonomous agent when new data confronted. In such scenarios, the main concern is catastrophic forgetting(CF), i.e., while learning the sequentially, neural network underfits the old data when it confronted with new data. To tackle this CF problem many numerous studied have been proposed, however it is very difficult to compare their performance due to dissimilarity in their evaluation mechanism. Here we focus on the comparison of all algorithms which are having similar type of evaluation mechanism. Here we are comparing three types of incremental learning methods: (1) Exemplar based methods, (2) Memory based methods, and (3) Network based method. In this survey paper, methodology oriented study for catastrophic forgetting in incremental deep neural network is addressed. Furthermore, it contains the mathematical overview of impact-full methods which can be help researchers to deal with CF. | [
"['Ashutosh Kumar' 'Sonali Agarwal' 'D Jude Hemanth']"
]
|
null | null | 2405.08017 | null | null | http://arxiv.org/pdf/2405.08017v1 | 2024-05-11T13:23:43Z | 2024-05-11T13:23:43Z | Translating Expert Intuition into Quantifiable Features: Encode
Investigator Domain Knowledge via LLM for Enhanced Predictive Analytics | In the realm of predictive analytics, the nuanced domain knowledge of investigators often remains underutilized, confined largely to subjective interpretations and ad hoc decision-making. This paper explores the potential of Large Language Models (LLMs) to bridge this gap by systematically converting investigator-derived insights into quantifiable, actionable features that enhance model performance. We present a framework that leverages LLMs' natural language understanding capabilities to encode these red flags into a structured feature set that can be readily integrated into existing predictive models. Through a series of case studies, we demonstrate how this approach not only preserves the critical human expertise within the investigative process but also scales the impact of this knowledge across various prediction tasks. The results indicate significant improvements in risk assessment and decision-making accuracy, highlighting the value of blending human experiential knowledge with advanced machine learning techniques. This study paves the way for more sophisticated, knowledge-driven analytics in fields where expert insight is paramount. | [
"['Phoebe Jing' 'Yijing Gao' 'Yuanhang Zhang' 'Xianlong Zeng']"
]
|
null | null | 2405.08019 | null | null | http://arxiv.org/pdf/2405.08019v1 | 2024-05-11T15:06:24Z | 2024-05-11T15:06:24Z | AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss
Weighting | Knowledge distillation, a widely used model compression technique, works on the basis of transferring knowledge from a cumbersome teacher model to a lightweight student model. The technique involves jointly optimizing the task specific and knowledge distillation losses with a weight assigned to them. Despite these weights playing a crucial role in the performance of the distillation process, current methods provide equal weight to both losses, leading to suboptimal performance. In this paper, we propose Adaptive Knowledge Distillation, a novel technique inspired by curriculum learning to adaptively weigh the losses at instance level. This technique goes by the notion that sample difficulty increases with teacher loss. Our method follows a plug-and-play paradigm that can be applied on top of any task-specific and distillation objectives. Experiments show that our method performs better than conventional knowledge distillation method and existing instance-level loss functions. | [
"['Shreyan Ganguly' 'Roshan Nayak' 'Rakshith Rao' 'Ujan Deb' 'Prathosh AP']"
]
|
null | null | 2405.08020 | null | null | http://arxiv.org/pdf/2405.08020v1 | 2024-05-11T16:38:50Z | 2024-05-11T16:38:50Z | ReActXGB: A Hybrid Binary Convolutional Neural Network Architecture for
Improved Performance and Computational Efficiency | Binary convolutional neural networks (BCNNs) provide a potential solution to reduce the memory requirements and computational costs associated with deep neural networks (DNNs). However, achieving a trade-off between performance and computational resources remains a significant challenge. Furthermore, the fully connected layer of BCNNs has evolved into a significant computational bottleneck. This is mainly due to the conventional practice of excluding the input layer and fully connected layer from binarization to prevent a substantial loss in accuracy. In this paper, we propose a hybrid model named ReActXGB, where we replace the fully convolutional layer of ReActNet-A with XGBoost. This modification targets to narrow the performance gap between BCNNs and real-valued networks while maintaining lower computational costs. Experimental results on the FashionMNIST benchmark demonstrate that ReActXGB outperforms ReActNet-A by 1.47% in top-1 accuracy, along with a reduction of 7.14% in floating-point operations (FLOPs) and 1.02% in model size. | [
"['Po-Hsun Chu' 'Ching-Han Chen']"
]
|
null | null | 2405.08026 | null | null | http://arxiv.org/pdf/2405.08026v1 | 2024-05-12T11:42:05Z | 2024-05-12T11:42:05Z | ExplainableDetector: Exploring Transformer-based Language Modeling
Approach for SMS Spam Detection with Explainability Analysis | SMS, or short messaging service, is a widely used and cost-effective communication medium that has sadly turned into a haven for unwanted messages, commonly known as SMS spam. With the rapid adoption of smartphones and Internet connectivity, SMS spam has emerged as a prevalent threat. Spammers have taken notice of the significance of SMS for mobile phone users. Consequently, with the emergence of new cybersecurity threats, the number of SMS spam has expanded significantly in recent years. The unstructured format of SMS data creates significant challenges for SMS spam detection, making it more difficult to successfully fight spam attacks in the cybersecurity domain. In this work, we employ optimized and fine-tuned transformer-based Large Language Models (LLMs) to solve the problem of spam message detection. We use a benchmark SMS spam dataset for this spam detection and utilize several preprocessing techniques to get clean and noise-free data and solve the class imbalance problem using the text augmentation technique. The overall experiment showed that our optimized fine-tuned BERT (Bidirectional Encoder Representations from Transformers) variant model RoBERTa obtained high accuracy with 99.84%. We also work with Explainable Artificial Intelligence (XAI) techniques to calculate the positive and negative coefficient scores which explore and explain the fine-tuned model transparency in this text-based spam SMS detection task. In addition, traditional Machine Learning (ML) models were also examined to compare their performance with the transformer-based models. This analysis describes how LLMs can make a good impact on complex textual-based spam data in the cybersecurity field. | [
"['Mohammad Amaz Uddin' 'Muhammad Nazrul Islam' 'Leandros Maglaras'\n 'Helge Janicke' 'Iqbal H. Sarker']"
]
|
null | null | 2405.08027 | null | null | http://arxiv.org/pdf/2405.08027v1 | 2024-05-12T13:36:58Z | 2024-05-12T13:36:58Z | Automating Data Annotation under Strategic Human Agents: Risks and
Potential Solutions | As machine learning (ML) models are increasingly used in social domains to make consequential decisions about humans, they often have the power to reshape data distributions. Humans, as strategic agents, continuously adapt their behaviors in response to the learning system. As populations change dynamically, ML systems may need frequent updates to ensure high performance. However, acquiring high-quality human-annotated samples can be highly challenging and even infeasible in social domains. A common practice to address this issue is using the model itself to annotate unlabeled data samples. This paper investigates the long-term impacts when ML models are retrained with model-annotated samples when they incorporate human strategic responses. We first formalize the interactions between strategic agents and the model and then analyze how they evolve under such dynamic interactions. We find that agents are increasingly likely to receive positive decisions as the model gets retrained, whereas the proportion of agents with positive labels may decrease over time. We thus propose a refined retraining process to stabilize the dynamics. Last, we examine how algorithmic fairness can be affected by these retraining processes and find that enforcing common fairness constraints at every round may not benefit the disadvantaged group in the long run. Experiments on (semi-)synthetic and real data validate the theoretical findings. | [
"['Tian Xie' 'Xueru Zhang']"
]
|
null | null | 2405.08029 | null | null | http://arxiv.org/pdf/2405.08029v2 | 2024-05-15T05:46:01Z | 2024-05-12T18:22:16Z | PHUDGE: Phi-3 as Scalable Judge | In this paper cum technical report, we present PHUDGE A fine tuned Phi3 model that achieved SOTA results in 4 tasks as Feedback Test, Feedback OOD, MT Human, Preference Test surpassing each and every existing model in latency and throughput. It shows very strong correlation not only with GPT4 but with Human annotators too in unseen data as well as in both absolute and relative grading tasks. We have not only addressed the usage of small LMs for cost effective production grade systems but have also shown that Causal modelling is not only slow in nature but sometimes it can hinder models learning capabilities and should be replaced by simpler tasks whenever we can to make the overall system faster and better. We show that by following systematic ML experimentation, thoughtful data augmentation and re purposing the problem itself, we can even beat 10x bigger models even with lesser training data. To the best of our knowledge, we are re the first one to experiment and showcase the usage of generalised version of Earth Movers Distance AKA Wasserstein distance by using Minkowski Distance with a penalty to control loss smoothing and can be used as a loss function instead of Cross Entropy to get stable training and better results for grading tasks. | [
"['Mahesh Deshwal' 'Apoorva Chawla']"
]
|
null | null | 2405.08031 | null | null | http://arxiv.org/pdf/2405.08031v2 | 2024-05-18T08:53:37Z | 2024-05-12T21:34:03Z | HGTDR: Advancing Drug Repurposing with Heterogeneous Graph Transformers | Motivation: Drug repurposing is a viable solution for reducing the time and cost associated with drug development. However, thus far, the proposed drug repurposing approaches still need to meet expectations. Therefore, it is crucial to offer a systematic approach for drug repurposing to achieve cost savings and enhance human lives. In recent years, using biological network-based methods for drug repurposing has generated promising results. Nevertheless, these methods have limitations. Primarily, the scope of these methods is generally limited concerning the size and variety of data they can effectively handle. Another issue arises from the treatment of heterogeneous data, which needs to be addressed or converted into homogeneous data, leading to a loss of information. A significant drawback is that most of these approaches lack end-to-end functionality, necessitating manual implementation and expert knowledge in certain stages. Results: We propose a new solution, HGTDR (Heterogeneous Graph Transformer for Drug Repurposing), to address the challenges associated with drug repurposing. HGTDR is a three-step approach for knowledge graph-based drug re-purposing: 1) constructing a heterogeneous knowledge graph, 2) utilizing a heterogeneous graph transformer network, and 3) computing relationship scores using a fully connected network. By leveraging HGTDR, users gain the ability to manipulate input graphs, extract information from diverse entities, and obtain their desired output. In the evaluation step, we demonstrate that HGTDR performs comparably to previous methods. Furthermore, we review medical studies to validate our method's top ten drug repurposing suggestions, which have exhibited promising results. We also demon-strated HGTDR's capability to predict other types of relations through numerical and experimental validation, such as drug-protein and disease-protein inter-relations. | [
"['Ali Gharizadeh' 'Karim Abbasi' 'Amin Ghareyazi' 'Mohammad R. K. Mofrad'\n 'Hamid R. Rabiee']"
]
|
null | null | 2405.08033 | null | null | http://arxiv.org/pdf/2405.08033v1 | 2024-05-13T01:04:27Z | 2024-05-13T01:04:27Z | Predicting Ship Responses in Different Seaways using a Generalizable
Force Correcting Machine Learning Method | A machine learning (ML) method is generalizable if it can make predictions on inputs which differ from the training dataset. For predictions of wave-induced ship responses, generalizability is an important consideration if ML methods are to be useful in design evaluations. Furthermore, the size of the training dataset has a significant impact on the practicality of a method, especially when training data is generated using high-fidelity numerical tools which are expensive. This paper considers a hybrid machine learning method which corrects the force in a low-fidelity equation of motion. The method is applied to two different case studies: the nonlinear responses of a Duffing equation subject to irregular excitation, and high-fidelity heave and pitch response data of a Fast Displacement Ship (FDS) in head seas. The generalizability of the method is determined in both cases by making predictions of the response in irregular wave conditions that differ from those in the training dataset. The influence that low-fidelity physics-based terms in the hybrid model have on generalizability is also investigated. The predictions are compared to two benchmarks: a linear physics-based model and a data-driven LSTM model. It is found that the hybrid method offers an improvement in prediction accuracy and generalizability when trained on a small dataset. | [
"['Kyle E. Marlantes' 'Piotr J. Bandyk' 'Kevin J. Maki']"
]
|
null | null | 2405.08034 | null | null | http://arxiv.org/pdf/2405.08034v1 | 2024-05-13T02:47:57Z | 2024-05-13T02:47:57Z | Fighter flight trajectory prediction based on spatio-temporal graphcial
attention network | Quickly and accurately predicting the flight trajectory of a blue army fighter in close-range air combat helps a red army fighter gain a dominant situation, which is the winning factor in later air combat. However,due to the high speed and even hypersonic capabilities of advanced fighters, the diversity of tactical maneuvers,and the instantaneous nature of situational transitions,it is difficult to meet the requirements of practical combat applications in terms of prediction accuracy.To improve prediction accuracy,this paper proposes a spatio-temporal graph attention network (ST-GAT) using encoding and decoding structures to predict the flight trajectory. The encoder adopts a parallel structure of Transformer and GAT branches embedded with the multi-head self-attention mechanism in each front end. The Transformer branch network is used to extract the temporal characteristics of historical trajectories and capture the impact of the fighter's historical state on future trajectories, while the GAT branch network is used to extract spatial features in historical trajectories and capture potential spatial correlations between fighters.Then we concatenate the outputs of the two branches into a new feature vector and input it into a decoder composed of a fully connected network to predict the future position coordinates of the blue army fighter.The computer simulation results show that the proposed network significantly improves the prediction accuracy of flight trajectories compared to the enhanced CNN-LSTM network (ECNN-LSTM), with improvements of 47% and 34% in both ADE and FDE indicators,providing strong support for subsequent autonomous combat missions. | [
"['Yao Sun' 'Tengyu Jing' 'Jiapeng Wang' 'Wei Wang']"
]
|
null | null | 2405.08036 | null | null | http://arxiv.org/pdf/2405.08036v2 | 2024-05-15T05:53:23Z | 2024-05-13T03:27:35Z | POWQMIX: Weighted Value Factorization with Potentially Optimal Joint
Actions Recognition for Cooperative Multi-Agent Reinforcement Learning | Value function factorization methods are commonly used in cooperative multi-agent reinforcement learning, with QMIX receiving significant attention. Many QMIX-based methods introduce monotonicity constraints between the joint action value and individual action values to achieve decentralized execution. However, such constraints limit the representation capacity of value factorization, restricting the joint action values it can represent and hindering the learning of the optimal policy. To address this challenge, we propose the Potentially Optimal joint actions Weighted QMIX (POWQMIX) algorithm, which recognizes the potentially optimal joint actions and assigns higher weights to the corresponding losses of these joint actions during training. We theoretically prove that with such a weighted training approach the optimal policy is guaranteed to be recovered. Experiments in matrix games, predator-prey, and StarCraft II Multi-Agent Challenge environments demonstrate that our algorithm outperforms the state-of-the-art value-based multi-agent reinforcement learning methods. | [
"['Chang Huang' 'Junqiao Zhao' 'Shatong Zhu' 'Hongtu Zhou' 'Chen Ye'\n 'Tiantian Feng' 'Changjun Jiang']"
]
|
null | null | 2405.08038 | null | null | http://arxiv.org/pdf/2405.08038v1 | 2024-05-13T06:57:18Z | 2024-05-13T06:57:18Z | Feature Expansion and enhanced Compression for Class Incremental
Learning | Class incremental learning consists in training discriminative models to classify an increasing number of classes over time. However, doing so using only the newly added class data leads to the known problem of catastrophic forgetting of the previous classes. Recently, dynamic deep learning architectures have been shown to exhibit a better stability-plasticity trade-off by dynamically adding new feature extractors to the model in order to learn new classes followed by a compression step to scale the model back to its original size, thus avoiding a growing number of parameters. In this context, we propose a new algorithm that enhances the compression of previous class knowledge by cutting and mixing patches of previous class samples with the new images during compression using our Rehearsal-CutMix method. We show that this new data augmentation reduces catastrophic forgetting by specifically targeting past class information and improving its compression. Extensive experiments performed on the CIFAR and ImageNet datasets under diverse incremental learning evaluation protocols demonstrate that our approach consistently outperforms the state-of-the-art . The code will be made available upon publication of our work. | [
"['Quentin Ferdinand' 'Gilles Le Chenadec' 'Benoit Clement'\n 'Panagiotis Papadakis' 'Quentin Oliveau']"
]
|
null | null | 2405.08041 | null | null | http://arxiv.org/pdf/2405.08041v1 | 2024-05-13T09:41:34Z | 2024-05-13T09:41:34Z | DeepFMEA -- A Scalable Framework Harmonizing Process Expertise and
Data-Driven PHM | Machine Learning (ML) based prognostics and health monitoring (PHM) tools provide new opportunities for manufacturers to operate and maintain their equipment in a risk-optimized manner and utilize it more sustainably along its lifecycle. Yet, in most industrial settings, data is often limited in quantity, and its quality can be inconsistent - both critical for developing and operating reliable ML models. To bridge this gap in practice, successfully industrialized PHM tools rely on the introduction of domain expertise as a prior, to enable sufficiently accurate predictions, while enhancing their interpretability. Thus, a key challenge while developing data-driven PHM tools involves translating the experience and process knowledge of maintenance personnel, development, and service engineers into a data structure. This structure must not only capture the diversity and variability of the expertise but also render this knowledge accessible for various data-driven algorithms. This results in data models that are heavily tailored towards a specific application and the failure modes the development team aims to detect or predict. The lack of a standardized approach limits developments' extensibility to new failure modes, their transferability to new applications, and it inhibits the utilization of standard data management and MLOps tools, increasing the burden on the development team. DeepFMEA draws inspiration from the Failure Mode and Effects Analysis (FMEA) in its structured approach to the analysis of any technical system and the resulting standardized data model, while considering aspects that are crucial to capturing process and maintenance expertise in a way that is both intuitive to domain experts and the resulting information can be introduced as priors to ML algorithms. | [
"['Christoph Netsch' 'Till Schöpe' 'Benedikt Schindele' 'Joyam Jayakumar']"
]
|
null | null | 2405.08042 | null | null | http://arxiv.org/pdf/2405.08042v1 | 2024-05-13T12:40:18Z | 2024-05-13T12:40:18Z | LLAniMAtion: LLAMA Driven Gesture Animation | Co-speech gesturing is an important modality in conversation, providing context and social cues. In character animation, appropriate and synchronised gestures add realism, and can make interactive agents more engaging. Historically, methods for automatically generating gestures were predominantly audio-driven, exploiting the prosodic and speech-related content that is encoded in the audio signal. In this paper we instead experiment with using LLM features for gesture generation that are extracted from text using LLAMA2. We compare against audio features, and explore combining the two modalities in both objective tests and a user study. Surprisingly, our results show that LLAMA2 features on their own perform significantly better than audio features and that including both modalities yields no significant difference to using LLAMA2 features in isolation. We demonstrate that the LLAMA2 based model can generate both beat and semantic gestures without any audio input, suggesting LLMs can provide rich encodings that are well suited for gesture generation. | [
"['Jonathan Windle' 'Iain Matthews' 'Sarah Taylor']"
]
|
null | null | 2405.08043 | null | null | http://arxiv.org/pdf/2405.08043v1 | 2024-05-13T12:56:24Z | 2024-05-13T12:56:24Z | HRNet: Differentially Private Hierarchical and Multi-Resolution Network
for Human Mobility Data Synthesization | Human mobility data offers valuable insights for many applications such as urban planning and pandemic response, but its use also raises privacy concerns. In this paper, we introduce the Hierarchical and Multi-Resolution Network (HRNet), a novel deep generative model specifically designed to synthesize realistic human mobility data while guaranteeing differential privacy. We first identify the key difficulties inherent in learning human mobility data under differential privacy. In response to these challenges, HRNet integrates three components: a hierarchical location encoding mechanism, multi-task learning across multiple resolutions, and private pre-training. These elements collectively enhance the model's ability under the constraints of differential privacy. Through extensive comparative experiments utilizing a real-world dataset, HRNet demonstrates a marked improvement over existing methods in balancing the utility-privacy trade-off. | [
"['Shun Takagi' 'Li Xiong' 'Fumiyuki Kato' 'Yang Cao' 'Masatoshi Yoshikawa']"
]
|
null | null | 2405.08044 | null | null | http://arxiv.org/pdf/2405.08044v1 | 2024-05-13T13:55:34Z | 2024-05-13T13:55:34Z | Mitigating federated learning contribution allocation instability
through randomized aggregation | Federated learning (FL) is a novel collaborative machine learning framework designed to preserve privacy while enabling the creation of robust models. This paradigm addresses a growing need for data security by allowing multiple participants to contribute to a model without exposing their individual datasets. A pivotal issue within this framework, however, concerns the fair and accurate attribution of contributions from various participants to the creation of the joint global model. Incorrect contribution distribution can erode trust among participants, result in inequitable compensation, and ultimately diminish the willingness of parties to engage or actively contribute to the federation. While several methods for remunerating participants have been proposed, little attention was given to the analysis of the stability of these methods when evaluating contributions, which is critical to ensure the long-term viability and fairness of FL systems. In this paper, we analyse this stability through the calculation of contributions by gradient-based model reconstruction techniques with Shapley values. Our investigation reveals that Shapley values fail to reflect baseline contributions, especially when employing different aggregation techniques. To address this issue, we extend on established aggregation techniques by introducing FedRandom, which is designed to sample contributions in a more equitable and distributed manner. We demonstrate that this approach not only serves as a viable aggregation technique but also significantly improves the accuracy of contribution assessment compared to traditional methods. Our results suggest that FedRandom enhances the overall fairness and stability of the federated learning system, making it a superior choice for federations with limited number of participants. | [
"['Arno Geimer' 'Beltran Fiz' 'Radu State']"
]
|
null | null | 2405.08045 | null | null | http://arxiv.org/pdf/2405.08045v1 | 2024-05-13T14:51:02Z | 2024-05-13T14:51:02Z | Comparative analysis of neural network architectures for short-term
FOREX forecasting | The present document delineates the analysis, design, implementation, and benchmarking of various neural network architectures within a short-term frequency prediction system for the foreign exchange market (FOREX). Our aim is to simulate the judgment of the human expert (technical analyst) using a system that responds promptly to changes in market conditions, thus enabling the optimization of short-term trading strategies. We designed and implemented a series of LSTM neural network architectures which are taken as input the exchange rate values and generate the short-term market trend forecasting signal and an ANN custom architecture based on technical analysis indicator simulators We performed a comparative analysis of the results and came to useful conclusions regarding the suitability of each architecture and the cost in terms of time and computational power to implement them. The ANN custom architecture produces better prediction quality with higher sensitivity using fewer resources and spending less time than LSTM architectures. The ANN custom architecture appears to be ideal for use in low-power computing systems and for use cases that need fast decisions with the least possible computational cost. | [
"['Theodoros Zafeiriou' 'Dimitris Kalles']"
]
|
null | null | 2405.08047 | null | null | http://arxiv.org/pdf/2405.08047v1 | 2024-05-13T15:16:22Z | 2024-05-13T15:16:22Z | Autonomous Sparse Mean-CVaR Portfolio Optimization | The $ell_0$-constrained mean-CVaR model poses a significant challenge due to its NP-hard nature, typically tackled through combinatorial methods characterized by high computational demands. From a markedly different perspective, we propose an innovative autonomous sparse mean-CVaR portfolio model, capable of approximating the original $ell_0$-constrained mean-CVaR model with arbitrary accuracy. The core idea is to convert the $ell_0$ constraint into an indicator function and subsequently handle it through a tailed approximation. We then propose a proximal alternating linearized minimization algorithm, coupled with a nested fixed-point proximity algorithm (both convergent), to iteratively solve the model. Autonomy in sparsity refers to retaining a significant portion of assets within the selected asset pool during adjustments in pool size. Consequently, our framework offers a theoretically guaranteed approximation of the $ell_0$-constrained mean-CVaR model, improving computational efficiency while providing a robust asset selection scheme. | [
"['Yizun Lin' 'Yangyu Zhang' 'Zhao-Rong Lai' 'Cheng Li']"
]
|
null | null | 2405.08053 | null | null | http://arxiv.org/pdf/2405.08053v1 | 2024-05-13T16:48:16Z | 2024-05-13T16:48:16Z | Radio Resource Management and Path Planning in Intelligent
Transportation Systems via Reinforcement Learning for Environmental
Sustainability | Efficient and dynamic path planning has become an important topic for urban areas with larger density of connected vehicles (CV) which results in reduction of travel time and directly contributes to environmental sustainability through reducing energy consumption. CVs exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the vehicle-to-infrastructure (V2I) messages to the Base-station (BS) to improve situation awareness on urban roads. In this paper, we investigate radio resource management (RRM) in such a framework to minimize the age of information (AoI) so as to enhance path planning results. We use the fact that V2I messages with lower AoI value result in less error in estimating the road capacity and more accurate path planning. Through simulations, we compare road travel times and volume over capacity (V/C) against different levels of AoI and demonstrate the promising performance of the proposed framework. | [
"['S. Norouzi' 'N. Azarasa' 'M. R. Abedi' 'N. Mokari'\n 'S. E. Seyedabrishami' 'H. Saeedi' 'E. A. Jorswieck']"
]
|
null | null | 2405.08089 | null | null | http://arxiv.org/pdf/2405.08089v1 | 2024-05-13T18:10:34Z | 2024-05-13T18:10:34Z | Comparative Study of Bitcoin Price Prediction | Prediction of stock prices has been a crucial and challenging task, especially in the case of highly volatile digital currencies such as Bitcoin. This research examineS the potential of using neural network models, namely LSTMs and GRUs, to forecast Bitcoin's price movements. We employ five-fold cross-validation to enhance generalization and utilize L2 regularization to reduce overfitting and noise. Our study demonstrates that the GRUs models offer better accuracy than LSTMs model for predicting Bitcoin's price. Specifically, the GRU model has an MSE of 4.67, while the LSTM model has an MSE of 6.25 when compared to the actual prices in the test set data. This finding indicates that GRU models are better equipped to process sequential data with long-term dependencies, a characteristic of financial time series data such as Bitcoin prices. In summary, our results provide valuable insights into the potential of neural network models for accurate Bitcoin price prediction and emphasize the importance of employing appropriate regularization techniques to enhance model performance. | [
"['Ali Mohammadjafari']"
]
|
null | null | 2405.08097 | null | null | http://arxiv.org/pdf/2405.08097v2 | 2024-05-15T13:48:54Z | 2024-05-13T18:24:03Z | Learning functions on symmetric matrices and point clouds via
lightweight invariant features | In this work, we present a mathematical formulation for machine learning of (1) functions on symmetric matrices that are invariant with respect to the action of permutations by conjugation, and (2) functions on point clouds that are invariant with respect to rotations, reflections, and permutations of the points. To achieve this, we construct $O(n^2)$ invariant features derived from generators for the field of rational functions on $ntimes n$ symmetric matrices that are invariant under joint permutations of rows and columns. We show that these invariant features can separate all distinct orbits of symmetric matrices except for a measure zero set; such features can be used to universally approximate invariant functions on almost all weighted graphs. For point clouds in a fixed dimension, we prove that the number of invariant features can be reduced, generically without losing expressivity, to $O(n)$, where $n$ is the number of points. We combine these invariant features with DeepSets to learn functions on symmetric matrices and point clouds with varying sizes. We empirically demonstrate the feasibility of our approach on molecule property regression and point cloud distance prediction. | [
"['Ben Blum-Smith' 'Ningyuan Huang' 'Marco Cuturi' 'Soledad Villar']"
]
|
null | null | 2405.08100 | null | null | http://arxiv.org/pdf/2405.08100v1 | 2024-05-13T18:26:55Z | 2024-05-13T18:26:55Z | Graph Neural Networks for Parameterized Quantum Circuits Expressibility
Estimation | Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs is a measure that determines their capability to harness the full potential of the quantum state space. It is thus a crucial guidepost to know when selecting a particular PQC ansatz. However, the existing technique for expressibility computation through statistical estimation requires a large number of samples, which poses significant challenges due to time and computational resource constraints. This paper introduces a novel approach for expressibility estimation of PQCs using Graph Neural Networks (GNNs). We demonstrate the predictive power of our GNN model with a dataset consisting of 25,000 samples from the noiseless IBM QASM Simulator and 12,000 samples from three distinct noisy quantum backends. The model accurately estimates expressibility, with root mean square errors (RMSE) of 0.05 and 0.06 for the noiseless and noisy backends, respectively. We compare our model's predictions with reference circuits [Sim and others, QuTe'2019] and IBM Qiskit's hardware-efficient ansatz sets to further evaluate our model's performance. Our experimental evaluation in noiseless and noisy scenarios reveals a close alignment with ground truth expressibility values, highlighting the model's efficacy. Moreover, our model exhibits promising extrapolation capabilities, predicting expressibility values with low RMSE for out-of-range qubit circuits trained solely on only up to 5-qubit circuit sets. This work thus provides a reliable means of efficiently evaluating the expressibility of diverse PQCs on noiseless simulators and hardware. | [
"['Shamminuj Aktar' 'Andreas Bärtschi' 'Diane Oyen' 'Stephan Eidenbenz'\n 'Abdel-Hameed A. Badawy']"
]
|
null | null | 2405.08101 | null | null | http://arxiv.org/pdf/2405.08101v1 | 2024-05-13T18:28:39Z | 2024-05-13T18:28:39Z | Can machine learning unlock new insights into high-frequency trading? | We design and train machine learning models to capture the nonlinear interactions between financial market dynamics and high-frequency trading (HFT) activity. In doing so, we introduce new metrics to identify liquidity-demanding and -supplying HFT strategies. Both types of HFT strategies increase activity in response to information events and decrease it when trading speed is restricted, with liquidity-supplying strategies demonstrating greater responsiveness. Liquidity-demanding HFT is positively linked with latency arbitrage opportunities, whereas liquidity-supplying HFT is negatively related, aligning with theoretical expectations. Our metrics have implications for understanding the information production process in financial markets. | [
"['G. Ibikunle' 'B. Moews' 'K. Rzayev']"
]
|
null | null | 2405.08111 | null | null | http://arxiv.org/pdf/2405.08111v1 | 2024-05-13T18:45:25Z | 2024-05-13T18:45:25Z | Conformalized Physics-Informed Neural Networks | Physics-informed neural networks (PINNs) are an influential method of solving differential equations and estimating their parameters given data. However, since they make use of neural networks, they provide only a point estimate of differential equation parameters, as well as the solution at any given point, without any measure of uncertainty. Ensemble and Bayesian methods have been previously applied to quantify the uncertainty of PINNs, but these methods may require making strong assumptions on the data-generating process, and can be computationally expensive. Here, we introduce Conformalized PINNs (C-PINNs) that, without making any additional assumptions, utilize the framework of conformal prediction to quantify the uncertainty of PINNs by providing intervals that have finite-sample, distribution-free statistical validity. | [
"['Lena Podina' 'Mahdi Torabi Rad' 'Mohammad Kohandel']"
]
|
null | null | 2405.08125 | null | null | http://arxiv.org/pdf/2405.08125v2 | 2024-05-16T12:10:58Z | 2024-05-13T19:09:50Z | AI-Cybersecurity Education Through Designing AI-based Cyberharassment
Detection Lab | Cyberharassment is a critical, socially relevant cybersecurity problem because of the adverse effects it can have on targeted groups or individuals. While progress has been made in understanding cyber-harassment, its detection, attacks on artificial intelligence (AI) based cyberharassment systems, and the social problems in cyberharassment detectors, little has been done in designing experiential learning educational materials that engage students in this emerging social cybersecurity in the era of AI. Experiential learning opportunities are usually provided through capstone projects and engineering design courses in STEM programs such as computer science. While capstone projects are an excellent example of experiential learning, given the interdisciplinary nature of this emerging social cybersecurity problem, it can be challenging to use them to engage non-computing students without prior knowledge of AI. Because of this, we were motivated to develop a hands-on lab platform that provided experiential learning experiences to non-computing students with little or no background knowledge in AI and discussed the lessons learned in developing this lab. In this lab used by social science students at North Carolina A&T State University across two semesters (spring and fall) in 2022, students are given a detailed lab manual and are to complete a set of well-detailed tasks. Through this process, students learn AI concepts and the application of AI for cyberharassment detection. Using pre- and post-surveys, we asked students to rate their knowledge or skills in AI and their understanding of the concepts learned. The results revealed that the students moderately understood the concepts of AI and cyberharassment. | [
"['Ebuka Okpala' 'Nishant Vishwamitra' 'Keyan Guo' 'Song Liao' 'Long Cheng'\n 'Hongxin Hu' 'Yongkai Wu' 'Xiaohong Yuan' 'Jeannette Wade'\n 'Sajad Khorsandroo']"
]
|
null | null | 2405.08137 | null | null | http://arxiv.org/pdf/2405.08137v1 | 2024-05-13T19:31:00Z | 2024-05-13T19:31:00Z | LATTE: an atomic environment descriptor based on Cartesian tensor
contractions | We propose a new descriptor for local atomic environments, to be used in combination with machine learning models for the construction of interatomic potentials. The Local Atomic Tensors Trainable Expansion (LATTE) allows for the efficient construction of a variable number of many-body terms with learnable parameters, resulting in a descriptor that is efficient, expressive, and can be scaled to suit different accuracy and computational cost requirements. We compare this new descriptor to existing ones on several systems, showing it to be competitive with very fast potentials at one end of the spectrum, and extensible to an accuracy close to the state of the art. | [
"['Franco Pellegrini' 'Stefano de Gironcoli' 'Emine Küçükbenli']"
]
|
null | null | 2405.08174 | null | null | http://arxiv.org/pdf/2405.08174v1 | 2024-05-13T20:39:27Z | 2024-05-13T20:39:27Z | Estimating Direct and Indirect Causal Effects of Spatiotemporal
Interventions in Presence of Spatial Interference | Spatial interference (SI) occurs when the treatment at one location affects the outcomes at other locations. Accounting for spatial interference in spatiotemporal settings poses further challenges as interference violates the stable unit treatment value assumption, making it infeasible for standard causal inference methods to quantify the effects of time-varying treatment at spatially varying outcomes. In this paper, we first formalize the concept of spatial interference in case of time-varying treatment assignments by extending the potential outcome framework under the assumption of no unmeasured confounding. We then propose our deep learning based potential outcome model for spatiotemporal causal inference. We utilize latent factor modeling to reduce the bias due to time-varying confounding while leveraging the power of U-Net architecture to capture global and local spatial interference in data over time. Our causal estimators are an extension of average treatment effect (ATE) for estimating direct (DATE) and indirect effects (IATE) of spatial interference on treated and untreated data. Being the first of its kind deep learning based spatiotemporal causal inference technique, our approach shows advantages over several baseline methods based on the experiment results on two synthetic datasets, with and without spatial interference. Our results on real-world climate dataset also align with domain knowledge, further demonstrating the effectiveness of our proposed method. | [
"['Sahara Ali' 'Omar Faruque' 'Jianwu Wang']"
]
|
null | null | 2405.08175 | null | null | http://arxiv.org/abs/2405.08175v1 | 2024-05-13T20:51:23Z | 2024-05-13T20:51:23Z | Comparative Analysis of AWS Model Deployment Services | Amazon Web Services (AWS) offers three important Model Deployment Services for model developers: SageMaker, Lambda, and Elastic Container Service (ECS). These services have critical advantages and disadvantages, influencing model developer's adoption decisions. This comparative analysis reviews the merits and drawbacks of these services. This analysis found that Lambda AWS service leads in efficiency, autoscaling aspects, and integration during model development. However, ECS was found to be outstanding in terms of flexibility, scalability, and infrastructure control; conversely, ECS is better suited when it comes to managing complex container environments during model development, as well as addressing budget concerns -- it is, therefore, the preferred option for model developers whose objective is to achieve complete freedom and framework flexibility with horizontal scaling. ECS is better suited to ensuring performance requirements align with project goals and constraints. The AWS service selection process considered factors that include but are not limited to load balance and cost-effectiveness. ECS is a better choice when model development begins from the abstract. It offers unique benefits, such as the ability to scale horizontally and vertically, making it the best preferable tool for model deployment. | [
"['Rahul Bagai']"
]
|
null | null | 2405.08179 | null | null | http://arxiv.org/pdf/2405.08179v1 | 2024-05-13T20:57:01Z | 2024-05-13T20:57:01Z | Do Bayesian imaging methods report trustworthy probabilities? | Bayesian statistics is a cornerstone of imaging sciences, underpinning many and varied approaches from Markov random fields to score-based denoising diffusion models. In addition to powerful image estimation methods, the Bayesian paradigm also provides a framework for uncertainty quantification and for using image data as quantitative evidence. These probabilistic capabilities are important for the rigorous interpretation of experimental results and for robust interfacing of quantitative imaging pipelines with scientific and decision-making processes. However, are the probabilities delivered by existing Bayesian imaging methods meaningful under replication of an experiment, or are they only meaningful as subjective measures of belief? This paper presents a Monte Carlo method to explore this question. We then leverage the proposed Monte Carlo method and run a large experiment requiring 1,000 GPU-hours to probe the accuracy of five canonical Bayesian imaging methods that are representative of some of the main Bayesian imaging strategies from the past decades (a score-based denoising diffusion technique, a plug-and-play Langevin algorithm utilising a Lipschitz-regularised DnCNN denoiser, a Bayesian method with a dictionary-based prior trained subject to a log-concavity constraint, an empirical Bayesian method with a total-variation prior, and a hierarchical Bayesian Gibbs sampler based on a Gaussian Markov random field model). We find that, a few cases, the probabilities reported by modern Bayesian imaging techniques are in broad agreement with long-term averages as observed over a large number of replication of an experiment, but existing Bayesian imaging methods are generally not able to deliver reliable uncertainty quantification results. | [
"['David Y. W. Thong' 'Charlesquin Kemajou Mbakam' 'Marcelo Pereyra']"
]
|
null | null | 2405.08183 | null | null | http://arxiv.org/pdf/2405.08183v2 | 2024-07-09T16:46:19Z | 2024-05-13T21:02:31Z | Towards Energy-Aware Federated Learning via MARL: A Dual-Selection
Approach for Model and Client | Although Federated Learning (FL) is promising in knowledge sharing for heterogeneous Artificial Intelligence of Thing (AIoT) devices, their training performance and energy efficacy are severely restricted in practical battery-driven scenarios due to the ``wooden barrel effect'' caused by the mismatch between homogeneous model paradigms and heterogeneous device capability. As a result, due to various kinds of differences among devices, it is hard for existing FL methods to conduct training effectively in energy-constrained scenarios, such as battery constraints of devices. To tackle the above issues, we propose an energy-aware FL framework named DR-FL, which considers the energy constraints in both clients and heterogeneous deep learning models to enable energy-efficient FL. Unlike Vanilla FL, DR-FL adopts our proposed Muti-Agents Reinforcement Learning (MARL)-based dual-selection method, which allows participated devices to make contributions to the global model effectively and adaptively based on their computing capabilities and energy capacities in a MARL-based manner. Experiments conducted with various widely recognized datasets demonstrate that DR-FL has the capability to optimize the exchange of knowledge among diverse models in large-scale AIoT systems while adhering to energy limitations. Additionally, it improves the performance of each individual heterogeneous device's model. | [
"['Jun Xia' 'Yi Zhang' 'Yiyu Shi']"
]
|
null | null | 2405.08185 | null | null | http://arxiv.org/pdf/2405.08185v1 | 2024-05-13T21:06:53Z | 2024-05-13T21:06:53Z | Probabilistic Flux Limiters | The stable numerical integration of shocks in compressible flow simulations relies on the reduction or elimination of Gibbs phenomena (unstable, spurious oscillations). A popular method to virtually eliminate Gibbs oscillations caused by numerical discretization in under-resolved simulations is to use a flux limiter. A wide range of flux limiters has been studied in the literature, with recent interest in their optimization via machine learning methods trained on high-resolution datasets. The common use of flux limiters in numerical codes as plug-and-play blackbox components makes them key targets for design improvement. Moreover, while aleatoric (inherent randomness) and epistemic (lack of knowledge) uncertainty is commonplace in fluid dynamical systems, these effects are generally ignored in the design of flux limiters. Even for deterministic dynamical models, numerical uncertainty is introduced via coarse-graining required by insufficient computational power to solve all scales of motion. Here, we introduce a conceptually distinct type of flux limiter that is designed to handle the effects of randomness in the model and uncertainty in model parameters. This new, {it probabilistic flux limiter}, learned with high-resolution data, consists of a set of flux limiting functions with associated probabilities, which define the frequencies of selection for their use. Using the example of Burgers' equation, we show that a machine learned, probabilistic flux limiter may be used in a shock capturing code to more accurately capture shock profiles. In particular, we show that our probabilistic flux limiter outperforms standard limiters, and can be successively improved upon (up to a point) by expanding the set of probabilistically chosen flux limiting functions. | [
"['Nga T. T. Nguyen-Fotiadis' 'Robert Chiodi' 'Michael McKerns'\n 'Daniel Livescu' 'Andrew Sornborger']"
]
|
null | null | 2405.08190 | null | null | http://arxiv.org/pdf/2405.08190v1 | 2024-05-13T21:12:31Z | 2024-05-13T21:12:31Z | Barren plateaus induced by the dimension of qudits | Variational Quantum Algorithms (VQAs) have emerged as pivotal strategies for attaining quantum advantages in diverse scientific and technological domains, notably within Quantum Neural Networks. However, despite their potential, VQAs encounter significant obstacles, chief among them being the gradient vanishing problem, commonly referred to as barren plateaus. In this study, we unveil a direct correlation between the dimension of qudits and the occurrence of barren plateaus, a connection previously overlooked. Through meticulous analysis, we demonstrate that existing literature implicitly suggests the intrinsic influence of qudit dimensionality on barren plateaus. To instantiate these findings, we present numerical results that exemplify the impact of qudit dimensionality on barren plateaus. Additionally, despite the proposition of various error mitigation techniques, our results call for further scrutiny about their efficacy in the context of VQAs with qudits. | [
"['Lucas Friedrich' 'Tiago de Souza Farias' 'Jonas Maziero']"
]
|
null | null | 2405.08199 | null | null | http://arxiv.org/pdf/2405.08199v1 | 2024-05-13T21:30:50Z | 2024-05-13T21:30:50Z | Modeling of Time-varying Wireless Communication Channel with Fading and
Shadowing | The real-time quantification of the effect of a wireless channel on the transmitting signal is crucial for the analysis and the intelligent design of wireless communication systems for various services. Recent mechanisms to model channel characteristics independent of coding, modulation, signal processing, etc., using deep learning neural networks are promising solutions. However, the current approaches are neither statistically accurate nor able to adapt to the changing environment. In this paper, we propose a new approach that combines a deep learning neural network with a mixture density network model to derive the conditional probability density function (PDF) of receiving power given a communication distance in general wireless communication systems. Furthermore, a deep transfer learning scheme is designed and implemented to allow the channel model to dynamically adapt to changes in communication environments. Extensive experiments on Nakagami fading channel model and Log-normal shadowing channel model with path loss and noise show that the new approach is more statistically accurate, faster, and more robust than the previous deep learning-based channel models. | [
"['Lee Youngmin' 'Ma Xiaomin' 'Lang S. I. D Andrew']"
]
|
null | null | 2405.08205 | null | null | http://arxiv.org/pdf/2405.08205v2 | 2024-06-17T20:14:34Z | 2024-05-13T21:48:48Z | Generative Enzyme Design Guided by Functionally Important Sites and
Small-Molecule Substrates | Enzymes are genetically encoded biocatalysts capable of accelerating chemical reactions. How can we automatically design functional enzymes? In this paper, we propose EnzyGen, an approach to learn a unified model to design enzymes across all functional families. Our key idea is to generate an enzyme's amino acid sequence and their three-dimensional (3D) coordinates based on functionally important sites and substrates corresponding to a desired catalytic function. These sites are automatically mined from enzyme databases. EnzyGen consists of a novel interleaving network of attention and neighborhood equivariant layers, which captures both long-range correlation in an entire protein sequence and local influence from nearest amino acids in 3D space. To learn the generative model, we devise a joint training objective, including a sequence generation loss, a position prediction loss and an enzyme-substrate interaction loss. We further construct EnzyBench, a dataset with 3157 enzyme families, covering all available enzymes within the protein data bank (PDB). Experimental results show that our EnzyGen consistently achieves the best performance across all 323 testing families, surpassing the best baseline by 10.79% in terms of substrate binding affinity. These findings demonstrate EnzyGen's superior capability in designing well-folded and effective enzymes binding to specific substrates with high affinities. | [
"['Zhenqiao Song' 'Yunlong Zhao' 'Wenxian Shi' 'Wengong Jin' 'Yang Yang'\n 'Lei Li']"
]
|
null | null | 2405.08209 | null | null | http://arxiv.org/pdf/2405.08209v1 | 2024-05-13T21:53:06Z | 2024-05-13T21:53:06Z | Who's in and who's out? A case study of multimodal CLIP-filtering in
DataComp | As training datasets become increasingly drawn from unstructured, uncontrolled environments such as the web, researchers and industry practitioners have increasingly relied upon data filtering techniques to "filter out the noise" of web-scraped data. While datasets have been widely shown to reflect the biases and values of their creators, in this paper we contribute to an emerging body of research that assesses the filters used to create these datasets. We show that image-text data filtering also has biases and is value-laden, encoding specific notions of what is counted as "high-quality" data. In our work, we audit a standard approach of image-text CLIP-filtering on the academic benchmark DataComp's CommonPool by analyzing discrepancies of filtering through various annotation techniques across multiple modalities of image, text, and website source. We find that data relating to several imputed demographic groups -- such as LGBTQ+ people, older women, and younger men -- are associated with higher rates of exclusion. Moreover, we demonstrate cases of exclusion amplification: not only are certain marginalized groups already underrepresented in the unfiltered data, but CLIP-filtering excludes data from these groups at higher rates. The data-filtering step in the machine learning pipeline can therefore exacerbate representation disparities already present in the data-gathering step, especially when existing filters are designed to optimize a specifically-chosen downstream performance metric like zero-shot image classification accuracy. Finally, we show that the NSFW filter fails to remove sexually-explicit content from CommonPool, and that CLIP-filtering includes several categories of copyrighted content at high rates. Our conclusions point to a need for fundamental changes in dataset creation and filtering practices. | [
"['Rachel Hong' 'William Agnew' 'Tadayoshi Kohno' 'Jamie Morgenstern']"
]
|
null | null | 2405.08213 | null | null | http://arxiv.org/pdf/2405.08213v1 | 2024-05-13T22:01:03Z | 2024-05-13T22:01:03Z | Interpreting Latent Student Knowledge Representations in Programming
Assignments | Recent advances in artificial intelligence for education leverage generative large language models, including using them to predict open-ended student responses rather than their correctness only. However, the black-box nature of these models limits the interpretability of the learned student knowledge representations. In this paper, we conduct a first exploration into interpreting latent student knowledge representations by presenting InfoOIRT, an Information regularized Open-ended Item Response Theory model, which encourages the latent student knowledge states to be interpretable while being able to generate student-written code for open-ended programming questions. InfoOIRT maximizes the mutual information between a fixed subset of latent knowledge states enforced with simple prior distributions and generated student code, which encourages the model to learn disentangled representations of salient syntactic and semantic code features including syntactic styles, mastery of programming skills, and code structures. Through experiments on a real-world programming education dataset, we show that InfoOIRT can both accurately generate student code and lead to interpretable student knowledge representations. | [
"['Nigel Fernandez' 'Andrew Lan']"
]
|
null | null | 2405.08217 | null | null | http://arxiv.org/pdf/2405.08217v1 | 2024-05-13T22:10:00Z | 2024-05-13T22:10:00Z | Data Valuation with Gradient Similarity | High-quality data is crucial for accurate machine learning and actionable analytics, however, mislabeled or noisy data is a common problem in many domains. Distinguishing low- from high-quality data can be challenging, often requiring expert knowledge and considerable manual intervention. Data Valuation algorithms are a class of methods that seek to quantify the value of each sample in a dataset based on its contribution or importance to a given predictive task. These data values have shown an impressive ability to identify mislabeled observations, and filtering low-value data can boost machine learning performance. In this work, we present a simple alternative to existing methods, termed Data Valuation with Gradient Similarity (DVGS). This approach can be easily applied to any gradient descent learning algorithm, scales well to large datasets, and performs comparably or better than baseline valuation methods for tasks such as corrupted label discovery and noise quantification. We evaluate the DVGS method on tabular, image and RNA expression datasets to show the effectiveness of the method across domains. Our approach has the ability to rapidly and accurately identify low-quality data, which can reduce the need for expert knowledge and manual intervention in data cleaning tasks. | [
"['Nathaniel J. Evans' 'Gordon B. Mills' 'Guanming Wu' 'Xubo Song'\n 'Shannon McWeeney']"
]
|
null | null | 2405.08226 | null | null | http://arxiv.org/pdf/2405.08226v1 | 2024-05-13T22:45:44Z | 2024-05-13T22:45:44Z | SeNMo: A Self-Normalizing Deep Learning Model for Enhanced Multi-Omics
Data Analysis in Oncology | Multi-omics research has enhanced our understanding of cancer heterogeneity and progression. Investigating molecular data through multi-omics approaches is crucial for unraveling the complex biological mechanisms underlying cancer, thereby enabling effective diagnosis, treatment, and prevention strategies. However, predicting patient outcomes through integration of all available multi-omics data is an under-study research direction. Here, we present SeNMo (Self-normalizing Network for Multi-omics), a deep neural network trained on multi-omics data across 33 cancer types. SeNMo is efficient in handling multi-omics data characterized by high-width (many features) and low-length (fewer samples) attributes. We trained SeNMo for the task of overall survival using pan-cancer data involving 33 cancer sites from Genomics Data Commons (GDC). The training data includes gene expression, DNA methylation, miRNA expression, DNA mutations, protein expression modalities, and clinical data. We evaluated the model's performance in predicting overall survival using concordance index (C-Index). SeNMo performed consistently well in training regime, with the validation C-Index of 0.76 on GDC's public data. In the testing regime, SeNMo performed with a C-Index of 0.758 on a held-out test set. The model showed an average accuracy of 99.8% on the task of classifying the primary cancer type on the pan-cancer test cohort. SeNMo proved to be a mini-foundation model for multi-omics oncology data because it demonstrated robust performance, and adaptability not only across molecular data types but also on the classification task of predicting the primary cancer type of patients. SeNMo can be further scaled to any cancer site and molecular data type. We believe SeNMo and similar models are poised to transform the oncology landscape, offering hope for more effective, efficient, and patient-centric cancer care. | [
"['Asim Waqas' 'Aakash Tripathi' 'Sabeen Ahmed' 'Ashwin Mukund'\n 'Hamza Farooq' 'Matthew B. Schabath' 'Paul Stewart' 'Mia Naeini'\n 'Ghulam Rasool']"
]
|
null | null | 2405.08233 | null | null | http://arxiv.org/pdf/2405.08233v3 | 2024-07-07T06:01:45Z | 2024-05-13T23:19:02Z | A Deep Dive into the Factors Influencing Financial Success: A Machine
Learning Approach | This paper explores various socioeconomic factors that contribute to individual financial success using machine learning algorithms and approaches. Financial success, a critical aspect of all individual's well-being, is a complex concept influenced by various factors. This study aims to understand the determinants of financial success. It examines the survey data from the National Longitudinal Survey of Youth 1997 by the Bureau of Labor Statistics (1), consisting of a sample of 8,984 individuals's longitudinal data over years. The dataset comprises income variables and a large set of socioeconomic variables of individuals. An in-depth analysis shows the effectiveness of machine learning algorithms in financial success research, highlights the potential of leveraging longitudinal data to enhance prediction accuracy, and provides valuable insights into how various socioeconomic factors influence financial success. The findings highlight the significant influence of highest education degree, occupation and gender as the top three determinants of individual income among socioeconomic factors examined. Yearly working hours, age and work tenure follow as three secondary influencing factors, and all other factors including parental household income, industry, parents' highest grade and others are identified as tertiary factors. These insights allow researchers to better understand the complex nature of financial success, and are also crucial for fostering financial success among individuals and advancing broader societal well-being by providing insights for policymakers during decision-making process. | [
"['Michael Zhou' 'Ramin Ramezani']"
]
|
null | null | 2405.08235 | null | null | http://arxiv.org/pdf/2405.08235v1 | 2024-05-13T23:24:25Z | 2024-05-13T23:24:25Z | Additive-Effect Assisted Learning | It is quite popular nowadays for researchers and data analysts holding different datasets to seek assistance from each other to enhance their modeling performance. We consider a scenario where different learners hold datasets with potentially distinct variables, and their observations can be aligned by a nonprivate identifier. Their collaboration faces the following difficulties: First, learners may need to keep data values or even variable names undisclosed due to, e.g., commercial interest or privacy regulations; second, there are restrictions on the number of transmission rounds between them due to e.g., communication costs. To address these challenges, we develop a two-stage assisted learning architecture for an agent, Alice, to seek assistance from another agent, Bob. In the first stage, we propose a privacy-aware hypothesis testing-based screening method for Alice to decide on the usefulness of the data from Bob, in a way that only requires Bob to transmit sketchy data. Once Alice recognizes Bob's usefulness, Alice and Bob move to the second stage, where they jointly apply a synergistic iterative model training procedure. With limited transmissions of summary statistics, we show that Alice can achieve the oracle performance as if the training were from centralized data, both theoretically and numerically. | [
"['Jiawei Zhang' 'Yuhong Yang' 'Jie Ding']"
]
|
null | null | 2405.08246 | null | null | http://arxiv.org/pdf/2405.08246v1 | 2024-05-14T00:22:06Z | 2024-05-14T00:22:06Z | Compositional Text-to-Image Generation with Dense Blob Representations | Existing text-to-image models struggle to follow complex text prompts, raising the need for extra grounding inputs for better controllability. In this work, we propose to decompose a scene into visual primitives - denoted as dense blob representations - that contain fine-grained details of the scene while being modular, human-interpretable, and easy-to-construct. Based on blob representations, we develop a blob-grounded text-to-image diffusion model, termed BlobGEN, for compositional generation. Particularly, we introduce a new masked cross-attention module to disentangle the fusion between blob representations and visual features. To leverage the compositionality of large language models (LLMs), we introduce a new in-context learning approach to generate blob representations from text prompts. Our extensive experiments show that BlobGEN achieves superior zero-shot generation quality and better layout-guided controllability on MS-COCO. When augmented by LLMs, our method exhibits superior numerical and spatial correctness on compositional image generation benchmarks. Project page: https://blobgen-2d.github.io. | [
"['Weili Nie' 'Sifei Liu' 'Morteza Mardani' 'Chao Liu' 'Benjamin Eckart'\n 'Arash Vahdat']"
]
|
null | null | 2405.08252 | null | null | http://arxiv.org/pdf/2405.08252v1 | 2024-05-14T00:57:02Z | 2024-05-14T00:57:02Z | Smart Sampling: Self-Attention and Bootstrapping for Improved Ensembled
Q-Learning | We present a novel method aimed at enhancing the sample efficiency of ensemble Q learning. Our proposed approach integrates multi-head self-attention into the ensembled Q networks while bootstrapping the state-action pairs ingested by the ensemble. This not only results in performance improvements over the original REDQ (Chen et al. 2021) and its variant DroQ (Hi-raoka et al. 2022), thereby enhancing Q predictions, but also effectively reduces both the average normalized bias and standard deviation of normalized bias within Q-function ensembles. Importantly, our method also performs well even in scenarios with a low update-to-data (UTD) ratio. Notably, the implementation of our proposed method is straightforward, requiring minimal modifications to the base model. | [
"['Muhammad Junaid Khan' 'Syed Hammad Ahmed' 'Gita Sukthankar']"
]
|
null | null | 2405.08253 | null | null | http://arxiv.org/pdf/2405.08253v2 | 2024-05-17T05:58:07Z | 2024-05-14T01:01:05Z | Thompson Sampling for Infinite-Horizon Discounted Decision Processes | We model a Markov decision process, parametrized by an unknown parameter, and study the asymptotic behavior of a sampling-based algorithm, called Thompson sampling. The standard definition of regret is not always suitable to evaluate a policy, especially when the underlying chain structure is general. We show that the standard (expected) regret can grow (super-)linearly and fails to capture the notion of learning in realistic settings with non-trivial state evolution. By decomposing the standard (expected) regret, we develop a new metric, called the expected residual regret, which forgets the immutable consequences of past actions. Instead, it measures regret against the optimal reward moving forward from the current period. We show that the expected residual regret of the Thompson sampling algorithm is upper bounded by a term which converges exponentially fast to 0. We present conditions under which the posterior sampling error of Thompson sampling converges to 0 almost surely. We then introduce the probabilistic version of the expected residual regret and present conditions under which it converges to 0 almost surely. Thus, we provide a viable concept of learning for sampling algorithms which will serve useful in broader settings than had been considered previously. | [
"['Daniel Adelman' 'Cagla Keceli' 'Alba V. Olivares-Nadal']"
]
|
null | null | 2405.08276 | null | null | http://arxiv.org/pdf/2405.08276v1 | 2024-05-14T02:11:38Z | 2024-05-14T02:11:38Z | Scalable Subsampling Inference for Deep Neural Networks | Deep neural networks (DNN) has received increasing attention in machine learning applications in the last several years. Recently, a non-asymptotic error bound has been developed to measure the performance of the fully connected DNN estimator with ReLU activation functions for estimating regression models. The paper at hand gives a small improvement on the current error bound based on the latest results on the approximation ability of DNN. More importantly, however, a non-random subsampling technique--scalable subsampling--is applied to construct a `subagged' DNN estimator. Under regularity conditions, it is shown that the subagged DNN estimator is computationally efficient without sacrificing accuracy for either estimation or prediction tasks. Beyond point estimation/prediction, we propose different approaches to build confidence and prediction intervals based on the subagged DNN estimator. In addition to being asymptotically valid, the proposed confidence/prediction intervals appear to work well in finite samples. All in all, the scalable subsampling DNN estimator offers the complete package in terms of statistical inference, i.e., (a) computational efficiency; (b) point estimation/prediction accuracy; and (c) allowing for the construction of practically useful confidence and prediction intervals. | [
"['Kejin Wu' 'Dimitris N. Politis']"
]
|
null | null | 2405.08284 | null | null | http://arxiv.org/pdf/2405.08284v1 | 2024-05-14T02:50:23Z | 2024-05-14T02:50:23Z | Predicting NVIDIA's Next-Day Stock Price: A Comparative Analysis of
LSTM, MLP, ARIMA, and ARIMA-GARCH Models | Forecasting stock prices remains a considerable challenge in financial markets, bearing significant implications for investors, traders, and financial institutions. Amid the ongoing AI revolution, NVIDIA has emerged as a key player driving innovation across various sectors. Given its prominence, we chose NVIDIA as the subject of our study. | [
"['Yiluan Xing' 'Chao Yan' 'Cathy Chang Xie']"
]
|
null | null | 2405.08293 | null | null | http://arxiv.org/pdf/2405.08293v1 | 2024-05-14T03:27:15Z | 2024-05-14T03:27:15Z | Airport Delay Prediction with Temporal Fusion Transformers | Since flight delay hurts passengers, airlines, and airports, its prediction becomes crucial for the decision-making of all stakeholders in the aviation industry and thus has been attempted by various previous research. However, previous delay predictions are often categorical and at a highly aggregated level. To improve that, this study proposes to apply the novel Temporal Fusion Transformer model and predict numerical airport arrival delays at quarter hour level for U.S. top 30 airports. Inputs to our model include airport demand and capacity forecasts, historic airport operation efficiency information, airport wind and visibility conditions, as well as enroute weather and traffic conditions. The results show that our model achieves satisfactory performance measured by small prediction errors on the test set. In addition, the interpretability analysis of the model outputs identifies the important input factors for delay prediction. | [
"['Ke Liu' 'Kaijing Ding' 'Xi Cheng' 'Jianan Chen' 'Siyuan Feng' 'Hui Lin'\n 'Jilin Song' 'Chen Zhu']"
]
|
null | null | 2405.08297 | null | null | http://arxiv.org/pdf/2405.08297v1 | 2024-05-14T03:42:33Z | 2024-05-14T03:42:33Z | Distance-Restricted Explanations: Theoretical Underpinnings & Efficient
Implementation | The uses of machine learning (ML) have snowballed in recent years. In many cases, ML models are highly complex, and their operation is beyond the understanding of human decision-makers. Nevertheless, some uses of ML models involve high-stakes and safety-critical applications. Explainable artificial intelligence (XAI) aims to help human decision-makers in understanding the operation of such complex ML models, thus eliciting trust in their operation. Unfortunately, the majority of past XAI work is based on informal approaches, that offer no guarantees of rigor. Unsurprisingly, there exists comprehensive experimental and theoretical evidence confirming that informal methods of XAI can provide human-decision makers with erroneous information. Logic-based XAI represents a rigorous approach to explainability; it is model-based and offers the strongest guarantees of rigor of computed explanations. However, a well-known drawback of logic-based XAI is the complexity of logic reasoning, especially for highly complex ML models. Recent work proposed distance-restricted explanations, i.e. explanations that are rigorous provided the distance to a given input is small enough. Distance-restricted explainability is tightly related with adversarial robustness, and it has been shown to scale for moderately complex ML models, but the number of inputs still represents a key limiting factor. This paper investigates novel algorithms for scaling up the performance of logic-based explainers when computing and enumerating ML model explanations with a large number of inputs. | [
"['Yacine Izza' 'Xuanxiang Huang' 'Antonio Morgado' 'Jordi Planes'\n 'Alexey Ignatiev' 'Joao Marques-Silva']"
]
|
null | null | 2405.08298 | null | null | http://arxiv.org/pdf/2405.08298v1 | 2024-05-14T03:48:45Z | 2024-05-14T03:48:45Z | Deep Reinforcement Learning for Real-Time Ground Delay Program Revision
and Corresponding Flight Delay Assignments | This paper explores the optimization of Ground Delay Programs (GDP), a prevalent Traffic Management Initiative used in Air Traffic Management (ATM) to reconcile capacity and demand discrepancies at airports. Employing Reinforcement Learning (RL) to manage the inherent uncertainties in the national airspace system-such as weather variability, fluctuating flight demands, and airport arrival rates-we developed two RL models: Behavioral Cloning (BC) and Conservative Q-Learning (CQL). These models are designed to enhance GDP efficiency by utilizing a sophisticated reward function that integrates ground and airborne delays and terminal area congestion. We constructed a simulated single-airport environment, SAGDP_ENV, which incorporates real operational data along with predicted uncertainties to facilitate realistic decision-making scenarios. Utilizing the whole year 2019 data from Newark Liberty International Airport (EWR), our models aimed to preemptively set airport program rates. Despite thorough modeling and simulation, initial outcomes indicated that the models struggled to learn effectively, attributed potentially to oversimplified environmental assumptions. This paper discusses the challenges encountered, evaluates the models' performance against actual operational data, and outlines future directions to refine RL applications in ATM. | [
"['Ke Liu' 'Fan Hu' 'Hui Lin' 'Xi Cheng' 'Jianan Chen' 'Jilin Song'\n 'Siyuan Feng' 'Gaofeng Su' 'Chen Zhu']"
]
|
null | null | 2405.08299 | null | null | http://arxiv.org/pdf/2405.08299v3 | 2024-05-20T02:10:54Z | 2024-05-14T03:49:14Z | Differentially Private Federated Learning: A Systematic Review | In recent years, privacy and security concerns in machine learning have promoted trusted federated learning to the forefront of research. Differential privacy has emerged as the de facto standard for privacy protection in federated learning due to its rigorous mathematical foundation and provable guarantee. Despite extensive research on algorithms that incorporate differential privacy within federated learning, there remains an evident deficiency in systematic reviews that categorize and synthesize these studies. Our work presents a systematic overview of the differentially private federated learning. Existing taxonomies have not adequately considered objects and level of privacy protection provided by various differential privacy models in federated learning. To rectify this gap, we propose a new taxonomy of differentially private federated learning based on definition and guarantee of various differential privacy models and federated scenarios. Our classification allows for a clear delineation of the protected objects across various differential privacy models and their respective neighborhood levels within federated learning environments. Furthermore, we explore the applications of differential privacy in federated learning scenarios. Our work provide valuable insights into privacy-preserving federated learning and suggest practical directions for future research. | [
"['Jie Fu' 'Yuan Hong' 'Xinpeng Ling' 'Leixia Wang' 'Xun Ran' 'Zhiyu Sun'\n 'Wendy Hui Wang' 'Zhili Chen' 'Yang Cao']"
]
|
null | null | 2405.08318 | null | null | http://arxiv.org/pdf/2405.08318v1 | 2024-05-14T04:58:23Z | 2024-05-14T04:58:23Z | No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian
Processes | This paper investigates the challenge of learning in black-box games, where the underlying utility function is unknown to any of the agents. While there is an extensive body of literature on the theoretical analysis of algorithms for computing the Nash equilibrium with complete information about the game, studies on Nash equilibrium in black-box games are less common. In this paper, we focus on learning the Nash equilibrium when the only available information about an agent's payoff comes in the form of empirical queries. We provide a no-regret learning algorithm that utilizes Gaussian processes to identify the equilibrium in such games. Our approach not only ensures a theoretical convergence rate but also demonstrates effectiveness across a variety collection of games through experimental validation. | [
"['Minbiao Han' 'Fengxue Zhang' 'Yuxin Chen']"
]
|
null | null | 2405.08331 | null | null | http://arxiv.org/pdf/2405.08331v1 | 2024-05-14T05:57:22Z | 2024-05-14T05:57:22Z | Are Generics and Negativity about Social Groups Common on Social Media?
A Comparative Analysis of Twitter (X) Data | Generics (unquantified generalizations) are thought to be pervasive in communication and when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, however. Using machine learning (ML) techniques, we therefore developed an automatic classifier for social generics, applied it to more than a million tweets about people, and analyzed the tweets. We found that most tweets (78%) about people contained no generics. However, tweets with social generics received more 'likes' and retweets. Furthermore, while recent psychological research may lead to the prediction that tweets with generics about political groups are more common than tweets with generics about ethnic groups, we found the opposite. However, consistent with recent claims that political animosity is less constrained by social norms than animosity against gender and ethnic groups, negative tweets with generics about political groups were significantly more prevalent and retweeted than negative tweets about ethnic groups. Our study provides the first ML-based insights into the use and impact of social generics on Twitter. | [
"['Uwe Peters' 'Ignacio Ojea Quintana']"
]
|
null | null | 2405.08334 | null | null | http://arxiv.org/pdf/2405.08334v1 | 2024-05-14T06:09:08Z | 2024-05-14T06:09:08Z | Could Chemical LLMs benefit from Message Passing | Pretrained language models (LMs) showcase significant capabilities in processing molecular text, while concurrently, message passing neural networks (MPNNs) demonstrate resilience and versatility in the domain of molecular science. Despite these advancements, we find there are limited studies investigating the bidirectional interactions between molecular structures and their corresponding textual representations. Therefore, in this paper, we propose two strategies to evaluate whether an information integration can enhance the performance: contrast learning, which involves utilizing an MPNN to supervise the training of the LM, and fusion, which exploits information from both models. Our empirical analysis reveals that the integration approaches exhibit superior performance compared to baselines when applied to smaller molecular graphs, while these integration approaches do not yield performance enhancements on large scale graphs. | [
"['Jiaqing Xie' 'Ziheng Chi']"
]
|
null | null | 2405.08342 | null | null | http://arxiv.org/abs/2405.08342v1 | 2024-05-14T06:31:38Z | 2024-05-14T06:31:38Z | Abnormal Respiratory Sound Identification Using Audio-Spectrogram Vision
Transformer | Respiratory disease, the third leading cause of deaths globally, is considered a high-priority ailment requiring significant research on identification and treatment. Stethoscope-recorded lung sounds and artificial intelligence-powered devices have been used to identify lung disorders and aid specialists in making accurate diagnoses. In this study, audio-spectrogram vision transformer (AS-ViT), a new approach for identifying abnormal respiration sounds, was developed. The sounds of the lungs are converted into visual representations called spectrograms using a technique called short-time Fourier transform (STFT). These images are then analyzed using a model called vision transformer to identify different types of respiratory sounds. The classification was carried out using the ICBHI 2017 database, which includes various types of lung sounds with different frequencies, noise levels, and backgrounds. The proposed AS-ViT method was evaluated using three metrics and achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20 split ratio in terms of unweighted average recall and overall scores respectively for respiratory sound detection, surpassing previous state-of-the-art results. | [
"['Whenty Ariyanti' 'Kai-Chun Liu' 'Kuan-Yu Chen' 'Yu Tsao']"
]
|
null | null | 2405.08363 | null | null | http://arxiv.org/pdf/2405.08363v1 | 2024-05-14T07:05:18Z | 2024-05-14T07:05:18Z | UnMarker: A Universal Attack on Defensive Watermarking | Reports regarding the misuse of $textit{Generative AI}$ ($textit{GenAI}$) to create harmful deepfakes are emerging daily. Recently, defensive watermarking, which enables $textit{GenAI}$ providers to hide fingerprints in their images to later use for deepfake detection, has been on the rise. Yet, its potential has not been fully explored. We present $textit{UnMarker}$ -- the first practical $textit{universal}$ attack on defensive watermarking. Unlike existing attacks, $textit{UnMarker}$ requires no detector feedback, no unrealistic knowledge of the scheme or similar models, and no advanced denoising pipelines that may not be available. Instead, being the product of an in-depth analysis of the watermarking paradigm revealing that robust schemes must construct their watermarks in the spectral amplitudes, $textit{UnMarker}$ employs two novel adversarial optimizations to disrupt the spectra of watermarked images, erasing the watermarks. Evaluations against the $textit{SOTA}$ prove its effectiveness, not only defeating traditional schemes while retaining superior quality compared to existing attacks but also breaking $textit{semantic}$ watermarks that alter the image's structure, reducing the best detection rate to $43%$ and rendering them useless. To our knowledge, $textit{UnMarker}$ is the first practical attack on $textit{semantic}$ watermarks, which have been deemed the future of robust watermarking. $textit{UnMarker}$ casts doubts on the very penitential of this countermeasure and exposes its paradoxical nature as designing schemes for robustness inevitably compromises other robustness aspects. | [
"['Andre Kassis' 'Urs Hengartner']"
]
|
null | null | 2405.08366 | null | null | http://arxiv.org/pdf/2405.08366v3 | 2024-05-20T17:46:14Z | 2024-05-14T07:07:13Z | Towards Principled Evaluations of Sparse Autoencoders for
Interpretability and Control | Disentangling model activations into meaningful features is a central problem in interpretability. However, the absence of ground-truth for these features in realistic scenarios makes validating recent approaches, such as sparse dictionary learning, elusive. To address this challenge, we propose a framework for evaluating feature dictionaries in the context of specific tasks, by comparing them against emph{supervised} feature dictionaries. First, we demonstrate that supervised dictionaries achieve excellent approximation, control, and interpretability of model computations on the task. Second, we use the supervised dictionaries to develop and contextualize evaluations of unsupervised dictionaries along the same three axes. We apply this framework to the indirect object identification (IOI) task using GPT-2 Small, with sparse autoencoders (SAEs) trained on either the IOI or OpenWebText datasets. We find that these SAEs capture interpretable features for the IOI task, but they are less successful than supervised features in controlling the model. Finally, we observe two qualitative phenomena in SAE training: feature occlusion (where a causally relevant concept is robustly overshadowed by even slightly higher-magnitude ones in the learned features), and feature over-splitting (where binary features split into many smaller, less interpretable features). We hope that our framework will provide a useful step towards more objective and grounded evaluations of sparse dictionary learning methods. | [
"['Aleksandar Makelov' 'George Lange' 'Neel Nanda']"
]
|
null | null | 2405.08373 | null | null | http://arxiv.org/pdf/2405.08373v1 | 2024-05-14T07:16:36Z | 2024-05-14T07:16:36Z | PromptMind Team at MEDIQA-CORR 2024: Improving Clinical Text Correction
with Error Categorization and LLM Ensembles | This paper describes our approach to the MEDIQA-CORR shared task, which involves error detection and correction in clinical notes curated by medical professionals. This task involves handling three subtasks: detecting the presence of errors, identifying the specific sentence containing the error, and correcting it. Through our work, we aim to assess the capabilities of Large Language Models (LLMs) trained on a vast corpora of internet data that contain both factual and unreliable information. We propose to comprehensively address all subtasks together, and suggest employing a unique prompt-based in-context learning strategy. We will evaluate its efficacy in this specialized task demanding a combination of general reasoning and medical knowledge. In medical systems where prediction errors can have grave consequences, we propose leveraging self-consistency and ensemble methods to enhance error correction and error detection performance. | [
"['Satya Kesav Gundabathula' 'Sriram R Kolar']"
]
|
null | null | 2405.08380 | null | null | http://arxiv.org/pdf/2405.08380v1 | 2024-05-14T07:23:10Z | 2024-05-14T07:23:10Z | CIER: A Novel Experience Replay Approach with Causal Inference in Deep
Reinforcement Learning | In the training process of Deep Reinforcement Learning (DRL), agents require repetitive interactions with the environment. With an increase in training volume and model complexity, it is still a challenging problem to enhance data utilization and explainability of DRL training. This paper addresses these challenges by focusing on the temporal correlations within the time dimension of time series. We propose a novel approach to segment multivariate time series into meaningful subsequences and represent the time series based on these subsequences. Furthermore, the subsequences are employed for causal inference to identify fundamental causal factors that significantly impact training outcomes. We design a module to provide feedback on the causality during DRL training. Several experiments demonstrate the feasibility of our approach in common environments, confirming its ability to enhance the effectiveness of DRL training and impart a certain level of explainability to the training process. Additionally, we extended our approach with priority experience replay algorithm, and experimental results demonstrate the continued effectiveness of our approach. | [
"['Jingwen Wang' 'Dehui Du' 'Yida Li' 'Yiyang Li' 'Yikang Chen']"
]
|
null | null | 2405.08403 | null | null | http://arxiv.org/pdf/2405.08403v2 | 2024-05-17T07:47:16Z | 2024-05-14T07:56:09Z | TFWT: Tabular Feature Weighting with Transformer | In this paper, we propose a novel feature weighting method to address the limitation of existing feature processing methods for tabular data. Typically the existing methods assume equal importance across all samples and features in one dataset. This simplified processing methods overlook the unique contributions of each feature, and thus may miss important feature information. As a result, it leads to suboptimal performance in complex datasets with rich features. To address this problem, we introduce Tabular Feature Weighting with Transformer, a novel feature weighting approach for tabular data. Our method adopts Transformer to capture complex feature dependencies and contextually assign appropriate weights to discrete and continuous features. Besides, we employ a reinforcement learning strategy to further fine-tune the weighting process. Our extensive experimental results across various real-world datasets and diverse downstream tasks show the effectiveness of TFWT and highlight the potential for enhancing feature weighting in tabular data analysis. | [
"['Xinhao Zhang' 'Zaitian Wang' 'Lu Jiang' 'Wanfu Gao' 'Pengfei Wang'\n 'Kunpeng Liu']"
]
|
null | null | 2405.08424 | null | null | http://arxiv.org/pdf/2405.08424v2 | 2024-05-24T01:44:42Z | 2024-05-14T08:35:39Z | Tackling Prevalent Conditions in Unsupervised Combinatorial
Optimization: Cardinality, Minimum, Covering, and More | Combinatorial optimization (CO) is naturally discrete, making machine learning based on differentiable optimization inapplicable. Karalias & Loukas (2020) adapted the probabilistic method to incorporate CO into differentiable optimization. Their work ignited the research on unsupervised learning for CO, composed of two main components: probabilistic objectives and derandomization. However, each component confronts unique challenges. First, deriving objectives under various conditions (e.g., cardinality constraints and minimum) is nontrivial. Second, the derandomization process is underexplored, and the existing derandomization methods are either random sampling or naive rounding. In this work, we aim to tackle prevalent (i.e., commonly involved) conditions in unsupervised CO. First, we concretize the targets for objective construction and derandomization with theoretical justification. Then, for various conditions commonly involved in different CO problems, we derive nontrivial objectives and derandomization to meet the targets. Finally, we apply the derivations to various CO problems. Via extensive experiments on synthetic and real-world graphs, we validate the correctness of our derivations and show our empirical superiority w.r.t. both optimization quality and speed. | [
"['Fanchen Bu' 'Hyeonsoo Jo' 'Soo Yong Lee' 'Sungsoo Ahn' 'Kijung Shin']"
]
|
null | null | 2405.08440 | null | null | http://arxiv.org/pdf/2405.08440v1 | 2024-05-14T09:01:33Z | 2024-05-14T09:01:33Z | DGCformer: Deep Graph Clustering Transformer for Multivariate Time
Series Forecasting | Multivariate time series forecasting tasks are usually conducted in a channel-dependent (CD) way since it can incorporate more variable-relevant information. However, it may also involve a lot of irrelevant variables, and this even leads to worse performance than the channel-independent (CI) strategy. This paper combines the strengths of both strategies and proposes the Deep Graph Clustering Transformer (DGCformer) for multivariate time series forecasting. Specifically, it first groups these relevant variables by a graph convolutional network integrated with an autoencoder, and a former-latter masked self-attention mechanism is then considered with the CD strategy being applied to each group of variables while the CI one for different groups. Extensive experimental results on eight datasets demonstrate the superiority of our method against state-of-the-art models, and our code will be publicly available upon acceptance. | [
"['Qinshuo Liu' 'Yanwen Fang' 'Pengtao Jiang' 'Guodong Li']"
]
|
null | null | 2405.08443 | null | null | http://arxiv.org/pdf/2405.08443v1 | 2024-05-14T09:03:00Z | 2024-05-14T09:03:00Z | Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage
Control | Active voltage control presents a promising avenue for relieving power congestion and enhancing voltage quality, taking advantage of the distributed controllable generators in the power network, such as roof-top photovoltaics. While Multi-Agent Reinforcement Learning (MARL) has emerged as a compelling approach to address this challenge, existing MARL approaches tend to overlook the constrained optimization nature of this problem, failing in guaranteeing safety constraints. In this paper, we formalize the active voltage control problem as a constrained Markov game and propose a safety-constrained MARL algorithm. We expand the primal-dual optimization RL method to multi-agent settings, and augment it with a novel approach of double safety estimation to learn the policy and to update the Lagrange-multiplier. In addition, we proposed different cost functions and investigated their influences on the behavior of our constrained MARL method. We evaluate our approach in the power distribution network simulation environment with real-world scale scenarios. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art MARL methods. | [
"['Yang Qu' 'Jinming Ma' 'Feng Wu']"
]
|
null | null | 2405.08448 | null | null | http://arxiv.org/pdf/2405.08448v1 | 2024-05-14T09:12:30Z | 2024-05-14T09:12:30Z | Understanding the performance gap between online and offline alignment
algorithms | Reinforcement learning from human feedback (RLHF) is the canonical framework for large language model alignment. However, rising popularity in offline alignment algorithms challenge the need for on-policy sampling in RLHF. Within the context of reward over-optimization, we start with an opening set of experiments that demonstrate the clear advantage of online methods over offline methods. This prompts us to investigate the causes to the performance discrepancy through a series of carefully designed experimental ablations. We show empirically that hypotheses such as offline data coverage and data quality by itself cannot convincingly explain the performance difference. We also find that while offline algorithms train policy to become good at pairwise classification, it is worse at generations; in the meantime the policies trained by online algorithms are good at generations while worse at pairwise classification. This hints at a unique interplay between discriminative and generative capabilities, which is greatly impacted by the sampling process. Lastly, we observe that the performance discrepancy persists for both contrastive and non-contrastive loss functions, and appears not to be addressed by simply scaling up policy networks. Taken together, our study sheds light on the pivotal role of on-policy sampling in AI alignment, and hints at certain fundamental challenges of offline alignment algorithms. | [
"['Yunhao Tang' 'Daniel Zhaohan Guo' 'Zeyu Zheng' 'Daniele Calandriello'\n 'Yuan Cao' 'Eugene Tarassov' 'Rémi Munos' 'Bernardo Ávila Pires'\n 'Michal Valko' 'Yong Cheng' 'Will Dabney']"
]
|
null | null | 2405.08465 | null | null | http://arxiv.org/pdf/2405.08465v1 | 2024-05-14T09:38:44Z | 2024-05-14T09:38:44Z | How to Surprisingly Consider Recommendations? A Knowledge-Graph-based
Approach Relying on Complex Network Metrics | Traditional recommendation proposals, including content-based and collaborative filtering, usually focus on similarity between items or users. Existing approaches lack ways of introducing unexpectedness into recommendations, prioritizing globally popular items over exposing users to unforeseen items. This investigation aims to design and evaluate a novel layer on top of recommender systems suited to incorporate relational information and suggest items with a user-defined degree of surprise. We propose a Knowledge Graph (KG) based recommender system by encoding user interactions on item catalogs. Our study explores whether network-level metrics on KGs can influence the degree of surprise in recommendations. We hypothesize that surprisingness correlates with certain network metrics, treating user profiles as subgraphs within a larger catalog KG. The achieved solution reranks recommendations based on their impact on structural graph metrics. Our research contributes to optimizing recommendations to reflect the metrics. We experimentally evaluate our approach on two datasets of LastFM listening histories and synthetic Netflix viewing profiles. We find that reranking items based on complex network metrics leads to a more unexpected and surprising composition of recommendation lists. | [
"['Oliver Baumann' 'Durgesh Nandini' 'Anderson Rossanez' 'Mirco Schoenfeld'\n 'Julio Cesar dos Reis']"
]
|
null | null | 2405.08473 | null | null | http://arxiv.org/pdf/2405.08473v1 | 2024-05-14T09:55:03Z | 2024-05-14T09:55:03Z | Improving the Real-Data Driven Network Evaluation Model for Digital Twin
Networks | With the emergence and proliferation of new forms of large-scale services such as smart homes, virtual reality/augmented reality, the increasingly complex networks are raising concerns about significant operational costs. As a result, the need for network management automation is emphasized, and Digital Twin Networks (DTN) technology is expected to become the foundation technology for autonomous networks. DTN has the advantage of being able to operate and system networks based on real-time collected data in a closed-loop system, and currently it is mainly designed for optimization scenarios. To improve network performance in optimization scenarios, it is necessary to select appropriate configurations and perform accurate performance evaluation based on real data. However, most network evaluation models currently use simulation data. Meanwhile, according to DTN standards documents, artificial intelligence (AI) models can ensure scalability, real-time performance, and accuracy in large-scale networks. Various AI research and standardization work is ongoing to optimize the use of DTN. When designing AI models, it is crucial to consider the characteristics of the data. This paper presents an autoencoder-based skip connected message passing neural network (AE-SMPN) as a network evaluation model using real network data. The model is created by utilizing graph neural network (GNN) with recurrent neural network (RNN) models to capture the spatiotemporal features of network data. Additionally, an AutoEncoder (AE) is employed to extract initial features. The neural network was trained using the real DTN dataset provided by the Barcelona Neural Networking Center (BNN-UPC), and the paper presents the analysis of the model structure along with experimental results. | [
"['Hyeju Shin' 'Ibrahim Aliyu' 'Abubakar Isah' 'Jinsul Kim']"
]
|
null | null | 2405.08484 | null | null | http://arxiv.org/pdf/2405.08484v1 | 2024-05-14T10:12:47Z | 2024-05-14T10:12:47Z | Universal replication of chaotic characteristics by classical and
quantum machine learning | Replicating chaotic characteristics of non-linear dynamics by machine learning (ML) has recently drawn wide attentions. In this work, we propose that a ML model, trained to predict the state one-step-ahead from several latest historic states, can accurately replicate the bifurcation diagram and the Lyapunov exponents of discrete dynamic systems. The characteristics for different values of the hyper-parameters are captured universally by a single ML model, while the previous works considered training the ML model independently by fixing the hyper-parameters to be specific values. Our benchmarks on the one- and two-dimensional Logistic maps show that variational quantum circuit can reproduce the long-term characteristics with higher accuracy than the long short-term memory (a well-recognized classical ML model). Our work reveals an essential difference between the ML for the chaotic characteristics and that for standard tasks, from the perspective of the relation between performance and model complexity. Our results suggest that quantum circuit model exhibits potential advantages on mitigating over-fitting, achieving higher accuracy and stability. | [
"['Sheng-Chen Bai' 'Shi-Ju Ran']"
]
|
null | null | 2405.08486 | null | null | http://arxiv.org/pdf/2405.08486v1 | 2024-05-14T10:23:57Z | 2024-05-14T10:23:57Z | Gradient Boosting Mapping for Dimensionality Reduction and Feature
Extraction | A fundamental problem in supervised learning is to find a good set of features or distance measures. If the new set of features is of lower dimensionality and can be obtained by a simple transformation of the original data, they can make the model understandable, reduce overfitting, and even help to detect distribution drift. We propose a supervised dimensionality reduction method Gradient Boosting Mapping (GBMAP), where the outputs of weak learners -- defined as one-layer perceptrons -- define the embedding. We show that the embedding coordinates provide better features for the supervised learning task, making simple linear models competitive with the state-of-the-art regressors and classifiers. We also use the embedding to find a principled distance measure between points. The features and distance measures automatically ignore directions irrelevant to the supervised learning task. We also show that we can reliably detect out-of-distribution data points with potentially large regression or classification errors. GBMAP is fast and works in seconds for dataset of million data points or hundreds of features. As a bonus, GBMAP provides a regression and classification performance comparable to the state-of-the-art supervised learning methods. | [
"['Anri Patron' 'Ayush Prasad' 'Hoang Phuc Hau Luu' 'Kai Puolamäki']"
]
|
null | null | 2405.08498 | null | null | http://arxiv.org/pdf/2405.08498v3 | 2024-06-28T13:31:48Z | 2024-05-14T10:55:04Z | Learning Decision Policies with Instrumental Variables through Double
Machine Learning | A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental variable (IV) regression, which utilises a key unconfounded variable known as the instrument, is a standard technique for learning causal relationships between confounded action, outcome, and context variables. Most recent IV regression algorithms use a two-stage approach, where a deep neural network (DNN) estimator learnt in the first stage is directly plugged into the second stage, in which another DNN is used to estimate the causal effect. Naively plugging the estimator can cause heavy bias in the second stage, especially when regularisation bias is present in the first stage estimator. We propose DML-IV, a non-linear IV regression method that reduces the bias in two-stage IV regressions and effectively learns high-performing policies. We derive a novel learning objective to reduce bias and design the DML-IV algorithm following the double/debiased machine learning (DML) framework. The learnt DML-IV estimator has strong convergence rate and $O(N^{-1/2})$ suboptimality guarantees that match those when the dataset is unconfounded. DML-IV outperforms state-of-the-art IV regression methods on IV regression benchmarks and learns high-performing policies in the presence of instruments. | [
"['Daqian Shao' 'Ashkan Soleymani' 'Francesco Quinzan' 'Marta Kwiatkowska']"
]
|
null | null | 2405.08514 | null | null | http://arxiv.org/pdf/2405.08514v1 | 2024-05-14T11:37:26Z | 2024-05-14T11:37:26Z | Falcon 7b for Software Mention Detection in Scholarly Documents | This paper aims to tackle the challenge posed by the increasing integration of software tools in research across various disciplines by investigating the application of Falcon-7b for the detection and classification of software mentions within scholarly texts. Specifically, the study focuses on solving Subtask I of the Software Mention Detection in Scholarly Publications (SOMD), which entails identifying and categorizing software mentions from academic literature. Through comprehensive experimentation, the paper explores different training strategies, including a dual-classifier approach, adaptive sampling, and weighted loss scaling, to enhance detection accuracy while overcoming the complexities of class imbalance and the nuanced syntax of scholarly writing. The findings highlight the benefits of selective labelling and adaptive sampling in improving the model's performance. However, they also indicate that integrating multiple strategies does not necessarily result in cumulative improvements. This research offers insights into the effective application of large language models for specific tasks such as SOMD, underlining the importance of tailored approaches to address the unique challenges presented by academic text analysis. | [
"['AmeerAli Khan' 'Qusai Ramadan' 'Cong Yang' 'Zeyd Boukhers']"
]
|
null | null | 2405.08527 | null | null | http://arxiv.org/pdf/2405.08527v1 | 2024-05-14T12:06:44Z | 2024-05-14T12:06:44Z | EEG-Features for Generalized Deepfake Detection | Since the advent of Deepfakes in digital media, the development of robust and reliable detection mechanism is urgently called for. In this study, we explore a novel approach to Deepfake detection by utilizing electroencephalography (EEG) measured from the neural processing of a human participant who viewed and categorized Deepfake stimuli from the FaceForensics++ datset. These measurements serve as input features to a binary support vector classifier, trained to discriminate between real and manipulated facial images. We examine whether EEG data can inform Deepfake detection and also if it can provide a generalized representation capable of identifying Deepfakes beyond the training domain. Our preliminary results indicate that human neural processing signals can be successfully integrated into Deepfake detection frameworks and hint at the potential for a generalized neural representation of artifacts in computer generated faces. Moreover, our study provides next steps towards the understanding of how digital realism is embedded in the human cognitive system, possibly enabling the development of more realistic digital avatars in the future. | [
"['Arian Beckmann' 'Tilman Stephani' 'Felix Klotzsche' 'Yonghao Chen'\n 'Simon M. Hofmann' 'Arno Villringer' 'Michael Gaebler' 'Vadim Nikulin'\n 'Sebastian Bosse' 'Peter Eisert' 'Anna Hilsmann']"
]
|
null | null | 2405.08538 | null | null | http://arxiv.org/pdf/2405.08538v1 | 2024-05-14T12:24:52Z | 2024-05-14T12:24:52Z | Self-Distillation Improves DNA Sequence Inference | Self-supervised pretraining (SSP) has been recognized as a method to enhance prediction accuracy in various downstream tasks. However, its efficacy for DNA sequences remains somewhat constrained. This limitation stems primarily from the fact that most existing SSP approaches in genomics focus on masked language modeling of individual sequences, neglecting the crucial aspect of encoding statistics across multiple sequences. To overcome this challenge, we introduce an innovative deep neural network model, which incorporates collaborative learning between a `student' and a `teacher' subnetwork. In this model, the student subnetwork employs masked learning on nucleotides and progressively adapts its parameters to the teacher subnetwork through an exponential moving average approach. Concurrently, both subnetworks engage in contrastive learning, deriving insights from two augmented representations of the input sequences. This self-distillation process enables our model to effectively assimilate both contextual information from individual sequences and distributional data across the sequence population. We validated our approach with preliminary pretraining using the human reference genome, followed by applying it to 20 downstream inference tasks. The empirical results from these experiments demonstrate that our novel method significantly boosts inference performance across the majority of these tasks. Our code is available at https://github.com/wiedersehne/FinDNA. | [
"['Tong Yu' 'Lei Cheng' 'Ruslan Khalitov' 'Erland Brandser Olsson'\n 'Zhirong Yang']"
]
|
null | null | 2405.08540 | null | null | http://arxiv.org/pdf/2405.08540v1 | 2024-05-14T12:26:19Z | 2024-05-14T12:26:19Z | Generalizing Knowledge Graph Embedding with Universal Orthogonal
Parameterization | Recent advances in knowledge graph embedding (KGE) rely on Euclidean/hyperbolic orthogonal relation transformations to model intrinsic logical patterns and topological structures. However, existing approaches are confined to rigid relational orthogonalization with restricted dimension and homogeneous geometry, leading to deficient modeling capability. In this work, we move beyond these approaches in terms of both dimension and geometry by introducing a powerful framework named GoldE, which features a universal orthogonal parameterization based on a generalized form of Householder reflection. Such parameterization can naturally achieve dimensional extension and geometric unification with theoretical guarantees, enabling our framework to simultaneously capture crucial logical patterns and inherent topological heterogeneity of knowledge graphs. Empirically, GoldE achieves state-of-the-art performance on three standard benchmarks. Codes are available at https://github.com/xxrep/GoldE. | [
"['Rui Li' 'Chaozhuo Li' 'Yanming Shen' 'Zeyu Zhang' 'Xu Chen']"
]
|
null | null | 2405.08550 | null | null | http://arxiv.org/pdf/2405.08550v1 | 2024-05-14T12:40:25Z | 2024-05-14T12:40:25Z | Learning Multi-Agent Communication from Graph Modeling Perspective | In numerous artificial intelligence applications, the collaborative efforts of multiple intelligent agents are imperative for the successful attainment of target objectives. To enhance coordination among these agents, a distributed communication framework is often employed. However, information sharing among all agents proves to be resource-intensive, while the adoption of a manually pre-defined communication architecture imposes limitations on inter-agent communication, thereby constraining the potential for collaborative efforts. In this study, we introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. We formulate this problem as the task of determining the communication graph while enabling the architecture parameters to update normally, thus necessitating a bi-level optimization process. Utilizing continuous relaxation of the graph representation and incorporating attention units, our proposed approach, CommFormer, efficiently optimizes the communication graph and concurrently refines architectural parameters through gradient descent in an end-to-end manner. Extensive experiments on a variety of cooperative tasks substantiate the robustness of our model across diverse cooperative scenarios, where agents are able to develop more coordinated and sophisticated strategies regardless of changes in the number of agents. | [
"['Shengchao Hu' 'Li Shen' 'Ya Zhang' 'Dacheng Tao']"
]
|
null | null | 2405.08553 | null | null | http://arxiv.org/pdf/2405.08553v2 | 2024-06-04T14:49:36Z | 2024-05-14T12:41:11Z | Improving Transformers with Dynamically Composable Multi-Head Attention | Multi-Head Attention (MHA) is a key component of Transformer. In MHA, attention heads work independently, causing problems such as low-rank bottleneck of attention score matrices and head redundancy. We propose Dynamically Composable Multi-Head Attention (DCMHA), a parameter and computation efficient attention architecture that tackles the shortcomings of MHA and increases the expressive power of the model by dynamically composing attention heads. At the core of DCMHA is a $it{Compose}$ function that transforms the attention score and weight matrices in an input-dependent way. DCMHA can be used as a drop-in replacement of MHA in any transformer architecture to obtain the corresponding DCFormer. DCFormer significantly outperforms Transformer on different architectures and model scales in language modeling, matching the performance of models with ~1.7x-2.0x compute. For example, DCPythia-6.9B outperforms open source Pythia-12B on both pretraining perplexity and downstream task evaluation. The code and models are available at https://github.com/Caiyun-AI/DCFormer. | [
"['Da Xiao' 'Qingye Meng' 'Shengping Li' 'Xingyuan Yuan']"
]
|
null | null | 2405.08556 | null | null | http://arxiv.org/pdf/2405.08556v2 | 2024-07-09T13:32:24Z | 2024-05-14T12:45:49Z | Shape-aware synthesis of pathological lung CT scans using CycleGAN for
enhanced semi-supervised lung segmentation | This paper addresses the problem of pathological lung segmentation, a significant challenge in medical image analysis, particularly pronounced in cases of peripheral opacities (severe fibrosis and consolidation) because of the textural similarity between lung tissue and surrounding areas. To overcome these challenges, this paper emphasizes the use of CycleGAN for unpaired image-to-image translation, in order to provide an augmentation method able to generate fake pathological images matching an existing ground truth. Although previous studies have employed CycleGAN, they often neglect the challenge of shape deformation, which is crucial for accurate medical image segmentation. Our work introduces an innovative strategy that incorporates additional loss functions. Specifically, it proposes an L1 loss based on the lung surrounding which shape is constrained to remain unchanged at the transition from the healthy to pathological domains. The lung surrounding is derived based on ground truth lung masks available in the healthy domain. Furthermore, preprocessing steps, such as cropping based on ribs/vertebra locations, are applied to refine the input for the CycleGAN, ensuring that the network focus on the lung region. This is essential to avoid extraneous biases, such as the zoom effect bias, which can divert attention from the main task. The method is applied to enhance in semi-supervised manner the lung segmentation process by employing a U-Net model trained with on-the-fly data augmentation incorporating synthetic pathological tissues generated by the CycleGAN model. Preliminary results from this research demonstrate significant qualitative and quantitative improvements, setting a new benchmark in the field of pathological lung segmentation. Our code is available at https://github.com/noureddinekhiati/Semi-supervised-lung-segmentation | [
"['Rezkellah Noureddine Khiati' 'Pierre-Yves Brillet' 'Aurélien Justet'\n 'Radu Ispas' 'Catalin Fetita']"
]
|
null | null | 2405.08558 | null | null | http://arxiv.org/pdf/2405.08558v1 | 2024-05-14T12:46:12Z | 2024-05-14T12:46:12Z | PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced
order models for nonlinear parametrized PDEs | The coupling of Proper Orthogonal Decomposition (POD) and deep learning-based ROMs (DL-ROMs) has proved to be a successful strategy to construct non-intrusive, highly accurate, surrogates for the real time solution of parametric nonlinear time-dependent PDEs. Inexpensive to evaluate, POD-DL-ROMs are also relatively fast to train, thanks to their limited complexity. However, POD-DL-ROMs account for the physical laws governing the problem at hand only through the training data, that are usually obtained through a full order model (FOM) relying on a high-fidelity discretization of the underlying equations. Moreover, the accuracy of POD-DL-ROMs strongly depends on the amount of available data. In this paper, we consider a major extension of POD-DL-ROMs by enforcing the fulfillment of the governing physical laws in the training process -- that is, by making them physics-informed -- to compensate for possible scarce and/or unavailable data and improve the overall reliability. To do that, we first complement POD-DL-ROMs with a trunk net architecture, endowing them with the ability to compute the problem's solution at every point in the spatial domain, and ultimately enabling a seamless computation of the physics-based loss by means of the strong continuous formulation. Then, we introduce an efficient training strategy that limits the notorious computational burden entailed by a physics-informed training phase. In particular, we take advantage of the few available data to develop a low-cost pre-training procedure; then, we fine-tune the architecture in order to further improve the prediction reliability. Accuracy and efficiency of the resulting pre-trained physics-informed DL-ROMs (PTPI-DL-ROMs) are then assessed on a set of test cases ranging from non-affinely parametrized advection-diffusion-reaction equations, to nonlinear problems like the Navier-Stokes equations for fluid flows. | [
"['Simone Brivio' 'Stefania Fresca' 'Andrea Manzoni']"
]
|
null | null | 2405.08567 | null | null | http://arxiv.org/pdf/2405.08567v1 | 2024-05-14T13:01:04Z | 2024-05-14T13:01:04Z | Python-Based Reinforcement Learning on Simulink Models | This paper proposes a framework for training Reinforcement Learning agents using Python in conjunction with Simulink models. Leveraging Python's superior customization options and popular libraries like Stable Baselines3, we aim to bridge the gap between the established Simulink environment and the flexibility of Python for training bleeding edge agents. Our approach is demonstrated on the Quanser Aero 2, a versatile dual-rotor helicopter. We show that policies trained on Simulink models can be seamlessly transferred to the real system, enabling efficient development and deployment of Reinforcement Learning agents for control tasks. Through systematic integration steps, including C-code generation from Simulink, DLL compilation, and Python interface development, we establish a robust framework for training agents on Simulink models. Experimental results demonstrate the effectiveness of our approach, surpassing previous efforts and highlighting the potential of combining Simulink with Python for Reinforcement Learning research and applications. | [
"['Georg Schäfer' 'Max Schirl' 'Jakob Rehrl' 'Stefan Huber'\n 'Simon Hirlaender']"
]
|
null | null | 2405.08576 | null | null | http://arxiv.org/pdf/2405.08576v1 | 2024-05-14T13:16:46Z | 2024-05-14T13:16:46Z | Hearing Touch: Audio-Visual Pretraining for Contact-Rich Manipulation | Although pre-training on a large amount of data is beneficial for robot learning, current paradigms only perform large-scale pretraining for visual representations, whereas representations for other modalities are trained from scratch. In contrast to the abundance of visual data, it is unclear what relevant internet-scale data may be used for pretraining other modalities such as tactile sensing. Such pretraining becomes increasingly crucial in the low-data regimes common in robotics applications. In this paper, we address this gap by using contact microphones as an alternative tactile sensor. Our key insight is that contact microphones capture inherently audio-based information, allowing us to leverage large-scale audio-visual pretraining to obtain representations that boost the performance of robotic manipulation. To the best of our knowledge, our method is the first approach leveraging large-scale multisensory pre-training for robotic manipulation. For supplementary information including videos of real robot experiments, please see https://sites.google.com/view/hearing-touch. | [
"['Jared Mejia' 'Victoria Dean' 'Tess Hellebrekers' 'Abhinav Gupta']"
]
|
null | null | 2405.08597 | null | null | http://arxiv.org/pdf/2405.08597v3 | 2024-05-29T10:05:40Z | 2024-05-14T13:37:36Z | Risks and Opportunities of Open-Source Generative AI | Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation, in particular from some of the major tech companies who are leading in AI development. This regulation is likely to put at risk the budding field of open-source generative AI. Using a three-stage framework for Gen AI development (near, mid and long-term), we analyze the risks and opportunities of open-source generative AI models with similar capabilities to the ones currently available (near to mid-term) and with greater capabilities (long-term). We argue that, overall, the benefits of open-source Gen AI outweigh its risks. As such, we encourage the open sourcing of models, training and evaluation data, and provide a set of recommendations and best practices for managing risks associated with open-source generative AI. | [
"['Francisco Eiras' 'Aleksandar Petrov' 'Bertie Vidgen'\n 'Christian Schroeder' 'Fabio Pizzati' 'Katherine Elkins'\n 'Supratik Mukhopadhyay' 'Adel Bibi' 'Aaron Purewal' 'Csaba Botos'\n 'Fabro Steibel' 'Fazel Keshtkar' 'Fazl Barez' 'Genevieve Smith'\n 'Gianluca Guadagni' 'Jon Chun' 'Jordi Cabot' 'Joseph Imperial'\n 'Juan Arturo Nolazco' 'Lori Landay' 'Matthew Jackson'\n 'Phillip H. S. Torr' 'Trevor Darrell' 'Yong Lee' 'Jakob Foerster']"
]
|
null | null | 2405.08602 | null | null | http://arxiv.org/pdf/2405.08602v1 | 2024-05-14T13:41:44Z | 2024-05-14T13:41:44Z | Optimizing Deep Reinforcement Learning for American Put Option Hedging | This paper contributes to the existing literature on hedging American options with Deep Reinforcement Learning (DRL). The study first investigates hyperparameter impact on hedging performance, considering learning rates, training episodes, neural network architectures, training steps, and transaction cost penalty functions. Results highlight the importance of avoiding certain combinations, such as high learning rates with a high number of training episodes or low learning rates with few training episodes and emphasize the significance of utilizing moderate values for optimal outcomes. Additionally, the paper warns against excessive training steps to prevent instability and demonstrates the superiority of a quadratic transaction cost penalty function over a linear version. This study then expands upon the work of Pickard et al. (2024), who utilize a Chebyshev interpolation option pricing method to train DRL agents with market calibrated stochastic volatility models. While the results of Pickard et al. (2024) showed that these DRL agents achieve satisfactory performance on empirical asset paths, this study introduces a novel approach where new agents at weekly intervals to newly calibrated stochastic volatility models. Results show DRL agents re-trained using weekly market data surpass the performance of those trained solely on the sale date. Furthermore, the paper demonstrates that both single-train and weekly-train DRL agents outperform the Black-Scholes Delta method at transaction costs of 1% and 3%. This practical relevance suggests that practitioners can leverage readily available market data to train DRL agents for effective hedging of options in their portfolios. | [
"['Reilly Pickard' 'F. Wredenhagen' 'Y. Lawryshyn']"
]
|
null | null | 2405.08604 | null | null | http://arxiv.org/pdf/2405.08604v2 | 2024-05-24T03:36:10Z | 2024-05-14T13:42:19Z | Towards Geometry-Aware Pareto Set Learning for Neural Multi-Objective
Combinatorial Optimization | Multi-objective combinatorial optimization (MOCO) problems are prevalent in various real-world applications. Most existing neural MOCO methods rely on problem decomposition to transform an MOCO problem into a series of singe-objective combinatorial optimization (SOCO) problems. However, these methods often approximate partial regions of the Pareto front and spend excessive time on diversity enhancement because of ambiguous decomposition and time-consuming precise hypervolume calculation. To address these limitations, we design a Geometry-Aware Pareto set Learning algorithm named GAPL, which provides a novel geometric perspective for neural MOCO via a Pareto attention model based on hypervolume expectation maximization. In addition, we propose a hypervolume residual update strategy to enable the Pareto attention model to capture both local and non-local information of the Pareto set/front. We also design a novel inference approach to further improve quality of the solution set and speed up hypervolume calculation. Experimental results on three classic MOCO problems demonstrate that our GAPL outperforms several state-of-the-art baselines via superior decomposition and efficient diversity enhancement. | [
"['Yongfan Lu' 'Zixiang Di' 'Bingdong Li' 'Shengcai Liu' 'Hong Qian'\n 'Peng Yang' 'Ke Tang' 'Aimin Zhou']"
]
|
null | null | 2405.08613 | null | null | http://arxiv.org/pdf/2405.08613v1 | 2024-05-14T13:56:12Z | 2024-05-14T13:56:12Z | GN-SINDy: Greedy Sampling Neural Network in Sparse Identification of
Nonlinear Partial Differential Equations | The sparse identification of nonlinear dynamical systems (SINDy) is a data-driven technique employed for uncovering and representing the fundamental dynamics of intricate systems based on observational data. However, a primary obstacle in the discovery of models for nonlinear partial differential equations (PDEs) lies in addressing the challenges posed by the curse of dimensionality and large datasets. Consequently, the strategic selection of the most informative samples within a given dataset plays a crucial role in reducing computational costs and enhancing the effectiveness of SINDy-based algorithms. To this aim, we employ a greedy sampling approach to the snapshot matrix of a PDE to obtain its valuable samples, which are suitable to train a deep neural network (DNN) in a SINDy framework. SINDy based algorithms often consist of a data collection unit, constructing a dictionary of basis functions, computing the time derivative, and solving a sparse identification problem which ends to regularised least squares minimization. In this paper, we extend the results of a SINDy based deep learning model discovery (DeePyMoD) approach by integrating greedy sampling technique in its data collection unit and new sparsity promoting algorithms in the least squares minimization unit. In this regard we introduce the greedy sampling neural network in sparse identification of nonlinear partial differential equations (GN-SINDy) which blends a greedy sampling method, the DNN, and the SINDy algorithm. In the implementation phase, to show the effectiveness of GN-SINDy, we compare its results with DeePyMoD by using a Python package that is prepared for this purpose on numerous PDE discovery | [
"['Ali Forootani' 'Peter Benner']"
]
|
null | null | 2405.08631 | null | null | http://arxiv.org/pdf/2405.08631v1 | 2024-05-14T14:10:48Z | 2024-05-14T14:10:48Z | A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net
Penalized Regression via Block-Coordinate Descent | We develop fast and scalable algorithms based on block-coordinate descent to solve the group lasso and the group elastic net for generalized linear models along a regularization path. Special attention is given when the loss is the usual least squares loss (Gaussian loss). We show that each block-coordinate update can be solved efficiently using Newton's method and further improved using an adaptive bisection method, solving these updates with a quadratic convergence rate. Our benchmarks show that our package adelie performs 3 to 10 times faster than the next fastest package on a wide array of both simulated and real datasets. Moreover, we demonstrate that our package is a competitive lasso solver as well, matching the performance of the popular lasso package glmnet. | [
"['James Yang' 'Trevor Hastie']"
]
|
null | null | 2405.08636 | null | null | http://arxiv.org/pdf/2405.08636v1 | 2024-05-14T14:14:23Z | 2024-05-14T14:14:23Z | Optimal design of experiments in the context of machine-learning
inter-atomic potentials: improving the efficiency and transferability of
kernel based methods | Data-driven, machine learning (ML) models of atomistic interactions are often based on flexible and non-physical functions that can relate nuanced aspects of atomic arrangements into predictions of energies and forces. As a result, these potentials are as good as the training data (usually results of so-called ab initio simulations) and we need to make sure that we have enough information for a model to become sufficiently accurate, reliable and transferable. The main challenge stems from the fact that descriptors of chemical environments are often sparse high-dimensional objects without a well-defined continuous metric. Therefore, it is rather unlikely that any ad hoc method of choosing training examples will be indiscriminate, and it will be easy to fall into the trap of confirmation bias, where the same narrow and biased sampling is used to generate train- and test- sets. We will demonstrate that classical concepts of statistical planning of experiments and optimal design can help to mitigate such problems at a relatively low computational cost. The key feature of the method we will investigate is that they allow us to assess the informativeness of data (how much we can improve the model by adding/swapping a training example) and verify if the training is feasible with the current set before obtaining any reference energies and forces -- a so-called off-line approach. In other words, we are focusing on an approach that is easy to implement and doesn't require sophisticated frameworks that involve automated access to high-performance computational (HPC). | [
"['Bartosz Barzdajn' 'Christopher P. Race']"
]
|
null | null | 2405.08637 | null | null | http://arxiv.org/pdf/2405.08637v1 | 2024-05-14T14:15:31Z | 2024-05-14T14:15:31Z | Drift Detection: Introducing Gaussian Split Detector | Recent research yielded a wide array of drift detectors. However, in order to achieve remarkable performance, the true class labels must be available during the drift detection phase. This paper targets at detecting drift when the ground truth is unknown during the detection phase. To that end, we introduce Gaussian Split Detector (GSD) a novel drift detector that works in batch mode. GSD is designed to work when the data follow a normal distribution and makes use of Gaussian mixture models to monitor changes in the decision boundary. The algorithm is designed to handle multi-dimension data streams and to work without the ground truth labels during the inference phase making it pertinent for real world use. In an extensive experimental study on real and synthetic datasets, we evaluate our detector against the state of the art. We show that our detector outperforms the state of the art in detecting real drift and in ignoring virtual drift which is key to avoid false alarms. | [
"['Maxime Fuccellaro' 'Laurent Simon' 'Akka Zemmari']"
]
|
null | null | 2405.08638 | null | null | http://arxiv.org/pdf/2405.08638v1 | 2024-05-14T14:18:25Z | 2024-05-14T14:18:25Z | vMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of
Gradient Directions for Policy Improvement | Reinforcement Learning (RL) is a widely employed technique in decision-making problems, encompassing two fundamental operations -- policy evaluation and policy improvement. Enhancing learning efficiency remains a key challenge in RL, with many efforts focused on using ensemble critics to boost policy evaluation efficiency. However, when using multiple critics, the actor in the policy improvement process can obtain different gradients. Previous studies have combined these gradients without considering their disagreements. Therefore, optimizing the policy improvement process is crucial to enhance learning efficiency. This study focuses on investigating the impact of gradient disagreements caused by ensemble critics on policy improvement. We introduce the concept of uncertainty of gradient directions as a means to measure the disagreement among gradients utilized in the policy improvement process. Through measuring the disagreement among gradients, we find that transitions with lower uncertainty of gradient directions are more reliable in the policy improvement process. Building on this analysis, we propose a method called von Mises-Fisher Experience Resampling (vMFER), which optimizes the policy improvement process by resampling transitions and assigning higher confidence to transitions with lower uncertainty of gradient directions. Our experiments demonstrate that vMFER significantly outperforms the benchmark and is particularly well-suited for ensemble structures in RL. | [
"['Yiwen Zhu' 'Jinyi Liu' 'Wenya Wei' 'Qianyi Fu' 'Yujing Hu' 'Zhou Fang'\n 'Bo An' 'Jianye Hao' 'Tangjie Lv' 'Changjie Fan']"
]
|
null | null | 2405.08645 | null | null | http://arxiv.org/pdf/2405.08645v1 | 2024-05-14T14:21:55Z | 2024-05-14T14:21:55Z | Certifying Robustness of Graph Convolutional Networks for Node
Perturbation with Polyhedra Abstract Interpretation | Graph convolutional neural networks (GCNs) are powerful tools for learning graph-based knowledge representations from training data. However, they are vulnerable to small perturbations in the input graph, which makes them susceptible to input faults or adversarial attacks. This poses a significant problem for GCNs intended to be used in critical applications, which need to provide certifiably robust services even in the presence of adversarial perturbations. We propose an improved GCN robustness certification technique for node classification in the presence of node feature perturbations. We introduce a novel polyhedra-based abstract interpretation approach to tackle specific challenges of graph data and provide tight upper and lower bounds for the robustness of the GCN. Experiments show that our approach simultaneously improves the tightness of robustness bounds as well as the runtime performance of certification. Moreover, our method can be used during training to further improve the robustness of GCNs. | [
"['Boqi Chen' 'Kristóf Marussy' 'Oszkár Semeráth' 'Gunter Mussbacher'\n 'Dániel Varró']"
]
|
null | null | 2405.08647 | null | null | http://arxiv.org/pdf/2405.08647v1 | 2024-05-14T14:22:14Z | 2024-05-14T14:22:14Z | Output-decomposed Learning of Mealy Machines | We present an active automata learning algorithm which learns a decomposition of a finite state machine, based on projecting onto individual outputs. This is dual to a recent compositional learning algorithm by Labbaf et al. (2023). When projecting the outputs to a smaller set, the model itself is reduced in size. By having several such projections, we do not lose any information and the full system can be reconstructed. Depending on the structure of the system this reduces the number of queries drastically, as shown by a preliminary evaluation of the algorithm. | [
"['Rick Koenders' 'Joshua Moerman']"
]
|
null | null | 2405.08654 | null | null | http://arxiv.org/pdf/2405.08654v2 | 2024-05-21T07:38:02Z | 2024-05-14T14:32:58Z | Can we Defend Against the Unknown? An Empirical Study About Threshold
Selection for Neural Network Monitoring | With the increasing use of neural networks in critical systems, runtime monitoring becomes essential to reject unsafe predictions during inference. Various techniques have emerged to establish rejection scores that maximize the separability between the distributions of safe and unsafe predictions. The efficacy of these approaches is mostly evaluated using threshold-agnostic metrics, such as the area under the receiver operating characteristic curve. However, in real-world applications, an effective monitor also requires identifying a good threshold to transform these scores into meaningful binary decisions. Despite the pivotal importance of threshold optimization, this problem has received little attention. A few studies touch upon this question, but they typically assume that the runtime data distribution mirrors the training distribution, which is a strong assumption as monitors are supposed to safeguard a system against potentially unforeseen threats. In this work, we present rigorous experiments on various image datasets to investigate: 1. The effectiveness of monitors in handling unforeseen threats, which are not available during threshold adjustments. 2. Whether integrating generic threats into the threshold optimization scheme can enhance the robustness of monitors. | [
"['Khoi Tran Dang' 'Kevin Delmas' 'Jérémie Guiochet' 'Joris Guérin']"
]
|
null | null | 2405.08658 | null | null | http://arxiv.org/pdf/2405.08658v1 | 2024-05-14T14:35:35Z | 2024-05-14T14:35:35Z | Beyond the Black Box: Do More Complex Models Provide Superior XAI
Explanations? | The increasing complexity of Artificial Intelligence models poses challenges to interpretability, particularly in the healthcare sector. This study investigates the impact of deep learning model complexity and Explainable AI (XAI) efficacy, utilizing four ResNet architectures (ResNet-18, 34, 50, 101). Through methodical experimentation on 4,369 lung X-ray images of COVID-19-infected and healthy patients, the research evaluates models' classification performance and the relevance of corresponding XAI explanations with respect to the ground-truth disease masks. Results indicate that the increase in model complexity is associated with a decrease in classification accuracy and AUC-ROC scores (ResNet-18: 98.4%, 0.997; ResNet-101: 95.9%, 0.988). Notably, in eleven out of twelve statistical tests performed, no statistically significant differences occurred between XAI quantitative metrics - Relevance Rank Accuracy and the proposed Positive Attribution Ratio - across trained models. These results suggest that increased model complexity does not consistently lead to higher performance or relevance of explanations for models' decision-making processes. | [
"['Mateusz Cedro' 'Marcin Chlebus']"
]
|
null | null | 2405.08661 | null | null | http://arxiv.org/abs/2405.08661v1 | 2024-05-14T14:41:58Z | 2024-05-14T14:41:58Z | Gradient Estimation and Variance Reduction in Stochastic and
Deterministic Models | It seems that in the current age, computers, computation, and data have an increasingly important role to play in scientific research and discovery. This is reflected in part by the rise of machine learning and artificial intelligence, which have become great areas of interest not just for computer science but also for many other fields of study. More generally, there have been trends moving towards the use of bigger, more complex and higher capacity models. It also seems that stochastic models, and stochastic variants of existing deterministic models, have become important research directions in various fields. For all of these types of models, gradient-based optimization remains as the dominant paradigm for model fitting, control, and more. This dissertation considers unconstrained, nonlinear optimization problems, with a focus on the gradient itself, that key quantity which enables the solution of such problems. In chapter 1, we introduce the notion of reverse differentiation, a term which describes the body of techniques which enables the efficient computation of gradients. We cover relevant techniques both in the deterministic and stochastic cases. We present a new framework for calculating the gradient of problems which involve both deterministic and stochastic elements. In chapter 2, we analyze the properties of the gradient estimator, with a focus on those properties which are typically assumed in convergence proofs of optimization algorithms. Chapter 3 gives various examples of applying our new gradient estimator. We further explore the idea of working with piecewise continuous models, that is, models with distinct branches and if statements which define what specific branch to use. | [
"['Ronan Keane']"
]
|
null | null | 2405.08668 | null | null | http://arxiv.org/pdf/2405.08668v1 | 2024-05-14T14:51:12Z | 2024-05-14T14:51:12Z | Promoting AI Equity in Science: Generalized Domain Prompt Learning for
Accessible VLM Research | Large-scale Vision-Language Models (VLMs) have demonstrated exceptional performance in natural vision tasks, motivating researchers across domains to explore domain-specific VLMs. However, the construction of powerful domain-specific VLMs demands vast amounts of annotated data, substantial electrical energy, and computing resources, primarily accessible to industry, yet hindering VLM research in academia. To address this challenge and foster sustainable and equitable VLM research, we present the Generalized Domain Prompt Learning (GDPL) framework. GDPL facilitates the transfer of VLMs' robust recognition capabilities from natural vision to specialized domains, without the need for extensive data or resources. By leveraging small-scale domain-specific foundation models and minimal prompt samples, GDPL empowers the language branch with domain knowledge through quaternion networks, uncovering cross-modal relationships between domain-specific vision features and natural vision-based contextual embeddings. Simultaneously, GDPL guides the vision branch into specific domains through hierarchical propagation of generated vision prompt features, grounded in well-matched vision-language relations. Furthermore, to fully harness the domain adaptation potential of VLMs, we introduce a novel low-rank adaptation approach. Extensive experiments across diverse domains like remote sensing, medical imaging, geology, Synthetic Aperture Radar, and fluid dynamics, validate the efficacy of GDPL, demonstrating its ability to achieve state-of-the-art domain recognition performance in a prompt learning paradigm. Our framework paves the way for sustainable and inclusive VLM research, transcending the barriers between academia and industry. | [
"['Qinglong Cao' 'Yuntian Chen' 'Lu Lu' 'Hao Sun' 'Zhenzhong Zeng'\n 'Xiaokang Yang' 'Dongxiao Zhang']"
]
|
null | null | 2405.08674 | null | null | http://arxiv.org/pdf/2405.08674v1 | 2024-05-14T14:55:57Z | 2024-05-14T14:55:57Z | Expensive Multi-Objective Bayesian Optimization Based on Diffusion
Models | Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling complex distributions of the Pareto optimal solutions is difficult with limited function evaluations. Existing Pareto set learning algorithms may exhibit considerable instability in such expensive scenarios, leading to significant deviations between the obtained solution set and the Pareto set (PS). In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm, namely CDM-PSL, for expensive MOBO. CDM-PSL includes both unconditional and conditional diffusion model for generating high-quality samples. Besides, we introduce an information entropy based weighting method to balance different objectives of EMOPs. This method is integrated with the guiding strategy, ensuring that all the objectives are appropriately balanced and given due consideration during the optimization process; Extensive experimental results on both synthetic benchmarks and real-world problems demonstrates that our proposed algorithm attains superior performance compared with various state-of-the-art MOBO algorithms. | [
"['Bingdong Li' 'Zixiang Di' 'Yongfan Lu' 'Hong Qian' 'Feng Wang'\n 'Peng Yang' 'Ke Tang' 'Aimin Zhou']"
]
|
null | null | 2405.08679 | null | null | http://arxiv.org/pdf/2405.08679v1 | 2024-05-14T15:00:09Z | 2024-05-14T15:00:09Z | Investigating Design Choices in Joint-Embedding Predictive Architectures
for General Audio Representation Learning | This paper addresses the problem of self-supervised general-purpose audio representation learning. We explore the use of Joint-Embedding Predictive Architectures (JEPA) for this task, which consists of splitting an input mel-spectrogram into two parts (context and target), computing neural representations for each, and training the neural network to predict the target representations from the context representations. We investigate several design choices within this framework and study their influence through extensive experiments by evaluating our models on various audio classification benchmarks, including environmental sounds, speech and music downstream tasks. We focus notably on which part of the input data is used as context or target and show experimentally that it significantly impacts the model's quality. In particular, we notice that some effective design choices in the image domain lead to poor performance on audio, thus highlighting major differences between these two modalities. | [
"['Alain Riou' 'Stefan Lattner' 'Gaëtan Hadjeres' 'Geoffroy Peeters']"
]
|
null | null | 2405.08698 | null | null | http://arxiv.org/pdf/2405.08698v2 | 2024-07-08T17:48:43Z | 2024-05-14T15:37:56Z | Byzantine-Resilient Secure Aggregation for Federated Learning Without
Privacy Compromises | Federated learning (FL) shows great promise in large scale machine learning, but brings new risks in terms of privacy and security. We propose ByITFL, a novel scheme for FL that provides resilience against Byzantine users while keeping the users' data private from the federator and private from other users. The scheme builds on the preexisting non-private FLTrust scheme, which tolerates malicious users through trust scores (TS) that attenuate or amplify the users' gradients. The trust scores are based on the ReLU function, which we approximate by a polynomial. The distributed and privacy-preserving computation in ByITFL is designed using a combination of Lagrange coded computing, verifiable secret sharing and re-randomization steps. ByITFL is the first Byzantine resilient scheme for FL with full information-theoretic privacy. | [
"['Yue Xia' 'Christoph Hofmeister' 'Maximilian Egger' 'Rawad Bitar']"
]
|
null | null | 2405.08699 | null | null | http://arxiv.org/pdf/2405.08699v1 | 2024-05-14T15:39:22Z | 2024-05-14T15:39:22Z | Weakly-supervised causal discovery based on fuzzy knowledge and complex
data complementarity | Causal discovery based on observational data is important for deciphering the causal mechanism behind complex systems. However, the effectiveness of existing causal discovery methods is limited due to inferior prior knowledge, domain inconsistencies, and the challenges of high-dimensional datasets with small sample sizes. To address this gap, we propose a novel weakly-supervised fuzzy knowledge and data co-driven causal discovery method named KEEL. KEEL adopts a fuzzy causal knowledge schema to encapsulate diverse types of fuzzy knowledge, and forms corresponding weakened constraints. This schema not only lessens the dependency on expertise but also allows various types of limited and error-prone fuzzy knowledge to guide causal discovery. It can enhance the generalization and robustness of causal discovery, especially in high-dimensional and small-sample scenarios. In addition, we integrate the extended linear causal model (ELCM) into KEEL for dealing with the multi-distribution and incomplete data. Extensive experiments with different datasets demonstrate the superiority of KEEL over several state-of-the-art methods in accuracy, robustness and computational efficiency. For causal discovery in real protein signal transduction processes, KEEL outperforms the benchmark method with limited data. In summary, KEEL is effective to tackle the causal discovery tasks with higher accuracy while alleviating the requirement for extensive domain expertise. | [
"['Wenrui Li' 'Wei Zhang' 'Qinghao Zhang' 'Xuegong Zhang' 'Xiaowo Wang']"
]
|
null | null | 2405.08703 | null | null | http://arxiv.org/pdf/2405.08703v1 | 2024-05-14T15:42:27Z | 2024-05-14T15:42:27Z | Using autoencoders and deep transfer learning to determine the stellar
parameters of 286 CARMENES M dwarfs | Deep learning (DL) techniques are a promising approach among the set of methods used in the ever-challenging determination of stellar parameters in M dwarfs. In this context, transfer learning could play an important role in mitigating uncertainties in the results due to the synthetic gap (i.e. difference in feature distributions between observed and synthetic data). We propose a feature-based deep transfer learning (DTL) approach based on autoencoders to determine stellar parameters from high-resolution spectra. Using this methodology, we provide new estimations for the effective temperature, surface gravity, metallicity, and projected rotational velocity for 286 M dwarfs observed by the CARMENES survey. Using autoencoder architectures, we projected synthetic PHOENIX-ACES spectra and observed CARMENES spectra onto a new feature space of lower dimensionality in which the differences between the two domains are reduced. We used this low-dimensional new feature space as input for a convolutional neural network to obtain the stellar parameter determinations. We performed an extensive analysis of our estimated stellar parameters, ranging from 3050 to 4300 K, 4.7 to 5.1 dex, and -0.53 to 0.25 dex for Teff, logg, and [Fe/H], respectively. Our results are broadly consistent with those of recent studies using CARMENES data, with a systematic deviation in our Teff scale towards hotter values for estimations above 3750 K. Furthermore, our methodology mitigates the deviations in metallicity found in previous DL techniques due to the synthetic gap. We consolidated a DTL-based methodology to determine stellar parameters in M dwarfs from synthetic spectra, with no need for high-quality measurements involved in the knowledge transfer. These results suggest the great potential of DTL to mitigate the differences in feature distributions between the observations and the PHOENIX-ACES spectra. | [
"['P. Mas-Buitrago' 'A. González-Marcos' 'E. Solano' 'V. M. Passegger'\n 'M. Cortés-Contreras' 'J. Ordieres-Meré' 'A. Bello-García'\n 'J. A. Caballero' 'A. Schweitzer' 'H. M. Tabernero' 'D. Montes'\n 'C. Cifuentes']"
]
|
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