categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2404.17508 | null | null | http://arxiv.org/pdf/2404.17508v1 | 2024-04-26T16:20:04Z | 2024-04-26T16:20:04Z | Constrained Neural Networks for Interpretable Heuristic Creation to
Optimise Computer Algebra Systems | We present a new methodology for utilising machine learning technology in symbolic computation research. We explain how a well known human-designed heuristic to make the choice of variable ordering in cylindrical algebraic decomposition may be represented as a constrained neural network. This allows us to then use machine learning methods to further optimise the heuristic, leading to new networks of similar size, representing new heuristics of similar complexity as the original human-designed one. We present this as a form of ante-hoc explainability for use in computer algebra development. | [
"['Dorian Florescu' 'Matthew England']"
]
|
null | null | 2404.17511 | null | null | http://arxiv.org/pdf/2404.17511v1 | 2024-04-26T16:26:11Z | 2024-04-26T16:26:11Z | Bridging the Fairness Divide: Achieving Group and Individual Fairness in
Graph Neural Networks | Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and learning from complex data structured as graphs, demonstrating remarkable effectiveness in various applications, such as social network analysis, recommendation systems, and drug discovery. However, despite their impressive performance, the fairness problem has increasingly gained attention as a crucial aspect to consider. Existing research in graph learning focuses on either group fairness or individual fairness. However, since each concept provides unique insights into fairness from distinct perspectives, integrating them into a fair graph neural network system is crucial. To the best of our knowledge, no study has yet to comprehensively tackle both individual and group fairness simultaneously. In this paper, we propose a new concept of individual fairness within groups and a novel framework named Fairness for Group and Individual (FairGI), which considers both group fairness and individual fairness within groups in the context of graph learning. FairGI employs the similarity matrix of individuals to achieve individual fairness within groups, while leveraging adversarial learning to address group fairness in terms of both Equal Opportunity and Statistical Parity. The experimental results demonstrate that our approach not only outperforms other state-of-the-art models in terms of group fairness and individual fairness within groups, but also exhibits excellent performance in population-level individual fairness, while maintaining comparable prediction accuracy. | [
"['Duna Zhan' 'Dongliang Guo' 'Pengsheng Ji' 'Sheng Li']"
]
|
null | null | 2404.17525 | null | null | http://arxiv.org/pdf/2404.17525v2 | 2024-05-09T15:31:08Z | 2024-04-26T16:41:24Z | Large Language Model Agent as a Mechanical Designer | Conventional mechanical design paradigms rely on experts systematically refining concepts through experience-guided modification and FEA to meet specific requirements. However, this approach can be time-consuming and heavily dependent on prior knowledge and experience. While numerous machine learning models have been developed to streamline this intensive and expert-driven iterative process, these methods typically demand extensive training data and considerable computational resources. Furthermore, methods based on deep learning are usually restricted to the specific domains and tasks for which they were trained, limiting their applicability across different tasks. This creates a trade-off between the efficiency of automation and the demand for resources. In this study, we present a novel approach that integrates pre-trained LLMs with a FEM module. The FEM module evaluates each design and provides essential feedback, guiding the LLMs to continuously learn, plan, generate, and optimize designs without the need for domain-specific training. We demonstrate the effectiveness of our proposed framework in managing the iterative optimization of truss structures, showcasing its capability to reason about and refine designs according to structured feedback and criteria. Our results reveal that these LLM-based agents can successfully generate truss designs that comply with natural language specifications with a success rate of up to 90%, which varies according to the applied constraints. By employing prompt-based optimization techniques we show that LLM based agents exhibit optimization behavior when provided with solution-score pairs to iteratively refine designs to meet specifications. This ability of LLM agents to produce viable designs and optimize them based on their inherent reasoning capabilities highlights their potential to develop and implement effective design strategies autonomously. | [
"['Yayati Jadhav' 'Amir Barati Farimani']"
]
|
null | null | 2404.17535 | null | null | http://arxiv.org/pdf/2404.17535v1 | 2024-04-26T17:01:38Z | 2024-04-26T17:01:38Z | Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical
Systems | The recently introduced class of architectures known as Neural Operators has emerged as highly versatile tools applicable to a wide range of tasks in the field of Scientific Machine Learning (SciML), including data representation and forecasting. In this study, we investigate the capabilities of Neural Implicit Flow (NIF), a recently developed mesh-agnostic neural operator, for representing the latent dynamics of canonical systems such as the Kuramoto-Sivashinsky (KS), forced Korteweg-de Vries (fKdV), and Sine-Gordon (SG) equations, as well as for extracting dynamically relevant information from them. Finally we assess the applicability of NIF as a dimensionality reduction algorithm and conduct a comparative analysis with another widely recognized family of neural operators, known as Deep Operator Networks (DeepONets). | [
"['Imran Nasim' 'Joaõ Lucas de Sousa Almeida']"
]
|
null | null | 2404.17546 | null | null | http://arxiv.org/pdf/2404.17546v1 | 2024-04-26T17:18:32Z | 2024-04-26T17:18:32Z | Probabilistic Inference in Language Models via Twisted Sequential Monte
Carlo | Numerous capability and safety techniques of Large Language Models (LLMs), including RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling from an unnormalized target distribution defined by a given reward or potential function over the full sequence. In this work, we leverage the rich toolkit of Sequential Monte Carlo (SMC) for these probabilistic inference problems. In particular, we use learned twist functions to estimate the expected future value of the potential at each timestep, which enables us to focus inference-time computation on promising partial sequences. We propose a novel contrastive method for learning the twist functions, and establish connections with the rich literature of soft reinforcement learning. As a complementary application of our twisted SMC framework, we present methods for evaluating the accuracy of language model inference techniques using novel bidirectional SMC bounds on the log partition function. These bounds can be used to estimate the KL divergence between the inference and target distributions in both directions. We apply our inference evaluation techniques to show that twisted SMC is effective for sampling undesirable outputs from a pretrained model (a useful component of harmlessness training and automated red-teaming), generating reviews with varied sentiment, and performing infilling tasks. | [
"['Stephen Zhao' 'Rob Brekelmans' 'Alireza Makhzani' 'Roger Grosse']"
]
|
null | null | 2404.17552 | null | null | http://arxiv.org/pdf/2404.17552v1 | 2024-04-26T17:30:36Z | 2024-04-26T17:30:36Z | A Semi-Automatic Approach to Create Large Gender- and Age-Balanced
Speaker Corpora: Usefulness of Speaker Diarization & Identification | This paper presents a semi-automatic approach to create a diachronic corpus of voices balanced for speaker's age, gender, and recording period, according to 32 categories (2 genders, 4 age ranges and 4 recording periods). Corpora were selected at French National Institute of Audiovisual (INA) to obtain at least 30 speakers per category (a total of 960 speakers; only 874 have be found yet). For each speaker, speech excerpts were extracted from audiovisual documents using an automatic pipeline consisting of speech detection, background music and overlapped speech removal and speaker diarization, used to present clean speaker segments to human annotators identifying target speakers. This pipeline proved highly effective, cutting down manual processing by a factor of ten. Evaluation of the quality of the automatic processing and of the final output is provided. It shows the automatic processing compare to up-to-date process, and that the output provides high quality speech for most of the selected excerpts. This method shows promise for creating large corpora of known target speakers. | [
"['Rémi Uro' 'David Doukhan' 'Albert Rilliard' 'Laëtitia Larcher'\n 'Anissa-Claire Adgharouamane' 'Marie Tahon' 'Antoine Laurent']"
]
|
null | null | 2404.17553 | null | null | http://arxiv.org/pdf/2404.17553v2 | 2024-05-01T10:21:49Z | 2024-04-26T17:31:41Z | Federated Transfer Component Analysis Towards Effective VNF Profiling | The increasing concerns of knowledge transfer and data privacy challenge the traditional gather-and-analyse paradigm in networks. Specifically, the intelligent orchestration of Virtual Network Functions (VNFs) requires understanding and profiling the resource consumption. However, profiling all kinds of VNFs is time-consuming. It is important to consider transferring the well-profiled VNF knowledge to other lack-profiled VNF types while keeping data private. To this end, this paper proposes a Federated Transfer Component Analysis (FTCA) method between the source and target VNFs. FTCA first trains Generative Adversarial Networks (GANs) based on the source VNF profiling data, and the trained GANs model is sent to the target VNF domain. Then, FTCA realizes federated domain adaptation by using the generated source VNF data and less target VNF profiling data, while keeping the raw data locally. Experiments show that the proposed FTCA can effectively predict the required resources for the target VNF. Specifically, the RMSE index of the regression model decreases by 38.5% and the R-squared metric advances up to 68.6%. | [
"['Xunzheng Zhang' 'Shadi Moazzeni' 'Juan Marcelo Parra-Ullauri'\n 'Reza Nejabati' 'Dimitra Simeonidou']"
]
|
null | null | 2404.17563 | null | null | http://arxiv.org/pdf/2404.17563v2 | 2024-07-14T15:28:01Z | 2024-04-26T17:45:32Z | An exactly solvable model for emergence and scaling laws | Deep learning models can exhibit what appears to be a sudden ability to solve a new problem as training time, training data, or model size increases, a phenomenon known as emergence. In this paper, we present a framework where each new ability (a skill) is represented as a basis function. We solve a simple multi-linear model in this skill-basis, finding analytic expressions for the emergence of new skills, as well as for scaling laws of the loss with training time, data size, model size, and optimal compute ($C$). We compare our detailed calculations to direct simulations of a two-layer neural network trained on multitask sparse parity, where the tasks in the dataset are distributed according to a power-law. Our simple model captures, using a single fit parameter, the sigmoidal emergence of multiple new skills as training time, data size or model size increases in the neural network. | [
"['Yoonsoo Nam' 'Nayara Fonseca' 'Seok Hyeong Lee' 'Chris Mingard'\n 'Ard A. Louis']"
]
|
null | null | 2404.17582 | null | null | http://arxiv.org/pdf/2404.17582v1 | 2024-04-04T02:21:38Z | 2024-04-04T02:21:38Z | Data Quality in Crowdsourcing and Spamming Behavior Detection | As crowdsourcing emerges as an efficient and cost-effective method for obtaining labels for machine learning datasets, it is important to assess the quality of crowd-provided data, so as to improve analysis performance and reduce biases in subsequent machine learning tasks. Given the lack of ground truth in most cases of crowdsourcing, we refer to data quality as annotators' consistency and credibility. Unlike the simple scenarios where Kappa coefficient and intraclass correlation coefficient usually can apply, online crowdsourcing requires dealing with more complex situations. We introduce a systematic method for evaluating data quality and detecting spamming threats via variance decomposition, and we classify spammers into three categories based on their different behavioral patterns. A spammer index is proposed to assess entire data consistency and two metrics are developed to measure crowd worker's credibility by utilizing the Markov chain and generalized random effects models. Furthermore, we showcase the practicality of our techniques and their advantages by applying them on a face verification task with both simulation and real-world data collected from two crowdsourcing platforms. | [
"['Yang Ba' 'Michelle V. Mancenido' 'Erin K. Chiou' 'Rong Pan']"
]
|
null | null | 2404.17583 | null | null | http://arxiv.org/pdf/2404.17583v1 | 2024-04-05T12:40:03Z | 2024-04-05T12:40:03Z | Physically recurrent neural network for rate and path-dependent
heterogeneous materials in a finite strain framework | In this work, a hybrid physics-based data-driven surrogate model for the microscale analysis of heterogeneous material is investigated. The proposed model benefits from the physics-based knowledge contained in the constitutive models used in the full-order micromodel by embedding them in a neural network. Following previous developments, this paper extends the applicability of the physically recurrent neural network (PRNN) by introducing an architecture suitable for rate-dependent materials in a finite strain framework. In this model, the homogenized deformation gradient of the micromodel is encoded into a set of deformation gradients serving as input to the embedded constitutive models. These constitutive models compute stresses, which are combined in a decoder to predict the homogenized stress, such that the internal variables of the history-dependent constitutive models naturally provide physics-based memory for the network. To demonstrate the capabilities of the surrogate model, we consider a unidirectional composite micromodel with transversely isotropic elastic fibers and elasto-viscoplastic matrix material. The extrapolation properties of the surrogate model trained to replace such micromodel are tested on loading scenarios unseen during training, ranging from different strain-rates to cyclic loading and relaxation. Speed-ups of three orders of magnitude with respect to the runtime of the original micromodel are obtained. | [
"['M. A. Maia' 'I. B. C. M. Rocha' 'D. Kovačević' 'F. P. van der Meer']"
]
|
null | null | 2404.17584 | null | null | http://arxiv.org/pdf/2404.17584v1 | 2024-04-05T14:49:01Z | 2024-04-05T14:49:01Z | Equivariant graph convolutional neural networks for the representation
of homogenized anisotropic microstructural mechanical response | Composite materials with different microstructural material symmetries are common in engineering applications where grain structure, alloying and particle/fiber packing are optimized via controlled manufacturing. In fact these microstructural tunings can be done throughout a part to achieve functional gradation and optimization at a structural level. To predict the performance of particular microstructural configuration and thereby overall performance, constitutive models of materials with microstructure are needed. In this work we provide neural network architectures that provide effective homogenization models of materials with anisotropic components. These models satisfy equivariance and material symmetry principles inherently through a combination of equivariant and tensor basis operations. We demonstrate them on datasets of stochastic volume elements with different textures and phases where the material undergoes elastic and plastic deformation, and show that the these network architectures provide significant performance improvements. | [
"['Ravi Patel' 'Cosmin Safta' 'Reese E. Jones']"
]
|
null | null | 2404.17585 | null | null | http://arxiv.org/pdf/2404.17585v2 | 2024-05-13T13:55:11Z | 2024-04-10T18:32:22Z | NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep
Stage Classification Using Single-Channel EEG | The classification of sleep stages is a pivotal aspect of diagnosing sleep disorders and evaluating sleep quality. However, the conventional manual scoring process, conducted by clinicians, is time-consuming and prone to human bias. Recent advancements in deep learning have substantially propelled the automation of sleep stage classification. Nevertheless, challenges persist, including the need for large datasets with labels and the inherent biases in human-generated annotations. This paper introduces NeuroNet, a self-supervised learning (SSL) framework designed to effectively harness unlabeled single-channel sleep electroencephalogram (EEG) signals by integrating contrastive learning tasks and masked prediction tasks. NeuroNet demonstrates superior performance over existing SSL methodologies through extensive experimentation conducted across three polysomnography (PSG) datasets. Additionally, this study proposes a Mamba-based temporal context module to capture the relationships among diverse EEG epochs. Combining NeuroNet with the Mamba-based temporal context module has demonstrated the capability to achieve, or even surpass, the performance of the latest supervised learning methodologies, even with a limited amount of labeled data. This study is expected to establish a new benchmark in sleep stage classification, promising to guide future research and applications in the field of sleep analysis. | [
"['Cheol-Hui Lee' 'Hakseung Kim' 'Hyun-jee Han' 'Min-Kyung Jung'\n 'Byung C. Yoon' 'Dong-Joo Kim']"
]
|
null | null | 2404.17589 | null | null | http://arxiv.org/pdf/2404.17589v2 | 2024-05-07T00:38:37Z | 2024-04-19T08:43:03Z | An Off-Policy Reinforcement Learning Algorithm Customized for Multi-Task
Fusion in Large-Scale Recommender Systems | As the last critical stage of RSs, Multi-Task Fusion (MTF) is responsible for combining multiple scores outputted by Multi-Task Learning (MTL) into a final score to maximize user satisfaction, which determines the ultimate recommendation results. Recently, to optimize long-term user satisfaction within a recommendation session, Reinforcement Learning (RL) is used for MTF in the industry. However, the off-policy RL algorithms used for MTF so far have the following severe problems: 1) to avoid out-of-distribution (OOD) problem, their constraints are overly strict, which seriously damage their performance; 2) they are unaware of the exploration policy used for producing training data and never interact with real environment, so only suboptimal policy can be learned; 3) the traditional exploration policies are inefficient and hurt user experience. To solve the above problems, we propose a novel method named IntegratedRL-MTF customized for MTF in large-scale RSs. IntegratedRL-MTF integrates off-policy RL model with our online exploration policy to relax overstrict and complicated constraints, which significantly improves its performance. We also design an extremely efficient exploration policy, which eliminates low-value exploration space and focuses on exploring potential high-value state-action pairs. Moreover, we adopt progressive training mode to further enhance our model's performance with the help of our exploration policy. We conduct extensive offline and online experiments in the short video channel of Tencent News. The results demonstrate that our model outperforms other models remarkably. IntegratedRL-MTF has been fully deployed in our RS and other large-scale RSs in Tencent, which have achieved significant improvements. | [
"['Peng Liu' 'Cong Xu' 'Ming Zhao' 'Jiawei Zhu' 'Bin Wang' 'Yi Ren']"
]
|
null | null | 2404.17591 | null | null | http://arxiv.org/abs/2404.17591v1 | 2024-04-19T13:28:36Z | 2024-04-19T13:28:36Z | Large Language Models for Next Point-of-Interest Recommendation | The next Point of Interest (POI) recommendation task is to predict users' immediate next POI visit given their historical data. Location-Based Social Network (LBSN) data, which is often used for the next POI recommendation task, comes with challenges. One frequently disregarded challenge is how to effectively use the abundant contextual information present in LBSN data. Previous methods are limited by their numerical nature and fail to address this challenge. In this paper, we propose a framework that uses pretrained Large Language Models (LLMs) to tackle this challenge. Our framework allows us to preserve heterogeneous LBSN data in its original format, hence avoiding the loss of contextual information. Furthermore, our framework is capable of comprehending the inherent meaning of contextual information due to the inclusion of commonsense knowledge. In experiments, we test our framework on three real-world LBSN datasets. Our results show that the proposed framework outperforms the state-of-the-art models in all three datasets. Our analysis demonstrates the effectiveness of the proposed framework in using contextual information as well as alleviating the commonly encountered cold-start and short trajectory problems. | [
"['Peibo Li' 'Maarten de Rijke' 'Hao Xue' 'Shuang Ao' 'Yang Song'\n 'Flora D. Salim']"
]
|
null | null | 2404.17592 | null | null | http://arxiv.org/pdf/2404.17592v1 | 2024-04-19T23:10:12Z | 2024-04-19T23:10:12Z | Low-Rank Online Dynamic Assortment with Dual Contextual Information | As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features to optimize assortments over time. In this paper, we consider the dynamic assortment problem with dual contexts -- user and item features. In high-dimensional scenarios, the quadratic growth of dimensions complicates computation and estimation. To tackle this challenge, we introduce a new low-rank dynamic assortment model to transform this problem into a manageable scale. Then we propose an efficient algorithm that estimates the intrinsic subspaces and utilizes the upper confidence bound approach to address the exploration-exploitation trade-off in online decision making. Theoretically, we establish a regret bound of $tilde{O}((d_1+d_2)rsqrt{T})$, where $d_1, d_2$ represent the dimensions of the user and item features respectively, $r$ is the rank of the parameter matrix, and $T$ denotes the time horizon. This bound represents a substantial improvement over prior literature, made possible by leveraging the low-rank structure. Extensive simulations and an application to the Expedia hotel recommendation dataset further demonstrate the advantages of our proposed method. | [
"['Seong Jin Lee' 'Will Wei Sun' 'Yufeng Liu']"
]
|
null | null | 2404.17598 | null | null | http://arxiv.org/pdf/2404.17598v1 | 2024-04-23T06:43:58Z | 2024-04-23T06:43:58Z | Revealing and Utilizing In-group Favoritism for Graph-based
Collaborative Filtering | When it comes to a personalized item recommendation system, It is essential to extract users' preferences and purchasing patterns. Assuming that users in the real world form a cluster and there is common favoritism in each cluster, in this work, we introduce Co-Clustering Wrapper (CCW). We compute co-clusters of users and items with co-clustering algorithms and add CF subnetworks for each cluster to extract the in-group favoritism. Combining the features from the networks, we obtain rich and unified information about users. We experimented real world datasets considering two aspects: Finding the number of groups divided according to in-group preference, and measuring the quantity of improvement of the performance. | [
"['Hoin Jung' 'Hyunsoo Cho' 'Myungje Choi' 'Joowon Lee' 'Jung Ho Park'\n 'Myungjoo Kang']"
]
|
null | null | 2404.17601 | null | null | http://arxiv.org/pdf/2404.17601v1 | 2024-04-23T23:10:08Z | 2024-04-23T23:10:08Z | Nested Inheritance Dynamics | The idea of the inheritance of biological processes, such as the developmental process or the life cycle of an organism, has been discussed in the biology literature, but formal mathematical descriptions and plausible data analysis frameworks are lacking. We introduce an extension of the nested Dirichlet Process (nDP) to a multiscale model to aid in understanding the mechanisms by which biological processes are inherited, remain stable, and are modified across generations. To address these issues, we introduce Nested Inheritance Dynamics Algorithm (NIDA). At its primary level, NIDA encompasses all processes unfolding within an individual organism's lifespan. The secondary level delineates the dynamics through which these processes evolve or remain stable over time. This framework allows for the specification of a physical system model at either scale, thus promoting seamless integration with established models of development and heredity. | [
"['Bahman Moraffah']"
]
|
null | null | 2404.17606 | null | null | http://arxiv.org/pdf/2404.17606v1 | 2024-04-25T02:05:30Z | 2024-04-25T02:05:30Z | SetCSE: Set Operations using Contrastive Learning of Sentence Embeddings | Taking inspiration from Set Theory, we introduce SetCSE, an innovative information retrieval framework. SetCSE employs sets to represent complex semantics and incorporates well-defined operations for structured information querying under the provided context. Within this framework, we introduce an inter-set contrastive learning objective to enhance comprehension of sentence embedding models concerning the given semantics. Furthermore, we present a suite of operations, including SetCSE intersection, difference, and operation series, that leverage sentence embeddings of the enhanced model for complex sentence retrieval tasks. Throughout this paper, we demonstrate that SetCSE adheres to the conventions of human language expressions regarding compounded semantics, provides a significant enhancement in the discriminatory capability of underlying sentence embedding models, and enables numerous information retrieval tasks involving convoluted and intricate prompts which cannot be achieved using existing querying methods. | [
"['Kang Liu']"
]
|
null | null | 2404.17607 | null | null | http://arxiv.org/pdf/2404.17607v1 | 2024-04-25T15:45:58Z | 2024-04-25T15:45:58Z | Utilizing Large Language Models to Identify Reddit Users Considering
Vaping Cessation for Digital Interventions | The widespread adoption of social media platforms globally not only enhances users' connectivity and communication but also emerges as a vital channel for the dissemination of health-related information, thereby establishing social media data as an invaluable organic data resource for public health research. The surge in popularity of vaping or e-cigarette use in the United States and other countries has caused an outbreak of e-cigarette and vaping use-associated lung injury (EVALI), leading to hospitalizations and fatalities in 2019, highlighting the urgency to comprehend vaping behaviors and develop effective strategies for cession. In this study, we extracted a sample dataset from one vaping sub-community on Reddit to analyze users' quit vaping intentions. Leveraging large language models including both the latest GPT-4 and traditional BERT-based language models for sentence-level quit-vaping intention prediction tasks, this study compares the outcomes of these models against human annotations. Notably, when compared to human evaluators, GPT-4 model demonstrates superior consistency in adhering to annotation guidelines and processes, showcasing advanced capabilities to detect nuanced user quit-vaping intentions that human evaluators might overlook. These preliminary findings emphasize the potential of GPT-4 in enhancing the accuracy and reliability of social media data analysis, especially in identifying subtle users' intentions that may elude human detection. | [
"['Sai Krishna Revanth Vuruma' 'Dezhi Wu' 'Saborny Sen Gupta' 'Lucas Aust'\n 'Valerie Lookingbill' 'Caleb Henry' 'Yang Ren' 'Erin Kasson'\n 'Li-Shiun Chen' 'Patricia Cavazos-Rehg' 'Dian Hu' 'Ming Huang']"
]
|
null | null | 2404.17608 | null | null | http://arxiv.org/pdf/2404.17608v1 | 2024-04-25T22:19:42Z | 2024-04-25T22:19:42Z | Synthesizing Audio from Silent Video using Sequence to Sequence Modeling | Generating audio from a video's visual context has multiple practical applications in improving how we interact with audio-visual media - for example, enhancing CCTV footage analysis, restoring historical videos (e.g., silent movies), and improving video generation models. We propose a novel method to generate audio from video using a sequence-to-sequence model, improving on prior work that used CNNs and WaveNet and faced sound diversity and generalization challenges. Our approach employs a 3D Vector Quantized Variational Autoencoder (VQ-VAE) to capture the video's spatial and temporal structures, decoding with a custom audio decoder for a broader range of sounds. Trained on the Youtube8M dataset segment, focusing on specific domains, our model aims to enhance applications like CCTV footage analysis, silent movie restoration, and video generation models. | [
"['Hugo Garrido-Lestache Belinchon' 'Helina Mulugeta' 'Adam Haile']"
]
|
null | null | 2404.17609 | null | null | http://arxiv.org/pdf/2404.17609v2 | 2024-06-19T13:34:24Z | 2024-04-26T02:04:05Z | CoSD: Collaborative Stance Detection with Contrastive Heterogeneous
Topic Graph Learning | Stance detection seeks to identify the viewpoints of individuals either in favor or against a given target or a controversial topic. Current advanced neural models for stance detection typically employ fully parametric softmax classifiers. However, these methods suffer from several limitations, including lack of explainability, insensitivity to the latent data structure, and unimodality, which greatly restrict their performance and applications. To address these challenges, we present a novel collaborative stance detection framework called (CoSD) which leverages contrastive heterogeneous topic graph learning to learn topic-aware semantics and collaborative signals among texts, topics, and stance labels for enhancing stance detection. During training, we construct a heterogeneous graph to structurally organize texts and stances through implicit topics via employing latent Dirichlet allocation. We then perform contrastive graph learning to learn heterogeneous node representations, aggregating informative multi-hop collaborative signals via an elaborate Collaboration Propagation Aggregation (CPA) module. During inference, we introduce a hybrid similarity scoring module to enable the comprehensive incorporation of topic-aware semantics and collaborative signals for stance detection. Extensive experiments on two benchmark datasets demonstrate the state-of-the-art detection performance of CoSD, verifying the effectiveness and explainability of our collaborative framework. | [
"['Yinghan Cheng' 'Qi Zhang' 'Chongyang Shi' 'Liang Xiao' 'Shufeng Hao'\n 'Liang Hu']"
]
|
null | null | 2404.17611 | null | null | http://arxiv.org/pdf/2404.17611v1 | 2024-04-26T06:31:44Z | 2024-04-26T06:31:44Z | MetaSD: A Unified Framework for Scalable Downscaling of Meteorological
Variables in Diverse Situations | Addressing complex meteorological processes at a fine spatial resolution requires substantial computational resources. To accelerate meteorological simulations, researchers have utilized neural networks to downscale meteorological variables from low-resolution simulations. Despite notable advancements, contemporary cutting-edge downscaling algorithms tailored to specific variables. Addressing meteorological variables in isolation overlooks their interconnectedness, leading to an incomplete understanding of atmospheric dynamics. Additionally, the laborious processes of data collection, annotation, and computational resources required for individual variable downscaling are significant hurdles. Given the limited versatility of existing models across different meteorological variables and their failure to account for inter-variable relationships, this paper proposes a unified downscaling approach leveraging meta-learning. This framework aims to facilitate the downscaling of diverse meteorological variables derived from various numerical models and spatiotemporal scales. Trained at variables consisted of temperature, wind, surface pressure and total precipitation from ERA5 and GFS, the proposed method can be extended to downscale convective precipitation, potential energy, height, humidity and ozone from CFS, S2S and CMIP6 at different spatiotemporal scales, which demonstrating its capability to capture the interconnections among diverse variables. Our approach represents the initial effort to create a generalized downscaling model. Experimental evidence demonstrates that the proposed model outperforms existing top downscaling methods in both quantitative and qualitative assessments. | [
"['Jing Hu' 'Honghu Zhang' 'Peng Zheng' 'Jialin Mu' 'Xiaomeng Huang'\n 'Xi Wu']"
]
|
null | null | 2404.17613 | null | null | http://arxiv.org/pdf/2404.17613v1 | 2024-04-26T08:42:58Z | 2024-04-26T08:42:58Z | Quantum Patch-Based Autoencoder for Anomaly Segmentation | Quantum Machine Learning investigates the possibility of quantum computers enhancing Machine Learning algorithms. Anomaly segmentation is a fundamental task in various domains to identify irregularities at sample level and can be addressed with both supervised and unsupervised methods. Autoencoders are commonly used in unsupervised tasks, where models are trained to reconstruct normal instances efficiently, allowing anomaly identification through high reconstruction errors. While quantum autoencoders have been proposed in the literature, their application to anomaly segmentation tasks remains unexplored. In this paper, we introduce a patch-based quantum autoencoder (QPB-AE) for image anomaly segmentation, with a number of parameters scaling logarithmically with patch size. QPB-AE reconstructs the quantum state of the embedded input patches, computing an anomaly map directly from measurement through a SWAP test without reconstructing the input image. We evaluate its performance across multiple datasets and parameter configurations and compare it against a classical counterpart. | [
"['Maria Francisca Madeira' 'Alessandro Poggiali' 'Jeanette Miriam Lorenz']"
]
|
null | null | 2404.17615 | null | null | http://arxiv.org/pdf/2404.17615v1 | 2024-04-26T09:15:26Z | 2024-04-26T09:15:26Z | DeepVARMA: A Hybrid Deep Learning and VARMA Model for Chemical Industry
Index Forecasting | Since the chemical industry index is one of the important indicators to measure the development of the chemical industry, forecasting it is critical for understanding the economic situation and trends of the industry. Taking the multivariable nonstationary series-synthetic material index as the main research object, this paper proposes a new prediction model: DeepVARMA, and its variants Deep-VARMA-re and DeepVARMA-en, which combine LSTM and VARMAX models. The new model firstly uses the deep learning model such as the LSTM remove the trends of the target time series and also learn the representation of endogenous variables, and then uses the VARMAX model to predict the detrended target time series with the embeddings of endogenous variables, and finally combines the trend learned by the LSTM and dependency learned by the VARMAX model to obtain the final predictive values. The experimental results show that (1) the new model achieves the best prediction accuracy by combining the LSTM encoding of the exogenous variables and the VARMAX model. (2) In multivariate non-stationary series prediction, DeepVARMA uses a phased processing strategy to show higher adaptability and accuracy compared to the traditional VARMA model as well as the machine learning models LSTM, RF and XGBoost. (3) Compared with smooth sequence prediction, the traditional VARMA and VARMAX models fluctuate more in predicting non-smooth sequences, while DeepVARMA shows more flexibility and robustness. This study provides more accurate tools and methods for future development and scientific decision-making in the chemical industry. | [
"['Xiang Li' 'Hu Yang']"
]
|
null | null | 2404.17617 | null | null | http://arxiv.org/pdf/2404.17617v1 | 2024-04-26T11:47:36Z | 2024-04-26T11:47:36Z | Beyond Traditional Threats: A Persistent Backdoor Attack on Federated
Learning | Backdoors on federated learning will be diluted by subsequent benign updates. This is reflected in the significant reduction of attack success rate as iterations increase, ultimately failing. We use a new metric to quantify the degree of this weakened backdoor effect, called attack persistence. Given that research to improve this performance has not been widely noted,we propose a Full Combination Backdoor Attack (FCBA) method. It aggregates more combined trigger information for a more complete backdoor pattern in the global model. Trained backdoored global model is more resilient to benign updates, leading to a higher attack success rate on the test set. We test on three datasets and evaluate with two models across various settings. FCBA's persistence outperforms SOTA federated learning backdoor attacks. On GTSRB, postattack 120 rounds, our attack success rate rose over 50% from baseline. The core code of our method is available at https://github.com/PhD-TaoLiu/FCBA. | [
"['Tao Liu' 'Yuhang Zhang' 'Zhu Feng' 'Zhiqin Yang' 'Chen Xu' 'Dapeng Man'\n 'Wu Yang']"
]
|
null | null | 2404.17620 | null | null | http://arxiv.org/pdf/2404.17620v1 | 2024-04-26T14:12:37Z | 2024-04-26T14:12:37Z | Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces | We propose a self-supervised approach for learning physics-based subspaces for real-time simulation. Existing learning-based methods construct subspaces by approximating pre-defined simulation data in a purely geometric way. However, this approach tends to produce high-energy configurations, leads to entangled latent space dimensions, and generalizes poorly beyond the training set. To overcome these limitations, we propose a self-supervised approach that directly minimizes the system's mechanical energy during training. We show that our method leads to learned subspaces that reflect physical equilibrium constraints, resolve overfitting issues of previous methods, and offer interpretable latent space parameters. | [
"['Jiahong Wang' 'Yinwei Du' 'Stelian Coros' 'Bernhard Thomaszewski']"
]
|
null | null | 2404.17621 | null | null | http://arxiv.org/abs/2404.17621v1 | 2024-04-26T14:25:07Z | 2024-04-26T14:25:07Z | Attention-aware non-rigid image registration for accelerated MR imaging | Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic image quality. In this work, we introduce an attention-aware deep learning-based framework that can perform non-rigid pairwise registration for fully sampled and accelerated MRI. We extract local visual representations to build similarity maps between the registered image pairs at multiple resolution levels and additionally leverage long-range contextual information using a transformer-based module to alleviate ambiguities in the presence of artifacts caused by undersampling. We combine local and global dependencies to perform simultaneous coarse and fine motion estimation. The proposed method was evaluated on in-house acquired fully sampled and accelerated data of 101 patients and 62 healthy subjects undergoing cardiac and thoracic MRI. The impact of motion estimation accuracy on the downstream task of motion-compensated reconstruction was analyzed. We demonstrate that our model derives reliable and consistent motion fields across different sampling trajectories (Cartesian and radial) and acceleration factors of up to 16x for cardiac motion and 30x for respiratory motion and achieves superior image quality in motion-compensated reconstruction qualitatively and quantitatively compared to conventional and recent deep learning-based approaches. The code is publicly available at https://github.com/lab-midas/GMARAFT. | [
"['Aya Ghoul' 'Jiazhen Pan' 'Andreas Lingg' 'Jens Kübler' 'Patrick Krumm'\n 'Kerstin Hammernik' 'Daniel Rueckert' 'Sergios Gatidis' 'Thomas Küstner']"
]
|
null | null | 2404.17625 | null | null | http://arxiv.org/pdf/2404.17625v2 | 2024-07-04T14:52:11Z | 2024-04-26T15:19:58Z | Alice's Adventures in a Differentiable Wonderland -- Volume I, A Tour of
the Land | Neural networks surround us, in the form of large language models, speech transcription systems, molecular discovery algorithms, robotics, and much more. Stripped of anything else, neural networks are compositions of differentiable primitives, and studying them means learning how to program and how to interact with these models, a particular example of what is called differentiable programming. This primer is an introduction to this fascinating field imagined for someone, like Alice, who has just ventured into this strange differentiable wonderland. I overview the basics of optimizing a function via automatic differentiation, and a selection of the most common designs for handling sequences, graphs, texts, and audios. The focus is on a intuitive, self-contained introduction to the most important design techniques, including convolutional, attentional, and recurrent blocks, hoping to bridge the gap between theory and code (PyTorch and JAX) and leaving the reader capable of understanding some of the most advanced models out there, such as large language models (LLMs) and multimodal architectures. | [
"['Simone Scardapane']"
]
|
null | null | 2404.17626 | null | null | http://arxiv.org/pdf/2404.17626v2 | 2024-05-07T16:21:28Z | 2024-04-26T16:39:50Z | Using Pre-training and Interaction Modeling for ancestry-specific
disease prediction in UK Biobank | Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals, underscoring a critical gap in genetic research. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data. We evaluate the performance of Group-LASSO INTERaction-NET (glinternet) and pretrained lasso in disease prediction focusing on diverse ancestries in the UK Biobank. Models were trained on data from White British and other ancestries and validated across a cohort of over 96,000 individuals for 8 diseases. Out of 96 models trained, we report 16 with statistically significant incremental predictive performance in terms of ROC-AUC scores (p-value < 0.05), found for diabetes, arthritis, gall stones, cystitis, asthma and osteoarthritis. For the interaction and pretrained models that outperformed the baseline, the PRS score was the primary driver behind prediction. Our findings indicate that both interaction terms and pre-training can enhance prediction accuracy but for a limited set of diseases and moderate improvements in accuracy | [
"['Thomas Le Menestrel' 'Erin Craig' 'Robert Tibshirani' 'Trevor Hastie'\n 'Manuel Rivas']"
]
|
null | null | 2404.17644 | null | null | http://arxiv.org/pdf/2404.17644v2 | 2024-05-03T10:48:56Z | 2024-04-26T18:08:15Z | A Conditional Independence Test in the Presence of Discretization | Testing conditional independence has many applications, such as in Bayesian network learning and causal discovery. Different test methods have been proposed. However, existing methods generally can not work when only discretized observations are available. Specifically, consider $X_1$, $tilde{X}_2$ and $X_3$ are observed variables, where $tilde{X}_2$ is a discretization of latent variables $X_2$. Applying existing test methods to the observations of $X_1$, $tilde{X}_2$ and $X_3$ can lead to a false conclusion about the underlying conditional independence of variables $X_1$, $X_2$ and $X_3$. Motivated by this, we propose a conditional independence test specifically designed to accommodate the presence of such discretization. To achieve this, we design the bridge equations to recover the parameter reflecting the statistical information of the underlying latent continuous variables. An appropriate test statistic and its asymptotic distribution under the null hypothesis of conditional independence have also been derived. Both theoretical results and empirical validation have been provided, demonstrating the effectiveness of our test methods. | [
"['Boyang Sun' 'Yu Yao' 'Huangyuan Hao' 'Yumou Qiu' 'Kun Zhang']"
]
|
null | null | 2404.17645 | null | null | http://arxiv.org/pdf/2404.17645v1 | 2024-03-08T09:58:13Z | 2024-03-08T09:58:13Z | Técnicas Quantum-Inspired en Tensor Networks para Contextos
Industriales | In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for industrial environments and contexts, with a compilation of the available literature and an analysis of the use cases that may be affected by such methods. In addition, we explore the limitations of such techniques in order to determine their potential scalability. | [
"['Alejandro Mata Ali' 'Iñigo Perez Delgado' 'Aitor Moreno Fdez. de Leceta']"
]
|
null | null | 2404.17651 | null | null | http://arxiv.org/pdf/2404.17651v1 | 2024-04-26T18:16:39Z | 2024-04-26T18:16:39Z | Hard ASH: Sparsity and the right optimizer make a continual learner | In class incremental learning, neural networks typically suffer from catastrophic forgetting. We show that an MLP featuring a sparse activation function and an adaptive learning rate optimizer can compete with established regularization techniques in the Split-MNIST task. We highlight the effectiveness of the Adaptive SwisH (ASH) activation function in this context and introduce a novel variant, Hard Adaptive SwisH (Hard ASH) to further enhance the learning retention. | [
"['Santtu Keskinen']"
]
|
null | null | 2404.17652 | null | null | http://arxiv.org/pdf/2404.17652v1 | 2024-04-26T18:18:25Z | 2024-04-26T18:18:25Z | Validating Deep-Learning Weather Forecast Models on Recent High-Impact
Extreme Events | The forecast accuracy of deep-learning-based weather prediction models is improving rapidly, leading many to speak of a "second revolution in weather forecasting". With numerous methods being developed, and limited physical guarantees offered by deep-learning models, there is a critical need for comprehensive evaluation of these emerging techniques. While this need has been partly fulfilled by benchmark datasets, they provide little information on rare and impactful extreme events, or on compound impact metrics, for which model accuracy might degrade due to misrepresented dependencies between variables. To address these issues, we compare deep-learning weather prediction models (GraphCast, PanguWeather, FourCastNet) and ECMWF's high-resolution forecast (HRES) system in three case studies: the 2021 Pacific Northwest heatwave, the 2023 South Asian humid heatwave, and the North American winter storm in 2021. We find evidence that machine learning (ML) weather prediction models can locally achieve similar accuracy to HRES on record-shattering events such as the 2021 Pacific Northwest heatwave and even forecast the compound 2021 North American winter storm substantially better. However, extrapolating to extreme conditions may impact machine learning models more severely than HRES, as evidenced by the comparable or superior spatially- and temporally-aggregated forecast accuracy of HRES for the two heatwaves studied. The ML forecasts also lack variables required to assess the health risks of events such as the 2023 South Asian humid heatwave. Generally, case-study-driven, impact-centric evaluation can complement existing research, increase public trust, and aid in developing reliable ML weather prediction models. | [
"['Olivier C. Pasche' 'Jonathan Wider' 'Zhongwei Zhang'\n 'Jakob Zscheischler' 'Sebastian Engelke']"
]
|
null | null | 2404.17667 | null | null | http://arxiv.org/pdf/2404.17667v1 | 2024-04-26T19:20:42Z | 2024-04-26T19:20:42Z | SiamQuality: A ConvNet-Based Foundation Model for Imperfect
Physiological Signals | Foundation models, especially those using transformers as backbones, have gained significant popularity, particularly in language and language-vision tasks. However, large foundation models are typically trained on high-quality data, which poses a significant challenge, given the prevalence of poor-quality real-world data. This challenge is more pronounced for developing foundation models for physiological data; such data are often noisy, incomplete, or inconsistent. The present work aims to provide a toolset for developing foundation models on physiological data. We leverage a large dataset of photoplethysmography (PPG) signals from hospitalized intensive care patients. For this data, we propose SimQuality, a novel self-supervised learning task based on convolutional neural networks (CNNs) as the backbone to enforce representations to be similar for good and poor quality signals that are from similar physiological states. We pre-trained the SimQuality on over 36 million 30-second PPG pairs and then fine-tuned and tested on six downstream tasks using external datasets. The results demonstrate the superiority of the proposed approach on all the downstream tasks, which are extremely important for heart monitoring on wearable devices. Our method indicates that CNNs can be an effective backbone for foundation models that are robust to training data quality. | [
"['Cheng Ding' 'Zhicheng Guo' 'Zhaoliang Chen' 'Randall J Lee'\n 'Cynthia Rudin' 'Xiao Hu']"
]
|
null | null | 2404.17670 | null | null | http://arxiv.org/pdf/2404.17670v1 | 2024-04-26T19:27:07Z | 2024-04-26T19:27:07Z | Federated Learning for Blind Image Super-Resolution | Traditional blind image SR methods need to model real-world degradations precisely. Consequently, current research struggles with this dilemma by assuming idealized degradations, which leads to limited applicability to actual user data. Moreover, the ideal scenario - training models on data from the targeted user base - presents significant privacy concerns. To address both challenges, we propose to fuse image SR with federated learning, allowing real-world degradations to be directly learned from users without invading their privacy. Furthermore, it enables optimization across many devices without data centralization. As this fusion is underexplored, we introduce new benchmarks specifically designed to evaluate new SR methods in this federated setting. By doing so, we employ known degradation modeling techniques from SR research. However, rather than aiming to mirror real degradations, our benchmarks use these degradation models to simulate the variety of degradations found across clients within a distributed user base. This distinction is crucial as it circumvents the need to precisely model real-world degradations, which limits contemporary blind image SR research. Our proposed benchmarks investigate blind image SR under new aspects, namely differently distributed degradation types among users and varying user numbers. We believe new methods tested within these benchmarks will perform more similarly in an application, as the simulated scenario addresses the variety while federated learning enables the training on actual degradations. | [
"['Brian B. Moser' 'Ahmed Anwar' 'Federico Raue' 'Stanislav Frolov'\n 'Andreas Dengel']"
]
|
null | null | 2404.17673 | null | null | http://arxiv.org/pdf/2404.17673v1 | 2024-04-26T19:40:19Z | 2024-04-26T19:40:19Z | Learning Manipulation Tasks in Dynamic and Shared 3D Spaces | Automating the segregation process is a need for every sector experiencing a high volume of materials handling, repetitive and exhaustive operations, in addition to risky exposures. Learning automated pick-and-place operations can be efficiently done by introducing collaborative autonomous systems (e.g. manipulators) in the workplace and among human operators. In this paper, we propose a deep reinforcement learning strategy to learn the place task of multi-categorical items from a shared workspace between dual-manipulators and to multi-goal destinations, assuming the pick has been already completed. The learning strategy leverages first a stochastic actor-critic framework to train an agent's policy network, and second, a dynamic 3D Gym environment where both static and dynamic obstacles (e.g. human factors and robot mate) constitute the state space of a Markov decision process. Learning is conducted in a Gazebo simulator and experiments show an increase in cumulative reward function for the agent further away from human factors. Future investigations will be conducted to enhance the task performance for both agents simultaneously. | [
"['Hariharan Arunachalam' 'Marc Hanheide' 'Sariah Mghames']"
]
|
null | null | 2404.17674 | null | null | http://arxiv.org/pdf/2404.17674v2 | 2024-05-29T17:54:47Z | 2024-04-26T19:41:08Z | Center-Based Relaxed Learning Against Membership Inference Attacks | Membership inference attacks (MIAs) are currently considered one of the main privacy attack strategies, and their defense mechanisms have also been extensively explored. However, there is still a gap between the existing defense approaches and ideal models in performance and deployment costs. In particular, we observed that the privacy vulnerability of the model is closely correlated with the gap between the model's data-memorizing ability and generalization ability. To address this, we propose a new architecture-agnostic training paradigm called center-based relaxed learning (CRL), which is adaptive to any classification model and provides privacy preservation by sacrificing a minimal or no loss of model generalizability. We emphasize that CRL can better maintain the model's consistency between member and non-member data. Through extensive experiments on standard classification datasets, we empirically show that this approach exhibits comparable performance without requiring additional model capacity or data costs. | [
"['Xingli Fang' 'Jung-Eun Kim']"
]
|
null | null | 2404.17683 | null | null | http://arxiv.org/pdf/2404.17683v1 | 2024-04-26T20:25:05Z | 2024-04-26T20:25:05Z | Energy Storage Arbitrage in Two-settlement Markets: A Transformer-Based
Approach | This paper presents an integrated model for bidding energy storage in day-ahead and real-time markets to maximize profits. We show that in integrated two-stage bidding, the real-time bids are independent of day-ahead settlements, while the day-ahead bids should be based on predicted real-time prices. We utilize a transformer-based model for real-time price prediction, which captures complex dynamical patterns of real-time prices, and use the result for day-ahead bidding design. For real-time bidding, we utilize a long short-term memory-dynamic programming hybrid real-time bidding model. We train and test our model with historical data from New York State, and our results showed that the integrated system achieved promising results of almost a 20% increase in profit compared to only bidding in real-time markets, and at the same time reducing the risk in terms of the number of days with negative profits. | [
"['Saud Alghumayjan' 'Jiajun Han' 'Ningkun Zheng' 'Ming Yi' 'Bolun Xu']"
]
|
null | null | 2404.17684 | null | null | http://arxiv.org/pdf/2404.17684v1 | 2024-04-26T20:27:10Z | 2024-04-26T20:27:10Z | Generalize by Touching: Tactile Ensemble Skill Transfer for Robotic
Furniture Assembly | Furniture assembly remains an unsolved problem in robotic manipulation due to its long task horizon and nongeneralizable operations plan. This paper presents the Tactile Ensemble Skill Transfer (TEST) framework, a pioneering offline reinforcement learning (RL) approach that incorporates tactile feedback in the control loop. TEST's core design is to learn a skill transition model for high-level planning, along with a set of adaptive intra-skill goal-reaching policies. Such design aims to solve the robotic furniture assembly problem in a more generalizable way, facilitating seamless chaining of skills for this long-horizon task. We first sample demonstration from a set of heuristic policies and trajectories consisting of a set of randomized sub-skill segments, enabling the acquisition of rich robot trajectories that capture skill stages, robot states, visual indicators, and crucially, tactile signals. Leveraging these trajectories, our offline RL method discerns skill termination conditions and coordinates skill transitions. Our evaluations highlight the proficiency of TEST on the in-distribution furniture assemblies, its adaptability to unseen furniture configurations, and its robustness against visual disturbances. Ablation studies further accentuate the pivotal role of two algorithmic components: the skill transition model and tactile ensemble policies. Results indicate that TEST can achieve a success rate of 90% and is over 4 times more efficient than the heuristic policy in both in-distribution and generalization settings, suggesting a scalable skill transfer approach for contact-rich manipulation. | [
"['Haohong Lin' 'Radu Corcodel' 'Ding Zhao']"
]
|
null | null | 2404.17687 | null | null | http://arxiv.org/pdf/2404.17687v1 | 2024-04-26T20:36:58Z | 2024-04-26T20:36:58Z | Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic
Review | Reinforcement Learning (RL) provides a framework in which agents can be trained, via trial and error, to solve complex decision-making problems. Learning with little supervision causes RL methods to require large amounts of data, which renders them too expensive for many applications (e.g. robotics). By reusing knowledge from a different task, knowledge transfer methods present an alternative to reduce the training time in RL. Given how severe data scarcity can be, there has been a growing interest for methods capable of transferring knowledge across different domains (i.e. problems with different representation) due to the flexibility they offer. This review presents a unifying analysis of methods focused on transferring knowledge across different domains. Through a taxonomy based on a transfer-approach categorization, and a characterization of works based on their data-assumption requirements, the objectives of this article are to 1) provide a comprehensive and systematic revision of knowledge transfer methods for the cross-domain RL setting, 2) categorize and characterize these methods to provide an analysis based on relevant features such as their transfer approach and data requirements, and 3) discuss the main challenges regarding cross-domain knowledge transfer, as well as ideas of future directions worth exploring to address these problems. | [
"['Sergio A. Serrano' 'Jose Martinez-Carranza' 'L. Enrique Sucar']"
]
|
null | null | 2404.17690 | null | null | http://arxiv.org/pdf/2404.17690v1 | 2024-04-26T20:39:08Z | 2024-04-26T20:39:08Z | A Biased Estimator for MinMax Sampling and Distributed Aggregation | MinMax sampling is a technique for downsampling a real-valued vector which minimizes the maximum variance over all vector components. This approach is useful for reducing the amount of data that must be sent over a constrained network link (e.g. in the wide-area). MinMax can provide unbiased estimates of the vector elements, along with unbiased estimates of aggregates when vectors are combined from multiple locations. In this work, we propose a biased MinMax estimation scheme, B-MinMax, which trades an increase in estimator bias for a reduction in variance. We prove that when no aggregation is performed, B-MinMax obtains a strictly lower MSE compared to the unbiased MinMax estimator. When aggregation is required, B-MinMax is preferable when sample sizes are small or the number of aggregated vectors is limited. Our experiments show that this approach can substantially reduce the MSE for MinMax sampling in many practical settings. | [
"['Joel Wolfrath' 'Abhishek Chandra']"
]
|
null | null | 2404.17699 | null | null | http://arxiv.org/pdf/2404.17699v3 | 2024-05-17T20:58:50Z | 2024-04-26T20:55:39Z | Deep Learning for Melt Pool Depth Contour Prediction From Surface
Thermal Images via Vision Transformers | Insufficient overlap between the melt pools produced during Laser Powder Bed Fusion (L-PBF) can lead to lack-of-fusion defects and deteriorated mechanical and fatigue performance. In-situ monitoring of the melt pool subsurface morphology requires specialized equipment that may not be readily accessible or scalable. Therefore, we introduce a machine learning framework to correlate in-situ two-color thermal images observed via high-speed color imaging to the two-dimensional profile of the melt pool cross-section. Specifically, we employ a hybrid CNN-Transformer architecture to establish a correlation between single bead off-axis thermal image sequences and melt pool cross-section contours measured via optical microscopy. In this architecture, a ResNet model embeds the spatial information contained within the thermal images to a latent vector, while a Transformer model correlates the sequence of embedded vectors to extract temporal information. Our framework is able to model the curvature of the subsurface melt pool structure, with improved performance in high energy density regimes compared to analytical melt pool models. The performance of this model is evaluated through dimensional and geometric comparisons to the corresponding experimental melt pool observations. | [
"['Francis Ogoke' 'Peter Myung-Won Pak' 'Alexander Myers'\n 'Guadalupe Quirarte' 'Jack Beuth' 'Jonathan Malen' 'Amir Barati Farimani']"
]
|
null | null | 2404.17701 | null | null | http://arxiv.org/pdf/2404.17701v3 | 2024-07-02T13:25:00Z | 2024-04-26T20:59:23Z | Embedded FPGA Developments in 130nm and 28nm CMOS for Machine Learning
in Particle Detector Readout | Embedded field programmable gate array (eFPGA) technology allows the implementation of reconfigurable logic within the design of an application-specific integrated circuit (ASIC). This approach offers the low power and efficiency of an ASIC along with the ease of FPGA configuration, particularly beneficial for the use case of machine learning in the data pipeline of next-generation collider experiments. An open-source framework called "FABulous" was used to design eFPGAs using 130 nm and 28 nm CMOS technology nodes, which were subsequently fabricated and verified through testing. The capability of an eFPGA to act as a front-end readout chip was assessed using simulation of high energy particles passing through a silicon pixel sensor. A machine learning-based classifier, designed for reduction of sensor data at the source, was synthesized and configured onto the eFPGA. A successful proof-of-concept was demonstrated through reproduction of the expected algorithm result on the eFPGA with perfect accuracy. Further development of the eFPGA technology and its application to collider detector readout is discussed. | [
"['Julia Gonski' 'Aseem Gupta' 'Haoyi Jia' 'Hyunjoon Kim' 'Lorenzo Rota'\n 'Larry Ruckman' 'Angelo Dragone' 'Ryan Herbst']"
]
|
null | null | 2404.17704 | null | null | http://arxiv.org/pdf/2404.17704v1 | 2024-04-26T21:30:36Z | 2024-04-26T21:30:36Z | SPLICE -- Streamlining Digital Pathology Image Processing | Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With the increasing availability of Whole Slide Images (WSIs), there's a growing demand for efficient retrieval, processing, and analysis of relevant images from vast biomedical archives. However, processing WSIs presents challenges due to their large size and content complexity. Full computer digestion of WSIs is impractical, and processing all patches individually is prohibitively expensive. In this paper, we propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE). This novel approach condenses a histopathology WSI into a compact set of representative patches, forming a "collage" of WSI while minimizing redundancy. SPLICE prioritizes patch quality and uniqueness by sequentially analyzing a WSI and selecting non-redundant representative features. We evaluated SPLICE for search and match applications, demonstrating improved accuracy, reduced computation time, and storage requirements compared to existing state-of-the-art methods. As an unsupervised method, SPLICE effectively reduces storage requirements for representing tissue images by 50%. This reduction enables numerous algorithms in computational pathology to operate much more efficiently, paving the way for accelerated adoption of digital pathology. | [
"['Areej Alsaafin' 'Peyman Nejat' 'Abubakr Shafique' 'Jibran Khan'\n 'Saghir Alfasly' 'Ghazal Alabtah' 'H. R. Tizhoosh']"
]
|
null | null | 2404.17709 | null | null | http://arxiv.org/pdf/2404.17709v1 | 2024-04-26T21:54:31Z | 2024-04-26T21:54:31Z | Low-rank Matrix Bandits with Heavy-tailed Rewards | In stochastic low-rank matrix bandit, the expected reward of an arm is equal to the inner product between its feature matrix and some unknown $d_1$ by $d_2$ low-rank parameter matrix $Theta^*$ with rank $r ll d_1wedge d_2$. While all prior studies assume the payoffs are mixed with sub-Gaussian noises, in this work we loosen this strict assumption and consider the new problem of underline{low}-rank matrix bandit with underline{h}eavy-underline{t}ailed underline{r}ewards (LowHTR), where the rewards only have finite $(1+delta)$ moment for some $delta in (0,1]$. By utilizing the truncation on observed payoffs and the dynamic exploration, we propose a novel algorithm called LOTUS attaining the regret bound of order $tilde O(d^frac{3}{2}r^frac{1}{2}T^frac{1}{1+delta}/tilde{D}_{rr})$ without knowing $T$, which matches the state-of-the-art regret bound under sub-Gaussian noises~citep{lu2021low,kang2022efficient} with $delta = 1$. Moreover, we establish a lower bound of the order $Omega(d^frac{delta}{1+delta} r^frac{delta}{1+delta} T^frac{1}{1+delta}) = Omega(T^frac{1}{1+delta})$ for LowHTR, which indicates our LOTUS is nearly optimal in the order of $T$. In addition, we improve LOTUS so that it does not require knowledge of the rank $r$ with $tilde O(dr^frac{3}{2}T^frac{1+delta}{1+2delta})$ regret bound, and it is efficient under the high-dimensional scenario. We also conduct simulations to demonstrate the practical superiority of our algorithm. | [
"['Yue Kang' 'Cho-Jui Hsieh' 'Thomas C. M. Lee']"
]
|
null | null | 2404.17714 | null | null | http://arxiv.org/pdf/2404.17714v1 | 2024-04-26T22:17:32Z | 2024-04-26T22:17:32Z | Lower Bounds for Private Estimation of Gaussian Covariance Matrices
under All Reasonable Parameter Regimes | We prove lower bounds on the number of samples needed to privately estimate the covariance matrix of a Gaussian distribution. Our bounds match existing upper bounds in the widest known setting of parameters. Our analysis relies on the Stein-Haff identity, an extension of the classical Stein's identity used in previous fingerprinting lemma arguments. | [
"['Victor S. Portella' 'Nick Harvey']"
]
|
null | null | 2404.17718 | null | null | http://arxiv.org/pdf/2404.17718v1 | 2024-04-26T22:46:17Z | 2024-04-26T22:46:17Z | Lessons from Deploying CropFollow++: Under-Canopy Agricultural
Navigation with Keypoints | We present a vision-based navigation system for under-canopy agricultural robots using semantic keypoints. Autonomous under-canopy navigation is challenging due to the tight spacing between the crop rows ($sim 0.75$ m), degradation in RTK-GPS accuracy due to multipath error, and noise in LiDAR measurements from the excessive clutter. Our system, CropFollow++, introduces modular and interpretable perception architecture with a learned semantic keypoint representation. We deployed CropFollow++ in multiple under-canopy cover crop planting robots on a large scale (25 km in total) in various field conditions and we discuss the key lessons learned from this. | [
"['Arun N. Sivakumar' 'Mateus V. Gasparino' 'Michael McGuire'\n 'Vitor A. H. Higuti' 'M. Ugur Akcal' 'Girish Chowdhary']"
]
|
null | null | 2404.17723 | null | null | http://arxiv.org/abs/2404.17723v2 | 2024-05-06T05:16:42Z | 2024-04-26T23:05:20Z | Retrieval-Augmented Generation with Knowledge Graphs for Customer
Service Question Answering | In customer service technical support, swiftly and accurately retrieving relevant past issues is critical for efficiently resolving customer inquiries. The conventional retrieval methods in retrieval-augmented generation (RAG) for large language models (LLMs) treat a large corpus of past issue tracking tickets as plain text, ignoring the crucial intra-issue structure and inter-issue relations, which limits performance. We introduce a novel customer service question-answering method that amalgamates RAG with a knowledge graph (KG). Our method constructs a KG from historical issues for use in retrieval, retaining the intra-issue structure and inter-issue relations. During the question-answering phase, our method parses consumer queries and retrieves related sub-graphs from the KG to generate answers. This integration of a KG not only improves retrieval accuracy by preserving customer service structure information but also enhances answering quality by mitigating the effects of text segmentation. Empirical assessments on our benchmark datasets, utilizing key retrieval (MRR, Recall@K, NDCG@K) and text generation (BLEU, ROUGE, METEOR) metrics, reveal that our method outperforms the baseline by 77.6% in MRR and by 0.32 in BLEU. Our method has been deployed within LinkedIn's customer service team for approximately six months and has reduced the median per-issue resolution time by 28.6%. | [
"['Zhentao Xu' 'Mark Jerome Cruz' 'Matthew Guevara' 'Tie Wang'\n 'Manasi Deshpande' 'Xiaofeng Wang' 'Zheng Li']"
]
|
null | null | 2404.17732 | null | null | http://arxiv.org/pdf/2404.17732v1 | 2024-04-26T23:46:10Z | 2024-04-26T23:46:10Z | Generative Dataset Distillation: Balancing Global Structure and Local
Details | In this paper, we propose a new dataset distillation method that considers balancing global structure and local details when distilling the information from a large dataset into a generative model. Dataset distillation has been proposed to reduce the size of the required dataset when training models. The conventional dataset distillation methods face the problem of long redeployment time and poor cross-architecture performance. Moreover, previous methods focused too much on the high-level semantic attributes between the synthetic dataset and the original dataset while ignoring the local features such as texture and shape. Based on the above understanding, we propose a new method for distilling the original image dataset into a generative model. Our method involves using a conditional generative adversarial network to generate the distilled dataset. Subsequently, we ensure balancing global structure and local details in the distillation process, continuously optimizing the generator for more information-dense dataset generation. | [
"['Longzhen Li' 'Guang Li' 'Ren Togo' 'Keisuke Maeda' 'Takahiro Ogawa'\n 'Miki Haseyama']"
]
|
null | null | 2404.17735 | null | null | http://arxiv.org/pdf/2404.17735v2 | 2024-05-08T20:43:47Z | 2024-04-27T00:09:26Z | Causal Diffusion Autoencoders: Toward Counterfactual Generation via
Diffusion Probabilistic Models | Diffusion probabilistic models (DPMs) have become the state-of-the-art in high-quality image generation. However, DPMs have an arbitrary noisy latent space with no interpretable or controllable semantics. Although there has been significant research effort to improve image sample quality, there is little work on representation-controlled generation using diffusion models. Specifically, causal modeling and controllable counterfactual generation using DPMs is an underexplored area. In this work, we propose CausalDiffAE, a diffusion-based causal representation learning framework to enable counterfactual generation according to a specified causal model. Our key idea is to use an encoder to extract high-level semantically meaningful causal variables from high-dimensional data and model stochastic variation using reverse diffusion. We propose a causal encoding mechanism that maps high-dimensional data to causally related latent factors and parameterize the causal mechanisms among latent factors using neural networks. To enforce the disentanglement of causal variables, we formulate a variational objective and leverage auxiliary label information in a prior to regularize the latent space. We propose a DDIM-based counterfactual generation procedure subject to do-interventions. Finally, to address the limited label supervision scenario, we also study the application of CausalDiffAE when a part of the training data is unlabeled, which also enables granular control over the strength of interventions in generating counterfactuals during inference. We empirically show that CausalDiffAE learns a disentangled latent space and is capable of generating high-quality counterfactual images. | [
"['Aneesh Komanduri' 'Chen Zhao' 'Feng Chen' 'Xintao Wu']"
]
|
null | null | 2404.17745 | null | null | http://arxiv.org/pdf/2404.17745v1 | 2024-04-27T01:22:45Z | 2024-04-27T01:22:45Z | An Attention-Based Deep Learning Architecture for Real-Time Monocular
Visual Odometry: Applications to GPS-free Drone Navigation | Drones are increasingly used in fields like industry, medicine, research, disaster relief, defense, and security. Technical challenges, such as navigation in GPS-denied environments, hinder further adoption. Research in visual odometry is advancing, potentially solving GPS-free navigation issues. Traditional visual odometry methods use geometry-based pipelines which, while popular, often suffer from error accumulation and high computational demands. Recent studies utilizing deep neural networks (DNNs) have shown improved performance, addressing these drawbacks. Deep visual odometry typically employs convolutional neural networks (CNNs) and sequence modeling networks like recurrent neural networks (RNNs) to interpret scenes and deduce visual odometry from video sequences. This paper presents a novel real-time monocular visual odometry model for drones, using a deep neural architecture with a self-attention module. It estimates the ego-motion of a camera on a drone, using consecutive video frames. An inference utility processes the live video feed, employing deep learning to estimate the drone's trajectory. The architecture combines a CNN for image feature extraction and a long short-term memory (LSTM) network with a multi-head attention module for video sequence modeling. Tested on two visual odometry datasets, this model converged 48% faster than a previous RNN model and showed a 22% reduction in mean translational drift and a 12% improvement in mean translational absolute trajectory error, demonstrating enhanced robustness to noise. | [
"['Olivier Brochu Dufour' 'Abolfazl Mohebbi' 'Sofiane Achiche']"
]
|
null | null | 2404.17746 | null | null | http://arxiv.org/pdf/2404.17746v1 | 2024-04-27T01:34:51Z | 2024-04-27T01:34:51Z | On the Rashomon ratio of infinite hypothesis sets | Given a classification problem and a family of classifiers, the Rashomon ratio measures the proportion of classifiers that yield less than a given loss. Previous work has explored the advantage of a large Rashomon ratio in the case of a finite family of classifiers. Here we consider the more general case of an infinite family. We show that a large Rashomon ratio guarantees that choosing the classifier with the best empirical accuracy among a random subset of the family, which is likely to improve generalizability, will not increase the empirical loss too much. We quantify the Rashomon ratio in two examples involving infinite classifier families in order to illustrate situations in which it is large. In the first example, we estimate the Rashomon ratio of the classification of normally distributed classes using an affine classifier. In the second, we obtain a lower bound for the Rashomon ratio of a classification problem with a modified Gram matrix when the classifier family consists of two-layer ReLU neural networks. In general, we show that the Rashomon ratio can be estimated using a training dataset along with random samples from the classifier family and we provide guarantees that such an estimation is close to the true value of the Rashomon ratio. | [
"['Evzenie Coupkova' 'Mireille Boutin']"
]
|
null | null | 2404.17752 | null | null | http://arxiv.org/pdf/2404.17752v1 | 2024-04-27T01:49:14Z | 2024-04-27T01:49:14Z | Generative Diffusion-based Downscaling for Climate | Downscaling, or super-resolution, provides decision-makers with detailed, high-resolution information about the potential risks and impacts of climate change, based on climate model output. Machine learning algorithms are proving themselves to be efficient and accurate approaches to downscaling. Here, we show how a generative, diffusion-based approach to downscaling gives accurate downscaled results. We focus on an idealised setting where we recover ERA5 at $0.25degree$~resolution from coarse grained version at $2degree$~resolution. The diffusion-based method provides superior accuracy compared to a standard U-Net, particularly at the fine scales, as highlighted by a spectral decomposition. Additionally, the generative approach provides users with a probability distribution which can be used for risk assessment. This research highlights the potential of diffusion-based downscaling techniques in providing reliable and detailed climate predictions. | [
"['Robbie A. Watt' 'Laura A. Mansfield']"
]
|
null | null | 2404.17760 | null | null | http://arxiv.org/pdf/2404.17760v1 | 2024-04-27T02:35:15Z | 2024-04-27T02:35:15Z | Adversarial Examples: Generation Proposal in the Context of Facial
Recognition Systems | In this paper we investigate the vulnerability that facial recognition systems present to adversarial examples by introducing a new methodology from the attacker perspective. The technique is based on the use of the autoencoder latent space, organized with principal component analysis. We intend to analyze the potential to craft adversarial examples suitable for both dodging and impersonation attacks, against state-of-the-art systems. Our initial hypothesis, which was not strongly favoured by the results, stated that it would be possible to separate between the "identity" and "facial expression" features to produce high-quality examples. Despite the findings not supporting it, the results sparked insights into adversarial examples generation and opened new research avenues in the area. | [
"['Marina Fuster' 'Ignacio Vidaurreta']"
]
|
null | null | 2404.17766 | null | null | http://arxiv.org/pdf/2404.17766v1 | 2024-04-27T03:09:39Z | 2024-04-27T03:09:39Z | Implementation of Big AI Models for Wireless Networks with Collaborative
Edge Computing | Big Artificial Intelligence (AI) models have emerged as a crucial element in various intelligent applications at the edge, such as voice assistants in smart homes and autonomous robotics in smart factories. Training big AI models, e.g., for personalized fine-tuning and continual model refinement, poses significant challenges to edge devices due to the inherent conflict between limited computing resources and intensive workload associated with training. Despite the constraints of on-device training, traditional approaches usually resort to aggregating training data and sending it to a remote cloud for centralized training. Nevertheless, this approach is neither sustainable, which strains long-range backhaul transmission and energy-consuming datacenters, nor safely private, which shares users' raw data with remote infrastructures. To address these challenges, we alternatively observe that prevalent edge environments usually contain a diverse collection of trusted edge devices with untapped idle resources, which can be leveraged for edge training acceleration. Motivated by this, in this article, we propose collaborative edge training, a novel training mechanism that orchestrates a group of trusted edge devices as a resource pool for expedited, sustainable big AI model training at the edge. As an initial step, we present a comprehensive framework for building collaborative edge training systems and analyze in-depth its merits and sustainable scheduling choices following its workflow. To further investigate the impact of its parallelism design, we empirically study a case of four typical parallelisms from the perspective of energy demand with realistic testbeds. Finally, we discuss open challenges for sustainable collaborative edge training to point to future directions of edge-centric big AI model training. | [
"['Liekang Zeng' 'Shengyuan Ye' 'Xu Chen' 'Yang Yang']"
]
|
null | null | 2404.17768 | null | null | http://arxiv.org/pdf/2404.17768v1 | 2024-04-27T03:30:50Z | 2024-04-27T03:30:50Z | Make the Most of Your Data: Changing the Training Data Distribution to
Improve In-distribution Generalization Performance | Can we modify the training data distribution to encourage the underlying optimization method toward finding solutions with superior generalization performance on in-distribution data? In this work, we approach this question for the first time by comparing the inductive bias of gradient descent (GD) with that of sharpness-aware minimization (SAM). By studying a two-layer CNN, we prove that SAM learns easy and difficult features more uniformly, particularly in early epochs. That is, SAM is less susceptible to simplicity bias compared to GD. Based on this observation, we propose USEFUL, an algorithm that clusters examples based on the network output early in training and upsamples examples with no easy features to alleviate the pitfalls of the simplicity bias. We show empirically that modifying the training data distribution in this way effectively improves the generalization performance on the original data distribution when training with (S)GD by mimicking the training dynamics of SAM. Notably, we demonstrate that our method can be combined with SAM and existing data augmentation strategies to achieve, to the best of our knowledge, state-of-the-art performance for training ResNet18 on CIFAR10, STL10, CINIC10, Tiny-ImageNet; ResNet34 on CIFAR100; and VGG19 and DenseNet121 on CIFAR10. | [
"['Dang Nguyen' 'Paymon Haddad' 'Eric Gan' 'Baharan Mirzasoleiman']"
]
|
null | null | 2404.17773 | null | null | http://arxiv.org/pdf/2404.17773v1 | 2024-04-27T04:09:49Z | 2024-04-27T04:09:49Z | Compressing Latent Space via Least Volume | This paper introduces Least Volume-a simple yet effective regularization inspired by geometric intuition-that can reduce the necessary number of latent dimensions needed by an autoencoder without requiring any prior knowledge of the intrinsic dimensionality of the dataset. We show that the Lipschitz continuity of the decoder is the key to making it work, provide a proof that PCA is just a linear special case of it, and reveal that it has a similar PCA-like importance ordering effect when applied to nonlinear models. We demonstrate the intuition behind the regularization on some pedagogical toy problems, and its effectiveness on several benchmark problems, including MNIST, CIFAR-10 and CelebA. | [
"['Qiuyi Chen' 'Mark Fuge']"
]
|
null | null | 2404.17789 | null | null | http://arxiv.org/pdf/2404.17789v2 | 2024-06-17T20:49:42Z | 2024-04-27T06:06:41Z | BiLO: Bilevel Local Operator Learning for PDE inverse problems | We propose a new neural network based method for solving inverse problems for partial differential equations (PDEs) by formulating the PDE inverse problem as a bilevel optimization problem. At the upper level, we minimize the data loss with respect to the PDE parameters. At the lower level, we train a neural network to locally approximate the PDE solution operator in the neighborhood of a given set of PDE parameters, which enables an accurate approximation of the descent direction for the upper level optimization problem. The lower level loss function includes the L2 norms of both the residual and its derivative with respect to the PDE parameters. We apply gradient descent simultaneously on both the upper and lower level optimization problems, leading to an effective and fast algorithm. The method, which we refer to as BiLO (Bilevel Local Operator learning), is also able to efficiently infer unknown functions in the PDEs through the introduction of an auxiliary variable. Through extensive experiments over multiple PDE systems, we demonstrate that our method enforces strong PDE constraints, is robust to sparse and noisy data, and eliminates the need to balance the residual and the data loss, which is inherent to the soft PDE constraints in many existing methods. | [
"['Ray Zirui Zhang' 'Xiaohui Xie' 'John Lowengrub']"
]
|
null | null | 2404.17799 | null | null | http://arxiv.org/pdf/2404.17799v1 | 2024-04-27T06:37:19Z | 2024-04-27T06:37:19Z | Personalized Federated Learning via Sequential Layer Expansion in
Representation Learning | Federated learning ensures the privacy of clients by conducting distributed training on individual client devices and sharing only the model weights with a central server. However, in real-world scenarios, the heterogeneity of data among clients necessitates appropriate personalization methods. In this paper, we aim to address this heterogeneity using a form of parameter decoupling known as representation learning. Representation learning divides deep learning models into 'base' and 'head' components. The base component, capturing common features across all clients, is shared with the server, while the head component, capturing unique features specific to individual clients, remains local. We propose a new representation learning-based approach that suggests decoupling the entire deep learning model into more densely divided parts with the application of suitable scheduling methods, which can benefit not only data heterogeneity but also class heterogeneity. In this paper, we compare and analyze two layer scheduling approaches, namely forward (textit{Vanilla}) and backward (textit{Anti}), in the context of data and class heterogeneity among clients. Our experimental results show that the proposed algorithm, when compared to existing personalized federated learning algorithms, achieves increased accuracy, especially under challenging conditions, while reducing computation costs. | [
"['Jaewon Jang' 'Bonjun Choi']"
]
|
null | null | 2404.17801 | null | null | http://arxiv.org/pdf/2404.17801v1 | 2024-04-27T06:44:39Z | 2024-04-27T06:44:39Z | Dynamical Mode Recognition of Coupled Flame Oscillators by Supervised
and Unsupervised Learning Approaches | Combustion instability in gas turbines and rocket engines, as one of the most challenging problems in combustion research, arises from the complex interactions among flames, which are also influenced by chemical reactions, heat and mass transfer, and acoustics. Identifying and understanding combustion instability is essential to ensure the safe and reliable operation of many combustion systems, where exploring and classifying the dynamical behaviors of complex flame systems is a core take. To facilitate fundamental studies, the present work concerns dynamical mode recognition of coupled flame oscillators made of flickering buoyant diffusion flames, which have gained increasing attention in recent years but are not sufficiently understood. The time series data of flame oscillators are generated by fully validated reacting flow simulations. Due to limitations of expertise-based models, a data-driven approach is adopted. In this study, a nonlinear dimensional reduction model of variational autoencoder (VAE) is used to project the simulation data onto a 2-dimensional latent space. Based on the phase trajectories in latent space, both supervised and unsupervised classifiers are proposed for datasets with well known labeling and without, respectively. For labeled datasets, we establish the Wasserstein-distance-based classifier (WDC) for mode recognition; for unlabeled datasets, we develop a novel unsupervised classifier (GMM-DTWC) combining dynamic time warping (DTW) and Gaussian mixture model (GMM). Through comparing with conventional approaches for dimensionality reduction and classification, the proposed supervised and unsupervised VAE-based approaches exhibit a prominent performance for distinguishing dynamical modes, implying their potential extension to dynamical mode recognition of complex combustion problems. | [
"['Weiming Xu' 'Tao Yang' 'Peng Zhang']"
]
|
null | null | 2404.17805 | null | null | http://arxiv.org/pdf/2404.17805v1 | 2024-04-27T07:05:41Z | 2024-04-27T07:05:41Z | From Optimization to Generalization: Fair Federated Learning against
Quality Shift via Inter-Client Sharpness Matching | Due to escalating privacy concerns, federated learning has been recognized as a vital approach for training deep neural networks with decentralized medical data. In practice, it is challenging to ensure consistent imaging quality across various institutions, often attributed to equipment malfunctions affecting a minority of clients. This imbalance in image quality can cause the federated model to develop an inherent bias towards higher-quality images, thus posing a severe fairness issue. In this study, we pioneer the identification and formulation of this new fairness challenge within the context of the imaging quality shift. Traditional methods for promoting fairness in federated learning predominantly focus on balancing empirical risks across diverse client distributions. This strategy primarily facilitates fair optimization across different training data distributions, yet neglects the crucial aspect of generalization. To address this, we introduce a solution termed Federated learning with Inter-client Sharpness Matching (FedISM). FedISM enhances both local training and global aggregation by incorporating sharpness-awareness, aiming to harmonize the sharpness levels across clients for fair generalization. Our empirical evaluations, conducted using the widely-used ICH and ISIC 2019 datasets, establish FedISM's superiority over current state-of-the-art federated learning methods in promoting fairness. Code is available at https://github.com/wnn2000/FFL4MIA. | [
"['Nannan Wu' 'Zhuo Kuang' 'Zengqiang Yan' 'Li Yu']"
]
|
null | null | 2404.17806 | null | null | http://arxiv.org/pdf/2404.17806v1 | 2024-04-27T07:05:48Z | 2024-04-27T07:05:48Z | T-CLAP: Temporal-Enhanced Contrastive Language-Audio Pretraining | Contrastive language-audio pretraining~(CLAP) has been developed to align the representations of audio and language, achieving remarkable performance in retrieval and classification tasks. However, current CLAP struggles to capture temporal information within audio and text features, presenting substantial limitations for tasks such as audio retrieval and generation. To address this gap, we introduce T-CLAP, a temporal-enhanced CLAP model. We use Large Language Models~(LLMs) and mixed-up strategies to generate temporal-contrastive captions for audio clips from extensive audio-text datasets. Subsequently, a new temporal-focused contrastive loss is designed to fine-tune the CLAP model by incorporating these synthetic data. We conduct comprehensive experiments and analysis in multiple downstream tasks. T-CLAP shows improved capability in capturing the temporal relationship of sound events and outperforms state-of-the-art models by a significant margin. | [
"['Yi Yuan' 'Zhuo Chen' 'Xubo Liu' 'Haohe Liu' 'Xuenan Xu' 'Dongya Jia'\n 'Yuanzhe Chen' 'Mark D. Plumbley' 'Wenwu Wang']"
]
|
null | null | 2404.17820 | null | null | http://arxiv.org/abs/2404.17820v1 | 2024-04-27T08:00:35Z | 2024-04-27T08:00:35Z | Motion planning for off-road autonomous driving based on human-like
cognition and weight adaptation | Driving in an off-road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human-like cognition and cost evaluation for off-road driving. First, we construct a multi-layer map describing different features of off-road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a CNN-LSTM network to learn the trajectories planned by human drivers in various off-road scenarios. Then, based on human-like generated trajectories in different environments, we design a primitive-based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off-road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off-road environment with complex terrain and varying road conditions. The experimental results show that the proposed human-like motion planner has excellent adaptability to different off-road conditions. It shows real-time operation, greater stability, and more human-like planning ability in diverse and challenging scenarios. | [
"['Yuchun Wang' 'Cheng Gong' 'Jianwei Gong' 'Peng Jia']"
]
|
null | null | 2404.17830 | null | null | http://arxiv.org/pdf/2404.17830v2 | 2024-05-03T02:29:09Z | 2024-04-27T08:40:33Z | Dynamic Against Dynamic: An Open-set Self-learning Framework | In open-set recognition, existing methods generally learn statically fixed decision boundaries using known classes to reject unknown classes. Though they have achieved promising results, such decision boundaries are evidently insufficient for universal unknown classes in dynamic and open scenarios as they can potentially appear at any position in the feature space. Moreover, these methods just simply reject unknown class samples during testing without any effective utilization for them. In fact, such samples completely can constitute the true instantiated representation of the unknown classes to further enhance the model's performance. To address these issues, this paper proposes a novel dynamic against dynamic idea, i.e., dynamic method against dynamic changing open-set world, where an open-set self-learning (OSSL) framework is correspondingly developed. OSSL starts with a good closed-set classifier trained by known classes and utilizes available test samples for model adaptation during testing, thus gaining the adaptability to changing data distributions. In particular, a novel self-matching module is designed for OSSL, which can achieve the adaptation in automatically identifying known class samples while rejecting unknown class samples which are further utilized to enhance the discriminability of the model as the instantiated representation of unknown classes. Our method establishes new performance milestones respectively in almost all standard and cross-data benchmarks. | [
"['Haifeng Yang' 'Chuanxing Geng' 'Pong C. Yuen' 'Songcan Chen']"
]
|
null | null | 2404.17847 | null | null | http://arxiv.org/pdf/2404.17847v1 | 2024-04-27T09:52:59Z | 2024-04-27T09:52:59Z | pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in
Heterogeneous Federated Learning | Model-heterogeneous personalized federated learning (MHPFL) enables FL clients to train structurally different personalized models on non-independent and identically distributed (non-IID) local data. Existing MHPFL methods focus on achieving client-level personalization, but cannot address batch-level data heterogeneity. To bridge this important gap, we propose a model-heterogeneous personalized Federated learning approach with Adaptive Feature Mixture (pFedAFM) for supervised learning tasks. It consists of three novel designs: 1) A sharing global homogeneous small feature extractor is assigned alongside each client's local heterogeneous model (consisting of a heterogeneous feature extractor and a prediction header) to facilitate cross-client knowledge fusion. The two feature extractors share the local heterogeneous model's prediction header containing rich personalized prediction knowledge to retain personalized prediction capabilities. 2) An iterative training strategy is designed to alternately train the global homogeneous small feature extractor and the local heterogeneous large model for effective global-local knowledge exchange. 3) A trainable weight vector is designed to dynamically mix the features extracted by both feature extractors to adapt to batch-level data heterogeneity. Theoretical analysis proves that pFedAFM can converge over time. Extensive experiments on 2 benchmark datasets demonstrate that it significantly outperforms 7 state-of-the-art MHPFL methods, achieving up to 7.93% accuracy improvement while incurring low communication and computation costs. | [
"['Liping Yi' 'Han Yu' 'Chao Ren' 'Heng Zhang' 'Gang Wang' 'Xiaoguang Liu'\n 'Xiaoxiao Li']"
]
|
null | null | 2404.17856 | null | null | http://arxiv.org/pdf/2404.17856v1 | 2024-04-27T10:20:41Z | 2024-04-27T10:20:41Z | Uncertainty quantification for iterative algorithms in linear models
with application to early stopping | This paper investigates the iterates $hbb^1,dots,hbb^T$ obtained from iterative algorithms in high-dimensional linear regression problems, in the regime where the feature dimension $p$ is comparable with the sample size $n$, i.e., $p asymp n$. The analysis and proposed estimators are applicable to Gradient Descent (GD), proximal GD and their accelerated variants such as Fast Iterative Soft-Thresholding (FISTA). The paper proposes novel estimators for the generalization error of the iterate $hbb^t$ for any fixed iteration $t$ along the trajectory. These estimators are proved to be $sqrt n$-consistent under Gaussian designs. Applications to early-stopping are provided: when the generalization error of the iterates is a U-shape function of the iteration $t$, the estimates allow to select from the data an iteration $hat t$ that achieves the smallest generalization error along the trajectory. Additionally, we provide a technique for developing debiasing corrections and valid confidence intervals for the components of the true coefficient vector from the iterate $hbb^t$ at any finite iteration $t$. Extensive simulations on synthetic data illustrate the theoretical results. | [
"['Pierre C. Bellec' 'Kai Tan']"
]
|
null | null | 2404.17868 | null | null | http://arxiv.org/pdf/2404.17868v1 | 2024-04-27T11:25:58Z | 2024-04-27T11:25:58Z | Error analysis for finite element operator learning methods for solving
parametric second-order elliptic PDEs | In this paper, we provide a theoretical analysis of a type of operator learning method without data reliance based on the classical finite element approximation, which is called the finite element operator network (FEONet). We first establish the convergence of this method for general second-order linear elliptic PDEs with respect to the parameters for neural network approximation. In this regard, we address the role of the condition number of the finite element matrix in the convergence of the method. Secondly, we derive an explicit error estimate for the self-adjoint case. For this, we investigate some regularity properties of the solution in certain function classes for a neural network approximation, verifying the sufficient condition for the solution to have the desired regularity. Finally, we will also conduct some numerical experiments that support the theoretical findings, confirming the role of the condition number of the finite element matrix in the overall convergence. | [
"['Youngjoon Hong' 'Seungchan Ko' 'Jaeyong Lee']"
]
|
null | null | 2404.17875 | null | null | http://arxiv.org/pdf/2404.17875v2 | 2024-05-08T06:56:53Z | 2024-04-27T12:19:08Z | Noisy Node Classification by Bi-level Optimization based Multi-teacher
Distillation | Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, we propose a new multi-teacher distillation method based on bi-level optimization (namely BO-NNC), to conduct noisy node classification on the graph data. Specifically, we first employ multiple self-supervised learning methods to train diverse teacher models, and then aggregate their predictions through a teacher weight matrix. Furthermore, we design a new bi-level optimization strategy to dynamically adjust the teacher weight matrix based on the training progress of the student model. Finally, we design a label improvement module to improve the label quality. Extensive experimental results on real datasets show that our method achieves the best results compared to state-of-the-art methods. | [
"['Yujing Liu' 'Zongqian Wu' 'Zhengyu Lu' 'Ci Nie' 'Guoqiu Wen' 'Ping Hu'\n 'Xiaofeng Zhu']"
]
|
null | null | 2404.17884 | null | null | http://arxiv.org/pdf/2404.17884v1 | 2024-04-27T12:43:02Z | 2024-04-27T12:43:02Z | Exploring the efficacy of a hybrid approach with modal decomposition
over fully deep learning models for flow dynamics forecasting | Fluid dynamics problems are characterized by being multidimensional and nonlinear, causing the experiments and numerical simulations being complex, time-consuming and monetarily expensive. In this sense, there is a need to find new ways to obtain data in a more economical manner. Thus, in this work we study the application of time series forecasting to fluid dynamics problems, where the aim is to predict the flow dynamics using only past information. We focus our study on models based on deep learning that do not require a high amount of data for training, as this is the problem we are trying to address. Specifically in this work we have tested three autoregressive models where two of them are fully based on deep learning and the other one is a hybrid model that combines modal decomposition with deep learning. We ask these models to generate $200$ time-ahead predictions of two datasets coming from a numerical simulation and experimental measurements, where the latter is characterized by being turbulent. We show how the hybrid model generates more reliable predictions in the experimental case, as it is physics-informed in the sense that the modal decomposition extracts the physics in a way that allows us to predict it. | [
"['Rodrigo Abadía-Heredia' 'Adrián Corrochano' 'Manuel Lopez-Martin'\n 'Soledad Le Clainche']"
]
|
null | null | 2404.17886 | null | null | http://arxiv.org/pdf/2404.17886v1 | 2024-04-27T12:47:37Z | 2024-04-27T12:47:37Z | Feature graphs for interpretable unsupervised tree ensembles:
centrality, interaction, and application in disease subtyping | Interpretable machine learning has emerged as central in leveraging artificial intelligence within high-stakes domains such as healthcare, where understanding the rationale behind model predictions is as critical as achieving high predictive accuracy. In this context, feature selection assumes a pivotal role in enhancing model interpretability by identifying the most important input features in black-box models. While random forests are frequently used in biomedicine for their remarkable performance on tabular datasets, the accuracy gained from aggregating decision trees comes at the expense of interpretability. Consequently, feature selection for enhancing interpretability in random forests has been extensively explored in supervised settings. However, its investigation in the unsupervised regime remains notably limited. To address this gap, the study introduces novel methods to construct feature graphs from unsupervised random forests and feature selection strategies to derive effective feature combinations from these graphs. Feature graphs are constructed for the entire dataset as well as individual clusters leveraging the parent-child node splits within the trees, such that feature centrality captures their relevance to the clustering task, while edge weights reflect the discriminating power of feature pairs. Graph-based feature selection methods are extensively evaluated on synthetic and benchmark datasets both in terms of their ability to reduce dimensionality while improving clustering performance, as well as to enhance model interpretability. An application on omics data for disease subtyping identifies the top features for each cluster, showcasing the potential of the proposed approach to enhance interpretability in clustering analyses and its utility in a real-world biomedical application. | [
"['Christel Sirocchi' 'Martin Urschler' 'Bastian Pfeifer']"
]
|
null | null | 2404.17892 | null | null | http://arxiv.org/abs/2404.17892v1 | 2024-04-27T13:01:05Z | 2024-04-27T13:01:05Z | Shared learning of powertrain control policies for vehicle fleets | Emerging data-driven approaches, such as deep reinforcement learning (DRL), aim at on-the-field learning of powertrain control policies that optimize fuel economy and other performance metrics. Indeed, they have shown great potential in this regard for individual vehicles on specific routes or drive cycles. However, for fleets of vehicles that must service a distribution of routes, DRL approaches struggle with learning stability issues that result in high variances and challenge their practical deployment. In this paper, we present a novel framework for shared learning among a fleet of vehicles through the use of a distilled group policy as the knowledge sharing mechanism for the policy learning computations at each vehicle. We detail the mathematical formulation that makes this possible. Several scenarios are considered to analyze the functionality, performance, and computational scalability of the framework with fleet size. Comparisons of the cumulative performance of fleets using our proposed shared learning approach with a baseline of individual learning agents and another state-of-the-art approach with a centralized learner show clear advantages to our approach. For example, we find a fleet average asymptotic improvement of 8.5 percent in fuel economy compared to the baseline while also improving on the metrics of acceleration error and shifting frequency for fleets serving a distribution of suburban routes. Furthermore, we include demonstrative results that show how the framework reduces variance within a fleet and also how it helps individual agents adapt better to new routes. | [
"['Lindsey Kerbel' 'Beshah Ayalew' 'Andrej Ivanco']"
]
|
null | null | 2404.17910 | null | null | http://arxiv.org/abs/2404.17910v1 | 2024-04-27T13:38:45Z | 2024-04-27T13:38:45Z | Reliable Student: Addressing Noise in Semi-Supervised 3D Object
Detection | Semi-supervised 3D object detection can benefit from the promising pseudo-labeling technique when labeled data is limited. However, recent approaches have overlooked the impact of noisy pseudo-labels during training, despite efforts to enhance pseudo-label quality through confidence-based filtering. In this paper, we examine the impact of noisy pseudo-labels on IoU-based target assignment and propose the Reliable Student framework, which incorporates two complementary approaches to mitigate errors. First, it involves a class-aware target assignment strategy that reduces false negative assignments in difficult classes. Second, it includes a reliability weighting strategy that suppresses false positive assignment errors while also addressing remaining false negatives from the first step. The reliability weights are determined by querying the teacher network for confidence scores of the student-generated proposals. Our work surpasses the previous state-of-the-art on KITTI 3D object detection benchmark on point clouds in the semi-supervised setting. On 1% labeled data, our approach achieves a 6.2% AP improvement for the pedestrian class, despite having only 37 labeled samples available. The improvements become significant for the 2% setting, achieving 6.0% AP and 5.7% AP improvements for the pedestrian and cyclist classes, respectively. | [
"['Farzad Nozarian' 'Shashank Agarwal' 'Farzaneh Rezaeianaran'\n 'Danish Shahzad' 'Atanas Poibrenski' 'Christian Müller'\n 'Philipp Slusallek']"
]
|
null | null | 2404.17912 | null | null | http://arxiv.org/pdf/2404.17912v1 | 2024-04-27T13:46:23Z | 2024-04-27T13:46:23Z | SERPENT-VLM : Self-Refining Radiology Report Generation Using Vision
Language Models | Radiology Report Generation (R2Gen) demonstrates how Multi-modal Large Language Models (MLLMs) can automate the creation of accurate and coherent radiological reports. Existing methods often hallucinate details in text-based reports that don't accurately reflect the image content. To mitigate this, we introduce a novel strategy, SERPENT-VLM (SElf Refining Radiology RePort GENeraTion using Vision Language Models), which improves the R2Gen task by integrating a self-refining mechanism into the MLLM framework. We employ a unique self-supervised loss that leverages similarity between pooled image representations and the contextual representations of the generated radiological text, alongside the standard Causal Language Modeling objective, to refine image-text representations. This allows the model to scrutinize and align the generated text through dynamic interaction between a given image and the generated text, therefore reducing hallucination and continuously enhancing nuanced report generation. SERPENT-VLM outperforms existing baselines such as LLaVA-Med, BiomedGPT, etc., achieving SoTA performance on the IU X-ray and Radiology Objects in COntext (ROCO) datasets, and also proves to be robust against noisy images. A qualitative case study emphasizes the significant advancements towards more sophisticated MLLM frameworks for R2Gen, opening paths for further research into self-supervised refinement in the medical imaging domain. | [
"['Manav Nitin Kapadnis' 'Sohan Patnaik' 'Abhilash Nandy' 'Sourjyadip Ray'\n 'Pawan Goyal' 'Debdoot Sheet']"
]
|
null | null | 2404.17916 | null | null | http://arxiv.org/pdf/2404.17916v1 | 2024-04-27T14:05:18Z | 2024-04-27T14:05:18Z | FedCRL: Personalized Federated Learning with Contrastive Shared
Representations for Label Heterogeneity in Non-IID Data | To deal with heterogeneity resulting from label distribution skew and data scarcity in distributed machine learning scenarios, this paper proposes a novel Personalized Federated Learning (PFL) algorithm, named Federated Contrastive Representation Learning (FedCRL). FedCRL introduces contrastive representation learning (CRL) on shared representations to facilitate knowledge acquisition of clients. Specifically, both local model parameters and averaged values of local representations are considered as shareable information to the server, both of which are then aggregated globally. CRL is applied between local representations and global representations to regularize personalized training by drawing similar representations closer and separating dissimilar ones, thereby enhancing local models with external knowledge and avoiding being harmed by label distribution skew. Additionally, FedCRL adopts local aggregation between each local model and the global model to tackle data scarcity. A loss-wise weighting mechanism is introduced to guide the local aggregation using each local model's contrastive loss to coordinate the global model involvement in each client, thus helping clients with scarce data. Our simulations demonstrate FedCRL's effectiveness in mitigating label heterogeneity by achieving accuracy improvements over existing methods on datasets with varying degrees of label heterogeneity. | [
"['Chenghao Huang' 'Xiaolu Chen' 'Yanru Zhang' 'Hao Wang']"
]
|
null | null | 2404.17925 | null | null | http://arxiv.org/pdf/2404.17925v1 | 2024-04-27T14:29:42Z | 2024-04-27T14:29:42Z | Accurate and fast anomaly detection in industrial processes and IoT
environments | We present a novel, simple and widely applicable semi-supervised procedure for anomaly detection in industrial and IoT environments, SAnD (Simple Anomaly Detection). SAnD comprises 5 steps, each leveraging well-known statistical tools, namely; smoothing filters, variance inflation factors, the Mahalanobis distance, threshold selection algorithms and feature importance techniques. To our knowledge, SAnD is the first procedure that integrates these tools to identify anomalies and help decipher their putative causes. We show how each step contributes to tackling technical challenges that practitioners face when detecting anomalies in industrial contexts, where signals can be highly multicollinear, have unknown distributions, and intertwine short-lived noise with the long(er)-lived actual anomalies. The development of SAnD was motivated by a concrete case study from our industrial partner, which we use here to show its effectiveness. We also evaluate the performance of SAnD by comparing it with a selection of semi-supervised methods on public datasets from the literature on anomaly detection. We conclude that SAnD is effective, broadly applicable, and outperforms existing approaches in both anomaly detection and runtime. | [
"['Simone Tonini' 'Andrea Vandin' 'Francesca Chiaromonte' 'Daniele Licari'\n 'Fernando Barsacchi']"
]
|
null | null | 2404.17926 | null | null | http://arxiv.org/pdf/2404.17926v1 | 2024-04-27T14:29:53Z | 2024-04-27T14:29:53Z | Pre-training on High Definition X-ray Images: An Experimental Study | Existing X-ray based pre-trained vision models are usually conducted on a relatively small-scale dataset (less than 500k samples) with limited resolution (e.g., 224 $times$ 224). However, the key to the success of self-supervised pre-training large models lies in massive training data, and maintaining high resolution in the field of X-ray images is the guarantee of effective solutions to difficult miscellaneous diseases. In this paper, we address these issues by proposing the first high-definition (1280 $times$ 1280) X-ray based pre-trained foundation vision model on our newly collected large-scale dataset which contains more than 1 million X-ray images. Our model follows the masked auto-encoder framework which takes the tokens after mask processing (with a high rate) is used as input, and the masked image patches are reconstructed by the Transformer encoder-decoder network. More importantly, we introduce a novel context-aware masking strategy that utilizes the chest contour as a boundary for adaptive masking operations. We validate the effectiveness of our model on two downstream tasks, including X-ray report generation and disease recognition. Extensive experiments demonstrate that our pre-trained medical foundation vision model achieves comparable or even new state-of-the-art performance on downstream benchmark datasets. The source code and pre-trained models of this paper will be released on https://github.com/Event-AHU/Medical_Image_Analysis. | [
"['Xiao Wang' 'Yuehang Li' 'Wentao Wu' 'Jiandong Jin' 'Yao Rong' 'Bo Jiang'\n 'Chuanfu Li' 'Jin Tang']"
]
|
null | null | 2404.17931 | null | null | http://arxiv.org/pdf/2404.17931v1 | 2024-04-27T15:04:30Z | 2024-04-27T15:04:30Z | Critical Review for One-class Classification: recent advances and the
reality behind them | This paper offers a comprehensive review of one-class classification (OCC), examining the technologies and methodologies employed in its implementation. It delves into various approaches utilized for OCC across diverse data types, such as feature data, image, video, time series, and others. Through a systematic review, this paper synthesizes promi-nent strategies used in OCC from its inception to its current advance-ments, with a particular emphasis on the promising application. Moreo-ver, the article criticizes the state-of-the-art (SOTA) image anomaly de-tection (AD) algorithms dominating one-class experiments. These algo-rithms include outlier exposure (binary classification) and pretrained model (multi-class classification), conflicting with the fundamental con-cept of learning from one class. Our investigation reveals that the top nine algorithms for one-class CIFAR10 benchmark are not OCC. We ar-gue that binary/multi-class classification algorithms should not be com-pared with OCC. | [
"['Toshitaka Hayashi' 'Dalibor Cimr' 'Hamido Fujita' 'Richard Cimler']"
]
|
null | null | 2404.17937 | null | null | http://arxiv.org/pdf/2404.17937v1 | 2024-04-27T15:25:03Z | 2024-04-27T15:25:03Z | DTization: A New Method for Supervised Feature Scaling | Artificial intelligence is currently a dominant force in shaping various aspects of the world. Machine learning is a sub-field in artificial intelligence. Feature scaling is one of the data pre-processing techniques that improves the performance of machine learning algorithms. The traditional feature scaling techniques are unsupervised where they do not have influence of the dependent variable in the scaling process. In this paper, we have presented a novel feature scaling technique named DTization that employs decision tree and robust scaler for supervised feature scaling. The proposed method utilizes decision tree to measure the feature importance and based on the importance, different features get scaled differently with the robust scaler algorithm. The proposed method has been extensively evaluated on ten classification and regression datasets on various evaluation matrices and the results show a noteworthy performance improvement compared to the traditional feature scaling methods. | [
"['Niful Islam']"
]
|
null | null | 2404.17940 | null | null | http://arxiv.org/pdf/2404.17940v1 | 2024-04-27T15:44:21Z | 2024-04-27T15:44:21Z | CBMAP: Clustering-based manifold approximation and projection for
dimensionality reduction | Dimensionality reduction methods are employed to decrease data dimensionality, either to enhance machine learning performance or to facilitate data visualization in two or three-dimensional spaces. These methods typically fall into two categories: feature selection and feature transformation. Feature selection retains significant features, while feature transformation projects data into a lower-dimensional space, with linear and nonlinear methods. While nonlinear methods excel in preserving local structures and capturing nonlinear relationships, they may struggle with interpreting global structures and can be computationally intensive. Recent algorithms, such as the t-SNE, UMAP, TriMap, and PaCMAP prioritize preserving local structures, often at the expense of accurately representing global structures, leading to clusters being spread out more in lower-dimensional spaces. Moreover, these methods heavily rely on hyperparameters, making their results sensitive to parameter settings. To address these limitations, this study introduces a clustering-based approach, namely CBMAP (Clustering-Based Manifold Approximation and Projection), for dimensionality reduction. CBMAP aims to preserve both global and local structures, ensuring that clusters in lower-dimensional spaces closely resemble those in high-dimensional spaces. Experimental evaluations on benchmark datasets demonstrate CBMAP's efficacy, offering speed, scalability, and minimal reliance on hyperparameters. Importantly, CBMAP enables low-dimensional projection of test data, addressing a critical need in machine learning applications. CBMAP is made freely available at https://github.com/doganlab/cbmap and can be installed from the Python Package Directory (PyPI) software repository with the command pip install cbmap. | [
"['Berat Dogan']"
]
|
null | null | 2404.17943 | null | null | http://arxiv.org/pdf/2404.17943v1 | 2024-04-27T15:46:54Z | 2024-04-27T15:46:54Z | Interaction Event Forecasting in Multi-Relational Recursive HyperGraphs:
A Temporal Point Process Approach | Modeling the dynamics of interacting entities using an evolving graph is an essential problem in fields such as financial networks and e-commerce. Traditional approaches focus primarily on pairwise interactions, limiting their ability to capture the complexity of real-world interactions involving multiple entities and their intricate relationship structures. This work addresses the problem of forecasting higher-order interaction events in multi-relational recursive hypergraphs. This is done using a dynamic graph representation learning framework that can capture complex relationships involving multiple entities. The proposed model, textit{Relational Recursive Hyperedge Temporal Point Process} (RRHyperTPP) uses an encoder that learns a dynamic node representation based on the historical interaction patterns and then a hyperedge link prediction based decoder to model the event's occurrence. These learned representations are then used for downstream tasks involving forecasting the type and time of interactions. The main challenge in learning from hyperedge events is that the number of possible hyperedges grows exponentially with the number of nodes in the network. This will make the computation of negative log-likelihood of the temporal point process expensive, as the calculation of survival function requires a summation over all possible hyperedges. In our work, we use noise contrastive estimation to learn the parameters of our model, and we have experimentally shown that our models perform better than previous state-of-the-art methods for interaction forecasting. | [
"['Tony Gracious' 'Ambedkar Dukkipati']"
]
|
null | null | 2404.17947 | null | null | http://arxiv.org/pdf/2404.17947v1 | 2024-04-27T15:57:35Z | 2024-04-27T15:57:35Z | Bounding the Expected Robustness of Graph Neural Networks Subject to
Node Feature Attacks | Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in various graph representation learning tasks. Recently, studies revealed their vulnerability to adversarial attacks. In this work, we theoretically define the concept of expected robustness in the context of attributed graphs and relate it to the classical definition of adversarial robustness in the graph representation learning literature. Our definition allows us to derive an upper bound of the expected robustness of Graph Convolutional Networks (GCNs) and Graph Isomorphism Networks subject to node feature attacks. Building on these findings, we connect the expected robustness of GNNs to the orthonormality of their weight matrices and consequently propose an attack-independent, more robust variant of the GCN, called the Graph Convolutional Orthonormal Robust Networks (GCORNs). We further introduce a probabilistic method to estimate the expected robustness, which allows us to evaluate the effectiveness of GCORN on several real-world datasets. Experimental experiments showed that GCORN outperforms available defense methods. Our code is publicly available at: href{https://github.com/Sennadir/GCORN}{https://github.com/Sennadir/GCORN}. | [
"['Yassine Abbahaddou' 'Sofiane Ennadir' 'Johannes F. Lutzeyer'\n 'Michalis Vazirgiannis' 'Henrik Boström']"
]
|
null | null | 2404.17951 | null | null | http://arxiv.org/pdf/2404.17951v1 | 2024-04-27T16:13:05Z | 2024-04-27T16:13:05Z | Cauchy-Schwarz Divergence Information Bottleneck for Regression | The information bottleneck (IB) approach is popular to improve the generalization, robustness and explainability of deep neural networks. Essentially, it aims to find a minimum sufficient representation $mathbf{t}$ by striking a trade-off between a compression term $I(mathbf{x};mathbf{t})$ and a prediction term $I(y;mathbf{t})$, where $I(cdot;cdot)$ refers to the mutual information (MI). MI is for the IB for the most part expressed in terms of the Kullback-Leibler (KL) divergence, which in the regression case corresponds to prediction based on mean squared error (MSE) loss with Gaussian assumption and compression approximated by variational inference. In this paper, we study the IB principle for the regression problem and develop a new way to parameterize the IB with deep neural networks by exploiting favorable properties of the Cauchy-Schwarz (CS) divergence. By doing so, we move away from MSE-based regression and ease estimation by avoiding variational approximations or distributional assumptions. We investigate the improved generalization ability of our proposed CS-IB and demonstrate strong adversarial robustness guarantees. We demonstrate its superior performance on six real-world regression tasks over other popular deep IB approaches. We additionally observe that the solutions discovered by CS-IB always achieve the best trade-off between prediction accuracy and compression ratio in the information plane. The code is available at url{https://github.com/SJYuCNEL/Cauchy-Schwarz-Information-Bottleneck}. | [
"['Shujian Yu' 'Xi Yu' 'Sigurd Løkse' 'Robert Jenssen' 'Jose C. Principe']"
]
|
null | null | 2404.17960 | null | null | http://arxiv.org/pdf/2404.17960v1 | 2024-04-27T17:13:49Z | 2024-04-27T17:13:49Z | PhishGuard: A Convolutional Neural Network Based Model for Detecting
Phishing URLs with Explainability Analysis | Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black box intelligent models decision to detect suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a 1D Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85%. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL. | [
"['Md Robiul Islam' 'Md Mahamodul Islam' 'Mst. Suraiya Afrin'\n 'Anika Antara' 'Nujhat Tabassum' 'Al Amin']"
]
|
null | null | 2404.17962 | null | null | http://arxiv.org/pdf/2404.17962v1 | 2024-04-27T17:22:12Z | 2024-04-27T17:22:12Z | Deep Learning for Low-Latency, Quantum-Ready RF Sensing | Recent work has shown the promise of applying deep learning to enhance software processing of radio frequency (RF) signals. In parallel, hardware developments with quantum RF sensors based on Rydberg atoms are breaking longstanding barriers in frequency range, resolution, and sensitivity. In this paper, we describe our implementations of quantum-ready machine learning approaches for RF signal classification. Our primary objective is latency: while deep learning offers a more powerful computational paradigm, it also traditionally incurs latency overheads that hinder wider scale deployment. Our work spans three axes. (1) A novel continuous wavelet transform (CWT) based recurrent neural network (RNN) architecture that enables flexible online classification of RF signals on-the-fly with reduced sampling time. (2) Low-latency inference techniques for both GPU and CPU that span over 100x reductions in inference time, enabling real-time operation with sub-millisecond inference. (3) Quantum-readiness validated through application of our models to physics-based simulation of Rydberg atom QRF sensors. Altogether, our work bridges towards next-generation RF sensors that use quantum technology to surpass previous physical limits, paired with latency-optimized AI/ML software that is suitable for real-time deployment. | [
"['Pranav Gokhale' 'Caitlin Carnahan' 'William Clark' 'Frederic T. Chong']"
]
|
null | null | 2404.17968 | null | null | http://arxiv.org/pdf/2404.17968v1 | 2024-04-27T18:04:28Z | 2024-04-27T18:04:28Z | Usefulness of Emotional Prosody in Neural Machine Translation | Neural Machine Translation (NMT) is the task of translating a text from one language to another with the use of a trained neural network. Several existing works aim at incorporating external information into NMT models to improve or control predicted translations (e.g. sentiment, politeness, gender). In this work, we propose to improve translation quality by adding another external source of information: the automatically recognized emotion in the voice. This work is motivated by the assumption that each emotion is associated with a specific lexicon that can overlap between emotions. Our proposed method follows a two-stage procedure. At first, we select a state-of-the-art Speech Emotion Recognition (SER) model to predict dimensional emotion values from all input audio in the dataset. Then, we use these predicted emotions as source tokens added at the beginning of input texts to train our NMT model. We show that integrating emotion information, especially arousal, into NMT systems leads to better translations. | [
"['Charles Brazier' 'Jean-Luc Rouas']"
]
|
null | null | 2404.17990 | null | null | http://arxiv.org/pdf/2404.17990v2 | 2024-06-25T07:46:30Z | 2024-04-27T19:40:35Z | TabVFL: Improving Latent Representation in Vertical Federated Learning | Autoencoders are popular neural networks that are able to compress high dimensional data to extract relevant latent information. TabNet is a state-of-the-art neural network model designed for tabular data that utilizes an autoencoder architecture for training. Vertical Federated Learning (VFL) is an emerging distributed machine learning paradigm that allows multiple parties to train a model collaboratively on vertically partitioned data while maintaining data privacy. The existing design of training autoencoders in VFL is to train a separate autoencoder in each participant and aggregate the latent representation later. This design could potentially break important correlations between feature data of participating parties, as each autoencoder is trained on locally available features while disregarding the features of others. In addition, traditional autoencoders are not specifically designed for tabular data, which is ubiquitous in VFL settings. Moreover, the impact of client failures during training on the model robustness is under-researched in the VFL scene. In this paper, we propose TabVFL, a distributed framework designed to improve latent representation learning using the joint features of participants. The framework (i) preserves privacy by mitigating potential data leakage with the addition of a fully-connected layer, (ii) conserves feature correlations by learning one latent representation vector, and (iii) provides enhanced robustness against client failures during training phase. Extensive experiments on five classification datasets show that TabVFL can outperform the prior work design, with 26.12% of improvement on f1-score. | [
"['Mohamed Rashad' 'Zilong Zhao' 'Jeremie Decouchant' 'Lydia Y. Chen']"
]
|
null | null | 2404.17997 | null | null | http://arxiv.org/pdf/2404.17997v1 | 2024-04-27T20:16:58Z | 2024-04-27T20:16:58Z | Optimal Initialization of Batch Bayesian Optimization | Field experiments and computer simulations are effective but time-consuming methods of measuring the quality of engineered systems at different settings. To reduce the total time required, experimenters may employ Bayesian optimization, which is parsimonious with measurements, and take measurements of multiple settings simultaneously, in a batch. In practice, experimenters use very few batches, thus, it is imperative that each batch be as informative as possible. Typically, the initial batch in a Batch Bayesian Optimization (BBO) is constructed from a quasi-random sample of settings values. We propose a batch-design acquisition function, Minimal Terminal Variance (MTV), that designs a batch by optimization rather than random sampling. MTV adapts a design criterion function from Design of Experiments, called I-Optimality, which minimizes the variance of the post-evaluation estimates of quality, integrated over the entire space of settings. MTV weights the integral by the probability that a setting is optimal, making it able to design not only an initial batch but all subsequent batches, as well. Applicability to both initialization and subsequent batches is novel among acquisition functions. Numerical experiments on test functions and simulators show that MTV compares favorably to other BBO methods. | [
"['Jiuge Ren' 'David Sweet']"
]
|
null | null | 2404.17999 | null | null | http://arxiv.org/pdf/2404.17999v1 | 2024-04-27T20:28:38Z | 2024-04-27T20:28:38Z | MediFact at MEDIQA-CORR 2024: Why AI Needs a Human Touch | Accurate representation of medical information is crucial for patient safety, yet artificial intelligence (AI) systems, such as Large Language Models (LLMs), encounter challenges in error-free clinical text interpretation. This paper presents a novel approach submitted to the MEDIQA-CORR 2024 shared task (Ben Abacha et al., 2024a), focusing on the automatic correction of single-word errors in clinical notes. Unlike LLMs that rely on extensive generic data, our method emphasizes extracting contextually relevant information from available clinical text data. Leveraging an ensemble of extractive and abstractive question-answering approaches, we construct a supervised learning framework with domain-specific feature engineering. Our methodology incorporates domain expertise to enhance error correction accuracy. By integrating domain expertise and prioritizing meaningful information extraction, our approach underscores the significance of a human-centric strategy in adapting AI for healthcare. | [
"['Nadia Saeed']"
]
|
null | null | 2404.18008 | null | null | http://arxiv.org/pdf/2404.18008v1 | 2024-04-27T21:00:38Z | 2024-04-27T21:00:38Z | Implicit Generative Prior for Bayesian Neural Networks | Predictive uncertainty quantification is crucial for reliable decision-making in various applied domains. Bayesian neural networks offer a powerful framework for this task. However, defining meaningful priors and ensuring computational efficiency remain significant challenges, especially for complex real-world applications. This paper addresses these challenges by proposing a novel neural adaptive empirical Bayes (NA-EB) framework. NA-EB leverages a class of implicit generative priors derived from low-dimensional distributions. This allows for efficient handling of complex data structures and effective capture of underlying relationships in real-world datasets. The proposed NA-EB framework combines variational inference with a gradient ascent algorithm. This enables simultaneous hyperparameter selection and approximation of the posterior distribution, leading to improved computational efficiency. We establish the theoretical foundation of the framework through posterior and classification consistency. We demonstrate the practical applications of our framework through extensive evaluations on a variety of tasks, including the two-spiral problem, regression, 10 UCI datasets, and image classification tasks on both MNIST and CIFAR-10 datasets. The results of our experiments highlight the superiority of our proposed framework over existing methods, such as sparse variational Bayesian and generative models, in terms of prediction accuracy and uncertainty quantification. | [
"['Yijia Liu' 'Xiao Wang']"
]
|
null | null | 2404.18017 | null | null | http://arxiv.org/pdf/2404.18017v1 | 2024-04-27T21:57:17Z | 2024-04-27T21:57:17Z | Application of Deep Learning for Factor Timing in Asset Management | The paper examines the performance of regression models (OLS linear regression, Ridge regression, Random Forest, and Fully-connected Neural Network) on the prediction of CMA (Conservative Minus Aggressive) factor premium and the performance of factor timing investment with them. Out-of-sample R-squared shows that more flexible models have better performance in explaining the variance in factor premium of the unseen period, and the back testing affirms that the factor timing based on more flexible models tends to over perform the ones with linear models. However, for flexible models like neural networks, the optimal weights based on their prediction tend to be unstable, which can lead to high transaction costs and market impacts. We verify that tilting down the rebalance frequency according to the historical optimal rebalancing scheme can help reduce the transaction costs. | [
"['Prabhu Prasad Panda' 'Maysam Khodayari Gharanchaei' 'Xilin Chen'\n 'Haoshu Lyu']"
]
|
null | null | 2404.18041 | null | null | http://arxiv.org/pdf/2404.18041v1 | 2024-04-28T00:58:28Z | 2024-04-28T00:58:28Z | Variational Optimization for Quantum Problems using Deep Generative
Networks | Optimization is one of the keystones of modern science and engineering. Its applications in quantum technology and machine learning helped nurture variational quantum algorithms and generative AI respectively. We propose a general approach to design variational optimization algorithms based on generative models: the Variational Generative Optimization Network (VGON). To demonstrate its broad applicability, we apply VGON to three quantum tasks: finding the best state in an entanglement-detection protocol, finding the ground state of a 1D quantum spin model with variational quantum circuits, and generating degenerate ground states of many-body quantum Hamiltonians. For the first task, VGON greatly reduces the optimization time compared to stochastic gradient descent while generating nearly optimal quantum states. For the second task, VGON alleviates the barren plateau problem in variational quantum circuits. For the final task, VGON can identify the degenerate ground state spaces after a single stage of training and generate a variety of states therein. | [
"['Lingxia Zhang' 'Xiaodie Lin' 'Peidong Wang' 'Kaiyan Yang' 'Xiao Zeng'\n 'Zhaohui Wei' 'Zizhu Wang']"
]
|
null | null | 2404.18043 | null | null | http://arxiv.org/pdf/2404.18043v1 | 2024-04-28T01:38:38Z | 2024-04-28T01:38:38Z | Utilizing Large Language Models for Information Extraction from Real
Estate Transactions | Real estate sales contracts contain crucial information for property transactions, but manual extraction of data can be time-consuming and error-prone. This paper explores the application of large language models, specifically transformer-based architectures, for automated information extraction from real estate contracts. We discuss challenges, techniques, and future directions in leveraging these models to improve efficiency and accuracy in real estate contract analysis. | [
"['Yu Zhao' 'Haoxiang Gao']"
]
|
null | null | 2404.18060 | null | null | http://arxiv.org/pdf/2404.18060v1 | 2024-04-28T03:28:27Z | 2024-04-28T03:28:27Z | Prompt Customization for Continual Learning | Contemporary continual learning approaches typically select prompts from a pool, which function as supplementary inputs to a pre-trained model. However, this strategy is hindered by the inherent noise of its selection approach when handling increasing tasks. In response to these challenges, we reformulate the prompting approach for continual learning and propose the prompt customization (PC) method. PC mainly comprises a prompt generation module (PGM) and a prompt modulation module (PMM). In contrast to conventional methods that employ hard prompt selection, PGM assigns different coefficients to prompts from a fixed-sized pool of prompts and generates tailored prompts. Moreover, PMM further modulates the prompts by adaptively assigning weights according to the correlations between input data and corresponding prompts. We evaluate our method on four benchmark datasets for three diverse settings, including the class, domain, and task-agnostic incremental learning tasks. Experimental results demonstrate consistent improvement (by up to 16.2%), yielded by the proposed method, over the state-of-the-art (SOTA) techniques. | [
"['Yong Dai' 'Xiaopeng Hong' 'Yabin Wang' 'Zhiheng Ma' 'Dongmei Jiang'\n 'Yaowei Wang']"
]
|
null | null | 2404.18063 | null | null | http://arxiv.org/pdf/2404.18063v1 | 2024-04-28T04:01:09Z | 2024-04-28T04:01:09Z | Machine Learning Techniques for Data Reduction of CFD Applications | We present an approach called guaranteed block autoencoder that leverages Tensor Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid dynamics (CFD) and other scientific applications. It uses a multidimensional block of tensors (spanning in space and time) for both input and output, capturing the spatiotemporal and interspecies relationship within a tensor. The tensor consists of species that represent different elements in a CFD simulation. To guarantee the error bound of the reconstructed data, principal component analysis (PCA) is applied to the residual between the original and reconstructed data. This yields a basis matrix, which is then used to project the residual of each instance. The resulting coefficients are retained to enable accurate reconstruction. Experimental results demonstrate that our approach can deliver two orders of magnitude in reduction while still keeping the errors of primary data under scientifically acceptable bounds. Compared to reduction-based approaches based on SZ, our method achieves a substantially higher compression ratio for a given error bound or a better error for a given compression ratio. | [
"['Jaemoon Lee' 'Ki Sung Jung' 'Qian Gong' 'Xiao Li' 'Scott Klasky'\n 'Jacqueline Chen' 'Anand Rangarajan' 'Sanjay Ranka']"
]
|
null | null | 2404.18071 | null | null | http://arxiv.org/pdf/2404.18071v1 | 2024-04-28T05:26:12Z | 2024-04-28T05:26:12Z | Can Perplexity Predict Fine-Tuning Performance? An Investigation of
Tokenization Effects on Sequential Language Models for Nepali | Recent language models use subwording mechanisms to handle Out-of-Vocabulary(OOV) words seen during test time and, their generation capacity is generally measured using perplexity, an intrinsic metric. It is known that increasing the subword granularity results in a decrease of perplexity value. However, the study of how subwording affects the understanding capacity of language models has been very few and only limited to a handful of languages. To reduce this gap we used 6 different tokenization schemes to pretrain relatively small language models in Nepali and used the representations learned to finetune on several downstream tasks. Although byte-level BPE algorithm has been used in recent models like GPT, RoBERTa we show that on average they are sub-optimal in comparison to algorithms such as SentencePiece in finetuning performances for Nepali. Additionally, similar recent studies have focused on the Bert-based language model. We, however, pretrain and finetune sequential transformer-based language models. | [
"['Nishant Luitel' 'Nirajan Bekoju' 'Anand Kumar Sah' 'Subarna Shakya']"
]
|
null | null | 2404.18077 | null | null | http://arxiv.org/pdf/2404.18077v1 | 2024-04-28T05:46:28Z | 2024-04-28T05:46:28Z | Generative AI for Low-Carbon Artificial Intelligence of Things | By integrating Artificial Intelligence (AI) with the Internet of Things (IoT), Artificial Intelligence of Things (AIoT) has revolutionized many fields. However, AIoT is facing the challenges of energy consumption and carbon emissions due to the continuous advancement of mobile technology. Fortunately, Generative AI (GAI) holds immense potential to reduce carbon emissions of AIoT due to its excellent reasoning and generation capabilities. In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT. Specifically, we first study the main impacts that cause carbon emissions in AIoT, and then introduce GAI techniques and their relations to carbon emissions. We then explore the application prospects of GAI in low-carbon AIoT, focusing on how GAI can reduce carbon emissions of network components. Subsequently, we propose a Large Language Model (LLM)-enabled carbon emission optimization framework, in which we design pluggable LLM and Retrieval Augmented Generation (RAG) modules to generate more accurate and reliable optimization problems. Furthermore, we utilize Generative Diffusion Models (GDMs) to identify optimal strategies for carbon emission reduction. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we insightfully provide open research directions for low-carbon AIoT. | [
"['Jinbo Wen' 'Ruichen Zhang' 'Dusit Niyato' 'Jiawen Kang' 'Hongyang Du'\n 'Yang Zhang' 'Zhu Han']"
]
|
null | null | 2404.18081 | null | null | http://arxiv.org/pdf/2404.18081v2 | 2024-04-30T14:14:26Z | 2024-04-28T06:17:42Z | ComposerX: Multi-Agent Symbolic Music Composition with LLMs | Music composition represents the creative side of humanity, and itself is a complex task that requires abilities to understand and generate information with long dependency and harmony constraints. While demonstrating impressive capabilities in STEM subjects, current LLMs easily fail in this task, generating ill-written music even when equipped with modern techniques like In-Context-Learning and Chain-of-Thoughts. To further explore and enhance LLMs' potential in music composition by leveraging their reasoning ability and the large knowledge base in music history and theory, we propose ComposerX, an agent-based symbolic music generation framework. We find that applying a multi-agent approach significantly improves the music composition quality of GPT-4. The results demonstrate that ComposerX is capable of producing coherent polyphonic music compositions with captivating melodies, while adhering to user instructions. | [
"['Qixin Deng' 'Qikai Yang' 'Ruibin Yuan' 'Yipeng Huang' 'Yi Wang'\n 'Xubo Liu' 'Zeyue Tian' 'Jiahao Pan' 'Ge Zhang' 'Hanfeng Lin' 'Yizhi Li'\n 'Yinghao Ma' 'Jie Fu' 'Chenghua Lin' 'Emmanouil Benetos' 'Wenwu Wang'\n 'Guangyu Xia' 'Wei Xue' 'Yike Guo']"
]
|
null | null | 2404.18101 | null | null | http://arxiv.org/pdf/2404.18101v1 | 2024-04-28T07:32:00Z | 2024-04-28T07:32:00Z | Advancing Supervised Learning with the Wave Loss Function: A Robust and
Smooth Approach | Loss function plays a vital role in supervised learning frameworks. The selection of the appropriate loss function holds the potential to have a substantial impact on the proficiency attained by the acquired model. The training of supervised learning algorithms inherently adheres to predetermined loss functions during the optimization process. In this paper, we present a novel contribution to the realm of supervised machine learning: an asymmetric loss function named wave loss. It exhibits robustness against outliers, insensitivity to noise, boundedness, and a crucial smoothness property. Theoretically, we establish that the proposed wave loss function manifests the essential characteristic of being classification-calibrated. Leveraging this breakthrough, we incorporate the proposed wave loss function into the least squares setting of support vector machines (SVM) and twin support vector machines (TSVM), resulting in two robust and smooth models termed Wave-SVM and Wave-TSVM, respectively. To address the optimization problem inherent in Wave-SVM, we utilize the adaptive moment estimation (Adam) algorithm. It is noteworthy that this paper marks the first instance of the Adam algorithm application to solve an SVM model. Further, we devise an iterative algorithm to solve the optimization problems of Wave-TSVM. To empirically showcase the effectiveness of the proposed Wave-SVM and Wave-TSVM, we evaluate them on benchmark UCI and KEEL datasets (with and without feature noise) from diverse domains. Moreover, to exemplify the applicability of Wave-SVM in the biomedical domain, we evaluate it on the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset. The experimental outcomes unequivocally reveal the prowess of Wave-SVM and Wave-TSVM in achieving superior prediction accuracy against the baseline models. | [
"['Mushir Akhtar' 'M. Tanveer' 'Mohd. Arshad']"
]
|
null | null | 2404.18134 | null | null | http://arxiv.org/pdf/2404.18134v1 | 2024-04-28T10:10:21Z | 2024-04-28T10:10:21Z | Enhancing Fairness in Neural Networks Using FairVIC | Mitigating bias in automated decision-making systems, specifically deep learning models, is a critical challenge in achieving fairness. This complexity stems from factors such as nuanced definitions of fairness, unique biases in each dataset, and the trade-off between fairness and model accuracy. To address such issues, we introduce FairVIC, an innovative approach designed to enhance fairness in neural networks by addressing inherent biases at the training stage. FairVIC differs from traditional approaches that typically address biases at the data preprocessing stage. Instead, it integrates variance, invariance and covariance into the loss function to minimise the model's dependency on protected characteristics for making predictions, thus promoting fairness. Our experimentation and evaluation consists of training neural networks on three datasets known for their biases, comparing our results to state-of-the-art algorithms, evaluating on different sizes of model architectures, and carrying out sensitivity analysis to examine the fairness-accuracy trade-off. Through our implementation of FairVIC, we observed a significant improvement in fairness across all metrics tested, without compromising the model's accuracy to a detrimental extent. Our findings suggest that FairVIC presents a straightforward, out-of-the-box solution for the development of fairer deep learning models, thereby offering a generalisable solution applicable across many tasks and datasets. | [
"['Charmaine Barker' 'Daniel Bethell' 'Dimitar Kazakov']"
]
|
null | null | 2404.18144 | null | null | http://arxiv.org/pdf/2404.18144v1 | 2024-04-28T11:27:30Z | 2024-04-28T11:27:30Z | Generative AI for Visualization: State of the Art and Future Directions | Generative AI (GenAI) has witnessed remarkable progress in recent years and demonstrated impressive performance in various generation tasks in different domains such as computer vision and computational design. Many researchers have attempted to integrate GenAI into visualization framework, leveraging the superior generative capacity for different operations. Concurrently, recent major breakthroughs in GenAI like diffusion model and large language model have also drastically increase the potential of GenAI4VIS. From a technical perspective, this paper looks back on previous visualization studies leveraging GenAI and discusses the challenges and opportunities for future research. Specifically, we cover the applications of different types of GenAI methods including sequence, tabular, spatial and graph generation techniques for different tasks of visualization which we summarize into four major stages: data enhancement, visual mapping generation, stylization and interaction. For each specific visualization sub-task, we illustrate the typical data and concrete GenAI algorithms, aiming to provide in-depth understanding of the state-of-the-art GenAI4VIS techniques and their limitations. Furthermore, based on the survey, we discuss three major aspects of challenges and research opportunities including evaluation, dataset, and the gap between end-to-end GenAI and generative algorithms. By summarizing different generation algorithms, their current applications and limitations, this paper endeavors to provide useful insights for future GenAI4VIS research. | [
"['Yilin Ye' 'Jianing Hao' 'Yihan Hou' 'Zhan Wang' 'Shishi Xiao' 'Yuyu Luo'\n 'Wei Zeng']"
]
|
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