categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2403.20202
| null | null |
http://arxiv.org/pdf/2403.20202v1
|
2024-03-29T14:31:36Z
|
2024-03-29T14:31:36Z
|
Voice Signal Processing for Machine Learning. The Case of Speaker
Isolation
|
The widespread use of automated voice assistants along with other recent technological developments have increased the demand for applications that process audio signals and human voice in particular. Voice recognition tasks are typically performed using artificial intelligence and machine learning models. Even though end-to-end models exist, properly pre-processing the signal can greatly reduce the complexity of the task and allow it to be solved with a simpler ML model and fewer computational resources. However, ML engineers who work on such tasks might not have a background in signal processing which is an entirely different area of expertise. The objective of this work is to provide a concise comparative analysis of Fourier and Wavelet transforms that are most commonly used as signal decomposition methods for audio processing tasks. Metrics for evaluating speech intelligibility are also discussed, namely Scale-Invariant Signal-to-Distortion Ratio (SI-SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short-Time Objective Intelligibility (STOI). The level of detail in the exposition is meant to be sufficient for an ML engineer to make informed decisions when choosing, fine-tuning, and evaluating a decomposition method for a specific ML model. The exposition contains mathematical definitions of the relevant concepts accompanied with intuitive non-mathematical explanations in order to make the text more accessible to engineers without deep expertise in signal processing. Formal mathematical definitions and proofs of theorems are intentionally omitted in order to keep the text concise.
|
[
"['Radan Ganchev']"
] |
null | null |
2403.20208
| null | null |
http://arxiv.org/pdf/2403.20208v6
|
2024-07-13T08:25:16Z
|
2024-03-29T14:41:21Z
|
Unleashing the Potential of Large Language Models for Predictive Tabular
Tasks in Data Science
|
In the domain of data science, the predictive tasks of classification, regression, and imputation of missing values are commonly encountered challenges associated with tabular data. This research endeavors to apply Large Language Models (LLMs) towards addressing these predictive tasks. Despite their proficiency in comprehending natural language, LLMs fall short in dealing with structured tabular data. This limitation stems from their lacking exposure to the intricacies of tabular data during their foundational training. Our research aims to mitigate this gap by compiling a comprehensive corpus of tables annotated with instructions and executing large-scale training of Llama-2 on this enriched dataset. Furthermore, we investigate the practical application of applying the trained model to zero-shot prediction, few-shot prediction, and in-context learning scenarios. Through extensive experiments, our methodology has shown significant improvements over existing benchmarks. These advancements highlight the efficacy of tailoring LLM training to solve table-related problems in data science, thereby establishing a new benchmark in the utilization of LLMs for enhancing tabular intelligence.
|
[
"['Yazheng Yang' 'Yuqi Wang' 'Sankalok Sen' 'Lei Li' 'Qi Liu']"
] |
null | null |
2403.20212
| null | null |
http://arxiv.org/pdf/2403.20212v1
|
2024-03-29T14:47:54Z
|
2024-03-29T14:47:54Z
|
On Size and Hardness Generalization in Unsupervised Learning for the
Travelling Salesman Problem
|
We study the generalization capability of Unsupervised Learning in solving the Travelling Salesman Problem (TSP). We use a Graph Neural Network (GNN) trained with a surrogate loss function to generate an embedding for each node. We use these embeddings to construct a heat map that indicates the likelihood of each edge being part of the optimal route. We then apply local search to generate our final predictions. Our investigation explores how different training instance sizes, embedding dimensions, and distributions influence the outcomes of Unsupervised Learning methods. Our results show that training with larger instance sizes and increasing embedding dimensions can build a more effective representation, enhancing the model's ability to solve TSP. Furthermore, in evaluating generalization across different distributions, we first determine the hardness of various distributions and explore how different hardnesses affect the final results. Our findings suggest that models trained on harder instances exhibit better generalization capabilities, highlighting the importance of selecting appropriate training instances in solving TSP using Unsupervised Learning.
|
[
"['Yimeng Min' 'Carla P. Gomes']"
] |
null | null |
2403.20221
| null | null |
http://arxiv.org/pdf/2403.20221v1
|
2024-03-29T15:05:57Z
|
2024-03-29T15:05:57Z
|
Graph Neural Aggregation-diffusion with Metastability
|
Continuous graph neural models based on differential equations have expanded the architecture of graph neural networks (GNNs). Due to the connection between graph diffusion and message passing, diffusion-based models have been widely studied. However, diffusion naturally drives the system towards an equilibrium state, leading to issues like over-smoothing. To this end, we propose GRADE inspired by graph aggregation-diffusion equations, which includes the delicate balance between nonlinear diffusion and aggregation induced by interaction potentials. The node representations obtained through aggregation-diffusion equations exhibit metastability, indicating that features can aggregate into multiple clusters. In addition, the dynamics within these clusters can persist for long time periods, offering the potential to alleviate over-smoothing effects. This nonlinear diffusion in our model generalizes existing diffusion-based models and establishes a connection with classical GNNs. We prove that GRADE achieves competitive performance across various benchmarks and alleviates the over-smoothing issue in GNNs evidenced by the enhanced Dirichlet energy.
|
[
"['Kaiyuan Cui' 'Xinyan Wang' 'Zicheng Zhang' 'Weichen Zhao']"
] |
null | null |
2403.20230
| null | null |
http://arxiv.org/pdf/2403.20230v1
|
2024-03-29T15:20:33Z
|
2024-03-29T15:20:33Z
|
An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer
Hybrid EfficientViT
|
Vision Transformers (ViTs) have achieved significant success in computer vision. However, their intensive computations and massive memory footprint challenge ViTs' deployment on embedded devices, calling for efficient ViTs. Among them, EfficientViT, the state-of-the-art one, features a Convolution-Transformer hybrid architecture, enhancing both accuracy and hardware efficiency. Unfortunately, existing accelerators cannot fully exploit the hardware benefits of EfficientViT due to its unique architecture. In this paper, we propose an FPGA-based accelerator for EfficientViT to advance the hardware efficiency frontier of ViTs. Specifically, we design a reconfigurable architecture to efficiently support various operation types, including lightweight convolutions and attention, boosting hardware utilization. Additionally, we present a time-multiplexed and pipelined dataflow to facilitate both intra- and inter-layer fusions, reducing off-chip data access costs. Experimental results show that our accelerator achieves up to 780.2 GOPS in throughput and 105.1 GOPS/W in energy efficiency at 200MHz on the Xilinx ZCU102 FPGA, which significantly outperforms prior works.
|
[
"['Haikuo Shao' 'Huihong Shi' 'Wendong Mao' 'Zhongfeng Wang']"
] |
null | null |
2403.20233
| null | null |
http://arxiv.org/pdf/2403.20233v3
|
2024-06-13T13:43:42Z
|
2024-03-29T15:22:03Z
|
Functional Bilevel Optimization for Machine Learning
|
In this paper, we introduce a new functional point of view on bilevel optimization problems for machine learning, where the inner objective is minimized over a function space. These types of problems are most often solved by using methods developed in the parametric setting, where the inner objective is strongly convex with respect to the parameters of the prediction function. The functional point of view does not rely on this assumption and notably allows using over-parameterized neural networks as the inner prediction function. We propose scalable and efficient algorithms for the functional bilevel optimization problem and illustrate the benefits of our approach on instrumental regression and reinforcement learning tasks.
|
[
"['Ieva Petrulionyte' 'Julien Mairal' 'Michael Arbel']"
] |
null | null |
2403.20246
| null | null |
http://arxiv.org/pdf/2403.20246v1
|
2024-03-29T15:45:25Z
|
2024-03-29T15:45:25Z
|
Enhancing Dimension-Reduced Scatter Plots with Class and Feature
Centroids
|
Dimension reduction is increasingly applied to high-dimensional biomedical data to improve its interpretability. When datasets are reduced to two dimensions, each observation is assigned an x and y coordinates and is represented as a point on a scatter plot. A significant challenge lies in interpreting the meaning of the x and y axes due to the complexities inherent in dimension reduction. This study addresses this challenge by using the x and y coordinates derived from dimension reduction to calculate class and feature centroids, which can be overlaid onto the scatter plots. This method connects the low-dimension space to the original high-dimensional space. We illustrate the utility of this approach with data derived from the phenotypes of three neurogenetic diseases and demonstrate how the addition of class and feature centroids increases the interpretability of scatter plots.
|
[
"['Daniel B. Hier' 'Tayo Obafemi-Ajayi' 'Gayla R. Olbricht'\n 'Devin M. Burns' 'Sasha Petrenko' 'Donald C. Wunsch II']"
] |
null | null |
2403.20250
| null | null |
http://arxiv.org/pdf/2403.20250v1
|
2024-03-29T15:55:06Z
|
2024-03-29T15:55:06Z
|
Optimal Policy Learning with Observational Data in Multi-Action
Scenarios: Estimation, Risk Preference, and Potential Failures
|
This paper deals with optimal policy learning (OPL) with observational data, i.e. data-driven optimal decision-making, in multi-action (or multi-arm) settings, where a finite set of decision options is available. It is organized in three parts, where I discuss respectively: estimation, risk preference, and potential failures. The first part provides a brief review of the key approaches to estimating the reward (or value) function and optimal policy within this context of analysis. Here, I delineate the identification assumptions and statistical properties related to offline optimal policy learning estimators. In the second part, I delve into the analysis of decision risk. This analysis reveals that the optimal choice can be influenced by the decision maker's attitude towards risks, specifically in terms of the trade-off between reward conditional mean and conditional variance. Here, I present an application of the proposed model to real data, illustrating that the average regret of a policy with multi-valued treatment is contingent on the decision-maker's attitude towards risk. The third part of the paper discusses the limitations of optimal data-driven decision-making by highlighting conditions under which decision-making can falter. This aspect is linked to the failure of the two fundamental assumptions essential for identifying the optimal choice: (i) overlapping, and (ii) unconfoundedness. Some conclusions end the paper.
|
[
"['Giovanni Cerulli']"
] |
null | null |
2403.20252
| null | null |
http://arxiv.org/pdf/2403.20252v1
|
2024-03-29T15:58:46Z
|
2024-03-29T15:58:46Z
|
Using LLMs to Model the Beliefs and Preferences of Targeted Populations
|
We consider the problem of aligning a large language model (LLM) to model the preferences of a human population. Modeling the beliefs, preferences, and behaviors of a specific population can be useful for a variety of different applications, such as conducting simulated focus groups for new products, conducting virtual surveys, and testing behavioral interventions, especially for interventions that are expensive, impractical, or unethical. Existing work has had mixed success using LLMs to accurately model human behavior in different contexts. We benchmark and evaluate two well-known fine-tuning approaches and evaluate the resulting populations on their ability to match the preferences of real human respondents on a survey of preferences for battery electric vehicles (BEVs). We evaluate our models against their ability to match population-wide statistics as well as their ability to match individual responses, and we investigate the role of temperature in controlling the trade-offs between these two. Additionally, we propose and evaluate a novel loss term to improve model performance on responses that require a numeric response.
|
[
"['Keiichi Namikoshi' 'Alex Filipowicz' 'David A. Shamma' 'Rumen Iliev'\n 'Candice L. Hogan' 'Nikos Arechiga']"
] |
null | null |
2403.20253
| null | null |
http://arxiv.org/pdf/2403.20253v2
|
2024-06-19T16:21:34Z
|
2024-03-29T15:59:11Z
|
MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image
Segmentation
|
Medical image segmentation of anatomical structures and pathology is crucial in modern clinical diagnosis, disease study, and treatment planning. To date, great progress has been made in deep learning-based segmentation techniques, but most methods still lack data efficiency, generalizability, and interactability. Consequently, the development of new, precise segmentation methods that demand fewer labeled datasets is of utmost importance in medical image analysis. Recently, the emergence of foundation models, such as CLIP and Segment-Anything-Model (SAM), with comprehensive cross-domain representation opened the door for interactive and universal image segmentation. However, exploration of these models for data-efficient medical image segmentation is still limited, but is highly necessary. In this paper, we propose a novel framework, called MedCLIP-SAM that combines CLIP and SAM models to generate segmentation of clinical scans using text prompts in both zero-shot and weakly supervised settings. To achieve this, we employed a new Decoupled Hard Negative Noise Contrastive Estimation (DHN-NCE) loss to fine-tune the BiomedCLIP model and the recent gScoreCAM to generate prompts to obtain segmentation masks from SAM in a zero-shot setting. Additionally, we explored the use of zero-shot segmentation labels in a weakly supervised paradigm to improve the segmentation quality further. By extensively testing three diverse segmentation tasks and medical image modalities (breast tumor ultrasound, brain tumor MRI, and lung X-ray), our proposed framework has demonstrated excellent accuracy. Code is available at https://github.com/HealthX-Lab/MedCLIP-SAM.
|
[
"['Taha Koleilat' 'Hojat Asgariandehkordi' 'Hassan Rivaz' 'Yiming Xiao']"
] |
null | null |
2403.20261
| null | null |
http://arxiv.org/pdf/2403.20261v3
|
2024-04-07T07:35:01Z
|
2024-03-29T16:10:34Z
|
FABind+: Enhancing Molecular Docking through Improved Pocket Prediction
and Pose Generation
|
Molecular docking is a pivotal process in drug discovery. While traditional techniques rely on extensive sampling and simulation governed by physical principles, these methods are often slow and costly. The advent of deep learning-based approaches has shown significant promise, offering increases in both accuracy and efficiency. Building upon the foundational work of FABind, a model designed with a focus on speed and accuracy, we present FABind+, an enhanced iteration that largely boosts the performance of its predecessor. We identify pocket prediction as a critical bottleneck in molecular docking and propose a novel methodology that significantly refines pocket prediction, thereby streamlining the docking process. Furthermore, we introduce modifications to the docking module to enhance its pose generation capabilities. In an effort to bridge the gap with conventional sampling/generative methods, we incorporate a simple yet effective sampling technique coupled with a confidence model, requiring only minor adjustments to the regression framework of FABind. Experimental results and analysis reveal that FABind+ remarkably outperforms the original FABind, achieves competitive state-of-the-art performance, and delivers insightful modeling strategies. This demonstrates FABind+ represents a substantial step forward in molecular docking and drug discovery. Our code is in https://github.com/QizhiPei/FABind.
|
[
"['Kaiyuan Gao' 'Qizhi Pei' 'Jinhua Zhu' 'Kun He' 'Lijun Wu']"
] |
null | null |
2403.20262
| null | null |
http://arxiv.org/pdf/2403.20262v1
|
2024-03-29T16:13:31Z
|
2024-03-29T16:13:31Z
|
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language
Models
|
Research on Large Language Models (LLMs) has recently witnessed an increasing interest in extending models' context size to better capture dependencies within long documents. While benchmarks have been proposed to assess long-range abilities, existing efforts primarily considered generic tasks that are not necessarily aligned with real-world applications. In contrast, our work proposes a new benchmark for long-context LLMs focused on a practical meeting assistant scenario. In this scenario, the long contexts consist of transcripts obtained by automatic speech recognition, presenting unique challenges for LLMs due to the inherent noisiness and oral nature of such data. Our benchmark, named ELITR-Bench, augments the existing ELITR corpus' transcripts with 271 manually crafted questions and their ground-truth answers. Our experiments with recent long-context LLMs on ELITR-Bench highlight a gap between open-source and proprietary models, especially when questions are asked sequentially within a conversation. We also provide a thorough analysis of our GPT-4-based evaluation method, encompassing insights from a crowdsourcing study. Our findings suggest that while GPT-4's evaluation scores are correlated with human judges', its ability to differentiate among more than three score levels may be limited.
|
[
"['Thibaut Thonet' 'Jos Rozen' 'Laurent Besacier']"
] |
null | null |
2403.20266
| null | null |
http://arxiv.org/pdf/2403.20266v1
|
2024-03-29T16:16:48Z
|
2024-03-29T16:16:48Z
|
Latxa: An Open Language Model and Evaluation Suite for Basque
|
We introduce Latxa, a family of large language models for Basque ranging from 7 to 70 billion parameters. Latxa is based on Llama 2, which we continue pretraining on a new Basque corpus comprising 4.3M documents and 4.2B tokens. Addressing the scarcity of high-quality benchmarks for Basque, we further introduce 4 multiple choice evaluation datasets: EusProficiency, comprising 5,169 questions from official language proficiency exams; EusReading, comprising 352 reading comprehension questions; EusTrivia, comprising 1,715 trivia questions from 5 knowledge areas; and EusExams, comprising 16,774 questions from public examinations. In our extensive evaluation, Latxa outperforms all previous open models we compare to by a large margin. In addition, it is competitive with GPT-4 Turbo in language proficiency and understanding, despite lagging behind in reading comprehension and knowledge-intensive tasks. Both the Latxa family of models, as well as our new pretraining corpora and evaluation datasets, are publicly available under open licenses at https://github.com/hitz-zentroa/latxa. Our suite enables reproducible research on methods to build LLMs for low-resource languages.
|
[
"['Julen Etxaniz' 'Oscar Sainz' 'Naiara Perez' 'Itziar Aldabe'\n 'German Rigau' 'Eneko Agirre' 'Aitor Ormazabal' 'Mikel Artetxe'\n 'Aitor Soroa']"
] |
null | null |
2403.20280
| null | null |
http://arxiv.org/pdf/2403.20280v1
|
2024-03-29T16:49:40Z
|
2024-03-29T16:49:40Z
|
Sparse multimodal fusion with modal channel attention
|
The ability of masked multimodal transformer architectures to learn a robust embedding space when modality samples are sparsely aligned is studied by measuring the quality of generated embedding spaces as a function of modal sparsity. An extension to the masked multimodal transformer model is proposed which incorporates modal-incomplete channels in the multihead attention mechanism called modal channel attention (MCA). Two datasets with 4 modalities are used, CMU-MOSEI for multimodal sentiment recognition and TCGA for multiomics. Models are shown to learn uniform and aligned embedding spaces with only two out of four modalities in most samples. It was found that, even with no modal sparsity, the proposed MCA mechanism improves the quality of generated embedding spaces, recall metrics, and subsequent performance on downstream tasks.
|
[
"['Josiah Bjorgaard']"
] |
null | null |
2403.20284
| null | null |
http://arxiv.org/pdf/2403.20284v1
|
2024-03-29T16:53:11Z
|
2024-03-29T16:53:11Z
|
LayerNorm: A key component in parameter-efficient fine-tuning
|
Fine-tuning a pre-trained model, such as Bidirectional Encoder Representations from Transformers (BERT), has been proven to be an effective method for solving many natural language processing (NLP) tasks. However, due to the large number of parameters in many state-of-the-art NLP models, including BERT, the process of fine-tuning is computationally expensive. One attractive solution to this issue is parameter-efficient fine-tuning, which involves modifying only a minimal segment of the model while keeping the remainder unchanged. Yet, it remains unclear which segment of the BERT model is crucial for fine-tuning. In this paper, we first analyze different components in the BERT model to pinpoint which one undergoes the most significant changes after fine-tuning. We find that output LayerNorm changes more than any other components when fine-tuned for different General Language Understanding Evaluation (GLUE) tasks. Then we show that only fine-tuning the LayerNorm can reach comparable, or in some cases better, performance to full fine-tuning and other parameter-efficient fine-tuning methods. Moreover, we use Fisher information to determine the most critical subset of LayerNorm and demonstrate that many NLP tasks in the GLUE benchmark can be solved by fine-tuning only a small portion of LayerNorm with negligible performance degradation.
|
[
"['Taha ValizadehAslani' 'Hualou Liang']"
] |
null | null |
2403.20287
| null | null |
http://arxiv.org/pdf/2403.20287v2
|
2024-06-10T14:47:46Z
|
2024-03-29T16:58:13Z
|
Benchmarking Counterfactual Image Generation
|
Generative AI has revolutionised visual content editing, empowering users to effortlessly modify images and videos. However, not all edits are equal. To perform realistic edits in domains such as natural image or medical imaging, modifications must respect causal relationships inherent to the data generation process. Such image editing falls into the counterfactual image generation regime. Evaluating counterfactual image generation is substantially complex: not only it lacks observable ground truths, but also requires adherence to causal constraints. Although several counterfactual image generation methods and evaluation metrics exist, a comprehensive comparison within a unified setting is lacking. We present a comparison framework to thoroughly benchmark counterfactual image generation methods. We integrate all models that have been used for the task at hand and expand them to novel datasets and causal graphs, demonstrating the superiority of Hierarchical VAEs across most datasets and metrics. Our framework is implemented in a user-friendly Python package that can be extended to incorporate additional SCMs, causal methods, generative models, and datasets for the community to build on.
|
[
"['Thomas Melistas' 'Nikos Spyrou' 'Nefeli Gkouti' 'Pedro Sanchez'\n 'Athanasios Vlontzos' 'Yannis Panagakis' 'Giorgos Papanastasiou'\n 'Sotirios A. Tsaftaris']"
] |
null | null |
2403.20298
| null | null |
http://arxiv.org/pdf/2403.20298v1
|
2024-03-29T17:15:21Z
|
2024-03-29T17:15:21Z
|
Review-Based Cross-Domain Recommendation via Hyperbolic Embedding and
Hierarchy-Aware Domain Disentanglement
|
The issue of data sparsity poses a significant challenge to recommender systems. In response to this, algorithms that leverage side information such as review texts have been proposed. Furthermore, Cross-Domain Recommendation (CDR), which captures domain-shareable knowledge and transfers it from a richer domain (source) to a sparser one (target), has received notable attention. Nevertheless, the majority of existing methodologies assume a Euclidean embedding space, encountering difficulties in accurately representing richer text information and managing complex interactions between users and items. This paper advocates a hyperbolic CDR approach based on review texts for modeling user-item relationships. We first emphasize that conventional distance-based domain alignment techniques may cause problems because small modifications in hyperbolic geometry result in magnified perturbations, ultimately leading to the collapse of hierarchical structures. To address this challenge, we propose hierarchy-aware embedding and domain alignment schemes that adjust the scale to extract domain-shareable information without disrupting structural forms. The process involves the initial embedding of review texts in hyperbolic space, followed by feature extraction incorporating degree-based normalization and structure alignment. We conducted extensive experiments to substantiate the efficiency, robustness, and scalability of our proposed model in comparison to state-of-the-art baselines.
|
[
"['Yoonhyuk Choi']"
] |
null | null |
2403.20320
| null | null |
http://arxiv.org/pdf/2403.20320v1
|
2024-03-29T17:43:58Z
|
2024-03-29T17:43:58Z
|
MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning
|
Adapting models pre-trained on large-scale datasets to a variety of downstream tasks is a common strategy in deep learning. Consequently, parameter-efficient fine-tuning methods have emerged as a promising way to adapt pre-trained models to different tasks while training only a minimal number of parameters. While most of these methods are designed for single-task adaptation, parameter-efficient training in Multi-Task Learning (MTL) architectures is still unexplored. In this paper, we introduce MTLoRA, a novel framework for parameter-efficient training of MTL models. MTLoRA employs Task-Agnostic and Task-Specific Low-Rank Adaptation modules, which effectively disentangle the parameter space in MTL fine-tuning, thereby enabling the model to adeptly handle both task specialization and interaction within MTL contexts. We applied MTLoRA to hierarchical-transformer-based MTL architectures, adapting them to multiple downstream dense prediction tasks. Our extensive experiments on the PASCAL dataset show that MTLoRA achieves higher accuracy on downstream tasks compared to fully fine-tuning the MTL model while reducing the number of trainable parameters by 3.6x. Furthermore, MTLoRA establishes a Pareto-optimal trade-off between the number of trainable parameters and the accuracy of the downstream tasks, outperforming current state-of-the-art parameter-efficient training methods in both accuracy and efficiency. Our code is publicly available.
|
[
"['Ahmed Agiza' 'Marina Neseem' 'Sherief Reda']"
] |
null | null |
2403.20324
| null | null |
http://arxiv.org/pdf/2403.20324v1
|
2024-03-29T17:51:50Z
|
2024-03-29T17:51:50Z
|
Localising the Seizure Onset Zone from Single-Pulse Electrical
Stimulation Responses with a Transformer
|
Epilepsy is one of the most common neurological disorders, and many patients require surgical intervention when medication fails to control seizures. For effective surgical outcomes, precise localisation of the epileptogenic focus - often approximated through the Seizure Onset Zone (SOZ) - is critical yet remains a challenge. Active probing through electrical stimulation is already standard clinical practice for identifying epileptogenic areas. This paper advances the application of deep learning for SOZ localisation using Single Pulse Electrical Stimulation (SPES) responses. We achieve this by introducing Transformer models that incorporate cross-channel attention. We evaluate these models on held-out patient test sets to assess their generalisability to unseen patients and electrode placements. Our study makes three key contributions: Firstly, we implement an existing deep learning model to compare two SPES analysis paradigms - namely, divergent and convergent. These paradigms evaluate outward and inward effective connections, respectively. Our findings reveal a notable improvement in moving from a divergent (AUROC: 0.574) to a convergent approach (AUROC: 0.666), marking the first application of the latter in this context. Secondly, we demonstrate the efficacy of the Transformer models in handling heterogeneous electrode placements, increasing the AUROC to 0.730. Lastly, by incorporating inter-trial variability, we further refine the Transformer models, with an AUROC of 0.745, yielding more consistent predictions across patients. These advancements provide a deeper insight into SOZ localisation and represent a significant step in modelling patient-specific intracranial EEG electrode placements in SPES. Future work will explore integrating these models into clinical decision-making processes to bridge the gap between deep learning research and practical healthcare applications.
|
[
"['Jamie Norris' 'Aswin Chari' 'Gerald Cooray' 'Martin Tisdall'\n 'Karl Friston' 'Richard Rosch']"
] |
null | null |
2403.20328
| null | null |
http://arxiv.org/pdf/2403.20328v1
|
2024-03-29T17:59:05Z
|
2024-03-29T17:59:05Z
|
Learning Visual Quadrupedal Loco-Manipulation from Demonstrations
|
Quadruped robots are progressively being integrated into human environments. Despite the growing locomotion capabilities of quadrupedal robots, their interaction with objects in realistic scenes is still limited. While additional robotic arms on quadrupedal robots enable manipulating objects, they are sometimes redundant given that a quadruped robot is essentially a mobile unit equipped with four limbs, each possessing 3 degrees of freedom (DoFs). Hence, we aim to empower a quadruped robot to execute real-world manipulation tasks using only its legs. We decompose the loco-manipulation process into a low-level reinforcement learning (RL)-based controller and a high-level Behavior Cloning (BC)-based planner. By parameterizing the manipulation trajectory, we synchronize the efforts of the upper and lower layers, thereby leveraging the advantages of both RL and BC. Our approach is validated through simulations and real-world experiments, demonstrating the robot's ability to perform tasks that demand mobility and high precision, such as lifting a basket from the ground while moving, closing a dishwasher, pressing a button, and pushing a door. Project website: https://zhengmaohe.github.io/leg-manip
|
[
"['Zhengmao He' 'Kun Lei' 'Yanjie Ze' 'Koushil Sreenath' 'Zhongyu Li'\n 'Huazhe Xu']"
] |
null | null |
2403.20329
| null | null |
http://arxiv.org/pdf/2403.20329v1
|
2024-03-29T17:59:06Z
|
2024-03-29T17:59:06Z
|
ReALM: Reference Resolution As Language Modeling
|
Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.
|
[
"['Joel Ruben Antony Moniz' 'Soundarya Krishnan' 'Melis Ozyildirim'\n 'Prathamesh Saraf' 'Halim Cagri Ates' 'Yuan Zhang' 'Hong Yu'\n 'Nidhi Rajshree']"
] |
null | null |
2403.20331
| null | null |
http://arxiv.org/pdf/2403.20331v1
|
2024-03-29T17:59:53Z
|
2024-03-29T17:59:53Z
|
Unsolvable Problem Detection: Evaluating Trustworthiness of Vision
Language Models
|
This paper introduces a novel and significant challenge for Vision Language Models (VLMs), termed Unsolvable Problem Detection (UPD). UPD examines the VLM's ability to withhold answers when faced with unsolvable problems in the context of Visual Question Answering (VQA) tasks. UPD encompasses three distinct settings: Absent Answer Detection (AAD), Incompatible Answer Set Detection (IASD), and Incompatible Visual Question Detection (IVQD). To deeply investigate the UPD problem, extensive experiments indicate that most VLMs, including GPT-4V and LLaVA-Next-34B, struggle with our benchmarks to varying extents, highlighting significant room for the improvements. To address UPD, we explore both training-free and training-based solutions, offering new insights into their effectiveness and limitations. We hope our insights, together with future efforts within the proposed UPD settings, will enhance the broader understanding and development of more practical and reliable VLMs.
|
[
"['Atsuyuki Miyai' 'Jingkang Yang' 'Jingyang Zhang' 'Yifei Ming' 'Qing Yu'\n 'Go Irie' 'Yixuan Li' 'Hai Li' 'Ziwei Liu' 'Kiyoharu Aizawa']"
] |
null | null |
2404.00013
| null | null |
http://arxiv.org/pdf/2404.00013v1
|
2024-03-15T13:01:09Z
|
2024-03-15T13:01:09Z
|
Missing Data Imputation With Granular Semantics and AI-driven Pipeline
for Bankruptcy Prediction
|
This work focuses on designing a pipeline for the prediction of bankruptcy. The presence of missing values, high dimensional data, and highly class-imbalance databases are the major challenges in the said task. A new method for missing data imputation with granular semantics has been introduced here. The merits of granular computing have been explored here to define this method. The missing values have been predicted using the feature semantics and reliable observations in a low-dimensional space, in the granular space. The granules are formed around every missing entry, considering a few of the highly correlated features and most reliable closest observations to preserve the relevance and reliability, the context, of the database against the missing entries. An intergranular prediction is then carried out for the imputation within those contextual granules. That is, the contextual granules enable a small relevant fraction of the huge database to be used for imputation and overcome the need to access the entire database repetitively for each missing value. This method is then implemented and tested for the prediction of bankruptcy with the Polish Bankruptcy dataset. It provides an efficient solution for big and high-dimensional datasets even with large imputation rates. Then an AI-driven pipeline for bankruptcy prediction has been designed using the proposed granular semantic-based data filling method followed by the solutions to the issues like high dimensional dataset and high class-imbalance in the dataset. The rest of the pipeline consists of feature selection with the random forest for reducing dimensionality, data balancing with SMOTE, and prediction with six different popular classifiers including deep NN. All methods defined here have been experimentally verified with suitable comparative studies and proven to be effective on all the data sets captured over the five years.
|
[
"['Debarati Chakraborty' 'Ravi Ranjan']"
] |
null | null |
2404.00015
| null | null |
http://arxiv.org/pdf/2404.00015v3
|
2024-04-03T10:09:05Z
|
2024-03-15T16:42:03Z
|
Empowering Credit Scoring Systems with Quantum-Enhanced Machine Learning
|
Quantum Kernels are projected to provide early-stage usefulness for quantum machine learning. However, highly sophisticated classical models are hard to surpass without losing interpretability, particularly when vast datasets can be exploited. Nonetheless, classical models struggle once data is scarce and skewed. Quantum feature spaces are projected to find better links between data features and the target class to be predicted even in such challenging scenarios and most importantly, enhanced generalization capabilities. In this work, we propose a novel approach called Systemic Quantum Score (SQS) and provide preliminary results indicating potential advantage over purely classical models in a production grade use case for the Finance sector. SQS shows in our specific study an increased capacity to extract patterns out of fewer data points as well as improved performance over data-hungry algorithms such as XGBoost, providing advantage in a competitive market as it is the FinTech and Neobank regime.
|
[
"['Javier Mancilla' 'André Sequeira' 'Tomas Tagliani' 'Francisco Llaneza'\n 'Claudio Beiza']"
] |
null | null |
2404.00016
| null | null |
http://arxiv.org/pdf/2404.00016v2
|
2024-05-22T11:58:22Z
|
2024-03-15T17:02:59Z
|
SOMson -- Sonification of Multidimensional Data in Kohonen Maps
|
Kohonen Maps, aka. Self-organizing maps (SOMs) are neural networks that visualize a high-dimensional feature space on a low-dimensional map. While SOMs are an excellent tool for data examination and exploration, they inherently cause a loss of detail. Visualizations of the underlying data do not integrate well and, therefore, fail to provide an overall picture. Consequently, we suggest SOMson, an interactive sonification of the underlying data, as a data augmentation technique. The sonification increases the amount of information provided simultaneously by the SOM. Instead of a user study, we present an interactive online example, so readers can explore SOMson themselves. Its strengths, weaknesses, and prospects are discussed.
|
[
"['Simon Linke' 'Tim Ziemer']"
] |
null | null |
2404.00019
| null | null |
http://arxiv.org/pdf/2404.00019v1
|
2024-03-19T11:43:41Z
|
2024-03-19T11:43:41Z
|
Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review
and Research Roadmap
|
Given the uncertainty surrounding how existing explainability methods for autonomous vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is imperative to determine the contexts requiring explanations and suitable interaction strategies. A comprehensive review becomes crucial to assess the alignment of current approaches with the varied interests and expectations within the AV ecosystem. This study presents a review to discuss the complexities associated with explanation generation and presentation to facilitate the development of more effective and inclusive explainable AV systems. Our investigation led to categorising existing literature into three primary topics: explanatory tasks, explanatory information, and explanatory information communication. Drawing upon our insights, we have proposed a comprehensive roadmap for future research centred on (i) knowing the interlocutor, (ii) generating timely explanations, (ii) communicating human-friendly explanations, and (iv) continuous learning. Our roadmap is underpinned by principles of responsible research and innovation, emphasising the significance of diverse explanation requirements. To effectively tackle the challenges associated with implementing explainable AV systems, we have delineated various research directions, including the development of privacy-preserving data integration, ethical frameworks, real-time analytics, human-centric interaction design, and enhanced cross-disciplinary collaborations. By exploring these research directions, the study aims to guide the development and deployment of explainable AVs, informed by a holistic understanding of user needs, technological advancements, regulatory compliance, and ethical considerations, thereby ensuring safer and more trustworthy autonomous driving experiences.
|
[
"['Sule Tekkesinoglu' 'Azra Habibovic' 'Lars Kunze']"
] |
null | null |
2404.00026
| null | null |
http://arxiv.org/pdf/2404.00026v3
|
2024-04-22T08:30:28Z
|
2024-03-20T21:02:16Z
|
Ink and Individuality: Crafting a Personalised Narrative in the Age of
LLMs
|
Individuality and personalization comprise the distinctive characteristics that make each writer unique and influence their words in order to effectively engage readers while conveying authenticity. However, our growing reliance on LLM-based writing assistants risks compromising our creativity and individuality over time. We often overlook the negative impacts of this trend on our creativity and uniqueness, despite the possible consequences. This study investigates these concerns by performing a brief survey to explore different perspectives and concepts, as well as trying to understand people's viewpoints, in conjunction with past studies in the area. Addressing these issues is essential for improving human-computer interaction systems and enhancing writing assistants for personalization and individuality.
|
[
"['Azmine Toushik Wasi' 'Raima Islam' 'Mst Rafia Islam']"
] |
null | null |
2404.00027
| null | null |
http://arxiv.org/pdf/2404.00027v3
|
2024-04-22T08:30:30Z
|
2024-03-20T21:06:42Z
|
LLMs as Writing Assistants: Exploring Perspectives on Sense of Ownership
and Reasoning
|
Sense of ownership in writing confines our investment of thoughts, time, and contribution, leading to attachment to the output. However, using writing assistants introduces a mental dilemma, as some content isn't directly our creation. For instance, we tend to credit Large Language Models (LLMs) more in creative tasks, even though all tasks are equal for them. Additionally, while we may not claim complete ownership of LLM-generated content, we freely claim authorship. We conduct a short survey to examine these issues and understand underlying cognitive processes in order to gain a better knowledge of human-computer interaction in writing and improve writing aid systems.
|
[
"['Azmine Toushik Wasi' 'Mst Rafia Islam' 'Raima Islam']"
] |
null | null |
2404.00030
| null | null |
http://arxiv.org/pdf/2404.00030v1
|
2024-03-22T07:13:10Z
|
2024-03-22T07:13:10Z
|
Visualization of Unstructured Sports Data -- An Example of Cricket Short
Text Commentary
|
Sports visualization focuses on the use of structured data, such as box-score data and tracking data. Unstructured data sources pertaining to sports are available in various places such as blogs, social media posts, and online news articles. Sports visualization methods either not fully exploited the information present in these sources or the proposed visualizations through the use of these sources did not augment to the body of sports visualization methods. We propose the use of unstructured data, namely cricket short text commentary for visualization. The short text commentary data is used for constructing individual player's strength rules and weakness rules. A computationally feasible definition for player's strength rule and weakness rule is proposed. A visualization method for the constructed rules is presented. In addition, players having similar strength rules or weakness rules is computed and visualized. We demonstrate the usefulness of short text commentary in visualization by analyzing the strengths and weaknesses of cricket players using more than one million text commentaries. We validate the constructed rules through two validation methods. The collected data, source code, and obtained results on more than 500 players are made publicly available.
|
[
"['Swarup Ranjan Behera' 'Vijaya V Saradhi']"
] |
null | null |
2404.00031
| null | null |
http://arxiv.org/pdf/2404.00031v2
|
2024-05-17T16:12:25Z
|
2024-03-22T13:34:46Z
|
Towards gaze-independent c-VEP BCI: A pilot study
|
A limitation of brain-computer interface (BCI) spellers is that they require the user to be able to move the eyes to fixate on targets. This poses an issue for users who cannot voluntarily control their eye movements, for instance, people living with late-stage amyotrophic lateral sclerosis (ALS). This pilot study makes the first step towards a gaze-independent speller based on the code-modulated visual evoked potential (c-VEP). Participants were presented with two bi-laterally located stimuli, one of which was flashing, and were tasked to attend to one of these stimuli either by directly looking at the stimuli (overt condition) or by using spatial attention, eliminating the need for eye movement (covert condition). The attended stimuli were decoded from electroencephalography (EEG) and classification accuracies of 88% and 100% were obtained for the covert and overt conditions, respectively. These fundamental insights show the promising feasibility of utilizing the c-VEP protocol for gaze-independent BCIs that use covert spatial attention when both stimuli flash simultaneously.
|
[
"['S. Narayanan' 'S. Ahmadi' 'P. Desain' 'J. Thielen']"
] |
null | null |
2404.00034
| null | null |
http://arxiv.org/pdf/2404.00034v1
|
2024-03-23T09:36:31Z
|
2024-03-23T09:36:31Z
|
Investigating Similarities Across Decentralized Financial (DeFi)
Services
|
We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contracts that are utilized in combination within single transactions and encapsulate the logic to conduct specific financial services such as swapping or lending cryptoassets. We propose a method to categorize these blocks into clusters based on their smart contract attributes and the graph structure of their smart contract calls. We employ GRL to create embedding vectors from building blocks and agglomerative models for clustering them. To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label. We find that in the best-case scenario purity reaches .888. We use additional information to associate the building blocks with protocol-specific target labels, obtaining comparable purity (.864) but higher V-Measure (.571); we discuss plausible explanations for this difference. In summary, this method helps categorize existing financial products offered by DeFi protocols, and can effectively automatize the detection of similar DeFi services, especially within protocols.
|
[
"['Junliang Luo' 'Stefan Kitzler' 'Pietro Saggese']"
] |
null | null |
2404.00039
| null | null |
http://arxiv.org/pdf/2404.00039v1
|
2024-03-24T02:45:34Z
|
2024-03-24T02:45:34Z
|
MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing
Algorithms for TinyML systems
|
Hyperdimensional computing (HDC) is emerging as a promising AI approach that can effectively target TinyML applications thanks to its lightweight computing and memory requirements. Previous works on HDC showed that limiting the standard 10k dimensions of the hyperdimensional space to much lower values is possible, reducing even more HDC resource requirements. Similarly, other studies demonstrated that binary values can be used as elements of the generated hypervectors, leading to significant efficiency gains at the cost of some degree of accuracy degradation. Nevertheless, current optimization attempts do not concurrently co-optimize HDC hyper-parameters, and accuracy degradation is not directly controlled, resulting in sub-optimal HDC models providing several applications with unacceptable output qualities. In this work, we propose MicroHD, a novel accuracy-driven HDC optimization approach that iteratively tunes HDC hyper-parameters, reducing memory and computing requirements while ensuring user-defined accuracy levels. The proposed method can be applied to HDC implementations using different encoding functions, demonstrates good scalability for larger HDC workloads, and achieves compression and efficiency gains up to 200x when compared to baseline implementations for accuracy degradations lower than 1%.
|
[
"['Flavio Ponzina' 'Tajana Rosing']"
] |
null | null |
2404.00042
| null | null |
http://arxiv.org/pdf/2404.00042v1
|
2024-03-24T14:45:11Z
|
2024-03-24T14:45:11Z
|
Stochastic Optimization with Constraints: A Non-asymptotic
Instance-Dependent Analysis
|
We consider the problem of stochastic convex optimization under convex constraints. We analyze the behavior of a natural variance reduced proximal gradient (VRPG) algorithm for this problem. Our main result is a non-asymptotic guarantee for VRPG algorithm. Contrary to minimax worst case guarantees, our result is instance-dependent in nature. This means that our guarantee captures the complexity of the loss function, the variability of the noise, and the geometry of the constraint set. We show that the non-asymptotic performance of the VRPG algorithm is governed by the scaled distance (scaled by $sqrt{N}$) between the solutions of the given problem and that of a certain small perturbation of the given problem -- both solved under the given convex constraints; here, $N$ denotes the number of samples. Leveraging a well-established connection between local minimax lower bounds and solutions to perturbed problems, we show that as $N rightarrow infty$, the VRPG algorithm achieves the renowned local minimax lower bound by H`{a}jek and Le Cam up to universal constants and a logarithmic factor of the sample size.
|
[
"['Koulik Khamaru']"
] |
null | null |
2404.00043
| null | null |
http://arxiv.org/pdf/2404.00043v1
|
2024-03-24T21:19:17Z
|
2024-03-24T21:19:17Z
|
Improve accessibility for Low Vision and Blind people using Machine
Learning and Computer Vision
|
With the ever-growing expansion of mobile technology worldwide, there is an increasing need for accommodation for those who are disabled. This project explores how machine learning and computer vision could be utilized to improve accessibility for people with visual impairments. There have been many attempts to develop various software that would improve accessibility in the day-to-day lives of blind people. However, applications on the market have low accuracy and only provide audio feedback. This project will concentrate on building a mobile application that helps blind people to orient in space by receiving audio and haptic feedback, e.g. vibrations, about their surroundings in real-time. The mobile application will have 3 main features. The initial feature is scanning text from the camera and reading it to a user. This feature can be used on paper with text, in the environment, and on road signs. The second feature is detecting objects around the user, and providing audio feedback about those objects. It also includes providing the description of the objects and their location, and giving haptic feedback if the user is too close to an object. The last feature is currency detection which provides a total amount of currency value to the user via the camera.
|
[
"['Jasur Shukurov']"
] |
null | null |
2404.00044
| null | null |
http://arxiv.org/pdf/2404.00044v2
|
2024-04-19T09:52:52Z
|
2024-03-25T03:23:03Z
|
UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction
with Unsupervised SMILES Alignment
|
Motivation: Retrosynthesis planning poses a formidable challenge in the organic chemical industry. Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recent years due to advancements in AI for science. Various deep learning-based methods have been proposed for this task in recent years, incorporating diverse levels of additional chemical knowledge dependency. Results: This paper introduces UAlign, a template-free graph-to-sequence pipeline for retrosynthesis prediction. By combining graph neural networks and Transformers, our method can more effectively leverage the inherent graph structure of molecules. Based on the fact that the majority of molecule structures remain unchanged during a chemical reaction, we propose a simple yet effective SMILES alignment technique to facilitate the reuse of unchanged structures for reactant generation. Extensive experiments show that our method substantially outperforms state-of-the-art template-free and semi-template-based approaches. Importantly, our template-free method achieves effectiveness comparable to, or even surpasses, established powerful template-based methods. Scientific contribution: We present a novel graph-to-sequence template-free retrosynthesis prediction pipeline that overcomes the limitations of Transformer-based methods in molecular representation learning and insufficient utilization of chemical information. We propose an unsupervised learning mechanism for establishing product-atom correspondence with reactant SMILES tokens, achieving even better results than supervised SMILES alignment methods. Extensive experiments demonstrate that UAlign significantly outperforms state-of-the-art template-free methods and rivals or surpasses template-based approaches, with up to 5% (top-5) and 5.4% (top-10) increased accuracy over the strongest baseline.
|
[
"['Kaipeng Zeng' 'Bo yang' 'Xin Zhao' 'Yu Zhang' 'Fan Nie' 'Xiaokang Yang'\n 'Yaohui Jin' 'Yanyan Xu']"
] |
null | null |
2404.00045
| null | null |
http://arxiv.org/pdf/2404.00045v1
|
2024-03-25T04:45:28Z
|
2024-03-25T04:45:28Z
|
Policy Optimization finds Nash Equilibrium in Regularized General-Sum LQ
Games
|
In this paper, we investigate the impact of introducing relative entropy regularization on the Nash Equilibria (NE) of General-Sum $N$-agent games, revealing the fact that the NE of such games conform to linear Gaussian policies. Moreover, it delineates sufficient conditions, contingent upon the adequacy of entropy regularization, for the uniqueness of the NE within the game. As Policy Optimization serves as a foundational approach for Reinforcement Learning (RL) techniques aimed at finding the NE, in this work we prove the linear convergence of a policy optimization algorithm which (subject to the adequacy of entropy regularization) is capable of provably attaining the NE. Furthermore, in scenarios where the entropy regularization proves insufficient, we present a $delta$-augmentation technique, which facilitates the achievement of an $epsilon$-NE within the game.
|
[
"['Muhammad Aneeq uz Zaman' 'Shubham Aggarwal' 'Melih Bastopcu'\n 'Tamer Başar']"
] |
null | null |
2404.00048
| null | null |
http://arxiv.org/abs/2404.00048v1
|
2024-03-25T11:10:49Z
|
2024-03-25T11:10:49Z
|
SLIMBRAIN: Augmented Reality Real-Time Acquisition and Processing System
For Hyperspectral Classification Mapping with Depth Information for In-Vivo
Surgical Procedures
|
Over the last two decades, augmented reality (AR) has led to the rapid development of new interfaces in various fields of social and technological application domains. One such domain is medicine, and to a higher extent surgery, where these visualization techniques help to improve the effectiveness of preoperative and intraoperative procedures. Following this trend, this paper presents SLIMBRAIN, a real-time acquisition and processing AR system suitable to classify and display brain tumor tissue from hyperspectral (HS) information. This system captures and processes HS images at 14 frames per second (FPS) during the course of a tumor resection operation to detect and delimit cancer tissue at the same time the neurosurgeon operates. The result is represented in an AR visualization where the classification results are overlapped with the RGB point cloud captured by a LiDAR camera. This representation allows natural navigation of the scene at the same time it is captured and processed, improving the visualization and hence effectiveness of the HS technology to delimit tumors. The whole system has been verified in real brain tumor resection operations.
|
[
"['Jaime Sancho' 'Manuel Villa' 'Miguel Chavarrías' 'Eduardo Juarez'\n 'Alfonso Lagares' 'César Sanz']"
] |
null | null |
2404.00050
| null | null |
http://arxiv.org/pdf/2404.00050v1
|
2024-03-25T15:11:15Z
|
2024-03-25T15:11:15Z
|
Grappa -- A Machine Learned Molecular Mechanics Force Field
|
Simulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than classical molecular mechanics (MM) force fields. Here, we propose a novel machine learning architecture to predict MM parameters from the molecular graph, employing a graph attentional neural network and a transformer with symmetry-preserving positional encoding. The resulting force field, Grappa, outperforms established and other machine-learned MM force fields in terms of accuracy at the same computational efficiency and can be used in existing Molecular Dynamics (MD) engines like GROMACS and OpenMM. It predicts energies and forces of small molecules, peptides, RNA and - showcasing its extensibility to uncharted regions of chemical space - radicals at state-of-the-art MM accuracy. We demonstrate Grappa's transferability to macromolecules in MD simulations, during which large protein are kept stable and small proteins can fold. Our force field sets the stage for biomolecular simulations close to chemical accuracy, but with the same computational cost as established protein force fields.
|
[
"['Leif Seute' 'Eric Hartmann' 'Jan Stühmer' 'Frauke Gräter']"
] |
null | null |
2404.00051
| null | null |
http://arxiv.org/pdf/2404.00051v1
|
2024-03-25T17:25:40Z
|
2024-03-25T17:25:40Z
|
Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal
Knowledge Graph Reasoning
|
Temporal Knowledge Graph Reasoning (TKGR) is the task of inferring missing facts for incomplete TKGs in complex scenarios (e.g., transductive and inductive settings), which has been gaining increasing attention. Recently, to mitigate dependence on structured connections in TKGs, text-based methods have been developed to utilize rich linguistic information from entity descriptions. However, suffering from the enormous parameters and inflexibility of pre-trained language models, existing text-based methods struggle to balance the textual knowledge and temporal information with computationally expensive purpose-built training strategies. To tap the potential of text-based models for TKGR in various complex scenarios, we propose ChapTER, a Contrastive historical modeling framework with prefix-tuning for TEmporal Reasoning. ChapTER feeds history-contextualized text into the pseudo-Siamese encoders to strike a textual-temporal balance via contrastive estimation between queries and candidates. By introducing virtual time prefix tokens, it applies a prefix-based tuning method to facilitate the frozen PLM capable for TKGR tasks under different settings. We evaluate ChapTER on four transductive and three few-shot inductive TKGR benchmarks, and experimental results demonstrate that ChapTER achieves superior performance compared to competitive baselines with only 0.17% tuned parameters. We conduct thorough analysis to verify the effectiveness, flexibility and efficiency of ChapTER.
|
[
"['Miao Peng' 'Ben Liu' 'Wenjie Xu' 'Zihao Jiang' 'Jiahui Zhu' 'Min Peng']"
] |
null | null |
2404.00054
| null | null |
http://arxiv.org/pdf/2404.00054v1
|
2024-03-26T01:42:13Z
|
2024-03-26T01:42:13Z
|
Choreographing the Digital Canvas: A Machine Learning Approach to
Artistic Performance
|
This paper introduces the concept of a design tool for artistic performances based on attribute descriptions. To do so, we used a specific performance of falling actions. The platform integrates a novel machine-learning (ML) model with an interactive interface to generate and visualize artistic movements. Our approach's core is a cyclic Attribute-Conditioned Variational Autoencoder (AC-VAE) model developed to address the challenge of capturing and generating realistic 3D human body motions from motion capture (MoCap) data. We created a unique dataset focused on the dynamics of falling movements, characterized by a new ontology that divides motion into three distinct phases: Impact, Glitch, and Fall. The ML model's innovation lies in its ability to learn these phases separately. It is achieved by applying comprehensive data augmentation techniques and an initial pose loss function to generate natural and plausible motion. Our web-based interface provides an intuitive platform for artists to engage with this technology, offering fine-grained control over motion attributes and interactive visualization tools, including a 360-degree view and a dynamic timeline for playback manipulation. Our research paves the way for a future where technology amplifies the creative potential of human expression, making sophisticated motion generation accessible to a wider artistic community.
|
[
"['Siyuan Peng' 'Kate Ladenheim' 'Snehesh Shrestha' 'Cornelia Fermüller']"
] |
null | null |
2404.00056
| null | null |
http://arxiv.org/pdf/2404.00056v1
|
2024-03-26T17:24:28Z
|
2024-03-26T17:24:28Z
|
Fingerprinting web servers through Transformer-encoded HTTP response
headers
|
We explored leveraging state-of-the-art deep learning, big data, and natural language processing to enhance the detection of vulnerable web server versions. Focusing on improving accuracy and specificity over rule-based systems, we conducted experiments by sending various ambiguous and non-standard HTTP requests to 4.77 million domains and capturing HTTP response status lines. We represented these status lines through training a BPE tokenizer and RoBERTa encoder for unsupervised masked language modeling. We then dimensionality reduced and concatenated encoded response lines to represent each domain's web server. A Random Forest and multilayer perceptron (MLP) classified these web servers, and achieved 0.94 and 0.96 macro F1-score, respectively, on detecting the five most popular origin web servers. The MLP achieved a weighted F1-score of 0.55 on classifying 347 major type and minor version pairs. Analysis indicates that our test cases are meaningful discriminants of web server types. Our approach demonstrates promise as a powerful and flexible alternative to rule-based systems.
|
[
"['Patrick Darwinkel']"
] |
null | null |
2404.00060
| null | null |
http://arxiv.org/pdf/2404.00060v1
|
2024-03-27T07:17:16Z
|
2024-03-27T07:17:16Z
|
Temporal Graph Networks for Graph Anomaly Detection in Financial
Networks
|
This paper explores the utilization of Temporal Graph Networks (TGN) for financial anomaly detection, a pressing need in the era of fintech and digitized financial transactions. We present a comprehensive framework that leverages TGN, capable of capturing dynamic changes in edges within financial networks, for fraud detection. Our study compares TGN's performance against static Graph Neural Network (GNN) baselines, as well as cutting-edge hypergraph neural network baselines using DGraph dataset for a realistic financial context. Our results demonstrate that TGN significantly outperforms other models in terms of AUC metrics. This superior performance underlines TGN's potential as an effective tool for detecting financial fraud, showcasing its ability to adapt to the dynamic and complex nature of modern financial systems. We also experimented with various graph embedding modules within the TGN framework and compared the effectiveness of each module. In conclusion, we demonstrated that, even with variations within TGN, it is possible to achieve good performance in the anomaly detection task.
|
[
"['Yejin Kim' 'Youngbin Lee' 'Minyoung Choe' 'Sungju Oh' 'Yongjae Lee']"
] |
null | null |
2404.00068
| null | null |
http://arxiv.org/pdf/2404.00068v1
|
2024-03-28T09:41:24Z
|
2024-03-28T09:41:24Z
|
A Data-Driven Predictive Analysis on Cyber Security Threats with Key
Risk Factors
|
Cyber risk refers to the risk of defacing reputation, monetary losses, or disruption of an organization or individuals, and this situation usually occurs by the unconscious use of cyber systems. The cyber risk is unhurriedly increasing day by day and it is right now a global threat. Developing countries like Bangladesh face major cyber risk challenges. The growing cyber threat worldwide focuses on the need for effective modeling to predict and manage the associated risk. This paper exhibits a Machine Learning(ML) based model for predicting individuals who may be victims of cyber attacks by analyzing socioeconomic factors. We collected the dataset from victims and non-victims of cyberattacks based on socio-demographic features. The study involved the development of a questionnaire to gather data, which was then used to measure the significance of features. Through data augmentation, the dataset was expanded to encompass 3286 entries, setting the stage for our investigation and modeling. Among several ML models with 19, 20, 21, and 26 features, we proposed a novel Pertinent Features Random Forest (RF) model, which achieved maximum accuracy with 20 features (95.95%) and also demonstrated the association among the selected features using the Apriori algorithm with Confidence (above 80%) according to the victim. We generated 10 important association rules and presented the framework that is rigorously evaluated on real-world datasets, demonstrating its potential to predict cyberattacks and associated risk factors effectively. Looking ahead, future efforts will be directed toward refining the predictive model's precision and delving into additional risk factors, to fortify the proposed framework's efficacy in navigating the complex terrain of cybersecurity threats.
|
[
"['Fatama Tuz Johora' 'Md Shahedul Islam Khan' 'Esrath Kanon'\n 'Mohammad Abu Tareq Rony' 'Md Zubair' 'Iqbal H. Sarker']"
] |
null | null |
2404.00069
| null | null |
http://arxiv.org/pdf/2404.00069v1
|
2024-03-28T14:44:44Z
|
2024-03-28T14:44:44Z
|
A Two-Phase Recall-and-Select Framework for Fast Model Selection
|
As the ubiquity of deep learning in various machine learning applications has amplified, a proliferation of neural network models has been trained and shared on public model repositories. In the context of a targeted machine learning assignment, utilizing an apt source model as a starting point typically outperforms the strategy of training from scratch, particularly with limited training data. Despite the investigation and development of numerous model selection strategies in prior work, the process remains time-consuming, especially given the ever-increasing scale of model repositories. In this paper, we propose a two-phase (coarse-recall and fine-selection) model selection framework, aiming to enhance the efficiency of selecting a robust model by leveraging the models' training performances on benchmark datasets. Specifically, the coarse-recall phase clusters models showcasing similar training performances on benchmark datasets in an offline manner. A light-weight proxy score is subsequently computed between this model cluster and the target dataset, which serves to recall a significantly smaller subset of potential candidate models in a swift manner. In the following fine-selection phase, the final model is chosen by fine-tuning the recalled models on the target dataset with successive halving. To accelerate the process, the final fine-tuning performance of each potential model is predicted by mining the model's convergence trend on the benchmark datasets, which aids in filtering lower performance models more earlier during fine-tuning. Through extensive experimentation on tasks covering natural language processing and computer vision, it has been demonstrated that the proposed methodology facilitates the selection of a high-performing model at a rate about 3x times faster than conventional baseline methods. Our code is available at https://github.com/plasware/two-phase-selection.
|
[
"['Jianwei Cui' 'Wenhang Shi' 'Honglin Tao' 'Wei Lu' 'Xiaoyong Du']"
] |
null | null |
2404.00074
| null | null |
http://arxiv.org/pdf/2404.00074v2
|
2024-06-03T09:03:10Z
|
2024-03-28T19:57:48Z
|
A finite operator learning technique for mapping the elastic properties
of microstructures to their mechanical deformations
|
To obtain fast solutions for governing physical equations in solid mechanics, we introduce a method that integrates the core ideas of the finite element method with physics-informed neural networks and concept of neural operators. This approach generalizes and enhances each method, learning the parametric solution for mechanical problems without relying on data from other resources (e.g. other numerical solvers). We propose directly utilizing the available discretized weak form in finite element packages to construct the loss functions algebraically, thereby demonstrating the ability to find solutions even in the presence of sharp discontinuities. Our focus is on micromechanics as an example, where knowledge of deformation and stress fields for a given heterogeneous microstructure is crucial for further design applications. The primary parameter under investigation is the Young's modulus distribution within the heterogeneous solid system. Our investigations reveal that physics-based training yields higher accuracy compared to purely data-driven approaches for unseen microstructures. Additionally, we offer two methods to directly improve the process of obtaining high-resolution solutions, avoiding the need to use basic interpolation techniques. First is based on an autoencoder approach to enhance the efficiency for calculation on high resolution grid point. Next, Fourier-based parametrization is utilized to address complex 2D and 3D problems in micromechanics. The latter idea aims to represent complex microstructures efficiently using Fourier coefficients. Comparisons with other well-known operator learning algorithms, further emphasize the advantages of the newly proposed method.
|
[
"['Shahed Rezaei' 'Reza Najian Asl' 'Shirko Faroughi' 'Mahdi Asgharzadeh'\n 'Ali Harandi' 'Rasoul Najafi Koopas' 'Gottfried Laschet' 'Stefanie Reese'\n 'Markus Apel']"
] |
null | null |
2404.00075
| null | null |
http://arxiv.org/pdf/2404.00075v1
|
2024-03-28T20:17:58Z
|
2024-03-28T20:17:58Z
|
BEACON: Bayesian Experimental design Acceleration with Conditional
Normalizing flows $-$ a case study in optimal monitor well placement for
CO$_2$ sequestration
|
CO$_2$ sequestration is a crucial engineering solution for mitigating climate change. However, the uncertain nature of reservoir properties, necessitates rigorous monitoring of CO$_2$ plumes to prevent risks such as leakage, induced seismicity, or breaching licensed boundaries. To address this, project managers use borehole wells for direct CO$_2$ and pressure monitoring at specific locations. Given the high costs associated with drilling, it is crucial to strategically place a limited number of wells to ensure maximally effective monitoring within budgetary constraints. Our approach for selecting well locations integrates fluid-flow solvers for forecasting plume trajectories with generative neural networks for plume inference uncertainty. Our methodology is extensible to three-dimensional domains and is developed within a Bayesian framework for optimal experimental design, ensuring scalability and mathematical optimality. We use a realistic case study to verify these claims by demonstrating our method's application in a large scale domain and optimal performance as compared to baseline well placement.
|
[
"['Rafael Orozco' 'Abhinav Gahlot' 'Felix J. Herrmann']"
] |
null | null |
2404.00076
| null | null |
http://arxiv.org/abs/2404.00076v2
|
2024-04-07T04:38:37Z
|
2024-03-29T00:09:48Z
|
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping
Attacks
|
Audio-based machine learning systems frequently use public or third-party data, which might be inaccurate. This exposes deep neural network (DNN) models trained on such data to potential data poisoning attacks. In this type of assault, attackers can train the DNN model using poisoned data, potentially degrading its performance. Another type of data poisoning attack that is extremely relevant to our investigation is label flipping, in which the attacker manipulates the labels for a subset of data. It has been demonstrated that these assaults may drastically reduce system performance, even for attackers with minimal abilities. In this study, we propose a backdoor attack named 'DirtyFlipping', which uses dirty label techniques, "label-on-label", to input triggers (clapping) in the selected data patterns associated with the target class, thereby enabling a stealthy backdoor.
|
[
"['Orson Mengara']"
] |
null | null |
2404.00081
| null | null |
http://arxiv.org/pdf/2404.00081v1
|
2024-03-29T08:55:39Z
|
2024-03-29T08:55:39Z
|
Molecular Generative Adversarial Network with Multi-Property
Optimization
|
Deep generative models, such as generative adversarial networks (GANs), have been employed for $de~novo$ molecular generation in drug discovery. Most prior studies have utilized reinforcement learning (RL) algorithms, particularly Monte Carlo tree search (MCTS), to handle the discrete nature of molecular representations in GANs. However, due to the inherent instability in training GANs and RL models, along with the high computational cost associated with MCTS sampling, MCTS RL-based GANs struggle to scale to large chemical databases. To tackle these challenges, this study introduces a novel GAN based on actor-critic RL with instant and global rewards, called InstGAN, to generate molecules at the token-level with multi-property optimization. Furthermore, maximized information entropy is leveraged to alleviate the mode collapse. The experimental results demonstrate that InstGAN outperforms other baselines, achieves comparable performance to state-of-the-art models, and efficiently generates molecules with multi-property optimization. The source code will be released upon acceptance of the paper.
|
[
"['Huidong Tang' 'Chen Li' 'Sayaka Kamei' 'Yoshihiro Yamanishi'\n 'Yasuhiko Morimoto']"
] |
null | null |
2404.00082
| null | null |
http://arxiv.org/pdf/2404.00082v2
|
2024-05-17T11:56:07Z
|
2024-03-29T10:48:32Z
|
Data-Driven Room Acoustic Modeling Via Differentiable Feedback Delay
Networks With Learnable Delay Lines
|
Over the past few decades, extensive research has been devoted to the design of artificial reverberation algorithms aimed at emulating the room acoustics of physical environments. Despite significant advancements, automatic parameter tuning of delay-network models remains an open challenge. We introduce a novel method for finding the parameters of a Feedback Delay Network (FDN) such that its output renders target attributes of a measured room impulse response. The proposed approach involves the implementation of a differentiable FDN with trainable delay lines, which, for the first time, allows us to simultaneously learn each and every delay-network parameter via backpropagation. The iterative optimization process seeks to minimize a perceptually-motivated time-domain loss function incorporating differentiable terms accounting for energy decay and echo density. Through experimental validation, we show that the proposed method yields time-invariant frequency-independent FDNs capable of closely matching the desired acoustical characteristics, and outperforms existing methods based on genetic algorithms and analytical FDN design.
|
[
"['Alessandro Ilic Mezza' 'Riccardo Giampiccolo' 'Enzo De Sena'\n 'Alberto Bernardini']"
] |
null | null |
2404.00085
| null | null |
http://arxiv.org/pdf/2404.00085v1
|
2024-03-29T17:32:42Z
|
2024-03-29T17:32:42Z
|
Bayesian Nonparametrics: An Alternative to Deep Learning
|
Bayesian nonparametric models offer a flexible and powerful framework for statistical model selection, enabling the adaptation of model complexity to the intricacies of diverse datasets. This survey intends to delve into the significance of Bayesian nonparametrics, particularly in addressing complex challenges across various domains such as statistics, computer science, and electrical engineering. By elucidating the basic properties and theoretical foundations of these nonparametric models, this survey aims to provide a comprehensive understanding of Bayesian nonparametrics and their relevance in addressing complex problems, particularly in the domain of multi-object tracking. Through this exploration, we uncover the versatility and efficacy of Bayesian nonparametric methodologies, paving the way for innovative solutions to intricate challenges across diverse disciplines.
|
[
"['Bahman Moraffah']"
] |
null | null |
2404.00103
| null | null |
http://arxiv.org/pdf/2404.00103v1
|
2024-03-29T18:23:34Z
|
2024-03-29T18:23:34Z
|
PikeLPN: Mitigating Overlooked Inefficiencies of Low-Precision Neural
Networks
|
Low-precision quantization is recognized for its efficacy in neural network optimization. Our analysis reveals that non-quantized elementwise operations which are prevalent in layers such as parameterized activation functions, batch normalization, and quantization scaling dominate the inference cost of low-precision models. These non-quantized elementwise operations are commonly overlooked in SOTA efficiency metrics such as Arithmetic Computation Effort (ACE). In this paper, we propose ACEv2 - an extended version of ACE which offers a better alignment with the inference cost of quantized models and their energy consumption on ML hardware. Moreover, we introduce PikeLPN, a model that addresses these efficiency issues by applying quantization to both elementwise operations and multiply-accumulate operations. In particular, we present a novel quantization technique for batch normalization layers named QuantNorm which allows for quantizing the batch normalization parameters without compromising the model performance. Additionally, we propose applying Double Quantization where the quantization scaling parameters are quantized. Furthermore, we recognize and resolve the issue of distribution mismatch in Separable Convolution layers by introducing Distribution-Heterogeneous Quantization which enables quantizing them to low-precision. PikeLPN achieves Pareto-optimality in efficiency-accuracy trade-off with up to 3X efficiency improvement compared to SOTA low-precision models.
|
[
"['Marina Neseem' 'Conor McCullough' 'Randy Hsin' 'Chas Leichner' 'Shan Li'\n 'In Suk Chong' 'Andrew G. Howard' 'Lukasz Lew' 'Sherief Reda'\n 'Ville-Mikko Rautio' 'Daniele Moro']"
] |
null | null |
2404.00130
| null | null |
http://arxiv.org/pdf/2404.00130v1
|
2024-03-29T19:51:34Z
|
2024-03-29T19:51:34Z
|
FISBe: A real-world benchmark dataset for instance segmentation of
long-range thin filamentous structures
|
Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.
|
[
"['Lisa Mais' 'Peter Hirsch' 'Claire Managan' 'Ramya Kandarpa'\n 'Josef Lorenz Rumberger' 'Annika Reinke' 'Lena Maier-Hein' 'Gudrun Ihrke'\n 'Dagmar Kainmueller']"
] |
null | null |
2404.00137
| null | null |
http://arxiv.org/pdf/2404.00137v1
|
2024-03-29T20:19:36Z
|
2024-03-29T20:19:36Z
|
Budget-aware Query Tuning: An AutoML Perspective
|
Modern database systems rely on cost-based query optimizers to come up with good execution plans for input queries. Such query optimizers rely on cost models to estimate the costs of candidate query execution plans. A cost model represents a function from a set of cost units to query execution cost, where each cost unit specifies the unit cost of executing a certain type of query processing operation (such as table scan or join). These cost units are traditionally viewed as constants, whose values only depend on the platform configuration where the database system runs on top of but are invariant for queries processed by the database system. In this paper, we challenge this classic view by thinking of these cost units as variables instead. We show that, by varying the cost-unit values one can obtain query plans that significantly outperform the default query plans returned by the query optimizer when viewing the cost units as constants. We term this cost-unit tuning process "query tuning" (QT) and show that it is similar to the well-known hyper-parameter optimization (HPO) problem in AutoML. As a result, any state-of-the-art HPO technologies can be applied to QT. We study the QT problem in the context of anytime tuning, which is desirable in practice by constraining the total time spent on QT within a given budget -- we call this problem budget-aware query tuning. We further extend our study from tuning a single query to tuning a workload with multiple queries, and we call this generalized problem budget-aware workload tuning (WT), which aims for minimizing the execution time of the entire workload. WT is more challenging as one needs to further prioritize individual query tuning within the given time budget. We propose solutions to both QT and WT and experimental evaluation using both benchmark and real workloads demonstrates the efficacy of our proposed solutions.
|
[
"['Wentao Wu' 'Chi Wang']"
] |
null | null |
2404.00140
| null | null |
http://arxiv.org/pdf/2404.00140v1
|
2024-03-29T20:28:42Z
|
2024-03-29T20:28:42Z
|
Does Faithfulness Conflict with Plausibility? An Empirical Study in
Explainable AI across NLP Tasks
|
Explainability algorithms aimed at interpreting decision-making AI systems usually consider balancing two critical dimensions: 1) textit{faithfulness}, where explanations accurately reflect the model's inference process. 2) textit{plausibility}, where explanations are consistent with domain experts. However, the question arises: do faithfulness and plausibility inherently conflict? In this study, through a comprehensive quantitative comparison between the explanations from the selected explainability methods and expert-level interpretations across three NLP tasks: sentiment analysis, intent detection, and topic labeling, we demonstrate that traditional perturbation-based methods Shapley value and LIME could attain greater faithfulness and plausibility. Our findings suggest that rather than optimizing for one dimension at the expense of the other, we could seek to optimize explainability algorithms with dual objectives to achieve high levels of accuracy and user accessibility in their explanations.
|
[
"['Xiaolei Lu' 'Jianghong Ma']"
] |
null | null |
2404.00145
| null | null |
http://arxiv.org/pdf/2404.00145v1
|
2024-03-29T20:36:58Z
|
2024-03-29T20:36:58Z
|
Verifying the Selected Completely at Random Assumption in
Positive-Unlabeled Learning
|
The goal of positive-unlabeled (PU) learning is to train a binary classifier on the basis of training data containing positive and unlabeled instances, where unlabeled observations can belong either to the positive class or to the negative class. Modeling PU data requires certain assumptions on the labeling mechanism that describes which positive observations are assigned a label. The simplest assumption, considered in early works, is SCAR (Selected Completely at Random Assumption), according to which the propensity score function, defined as the probability of assigning a label to a positive observation, is constant. On the other hand, a much more realistic assumption is SAR (Selected at Random), which states that the propensity function solely depends on the observed feature vector. SCAR-based algorithms are much simpler and computationally much faster compared to SAR-based algorithms, which usually require challenging estimation of the propensity score. In this work, we propose a relatively simple and computationally fast test that can be used to determine whether the observed data meet the SCAR assumption. Our test is based on generating artificial labels conforming to the SCAR case, which in turn allows to mimic the distribution of the test statistic under the null hypothesis of SCAR. We justify our method theoretically. In experiments, we demonstrate that the test successfully detects various deviations from SCAR scenario and at the same time it is possible to effectively control the type I error. The proposed test can be recommended as a pre-processing step to decide which final PU algorithm to choose in cases when nature of labeling mechanism is not known.
|
[
"['Paweł Teisseyre' 'Konrad Furmańczyk' 'Jan Mielniczuk']"
] |
null | null |
2404.00158
| null | null |
http://arxiv.org/pdf/2404.00158v1
|
2024-03-29T21:12:25Z
|
2024-03-29T21:12:25Z
|
Fully Zeroth-Order Bilevel Programming via Gaussian Smoothing
|
In this paper, we study and analyze zeroth-order stochastic approximation algorithms for solving bilvel problems, when neither the upper/lower objective values, nor their unbiased gradient estimates are available. In particular, exploiting Stein's identity, we first use Gaussian smoothing to estimate first- and second-order partial derivatives of functions with two independent block of variables. We then used these estimates in the framework of a stochastic approximation algorithm for solving bilevel optimization problems and establish its non-asymptotic convergence analysis. To the best of our knowledge, this is the first time that sample complexity bounds are established for a fully stochastic zeroth-order bilevel optimization algorithm.
|
[
"['Alireza Aghasi' 'Saeed Ghadimi']"
] |
null | null |
2404.00162
| null | null |
http://arxiv.org/pdf/2404.00162v2
|
2024-04-03T03:28:48Z
|
2024-03-29T21:37:23Z
|
Modeling Large-Scale Walking and Cycling Networks: A Machine Learning
Approach Using Mobile Phone and Crowdsourced Data
|
Walking and cycling are known to bring substantial health, environmental, and economic advantages. However, the development of evidence-based active transportation planning and policies has been impeded by significant data limitations, such as biases in crowdsourced data and representativeness issues of mobile phone data. In this study, we develop and apply a machine learning based modeling approach for estimating daily walking and cycling volumes across a large-scale regional network in New South Wales, Australia that includes 188,999 walking links and 114,885 cycling links. The modeling methodology leverages crowdsourced and mobile phone data as well as a range of other datasets on population, land use, topography, climate, etc. The study discusses the unique challenges and limitations related to all three aspects of model training, testing, and inference given the large geographical extent of the modeled networks and relative scarcity of observed walking and cycling count data. The study also proposes a new technique to identify model estimate outliers and to mitigate their impact. Overall, the study provides a valuable resource for transportation modelers, policymakers and urban planners seeking to enhance active transportation infrastructure planning and policies with advanced emerging data-driven modeling methodologies.
|
[
"['Meead Saberi' 'Tanapon Lilasathapornkit']"
] |
null | null |
2404.00165
| null | null |
http://arxiv.org/pdf/2404.00165v1
|
2024-03-29T21:44:24Z
|
2024-03-29T21:44:24Z
|
Individual Text Corpora Predict Openness, Interests, Knowledge and Level
of Education
|
Here we examine whether the personality dimension of openness to experience can be predicted from the individual google search history. By web scraping, individual text corpora (ICs) were generated from 214 participants with a mean number of 5 million word tokens. We trained word2vec models and used the similarities of each IC to label words, which were derived from a lexical approach of personality. These IC-label-word similarities were utilized as predictive features in neural models. For training and validation, we relied on 179 participants and held out a test sample of 35 participants. A grid search with varying number of predictive features, hidden units and boost factor was performed. As model selection criterion, we used R2 in the validation samples penalized by the absolute R2 difference between training and validation. The selected neural model explained 35% of the openness variance in the test sample, while an ensemble model with the same architecture often provided slightly more stable predictions for intellectual interests, knowledge in humanities and level of education. Finally, a learning curve analysis suggested that around 500 training participants are required for generalizable predictions. We discuss ICs as a complement or replacement of survey-based psychodiagnostics.
|
[
"['Markus J. Hofmann' 'Markus T. Jansen' 'Christoph Wigbels'\n 'Benny Briesemeister' 'Arthur M. Jacobs']"
] |
null | null |
2404.00172
| null | null |
http://arxiv.org/pdf/2404.00172v1
|
2024-03-29T22:03:53Z
|
2024-03-29T22:03:53Z
|
Universal Bovine Identification via Depth Data and Deep Metric Learning
|
This paper proposes and evaluates, for the first time, a top-down (dorsal view), depth-only deep learning system for accurately identifying individual cattle and provides associated code, datasets, and training weights for immediate reproducibility. An increase in herd size skews the cow-to-human ratio at the farm and makes the manual monitoring of individuals more challenging. Therefore, real-time cattle identification is essential for the farms and a crucial step towards precision livestock farming. Underpinned by our previous work, this paper introduces a deep-metric learning method for cattle identification using depth data from an off-the-shelf 3D camera. The method relies on CNN and MLP backbones that learn well-generalised embedding spaces from the body shape to differentiate individuals -- requiring neither species-specific coat patterns nor close-up muzzle prints for operation. The network embeddings are clustered using a simple algorithm such as $k$-NN for highly accurate identification, thus eliminating the need to retrain the network for enrolling new individuals. We evaluate two backbone architectures, ResNet, as previously used to identify Holstein Friesians using RGB images, and PointNet, which is specialised to operate on 3D point clouds. We also present CowDepth2023, a new dataset containing 21,490 synchronised colour-depth image pairs of 99 cows, to evaluate the backbones. Both ResNet and PointNet architectures, which consume depth maps and point clouds, respectively, led to high accuracy that is on par with the coat pattern-based backbone.
|
[
"['Asheesh Sharma' 'Lucy Randewich' 'William Andrew' 'Sion Hannuna'\n 'Neill Campbell' 'Siobhan Mullan' 'Andrew W. Dowsey' 'Melvyn Smith'\n 'Mark Hansen' 'Tilo Burghardt']"
] |
null | null |
2404.00173
| null | null |
http://arxiv.org/pdf/2404.00173v2
|
2024-06-10T12:46:22Z
|
2024-03-29T22:05:26Z
|
Comparing Hyper-optimized Machine Learning Models for Predicting
Efficiency Degradation in Organic Solar Cells
|
This work presents a set of optimal machine learning (ML) models to represent the temporal degradation suffered by the power conversion efficiency (PCE) of polymeric organic solar cells (OSCs) with a multilayer structure ITO/PEDOT:PSS/P3HT:PCBM/Al. To that aim, we generated a database with 996 entries, which includes up to 7 variables regarding both the manufacturing process and environmental conditions for more than 180 days. Then, we relied on a software framework that brings together a conglomeration of automated ML protocols that execute sequentially against our database by simply command-line interface. This easily permits hyper-optimizing and randomizing seeds of the ML models through exhaustive benchmarking so that optimal models are obtained. The accuracy achieved reaches values of the coefficient determination (R2) widely exceeding 0.90, whereas the root mean squared error (RMSE), sum of squared error (SSE), and mean absolute error (MAE)>1% of the target value, the PCE. Additionally, we contribute with validated models able to screen the behavior of OSCs never seen in the database. In that case, R2~0.96-0.97 and RMSE~1%, thus confirming the reliability of the proposal to predict. For comparative purposes, classical Bayesian regression fitting based on non-linear mean squares (LMS) are also presented, which only perform sufficiently for univariate cases of single OSCs. Hence they fail to outperform the breadth of the capabilities shown by the ML models. Finally, thanks to the standardized results offered by the ML framework, we study the dependencies between the variables of the dataset and their implications for the optimal performance and stability of the OSCs. Reproducibility is ensured by a standardized report altogether with the dataset, which are publicly available at Github.
|
[
"['David Valiente' 'Fernando Rodríguez-Mas' 'Juan V. Alegre-Requena'\n 'David Dalmau' 'Juan C. Ferrer']"
] |
null | null |
2404.00178
| null | null |
http://arxiv.org/pdf/2404.00178v1
|
2024-03-29T22:23:35Z
|
2024-03-29T22:23:35Z
|
Beyond Suspension: A Two-phase Methodology for Concluding Sports Leagues
|
Problem definition: Professional sports leagues may be suspended due to various reasons such as the recent COVID-19 pandemic. A critical question the league must address when re-opening is how to appropriately select a subset of the remaining games to conclude the season in a shortened time frame. Academic/practical relevance: Despite the rich literature on scheduling an entire season starting from a blank slate, concluding an existing season is quite different. Our approach attempts to achieve team rankings similar to that which would have resulted had the season been played out in full. Methodology: We propose a data-driven model which exploits predictive and prescriptive analytics to produce a schedule for the remainder of the season comprised of a subset of originally-scheduled games. Our model introduces novel rankings-based objectives within a stochastic optimization model, whose parameters are first estimated using a predictive model. We introduce a deterministic equivalent reformulation along with a tailored Frank-Wolfe algorithm to efficiently solve our problem, as well as a robust counterpart based on min-max regret. Results: We present simulation-based numerical experiments from previous National Basketball Association (NBA) seasons 2004--2019, and show that our models are computationally efficient, outperform a greedy benchmark that approximates a non-rankings-based scheduling policy, and produce interpretable results. Managerial implications: Our data-driven decision-making framework may be used to produce a shortened season with 25-50% fewer games while still producing an end-of-season ranking similar to that of the full season, had it been played.
|
[
"['Ali Hassanzadeh' 'Mojtaba Hosseini' 'John G. Turner']"
] |
null | null |
2404.00179
| null | null |
http://arxiv.org/pdf/2404.00179v1
|
2024-03-29T22:24:12Z
|
2024-03-29T22:24:12Z
|
Multi-Region Transfer Learning for Segmentation of Crop Field Boundaries
in Satellite Images with Limited Labels
|
The goal of field boundary delineation is to predict the polygonal boundaries and interiors of individual crop fields in overhead remotely sensed images (e.g., from satellites or drones). Automatic delineation of field boundaries is a necessary task for many real-world use cases in agriculture, such as estimating cultivated area in a region or predicting end-of-season yield in a field. Field boundary delineation can be framed as an instance segmentation problem, but presents unique research challenges compared to traditional computer vision datasets used for instance segmentation. The practical applicability of previous work is also limited by the assumption that a sufficiently-large labeled dataset is available where field boundary delineation models will be applied, which is not the reality for most regions (especially under-resourced regions such as Sub-Saharan Africa). We present an approach for segmentation of crop field boundaries in satellite images in regions lacking labeled data that uses multi-region transfer learning to adapt model weights for the target region. We show that our approach outperforms existing methods and that multi-region transfer learning substantially boosts performance for multiple model architectures. Our implementation and datasets are publicly available to enable use of the approach by end-users and serve as a benchmark for future work.
|
[
"['Hannah Kerner' 'Saketh Sundar' 'Mathan Satish']"
] |
null | null |
2404.00195
| null | null |
http://arxiv.org/pdf/2404.00195v2
|
2024-05-27T23:57:02Z
|
2024-03-29T23:55:25Z
|
Multiple-policy Evaluation via Density Estimation
|
We study the multiple-policy evaluation problem where we are given a set of $K$ policies and the goal is to evaluate their performance (expected total reward over a fixed horizon) to an accuracy $epsilon$ with probability at least $1-delta$. We propose an algorithm named $mathrm{CAESAR}$ for this problem. Our approach is based on computing an approximate optimal offline sampling distribution and using the data sampled from it to perform the simultaneous estimation of the policy values. $mathrm{CAESAR}$ has two phases. In the first we produce coarse estimates of the visitation distributions of the target policies at a low order sample complexity rate that scales with $tilde{O}(frac{1}{epsilon})$. In the second phase, we approximate the optimal offline sampling distribution and compute the importance weighting ratios for all target policies by minimizing a step-wise quadratic loss function inspired by the DualDICE cite{nachum2019dualdice} objective. Up to low order and logarithmic terms $mathrm{CAESAR}$ achieves a sample complexity $tilde{O}left(frac{H^4}{epsilon^2}sum_{h=1}^Hmax_{kin[K]}sum_{s,a}frac{(d_h^{pi^k}(s,a))^2}{mu^*_h(s,a)}right)$, where $d^{pi}$ is the visitation distribution of policy $pi$, $mu^*$ is the optimal sampling distribution, and $H$ is the horizon.
|
[
"['Yilei Chen' 'Aldo Pacchiano' 'Ioannis Ch. Paschalidis']"
] |
null | null |
2404.00204
| null | null |
http://arxiv.org/pdf/2404.00204v1
|
2024-03-30T00:46:43Z
|
2024-03-30T00:46:43Z
|
A PPO-based DRL Auto-Tuning Nonlinear PID Drone Controller for Robust
Autonomous Flights
|
This project aims to revolutionize drone flight control by implementing a nonlinear Deep Reinforcement Learning (DRL) agent as a replacement for traditional linear Proportional Integral Derivative (PID) controllers. The primary objective is to seamlessly transition drones between manual and autonomous modes, enhancing responsiveness and stability. We utilize the Proximal Policy Optimization (PPO) reinforcement learning strategy within the Gazebo simulator to train the DRL agent. Adding a $20,000 indoor Vicon tracking system offers <1mm positioning accuracy, which significantly improves autonomous flight precision. To navigate the drone in the shortest collision-free trajectory, we also build a 3 dimensional A* path planner and implement it into the real flight successfully.
|
[
"['Junyang Zhang' 'Cristian Emanuel Ocampo Rivera' 'Kyle Tyni'\n 'Steven Nguyen']"
] |
null | null |
2404.00207
| null | null |
http://arxiv.org/pdf/2404.00207v1
|
2024-03-30T01:08:25Z
|
2024-03-30T01:08:25Z
|
Causal Inference for Human-Language Model Collaboration
|
In this paper, we examine the collaborative dynamics between humans and language models (LMs), where the interactions typically involve LMs proposing text segments and humans editing or responding to these proposals. Productive engagement with LMs in such scenarios necessitates that humans discern effective text-based interaction strategies, such as editing and response styles, from historical human-LM interactions. This objective is inherently causal, driven by the counterfactual `what-if' question: how would the outcome of collaboration change if humans employed a different text editing/refinement strategy? A key challenge in answering this causal inference question is formulating an appropriate causal estimand: the conventional average treatment effect (ATE) estimand is inapplicable to text-based treatments due to their high dimensionality. To address this concern, we introduce a new causal estimand -- Incremental Stylistic Effect (ISE) -- which characterizes the average impact of infinitesimally shifting a text towards a specific style, such as increasing formality. We establish the conditions for the non-parametric identification of ISE. Building on this, we develop CausalCollab, an algorithm designed to estimate the ISE of various interaction strategies in dynamic human-LM collaborations. Our empirical investigations across three distinct human-LM collaboration scenarios reveal that CausalCollab effectively reduces confounding and significantly improves counterfactual estimation over a set of competitive baselines.
|
[
"['Bohan Zhang' 'Yixin Wang' 'Paramveer S. Dhillon']"
] |
null | null |
2404.00211
| null | null |
http://arxiv.org/pdf/2404.00211v1
|
2024-03-30T01:26:05Z
|
2024-03-30T01:26:05Z
|
Multi-Conditional Ranking with Large Language Models
|
Utilizing large language models (LLMs) to rank a set of items has become a common approach in recommendation and retrieval systems. Typically, these systems focus on ordering a substantial number of documents in a monotonic order based on a given query. However, real-world scenarios often present a different challenge: ranking a comparatively smaller set of items, but according to a variety of diverse and occasionally conflicting conditions. In this paper, we define and explore the task of multi-conditional ranking by introducing MCRank, a benchmark tailored for assessing multi-conditional ranking across various item types and conditions. Our analysis of LLMs using MCRank indicates a significant decrease in performance as the number and complexity of items and conditions grow. To overcome this limitation, we propose a novel decomposed reasoning method, consisting of EXtracting and Sorting the conditions, and then Iterativly Ranking the items (EXSIR). Our extensive experiments show that this decomposed reasoning method enhances LLMs' performance significantly, achieving up to a 12% improvement over existing LLMs. We also provide a detailed analysis of LLMs performance across various condition categories, and examine the effectiveness of decomposition step. Furthermore, we compare our method with existing approaches such as Chain-of-Thought and an encoder-type ranking model, demonstrating the superiority of our approach and complexity of MCR task. We released our dataset and code.
|
[
"['Pouya Pezeshkpour' 'Estevam Hruschka']"
] |
null | null |
2404.00218
| null | null |
http://arxiv.org/pdf/2404.00218v2
|
2024-07-15T05:18:42Z
|
2024-03-30T02:23:01Z
|
Functional-Edged Network Modeling
|
Contrasts with existing works which all consider nodes as functions and use edges to represent the relationships between different functions. We target at network modeling whose edges are functional data and transform the adjacency matrix into a functional adjacency tensor, introducing an additional dimension dedicated to function representation. Tucker functional decomposition is used for the functional adjacency tensor, and to further consider the community between nodes, we regularize the basis matrices to be symmetrical. Furthermore, to deal with irregular observations of the functional edges, we conduct model inference to solve a tensor completion problem. It is optimized by a Riemann conjugate gradient descent method. Besides these, we also derive several theorems to show the desirable properties of the functional edged network model. Finally, we evaluate the efficacy of our proposed model using simulation data and real metro system data from Hong Kong and Singapore.
|
[
"['Haijie Xu' 'Chen Zhang']"
] |
null | null |
2404.00220
| null | null |
http://arxiv.org/pdf/2404.00220v1
|
2024-03-30T02:32:53Z
|
2024-03-30T02:32:53Z
|
Partially-Observable Sequential Change-Point Detection for
Autocorrelated Data via Upper Confidence Region
|
Sequential change point detection for multivariate autocorrelated data is a very common problem in practice. However, when the sensing resources are limited, only a subset of variables from the multivariate system can be observed at each sensing time point. This raises the problem of partially observable multi-sensor sequential change point detection. For it, we propose a detection scheme called adaptive upper confidence region with state space model (AUCRSS). It models multivariate time series via a state space model (SSM), and uses an adaptive sampling policy for efficient change point detection and localization. A partially-observable Kalman filter algorithm is developed for online inference of SSM, and accordingly, a change point detection scheme based on a generalized likelihood ratio test is developed. How its detection power relates to the adaptive sampling strategy is analyzed. Meanwhile, by treating the detection power as a reward, its connection with the online combinatorial multi-armed bandit (CMAB) problem is formulated and an adaptive upper confidence region algorithm is proposed for adaptive sampling policy design. Theoretical analysis of the asymptotic average detection delay is performed, and thorough numerical studies with synthetic data and real-world data are conducted to demonstrate the effectiveness of our method.
|
[
"['Haijie Xu' 'Xiaochen Xian' 'Chen Zhang' 'Kaibo Liu']"
] |
null | null |
2404.00225
| null | null |
http://arxiv.org/pdf/2404.00225v1
|
2024-03-30T02:55:49Z
|
2024-03-30T02:55:49Z
|
Heterogeneous Contrastive Learning for Foundation Models and Beyond
|
In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive self-supervised learning to model large-scale heterogeneous data. Many existing foundation models benefit from the generalization capability of contrastive self-supervised learning by learning compact and high-quality representations without relying on any label information. Amidst the explosive advancements in foundation models across multiple domains, including natural language processing and computer vision, a thorough survey on heterogeneous contrastive learning for the foundation model is urgently needed. In response, this survey critically evaluates the current landscape of heterogeneous contrastive learning for foundation models, highlighting the open challenges and future trends of contrastive learning. In particular, we first present how the recent advanced contrastive learning-based methods deal with view heterogeneity and how contrastive learning is applied to train and fine-tune the multi-view foundation models. Then, we move to contrastive learning methods for task heterogeneity, including pretraining tasks and downstream tasks, and show how different tasks are combined with contrastive learning loss for different purposes. Finally, we conclude this survey by discussing the open challenges and shedding light on the future directions of contrastive learning.
|
[
"['Lecheng Zheng' 'Baoyu Jing' 'Zihao Li' 'Hanghang Tong' 'Jingrui He']"
] |
null | null |
2404.00228
| null | null |
http://arxiv.org/pdf/2404.00228v3
|
2024-04-03T07:15:05Z
|
2024-03-30T03:16:37Z
|
InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning
|
Continual learning requires the model to learn multiple tasks sequentially. In continual learning, the model should possess the ability to maintain its performance on old tasks (stability) and the ability to adapt to new tasks continuously (plasticity). Recently, parameter-efficient fine-tuning (PEFT), which involves freezing a pre-trained model and injecting a small number of learnable parameters to adapt to downstream tasks, has gained increasing popularity in continual learning. Although existing continual learning methods based on PEFT have demonstrated superior performance compared to those not based on PEFT, most of them do not consider how to eliminate the interference of the new task on the old tasks, which inhibits the model from making a good trade-off between stability and plasticity. In this work, we propose a new PEFT method, called interference-free low-rank adaptation (InfLoRA), for continual learning. InfLoRA injects a small number of parameters to reparameterize the pre-trained weights and shows that fine-tuning these injected parameters is equivalent to fine-tuning the pre-trained weights within a subspace. Furthermore, InfLoRA designs this subspace to eliminate the interference of the new task on the old tasks, making a good trade-off between stability and plasticity. Experimental results show that InfLoRA outperforms existing state-of-the-art continual learning methods on multiple datasets.
|
[
"['Yan-Shuo Liang' 'Wu-Jun Li']"
] |
null | null |
2404.00231
| null | null |
http://arxiv.org/pdf/2404.00231v3
|
2024-05-01T01:47:43Z
|
2024-03-30T03:23:52Z
|
Attention-based Shape-Deformation Networks for Artifact-Free Geometry
Reconstruction of Lumbar Spine from MR Images
|
Lumbar disc degeneration, a progressive structural wear and tear of lumbar intervertebral disc, is regarded as an essential role on low back pain, a significant global health concern. Automated lumbar spine geometry reconstruction from MR images will enable fast measurement of medical parameters to evaluate the lumbar status, in order to determine a suitable treatment. Existing image segmentation-based techniques often generate erroneous segments or unstructured point clouds, unsuitable for medical parameter measurement. In this work, we present $textit{UNet-DeformSA}$ and $textit{TransDeformer}$: novel attention-based deep neural networks that reconstruct the geometry of the lumbar spine with high spatial accuracy and mesh correspondence across patients, and we also present a variant of $textit{TransDeformer}$ for error estimation. Specially, we devise new attention modules with a new attention formula, which integrate image features and tokenized contour features to predict the displacements of the points on a shape template without the need for image segmentation. The deformed template reveals the lumbar spine geometry in an image. Experiment results show that our networks generate artifact-free geometry outputs, and the variant of $textit{TransDeformer}$ can predict the errors of a reconstructed geometry. Our code is available at https://github.com/linchenq/TransDeformer-Mesh.
|
[
"['Linchen Qian' 'Jiasong Chen' 'Linhai Ma' 'Timur Urakov' 'Weiyong Gu'\n 'Liang Liang']"
] |
null | null |
2404.00232
| null | null |
http://arxiv.org/pdf/2404.00232v1
|
2024-03-30T03:26:51Z
|
2024-03-30T03:26:51Z
|
Efficient Automatic Tuning for Data-driven Model Predictive Control via
Meta-Learning
|
AutoMPC is a Python package that automates and optimizes data-driven model predictive control. However, it can be computationally expensive and unstable when exploring large search spaces using pure Bayesian Optimization (BO). To address these issues, this paper proposes to employ a meta-learning approach called Portfolio that improves AutoMPC's efficiency and stability by warmstarting BO. Portfolio optimizes initial designs for BO using a diverse set of configurations from previous tasks and stabilizes the tuning process by fixing initial configurations instead of selecting them randomly. Experimental results demonstrate that Portfolio outperforms the pure BO in finding desirable solutions for AutoMPC within limited computational resources on 11 nonlinear control simulation benchmarks and 1 physical underwater soft robot dataset.
|
[
"['Baoyu Li' 'William Edwards' 'Kris Hauser']"
] |
null | null |
2404.00235
| null | null |
http://arxiv.org/abs/2404.00235v1
|
2024-03-30T03:52:58Z
|
2024-03-30T03:52:58Z
|
Information Security and Privacy in the Digital World: Some Selected
Topics
|
In the era of generative artificial intelligence and the Internet of Things, while there is explosive growth in the volume of data and the associated need for processing, analysis, and storage, several new challenges are faced in identifying spurious and fake information and protecting the privacy of sensitive data. This has led to an increasing demand for more robust and resilient schemes for authentication, integrity protection, encryption, non-repudiation, and privacy-preservation of data. The chapters in this book present some of the state-of-the-art research works in the field of cryptography and security in computing and communications.
|
[
"['Jaydip Sen' 'Joceli Mayer' 'Subhasis Dasgupta' 'Subrata Nandi'\n 'Srinivasan Krishnaswamy' 'Pinaki Mitra' 'Mahendra Pratap Singh'\n 'Naga Prasanthi Kundeti' 'Chandra Sekhara Rao MVP' 'Sudha Sree Chekuri'\n 'Seshu Babu Pallapothu' 'Preethi Nanjundan' 'Jossy P. George'\n 'Abdelhadi El Allahi' 'Ilham Morino' 'Salma AIT Oussous'\n 'Siham Beloualid' 'Ahmed Tamtaoui' 'Abderrahim Bajit']"
] |
null | null |
2404.00247
| null | null |
http://arxiv.org/pdf/2404.00247v2
|
2024-05-01T05:55:34Z
|
2024-03-30T04:58:59Z
|
Facilitating Reinforcement Learning for Process Control Using Transfer
Learning: Perspectives
|
This paper provides insights into deep reinforcement learning (DRL) for process control from the perspective of transfer learning. We analyze the challenges of applying DRL in the field of process industries and the necessity of introducing transfer learning. Furthermore, recommendations and prospects are provided for future research directions on how transfer learning can be integrated with DRL to empower process control.
|
[
"['Runze Lin' 'Junghui Chen' 'Lei Xie' 'Hongye Su' 'Biao Huang']"
] |
null | null |
2404.00254
| null | null |
http://arxiv.org/pdf/2404.00254v1
|
2024-03-30T05:51:09Z
|
2024-03-30T05:51:09Z
|
Clustering for Protein Representation Learning
|
Protein representation learning is a challenging task that aims to capture the structure and function of proteins from their amino acid sequences. Previous methods largely ignored the fact that not all amino acids are equally important for protein folding and activity. In this article, we propose a neural clustering framework that can automatically discover the critical components of a protein by considering both its primary and tertiary structure information. Our framework treats a protein as a graph, where each node represents an amino acid and each edge represents a spatial or sequential connection between amino acids. We then apply an iterative clustering strategy to group the nodes into clusters based on their 1D and 3D positions and assign scores to each cluster. We select the highest-scoring clusters and use their medoid nodes for the next iteration of clustering, until we obtain a hierarchical and informative representation of the protein. We evaluate on four protein-related tasks: protein fold classification, enzyme reaction classification, gene ontology term prediction, and enzyme commission number prediction. Experimental results demonstrate that our method achieves state-of-the-art performance.
|
[
"['Ruijie Quan' 'Wenguan Wang' 'Fan Ma' 'Hehe Fan' 'Yi Yang']"
] |
null | null |
2404.00257
| null | null |
http://arxiv.org/pdf/2404.00257v2
|
2024-04-22T14:38:25Z
|
2024-03-30T06:17:39Z
|
YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel
Class Discovery
|
Because of its use in practice, open-world object detection (OWOD) has gotten a lot of attention recently. The challenge is how can a model detect novel classes and then incrementally learn them without forgetting previously known classes. Previous approaches hinge on strongly-supervised or weakly-supervised novel-class data for novel-class detection, which may not apply to real applications. We construct a new benchmark that novel classes are only encountered at the inference stage. And we propose a new OWOD detector YOLOOC, based on the YOLO architecture yet for the Open-Class setup. We introduce label smoothing to prevent the detector from over-confidently mapping novel classes to known classes and to discover novel classes. Extensive experiments conducted on our more realistic setup demonstrate the effectiveness of our method for discovering novel classes in our new benchmark.
|
[
"['Qian Wan' 'Xiang Xiang' 'Qinhao Zhou']"
] |
null | null |
2404.00264
| null | null |
http://arxiv.org/pdf/2404.00264v1
|
2024-03-30T06:40:54Z
|
2024-03-30T06:40:54Z
|
DiLM: Distilling Dataset into Language Model for Text-level Dataset
Distillation
|
Dataset distillation aims to compress a training dataset by creating a small number of informative synthetic samples such that neural networks trained on them perform as well as those trained on the original training dataset. Current text dataset distillation methods create each synthetic sample as a sequence of word embeddings instead of a text to apply gradient-based optimization; however, such embedding-level distilled datasets cannot be used for training other models whose word embedding weights are different from the model used for distillation. To address this issue, we propose a novel text dataset distillation approach, called Distilling dataset into Language Model (DiLM), which trains a language model to generate informative synthetic training samples as text data, instead of directly optimizing synthetic samples. We evaluated DiLM on various text classification datasets and showed that distilled synthetic datasets from DiLM outperform those from current coreset selection methods. DiLM achieved remarkable generalization performance in training different types of models and in-context learning of large language models. Our code will be available at https://github.com/arumaekawa/DiLM.
|
[
"['Aru Maekawa' 'Satoshi Kosugi' 'Kotaro Funakoshi' 'Manabu Okumura']"
] |
null | null |
2404.00271
| null | null |
http://arxiv.org/pdf/2404.00271v1
|
2024-03-30T07:25:30Z
|
2024-03-30T07:25:30Z
|
TG-NAS: Leveraging Zero-Cost Proxies with Transformer and Graph
Convolution Networks for Efficient Neural Architecture Search
|
Neural architecture search (NAS) is an effective method for discovering new convolutional neural network (CNN) architectures. However, existing approaches often require time-consuming training or intensive sampling and evaluations. Zero-shot NAS aims to create training-free proxies for architecture performance prediction. However, existing proxies have suboptimal performance, and are often outperformed by simple metrics such as model parameter counts or the number of floating-point operations. Besides, existing model-based proxies cannot be generalized to new search spaces with unseen new types of operators without golden accuracy truth. A universally optimal proxy remains elusive. We introduce TG-NAS, a novel model-based universal proxy that leverages a transformer-based operator embedding generator and a graph convolution network (GCN) to predict architecture performance. This approach guides neural architecture search across any given search space without the need of retraining. Distinct from other model-based predictor subroutines, TG-NAS itself acts as a zero-cost (ZC) proxy, guiding architecture search with advantages in terms of data independence, cost-effectiveness, and consistency across diverse search spaces. Our experiments showcase its advantages over existing proxies across various NAS benchmarks, suggesting its potential as a foundational element for efficient architecture search. TG-NAS achieves up to 300X improvements in search efficiency compared to previous SOTA ZC proxy methods. Notably, it discovers competitive models with 93.75% CIFAR-10 accuracy on the NAS-Bench-201 space and 74.5% ImageNet top-1 accuracy on the DARTS space.
|
[
"['Ye Qiao' 'Haocheng Xu' 'Sitao Huang']"
] |
null | null |
2404.00282
| null | null |
http://arxiv.org/pdf/2404.00282v1
|
2024-03-30T08:28:08Z
|
2024-03-30T08:28:08Z
|
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept,
Taxonomy, and Methods
|
With extensive pre-trained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects such as multi-task learning, sample efficiency, and task planning. In this survey, we provide a comprehensive review of the existing literature in $textit{LLM-enhanced RL}$ and summarize its characteristics compared to conventional RL methods, aiming to clarify the research scope and directions for future studies. Utilizing the classical agent-environment interaction paradigm, we propose a structured taxonomy to systematically categorize LLMs' functionalities in RL, including four roles: information processor, reward designer, decision-maker, and generator. Additionally, for each role, we summarize the methodologies, analyze the specific RL challenges that are mitigated, and provide insights into future directions. Lastly, potential applications, prospective opportunities and challenges of the $textit{LLM-enhanced RL}$ are discussed.
|
[
"['Yuji Cao' 'Huan Zhao' 'Yuheng Cheng' 'Ting Shu' 'Guolong Liu'\n 'Gaoqi Liang' 'Junhua Zhao' 'Yun Li']"
] |
null | null |
2404.00297
| null | null |
http://arxiv.org/pdf/2404.00297v2
|
2024-05-16T14:35:36Z
|
2024-03-30T09:20:43Z
|
TRABSA: Interpretable Sentiment Analysis of Tweets using Attention-based
BiLSTM and Twitter-RoBERTa
|
Sentiment analysis is crucial for understanding public opinion and consumer behavior. Existing models face challenges with linguistic diversity, generalizability, and explainability. We propose TRABSA, a hybrid framework integrating transformer-based architectures, attention mechanisms, and BiLSTM networks to address this. Leveraging RoBERTa-trained on 124M tweets, we bridge gaps in sentiment analysis benchmarks, ensuring state-of-the-art accuracy. Augmenting datasets with tweets from 32 countries and US states, we compare six word-embedding techniques and three lexicon-based labeling techniques, selecting the best for optimal sentiment analysis. TRABSA outperforms traditional ML and deep learning models with 94% accuracy and significant precision, recall, and F1-score gains. Evaluation across diverse datasets demonstrates consistent superiority and generalizability. SHAP and LIME analyses enhance interpretability, improving confidence in predictions. Our study facilitates pandemic resource management, aiding resource planning, policy formation, and vaccination tactics.
|
[
"['Md Abrar Jahin' 'Md Sakib Hossain Shovon' 'M. F. Mridha'\n 'Md Rashedul Islam' 'Yutaka Watanobe']"
] |
null | null |
2404.00323
| null | null |
http://arxiv.org/pdf/2404.00323v1
|
2024-03-30T11:28:05Z
|
2024-03-30T11:28:05Z
|
CLIP-driven Outliers Synthesis for few-shot OOD detection
|
Few-shot OOD detection focuses on recognizing out-of-distribution (OOD) images that belong to classes unseen during training, with the use of only a small number of labeled in-distribution (ID) images. Up to now, a mainstream strategy is based on large-scale vision-language models, such as CLIP. However, these methods overlook a crucial issue: the lack of reliable OOD supervision information, which can lead to biased boundaries between in-distribution (ID) and OOD. To tackle this problem, we propose CLIP-driven Outliers Synthesis~(CLIP-OS). Firstly, CLIP-OS enhances patch-level features' perception by newly proposed patch uniform convolution, and adaptively obtains the proportion of ID-relevant information by employing CLIP-surgery-discrepancy, thus achieving separation between ID-relevant and ID-irrelevant. Next, CLIP-OS synthesizes reliable OOD data by mixing up ID-relevant features from different classes to provide OOD supervision information. Afterward, CLIP-OS leverages synthetic OOD samples by unknown-aware prompt learning to enhance the separability of ID and OOD. Extensive experiments across multiple benchmarks demonstrate that CLIP-OS achieves superior few-shot OOD detection capability.
|
[
"['Hao Sun' 'Rundong He' 'Zhongyi Han' 'Zhicong Lin' 'Yongshun Gong'\n 'Yilong Yin']"
] |
null | null |
2404.00327
| null | null |
http://arxiv.org/pdf/2404.00327v2
|
2024-07-05T03:55:57Z
|
2024-03-30T11:41:19Z
|
YNetr: Dual-Encoder architecture on Plain Scan Liver Tumors (PSLT)
|
Background: Liver tumors are abnormal growths in the liver that can be either benign or malignant, with liver cancer being a significant health concern worldwide. However, there is no dataset for plain scan segmentation of liver tumors, nor any related algorithms. To fill this gap, we propose Plain Scan Liver Tumors(PSLT) and YNetr. Methods: A collection of 40 liver tumor plain scan segmentation datasets was assembled and annotated. Concurrently, we utilized Dice coefficient as the metric for assessing the segmentation outcomes produced by YNetr, having advantage of capturing different frequency information. Results: The YNetr model achieved a Dice coefficient of 62.63% on the PSLT dataset, surpassing the other publicly available model by an accuracy margin of 1.22%. Comparative evaluations were conducted against a range of models including UNet 3+, XNet, UNetr, Swin UNetr, Trans-BTS, COTr, nnUNetv2 (2D), nnUNetv2 (3D fullres), MedNext (2D) and MedNext(3D fullres). Conclusions: We not only proposed a dataset named PSLT(Plain Scan Liver Tumors), but also explored a structure called YNetr that utilizes wavelet transform to extract different frequency information, which having the SOTA in PSLT by experiments.
|
[
"['Wen Sheng' 'Zhong Zheng' 'Jiajun Liu' 'Han Lu' 'Hanyuan Zhang'\n 'Zhengyong Jiang' 'Zhihong Zhang' 'Daoping Zhu']"
] |
null | null |
2404.00357
| null | null |
http://arxiv.org/pdf/2404.00357v1
|
2024-03-30T13:18:27Z
|
2024-03-30T13:18:27Z
|
Revisiting Random Weight Perturbation for Efficiently Improving
Generalization
|
Improving the generalization ability of modern deep neural networks (DNNs) is a fundamental challenge in machine learning. Two branches of methods have been proposed to seek flat minima and improve generalization: one led by sharpness-aware minimization (SAM) minimizes the worst-case neighborhood loss through adversarial weight perturbation (AWP), and the other minimizes the expected Bayes objective with random weight perturbation (RWP). While RWP offers advantages in computation and is closely linked to AWP on a mathematical basis, its empirical performance has consistently lagged behind that of AWP. In this paper, we revisit the use of RWP for improving generalization and propose improvements from two perspectives: i) the trade-off between generalization and convergence and ii) the random perturbation generation. Through extensive experimental evaluations, we demonstrate that our enhanced RWP methods achieve greater efficiency in enhancing generalization, particularly in large-scale problems, while also offering comparable or even superior performance to SAM. The code is released at https://github.com/nblt/mARWP.
|
[
"['Tao Li' 'Qinghua Tao' 'Weihao Yan' 'Zehao Lei' 'Yingwen Wu' 'Kun Fang'\n 'Mingzhen He' 'Xiaolin Huang']"
] |
null | null |
2404.00371
| null | null |
http://arxiv.org/abs/2404.00371v1
|
2024-03-30T13:49:59Z
|
2024-03-30T13:49:59Z
|
From Learning to Analytics: Improving Model Efficacy with Goal-Directed
Client Selection
|
Federated learning (FL) is an appealing paradigm for learning a global model among distributed clients while preserving data privacy. Driven by the demand for high-quality user experiences, evaluating the well-trained global model after the FL process is crucial. In this paper, we propose a closed-loop model analytics framework that allows for effective evaluation of the trained global model using clients' local data. To address the challenges posed by system and data heterogeneities in the FL process, we study a goal-directed client selection problem based on the model analytics framework by selecting a subset of clients for the model training. This problem is formulated as a stochastic multi-armed bandit (SMAB) problem. We first put forth a quick initial upper confidence bound (Quick-Init UCB) algorithm to solve this SMAB problem under the federated analytics (FA) framework. Then, we further propose a belief propagation-based UCB (BP-UCB) algorithm under the democratized analytics (DA) framework. Moreover, we derive two regret upper bounds for the proposed algorithms, which increase logarithmically over the time horizon. The numerical results demonstrate that the proposed algorithms achieve nearly optimal performance, with a gap of less than 1.44% and 3.12% under the FA and DA frameworks, respectively.
|
[
"['Jingwen Tong' 'Zhenzhen Chen' 'Liqun Fu' 'Jun Zhang' 'Zhu Han']"
] |
null | null |
2404.00385
| null | null |
http://arxiv.org/pdf/2404.00385v1
|
2024-03-30T14:58:40Z
|
2024-03-30T14:58:40Z
|
Constrained Layout Generation with Factor Graphs
|
This paper addresses the challenge of object-centric layout generation under spatial constraints, seen in multiple domains including floorplan design process. The design process typically involves specifying a set of spatial constraints that include object attributes like size and inter-object relations such as relative positioning. Existing works, which typically represent objects as single nodes, lack the granularity to accurately model complex interactions between objects. For instance, often only certain parts of an object, like a room's right wall, interact with adjacent objects. To address this gap, we introduce a factor graph based approach with four latent variable nodes for each room, and a factor node for each constraint. The factor nodes represent dependencies among the variables to which they are connected, effectively capturing constraints that are potentially of a higher order. We then develop message-passing on the bipartite graph, forming a factor graph neural network that is trained to produce a floorplan that aligns with the desired requirements. Our approach is simple and generates layouts faithful to the user requirements, demonstrated by a large improvement in IOU scores over existing methods. Additionally, our approach, being inferential and accurate, is well-suited to the practical human-in-the-loop design process where specifications evolve iteratively, offering a practical and powerful tool for AI-guided design.
|
[
"['Mohammed Haroon Dupty' 'Yanfei Dong' 'Sicong Leng' 'Guoji Fu'\n 'Yong Liang Goh' 'Wei Lu' 'Wee Sun Lee']"
] |
null | null |
2404.00390
| null | null |
http://arxiv.org/pdf/2404.00390v1
|
2024-03-30T15:03:52Z
|
2024-03-30T15:03:52Z
|
Learning truly monotone operators with applications to nonlinear inverse
problems
|
This article introduces a novel approach to learning monotone neural networks through a newly defined penalization loss. The proposed method is particularly effective in solving classes of variational problems, specifically monotone inclusion problems, commonly encountered in image processing tasks. The Forward-Backward-Forward (FBF) algorithm is employed to address these problems, offering a solution even when the Lipschitz constant of the neural network is unknown. Notably, the FBF algorithm provides convergence guarantees under the condition that the learned operator is monotone. Building on plug-and-play methodologies, our objective is to apply these newly learned operators to solving non-linear inverse problems. To achieve this, we initially formulate the problem as a variational inclusion problem. Subsequently, we train a monotone neural network to approximate an operator that may not inherently be monotone. Leveraging the FBF algorithm, we then show simulation examples where the non-linear inverse problem is successfully solved.
|
[
"['Younes Belkouchi' 'Jean-Christophe Pesquet' 'Audrey Repetti'\n 'Hugues Talbot']"
] |
null | null |
2404.00399
| null | null |
http://arxiv.org/pdf/2404.00399v2
|
2024-04-23T13:45:48Z
|
2024-03-30T15:38:54Z
|
Aurora-M: The First Open Source Multilingual Language Model Red-teamed
according to the U.S. Executive Order
|
Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility. Initiatives such as BLOOM and StarCoder aim to democratize access to pretrained models for collaborative community development. However, such existing models face challenges: limited multilingual capabilities, continual pretraining causing catastrophic forgetting, whereas pretraining from scratch is computationally expensive, and compliance with AI safety and development laws. This paper presents Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code. Continually pretrained from StarCoderPlus on 435 billion additional tokens, Aurora-M surpasses 2 trillion tokens in total training token count. It is the first open-source multilingual model fine-tuned on human-reviewed safety instructions, thus aligning its development not only with conventional red-teaming considerations, but also with the specific concerns articulated in the Biden-Harris Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Aurora-M is rigorously evaluated across various tasks and languages, demonstrating robustness against catastrophic forgetting and outperforming alternatives in multilingual settings, particularly in safety evaluations. To promote responsible open-source LLM development, Aurora-M and its variants are released at https://huggingface.co/collections/aurora-m/aurora-m-models-65fdfdff62471e09812f5407 .
|
[
"['Taishi Nakamura' 'Mayank Mishra' 'Simone Tedeschi' 'Yekun Chai'\n 'Jason T Stillerman' 'Felix Friedrich' 'Prateek Yadav' 'Tanmay Laud'\n 'Vu Minh Chien' 'Terry Yue Zhuo' 'Diganta Misra' 'Ben Bogin'\n 'Xuan-Son Vu' 'Marzena Karpinska' 'Arnav Varma Dantuluri' 'Wojciech Kusa'\n 'Tommaso Furlanello' 'Rio Yokota' 'Niklas Muennighoff' 'Suhas Pai'\n 'Tosin Adewumi' 'Veronika Laippala' 'Xiaozhe Yao' 'Adalberto Junior'\n 'Alpay Ariyak' 'Aleksandr Drozd' 'Jordan Clive' 'Kshitij Gupta'\n 'Liangyu Chen' 'Qi Sun' 'Ken Tsui' 'Noah Persaud' 'Nour Fahmy'\n 'Tianlong Chen' 'Mohit Bansal' 'Nicolo Monti' 'Tai Dang' 'Ziyang Luo'\n 'Tien-Tung Bui' 'Roberto Navigli' 'Virendra Mehta' 'Matthew Blumberg'\n 'Victor May' 'Huu Nguyen' 'Sampo Pyysalo']"
] |
null | null |
2404.00408
| null | null |
http://arxiv.org/pdf/2404.00408v1
|
2024-03-30T16:34:28Z
|
2024-03-30T16:34:28Z
|
Deep Learning with Parametric Lenses
|
We propose a categorical semantics for machine learning algorithms in terms of lenses, parametric maps, and reverse derivative categories. This foundation provides a powerful explanatory and unifying framework: it encompasses a variety of gradient descent algorithms such as ADAM, AdaGrad, and Nesterov momentum, as well as a variety of loss functions such as MSE and Softmax cross-entropy, and different architectures, shedding new light on their similarities and differences. Furthermore, our approach to learning has examples generalising beyond the familiar continuous domains (modelled in categories of smooth maps) and can be realised in the discrete setting of Boolean and polynomial circuits. We demonstrate the practical significance of our framework with an implementation in Python.
|
[
"['Geoffrey S. H. Cruttwell' 'Bruno Gavranovic' 'Neil Ghani' 'Paul Wilson'\n 'Fabio Zanasi']"
] |
null | null |
2404.00411
| null | null |
http://arxiv.org/pdf/2404.00411v3
|
2024-07-13T19:58:08Z
|
2024-03-30T16:41:24Z
|
Aardvark weather: end-to-end data-driven weather forecasting
|
Weather forecasting is critical for a range of human activities including transportation, agriculture, industry, as well as the safety of the general public. Machine learning models have the potential to transform the complex weather prediction pipeline, but current approaches still rely on numerical weather prediction (NWP) systems, limiting forecast speed and accuracy. Here we demonstrate that a machine learning model can replace the entire operational NWP pipeline. Aardvark Weather, an end-to-end data-driven weather prediction system, ingests raw observations and outputs global gridded forecasts and local station forecasts. Further, it can be optimised end-to-end to maximise performance over quantities of interest. Global forecasts outperform an operational NWP baseline for multiple variables and lead times. Local station forecasts are skillful up to ten days lead time and achieve comparable and often lower errors than a post-processed global NWP baseline and a state-of-the-art end-to-end forecasting system with input from human forecasters. These forecasts are produced with a remarkably simple neural process model using just 8% of the input data and three orders of magnitude less compute than existing NWP and hybrid AI-NWP methods. We anticipate that Aardvark Weather will be the starting point for a new generation of end-to-end machine learning models for medium-range forecasting that will reduce computational costs by orders of magnitude and enable the rapid and cheap creation of bespoke models for users in a variety of fields, including for the developing world where state-of-the-art local models are not currently available.
|
[
"['Anna Vaughan' 'Stratis Markou' 'Will Tebbutt' 'James Requeima'\n 'Wessel P. Bruinsma' 'Tom R. Andersson' 'Michael Herzog'\n 'Nicholas D. Lane' 'Matthew Chantry' 'J. Scott Hosking'\n 'Richard E. Turner']"
] |
null | null |
2404.00412
| null | null |
http://arxiv.org/pdf/2404.00412v1
|
2024-03-30T16:43:40Z
|
2024-03-30T16:43:40Z
|
SVGCraft: Beyond Single Object Text-to-SVG Synthesis with Comprehensive
Canvas Layout
|
Generating VectorArt from text prompts is a challenging vision task, requiring diverse yet realistic depictions of the seen as well as unseen entities. However, existing research has been mostly limited to the generation of single objects, rather than comprehensive scenes comprising multiple elements. In response, this work introduces SVGCraft, a novel end-to-end framework for the creation of vector graphics depicting entire scenes from textual descriptions. Utilizing a pre-trained LLM for layout generation from text prompts, this framework introduces a technique for producing masked latents in specified bounding boxes for accurate object placement. It introduces a fusion mechanism for integrating attention maps and employs a diffusion U-Net for coherent composition, speeding up the drawing process. The resulting SVG is optimized using a pre-trained encoder and LPIPS loss with opacity modulation to maximize similarity. Additionally, this work explores the potential of primitive shapes in facilitating canvas completion in constrained environments. Through both qualitative and quantitative assessments, SVGCraft is demonstrated to surpass prior works in abstraction, recognizability, and detail, as evidenced by its performance metrics (CLIP-T: 0.4563, Cosine Similarity: 0.6342, Confusion: 0.66, Aesthetic: 6.7832). The code will be available at https://github.com/ayanban011/SVGCraft.
|
[
"['Ayan Banerjee' 'Nityanand Mathur' 'Josep Lladós' 'Umapada Pal'\n 'Anjan Dutta']"
] |
null | null |
2404.00413
| null | null |
http://arxiv.org/pdf/2404.00413v1
|
2024-03-30T16:43:59Z
|
2024-03-30T16:43:59Z
|
Language Models are Spacecraft Operators
|
Recent trends are emerging in the use of Large Language Models (LLMs) as autonomous agents that take actions based on the content of the user text prompts. We intend to apply these concepts to the field of Guidance, Navigation, and Control in space, enabling LLMs to have a significant role in the decision-making process for autonomous satellite operations. As a first step towards this goal, we have developed a pure LLM-based solution for the Kerbal Space Program Differential Games (KSPDG) challenge, a public software design competition where participants create autonomous agents for maneuvering satellites involved in non-cooperative space operations, running on the KSP game engine. Our approach leverages prompt engineering, few-shot prompting, and fine-tuning techniques to create an effective LLM-based agent that ranked 2nd in the competition. To the best of our knowledge, this work pioneers the integration of LLM agents into space research. Code is available at https://github.com/ARCLab-MIT/kspdg.
|
[
"['Victor Rodriguez-Fernandez' 'Alejandro Carrasco' 'Jason Cheng'\n 'Eli Scharf' 'Peng Mun Siew' 'Richard Linares']"
] |
null | null |
2404.00417
| null | null |
http://arxiv.org/pdf/2404.00417v1
|
2024-03-30T16:53:10Z
|
2024-03-30T16:53:10Z
|
Orchestrate Latent Expertise: Advancing Online Continual Learning with
Multi-Level Supervision and Reverse Self-Distillation
|
To accommodate real-world dynamics, artificial intelligence systems need to cope with sequentially arriving content in an online manner. Beyond regular Continual Learning (CL) attempting to address catastrophic forgetting with offline training of each task, Online Continual Learning (OCL) is a more challenging yet realistic setting that performs CL in a one-pass data stream. Current OCL methods primarily rely on memory replay of old training samples. However, a notable gap from CL to OCL stems from the additional overfitting-underfitting dilemma associated with the use of rehearsal buffers: the inadequate learning of new training samples (underfitting) and the repeated learning of a few old training samples (overfitting). To this end, we introduce a novel approach, Multi-level Online Sequential Experts (MOSE), which cultivates the model as stacked sub-experts, integrating multi-level supervision and reverse self-distillation. Supervision signals across multiple stages facilitate appropriate convergence of the new task while gathering various strengths from experts by knowledge distillation mitigates the performance decline of old tasks. MOSE demonstrates remarkable efficacy in learning new samples and preserving past knowledge through multi-level experts, thereby significantly advancing OCL performance over state-of-the-art baselines (e.g., up to 7.3% on Split CIFAR-100 and 6.1% on Split Tiny-ImageNet).
|
[
"['HongWei Yan' 'Liyuan Wang' 'Kaisheng Ma' 'Yi Zhong']"
] |
null | null |
2404.00418
| null | null |
http://arxiv.org/pdf/2404.00418v1
|
2024-03-30T16:54:35Z
|
2024-03-30T16:54:35Z
|
Continual Learning for Autonomous Robots: A Prototype-based Approach
|
Humans and animals learn throughout their lives from limited amounts of sensed data, both with and without supervision. Autonomous, intelligent robots of the future are often expected to do the same. The existing continual learning (CL) methods are usually not directly applicable to robotic settings: they typically require buffering and a balanced replay of training data. A few-shot online continual learning (FS-OCL) setting has been proposed to address more realistic scenarios where robots must learn from a non-repeated sparse data stream. To enable truly autonomous life-long learning, an additional challenge of detecting novelties and learning new items without supervision needs to be addressed. We address this challenge with our new prototype-based approach called Continually Learning Prototypes (CLP). In addition to being capable of FS-OCL learning, CLP also detects novel objects and learns them without supervision. To mitigate forgetting, CLP utilizes a novel metaplasticity mechanism that adapts the learning rate individually per prototype. CLP is rehearsal-free, hence does not require a memory buffer, and is compatible with neuromorphic hardware, characterized by ultra-low power consumption, real-time processing abilities, and on-chip learning. Indeed, we have open-sourced a simple version of CLP in the neuromorphic software framework Lava, targetting Intel's neuromorphic chip Loihi 2. We evaluate CLP on a robotic vision dataset, OpenLORIS. In a low-instance FS-OCL scenario, CLP shows state-of-the-art results. In the open world, CLP detects novelties with superior precision and recall and learns features of the detected novel classes without supervision, achieving a strong baseline of 99% base class and 65%/76% (5-shot/10-shot) novel class accuracy.
|
[
"['Elvin Hajizada' 'Balachandran Swaminathan' 'Yulia Sandamirskaya']"
] |
null | null |
2404.00420
| null | null |
http://arxiv.org/pdf/2404.00420v1
|
2024-03-30T16:58:42Z
|
2024-03-30T16:58:42Z
|
Learning Service Selection Decision Making Behaviors During Scientific
Workflow Development
|
Increasingly, more software services have been published onto the Internet, making it a big challenge to recommend services in the process of a scientific workflow composition. In this paper, a novel context-aware approach is proposed to recommending next services in a workflow development process, through learning service representation and service selection decision making behaviors from workflow provenance. Inspired by natural language sentence generation, the composition process of a scientific workflow is formalized as a step-wise procedure within the context of the goal of workflow, and the problem of next service recommendation is mapped to next word prediction. Historical service dependencies are first extracted from scientific workflow provenance to build a knowledge graph. Service sequences are then generated based on diverse composition path generation strategies. Afterwards, the generated corpus of composition paths are leveraged to study previous decision making strategies. Such a trained goal-oriented next service prediction model will be used to recommend top K candidate services during workflow composition process. Extensive experiments on a real-word repository have demonstrated the effectiveness of this approach.
|
[
"['Xihao Xie' 'Jia Zhang' 'Rahul Ramachandran' 'Tsengdar J. Lee'\n 'Seungwon Lee']"
] |
null | null |
2404.00431
| null | null |
http://arxiv.org/pdf/2404.00431v1
|
2024-03-30T17:32:26Z
|
2024-03-30T17:32:26Z
|
Visualizing Routes with AI-Discovered Street-View Patterns
|
Street-level visual appearances play an important role in studying social systems, such as understanding the built environment, driving routes, and associated social and economic factors. It has not been integrated into a typical geographical visualization interface (e.g., map services) for planning driving routes. In this paper, we study this new visualization task with several new contributions. First, we experiment with a set of AI techniques and propose a solution of using semantic latent vectors for quantifying visual appearance features. Second, we calculate image similarities among a large set of street-view images and then discover spatial imagery patterns. Third, we integrate these discovered patterns into driving route planners with new visualization techniques. Finally, we present VivaRoutes, an interactive visualization prototype, to show how visualizations leveraged with these discovered patterns can help users effectively and interactively explore multiple routes. Furthermore, we conducted a user study to assess the usefulness and utility of VivaRoutes.
|
[
"['Tsung Heng Wu' 'Md Amiruzzaman' 'Ye Zhao' 'Deepshikha Bhati' 'Jing Yang']"
] |
null | null |
2404.00437
| null | null |
http://arxiv.org/abs/2404.00437v1
|
2024-03-30T17:59:43Z
|
2024-03-30T17:59:43Z
|
Automatic explanation of the classification of Spanish legal judgments
in jurisdiction-dependent law categories with tree estimators
|
Automatic legal text classification systems have been proposed in the literature to address knowledge extraction from judgments and detect their aspects. However, most of these systems are black boxes even when their models are interpretable. This may raise concerns about their trustworthiness. Accordingly, this work contributes with a system combining Natural Language Processing (NLP) with Machine Learning (ML) to classify legal texts in an explainable manner. We analyze the features involved in the decision and the threshold bifurcation values of the decision paths of tree structures and present this information to the users in natural language. This is the first work on automatic analysis of legal texts combining NLP and ML along with Explainable Artificial Intelligence techniques to automatically make the models' decisions understandable to end users. Furthermore, legal experts have validated our solution, and this knowledge has also been incorporated into the explanation process as "expert-in-the-loop" dictionaries. Experimental results on an annotated data set in law categories by jurisdiction demonstrate that our system yields competitive classification performance, with accuracy values well above 90%, and that its automatic explanations are easily understandable even to non-expert users.
|
[
"['Jaime González-González' 'Francisco de Arriba-Pérez'\n 'Silvia García-Méndez' 'Andrea Busto-Castiñeira'\n 'Francisco J. González-Castaño']"
] |
null | null |
2404.00438
| null | null |
http://arxiv.org/pdf/2404.00438v1
|
2024-03-30T18:07:29Z
|
2024-03-30T18:07:29Z
|
Communication Efficient Distributed Training with Distributed Lion
|
The Lion optimizer has been a promising competitor with the AdamW for training large AI models, with advantages on memory, computation, and sample efficiency. In this paper, we introduce Distributed Lion, an innovative adaptation of Lion for distributed training environments. Leveraging the sign operator in Lion, our Distributed Lion only requires communicating binary or lower-precision vectors between workers to the center server, significantly reducing the communication cost. Our theoretical analysis confirms Distributed Lion's convergence properties. Empirical results demonstrate its robustness across a range of tasks, worker counts, and batch sizes, on both vision and language problems. Notably, Distributed Lion attains comparable performance to standard Lion or AdamW optimizers applied on aggregated gradients, but with significantly reduced communication bandwidth. This feature is particularly advantageous for training large models. In addition, we also demonstrate that Distributed Lion presents a more favorable performance-bandwidth balance compared to existing efficient distributed methods such as deep gradient compression and ternary gradients.
|
[
"['Bo Liu' 'Lemeng Wu' 'Lizhang Chen' 'Kaizhao Liang' 'Jiaxu Zhu'\n 'Chen Liang' 'Raghuraman Krishnamoorthi' 'Qiang Liu']"
] |
null | null |
2404.00450
| null | null |
http://arxiv.org/pdf/2404.00450v2
|
2024-04-04T05:33:07Z
|
2024-03-30T18:41:51Z
|
Planning and Editing What You Retrieve for Enhanced Tool Learning
|
Recent advancements in integrating external tools with Large Language Models (LLMs) have opened new frontiers, with applications in mathematical reasoning, code generators, and smart assistants. However, existing methods, relying on simple one-time retrieval strategies, fall short on effectively and accurately shortlisting relevant tools. This paper introduces a novel PLUTO (Planning, Learning, and Understanding for TOols) approach, encompassing `Plan-and-Retrieve (P&R)` and `Edit-and-Ground (E&G)` paradigms. The P&R paradigm consists of a neural retrieval module for shortlisting relevant tools and an LLM-based query planner that decomposes complex queries into actionable tasks, enhancing the effectiveness of tool utilization. The E&G paradigm utilizes LLMs to enrich tool descriptions based on user scenarios, bridging the gap between user queries and tool functionalities. Experiment results demonstrate that these paradigms significantly improve the recall and NDCG in tool retrieval tasks, significantly surpassing current state-of-the-art models.
|
[
"['Tenghao Huang' 'Dongwon Jung' 'Muhao Chen']"
] |
null | null |
2404.00456
| null | null |
http://arxiv.org/pdf/2404.00456v1
|
2024-03-30T19:20:06Z
|
2024-03-30T19:20:06Z
|
QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
|
We introduce QuaRot, a new Quantization scheme based on Rotations, which is able to quantize LLMs end-to-end, including all weights, activations, and KV cache in 4 bits. QuaRot rotates LLMs in a way that removes outliers from the hidden state without changing the output, making quantization easier. This computational invariance is applied to the hidden state (residual) of the LLM, as well as to the activations of the feed-forward components, aspects of the attention mechanism and to the KV cache. The result is a quantized model where all matrix multiplications are performed in 4-bits, without any channels identified for retention in higher precision. Our quantized LLaMa2-70B model has losses of at most 0.29 WikiText-2 perplexity and retains 99% of the zero-shot performance. Code is available at: https://github.com/spcl/QuaRot.
|
[
"['Saleh Ashkboos' 'Amirkeivan Mohtashami' 'Maximilian L. Croci' 'Bo Li'\n 'Martin Jaggi' 'Dan Alistarh' 'Torsten Hoefler' 'James Hensman']"
] |
null | null |
2404.00461
| null | null |
http://arxiv.org/pdf/2404.00461v1
|
2024-03-30T20:02:36Z
|
2024-03-30T20:02:36Z
|
Shortcuts Arising from Contrast: Effective and Covert Clean-Label
Attacks in Prompt-Based Learning
|
Prompt-based learning paradigm has demonstrated remarkable efficacy in enhancing the adaptability of pretrained language models (PLMs), particularly in few-shot scenarios. However, this learning paradigm has been shown to be vulnerable to backdoor attacks. The current clean-label attack, employing a specific prompt as a trigger, can achieve success without the need for external triggers and ensure correct labeling of poisoned samples, which is more stealthy compared to the poisoned-label attack, but on the other hand, it faces significant issues with false activations and poses greater challenges, necessitating a higher rate of poisoning. Using conventional negative data augmentation methods, we discovered that it is challenging to trade off between effectiveness and stealthiness in a clean-label setting. In addressing this issue, we are inspired by the notion that a backdoor acts as a shortcut and posit that this shortcut stems from the contrast between the trigger and the data utilized for poisoning. In this study, we propose a method named Contrastive Shortcut Injection (CSI), by leveraging activation values, integrates trigger design and data selection strategies to craft stronger shortcut features. With extensive experiments on full-shot and few-shot text classification tasks, we empirically validate CSI's high effectiveness and high stealthiness at low poisoning rates. Notably, we found that the two approaches play leading roles in full-shot and few-shot settings, respectively.
|
[
"['Xiaopeng Xie' 'Ming Yan' 'Xiwen Zhou' 'Chenlong Zhao' 'Suli Wang'\n 'Yong Zhang' 'Joey Tianyi Zhou']"
] |
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