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272827322 | 2409.13903 | 2024-09-20 | CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data | Advances in generative AI point towards a new era of personalized applications that perform diverse tasks on behalf of users. While general AI assistants have yet to fully emerge, their potential to share personal data raises significant privacy challenges. This paper introduces CI-Bench, a comprehensive synthetic benc... | [
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272770565 | 2409.13427 | 2024-09-20 | A User Study on Contrastive Explanations for Multi-Effector Temporal Planning with Non-Stationary Costs | In this paper, we adopt constrastive explanations within an end-user application for temporal planning of smart homes. In this application, users have requirements on the execution of appliance tasks, pay for energy according to dynamic energy tariffs, have access to high-capacity battery storage, and are able to sell ... | [
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272770490 | 2409.13202 | 2024-09-20 | CITI: Enhancing Tool Utilizing Ability in Large Language Models without Sacrificing General Performance | Tool learning enables the Large Language Models (LLMs) to interact with the external environment by invoking tools, enriching the accuracy and capability scope of LLMs. However, previous works predominantly focus on improving model's tool-utilizing accuracy and the ability to generalize to new, unseen tools, excessivel... | [
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272770462 | 2409.13669 | 2024-09-20 | A Spacetime Perspective on Dynamical Computation in Neural Information Processing Systems | There is now substantial evidence for traveling waves and other structured spatiotemporal recurrent neural dynamics in cortical structures; but these observations have typically been difficult to reconcile with notions of topographically organized selectivity and feedforward receptive fields. We introduce a new 'spacet... | [
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272770296 | 2409.13222 | 2024-09-20 | 3D-GSW: 3D Gaussian Splatting for Robust Watermarking | As 3D Gaussian Splatting (3D-GS) gains significant attention and its commercial usage increases, the need for watermarking technologies to prevent unauthorized use of the 3D-GS models and rendered images has become increasingly important. In this paper, we introduce a robust watermarking method for 3D-GS that secures c... | [
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272827316 | 2409.13912 | 2024-09-20 | OneBEV: Using One Panoramic Image for Bird's-Eye-View Semantic Mapping | In the field of autonomous driving, Bird's-Eye-View (BEV) perception has attracted increasing attention in the community since it provides more comprehensive information compared with pinhole front-view images and panoramas. Traditional BEV methods, which rely on multiple narrow-field cameras and complex pose estimatio... | [
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272770363 | 2409.13482 | 2024-09-20 | Invertible ResNets for Inverse Imaging Problems: Competitive Performance with Provable Regularization Properties | Learning-based methods have demonstrated remarkable performance in solving inverse problems, particularly in image reconstruction tasks. Despite their success, these approaches often lack theoretical guarantees, which are crucial in sensitive applications such as medical imaging. Recent works by Arndt et al addressed t... | [
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272770598 | 2409.13407 | 2024-09-20 | Instruction-guided Multi-Granularity Segmentation and Captioning with Large Multimodal Model | Large Multimodal Models (LMMs) have achieved significant progress by extending large language models. Building on this progress, the latest developments in LMMs demonstrate the ability to generate dense pixel-wise segmentation through the integration of segmentation models.Despite the innovations, the textual responses... | [
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272770564 | 2409.13689 | 2024-09-20 | Temporally Aligned Audio for Video with Autoregression | We introduce V-AURA, the first autoregressive model to achieve high temporal alignment and relevance in video-to-audio generation. V-AURA uses a high-framerate visual feature extractor and a cross-modal audio-visual feature fusion strategy to capture fine-grained visual motion events and ensure precise temporal alignme... | [
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272770412 | 2409.13496 | 2024-09-20 | DAP-LED: Learning Degradation-Aware Priors with CLIP for Joint Low-light Enhancement and Deblurring | Autonomous vehicles and robots often struggle with reliable visual perception at night due to the low illumination and motion blur caused by the long exposure time of RGB cameras. Existing methods address this challenge by sequentially connecting the off-the-shelf pretrained low-light enhancement and deblurring models.... | [
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272770539 | 2409.13464 | 2024-09-20 | Robust Salient Object Detection on Compressed Images Using Convolutional Neural Networks | Salient object detection (SOD) has achieved substantial progress in recent years. In practical scenarios, compressed images (CI) serve as the primary medium for data transmission and storage. However, scant attention has been directed towards SOD for compressed images using convolutional neural networks (CNNs). In this... | [
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272770506 | 2409.13483 | 2024-09-20 | A Multimodal Dense Retrieval Approach for Speech-Based Open-Domain Question Answering | Speech-based open-domain question answering (QA over a large corpus of text passages with spoken questions) has emerged as an important task due to the increasing number of users interacting with QA systems via speech interfaces. Passage retrieval is a key task in speech-based open-domain QA. So far, previous works ado... | [
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272826744 | 2409.13825 | 2024-09-20 | A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics | Understanding the structure and motion of the heart is crucial for diagnosing and managing cardiovascular diseases, the leading cause of global death. There is wide variation in cardiac shape and motion patterns, influenced by demographic, anthropometric and disease factors. Unravelling normal patterns of shape and mot... | [
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272827587 | 2409.13884 | 2024-09-20 | A Multi-LLM Debiasing Framework | Large Language Models (LLMs) are powerful tools with the potential to benefit society immensely, yet, they have demonstrated biases that perpetuate societal inequalities. Despite significant advancements in bias mitigation techniques using data augmentation, zero-shot prompting, and model fine-tuning, biases continuous... | [
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272770369 | 2409.13540 | 2024-09-20 | FullAnno: A Data Engine for Enhancing Image Comprehension of MLLMs | Multimodal Large Language Models (MLLMs) have shown promise in a broad range of vision-language tasks with their strong reasoning and generalization capabilities. However, they heavily depend on high-quality data in the Supervised Fine-Tuning (SFT) phase. The existing approaches aim to curate high-quality data via GPT-... | [
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272770521 | 2409.13551 | 2024-09-20 | Contextualized Data-Wrangling Code Generation in Computational Notebooks | Data wrangling, the process of preparing raw data for further analysis in computational notebooks, is a crucial yet time-consuming step in data science. Code generation has the potential to automate the data wrangling process to reduce analysts' overhead by translating user intents into executable code. Precisely gener... | [
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272770213 | 2409.13428 | 2024-09-20 | Methods for Solving Variational Inequalities with Markovian Stochasticity | In this paper, we present a novel stochastic method for solving variational inequalities (VI) in the context of Markovian noise. By leveraging Extragradient technique, we can productively solve VI optimization problems characterized by Markovian dynamics. We demonstrate the efficacy of proposed method through rigorous ... | [
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272827691 | 2409.13945 | 2024-09-20 | PureDiffusion: Using Backdoor to Counter Backdoor in Generative Diffusion Models | Diffusion models (DMs) are advanced deep learning models that achieved state-of-the-art capability on a wide range of generative tasks. However, recent studies have shown their vulnerability regarding backdoor attacks, in which backdoored DMs consistently generate a designated result (e.g., a harmful image) called back... | [
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272770259 | 2409.13376 | 2024-09-20 | More Clustering Quality Metrics for ABCDE | ABCDE is a technique for evaluating clusterings of very large populations of items. Given two clusterings, namely a Baseline clustering and an Experiment clustering, ABCDE can characterize their differences with impact and quality metrics, and thus help to determine which clustering to prefer. We previously described t... | [
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272770802 | 2409.13335 | 2024-09-20 | Beyond the binary: Limitations and possibilities of gender-related speech technology research | This paper presents a review of 107 research papers relating to speech and sex or gender in ISCA Interspeech publications between 2013 and 2023. We note the scarcity of work on this topic and find that terminology, particularly the word gender, is used in ways that are underspecified and often out of step with the prev... | [
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272827877 | 2409.13919 | 2024-09-20 | Measuring Error Alignment for Decision-Making Systems | Given that AI systems are set to play a pivotal role in future decision-making processes, their trustworthiness and reliability are of critical concern. Due to their scale and complexity, modern AI systems resist direct interpretation, and alternative ways are needed to establish trust in those systems, and determine h... | [
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272770310 | 2409.13437 | 2024-09-20 | Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Models | Brain imaging has allowed neuroscientists to analyze brain morphology in genetic and neurodevelopmental disorders, such as Down syndrome, pinpointing regions of interest to unravel the neuroanatomical underpinnings of cognitive impairment and memory deficits. However, the connections between brain anatomy, cognitive pe... | [
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272828179 | 2409.13920 | 2024-09-20 | One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks | Morphologically rich languages are notoriously challenging to process for downstream NLP applications. This paper presents a new pretrained language model, ByT5-Sanskrit, designed for NLP applications involving the morphologically rich language Sanskrit. We evaluate ByT5-Sanskrit on established Sanskrit word segmentati... | [
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272827570 | 2409.13886 | 2024-09-20 | Learning to Play Video Games with Intuitive Physics Priors | Video game playing is an extremely structured domain where algorithmic decision-making can be tested without adverse real-world consequences. While prevailing methods rely on image inputs to avoid the problem of hand-crafting state space representations, this approach systematically diverges from the way humans actuall... | [
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272770477 | 2409.13175 | 2024-09-20 | RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems | Modern recommender systems are built upon computation-intensive infrastructure, and it is challenging to perform real-time computation for each request, especially in peak periods, due to the limited computational resources. Recommending by user-wise result caches is widely used when the system cannot afford a real-tim... | [
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272770255 | 2409.13156 | 2024-09-20 | RRM: Robust Reward Model Training Mitigates Reward Hacking | Reward models (RMs) play a pivotal role in aligning large language models (LLMs) with human preferences. However, traditional RM training, which relies on response pairs tied to specific prompts, struggles to disentangle prompt-driven preferences from prompt-independent artifacts, such as response length and format. In... | [
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272770613 | 2409.13253 | 2024-09-20 | Inductive Spatial Temporal Prediction Under Data Drift with Informative Graph Neural Network | Inductive spatial temporal prediction can generalize historical data to predict unseen data, crucial for highly dynamic scenarios (e.g., traffic systems, stock markets). However, external events (e.g., urban structural growth, market crash) and emerging new entities (e.g., locations, stocks) can undermine prediction ac... | [
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271539938 | 2409.13346 | 2024-09-20 | Imagine yourself: Tuning-Free Personalized Image Generation | Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for personalized image generation. Unlike conventional tuning-based personalization techniques, Imagine yourself operates as a tuning-free model,... | [
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272770526 | 2409.13181 | 2024-09-20 | Overcoming Data Limitations in Internet Traffic Forecasting: LSTM Models with Transfer Learning and Wavelet Augmentation | Effective internet traffic prediction in smaller ISP networks is challenged by limited data availability. This paper explores this issue using transfer learning and data augmentation techniques with two LSTM-based models, LSTMSeq2Seq and LSTMSeq2SeqAtn, initially trained on a comprehensive dataset provided by Juniper N... | [
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272770655 | 2409.13561 | 2024-09-20 | Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis | Logs are imperative in the maintenance of online service systems, which often encompass important information for effective failure mitigation. While existing anomaly detection methodologies facilitate the identification of anomalous logs within extensive runtime data, manual investigation of log messages by engineers ... | [
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272770428 | 2409.13507 | 2024-09-20 | Sketching With Your Voice: "Non-Phonorealistic" Rendering of Sounds via Vocal Imitation | We present a method for automatically producing human-like vocal imitations of sounds: the equivalent of "sketching," but for auditory rather than visual representation. Starting with a simulated model of the human vocal tract, we first try generating vocal imitations by tuning the model's control parameters to make th... | [
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272770508 | 2409.13612 | 2024-09-20 | FIHA: Autonomous Hallucination Evaluation in Vision-Language Models with Davidson Scene Graphs | The rapid development of Large Vision-Language Models (LVLMs) often comes with widespread hallucination issues, making cost-effective and comprehensive assessments increasingly vital. Current approaches mainly rely on costly annotations and are not comprehensive -- in terms of evaluating all aspects such as relations, ... | [
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272827378 | 2409.13908 | 2024-09-20 | Nonlinear Inverse Design of Mechanical Multi-Material Metamaterials Enabled by Video Denoising Diffusion and Structure Identifier | Metamaterials, synthetic materials with customized properties, have emerged as a promising field due to advancements in additive manufacturing. These materials derive unique mechanical properties from their internal lattice structures, which are often composed of multiple materials that repeat geometric patterns. While... | [
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272770697 | 2409.13163 | 2024-09-20 | Hidden Activations Are Not Enough: A General Approach to Neural Network Predictions | We introduce a novel mathematical framework for analyzing neural networks using tools from quiver representation theory. This framework enables us to quantify the similarity between a new data sample and the training data, as perceived by the neural network. By leveraging the induced quiver representation of a data sam... | [
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272770561 | 2409.13299 | 2024-09-20 | OMG-RL:Offline Model-based Guided Reward Learning for Heparin Treatment | Accurate medication dosing holds an important position in the overall patient therapeutic process. Therefore, much research has been conducted to develop optimal administration strategy based on Reinforcement learning (RL). However, Relying solely on a few explicitly defined reward functions makes it difficult to learn... | [
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272770633 | 2409.13291 | 2024-09-20 | Localized Gaussians as Self-Attention Weights for Point Clouds Correspondence | Current data-driven methodologies for point cloud matching demand extensive training time and computational resources, presenting significant challenges for model deployment and application. In the point cloud matching task, recent advancements with an encoder-only Transformer architecture have revealed the emergence o... | [
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272770570 | 2409.13235 | 2024-09-20 | Balancing Label Imbalance in Federated Environments Using Only Mixup and Artificially-Labeled Noise | Clients in a distributed or federated environment will often hold data skewed towards differing subsets of labels. This scenario, referred to as heterogeneous or non-iid federated learning, has been shown to significantly hinder model training and performance. In this work, we explore the limits of a simple yet effecti... | [
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272770455 | 2409.13686 | 2024-09-20 | The Impact of Large Language Models in Academia: from Writing to Speaking | Large language models (LLMs) are increasingly impacting human society, particularly in textual information. Based on more than 30,000 papers and 1,000 presentations from machine learning conferences, we examined and compared the words used in writing and speaking, representing the first large-scale study of how LLMs in... | [
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272770275 | 2409.13652 | 2024-09-20 | OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition | The recent paradigm shift to large-scale foundation models has brought about a new era for deep learning that, while has found great success in practice, has also been plagued by prohibitively expensive costs in terms of high memory consumption and compute. To mitigate these issues, there has been a concerted effort in... | [
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272827883 | 2409.13852 | 2024-09-20 | Do language models practice what they preach? Examining language ideologies about gendered language reform encoded in LLMs | We study language ideologies in text produced by LLMs through a case study on English gendered language reform (related to role nouns like congressperson/-woman/-man, and singular they). First, we find political bias: when asked to use language that is "correct" or "natural", LLMs use language most similarly to when as... | [
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272770661 | 2409.13385 | 2024-09-20 | Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey | Large Language Models (LLMs) showcase remarkable abilities, yet they struggle with limitations such as hallucinations, outdated knowledge, opacity, and inexplicable reasoning. To address these challenges, Retrieval-Augmented Generation (RAG) has proven to be a viable solution, leveraging external databases to improve t... | [
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272770604 | 2409.13148 | 2024-09-20 | UniTabNet: Bridging Vision and Language Models for Enhanced Table Structure Recognition | In the digital era, table structure recognition technology is a critical tool for processing and analyzing large volumes of tabular data. Previous methods primarily focus on visual aspects of table structure recovery but often fail to effectively comprehend the textual semantics within tables, particularly for descript... | [
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272770616 | 2409.13392 | 2024-09-20 | Elite-EvGS: Learning Event-based 3D Gaussian Splatting by Distilling Event-to-Video Priors | Event cameras are bio-inspired sensors that output asynchronous and sparse event streams, instead of fixed frames. Benefiting from their distinct advantages, such as high dynamic range and high temporal resolution, event cameras have been applied to address 3D reconstruction, important for robotic mapping. Recently, ne... | [
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272770695 | 2409.13591 | 2024-09-20 | Portrait Video Editing Empowered by Multimodal Generative Priors | We introduce PortraitGen, a powerful portrait video editing method that achieves consistent and expressive stylization with multimodal prompts. Traditional portrait video editing methods often struggle with 3D and temporal consistency, and typically lack in rendering quality and efficiency. To address these issues, we ... | [
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272770277 | 2409.13146 | 2024-09-20 | GASA-UNet: Global Axial Self-Attention U-Net for 3D Medical Image Segmentation | Accurate segmentation of multiple organs and the differentiation of pathological tissues in medical imaging are crucial but challenging, especially for nuanced classifications and ambiguous organ boundaries. To tackle these challenges, we introduce GASA-UNet, a refined U-Net-like model featuring a novel Global Axial Se... | [
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272770250 | 2409.13261 | 2024-09-20 | Anti-jamming Transmission of Downlink Cell Free Millimeter-Wave MIMO System | In this letter, the maximization of resistible jamming power is studied for multi-user downlink millimeter-wave cell-free multiple-input-multiple-output (CF-MIMO) systems. We propose an alternate optimization-based anti-jamming hybrid beamforming (AO-AJHBF) scheme. For receiving beamforming, more practical prior about ... | [
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272770575 | 2409.13321 | 2024-09-20 | SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation | Inspired by the success of large language models (LLMs), there is growing research interest in developing LLMs in the medical domain to assist clinicians. However, for hospitals, using closed-source commercial LLMs involves privacy issues, and developing open-source public LLMs requires large-scale computational resour... | [
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272770738 | 2409.13259 | 2024-09-20 | A generalizable framework for unlocking missing reactions in genome-scale metabolic networks using deep learning | Incomplete knowledge of metabolic processes hinders the accuracy of GEnome-scale Metabolic models (GEMs), which in turn impedes advancements in systems biology and metabolic engineering. Existing gap-filling methods typically rely on phenotypic data to minimize the disparity between computational predictions and experi... | [
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272770675 | 2409.13174 | 2024-09-20 | Manipulation Facing Threats: Evaluating Physical Vulnerabilities in End-to-End Vision Language Action Models | Recently, driven by advancements in Multimodal Large Language Models (MLLMs), Vision Language Action Models (VLAMs) are being proposed to achieve better performance in open-vocabulary scenarios for robotic manipulation tasks. Since manipulation tasks involve direct interaction with the physical world, ensuring robustne... | [
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272770530 | 2409.13582 | 2024-09-20 | Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detection | Speech dysfluency modeling is a task to detect dysfluencies in speech, such as repetition, block, insertion, replacement, and deletion. Most recent advancements treat this problem as a time-based object detection problem. In this work, we revisit this problem from a new perspective: tokenizing dysfluencies and modeling... | [
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272770535 | 2409.13221 | 2024-09-20 | Optimizing RLHF Training for Large Language Models with Stage Fusion | We present RLHFuse, an efficient training system with stage fusion for Reinforcement Learning from Human Feedback (RLHF). Due to the intrinsic nature of RLHF training, i.e., the data skewness in the generation stage and the pipeline bubbles in the training stage, existing RLHF systems suffer from low GPU utilization. R... | [
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272770332 | 2409.13598 | 2024-09-20 | Prithvi WxC: Foundation Model for Weather and Climate | Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting. While the parallel developments in the AI liter... | [
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272770193 | 2409.13644 | 2024-09-20 | Non-overlapping, Schwarz-type Domain Decomposition Method for Physics and Equality Constrained Artificial Neural Networks | We present a non-overlapping, Schwarz-type domain decomposition method with a generalized interface condition, designed for physics-informed machine learning of partial differential equations (PDEs) in both forward and inverse contexts. Our approach employs physics and equality-constrained artificial neural networks (P... | [
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272826874 | 2409.13948 | 2024-09-20 | Aligning Language Models Using Follow-up Likelihood as Reward Signal | In natural human-to-human conversations, participants often receive feedback signals from one another based on their follow-up reactions. These reactions can include verbal responses, facial expressions, changes in emotional state, and other non-verbal cues. Similarly, in human-machine interactions, the machine can lev... | [
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272828144 | 2409.13783 | 2024-09-20 | A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles | To solve the problem of lateral and logitudinal joint decision-making of multi-vehicle cooperative driving for connected and automated vehicles (CAVs), this paper proposes a Monte Carlo tree search (MCTS) method with parallel update for multi-agent Markov game with limited horizon and time discounted setting. By analyz... | [
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272827403 | 2409.13851 | 2024-09-20 | Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks | Graph convolutional neural networks (GCNNs) have become a machine learning workhorse for screening the chemical space of crystalline materials in fields such as catalysis and energy storage, by predicting properties from structures. Multicomponent materials, however, present a unique challenge since they can exhibit ch... | [
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272770247 | 2409.13317 | 2024-09-20 | JMedBench: A Benchmark for Evaluating Japanese Biomedical Large Language Models | Recent developments in Japanese large language models (LLMs) primarily focus on general domains, with fewer advancements in Japanese biomedical LLMs. One obstacle is the absence of a comprehensive, large-scale benchmark for comparison. Furthermore, the resources for evaluating Japanese biomedical LLMs are insufficient.... | [
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272827340 | 2409.13894 | 2024-09-20 | PTQ4ADM: Post-Training Quantization for Efficient Text Conditional Audio Diffusion Models | Denoising diffusion models have emerged as state-of-the-art in generative tasks across image, audio, and video domains, producing high-quality, diverse, and contextually relevant data. However, their broader adoption is limited by high computational costs and large memory footprints. Post-training quantization (PTQ) of... | [
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272770142 | 2409.13508 | 2024-09-20 | Quantum-Assisted Joint Virtual Network Function Deployment and Maximum Flow Routing for Space Information Networks | Network function virtualization (NFV)-enabled space information network (SIN) has emerged as a promising method to facilitate global coverage and seamless service. This paper proposes a novel NFV-enabled SIN to provide end-to-end communication and computation services for ground users. Based on the multi-functional tim... | [
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272770711 | 2409.13280 | 2024-09-20 | Efficient Training of Deep Neural Operator Networks via Randomized Sampling | Neural operators (NOs) employ deep neural networks to learn mappings between infinite-dimensional function spaces. Deep operator network (DeepONet), a popular NO architecture, has demonstrated success in the real-time prediction of complex dynamics across various scientific and engineering applications. In this work, w... | [
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