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272753734 | 2409.12919 | 2024-09-19 | Swine Diet Design using Multi-objective Regionalized Bayesian Optimization | The design of food diets in the context of animal nutrition is a complex problem that aims to develop cost-effective formulations while balancing minimum nutritional content. Traditional approaches based on theoretical models of metabolic responses and concentrations of digestible energy in raw materials face limitatio... | [
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272770537 | 2409.12992 | 2024-09-19 | DiffEditor: Enhancing Speech Editing with Semantic Enrichment and Acoustic Consistency | As text-based speech editing becomes increasingly prevalent, the demand for unrestricted free-text editing continues to grow. However, existing speech editing techniques encounter significant challenges, particularly in maintaining intelligibility and acoustic consistency when dealing with out-of-domain (OOD) text. In ... | [
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272753140 | 2409.12853 | 2024-09-19 | A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights | Attention-Deficit/Hyperactivity Disorder (ADHD) is a challenging disorder to study due to its complex symptomatology and diverse contributing factors. To explore how we can gain deeper insights on this topic, we performed a network analysis on a comprehensive knowledge graph (KG) of ADHD, constructed by integrating sci... | [
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272753200 | 2409.12576 | 2024-09-19 | StoryMaker: Towards Holistic Consistent Characters in Text-to-image Generation | Tuning-free personalized image generation methods have achieved significant success in maintaining facial consistency, i.e., identities, even with multiple characters. However, the lack of holistic consistency in scenes with multiple characters hampers these methods' ability to create a cohesive narrative. In this pape... | [
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272753646 | 2409.12695 | 2024-09-19 | Exploring Large Language Models for Product Attribute Value Identification | Product attribute value identification (PAVI) involves automatically identifying attributes and their values from product information, enabling features like product search, recommendation, and comparison. Existing methods primarily rely on fine-tuning pre-trained language models, such as BART and T5, which require ext... | [
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263810377 | 2409.12939 | 2024-09-19 | Accelerating AI and Computer Vision for Satellite Pose Estimation on the Intel Myriad X Embedded SoC | The challenging deployment of Artificial Intelligence (AI) and Computer Vision (CV) algorithms at the edge pushes the community of embedded computing to examine heterogeneous System-on-Chips (SoCs). Such novel computing platforms provide increased diversity in interfaces, processors and storage, however, the efficient ... | [
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272770441 | 2409.13082 | 2024-09-19 | AutoVerus: Automated Proof Generation for Rust Code | Generative AI has shown its values for many software engineering tasks. Still in its infancy, large language model (LLM)-based proof generation lags behind LLM-based code generation. In this paper, we present AutoVerus. AutoVerus uses LLMs to automatically generate correctness proof for Rust code. AutoVerus is designed... | [
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272753572 | 2409.12959 | 2024-09-19 | MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines | The advent of Large Language Models (LLMs) has paved the way for AI search engines, e.g., SearchGPT, showcasing a new paradigm in human-internet interaction. However, most current AI search engines are limited to text-only settings, neglecting the multimodal user queries and the text-image interleaved nature of website... | [
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272753551 | 2409.12479 | 2024-09-19 | Learning Multi-Manifold Embedding for Out-Of-Distribution Detection | Detecting out-of-distribution (OOD) samples is crucial for trustworthy AI in real-world applications. Leveraging recent advances in representation learning and latent embeddings, Various scoring algorithms estimate distributions beyond the training data. However, a single embedding space falls short in characterizing i... | [
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272753544 | 2409.12388 | 2024-09-19 | Disentangling Speakers in Multi-Talker Speech Recognition with Speaker-Aware CTC | Multi-talker speech recognition (MTASR) faces unique challenges in disentangling and transcribing overlapping speech. To address these challenges, this paper investigates the role of Connectionist Temporal Classification (CTC) in speaker disentanglement when incorporated with Serialized Output Training (SOT) for MTASR.... | [
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272753189 | 2409.12724 | 2024-09-19 | PVContext: Hybrid Context Model for Point Cloud Compression | Efficient storage of large-scale point cloud data has become increasingly challenging due to advancements in scanning technology. Recent deep learning techniques have revolutionized this field; However, most existing approaches rely on single-modality contexts, such as octree nodes or voxel occupancy, limiting their ab... | [
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272753725 | 2409.12535 | 2024-09-19 | Deep Probability Segmentation: Are segmentation models probability estimators? | Deep learning has revolutionized various fields by enabling highly accurate predictions and estimates. One important application is probabilistic prediction, where models estimate the probability of events rather than deterministic outcomes. This approach is particularly relevant and, therefore, still unexplored for se... | [
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272753493 | 2409.12425 | 2024-09-19 | Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels | Large Language Models (LLMs) have demonstrated remarkable performance through supervised fine-tuning or in-context learning using gold labels. However, this paradigm is limited by the availability of gold labels, while in certain scenarios, LLMs may need to perform tasks that are too complex for humans to provide such ... | [
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272832378 | 2409.15373 | 2024-09-19 | Enhancing Performance and Scalability of Large-Scale Recommendation Systems with Jagged Flash Attention | The integration of hardware accelerators has significantly advanced the capabilities of modern recommendation systems, enabling the exploration of complex ranking paradigms previously deemed impractical. However, the GPU-based computational costs present substantial challenges. In this paper, we demonstrate our develop... | [
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272753674 | 2409.12963 | 2024-09-19 | Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner | Advancements in Large Language Models (LLMs) inspire various strategies for integrating video modalities. A key approach is Video-LLMs, which incorporate an optimizable interface linking sophisticated video encoders to LLMs. However, due to computation and data limitations, these Video-LLMs are typically pre-trained to... | [
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272753084 | 2409.12699 | 2024-09-19 | PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs) | The capability of generating high-quality source code using large language models (LLMs) reduces software development time and costs. However, they often introduce security vulnerabilities due to training on insecure open-source data. This highlights the need for ensuring secure and functional code generation. This pap... | [
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272753333 | 2409.12390 | 2024-09-19 | A Novel Perspective for Multi-modal Multi-label Skin Lesion Classification | The efficacy of deep learning-based Computer-Aided Diagnosis (CAD) methods for skin diseases relies on analyzing multiple data modalities (i.e., clinical+dermoscopic images, and patient metadata) and addressing the challenges of multi-label classification. Current approaches tend to rely on limited multi-modal techniqu... | [
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272753395 | 2409.12760 | 2024-09-19 | COCO-OLAC: A Benchmark for Occluded Panoptic Segmentation and Image Understanding | To help address the occlusion problem in panoptic segmentation and image understanding, this paper proposes a new large-scale dataset named COCO-OLAC (COCO Occlusion Labels for All Computer Vision Tasks), which is derived from the COCO dataset by manually labelling images into three perceived occlusion levels. Using CO... | [
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272753303 | 2409.12412 | 2024-09-19 | How to predict on-road air pollution based on street view images and machine learning: a quantitative analysis of the optimal strategy | On-road air pollution exhibits substantial variability over short distances due to emission sources, dilution, and physicochemical processes. Integrating mobile monitoring data with street view images (SVIs) holds promise for predicting local air pollution. However, algorithms, sampling strategies, and image quality in... | [
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272770727 | 2409.13106 | 2024-09-19 | UL-VIO: Ultra-lightweight Visual-Inertial Odometry with Noise Robust Test-time Adaptation | Data-driven visual-inertial odometry (VIO) has received highlights for its performance since VIOs are a crucial compartment in autonomous robots. However, their deployment on resource-constrained devices is non-trivial since large network parameters should be accommodated in the device memory. Furthermore, these networ... | [
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272770516 | 2409.13084 | 2024-09-19 | Real-time estimation of overt attention from dynamic features of the face using deep-learning | Students often drift in and out of focus during class. Effective teachers recognize this and re-engage them when necessary. With the shift to remote learning, teachers have lost the visual feedback needed to adapt to varying student engagement. We propose using readily available front-facing video to infer attention le... | [
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273023139 | 2410.00031 | 2024-09-19 | Strategic Collusion of LLM Agents: Market Division in Multi-Commodity Competitions | Machine-learning technologies are seeing increased deployment in real-world market scenarios. In this work, we explore the strategic behaviors of large language models (LLMs) when deployed as autonomous agents in multi-commodity markets, specifically within Cournot competition frameworks. We examine whether LLMs can in... | [
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272753553 | 2409.12900 | 2024-09-19 | Recognition of Harmful Phytoplankton from Microscopic Images using Deep Learning | Monitoring plankton distribution, particularly harmful phytoplankton, is vital for preserving aquatic ecosystems, regulating the global climate, and ensuring environmental protection. Traditional methods for monitoring are often time-consuming, expensive, error-prone, and unsuitable for large-scale applications, highli... | [
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272753224 | 2409.12801 | 2024-09-19 | Exploring the Lands Between: A Method for Finding Differences between AI-Decisions and Human Ratings through Generated Samples | Many important decisions in our everyday lives, such as authentication via biometric models, are made by Artificial Intelligence (AI) systems. These can be in poor alignment with human expectations, and testing them on clear-cut existing data may not be enough to uncover those cases. We propose a method to find samples... | [
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272753654 | 2409.12828 | 2024-09-19 | Power System State Estimation by Phase Synchronization and Eigenvectors | To estimate accurate voltage phasors from inaccurate voltage magnitude and complex power measurements, the standard approach is to iteratively refine a good initial guess using the Gauss--Newton method. But the nonconvexity of the estimation makes the Gauss--Newton method sensitive to its initial guess, so human interv... | [
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272753581 | 2409.12741 | 2024-09-19 | Fine Tuning Large Language Models for Medicine: The Role and Importance of Direct Preference Optimization | Large Language Model (LLM) fine tuning is underutilized in the field of medicine. Two of the most common methods of fine tuning are Supervised Fine Tuning (SFT) and Direct Preference Optimization (DPO), but there is little guidance informing users when to use either technique. In this investigation, we compare the perf... | [
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272753369 | 2409.12419 | 2024-09-19 | Shape-Space Deformer: Unified Visuo-Tactile Representations for Robotic Manipulation of Deformable Objects | Accurate modelling of object deformations is crucial for a wide range of robotic manipulation tasks, where interacting with soft or deformable objects is essential. Current methods struggle to generalise to unseen forces or adapt to new objects, limiting their utility in real-world applications. We propose Shape-Space ... | [
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272753147 | 2409.12437 | 2024-09-19 | Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data | Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with complex logical reasoning tasks that involve long reasoning chains. In this work, we explore the potential and limitations of using graph-based synthetic reasoning data as trainin... | [
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272753353 | 2409.12448 | 2024-09-19 | Infrared Small Target Detection in Satellite Videos: A New Dataset and A Novel Recurrent Feature Refinement Framework | Multi-frame infrared small target (MIRST) detection in satellite videos is a long-standing, fundamental yet challenging task for decades, and the challenges can be summarized as: First, extremely small target size, highly complex clutters & noises, various satellite motions result in limited feature representation, hig... | [
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272753032 | 2409.12454 | 2024-09-19 | FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling | Electroencephalography (EEG) is a vital tool to measure and record brain activity in neuroscience and clinical applications, yet its potential is constrained by signal heterogeneity, low signal-to-noise ratios, and limited labeled datasets. In this paper, we propose FoME (Foundation Model for EEG), a novel approach usi... | [
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272753393 | 2409.12447 | 2024-09-19 | Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts | Generative pre-trained models power intelligent software features used by millions of users controlled by developer-written natural language prompts. Despite the impact of prompt-powered software, little is known about its development process and its relationship to programming. In this work, we argue that some prompts... | [
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272770125 | 2409.13064 | 2024-09-19 | Fear and Loathing on the Frontline: Decoding the Language of Othering by Russia-Ukraine War Bloggers | Othering, the act of portraying outgroups as fundamentally different from the ingroup, often escalates into framing them as existential threats--fueling intergroup conflict and justifying exclusion and violence. These dynamics are alarmingly pervasive, spanning from the extreme historical examples of genocides against ... | [
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272753184 | 2409.12816 | 2024-09-19 | Hierarchical Gradient-Based Genetic Sampling for Accurate Prediction of Biological Oscillations | Biological oscillations are periodic changes in various signaling processes crucial for the proper functioning of living organisms. These oscillations are modeled by ordinary differential equations, with coefficient variations leading to diverse periodic behaviors, typically measured by oscillatory frequencies. This pa... | [
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272753387 | 2409.12435 | 2024-09-19 | Linguistic Minimal Pairs Elicit Linguistic Similarity in Large Language Models | We introduce a novel analysis that leverages linguistic minimal pairs to probe the internal linguistic representations of Large Language Models (LLMs). By measuring the similarity between LLM activation differences across minimal pairs, we quantify the and gain insight into the linguistic knowledge captured by LLMs. Ou... | [
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272753133 | 2409.12805 | 2024-09-19 | Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning | We propose a new data representation method based on Quantum Cognition Machine Learning and apply it to manifold learning, specifically to the estimation of intrinsic dimension of data sets. The idea is to learn a representation of each data point as a quantum state, encoding both local properties of the point as well ... | [
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272827040 | 2409.13939 | 2024-09-20 | Simple Unsupervised Knowledge Distillation With Space Similarity | As per recent studies, Self-supervised learning (SSL) does not readily extend to smaller architectures. One direction to mitigate this shortcoming while simultaneously training a smaller network without labels is to adopt unsupervised knowledge distillation (UKD). Existing UKD approaches handcraft preservation worthy i... | [
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272770453 | 2409.13447 | 2024-09-20 | AQA: Adaptive Question Answering in a Society of LLMs via Contextual Multi-Armed Bandit | In question answering (QA), different questions can be effectively addressed with different answering strategies. Some require a simple lookup, while others need complex, multi-step reasoning to be answered adequately. This observation motivates the development of a dynamic method that adaptively selects the most suita... | [
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272827749 | 2409.13915 | 2024-09-20 | Data Pruning via Separability, Integrity, and Model Uncertainty-Aware Importance Sampling | This paper improves upon existing data pruning methods for image classification by introducing a novel pruning metric and pruning procedure based on importance sampling. The proposed pruning metric explicitly accounts for data separability, data integrity, and model uncertainty, while the sampling procedure is adaptive... | [
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272770579 | 2409.13555 | 2024-09-20 | Generating Visual Stories with Grounded and Coreferent Characters | Characters are important in narratives. They move the plot forward, create emotional connections, and embody the story's themes. Visual storytelling methods focus more on the plot and events relating to it, without building the narrative around specific characters. As a result, the generated stories feel generic, with ... | [
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272770338 | 2409.13315 | 2024-09-20 | Exploring the Performance-Reproducibility Trade-off in Quality-Diversity | Quality-Diversity (QD) algorithms have exhibited promising results across many domains and applications. However, uncertainty in fitness and behaviour estimations of solutions remains a major challenge when QD is used in complex real-world applications. While several approaches have been proposed to improve the perform... | [
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272770316 | 2409.13621 | 2024-09-20 | Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network | Event Causality Identification (ECI) focuses on extracting causal relations between events in texts. Existing methods for ECI primarily rely on causal features and external knowledge. However, these approaches fall short in two dimensions: (1) causal features between events in a text often lack explicit clues, and (2) ... | [
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