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272653776 | 2409.08925 | 2024-09-13 | Multi forests: Variable importance for multi-class outcomes | In prediction tasks with multi-class outcomes, identifying covariates specifically associated with one or more outcome classes can be important. Conventional variable importance measures (VIMs) from random forests (RFs), like permutation and Gini importance, focus on overall predictive performance or node purity, witho... | [
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272653735 | 2409.08824 | 2024-09-13 | Pathfinder for Low-altitude Aircraft with Binary Neural Network | A prior global topological map (e.g., the OpenStreetMap, OSM) can boost the performance of autonomous mapping by a ground mobile robot. However, the prior map is usually incomplete due to lacking labeling in partial paths. To solve this problem, this paper proposes an OSM maker using airborne sensors carried by low-alt... | [
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272653652 | 2409.08583 | 2024-09-13 | LHQ-SVC: Lightweight and High Quality Singing Voice Conversion Modeling | Singing Voice Conversion (SVC) has emerged as a significant subfield of Voice Conversion (VC), enabling the transformation of one singer's voice into another while preserving musical elements such as melody, rhythm, and timbre. Traditional SVC methods have limitations in terms of audio quality, data requirements, and c... | [
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272654178 | 2409.09009 | 2024-09-13 | Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach | Direct speech translation (ST) models often struggle with rare words. Incorrect translation of these words can have severe consequences, impacting translation quality and user trust. While rare word translation is inherently challenging for neural models due to sparse learning signals, real-world scenarios often allow ... | [
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272653897 | 2409.08554 | 2024-09-13 | LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study | Grapheme-to-phoneme (G2P) conversion is critical in speech processing, particularly for applications like speech synthesis. G2P systems must possess linguistic understanding and contextual awareness of languages with polyphone words and context-dependent phonemes. Large language models (LLMs) have recently demonstrated... | [
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272653748 | 2409.08958 | 2024-09-13 | PINNfluence: Influence Functions for Physics-Informed Neural Networks | Recently, physics-informed neural networks (PINNs) have emerged as a flexible and promising application of deep learning to partial differential equations in the physical sciences. While offering strong performance and competitive inference speeds on forward and inverse problems, their black-box nature limits interpret... | [
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272689267 | 2409.09217 | 2024-09-13 | Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme | Conventional WENO3 methods are known to be highly dissipative at lower resolutions, introducing significant errors in the pre-asymptotic regime. In this paper, we employ a rational neural network to accurately estimate the local smoothness of the solution, dynamically adapting the stencil weights based on local solutio... | [
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272384239 | 2409.08573 | 2024-09-13 | HTR-VT: Handwritten Text Recognition with Vision Transformer | We explore the application of Vision Transformer (ViT) for handwritten text recognition. The limited availability of labeled data in this domain poses challenges for achieving high performance solely relying on ViT. Previous transformer-based models required external data or extensive pre-training on large datasets to ... | [
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272690165 | 2409.09196 | 2024-09-13 | Are Sparse Neural Networks Better Hard Sample Learners? | While deep learning has demonstrated impressive progress, it remains a daunting challenge to learn from hard samples as these samples are usually noisy and intricate. These hard samples play a crucial role in the optimal performance of deep neural networks. Most research on Sparse Neural Networks (SNNs) has focused on ... | [
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272654120 | 2409.08647 | 2024-09-13 | Training Gradient Boosted Decision Trees on Tabular Data Containing Label Noise for Classification Tasks | Label noise, which refers to the mislabeling of instances in a dataset, can significantly impair classifier performance, increase model complexity, and affect feature selection. While most research has concentrated on deep neural networks for image and text data, this study explores the impact of label noise on gradien... | [
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272653602 | 2409.08482 | 2024-09-13 | Risks When Sharing LoRA Fine-Tuned Diffusion Model Weights | With the emerging trend in generative models and convenient public access to diffusion models pre-trained on large datasets, users can fine-tune these models to generate images of personal faces or items in new contexts described by natural language. Parameter efficient fine-tuning (PEFT) such as Low Rank Adaptation (L... | [
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272654079 | 2409.08711 | 2024-09-13 | Text-To-Speech Synthesis In The Wild | Traditional Text-to-Speech (TTS) systems rely on studio-quality speech recorded in controlled settings.a Recently, an effort known as noisy-TTS training has emerged, aiming to utilize in-the-wild data. However, the lack of dedicated datasets has been a significant limitation. We introduce the TTS In the Wild (TITW) dat... | [
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272653943 | 2409.08769 | 2024-09-13 | Causal Transformer for Fusion and Pose Estimation in Deep Visual Inertial Odometry | In recent years, transformer-based architectures become the de facto standard for sequence modeling in deep learning frameworks. Inspired by the successful examples, we propose a causal visual-inertial fusion transformer (VIFT) for pose estimation in deep visual-inertial odometry. This study aims to improve pose estima... | [
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272653868 | 2409.08522 | 2024-09-13 | MAPX: An explainable model-agnostic framework for the detection of false information on social media networks | The automated detection of false information has become a fundamental task in combating the spread of "fake news" on online social media networks (OSMN) as it reduces the need for manual discernment by individuals. In the literature, leveraging various content or context features of OSMN documents have been found usefu... | [
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272693906 | 2409.10562 | 2024-09-13 | Are Existing Road Design Guidelines Suitable for Autonomous Vehicles? | The emergence of Autonomous Vehicles (AVs) has spurred research into testing the resilience of their perception systems, i.e. to ensure they are not susceptible to making critical misjudgements. It is important that they are tested not only with respect to other vehicles on the road, but also those objects placed on th... | [
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272653863 | 2409.08687 | 2024-09-13 | xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editing | Reusing pre-collected data from different domains is an appealing solution for decision-making tasks, especially when data in the target domain are limited. Existing cross-domain policy transfer methods mostly aim at learning domain correspondences or corrections to facilitate policy learning, such as learning task/dom... | [
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273022670 | 2410.00005 | 2024-09-13 | Winning Solution For Meta KDD Cup' 24 | This paper describes the winning solutions of all tasks in Meta KDD Cup 24 from db3 team. The challenge is to build a RAG system from web sources and knowledge graphs. We are given multiple sources for each query to help us answer the question. The CRAG challenge involves three tasks: (1) condensing information from we... | [
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272654099 | 2409.08669 | 2024-09-13 | AdR-Gaussian: Accelerating Gaussian Splatting with Adaptive Radius | 3D Gaussian Splatting (3DGS) is a recent explicit 3D representation that has achieved high-quality reconstruction and real-time rendering of complex scenes. However, the rasterization pipeline still suffers from unnecessary overhead resulting from avoidable serial Gaussian culling, and uneven load due to the distinct n... | [
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272653609 | 2409.08805 | 2024-09-13 | Exploring SSL Discrete Tokens for Multilingual ASR | With the advancement of Self-supervised Learning (SSL) in speech-related tasks, there has been growing interest in utilizing discrete tokens generated by SSL for automatic speech recognition (ASR), as they offer faster processing techniques. However, previous studies primarily focused on multilingual ASR with Fbank fea... | [
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272653583 | 2409.08561 | 2024-09-13 | Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding | Large language models (LLMs) have demonstrated remarkable capabilities in tasks requiring reasoning and multi-step problem-solving through the use of chain-of-thought (CoT) prompting. However, generating the full CoT process results in significantly longer output sequences, leading to increased computational costs and ... | [
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272689797 | 2409.09221 | 2024-09-13 | Multi-modal Speech Transformer Decoders: When Do Multiple Modalities Improve Accuracy? | Decoder-only discrete-token language models have recently achieved significant success in automatic speech recognition. However, systematic analyses of how different modalities impact performance in specific scenarios remain limited. In this paper, we investigate the effects of multiple modalities on recognition accura... | [
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272654068 | 2409.08904 | 2024-09-13 | AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language Models | Training and deploying reinforcement learning (RL) policies for robots, especially in accomplishing specific tasks, presents substantial challenges. Recent advancements have explored diverse reward function designs, training techniques, simulation-to-reality (sim-to-real) transfers, and performance analysis methodologi... | [
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272653891 | 2409.08975 | 2024-09-13 | Accurate and Fast Estimation of Temporal Motifs using Path Sampling | Counting the number of small subgraphs, called motifs, is a fundamental problem in social network analysis and graph mining. Many real-world networks are directed and temporal, where edges have timestamps. Motif counting in directed, temporal graphs is especially challenging because there are a plethora of different ki... | [
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272653710 | 2409.08765 | 2024-09-13 | Cross-Country Comparative Analysis of Climate Resilience and Localized Mapping in Data-Sparse Regions | Climate resilience across sectors varies significantly in low-income countries (LICs), with agriculture being the most vulnerable to climate change. Existing studies typically focus on individual countries, offering limited insights into broader cross-country patterns of adaptation and vulnerability. This paper address... | [
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272653626 | 2409.08846 | 2024-09-13 | Fingerprint Vector: Enabling Scalable and Efficient Model Fingerprint Transfer via Vector Addition | Backdoor-based fingerprinting has emerged as an effective technique for tracing the ownership of large language models. However, in real-world deployment scenarios, developers often instantiate multiple downstream models from a shared base model, and applying fingerprinting to each variant individually incurs prohibiti... | [
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272653594 | 2409.08864 | 2024-09-13 | Exploring Graph Structure Comprehension Ability of Multimodal Large Language Models: Case Studies | Large Language Models (LLMs) have shown remarkable capabilities in processing various data structures, including graphs. While previous research has focused on developing textual encoding methods for graph representation, the emergence of multimodal LLMs presents a new frontier for graph comprehension. These advanced m... | [
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272654129 | 2409.08872 | 2024-09-13 | Exploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages | This study investigates the efficacy of data augmentation techniques for low-resource automatic speech recognition (ASR), focusing on two endangered Austronesian languages, Amis and Seediq. Recognizing the potential of self-supervised learning (SSL) in low-resource settings, we explore the impact of data volume on the ... | [
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272653817 | 2409.08690 | 2024-09-13 | Generating Temporal Contact Graphs Using Random Walkers | We study human mobility networks through timeseries of contacts between individuals. Our proposed Random Walkers Induced temporal Graph (RWIG) model generates temporal graph sequences based on independent random walkers that traverse an underlying graph in discrete time steps. Co-location of walkers at a given node and... | [
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272653774 | 2409.09018 | 2024-09-13 | An Efficient and Streaming Audio Visual Active Speaker Detection System | This paper delves into the challenging task of Active Speaker Detection (ASD), where the system needs to determine in real-time whether a person is speaking or not in a series of video frames. While previous works have made significant strides in improving network architectures and learning effective representations fo... | [
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272654023 | 2409.08738 | 2024-09-13 | DataliVR: Transformation of Data Literacy Education through Virtual Reality with ChatGPT-Powered Enhancements | Data literacy is essential in today's data-driven world, emphasizing individuals' abilities to effectively manage data and extract meaningful insights. However, traditional classroom-based educational approaches often struggle to fully address the multifaceted nature of data literacy. As education undergoes digital tra... | [
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272654095 | 2409.08508 | 2024-09-13 | Identifying Human Indoor Daily Life Behavior employing Thermal Sensor Arrays (TSAs) | Daily activity monitoring systems used in households provide vital information for health status, particularly with aging residents. Multiple approaches have been introduced to achieve such goals, typically obtrusive and non-obtrusive. Amongst the obtrusive approaches are the wearable devices, and among the non-obtrusi... | [
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272653892 | 2409.08744 | 2024-09-13 | Uncertainty and Generalizability in Foundation Models for Earth Observation | We take the perspective in which we want to design a downstream task (such as estimating vegetation coverage) on a certain area of interest (AOI) with a limited labeling budget. By leveraging an existing Foundation Model (FM) we must decide whether we train a downstream model on a different but label-rich AOI hoping it... | [
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272653607 | 2409.08905 | 2024-09-13 | D2-MLP: Dynamic Decomposed MLP Mixer for Medical Image Segmentation | Convolutional neural networks are widely used in various segmentation tasks in medical images. However, they are challenged to learn global features adaptively due to the inherent locality of convolutional operations. In contrast, MLP Mixers are proposed as a backbone to learn global information across channels with lo... | [
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272689058 | 2409.09149 | 2024-09-13 | Adaptive Multi-Modal Control of Digital Human Hand Synthesis Using a Region-Aware Cycle Loss | Diffusion models have shown their remarkable ability to synthesize images, including the generation of humans in specific poses. However, current models face challenges in adequately expressing conditional control for detailed hand pose generation, leading to significant distortion in the hand regions. To tackle this p... | [
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272689127 | 2409.09202 | 2024-09-13 | HotSwap: Enabling Live Dependency Sharing in Serverless Computing | This work presents HotSwap, a novel provider-side cold-start optimization for serverless computing. This optimization reduces cold-start time when booting and loading dependencies at runtime inside a function container. Previous research has extensively focused on reducing cold-start latency for specific functions. How... | [
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272653858 | 2409.08695 | 2024-09-13 | Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia Feeding | Traditional fish farming practices often lead to inefficient feeding, resulting in environmental issues and reduced productivity. We developed an innovative system combining computer vision and IoT technologies for precise Tilapia feeding. Our solution uses real-time IoT sensors to monitor water quality parameters and ... | [
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273023293 | 2410.00010 | 2024-09-13 | PHemoNet: A Multimodal Network for Physiological Signals | Emotion recognition is essential across numerous fields, including medical applications and brain-computer interface (BCI). Emotional responses include behavioral reactions, such as tone of voice and body movement, and changes in physiological signals, such as the electroencephalogram (EEG). The latter are involuntary,... | [
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272690047 | 2409.09130 | 2024-09-13 | FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature Selection | Due to the vast testing space, the increasing demand for effective and efficient testing of deep neural networks (DNNs) has led to the development of various DNN test case prioritization techniques. However, the fact that DNNs can deliver high-confidence predictions for incorrectly predicted examples, known as the over... | [
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272653853 | 2409.08631 | 2024-09-13 | Sybil Detection using Graph Neural Networks | This paper presents SYBILGAT, a novel approach to Sybil detection in social networks using Graph Attention Networks (GATs). Traditional methods for Sybil detection primarily leverage structural properties of networks; however, they tend to struggle with a large number of attack edges and are often unable to simultaneou... | [
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272653618 | 2409.08775 | 2024-09-13 | What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use | Prompting LLMs for complex tasks (e.g., building a trip advisor chatbot) needs humans to clearly articulate customized requirements (e.g., "start the response with a tl;dr"). However, existing prompt engineering instructions often lack focused training on requirement articulation and instead tend to emphasize increasin... | [
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272690093 | 2409.09199 | 2024-09-13 | Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features | Multi-armed Bandits (MABs) are increasingly employed in online platforms and e-commerce to optimize decision making for personalized user experiences. In this work, we focus on the Contextual Bandit problem with linear rewards, under conditions of sparsity and batched data. We address the challenge of fairness by exclu... | [
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272653690 | 2409.08881 | 2024-09-13 | Data Efficient Child-Adult Speaker Diarization with Simulated Conversations | Automating child speech analysis is crucial for applications such as neurocognitive assessments. Speaker diarization, which identifies ``who spoke when'', is an essential component of the automated analysis. However, publicly available child-adult speaker diarization solutions are scarce due to privacy concerns and a l... | [
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{
"author_id": "t wei_12",
"name": "Tianqi Wei",
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"num_cita... | null |
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