id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.00108 | Act Now: A Novel Online Forecasting Framework for Large-Scale Streaming
Data | [
"cs.LG"
] | In this paper, we find that existing online forecasting methods have the following issues: 1) They do not consider the update frequency of streaming data and directly use labels (future signals) to update the model, leading to information leakage. 2) Eliminating information leakage can exacerbate concept drift and onli... | {
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2412.00109 | Deep Neural Network-Based Prediction of B-Cell Epitopes for SARS-CoV and
SARS-CoV-2: Enhancing Vaccine Design through Machine Learning | [
"cs.LG",
"cs.CE",
"q-bio.BM",
"stat.ML"
] | The accurate prediction of B-cell epitopes is critical for guiding vaccine development against infectious diseases, including SARS and COVID-19. This study explores the use of a deep neural network (DNN) model to predict B-cell epitopes for SARS-CoVandSARS-CoV-2,leveraging a dataset that incorporates essential protein ... | {
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2412.00110 | Demographic Predictability in 3D CT Foundation Embeddings | [
"cs.CV",
"cs.AI",
"cs.ET",
"cs.LG"
] | Self-supervised foundation models have recently been successfully extended to encode three-dimensional (3D) computed tomography (CT) images, with excellent performance across several downstream tasks, such as intracranial hemorrhage detection and lung cancer risk forecasting. However, as self-supervised models learn fr... | {
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2412.00111 | Video Set Distillation: Information Diversification and Temporal
Densification | [
"cs.CV"
] | The rapid development of AI models has led to a growing emphasis on enhancing their capabilities for complex input data such as videos. While large-scale video datasets have been introduced to support this growth, the unique challenges of reducing redundancies in video \textbf{sets} have not been explored. Compared to ... | {
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2412.00112 | BiPO: Bidirectional Partial Occlusion Network for Text-to-Motion
Synthesis | [
"cs.CV",
"cs.GR"
] | Generating natural and expressive human motions from textual descriptions is challenging due to the complexity of coordinating full-body dynamics and capturing nuanced motion patterns over extended sequences that accurately reflect the given text. To address this, we introduce BiPO, Bidirectional Partial Occlusion Netw... | {
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2412.00113 | Boundary-Decoder network for inverse prediction of capacitor
electrostatic analysis | [
"cs.LG",
"cs.AI",
"cs.CE"
] | Traditional electrostatic simulation are meshed-based methods which convert partial differential equations into an algebraic system of equations and their solutions are approximated through numerical methods. These methods are time consuming and any changes in their initial or boundary conditions will require solving t... | {
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2412.00114 | SceneTAP: Scene-Coherent Typographic Adversarial Planner against
Vision-Language Models in Real-World Environments | [
"cs.CV",
"cs.AI"
] | Large vision-language models (LVLMs) have shown remarkable capabilities in interpreting visual content. While existing works demonstrate these models' vulnerability to deliberately placed adversarial texts, such texts are often easily identifiable as anomalous. In this paper, we present the first approach to generate s... | {
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2412.00115 | OpenHumanVid: A Large-Scale High-Quality Dataset for Enhancing
Human-Centric Video Generation | [
"cs.CV"
] | Recent advancements in visual generation technologies have markedly increased the scale and availability of video datasets, which are crucial for training effective video generation models. However, a significant lack of high-quality, human-centric video datasets presents a challenge to progress in this field. To bridg... | {
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2412.00117 | Proceedings of the 2024 XCSP3 Competition | [
"cs.AI"
] | This document represents the proceedings of the 2024 XCSP3 Competition. The results of this competition of constraint solvers were presented at CP'24 (30th International Conference on Principles and Practice of Constraint Programming). | {
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2412.00118 | Boundary Control Behaviors of Multiple Low-cost AUVs Using Acoustic
Communication | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This study presents acoustic-based methods for the control of multiple autonomous underwater vehicles (AUV). This study proposes two different models for implementing boundary and path control on low-cost AUVs using acoustic communication and a single central acoustic beacon. Two methods are presented: the Range Variat... | {
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2412.00119 | Training Multi-Layer Binary Neural Networks With Local Binary Error
Signals | [
"cs.LG",
"cs.CV"
] | Binary Neural Networks (BNNs) hold the potential for significantly reducing computational complexity and memory demand in machine and deep learning. However, most successful training algorithms for BNNs rely on quantization-aware floating-point Stochastic Gradient Descent (SGD), with full-precision hidden weights used ... | {
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2412.00120 | Relation-Aware Meta-Learning for Zero-shot Sketch-Based Image Retrieval | [
"cs.CV",
"cs.AI"
] | Sketch-based image retrieval (SBIR) relies on free-hand sketches to retrieve natural photos within the same class. However, its practical application is limited by its inability to retrieve classes absent from the training set. To address this limitation, the task has evolved into Zero-Shot Sketch-Based Image Retrieval... | {
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2412.00121 | Hybrid Discriminative Attribute-Object Embedding Network for
Compositional Zero-Shot Learning | [
"cs.CV",
"cs.AI"
] | Compositional Zero-Shot Learning (CZSL) recognizes new combinations by learning from known attribute-object pairs. However, the main challenge of this task lies in the complex interactions between attributes and object visual representations, which lead to significant differences in images. In addition, the long-tail l... | {
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2412.00122 | Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific
Feedback | [
"cs.CV"
] | Learning from feedback has been shown to enhance the alignment between text prompts and images in text-to-image diffusion models. However, due to the lack of focus in feedback content, especially regarding the object type and quantity, these techniques struggle to accurately match text and images when faced with specif... | {
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2412.00123 | Electricity Price Prediction Using Multi-Kernel Gaussian Process
Regression Combined with Kernel-Based Support Vector Regression | [
"cs.LG",
"math.PR"
] | This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on combining Gaussian Process Regression (GPR) and Support Vector Regression (SVR). While GPR is a competent model for learning the stochastic pattern within the data and interpolation, its performance for out-of-sam... | {
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2412.00124 | Auto-Encoded Supervision for Perceptual Image Super-Resolution | [
"cs.CV",
"eess.IV"
] | This work tackles the fidelity objective in the perceptual super-resolution~(SR). Specifically, we address the shortcomings of pixel-level $L_\text{p}$ loss ($\mathcal{L}_\text{pix}$) in the GAN-based SR framework. Since $L_\text{pix}$ is known to have a trade-off relationship against perceptual quality, prior methods ... | {
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2412.00125 | Efficient Learning Content Retrieval with Knowledge Injection | [
"cs.CL"
] | With the rise of online education platforms, there is a growing abundance of educational content across various domain. It can be difficult to navigate the numerous available resources to find the most suitable training, especially in domains that include many interconnected areas, such as ICT. In this study, we propos... | {
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2412.00126 | Streamlined Federated Unlearning: Unite as One to Be Highly Efficient | [
"cs.LG"
] | Recently, the enactment of "right to be forgotten" laws and regulations has imposed new privacy requirements on federated learning (FL). Researchers aim to remove the influence of certain data from the trained model without training from scratch through federated unlearning (FU). While current FU research has shown pro... | {
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2412.00127 | Orthus: Autoregressive Interleaved Image-Text Generation with
Modality-Specific Heads | [
"cs.CV",
"cs.AI",
"cs.CL"
] | We introduce Orthus, an autoregressive (AR) transformer that excels in generating images given textual prompts, answering questions based on visual inputs, and even crafting lengthy image-text interleaved contents. Unlike prior arts on unified multimodal modeling, Orthus simultaneously copes with discrete text tokens a... | {
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2412.00129 | Scaling Particle Collision Data Analysis | [
"cs.LG",
"hep-ex",
"physics.data-an"
] | For decades, researchers have developed task-specific models to address scientific challenges across diverse disciplines. Recently, large language models (LLMs) have shown enormous capabilities in handling general tasks; however, these models encounter difficulties in addressing real-world scientific problems, particul... | {
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2412.00131 | Open-Sora Plan: Open-Source Large Video Generation Model | [
"cs.CV",
"cs.AI"
] | We introduce Open-Sora Plan, an open-source project that aims to contribute a large generation model for generating desired high-resolution videos with long durations based on various user inputs. Our project comprises multiple components for the entire video generation process, including a Wavelet-Flow Variational Aut... | {
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2412.00132 | Road User Classification from High-Frequency GNSS Data Using Distributed
Edge Intelligence | [
"cs.LG",
"cs.AI",
"cs.ET"
] | Real-world traffic involves diverse road users, ranging from pedestrians to heavy trucks, necessitating effective road user classification for various applications within Intelligent Transport Systems (ITS). Traditional approaches often rely on intrusive and/or expensive external hardware sensors. These systems typical... | {
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2412.00133 | Event-based Tracking of Any Point with Motion-Robust Correlation
Features | [
"cs.CV",
"cs.LG",
"cs.RO"
] | Tracking any point (TAP) recently shifted the motion estimation paradigm from focusing on individual salient points with local templates to tracking arbitrary points with global image contexts. However, while research has mostly focused on driving the accuracy of models in nominal settings, addressing scenarios with di... | {
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2412.00134 | PP-SSL : Priority-Perception Self-Supervised Learning for Fine-Grained
Recognition | [
"cs.CV"
] | Self-supervised learning is emerging in fine-grained visual recognition with promising results. However, existing self-supervised learning methods are often susceptible to irrelevant patterns in self-supervised tasks and lack the capability to represent the subtle differences inherent in fine-grained visual recognition... | {
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2412.00136 | FonTS: Text Rendering with Typography and Style Controls | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Visual text images are prevalent in various applications, requiring careful font selection and typographic choices. Recent advances in Diffusion Transformer (DiT)-based text-to-image (T2I) models show promise in automating these processes. However, these methods still face challenges such as inconsistent fonts, style v... | {
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2412.00138 | Unleashing the Power of Data Synthesis in Visual Localization | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | Visual localization, which estimates a camera's pose within a known scene, is a long-standing challenge in vision and robotics. Recent end-to-end methods that directly regress camera poses from query images have gained attention for fast inference. However, existing methods often struggle to generalize to unseen views.... | {
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2412.00139 | EFSA: Episodic Few-Shot Adaptation for Text-to-Image Retrieval | [
"cs.CV"
] | Text-to-image retrieval is a critical task for managing diverse visual content, but common benchmarks for the task rely on small, single-domain datasets that fail to capture real-world complexity. Pre-trained vision-language models tend to perform well with easy negatives but struggle with hard negatives--visually simi... | {
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2412.00140 | Differentiable Topology Estimating from Curvatures for 3D Shapes | [
"cs.CV",
"cs.GR"
] | In the field of data-driven 3D shape analysis and generation, the estimation of global topological features from localized representations such as point clouds, voxels, and neural implicit fields is a longstanding challenge. This paper introduces a novel, differentiable algorithm tailored to accurately estimate the glo... | {
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2412.00142 | Sparse Attention Vectors: Generative Multimodal Model Features Are
Discriminative Vision-Language Classifiers | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Generative Large Multimodal Models (LMMs) like LLaVA and Qwen-VL excel at a wide variety of vision-language (VL) tasks such as image captioning or visual question answering. Despite strong performance, LMMs are not directly suited for foundational discriminative vision-language tasks (i.e., tasks requiring discrete lab... | {
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2412.00143 | Is Oracle Pruning the True Oracle? | [
"cs.LG",
"cs.CV"
] | Oracle pruning, which selects unimportant weights by minimizing the pruned train loss, has been taken as the foundation for most neural network pruning methods for over 35 years, while few (if not none) have thought about how much the foundation really holds. This paper, for the first time, attempts to examine its vali... | {
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2412.00144 | MPQ-Diff: Mixed Precision Quantization for Diffusion Models | [
"cs.CV",
"cs.LG"
] | Diffusion models (DMs) generate remarkable high quality images via the stochastic denoising process, which unfortunately incurs high sampling time. Post-quantizing the trained diffusion models in fixed bit-widths, e.g., 4 bits on weights and 8 bits on activation, is shown effective in accelerating sampling time while m... | {
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2412.00145 | Semi-Supervised Neural Processes for Articulated Object Interactions | [
"cs.RO",
"cs.LG"
] | The scarcity of labeled action data poses a considerable challenge for developing machine learning algorithms for robotic object manipulation. It is expensive and often infeasible for a robot to interact with many objects. Conversely, visual data of objects, without interaction, is abundantly available and can be lever... | {
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2412.00146 | Knowledge-Augmented Explainable and Interpretable Learning for Anomaly
Detection and Diagnosis | [
"cs.LG",
"cs.AI"
] | Knowledge-augmented learning enables the combination of knowledge-based and data-driven approaches. For anomaly detection and diagnosis, understandability is typically an important factor, especially in high-risk areas. Therefore, explainability and interpretability are also major criteria in such contexts. This chapte... | {
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2412.00147 | Development of CPS Platform for Autonomous Construction | [
"cs.RO"
] | In recent years, labor shortages due to the declining birthrate and aging population have become significant challenges at construction sites in developed countries, including Japan. To address these challenges, we are developing an open platform called ROS2-TMS for Construction, a Cyber-Physical System (CPS) for const... | {
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2412.00148 | Motion Modes: What Could Happen Next? | [
"cs.CV"
] | Predicting diverse object motions from a single static image remains challenging, as current video generation models often entangle object movement with camera motion and other scene changes. While recent methods can predict specific motions from motion arrow input, they rely on synthetic data and predefined motions, l... | {
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2412.00150 | Curriculum Fine-tuning of Vision Foundation Model for Medical Image
Classification Under Label Noise | [
"cs.CV",
"eess.IV"
] | Deep neural networks have demonstrated remarkable performance in various vision tasks, but their success heavily depends on the quality of the training data. Noisy labels are a critical issue in medical datasets and can significantly degrade model performance. Previous clean sample selection methods have not utilized t... | {
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2412.00151 | DLaVA: Document Language and Vision Assistant for Answer Localization
with Enhanced Interpretability and Trustworthiness | [
"cs.CV",
"cs.AI"
] | Document Visual Question Answering (VQA) requires models to interpret textual information within complex visual layouts and comprehend spatial relationships to answer questions based on document images. Existing approaches often lack interpretability and fail to precisely localize answers within the document, hindering... | {
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2412.00152 | Dynamic Neural Curiosity Enhances Learning Flexibility for Autonomous
Goal Discovery | [
"cs.RO",
"cs.AI",
"cs.LG"
] | The autonomous learning of new goals in robotics remains a complex issue to address. Here, we propose a model where curiosity influence learning flexibility. To do so, this paper proposes to root curiosity and attention together by taking inspiration from the Locus Coeruleus-Norepinephrine system along with various cog... | {
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2412.00153 | ROSE: Revolutionizing Open-Set Dense Segmentation with Patch-Wise
Perceptual Large Multimodal Model | [
"cs.CV",
"cs.LG"
] | Advances in CLIP and large multimodal models (LMMs) have enabled open-vocabulary and free-text segmentation, yet existing models still require predefined category prompts, limiting free-form category self-generation. Most segmentation LMMs also remain confined to sparse predictions, restricting their applicability in o... | {
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2412.00154 | o1-Coder: an o1 Replication for Coding | [
"cs.SE",
"cs.AI"
] | The technical report introduces O1-CODER, an attempt to replicate OpenAI's o1 model with a focus on coding tasks. It integrates reinforcement learning (RL) and Monte Carlo Tree Search (MCTS) to enhance the model's System-2 thinking capabilities. The framework includes training a Test Case Generator (TCG) for standardiz... | {
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2412.00155 | T-3DGS: Removing Transient Objects for 3D Scene Reconstruction | [
"cs.CV",
"cs.LG"
] | We propose a novel framework to remove transient objects from input videos for 3D scene reconstruction using Gaussian Splatting. Our framework consists of the following steps. In the first step, we propose an unsupervised training strategy for a classification network to distinguish between transient objects and static... | {
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2412.00156 | VISION-XL: High Definition Video Inverse Problem Solver using Latent
Image Diffusion Models | [
"cs.CV",
"cs.AI",
"cs.LG",
"stat.ML"
] | In this paper, we propose a novel framework for solving high-definition video inverse problems using latent image diffusion models. Building on recent advancements in spatio-temporal optimization for video inverse problems using image diffusion models, our approach leverages latent-space diffusion models to achieve enh... | {
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2412.00157 | AerialGo: Walking-through City View Generation from Aerial Perspectives | [
"cs.CV",
"cs.LG"
] | High-quality 3D urban reconstruction is essential for applications in urban planning, navigation, and AR/VR. However, capturing detailed ground-level data across cities is both labor-intensive and raises significant privacy concerns related to sensitive information, such as vehicle plates, faces, and other personal ide... | {
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2412.00161 | STEP: Enhancing Video-LLMs' Compositional Reasoning by Spatio-Temporal
Graph-guided Self-Training | [
"cs.CV",
"cs.LG"
] | Video Large Language Models (Video-LLMs) have recently shown strong performance in basic video understanding tasks, such as captioning and coarse-grained question answering, but struggle with compositional reasoning that requires multi-step spatio-temporal inference across object relations, interactions, and events. Th... | {
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2412.00162 | Dynamic High-Order Control Barrier Functions with Diffuser for
Safety-Critical Trajectory Planning at Signal-Free Intersections | [
"cs.RO",
"cs.LG",
"cs.SY",
"eess.SY"
] | Planning safe and efficient trajectories through signal-free intersections presents significant challenges for autonomous vehicles (AVs), particularly in dynamic, multi-task environments with unpredictable interactions and an increased possibility of conflicts. This study aims to address these challenges by developing ... | {
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2412.00165 | Modelling Networked Dynamical System by Temporal Graph Neural ODE with
Irregularly Partial Observed Time-series Data | [
"cs.LG"
] | Modeling the evolution of system with time-series data is a challenging and critical task in a wide range of fields, especially when the time-series data is regularly sampled and partially observable. Some methods have been proposed to estimate the hidden dynamics between intervals like Neural ODE or Exponential decay ... | {
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2412.00166 | To Ensemble or Not: Assessing Majority Voting Strategies for Phishing
Detection with Large Language Models | [
"cs.CL",
"cs.AI"
] | The effectiveness of Large Language Models (LLMs) significantly relies on the quality of the prompts they receive. However, even when processing identical prompts, LLMs can yield varying outcomes due to differences in their training processes. To leverage the collective intelligence of multiple LLMs and enhance their p... | {
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2412.00167 | Origin-Destination Demand Prediction: An Urban Radiation and Attraction
Perspective | [
"cs.LG",
"cs.AI"
] | In recent years, origin-destination (OD) demand prediction has gained significant attention for its profound implications in urban development. Existing data-driven deep learning methods primarily focus on the spatial or temporal dependency between regions yet neglecting regions' fundamental functional difference. Thou... | {
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2412.00171 | RoboMatrix: A Skill-centric Hierarchical Framework for Scalable Robot
Task Planning and Execution in Open-World | [
"cs.RO",
"cs.CV"
] | Existing policy learning methods predominantly adopt the task-centric paradigm, necessitating the collection of task data in an end-to-end manner. Consequently, the learned policy tends to fail to tackle novel tasks. Moreover, it is hard to localize the errors for a complex task with multiple stages due to end-to-end l... | {
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2412.00173 | Spatial Clustering of Molecular Localizations with Graph Neural Networks | [
"cs.LG",
"physics.bio-ph",
"physics.data-an",
"q-bio.QM"
] | Single-molecule localization microscopy generates point clouds corresponding to fluorophore localizations. Spatial cluster identification and analysis of these point clouds are crucial for extracting insights about molecular organization. However, this task becomes challenging in the presence of localization noise, hig... | {
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2412.00174 | SOLAMI: Social Vision-Language-Action Modeling for Immersive Interaction
with 3D Autonomous Characters | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Human beings are social animals. How to equip 3D autonomous characters with similar social intelligence that can perceive, understand and interact with humans remains an open yet foundamental problem. In this paper, we introduce SOLAMI, the first end-to-end Social vision-Language-Action (VLA) Modeling framework for Imm... | {
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2412.00175 | Circumventing shortcuts in audio-visual deepfake detection datasets with
unsupervised learning | [
"cs.CV",
"cs.LG",
"cs.SD",
"eess.AS",
"eess.IV"
] | Good datasets are essential for developing and benchmarking any machine learning system. Their importance is even more extreme for safety critical applications such as deepfake detection - the focus of this paper. Here we reveal that two of the most widely used audio-video deepfake datasets suffer from a previously uni... | {
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2412.00176 | Art-Free Generative Models: Art Creation Without Graphic Art Knowledge | [
"cs.CV"
] | We explore the question: "How much prior art knowledge is needed to create art?" To investigate this, we propose a text-to-image generation model trained without access to art-related content. We then introduce a simple yet effective method to learn an art adapter using only a few examples of selected artistic styles. ... | {
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2412.00177 | LumiNet: Latent Intrinsics Meets Diffusion Models for Indoor Scene
Relighting | [
"cs.CV",
"cs.GR",
"cs.LG"
] | We introduce LumiNet, a novel architecture that leverages generative models and latent intrinsic representations for effective lighting transfer. Given a source image and a target lighting image, LumiNet synthesizes a relit version of the source scene that captures the target's lighting. Our approach makes two key cont... | {
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2412.00198 | Improving the performance of weak supervision searches using data
augmentation | [
"hep-ph",
"cs.LG",
"hep-ex"
] | Weak supervision combines the advantages of training on real data with the ability to exploit signal properties. However, training a neural network using weak supervision often requires an excessive amount of signal data, which severely limits its practical applicability. In this study, we propose addressing this limit... | {
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2412.00200 | Scaling of Stochastic Normalizing Flows in $\mathrm{SU}(3)$ lattice
gauge theory | [
"hep-lat",
"cond-mat.stat-mech",
"cs.LG",
"stat.ML"
] | Non-equilibrium Markov Chain Monte Carlo (NE-MCMC) simulations provide a well-understood framework based on Jarzynski's equality to sample from a target probability distribution. By driving a base probability distribution out of equilibrium, observables are computed without the need to thermalize. If the base distribut... | {
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2412.00205 | Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty
Estimation | [
"cs.CV",
"cs.LG",
"stat.ML"
] | Despite the remarkable progress in generative modelling, current diffusion models lack a quantitative approach to assess image quality. To address this limitation, we propose to estimate the pixel-wise aleatoric uncertainty during the sampling phase of diffusion models and utilise the uncertainty to improve the sample ... | {
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2412.00206 | Towards the Ultimate Programming Language: Trust and Benevolence in the
Age of Artificial Intelligence | [
"cs.AI",
"cs.CY",
"cs.HC",
"cs.PL",
"cs.SE"
] | This article explores the evolving role of programming languages in the context of artificial intelligence. It highlights the need for programming languages to ensure human understanding while eliminating unnecessary implementation details and suggests that future programs should be designed to recognize and actively s... | {
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2412.00208 | Train Once for All: A Transitional Approach for Efficient Aspect
Sentiment Triplet Extraction | [
"cs.CL"
] | Aspect-Opinion Pair Extraction (AOPE) and Aspect Sentiment Triplet Extraction (ASTE) have drawn growing attention in NLP. However, most existing approaches extract aspects and opinions independently, optionally adding pairwise relations, often leading to error propagation and high time complexity. To address these chal... | {
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2412.00209 | Digital Twin in Industries: A Comprehensive Survey | [
"cs.AI"
] | Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as... | {
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2412.00211 | Dissipative iFIR filters for data-driven design | [
"eess.SY",
"cs.RO",
"cs.SY",
"math.OC"
] | We tackle the problem of providing closed-loop stability guarantees with a scalable data-driven design. We combine virtual reference feedback tuning with dissipativity constraints on the controller for closed-loop stability. The constraints are formulated as a set of linear inequalities in the frequency domain. This le... | {
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2412.00218 | NushuRescue: Revitalization of the Endangered Nushu Language with AI | [
"cs.CL",
"cs.LG"
] | The preservation and revitalization of endangered and extinct languages is a meaningful endeavor, conserving cultural heritage while enriching fields like linguistics and anthropology. However, these languages are typically low-resource, making their reconstruction labor-intensive and costly. This challenge is exemplif... | {
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2412.00224 | An AI-Driven Data Mesh Architecture Enhancing Decision-Making in
Infrastructure Construction and Public Procurement | [
"cs.AI",
"cs.DB",
"cs.MA"
] | Infrastructure construction, often dubbed an "industry of industries," is closely linked with government spending and public procurement, offering significant opportunities for improved efficiency and productivity through better transparency and information access. By leveraging these opportunities, we can achieve nota... | {
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2412.00225 | Meta-learning Loss Functions of Parametric Partial Differential
Equations Using Physics-Informed Neural Networks | [
"cs.LG",
"math.AP",
"physics.comp-ph"
] | This paper proposes a new way to learn Physics-Informed Neural Network loss functions using Generalized Additive Models. We apply our method by meta-learning parametric partial differential equations, PDEs, on Burger's and 2D Heat Equations. The goal is to learn a new loss function for each parametric PDE using meta-le... | {
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2412.00230 | Clinical Document Corpora -- Real Ones, Translated and Synthetic
Substitutes, and Assorted Domain Proxies: A Survey of Diversity in Corpus
Design, with Focus on German Text Data | [
"cs.CL"
] | We survey clinical document corpora, with focus on German textual data. Due to rigid data privacy legislation in Germany these resources, with only few exceptions, are stored in safe clinical data spaces and locked against clinic-external researchers. This situation stands in stark contrast with established workflows i... | {
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2412.00237 | Hybrid Spiking Neural Network -- Transformer Video Classification Model | [
"cs.CV",
"cs.LG"
] | In recent years, Spiking Neural Networks (SNNs) have gathered significant interest due to their temporal understanding capabilities. This work introduces, to the best of our knowledge, the first Cortical Column like hybrid architecture for the Time-Series Data Classification Task that leverages SNNs and is inspired by ... | {
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2412.00238 | Twisted Convolutional Networks (TCNs): Enhancing Feature Interactions
for Non-Spatial Data Classification | [
"cs.CV",
"cs.AI"
] | Twisted Convolutional Networks (TCNs) are introduced as a novel neural network architecture designed to effectively process one-dimensional data with arbitrary feature order and minimal spatial relationships. Unlike traditional Convolutional Neural Networks (CNNs), which excel at handling structured two-dimensional dat... | {
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2412.00239 | Generating a Low-code Complete Workflow via Task Decomposition and RAG | [
"cs.SE",
"cs.AI"
] | AI technologies are moving rapidly from research to production. With the popularity of Foundation Models (FMs) that generate text, images, and video, AI-based systems are increasing their complexity. Compared to traditional AI-based software, systems employing FMs, or GenAI-based systems, are more difficult to design d... | {
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2412.00241 | Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations | [
"cs.LG"
] | Graph Neural Networks (GNNs) have seen significant advances in recent years, yet their application to multigraphs, where parallel edges exist between the same pair of nodes, remains under-explored. Standard GNNs, designed for simple graphs, compute node representations by combining all connected edges at once, without ... | {
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2412.00242 | Uni-SLAM: Uncertainty-Aware Neural Implicit SLAM for Real-Time Dense
Indoor Scene Reconstruction | [
"cs.CV"
] | Neural implicit fields have recently emerged as a powerful representation method for multi-view surface reconstruction due to their simplicity and state-of-the-art performance. However, reconstructing thin structures of indoor scenes while ensuring real-time performance remains a challenge for dense visual SLAM systems... | {
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2412.00243 | Realistic Corner Case Generation for Autonomous Vehicles with Multimodal
Large Language Model | [
"cs.RO",
"cs.AI"
] | To guarantee the safety and reliability of autonomous vehicle (AV) systems, corner cases play a crucial role in exploring the system's behavior under rare and challenging conditions within simulation environments. However, current approaches often fall short in meeting diverse testing needs and struggle to generalize t... | {
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2412.00244 | Robust Testing for Deep Learning using Human Label Noise | [
"cs.LG"
] | In deep learning (DL) systems, label noise in training datasets often degrades model performance, as models may learn incorrect patterns from mislabeled data. The area of Learning with Noisy Labels (LNL) has introduced methods to effectively train DL models in the presence of noisily-labeled datasets. Traditionally, th... | {
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2412.00245 | Integrating Social Determinants of Health into Knowledge Graphs:
Evaluating Prediction Bias and Fairness in Healthcare | [
"cs.AI",
"cs.CY",
"cs.LG"
] | Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored. This study addresses this gap by constructing an SDoH-enriched knowledge graph using the MIMIC-III dataset and PrimeKG. We introduce a novel fairness formula... | {
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2412.00251 | Fine Tuning Large Language Models to Deliver CBT for Depression | [
"cs.AI",
"cs.HC"
] | Cognitive Behavioral Therapy (CBT) is a well-established, evidence-based treatment for Major Depressive Disorder. Unfortunately, there exist significant barriers to individuals accessing CBT, including cost, scarcity of therapists and stigma. This study explores the feasibility of fine-tuning small open weight large la... | {
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2412.00252 | Localization Phenomena in Large-Scale Networked Systems: Robustness and
Fragility of Dynamics | [
"eess.SY",
"cond-mat.dis-nn",
"cs.SY",
"math-ph",
"math.MP"
] | We study phenomena where some eigenvectors of a graph Laplacian are largely confined in small subsets of the graph. These localization phenomena are similar to those generally termed Anderson Localization in the Physics literature, and are related to the complexity of the structure of large graphs in still unexplored w... | {
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2412.00256 | Excretion Detection in Pigsties Using Convolutional and Transformerbased
Deep Neural Networks | [
"cs.CV"
] | Animal excretions in form of urine puddles and feces are a significant source of emissions in livestock farming. Automated detection of soiled floor in barns can contribute to improved management processes but also the derived information can be used to model emission dynamics. Previous research approaches to determine... | {
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2412.00259 | One-Shot Real-to-Sim via End-to-End Differentiable Simulation and
Rendering | [
"cs.RO",
"cs.CV",
"cs.GR"
] | Identifying predictive world models for robots in novel environments from sparse online observations is essential for robot task planning and execution in novel environments. However, existing methods that leverage differentiable simulators to identify world models are incapable of jointly optimizing the shape, appeara... | {
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2412.00261 | Attribute-Enhanced Similarity Ranking for Sparse Link Prediction | [
"cs.LG",
"cs.AI",
"cs.SI"
] | Link prediction is a fundamental problem in graph data. In its most realistic setting, the problem consists of predicting missing or future links between random pairs of nodes from the set of disconnected pairs. Graph Neural Networks (GNNs) have become the predominant framework for link prediction. GNN-based methods tr... | {
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2412.00268 | Grasping and Rolling In-plane Manipulation Using Deployable Tape spring
Appendages | [
"cs.RO"
] | Rigid multi-link robotic arms face a tradeoff between their overall reach distance (the workspace), and how compactly they can be collapsed (the storage volume). Increasing the workspace of a robot arm requires longer links, which adds weight to the system and requires a larger storage volume. However, the tradeoff bet... | {
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2412.00270 | Optimal Transmission Switching and Busbar Splitting in Hybrid AC/DC
Grids | [
"eess.SY",
"cs.SY"
] | Driven by global climate goals, an increasing amount of Renewable Energy Sources (RES) is currently being installed worldwide. Especially in the context of offshore wind integration, hybrid AC/DC grids are considered to be the most effective technology to transmit this RES power over long distances. As hybrid AC/DC sys... | {
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2412.00274 | Construction of observable and MDP convolutional codes with good
decodable properties for erasure channels by I/S/O representations | [
"cs.IT",
"math.IT"
] | This paper addresses the construction of observable convolutional codes that exhibit good performance with the available decoding algorithms for erasure channels. Our construction is based on the use of input/state/output (I/S/O) representations and the invariance of certain properties of linear systems under various g... | {
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2412.00277 | Facial Expression Recognition with Controlled Privacy Preservation and
Feature Compensation | [
"cs.CV"
] | Facial expression recognition (FER) systems raise significant privacy concerns due to the potential exposure of sensitive identity information. This paper presents a study on removing identity information while preserving FER capabilities. Drawing on the observation that low-frequency components predominantly contain i... | {
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2412.00278 | Average-Over-Time Spiking Neural Networks for Uncertainty Estimation in
Regression | [
"cs.LG",
"cs.AI",
"cs.NE"
] | Uncertainty estimation is a standard tool to quantify the reliability of modern deep learning models, and crucial for many real-world applications. However, efficient uncertainty estimation methods for spiking neural networks, particularly for regression models, have been lacking. Here, we introduce two methods that ad... | {
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2412.00281 | Streamlining the review process: AI-generated annotations in research
manuscripts | [
"cs.AI"
] | The increasing volume of research paper submissions poses a significant challenge to the traditional academic peer-review system, leading to an overwhelming workload for reviewers. This study explores the potential of integrating Large Language Models (LLMs) into the peer-review process to enhance efficiency without co... | {
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2412.00283 | Hyperspectral Images Efficient Spatial and Spectral non-Linear Model
with Bidirectional Feature Learning | [
"cs.CV"
] | Classifying hyperspectral images (HSIs) is a complex task in remote sensing due to the high-dimensional nature and volume of data involved. To address these challenges, we propose the Spectral-Spatial non-Linear Model, a novel framework that significantly reduces data volume while enhancing classification accuracy. Our... | {
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2412.00290 | Adapting the re-ID challenge for static sensors | [
"cs.CV",
"cs.AI"
] | In both 2016 and 2018, a census of the highly-endangered Grevy's zebra population was enabled by the Great Grevy's Rally (GGR), a citizen science event that produces population estimates via expert and algorithmic curation of volunteer-captured images. A complementary, scalable, and long-term Grevy's population monitor... | {
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2412.00291 | Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor
Environments | [
"cs.RO",
"cs.CV"
] | The creation of a metric-semantic map, which encodes human-prior knowledge, represents a high-level abstraction of environments. However, constructing such a map poses challenges related to the fusion of multi-modal sensor data, the attainment of real-time mapping performance, and the preservation of structural and sem... | {
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2412.00293 | Adaptformer: Sequence models as adaptive iterative planners | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Despite recent advances in learning-based behavioral planning for autonomous systems, decision-making in multi-task missions remains a challenging problem. For instance, a mission might require a robot to explore an unknown environment, locate the goals, and navigate to them, even if there are obstacles along the way. ... | {
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2412.00295 | Linear Simple Cycle Reservoirs at the edge of stability perform Fourier
decomposition of the input driving signals | [
"cs.NE",
"math.DS"
] | This paper explores the representational structure of linear Simple Cycle Reservoirs (SCR) operating at the edge of stability. We view SCR as providing in their state space feature representations of the input-driving time series. By endowing the state space with the canonical dot-product, we ``reverse engineer" the co... | {
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2412.00300 | PlanCritic: Formal Planning with Human Feedback | [
"cs.AI",
"cs.NE"
] | Real world planning problems are often too complex to be effectively tackled by a single unaided human. To alleviate this, some recent work has focused on developing a collaborative planning system to assist humans in complex domains, with bridging the gap between the system's problem representation and the real world ... | {
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"cs.NE": 1,
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} |
2412.00301 | Bandit Learning in Matching Markets: Utilitarian and Rawlsian
Perspectives | [
"cs.LG",
"cs.GT"
] | Two-sided matching markets have demonstrated significant impact in many real-world applications, including school choice, medical residency placement, electric vehicle charging, ride sharing, and recommender systems. However, traditional models often assume that preferences are known, which is not always the case in mo... | {
"Other": 1,
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} |
2412.00302 | HSLiNets: Hyperspectral Image and LiDAR Data Fusion Using Efficient Dual
Non-Linear Feature Learning Networks | [
"cs.CV",
"eess.IV"
] | The integration of hyperspectral imaging (HSI) and LiDAR data within new linear feature spaces offers a promising solution to the challenges posed by the high-dimensionality and redundancy inherent in HSIs. This study introduces a dual linear fused space framework that capitalizes on bidirectional reversed convolutiona... | {
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} |
2412.00306 | Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic
Alignment | [
"cs.CV"
] | Personalized image generation has emerged from the recent advancements in generative models. However, these generated personalized images often suffer from localized artifacts such as incorrect logos, reducing fidelity and fine-grained identity details of the generated results. Furthermore, there is little prior work t... | {
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} |
2412.00308 | BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for
Severely Episode-Limited RL Settings | [
"cs.LG",
"cs.AI",
"stat.ML"
] | In settings where the application of reinforcement learning (RL) requires running real-world trials, including the optimization of adaptive health interventions, the number of episodes available for learning can be severely limited due to cost or time constraints. In this setting, the bias-variance trade-off of context... | {
"Other": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.00309 | Towards Pixel-Level Prediction for Gaze Following: Benchmark and
Approach | [
"cs.CV"
] | Following the gaze of other people and analyzing the target they are looking at can help us understand what they are thinking, and doing, and predict the actions that may follow. Existing methods for gaze following struggle to perform well in natural scenes with diverse objects, and focus on gaze points rather than obj... | {
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} |
2412.00312 | Raw Audio Classification with Cosine Convolutional Neural Network
(CosCovNN) | [
"cs.SD",
"cs.AI",
"eess.AS"
] | This study explores the field of audio classification from raw waveform using Convolutional Neural Networks (CNNs), a method that eliminates the need for extracting specialised features in the pre-processing step. Unlike recent trends in literature, which often focuses on designing frontends or filters for only the ini... | {
"Other": 0,
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"cs.SD": 1,
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} |
2412.00315 | One Model for One Graph: A New Perspective for Pretraining with
Cross-domain Graphs | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate network patterns, achieving success across different domains. However, existing GNNs require careful domain-specific architecture designs and training from scratch on each dataset, leading to an expertise-intensive process with difficulty... | {
"Other": 0,
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"cs.MA": 0,
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"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.00316 | HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair
Spatial-Temporal Forecasting | [
"cs.LG",
"cs.AI",
"cs.CY"
] | Achieving fair prediction performance across nodes is crucial in the spatial-temporal domain, as it ensures the validity and reliability of forecasting outcomes. However, existing models focus primarily on improving the overall accuracy of the prediction, often neglecting the goal of achieving uniformity in the predict... | {
"Other": 0,
"cs.AI": 1,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2412.00318 | Bayesian FFT Modal Identification for Multi-setup Experimental Modal
Analysis | [
"cs.CE"
] | In full-scale forced vibration tests, the demand often arises to capture high-spatial-resolution mode shapes with limited number of sensors and shakers. Multi-setup experimental modal analysis (EMA) addresses this challenge by roving sensors and shakers across multiple setups. To enable fast and accurate multi-setup EM... | {
"Other": 0,
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"cs.RO": 0,
"cs.SD": 0,
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"cs.SY": 0
} |
2412.00319 | Improving speaker verification robustness with synthetic emotional
utterances | [
"cs.SD",
"cs.AI",
"eess.AS"
] | A speaker verification (SV) system offers an authentication service designed to confirm whether a given speech sample originates from a specific speaker. This technology has paved the way for various personalized applications that cater to individual preferences. A noteworthy challenge faced by SV systems is their abil... | {
"Other": 0,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 1,
"cs.SI": 0,
"cs.SY": 0
} |
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