id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.04857 | Neuro-Symbolic Data Generation for Math Reasoning | [
"cs.AI"
] | A critical question about Large Language Models (LLMs) is whether their apparent deficiency in mathematical reasoning is inherent, or merely a result of insufficient exposure to high-quality mathematical data. To explore this, we developed an automated method for generating high-quality, supervised mathematical dataset... | {
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2412.04858 | Rethink Deep Learning with Invariance in Data Representation | [
"cs.AI"
] | Integrating invariance into data representations is a principled design in intelligent systems and web applications. Representations play a fundamental role, where systems and applications are both built on meaningful representations of digital inputs (rather than the raw data). In fact, the proper design/learning of s... | {
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2412.04859 | Breaking Event Rumor Detection via Stance-Separated Multi-Agent Debate | [
"cs.CL",
"cs.MA"
] | The rapid spread of rumors on social media platforms during breaking events severely hinders the dissemination of the truth. Previous studies reveal that the lack of annotated resources hinders the direct detection of unforeseen breaking events not covered in yesterday's news. Leveraging large language models (LLMs) fo... | {
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2412.04861 | MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution | [
"cs.LG",
"eess.SP"
] | Electrocardiogram (ECG) signals play a crucial role in diagnosing cardiovascular diseases. To reduce power consumption in wearable or portable devices used for long-term ECG monitoring, super-resolution (SR) techniques have been developed, enabling these devices to collect and transmit signals at a lower sampling rate.... | {
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2412.04862 | EXAONE 3.5: Series of Large Language Models for Real-world Use Cases | [
"cs.CL"
] | This technical report introduces the EXAONE 3.5 instruction-tuned language models, developed and released by LG AI Research. The EXAONE 3.5 language models are offered in three configurations: 32B, 7.8B, and 2.4B. These models feature several standout capabilities: 1) exceptional instruction following capabilities in r... | {
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2412.04866 | Near-field Communications with Extremely Large-Scale Uniform Arc Arrays:
Channel Modelling and Performance Analysis | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this letter, we propose a new conformal array architecture, called extremely large-scale uniform arc array (XL-UAA), to improve near-field communication performance. Specifically,under the non-uniform spherical wavefront channel model, we establish mathematical modeling and performance analysis for XL-UAAs. It is sh... | {
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2412.04867 | MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection
Dataset for Tiny Objects | [
"cs.CV"
] | We present MANTA, a visual-text anomaly detection dataset for tiny objects. The visual component comprises over 137.3K images across 38 object categories spanning five typical domains, of which 8.6K images are labeled as anomalous with pixel-level annotations. Each image is captured from five distinct viewpoints to ens... | {
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2412.04868 | NebulaFL: Effective Asynchronous Federated Learning for JointCloud
Computing | [
"cs.DC",
"cs.AI",
"cs.NI"
] | With advancements in AI infrastructure and Trusted Execution Environment (TEE) technology, Federated Learning as a Service (FLaaS) through JointCloud Computing (JCC) is promising to break through the resource constraints caused by heterogeneous edge devices in the traditional Federated Learning (FL) paradigm. Specifica... | {
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2412.04871 | Building a Family of Data Augmentation Models for Low-cost LLM
Fine-tuning on the Cloud | [
"cs.CL"
] | Specializing LLMs in various domain-specific tasks has emerged as a critical step towards achieving high performance. However, the construction and annotation of datasets in specific domains are always very costly. Apart from using superior and expensive closed-source LLM APIs to construct datasets, some open-source mo... | {
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2412.04876 | Dynamic Interference Prediction for In-X 6G Sub-networks | [
"cs.IT",
"math.IT"
] | The sixth generation (6G) industrial Sub-networks (SNs) face several challenges in meeting extreme latency and reliability requirements in the order of 0.1-1 ms and 99.999 -to-99.99999 percentile, respectively. Interference management (IM) plays an integral role in addressing these requirements, especially in ultra-den... | {
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2412.04877 | Fluid Antenna Index Modulation for MIMO Systems: Robust Transmission and
Low-Complexity Detection | [
"cs.IT",
"eess.SP",
"math.IT"
] | The fluid antenna (FA) index modulation (IM)-enabled multiple-input multiple-output (MIMO) system, referred to as FA-IM, significantly enhances spectral efficiency (SE) compared to the conventional FA-assisted MIMO system. To improve robustness against the high spatial correlation among multiple activated ports of the ... | {
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2412.04879 | Automatic Tissue Differentiation in Parotidectomy using Hyperspectral
Imaging | [
"eess.IV",
"cs.CV",
"physics.med-ph"
] | In head and neck surgery, continuous intraoperative tissue differentiation is of great importance to avoid injury to sensitive structures such as nerves and vessels. Hyperspectral imaging (HSI) with neural network analysis could support the surgeon in tissue differentiation. A 3D Convolutional Neural Network with hyper... | {
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2412.04880 | MozzaVID: Mozzarella Volumetric Image Dataset | [
"cs.CV",
"eess.IV"
] | Influenced by the complexity of volumetric imaging, there is a shortage of established datasets useful for benchmarking volumetric deep-learning models. As a consequence, new and existing models are not easily comparable, limiting the development of architectures optimized specifically for volumetric data. To counterac... | {
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2412.04882 | Nonmyopic Global Optimisation via Approximate Dynamic Programming | [
"cs.LG",
"stat.ML"
] | Unconstrained global optimisation aims to optimise expensive-to-evaluate black-box functions without gradient information. Bayesian optimisation, one of the most well-known techniques, typically employs Gaussian processes as surrogate models, leveraging their probabilistic nature to balance exploration and exploitation... | {
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2412.04884 | AI-Driven Non-Invasive Detection and Staging of Steatosis in Fatty Liver
Disease Using a Novel Cascade Model and Information Fusion Techniques | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Non-alcoholic fatty liver disease (NAFLD) is one of the most widespread liver disorders on a global scale, posing a significant threat of progressing to more severe conditions like nonalcoholic steatohepatitis (NASH), liver fibrosis, cirrhosis, and hepatocellular carcinoma. Diagnosing and staging NAFLD presents challen... | {
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2412.04887 | Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large
Scene Reconstruction | [
"cs.CV"
] | 3D Gaussian Splatting has demonstrated notable success in large-scale scene reconstruction, but challenges persist due to high training memory consumption and storage overhead. Hybrid representations that integrate implicit and explicit features offer a way to mitigate these limitations. However, when applied in parall... | {
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2412.04888 | VTD: Visual and Tactile Database for Driver State and Behavior
Perception | [
"cs.RO",
"cs.AI"
] | In the domain of autonomous vehicles, the human-vehicle co-pilot system has garnered significant research attention. To address the subjective uncertainties in driver state and interaction behaviors, which are pivotal to the safety of Human-in-the-loop co-driving systems, we introduce a novel visual-tactile perception ... | {
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2412.04893 | Automatic Tongue Delineation from MRI Images with a Convolutional Neural
Network Approach | [
"cs.AI"
] | Tongue contour extraction from real-time magnetic resonance images is a nontrivial task due to the presence of artifacts manifesting in form of blurring or ghostly contours. In this work, we present results of automatic tongue delineation achieved by means of U-Net auto-encoder convolutional neural network. We present ... | {
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2412.04896 | Comprehensive Analysis and Improvements in Pansharpening Using Deep
Learning | [
"eess.IV",
"cs.CV"
] | Pansharpening is a crucial task in remote sensing, enabling the generation of high-resolution multispectral images by fusing low-resolution multispectral data with high-resolution panchromatic images. This paper provides a comprehensive analysis of traditional and deep learning-based pansharpening methods. While state-... | {
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2412.04898 | Mitigating Instance-Dependent Label Noise: Integrating Self-Supervised
Pretraining with Pseudo-Label Refinement | [
"cs.CV",
"cs.LG"
] | Deep learning models rely heavily on large volumes of labeled data to achieve high performance. However, real-world datasets often contain noisy labels due to human error, ambiguity, or resource constraints during the annotation process. Instance-dependent label noise (IDN), where the probability of a label being corru... | {
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2412.04903 | EACO: Enhancing Alignment in Multimodal LLMs via Critical Observation | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | Multimodal large language models (MLLMs) have achieved remarkable progress on various visual question answering and reasoning tasks leveraging instruction fine-tuning specific datasets. They can also learn from preference data annotated by human to enhance their reasoning ability and mitigate hallucinations. Most of pr... | {
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2412.04905 | DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models (LLMs) enabled dialogue systems have become one of the central modes in human-machine interaction, which bring about vast amounts of conversation logs and increasing demand for dialogue generation. The dialogue's life-cycle spans from $\textit{Prelude}$ through $\textit{Interlocution}$ to $\textit... | {
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2412.04908 | PERCY: A Multimodal Dataset and Conversational System for Personalized
and Emotionally Aware Human-Robot Interaction | [
"cs.HC",
"cs.ET",
"cs.RO"
] | The integration of conversational agents into our daily lives has become increasingly common, yet many of these agents cannot engage in deep interactions with humans. Despite this, there is a noticeable shortage of datasets that capture multimodal information from human-robot interaction dialogues. To address this gap,... | {
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2412.04910 | Learning High-Degree Parities: The Crucial Role of the Initialization | [
"cs.LG"
] | Parities have become a standard benchmark for evaluating learning algorithms. Recent works show that regular neural networks trained by gradient descent can efficiently learn degree $k$ parities on uniform inputs for constant $k$, but fail to do so when $k$ and $d-k$ grow with $d$ (here $d$ is the ambient dimension). H... | {
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2412.04912 | UniMIC: Towards Universal Multi-modality Perceptual Image Compression | [
"eess.IV",
"cs.CV"
] | We present UniMIC, a universal multi-modality image compression framework, intending to unify the rate-distortion-perception (RDP) optimization for multiple image codecs simultaneously through excavating cross-modality generative priors. Unlike most existing works that need to design and optimize image codecs from scra... | {
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2412.04914 | Achieving Group Fairness through Independence in Predictive Process
Monitoring | [
"cs.LG",
"stat.ML"
] | Predictive process monitoring focuses on forecasting future states of ongoing process executions, such as predicting the outcome of a particular case. In recent years, the application of machine learning models in this domain has garnered significant scientific attention. When using historical execution data, which may... | {
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2412.04915 | Beyond Boxes: Mask-Guided Spatio-Temporal Feature Aggregation for Video
Object Detection | [
"cs.CV"
] | The primary challenge in Video Object Detection (VOD) is effectively exploiting temporal information to enhance object representations. Traditional strategies, such as aggregating region proposals, often suffer from feature variance due to the inclusion of background information. We introduce a novel instance mask-base... | {
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2412.04919 | Hard Math -- Easy UVM: Pragmatic solutions for verifying hardware
algorithms using UVM | [
"cs.AI",
"cs.AR",
"cs.LO"
] | This paper presents pragmatic solutions for verifying complex mathematical algorithms implemented in hardware in an efficient and effective manner. Maximizing leverage of a known-answer-test strategy, based on predefined data scenarios combined with design-for-verification modes, we demonstrate how to find and isolate ... | {
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2412.04922 | Large Language Models for Ingredient Substitution in Food Recipes using
Supervised Fine-tuning and Direct Preference Optimization | [
"cs.CL"
] | In this paper, we address the challenge of recipe personalization through ingredient substitution. We make use of Large Language Models (LLMs) to build an ingredient substitution system designed to predict plausible substitute ingredients within a given recipe context. Given that the use of LLMs for this task has been ... | {
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2412.04923 | HyperGraphOS: A Meta Operating System for Science and Engineering | [
"cs.AI",
"cs.MA"
] | This paper presents HyperGraphOS, an innovative Operating System designed for the scientific and engineering domains. It combines model based engineering, graph modeling, data containers, and computational tools, offering users a dynamic workspace for creating and managing complex models represented as customizable gra... | {
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2412.04924 | Follow the money: a startup-based measure of AI exposure across
occupations, industries and regions | [
"econ.GN",
"cs.AI",
"q-fin.EC"
] | The integration of artificial intelligence (AI) into the workplace is advancing rapidly, necessitating robust metrics to evaluate its tangible impact on the labour market. Existing measures of AI occupational exposure largely focus on AI's theoretical potential to substitute or complement human labour on the basis of t... | {
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2412.04925 | $S^3$: Synonymous Semantic Space for Improving Zero-Shot Generalization
of Vision-Language Models | [
"cs.CV"
] | Recently, many studies have been conducted to enhance the zero-shot generalization ability of vision-language models (e.g., CLIP) by addressing the semantic misalignment between image and text embeddings in downstream tasks. Although many efforts have been made, existing methods barely consider the fact that a class of... | {
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2412.04929 | Continuous Video Process: Modeling Videos as Continuous
Multi-Dimensional Processes for Video Prediction | [
"cs.CV",
"cs.AI",
"cs.LG",
"stat.ML"
] | Diffusion models have made significant strides in image generation, mastering tasks such as unconditional image synthesis, text-image translation, and image-to-image conversions. However, their capability falls short in the realm of video prediction, mainly because they treat videos as a collection of independent image... | {
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2412.04930 | Video Decomposition Prior: A Methodology to Decompose Videos into Layers | [
"cs.CV",
"cs.LG"
] | In the evolving landscape of video enhancement and editing methodologies, a majority of deep learning techniques often rely on extensive datasets of observed input and ground truth sequence pairs for optimal performance. Such reliance often falters when acquiring data becomes challenging, especially in tasks like video... | {
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2412.04931 | DEYOLO: Dual-Feature-Enhancement YOLO for Cross-Modality Object
Detection | [
"cs.CV"
] | Object detection in poor-illumination environments is a challenging task as objects are usually not clearly visible in RGB images. As infrared images provide additional clear edge information that complements RGB images, fusing RGB and infrared images has potential to enhance the detection ability in poor-illumination ... | {
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2412.04935 | Uncertainty-aware retinal layer segmentation in OCT through
probabilistic signed distance functions | [
"eess.IV",
"cs.AI",
"cs.CV"
] | In this paper, we present a new approach for uncertainty-aware retinal layer segmentation in Optical Coherence Tomography (OCT) scans using probabilistic signed distance functions (SDF). Traditional pixel-wise and regression-based methods primarily encounter difficulties in precise segmentation and lack of geometrical ... | {
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2412.04936 | Probing the contents of semantic representations from text, behavior,
and brain data using the psychNorms metabase | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Semantic representations are integral to natural language processing, psycholinguistics, and artificial intelligence. Although often derived from internet text, recent years have seen a rise in the popularity of behavior-based (e.g., free associations) and brain-based (e.g., fMRI) representations, which promise improve... | {
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2412.04937 | Who Speaks Next? Multi-party AI Discussion Leveraging the Systematics of
Turn-taking in Murder Mystery Games | [
"cs.CL",
"cs.AI"
] | Multi-agent systems utilizing large language models (LLMs) have shown great promise in achieving natural dialogue. However, smooth dialogue control and autonomous decision making among agents still remain challenges. In this study, we focus on conversational norms such as adjacency pairs and turn-taking found in conver... | {
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2412.04938 | Solving 1D Poisson problem with a Variational Quantum Linear Solver | [
"cs.CE",
"cs.NA",
"math.NA"
] | Different hybrid quantum-classical algorithms have recently been developed as a near-term way to solve linear systems of equations on quantum devices. However, the focus has so far been mostly on the methods, rather than the problems that they need to tackle. In fact, these algorithms have been run on real hardware onl... | {
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2412.04939 | Verb Mirage: Unveiling and Assessing Verb Concept Hallucinations in
Multimodal Large Language Models | [
"cs.CV"
] | Multimodal Large Language Models (MLLMs) have garnered significant attention recently and demonstrate outstanding capabilities in various tasks such as OCR, VQA, captioning, $\textit{etc}$. However, hallucination remains a persistent issue. While numerous methods have been proposed to mitigate hallucinations, achieving... | {
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2412.04942 | A Federated Approach to Few-Shot Hate Speech Detection for Marginalized
Communities | [
"cs.CL",
"cs.AI"
] | Hate speech online remains an understudied issue for marginalized communities, and has seen rising relevance, especially in the Global South, which includes developing societies with increasing internet penetration. In this paper, we aim to provide marginalized communities living in societies where the dominant languag... | {
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2412.04945 | HOLa: HoloLens Object Labeling | [
"cs.CV",
"cs.LG"
] | In the context of medical Augmented Reality (AR) applications, object tracking is a key challenge and requires a significant amount of annotation masks. As segmentation foundation models like the Segment Anything Model (SAM) begin to emerge, zero-shot segmentation requires only minimal human participation obtaining hig... | {
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2412.04947 | C$^2$LEVA: Toward Comprehensive and Contamination-Free Language Model
Evaluation | [
"cs.CL"
] | Recent advances in large language models (LLMs) have shown significant promise, yet their evaluation raises concerns, particularly regarding data contamination due to the lack of access to proprietary training data. To address this issue, we present C$^2$LEVA, a comprehensive bilingual benchmark featuring systematic co... | {
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2412.04948 | KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view
Knowledge Graph Contrastive Learning | [
"cs.CL",
"cs.AI"
] | Autoregressive large language models (LLMs) pre-trained by next token prediction are inherently proficient in generative tasks. However, their performance on knowledge-driven tasks such as factual knowledge querying remains unsatisfactory. Knowledge graphs (KGs), as high-quality structured knowledge bases, can provide ... | {
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2412.04950 | Bed-Attached Vibration Sensor System: A Machine Learning Approach for
Fall Detection in Nursing Homes | [
"cs.LG",
"cs.AI"
] | The increasing shortage of nursing staff and the acute risk of falls in nursing homes pose significant challenges for the healthcare system. This study presents the development of an automated fall detection system integrated into care beds, aimed at enhancing patient safety without compromising privacy through wearabl... | {
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2412.04954 | Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for
Radiology Report Generation | [
"cs.CV",
"cs.CL",
"cs.LG"
] | We introduce a radiology-focused visual language model designed to generate radiology reports from chest X-rays. Building on previous findings that large language models (LLMs) can acquire multimodal capabilities when aligned with pretrained vision encoders, we demonstrate similar potential with chest X-ray images. Thi... | {
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2412.04955 | MixedGaussianAvatar: Realistically and Geometrically Accurate Head
Avatar via Mixed 2D-3D Gaussian Splatting | [
"cs.CV"
] | Reconstructing high-fidelity 3D head avatars is crucial in various applications such as virtual reality. The pioneering methods reconstruct realistic head avatars with Neural Radiance Fields (NeRF), which have been limited by training and rendering speed. Recent methods based on 3D Gaussian Splatting (3DGS) significant... | {
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2412.04964 | Flash Communication: Reducing Tensor Parallelization Bottleneck for Fast
Large Language Model Inference | [
"cs.AI"
] | The ever-increasing sizes of large language models necessitate distributed solutions for fast inference that exploit multi-dimensional parallelism, where computational loads are split across various accelerators such as GPU clusters. However, this approach often introduces significant communication overhead, especially... | {
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2412.04974 | Putting the Iterative Training of Decision Trees to the Test on a
Real-World Robotic Task | [
"cs.LG",
"cs.AI",
"cs.RO"
] | In previous research, we developed methods to train decision trees (DT) as agents for reinforcement learning tasks, based on deep reinforcement learning (DRL) networks. The samples from which the DTs are built, use the environment's state as features and the corresponding action as label. To solve the nontrivial task o... | {
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2412.04975 | PETapter: Leveraging PET-style classification heads for modular few-shot
parameter-efficient fine-tuning | [
"cs.CL"
] | Few-shot learning and parameter-efficient fine-tuning (PEFT) are crucial to overcome the challenges of data scarcity and ever growing language model sizes. This applies in particular to specialized scientific domains, where researchers might lack expertise and resources to fine-tune high-performing language models to n... | {
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2412.04981 | Causal discovery with endogenous context variables | [
"cs.LG",
"math.ST",
"stat.TH"
] | Causal systems often exhibit variations of the underlying causal mechanisms between the variables of the system. Often, these changes are driven by different environments or internal states in which the system operates, and we refer to context variables as those variables that indicate this change in causal mechanisms.... | {
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2412.04984 | Frontier Models are Capable of In-context Scheming | [
"cs.AI",
"cs.LG"
] | Frontier models are increasingly trained and deployed as autonomous agent. One safety concern is that AI agents might covertly pursue misaligned goals, hiding their true capabilities and objectives - also known as scheming. We study whether models have the capability to scheme in pursuit of a goal that we provide in-co... | {
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2412.04986 | Power Plant Detection for Energy Estimation using GIS with Remote
Sensing, CNN & Vision Transformers | [
"cs.LG",
"cs.CV"
] | In this research, we propose a hybrid model for power plant detection to assist energy estimation applications, by pipelining GIS (Geographical Information Systems) having Remote Sensing capabilities with CNN (Convolutional Neural Networks) and ViT (Vision Transformers). Our proposed approach enables real-time analysis... | {
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2412.04987 | FlowPolicy: Enabling Fast and Robust 3D Flow-based Policy via
Consistency Flow Matching for Robot Manipulation | [
"cs.RO"
] | Robots can acquire complex manipulation skills by learning policies from expert demonstrations, which is often known as vision-based imitation learning. Generating policies based on diffusion and flow matching models has been shown to be effective, particularly in robotic manipulation tasks. However, recursion-based ap... | {
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2412.04989 | Location-Driven Programmable Wireless Environments through
Light-emitting RIS (LeRIS) | [
"cs.NI",
"cs.IT",
"math.IT"
] | As 6G wireless networks seek to enable robust and dynamic programmable wireless environments (PWEs), reconfigurable intelligent surfaces (RISs) have emerged as a cornerstone for controlling electromagnetic wave propagation. However, realizing the potential of RISs for demanding PWE applications depends on precise and r... | {
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2412.04990 | ETLNet: An Efficient TCN-BiLSTM Network for Road Anomaly Detection Using
Smartphone Sensors | [
"cs.CV",
"cs.AI"
] | Road anomalies can be defined as irregularities on the road surface or in the surface itself. Some may be intentional (such as speedbumps), accidental (such as materials falling off a truck), or the result of roads' excessive use or low or no maintenance, such as potholes. Despite their varying origins, these irregular... | {
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2412.04999 | 'Debunk-It-Yourself': Health Professionals' Strategies for Responding to
Misinformation on TikTok | [
"cs.CR",
"cs.CY",
"cs.HC",
"cs.SI"
] | Misinformation is "sticky" in nature, requiring a considerable effort to undo its influence. One such effort is debunking or exposing the falsity of information. As an abundance of misinformation is on social media, platforms do bear some debunking responsibility in order to preserve their trustworthiness as informatio... | {
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2412.05000 | Noise Matters: Diffusion Model-based Urban Mobility Generation with
Collaborative Noise Priors | [
"cs.LG"
] | With global urbanization, the focus on sustainable cities has largely grown, driving research into equity, resilience, and urban planning, which often relies on mobility data. The rise of web-based apps and mobile devices has provided valuable user data for mobility-related research. However, real-world mobility data i... | {
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2412.05003 | SLayR: Scene Layout Generation with Rectified Flow | [
"cs.CV"
] | We introduce SLayR, Scene Layout Generation with Rectified flow. State-of-the-art text-to-image models achieve impressive results. However, they generate images end-to-end, exposing no fine-grained control over the process. SLayR presents a novel transformer-based rectified flow model for layout generation over a token... | {
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2412.05004 | Prompt Transfer for Dual-Aspect Cross Domain Cognitive Diagnosis | [
"cs.LG",
"cs.CY"
] | Cognitive Diagnosis (CD) aims to evaluate students' cognitive states based on their interaction data, enabling downstream applications such as exercise recommendation and personalized learning guidance. However, existing methods often struggle with accuracy drops in cross-domain cognitive diagnosis (CDCD), a practical ... | {
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2412.05006 | Analog-Only Beamforming for Near-Field Multiuser MIMO Communications | [
"cs.IT",
"eess.SP",
"math.IT"
] | For the existing near-field multiuser communications based on hybrid beamforming (HBF) architectures, high-quality effective channel estimation is required to obtain the channel state information (CSI) for the design of the digital beamformer. To simplify the system reconfiguration and eliminate the pilot overhead requ... | {
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2412.05010 | Backdooring Outlier Detection Methods: A Novel Attack Approach | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.CV"
] | There have been several efforts in backdoor attacks, but these have primarily focused on the closed-set performance of classifiers (i.e., classification). This has left a gap in addressing the threat to classifiers' open-set performance, referred to as outlier detection in the literature. Reliable outlier detection is ... | {
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2412.05011 | Galois self-orthogonal MDS codes with large dimensions | [
"cs.IT",
"math.IT"
] | Let $q=p^m$ be a prime power, $e$ be an integer with $0\leq e\leq m-1$ and $s=\gcd(e,m)$. In this paper, for a vector $v$ and a $q$-ary linear code $C$, we give some necessary and sufficient conditions for the equivalent code $vC$ of $C$ and the extended code of $vC$ to be $e$-Galois self-orthogonal. From this, we dire... | {
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2412.05012 | SAMCL: Empowering SAM to Continually Learn from Dynamic Domains | [
"cs.CV"
] | Segment Anything Model (SAM) struggles with segmenting objects in the open world, especially across diverse and dynamic domains. Continual segmentation (CS) is a potential technique to solve this issue, but a significant obstacle is the intractable balance between previous domains (stability) and new domains (plasticit... | {
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2412.05013 | Project Report: Requirements for a Social Robot as an Information
Provider in the Public Sector | [
"cs.RO",
"cs.AI"
] | Is it possible to integrate a humanoid social robot into the work processes or customer care in an official environment, e.g. in municipal offices? If so, what could such an application scenario look like and what skills would the robot need to have when interacting with human customers? What are requirements for this ... | {
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2412.05022 | Get It Right: Improving Comprehensibility with Adaptable Speech
Expression of a Humanoid Service Robot | [
"cs.RO",
"cs.AI"
] | As humanoid service robots are becoming more and more perceptible in public service settings for instance as a guide to welcome visitors or to explain a procedure to follow, it is desirable to improve the comprehensibility of complex issues for human customers and to adapt the level of difficulty of the information pro... | {
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2412.05023 | Steps are all you need: Rethinking STEM Education with Prompt
Engineering | [
"cs.CL"
] | Few shot and Chain-of-Thought prompting have shown promise when applied to Physics Question Answering Tasks, but are limited by the lack of mathematical ability inherent to LLMs, and are prone to hallucination. By utilizing a Mixture of Experts (MoE) Model, along with analogical prompting, we are able to show improved ... | {
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2412.05024 | Talking Like One of Us: Effects of Using Regional Language in a Humanoid
Social Robot | [
"cs.RO",
"cs.AI"
] | Social robots are becoming more and more perceptible in public service settings. For engaging people in a natural environment a smooth social interaction as well as acceptance by the users are important issues for future successful Human-Robot Interaction (HRI). The type of verbal communication has a special significan... | {
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2412.05028 | Unifying Dual-Space Embedding for Entity Alignment via Contrastive
Learning | [
"cs.CL"
] | Entity alignment aims to match identical entities across different knowledge graphs (KGs). Graph neural network-based entity alignment methods have achieved promising results in Euclidean space. However, KGs often contain complex structures, including both local and hierarchical ones, which make it challenging to effic... | {
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2412.05029 | Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial
Label Learning | [
"cs.LG"
] | In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. The conventional PLL assumes the noisy labels are randomly generated (instance-independent), while in practical scenarios, the noisy labels are always instance-dependent and... | {
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2412.05035 | SMIC: Semantic Multi-Item Compression based on CLIP dictionary | [
"eess.IV",
"cs.CV",
"cs.MM"
] | Semantic compression, a compression scheme where the distortion metric, typically MSE, is replaced with semantic fidelity metrics, tends to become more and more popular. Most recent semantic compression schemes rely on the foundation model CLIP. In this work, we extend such a scheme to image collection compression, whe... | {
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2412.05037 | Mechanical State Estimation with a Polynomial-Chaos-Based Statistical
Finite Element Method | [
"cs.CE"
] | The Statistical Finite Element Method (statFEM) offers a Bayesian framework for integrating computational models with observational data, thus providing improved predictions for structural health monitoring and digital twinning. This paper presents an efficient sampling-free statFEM tailored for non-conjugate, non-Gaus... | {
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2412.05042 | Improving Post-Earthquake Crack Detection using Semi-Synthetic Generated
Images | [
"cs.CV",
"cs.AI"
] | Following an earthquake, it is vital to quickly evaluate the safety of the impacted areas. Damage detection systems, powered by computer vision and deep learning, can assist experts in this endeavor. However, the lack of extensive, labeled datasets poses a challenge to the development of these systems. In this study, w... | {
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2412.05043 | ReF-LDM: A Latent Diffusion Model for Reference-based Face Image
Restoration | [
"cs.CV",
"cs.LG",
"eess.IV"
] | While recent works on blind face image restoration have successfully produced impressive high-quality (HQ) images with abundant details from low-quality (LQ) input images, the generated content may not accurately reflect the real appearance of a person. To address this problem, incorporating well-shot personal images a... | {
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2412.05049 | OCEAN: Open-World Contrastive Authorship Identification | [
"cs.AI",
"cs.CR"
] | In an era where cyberattacks increasingly target the software supply chain, the ability to accurately attribute code authorship in binary files is critical to improving cybersecurity measures. We propose OCEAN, a contrastive learning-based system for function-level authorship attribution. OCEAN is the first framework t... | {
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2412.05053 | EvTTC: An Event Camera Dataset for Time-to-Collision Estimation | [
"cs.RO",
"cs.CV"
] | Time-to-Collision (TTC) estimation lies in the core of the forward collision warning (FCW) functionality, which is key to all Automatic Emergency Braking (AEB) systems. Although the success of solutions using frame-based cameras (e.g., Mobileye's solutions) has been witnessed in normal situations, some extreme cases, s... | {
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2412.05058 | Spatial Bandwidth of Bilateral Near-Field Channels for Linear
Large-Scale Antenna Array System | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper analyzes the spatial bandwidth of line-of-sight (LoS) channels in massive MIMO systems. For the linear large-scale antenna arrays (LSAA) of transceivers placed in random locations in 3D space, a simple but accurate closed-form expression is derived to characterize the spatial bandwidth. Subsequent analysis o... | {
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2412.05065 | Reconstruction of 3D lumbar spine models from incomplete segmentations
using landmark detection | [
"eess.IV",
"cs.CV"
] | Patient-specific 3D spine models serve as a foundation for spinal treatment and surgery planning as well as analysis of loading conditions in biomechanical and biomedical research. Despite advancements in imaging technologies, the reconstruction of complete 3D spine models often faces challenges due to limitations in i... | {
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2412.05066 | BimArt: A Unified Approach for the Synthesis of 3D Bimanual Interaction
with Articulated Objects | [
"cs.CV",
"cs.GR",
"cs.RO"
] | We present BimArt, a novel generative approach for synthesizing 3D bimanual hand interactions with articulated objects. Unlike prior works, we do not rely on a reference grasp, a coarse hand trajectory, or separate modes for grasping and articulating. To achieve this, we first generate distance-based contact maps condi... | {
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2412.05074 | LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracking | [
"cs.CV",
"eess.SP"
] | Data-driven Wi-Fi localization and tracking have shown great promise due to their lower reliance on specialized hardware compared to model-based methods. However, most existing data collection techniques provide only coarse-grained ground truth or a limited number of labeled points, significantly hindering the advancem... | {
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2412.05076 | Improving analytical color and texture similarity estimation methods for
dataset-agnostic person reidentification | [
"cs.CV"
] | This paper studies a combined person reidentification (re-id) method that uses human parsing, analytical feature extraction and similarity estimation schemes. One of its prominent features is its low computational requirements so it can be implemented on edge devices. The method allows direct comparison of specific ima... | {
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2412.05078 | An Experimental Framework for Implementing Decentralized Autonomous
Database Systems in Rust | [
"cs.DB",
"cs.DC"
] | This paper presents an experimental framework for implementing Decentralized Autonomous Database Systems (DADBS) using the Rust programming language. As traditional centralized databases face challenges in scalability, security, and autonomy, DADBS emerge as a promising solution, using blockchain principles to create d... | {
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2412.05081 | Spinal ligaments detection on vertebrae meshes using registration and 3D
edge detection | [
"cs.CV"
] | Spinal ligaments are crucial elements in the complex biomechanical simulation models as they transfer forces on the bony structure, guide and limit movements and stabilize the spine. The spinal ligaments encompass seven major groups being responsible for maintaining functional interrelationships among the other spinal ... | {
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2412.05084 | Reconstructing Quantitative Cerebral Perfusion Images Directly From
Measured Sinogram Data Acquired Using C-arm Cone-Beam CT | [
"eess.IV",
"cs.CV",
"physics.med-ph"
] | To shorten the door-to-puncture time for better treating patients with acute ischemic stroke, it is highly desired to obtain quantitative cerebral perfusion images using C-arm cone-beam computed tomography (CBCT) equipped in the interventional suite. However, limited by the slow gantry rotation speed, the temporal reso... | {
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2412.05091 | Designing a Secure, Scalable, and Cost-Effective Cloud Storage Solution:
A Novel Approach to Data Management using NextCloud, TrueNAS, and QEMU/KVM | [
"cs.DB",
"cs.CR"
] | This paper presents a novel approach to cloud storage challenges by integrating NextCloud, TrueNAS, and QEMU/KVM. Our research demonstrates how this combination creates a robust, flexible, and economical cloud storage system suitable for various applications. We detail the architecture, highlighting TrueNAS's ZFS-based... | {
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2412.05095 | SoPo: Text-to-Motion Generation Using Semi-Online Preference
Optimization | [
"cs.CV"
] | Text-to-motion generation is essential for advancing the creative industry but often presents challenges in producing consistent, realistic motions. To address this, we focus on fine-tuning text-to-motion models to consistently favor high-quality, human-preferred motions, a critical yet largely unexplored problem. In t... | {
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2412.05098 | From Defects to Demands: A Unified, Iterative, and Heuristically Guided
LLM-Based Framework for Automated Software Repair and Requirement Realization | [
"cs.SE",
"cs.AI"
] | This manuscript signals a new era in the integration of artificial intelligence with software engineering, placing machines at the pinnacle of coding capability. We present a formalized, iterative methodology proving that AI can fully replace human programmers in all aspects of code creation and refinement. Our approac... | {
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2412.05101 | The Silent Prompt: Initial Noise as Implicit Guidance for Goal-Driven
Image Generation | [
"cs.CV"
] | Text-to-image synthesis (T2I) has advanced remarkably with the emergence of large-scale diffusion models. In the conventional setup, the text prompt provides explicit, user-defined guidance, directing the generation process by denoising a randomly sampled Gaussian noise. In this work, we reveal that the often-overlooke... | {
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2412.05102 | Exact Model Reduction for Continuous-Time Open Quantum Dynamics | [
"quant-ph",
"cs.SY",
"eess.SY",
"math-ph",
"math.MP"
] | We consider finite-dimensional many-body quantum systems described by time-independent Hamiltonians and Markovian master equations, and present a systematic method for constructing smaller-dimensional, reduced models that exactly reproduce the time evolution of a set of initial conditions or observables of interest. Ou... | {
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"cs.SY": 1
} |
2412.05103 | Integrating Semantic Communication and Human Decision-Making into an
End-to-End Sensing-Decision Framework | [
"eess.SP",
"cs.HC",
"cs.LG"
] | As early as 1949, Weaver defined communication in a very broad sense to include all procedures by which one mind or technical system can influence another, thus establishing the idea of semantic communication. With the recent success of machine learning in expert assistance systems where sensed information is wirelessl... | {
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} |
2412.05109 | Generating Rectifiable Measures through Neural Networks | [
"cs.LG",
"cs.IT",
"math.IT",
"math.PR",
"math.ST",
"stat.ML",
"stat.TH"
] | We derive universal approximation results for the class of (countably) $m$-rectifiable measures. Specifically, we prove that $m$-rectifiable measures can be approximated as push-forwards of the one-dimensional Lebesgue measure on $[0,1]$ using ReLU neural networks with arbitrarily small approximation error in terms of ... | {
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} |
2412.05112 | Modeling Task Immersion based on Goal Activation Mechanism | [
"cs.AI"
] | Immersion in a task is a prerequisite for creativity. However, excessive arousal in a single task has drawbacks, such as overlooking events outside of the task. To examine such a negative aspect, this study constructs a computational model of arousal dynamics where the excessively increased arousal makes the task trans... | {
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} |
2412.05114 | A*Net and NBFNet Learn Negative Patterns on Knowledge Graphs | [
"cs.AI"
] | In this technical report, we investigate the predictive performance differences of a rule-based approach and the GNN architectures NBFNet and A*Net with respect to knowledge graph completion. For the two most common benchmarks, we find that a substantial fraction of the performance difference can be explained by one un... | {
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} |
2412.05117 | Transformers Can Navigate Mazes With Multi-Step Prediction | [
"cs.LG"
] | Despite their remarkable success in language modeling, transformers trained to predict the next token in a sequence struggle with long-term planning. This limitation is particularly evident in tasks requiring foresight to plan multiple steps ahead such as maze navigation. The standard next single token prediction objec... | {
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} |
2412.05124 | Feature-Based Network Construction: From Sampling to What-if Analysis | [
"physics.soc-ph",
"cs.SI"
] | Networks are characterized by structural features, such as degree distribution, triangular closures, and assortativity. This paper addresses the problem of reconstructing instances of continuously (and non-negatively) weighted networks from given feature values. We introduce the gradient-based Feature-Based Network Con... | {
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} |
2412.05126 | Robust Computation with Intrinsic Heterogeneity | [
"cs.LG",
"cs.NE"
] | Intrinsic within-type neuronal heterogeneity is a ubiquitous feature of biological systems, with well-documented computational advantages. Recent works in machine learning have incorporated such diversities by optimizing neuronal parameters alongside synaptic connections and demonstrated state-of-the-art performance ac... | {
"Other": 0,
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} |
2412.05127 | The Prompt Canvas: A Literature-Based Practitioner Guide for Creating
Effective Prompts in Large Language Models | [
"cs.AI"
] | The rise of large language models (LLMs) has highlighted the importance of prompt engineering as a crucial technique for optimizing model outputs. While experimentation with various prompting methods, such as Few-shot, Chain-of-Thought, and role-based techniques, has yielded promising results, these advancements remain... | {
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} |
2412.05130 | Technology as uncharted territory: Contextual integrity and the notion
of AI as new ethical ground | [
"cs.AI"
] | Recent research illustrates how AI can be developed and deployed in a manner detached from the concrete social context of application. By abstracting from the contexts of AI application, practitioners also disengage from the distinct normative structures that govern them. Building upon Helen Nissenbaum's framework of c... | {
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} |
2412.05132 | Dirac-Equation Signal Processing: Physics Boosts Topological Machine
Learning | [
"cond-mat.dis-nn",
"cs.LG",
"cs.SI",
"physics.soc-ph",
"quant-ph"
] | Topological signals are variables or features associated with both nodes and edges of a network. Recently, in the context of Topological Machine Learning, great attention has been devoted to signal processing of such topological signals. Most of the previous topological signal processing algorithms treat node and edge ... | {
"Other": 0,
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} |
2412.05133 | Learning Hidden Physics and System Parameters with Deep Operator
Networks | [
"cs.LG"
] | Big data is transforming scientific progress by enabling the discovery of novel models, enhancing existing frameworks, and facilitating precise uncertainty quantification, while advancements in scientific machine learning complement this by providing powerful tools to solve inverse problems to identify the complex syst... | {
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} |
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