id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.06831 | TransitGPT: A Generative AI-based framework for interacting with GTFS
data using Large Language Models | [
"cs.CL",
"cs.AI",
"stat.AP"
] | This paper introduces a framework that leverages Large Language Models (LLMs) to answer natural language queries about General Transit Feed Specification (GTFS) data. The framework is implemented in a chatbot called TransitGPT with open-source code. TransitGPT works by guiding LLMs to generate Python code that extracts... | {
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2412.06832 | SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to
Question Answering | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.DC"
] | Retrieval Augmented Generation (RAG) enables Large Language Models (LLMs) to generalize to new information by decoupling reasoning capabilities from static knowledge bases. Traditional RAG enhancements have explored vertical scaling -- assigning subtasks to specialized modules -- and horizontal scaling -- replicating t... | {
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2412.06833 | Detecting Fake News on Social Media: A Novel Reliability Aware
Machine-Crowd Hybrid Intelligence-Based Method | [
"cs.LG",
"cs.AI",
"cs.SI"
] | Fake news on social media platforms poses a significant threat to societal systems, underscoring the urgent need for advanced detection methods. The existing detection methods can be divided into machine intelligence-based, crowd intelligence-based, and hybrid intelligence-based methods. Among them, hybrid intelligence... | {
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2412.06834 | Investigating social alignment via mirroring in a system of interacting
language models | [
"cs.MA",
"cs.AI",
"cs.CY"
] | Alignment is a social phenomenon wherein individuals share a common goal or perspective. Mirroring, or mimicking the behaviors and opinions of another individual, is one mechanism by which individuals can become aligned. Large scale investigations of the effect of mirroring on alignment have been limited due to the sca... | {
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2412.06835 | APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies
for Flood Forecasting | [
"cs.LG",
"cs.AI"
] | Accurate flood prediction is crucial for disaster prevention and mitigation. Hydrological data exhibit highly nonlinear temporal patterns and encompass complex spatial relationships between rainfall and flow. Existing flood prediction models struggle to capture these intricate temporal features and spatial dependencies... | {
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2412.06836 | GRUvader: Sentiment-Informed Stock Market Prediction | [
"cs.LG",
"cs.AI",
"stat.AP"
] | Stock price prediction is challenging due to global economic instability, high volatility, and the complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market prediction and further examined the influence of a sentiment analysis indicator on the prediction of stock p... | {
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2412.06837 | Innovative Sentiment Analysis and Prediction of Stock Price Using
FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach | [
"cs.LG",
"cs.AI",
"q-fin.ST",
"stat.AP",
"stat.CO"
] | This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share In... | {
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2412.06838 | Hardware implementation of timely reliable Bayesian decision-making
using memristors | [
"cs.LG",
"cs.AR"
] | Brains perform decision-making by Bayes theorem. The theorem quantifies events as probabilities and, based on probability rules, renders the decisions. Learning from this, Bayes theorem can be applied to enable efficient user-scene interactions. However, given the probabilistic nature, implementing Bayes theorem in har... | {
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2412.06839 | A Neural Model of Rule Discovery with Relatively Short-Term Sequence
Memory | [
"cs.LG",
"cs.AI"
] | This report proposes a neural cognitive model for discovering regularities in event sequences. In a fluid intelligence task, the subject is required to discover regularities from relatively short-term memory of the first-seen task. Some fluid intelligence tasks require discovering regularities in event sequences. Thus,... | {
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2412.06840 | MDiFF: Exploiting Multimodal Score-based Diffusion Models for New
Fashion Product Performance Forecasting | [
"cs.LG",
"cs.CV"
] | The fast fashion industry suffers from significant environmental impacts due to overproduction and unsold inventory. Accurately predicting sales volumes for unreleased products could significantly improve efficiency and resource utilization. However, predicting performance for entirely new items is challenging due to t... | {
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2412.06841 | The Helicobacter pylori AI-Clinician: Harnessing Artificial Intelligence
to Personalize H. pylori Treatment Recommendations | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | Helicobacter pylori (H. pylori) is the most common carcinogenic pathogen worldwide. Infecting roughly 1 in 2 individuals globally, it is the leading cause of peptic ulcer disease, chronic gastritis, and gastric cancer. To investigate whether personalized treatments would be optimal for patients suffering from infection... | {
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2412.06842 | Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An
Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures
of Experts | [
"cs.LG"
] | Physics-informed neural networks (PINNs) commonly address ill-posed inverse problems by uncovering unknown physics. This study presents a novel unsupervised learning framework that identifies spatial subdomains with specific governing physics. It uses the partition of unity networks (POUs) to divide the space into subd... | {
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2412.06843 | Semantic Loss Guided Data Efficient Supervised Fine Tuning for Safe
Responses in LLMs | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) generating unsafe responses to toxic prompts is a significant issue in their applications. While various efforts aim to address this safety concern, previous approaches often demand substantial human data collection or rely on the less dependable option of using another LLM to generate corr... | {
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2412.06845 | Fully Open Source Moxin-7B Technical Report | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Recently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities. Leading this evolution are proprietary LLMs like GPT-4 and GPT-o1, which have captured widespread attention in the AI community due to their remarkable performance and ve... | {
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2412.06846 | Classifier-free guidance in LLMs Safety | [
"cs.LG",
"cs.AI"
] | The paper describes LLM unlearning without a retaining dataset, using the ORPO reinforcement learning method with inference enhanced by modified classifier-free guidance. Significant improvement in unlearning, without degradation of the model, is achieved through direct training on synthetic replacement data in CFG-awa... | {
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2412.06847 | M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven
Drug Design and Discovery | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | This paper introduces M$^{3}$-20M, a large-scale Multi-Modal Molecular dataset that contains over 20 million molecules. Designed to support AI-driven drug design and discovery, M$^{3}$-20M is 71 times more in the number of molecules than the largest existing dataset, providing an unprecedented scale that can highly ben... | {
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2412.06849 | GL-Fusion: Rethinking the Combination of Graph Neural Network and Large
Language model | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Recent research on integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) typically follows two approaches: LLM-centered models, which convert graph data into tokens for LLM processing, and GNN-centered models, which use LLMs to encode text features into node and edge representations for GNN input. ... | {
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2412.06852 | EGEAN: An Exposure-Guided Embedding Alignment Network for Post-Click
Conversion Estimation | [
"cs.LG",
"cs.AI"
] | Accurate post-click conversion rate (CVR) estimation is crucial for online advertising systems. Despite significant advances in causal approaches designed to address the Sample Selection Bias problem, CVR estimation still faces challenges due to Covariate Shift. Given the intrinsic connection between the distribution o... | {
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2412.06853 | Tube Loss: A Novel Approach for Prediction Interval Estimation and
probabilistic forecasting | [
"cs.LG",
"cs.AI"
] | This paper proposes a novel loss function, called 'Tube Loss', for simultaneous estimation of bounds of a Prediction Interval (PI) in the regression setup, and also for generating probabilistic forecasts from time series data solving a single optimization problem. The PIs obtained by minimizing the empirical risk based... | {
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2412.06855 | Incentivized Symbiosis: A Paradigm for Human-Agent Coevolution | [
"cs.MA",
"cs.AI"
] | Cooperation is vital to our survival and progress. Evolutionary game theory offers a lens to understand the structures and incentives that enable cooperation to be a successful strategy. As artificial intelligence agents become integral to human systems, the dynamics of cooperation take on unprecedented significance. T... | {
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2412.06857 | Comb Tensor Networks vs. Matrix Product States: Enhanced Efficiency in
High-Dimensional Spaces | [
"cs.LG",
"quant-ph"
] | Modern approaches to generative modeling of continuous data using tensor networks incorporate compression layers to capture the most meaningful features of high-dimensional inputs. These methods, however, rely on traditional Matrix Product States (MPS) architectures. Here, we demonstrate that beyond a certain threshold... | {
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2412.06858 | Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM
Quantization | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Quantization is a critical step to enable efficient LLM serving under limited resource. However, previous research observes that certain weights in the LLM, known as outliers, are significantly sensitive to quantization noises. Existing quantization methods leave these outliers as floating points or higher precisions t... | {
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2412.06859 | Generating floorplans for various building functionalities via latent
diffusion model | [
"cs.LG",
"cs.AI",
"cs.CV"
] | In the domain of architectural design, the foundational essence of creativity and human intelligence lies in the mastery of solving floorplans, a skill demanding distinctive expertise and years of experience. Traditionally, the architectural design process of creating floorplans often requires substantial manual labour... | {
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2412.06860 | Balancing Efficiency and Effectiveness: An LLM-Infused Approach for
Optimized CTR Prediction | [
"cs.LG",
"cs.AI"
] | Click-Through Rate (CTR) prediction is essential in online advertising, where semantic information plays a pivotal role in shaping user decisions and enhancing CTR effectiveness. Capturing and modeling deep semantic information, such as a user's preference for "H\"aagen-Dazs' HEAVEN strawberry light ice cream" due to i... | {
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2412.06861 | Mining Limited Data Sufficiently: A BERT-inspired Approach for CSI Time
Series Application in Wireless Communication and Sensing | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Channel State Information (CSI) is the cornerstone in both wireless communication and sensing systems. In wireless communication systems, CSI provides essential insights into channel conditions, enabling system optimizations like channel compensation and dynamic resource allocation. However, the high computational comp... | {
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2412.06862 | Stock Type Prediction Model Based on Hierarchical Graph Neural Network | [
"cs.LG",
"q-fin.CP"
] | This paper introduces a novel approach to stock data analysis by employing a Hierarchical Graph Neural Network (HGNN) model that captures multi-level information and relational structures in the stock market. The HGNN model integrates stock relationship data and hierarchical attributes to predict stock types effectivel... | {
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2412.06864 | Political-LLM: Large Language Models in Political Science | [
"cs.CL",
"cs.AI"
] | In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection. Meanwhile, the need to systematically understand how LLMs can further revolutionize the field also becomes urgent. In... | {
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2412.06865 | FP=xINT:A Low-Bit Series Expansion Algorithm for Post-Training
Quantization | [
"cs.LG",
"cs.AI"
] | Post-Training Quantization (PTQ) converts pre-trained Full-Precision (FP) models into quantized versions without training. While existing methods reduce size and computational costs, they also significantly degrade performance and quantization efficiency at extremely low settings due to quantization noise. We introduce... | {
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2412.06866 | LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated
Autocorrelation for Time Series Forecasting | [
"cs.LG",
"cs.AI"
] | Time series forecasting is an important challenge with significant applications in areas such as weather prediction, stock market analysis, scientific simulations and industrial process analysis. In this work, we introduce LMS-AutoTSF, a novel time series forecasting architecture that incorporates autocorrelation while... | {
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2412.06867 | Lossless Model Compression via Joint Low-Rank Factorization Optimization | [
"cs.LG",
"cs.AI",
"cs.CC"
] | Low-rank factorization is a popular model compression technique that minimizes the error $\delta$ between approximated and original weight matrices. Despite achieving performances close to the original models when $\delta$ is optimized, a performance discrepancy remains due to the separate optimization processes for lo... | {
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2412.06868 | Compression for Better: A General and Stable Lossless Compression
Framework | [
"cs.CV",
"cs.AI"
] | This work focus on how to stabilize and lossless model compression, aiming to reduce model complexity and enhance efficiency without sacrificing performance due to compression errors. A key challenge is effectively leveraging compression errors and defining the boundaries for lossless compression to minimize model loss... | {
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2412.06869 | Safety Monitoring of Machine Learning Perception Functions: a Survey | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.SE"
] | Machine Learning (ML) models, such as deep neural networks, are widely applied in autonomous systems to perform complex perception tasks. New dependability challenges arise when ML predictions are used in safety-critical applications, like autonomous cars and surgical robots. Thus, the use of fault tolerance mechanisms... | {
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2412.06870 | Variable Selection for Comparing High-dimensional Time-Series Data | [
"stat.ME",
"cs.LG",
"stat.ML"
] | Given a pair of multivariate time-series data of the same length and dimensions, an approach is proposed to select variables and time intervals where the two series are significantly different. In applications where one time series is an output from a computationally expensive simulator, the approach may be used for va... | {
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2412.06871 | Predicting Subway Passenger Flows under Incident Situation with
Causality | [
"cs.LG",
"cs.AI"
] | In the context of rail transit operations, real-time passenger flow prediction is essential; however, most models primarily focus on normal conditions, with limited research addressing incident situations. There are several intrinsic challenges associated with prediction during incidents, such as a lack of interpretabi... | {
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2412.06874 | Real-Time Performance Optimization of Travel Reservation Systems Using
AI and Microservices | [
"cs.SE",
"cs.AI",
"cs.CE",
"cs.CL",
"cs.PL"
] | The rapid growth of the travel industry has increased the need for real-time optimization in reservation systems that could take care of huge data and transaction volumes. This study proposes a hybrid framework that ut folds an Artificial Intelligence and a Microservices approach for the performance optimization of the... | {
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2412.06875 | VQ4ALL: Efficient Neural Network Representation via a Universal Codebook | [
"cs.LG",
"cs.AI"
] | The rapid growth of the big neural network models puts forward new requirements for lightweight network representation methods. The traditional methods based on model compression have achieved great success, especially VQ technology which realizes the high compression ratio of models by sharing code words. However, bec... | {
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2412.06877 | LLMs for Generalizable Language-Conditioned Policy Learning under
Minimal Data Requirements | [
"cs.CL",
"cs.AI"
] | To develop autonomous agents capable of executing complex, multi-step decision-making tasks as specified by humans in natural language, existing reinforcement learning approaches typically require expensive labeled datasets or access to real-time experimentation. Moreover, conventional methods often face difficulties i... | {
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2412.06878 | SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model
with Transparent Explanations | [
"cs.CV",
"cs.LG"
] | With the rise of generative AI and rapid growth of high-quality video generation, video guardrails have become more crucial than ever to ensure safety and security across platforms. Current video guardrails, however, are either overly simplistic, relying on pure classification models trained on simple policies with lim... | {
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2412.06917 | Haptics in Micro- and Nano-Manipulation | [
"cs.RO"
] | One of the motivations for the development of wirelessly guided untethered magnetic devices (UMDs), such as microrobots and nanorobots, is the continuous demand to manipulate, sort, and assemble micro-objects with high level of accuracy and dexterity. UMDs can function as microgrippers or manipulators and move micro-ob... | {
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} |
2412.06924 | Efficient user history modeling with amortized inference for deep
learning recommendation models | [
"cs.LG",
"cs.IR"
] | We study user history modeling via Transformer encoders in deep learning recommendation models (DLRM). Such architectures can significantly improve recommendation quality, but usually incur high latency cost necessitating infrastructure upgrades or very small Transformer models. An important part of user history modeli... | {
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2412.06926 | When Every Token Counts: Optimal Segmentation for Low-Resource Language
Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Traditional greedy tokenization methods have been a critical step in Natural Language Processing (NLP), influencing how text is converted into tokens and directly impacting model performance. While subword tokenizers like Byte-Pair Encoding (BPE) are widely used, questions remain about their optimality across model sca... | {
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2412.06927 | Gradient-based facial encoding for key generation to encrypt and decrypt
multimedia data | [
"cs.CR",
"cs.CV"
] | Security systems relying on passwords are vulnerable to being forgotten, guessed, or breached. Likewise, biometric systems that operate independently are at risk of template spoofing and replay incidents. This paper introduces a biocryptosystem utilizing face recognition techniques to address these issues, allowing for... | {
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2412.06931 | Non-Prehensile Tool-Object Manipulation by Integrating LLM-Based
Planning and Manoeuvrability-Driven Controls | [
"cs.RO"
] | The ability to wield tools was once considered exclusive to human intelligence, but it's now known that many other animals, like crows, possess this capability. Yet, robotic systems still fall short of matching biological dexterity. In this paper, we investigate the use of Large Language Models (LLMs), tool affordances... | {
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2412.06935 | Hypermodularity and community detection in higher-order networks | [
"physics.soc-ph",
"cond-mat.dis-nn",
"cs.SI"
] | Numerous networked systems feature a structure of non-trivial communities, which often correspond to their functional modules. Such communities have been detected in real-world biological, social and technological systems, as well as in synthetic models thereof. While much effort has been devoted to develop methods for... | {
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2412.06936 | Creating a Cooperative AI Policymaking Platform through Open Source
Collaboration | [
"cs.CY",
"cs.AI",
"cs.LG"
] | Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits. Current incentive structures and regulatory delays may hinder responsible AI development and deployment, particularly in light of the transformat... | {
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2412.06940 | Digital Twin-Empowered Voltage Control for Power Systems | [
"cs.LG",
"eess.SP"
] | Emerging digital twin technology has the potential to revolutionize voltage control in power systems. However, the state-of-the-art digital twin method suffers from low computational and sampling efficiency, which hinders its applications. To address this issue, we propose a Gumbel-Consistency Digital Twin (GC-DT) meth... | {
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2412.06943 | Entrywise application of non-linear functions on orthogonally invariant
matrices | [
"math.SP",
"cond-mat.stat-mech",
"cs.LG",
"math.OA"
] | In this article, we investigate how the entrywise application of a non-linear function to symmetric orthogonally invariant random matrix ensembles alters the spectral distribution. We treat also the multivariate case where we apply multivariate functions to entries of several orthogonally invariant matrices; where even... | {
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2412.06946 | NRSurNN3dq4: A Deep Learning Powered Numerical Relativity Surrogate for
Binary Black Hole Waveforms | [
"gr-qc",
"astro-ph.HE",
"astro-ph.IM",
"cs.LG"
] | Gravitational wave approximants are widely used tools in gravitational-wave astronomy. They allow for dense coverage of the parameter space of binary black hole (BBH) mergers for purposes of parameter inference, or, more generally, match filtering tasks, while avoiding the computationally expensive full evolution of nu... | {
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2412.06947 | PyraNet: A Large Scale Hierarchical Verilog Dataset | [
"cs.AR",
"cs.AI",
"cs.LG",
"cs.PL"
] | Recently, there has been a growing interest in leveraging Large Language Models for Verilog code generation. However, the current quality of the generated Verilog code remains suboptimal. This is largely due to the absence of well-defined, well-organized datasets with high-quality samples, as well as a lack of innovati... | {
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} |
2412.06949 | Bridging Conversational and Collaborative Signals for Conversational
Recommendation | [
"cs.IR",
"cs.AI",
"cs.LG"
] | Conversational recommendation systems (CRS) leverage contextual information from conversations to generate recommendations but often struggle due to a lack of collaborative filtering (CF) signals, which capture user-item interaction patterns essential for accurate recommendations. We introduce Reddit-ML32M, a dataset t... | {
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} |
2412.06951 | Analysing Public Transport User Sentiment on Low Resource Multilingual
Data | [
"cs.CL",
"cs.LG"
] | Public transport systems in many Sub-Saharan countries often receive less attention compared to other sectors, underscoring the need for innovative solutions to improve the Quality of Service (QoS) and overall user experience. This study explored commuter opinion mining to understand sentiments toward existing public t... | {
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2412.06954 | CURE: A dataset for Clinical Understanding & Retrieval Evaluation | [
"cs.IR"
] | Given the dominance of dense retrievers that do not generalize well beyond their training dataset distributions, domain-specific test sets are essential in evaluating retrieval. There are few test datasets for retrieval systems intended for use by healthcare providers in a point-of-care setting. To fill this gap we hav... | {
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2412.06956 | Microcontroller-Driven MPPT System for Enhanced Photovoltaic Efficiency:
An Experimental Approach in Nepal | [
"eess.SY",
"cs.SY"
] | Solar energy utilization in places like Nepal, is often obstructed by unpredicted environmental factors and existing technological barriers. The challenges encountered often result in fluctuating energy outputs, hindering the transition to greener energy solutions. To tackle these issues, this study introduces a custom... | {
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2412.06958 | Enhancing operational wind downscaling capabilities over Canada:
Application of a Conditional Wasserstein GAN methodology | [
"cs.LG",
"cs.AI"
] | Wind downscaling is essential for improving the spatial resolution of weather forecasts, particularly in operational Numerical Weather Prediction (NWP). This study advances wind downscaling by extending the DownGAN framework introduced by Annau et al.,to operational datasets from the Global Deterministic Prediction Sys... | {
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2412.06959 | Geological and Well prior assisted full waveform inversion using
conditional diffusion models | [
"physics.geo-ph",
"cs.LG"
] | Full waveform inversion (FWI) often faces challenges due to inadequate seismic observations, resulting in band-limited and geologically inaccurate inversion results. Incorporating prior information from potential velocity distributions, well-log information, and our geological knowledge and expectations can significant... | {
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2412.06966 | Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI
Policy, Research, and Practice | [
"cs.LG",
"cs.AI",
"cs.CY"
] | We articulate fundamental mismatches between technical methods for machine unlearning in Generative AI, and documented aspirations for broader impact that these methods could have for law and policy. These aspirations are both numerous and varied, motivated by issues that pertain to privacy, copyright, safety, and more... | {
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2412.06967 | Effective Text Adaptation for LLM-based ASR through Soft Prompt
Fine-Tuning | [
"cs.CL",
"cs.SD",
"eess.AS"
] | The advent of Large Language Models (LLM) has reformed the Automatic Speech Recognition (ASR). Prompting LLM with audio embeddings to generate transcriptions becomes the new state-of-the-art ASR. Despite LLMs being trained with an extensive amount of text corpora, high-quality domain-specific text data can still signif... | {
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2412.06968 | SphereUFormer: A U-Shaped Transformer for Spherical 360 Perception | [
"cs.CV"
] | This paper proposes a novel method for omnidirectional 360$\degree$ perception. Most common previous methods relied on equirectangular projection. This representation is easily applicable to 2D operation layers but introduces distortions into the image. Other methods attempted to remove the distortions by maintaining a... | {
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2412.06974 | MV-DUSt3R+: Single-Stage Scene Reconstruction from Sparse Views In 2
Seconds | [
"cs.CV",
"cs.AI"
] | Recent sparse multi-view scene reconstruction advances like DUSt3R and MASt3R no longer require camera calibration and camera pose estimation. However, they only process a pair of views at a time to infer pixel-aligned pointmaps. When dealing with more than two views, a combinatorial number of error prone pairwise reco... | {
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2412.06975 | AutoReason: Automatic Few-Shot Reasoning Decomposition | [
"cs.CL",
"cs.AI"
] | Chain of Thought (CoT) was introduced in recent research as a method for improving step-by-step reasoning in Large Language Models. However, CoT has limited applications such as its need for hand-crafted few-shot exemplar prompts and no capability to adjust itself to different queries. In this work, we propose a syst... | {
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2412.06978 | Edge-SD-SR: Low Latency and Parameter Efficient On-device
Super-Resolution with Stable Diffusion via Bidirectional Conditioning | [
"cs.CV"
] | There has been immense progress recently in the visual quality of Stable Diffusion-based Super Resolution (SD-SR). However, deploying large diffusion models on computationally restricted devices such as mobile phones remains impractical due to the large model size and high latency. This is compounded for SR as it often... | {
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2412.06981 | Diffusing Differentiable Representations | [
"cs.CV",
"cs.LG"
] | We introduce a novel, training-free method for sampling differentiable representations (diffreps) using pretrained diffusion models. Rather than merely mode-seeking, our method achieves sampling by "pulling back" the dynamics of the reverse-time process--from the image space to the diffrep parameter space--and updating... | {
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2412.06983 | Collision-inclusive Manipulation Planning for Occluded Object Grasping
via Compliant Robot Motions | [
"cs.RO"
] | Traditional robotic manipulation mostly focuses on collision-free tasks. In practice, however, many manipulation tasks (e.g., occluded object grasping) require the robot to intentionally collide with the environment to reach a desired task configuration. By enabling compliant robot motions, collisions between the robot... | {
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2412.06985 | Ground Perturbation Detection via Lower-Limb Kinematic States During
Locomotion | [
"cs.RO"
] | Falls during daily ambulation activities are a leading cause of injury in older adults due to delayed physiological responses to disturbances of balance. Lower-limb exoskeletons have the potential to mitigate fall incidents by detecting and reacting to perturbations before the user. Although commonly used, the standard... | {
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2412.06989 | Learning About Algorithm Auditing in Five Steps: Scaffolding How High
School Youth Can Systematically and Critically Evaluate Machine Learning
Applications | [
"cs.HC",
"cs.AI",
"cs.CY"
] | While there is widespread interest in supporting young people to critically evaluate machine learning-powered systems, there is little research on how we can support them in inquiring about how these systems work and what their limitations and implications may be. Outside of K-12 education, an effective strategy in eva... | {
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2412.06990 | The Oracle Complexity of Simplex-based Matrix Games: Linear Separability
and Nash Equilibria | [
"cs.GT",
"cs.LG",
"math.OC"
] | We study the problem of solving matrix games of the form $\max_{\mathbf{w}\in\mathcal{W}}\min_{\mathbf{p}\in\Delta}\mathbf{p}^{\top}A\mathbf{w}$, where $A$ is some matrix and $\Delta$ is the probability simplex. This problem encapsulates canonical tasks such as finding a linear separator and computing Nash equilibria i... | {
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2412.06993 | Toward AI-Driven Digital Organism: Multiscale Foundation Models for
Predicting, Simulating and Programming Biology at All Levels | [
"cs.AI",
"cs.LG",
"q-bio.QM"
] | We present an approach of using AI to model and simulate biology and life. Why is it important? Because at the core of medicine, pharmacy, public health, longevity, agriculture and food security, environmental protection, and clean energy, it is biology at work. Biology in the physical world is too complex to manipulat... | {
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2412.07000 | Extreme AutoML: Analysis of Classification, Regression, and NLP
Performance | [
"cs.LG",
"cs.AI"
] | Utilizing machine learning techniques has always required choosing hyperparameters. This is true whether one uses a classical technique such as a KNN or very modern neural networks such as Deep Learning. Though in many applications, hyperparameters are chosen by hand, automated methods have become increasingly more com... | {
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2412.07003 | Understanding Gradient Descent through the Training Jacobian | [
"cs.LG"
] | We examine the geometry of neural network training using the Jacobian of trained network parameters with respect to their initial values. Our analysis reveals low-dimensional structure in the training process which is dependent on the input data but largely independent of the labels. We find that the singular value spe... | {
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2412.07005 | In-Application Defense Against Evasive Web Scans through Behavioral
Analysis | [
"cs.LG",
"cs.CR",
"cs.IT",
"math.IT"
] | Web traffic has evolved to include both human users and automated agents, ranging from benign web crawlers to adversarial scanners such as those capable of credential stuffing, command injection, and account hijacking at the web scale. The estimated financial costs of these adversarial activities are estimated to excee... | {
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2412.07009 | LUIEO: A Lightweight Model for Integrating Underwater Image Enhancement
and Object Detection | [
"cs.CV"
] | Underwater optical images inevitably suffer from various degradation factors such as blurring, low contrast, and color distortion, which hinder the accuracy of object detection tasks. Due to the lack of paired underwater/clean images, most research methods adopt a strategy of first enhancing and then detecting, resulti... | {
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2412.07010 | TAEN: A Model-Constrained Tikhonov Autoencoder Network for Forward and
Inverse Problems | [
"cs.LG",
"physics.comp-ph"
] | Efficient real-time solvers for forward and inverse problems are essential in engineering and science applications. Machine learning surrogate models have emerged as promising alternatives to traditional methods, offering substantially reduced computational time. Nevertheless, these models typically demand extensive tr... | {
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2412.07011 | Multi-Objective Communication Optimization for Temporal Continuity in
Dynamic Vehicular Networks | [
"cs.NE"
] | Vehicular Ad-hoc Networks (VANETs) operate in highly dynamic environments characterized by high mobility, time-varying channel conditions, and frequent network disruptions. Addressing these challenges, this paper presents a novel temporal-aware multi-objective robust optimization framework, which for the first time for... | {
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2412.07012 | ProVision: Programmatically Scaling Vision-centric Instruction Data for
Multimodal Language Models | [
"cs.CV",
"cs.AI"
] | With the rise of multimodal applications, instruction data has become critical for training multimodal language models capable of understanding complex image-based queries. Existing practices rely on powerful but costly large language models (LLMs) or multimodal language models (MLMs) to produce instruction data. These... | {
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2412.07017 | Asynchronous LLM Function Calling | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) use function calls to interface with external tools and data source. However, the current approach to LLM function calling is inherently synchronous, where each call blocks LLM inference, limiting LLM operation and concurrent function execution. In this work, we propose AsyncLM, a system fo... | {
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2412.07019 | Assessing the Impact of Conspiracy Theories Using Large Language Models | [
"cs.CL",
"cs.CY"
] | Measuring the relative impact of CTs is important for prioritizing responses and allocating resources effectively, especially during crises. However, assessing the actual impact of CTs on the public poses unique challenges. It requires not only the collection of CT-specific knowledge but also diverse information from s... | {
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2412.07021 | Sequential Compression Layers for Efficient Federated Learning in
Foundational Models | [
"cs.LG",
"cs.AI"
] | Federated Learning (FL) has gained popularity for fine-tuning large language models (LLMs) across multiple nodes, each with its own private data. While LoRA has been widely adopted for parameter efficient federated fine-tuning, recent theoretical and empirical studies highlight its suboptimal performance in the federat... | {
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2412.07022 | Dense Cross-Connected Ensemble Convolutional Neural Networks for
Enhanced Model Robustness | [
"cs.CV",
"cs.AI"
] | The resilience of convolutional neural networks against input variations and adversarial attacks remains a significant challenge in image recognition tasks. Motivated by the need for more robust and reliable image recognition systems, we propose the Dense Cross-Connected Ensemble Convolutional Neural Network (DCC-ECNN)... | {
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2412.07026 | GenAI4UQ: A Software for Inverse Uncertainty Quantification Using
Conditional Generative Models | [
"cs.LG",
"physics.geo-ph"
] | We introduce GenAI4UQ, a software package for inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting in scientific applications. GenAI4UQ leverages a generative artificial intelligence (AI) based conditional modeling framework to address the limitations of traditional in... | {
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2412.07027 | Deep Learning for Cross-Border Transaction Anomaly Detection in
Anti-Money Laundering Systems | [
"cs.LG",
"cs.CY",
"cs.SI",
"q-fin.RM"
] | In the context of globalization and the rapid expansion of the digital economy, anti-money laundering (AML) has become a crucial aspect of financial oversight, particularly in cross-border transactions. The rising complexity and scale of international financial flows necessitate more intelligent and adaptive AML system... | {
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2412.07030 | FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge
Distillation for Question Answering | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.IR",
"cs.LG"
] | Multimodal multihop question answering is a complex task that requires reasoning over multiple sources of information, such as images and text, to answer questions. While there has been significant progress in visual question answering, the multihop setting remains unexplored due to the lack of high-quality datasets. C... | {
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2412.07031 | Large Language Models: An Applied Econometric Framework | [
"econ.EM",
"cs.AI"
] | How can we use the novel capacities of large language models (LLMs) in empirical research? And how can we do so while accounting for their limitations, which are themselves only poorly understood? We develop an econometric framework to answer this question that distinguishes between two types of empirical tasks. Using ... | {
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2412.07039 | Data Augmentation with Variational Autoencoder for Imbalanced Dataset | [
"cs.LG"
] | Learning from an imbalanced distribution presents a major challenge in predictive modeling, as it generally leads to a reduction in the performance of standard algorithms. Various approaches exist to address this issue, but many of them concern classification problems, with a limited focus on regression. In this paper,... | {
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2412.07041 | Generalized Least Squares Kernelized Tensor Factorization | [
"stat.ML",
"cs.CV",
"cs.LG"
] | Completing multidimensional tensor-structured data with missing entries is a fundamental task for many real-world applications involving incomplete or corrupted datasets. For data with spatial or temporal side information, low-rank factorization models with smoothness constraints have demonstrated strong performance. A... | {
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2412.07042 | Generative AI Impact on Labor Market: Analyzing ChatGPT's Demand in Job
Advertisements | [
"cs.CY",
"cs.AI",
"econ.GN",
"q-fin.EC"
] | The rapid advancement of Generative AI (Gen AI) technologies, particularly tools like ChatGPT, is significantly impacting the labor market by reshaping job roles and skill requirements. This study examines the demand for ChatGPT-related skills in the U.S. labor market by analyzing job advertisements collected from majo... | {
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2412.07049 | Static Key Attention in Vision | [
"cs.CV"
] | The success of vision transformers is widely attributed to the expressive power of their dynamically parameterized multi-head self-attention mechanism. We examine the impact of substituting the dynamic parameterized key with a static key within the standard attention mechanism in Vision Transformers. Our findings revea... | {
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2412.07050 | Advancing clinical trial outcomes using deep learning and predictive
modelling: bridging precision medicine and patient-centered care | [
"cs.LG"
] | The integration of artificial intelligence [AI] into clinical trials has revolutionized the process of drug development and personalized medicine. Among these advancements, deep learning and predictive modelling have emerged as transformative tools for optimizing clinical trial design, patient recruitment, and real-tim... | {
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2412.07051 | A Misclassification Network-Based Method for Comparative Genomic
Analysis | [
"q-bio.GN",
"cs.LG"
] | Classifying genome sequences based on metadata has been an active area of research in comparative genomics for decades with many important applications across the life sciences. Established methods for classifying genomes can be broadly grouped into sequence alignment-based and alignment-free models. Conventional align... | {
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2412.07057 | A Note on Sample Complexity of Interactive Imitation Learning with Log
Loss | [
"stat.ML",
"cs.LG"
] | Imitation learning (IL) is a general paradigm for learning from experts in sequential decision-making problems. Recent advancements in IL have shown that offline imitation learning, specifically Behavior Cloning (BC) with log loss, is minimax optimal. Meanwhile, its interactive counterpart, DAgger, is shown to suffer f... | {
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"cs.SY": 0
} |
2412.07062 | Optimizing Personalized Federated Learning through Adaptive Layer-Wise
Learning | [
"cs.LG"
] | Real-life deployment of federated Learning (FL) often faces non-IID data, which leads to poor accuracy and slow convergence. Personalized FL (pFL) tackles these issues by tailoring local models to individual data sources and using weighted aggregation methods for client-specific learning. However, existing pFL methods ... | {
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} |
2412.07066 | The Mirage of Artificial Intelligence Terms of Use Restrictions | [
"cs.CY",
"cs.AI",
"cs.LG"
] | Artificial intelligence (AI) model creators commonly attach restrictive terms of use to both their models and their outputs. These terms typically prohibit activities ranging from creating competing AI models to spreading disinformation. Often taken at face value, these terms are positioned by companies as key enforcea... | {
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} |
2412.07067 | MoE-CAP: Cost-Accuracy-Performance Benchmarking for Mixture-of-Experts
Systems | [
"cs.LG",
"cs.DC"
] | The sparse Mixture-of-Experts (MoE) architecture is increasingly favored for scaling Large Language Models (LLMs) efficiently; however, MoE systems rely on heterogeneous compute and memory resources. These factors collectively influence the system's Cost, Accuracy, and Performance (CAP), creating a challenging trade-of... | {
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} |
2412.07069 | Enhancing radioisotope identification in gamma spectra with transfer
learning | [
"cs.LG",
"nucl-th"
] | Machine learning methods in gamma spectroscopy have the potential to provide accurate, real-time classification of unknown radioactive samples. However, obtaining sufficient experimental training data is often prohibitively expensive and time-consuming, and models trained solely on synthetic data can struggle to genera... | {
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} |
2412.07072 | Stable Mean Teacher for Semi-supervised Video Action Detection | [
"cs.CV"
] | In this work, we focus on semi-supervised learning for video action detection. Video action detection requires spatiotemporal localization in addition to classification, and a limited amount of labels makes the model prone to unreliable predictions. We present Stable Mean Teacher, a simple end-to-end teacher-based fram... | {
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} |
2412.07077 | Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models
with Prompt Ensembling | [
"cs.CV"
] | The advancement of vision-language models, particularly the Contrastive Language-Image Pre-training (CLIP) model, has revolutionized the field of machine learning by enabling robust zero-shot learning capabilities. These capabilities allow models to understand and respond to previously unseen data without task-specific... | {
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} |
2412.07078 | Defensive Dual Masking for Robust Adversarial Defense | [
"cs.CL",
"cs.AI"
] | The field of textual adversarial defenses has gained considerable attention in recent years due to the increasing vulnerability of natural language processing (NLP) models to adversarial attacks, which exploit subtle perturbations in input text to deceive models. This paper introduces the Defensive Dual Masking (DDM) a... | {
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} |
2412.07079 | Light Field Image Quality Assessment With Auxiliary Learning Based on
Depthwise and Anglewise Separable Convolutions | [
"eess.IV",
"cs.CV",
"cs.LG"
] | In multimedia broadcasting, no-reference image quality assessment (NR-IQA) is used to indicate the user-perceived quality of experience (QoE) and to support intelligent data transmission while optimizing user experience. This paper proposes an improved no-reference light field image quality assessment (NR-LFIQA) metric... | {
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} |
2412.07080 | EvRepSL: Event-Stream Representation via Self-Supervised Learning for
Event-Based Vision | [
"cs.CV",
"cs.AI",
"cs.MM"
] | Event-stream representation is the first step for many computer vision tasks using event cameras. It converts the asynchronous event-streams into a formatted structure so that conventional machine learning models can be applied easily. However, most of the state-of-the-art event-stream representations are manually desi... | {
"Other": 1,
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} |
2412.07081 | Sequential Controlled Langevin Diffusions | [
"stat.ML",
"cs.AI",
"cs.LG"
] | An effective approach for sampling from unnormalized densities is based on the idea of gradually transporting samples from an easy prior to the complicated target distribution. Two popular methods are (1) Sequential Monte Carlo (SMC), where the transport is performed through successive annealed densities via prescribed... | {
"Other": 0,
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} |
2412.07082 | User Authentication and Vital Signs Extraction from Low-Frame-Rate and
Monochrome No-contact Fingerprint Captures | [
"eess.IV",
"cs.CR",
"cs.LG"
] | We present our work on leveraging low-frame-rate monochrome (blue light) videos of fingertips, captured with an off-the-shelf fingerprint capture device, to extract vital signs and identify users. These videos utilize photoplethysmography (PPG), commonly used to measure vital signs like heart rate. While prior research... | {
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} |
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