id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.08807 | Multi-visual modality micro drone-based structural damage detection | [
"cs.CV"
] | Accurate detection and resilience of object detectors in structural damage detection are important in ensuring the continuous use of civil infrastructure. However, achieving robustness in object detectors remains a persistent challenge, impacting their ability to generalize effectively. This study proposes DetectorX, a... | {
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2501.08808 | A Bayesian Hierarchical Model for Generating Synthetic Unbalanced Power
Distribution Grids | [
"eess.SY",
"cs.SY"
] | The real-world data of power networks is often inaccessible due to privacy and security concerns, highlighting the need for tools to generate realistic synthetic network data. Existing methods leverage geographic tools like OpenStreetMap with heuristic rules to model system topology and typically focus on single-phase,... | {
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2501.08809 | XMusic: Towards a Generalized and Controllable Symbolic Music Generation
Framework | [
"cs.SD",
"cs.AI",
"eess.AS"
] | In recent years, remarkable advancements in artificial intelligence-generated content (AIGC) have been achieved in the fields of image synthesis and text generation, generating content comparable to that produced by humans. However, the quality of AI-generated music has not yet reached this standard, primarily due to t... | {
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2501.08814 | SAIF: A Comprehensive Framework for Evaluating the Risks of Generative
AI in the Public Sector | [
"cs.AI",
"cs.CL",
"cs.CY"
] | The rapid adoption of generative AI in the public sector, encompassing diverse applications ranging from automated public assistance to welfare services and immigration processes, highlights its transformative potential while underscoring the pressing need for thorough risk assessments. Despite its growing presence, ev... | {
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2501.08815 | Human Pose-Constrained UV Map Estimation | [
"cs.CV"
] | UV map estimation is used in computer vision for detailed analysis of human posture or activity. Previous methods assign pixels to body model vertices by comparing pixel descriptors independently, without enforcing global coherence or plausibility in the UV map. We propose Pose-Constrained Continuous Surface Embeddings... | {
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2501.08816 | IDEA: Image Description Enhanced CLIP-Adapter | [
"cs.CV",
"cs.AI",
"cs.LG"
] | CLIP (Contrastive Language-Image Pre-training) has attained great success in pattern recognition and computer vision. Transferring CLIP to downstream tasks (e.g. zero- or few-shot classification) is a hot topic in multimodal learning. However, current studies primarily focus on either prompt learning for text or adapte... | {
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2501.08819 | Boosting Diffusion Guidance via Learning Degradation-Aware Models for
Blind Super Resolution | [
"eess.IV",
"cs.CV"
] | Recently, diffusion-based blind super-resolution (SR) methods have shown great ability to generate high-resolution images with abundant high-frequency detail, but the detail is often achieved at the expense of fidelity. Meanwhile, another line of research focusing on rectifying the reverse process of diffusion models (... | {
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2501.08821 | A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection | [
"cs.LG"
] | Machine learning algorithms often encounter different or "out-of-distribution" (OOD) data at deployment time, and OOD detection is frequently employed to detect these examples. While it works reasonably well in practice, existing theoretical results on OOD detection are highly pessimistic. In this work, we take a close... | {
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2501.08822 | Deep Learning Meets Queue-Reactive: A Framework for Realistic Limit
Order Book Simulation | [
"q-fin.TR",
"cs.LG"
] | The Queue-Reactive model introduced by Huang et al. (2015) has become a standard tool for limit order book modeling, widely adopted by both researchers and practitioners for its simplicity and effectiveness. We present the Multidimensional Deep Queue-Reactive (MDQR) model, which extends this framework in three ways: it... | {
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2501.08828 | MMDocIR: Benchmarking Multi-Modal Retrieval for Long Documents | [
"cs.IR",
"cs.AI",
"cs.CL",
"cs.CV"
] | Multi-modal document retrieval is designed to identify and retrieve various forms of multi-modal content, such as figures, tables, charts, and layout information from extensive documents. Despite its significance, there is a notable lack of a robust benchmark to effectively evaluate the performance of systems in multi-... | {
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2501.08837 | MANTA: Diffusion Mamba for Efficient and Effective Stochastic Long-Term
Dense Anticipation | [
"cs.CV"
] | Our work addresses the problem of stochastic long-term dense anticipation. The goal of this task is to predict actions and their durations several minutes into the future based on provided video observations. Anticipation over extended horizons introduces high uncertainty, as a single observation can lead to multiple p... | {
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2501.08838 | ToMATO: Verbalizing the Mental States of Role-Playing LLMs for
Benchmarking Theory of Mind | [
"cs.CL",
"cs.AI"
] | Existing Theory of Mind (ToM) benchmarks diverge from real-world scenarios in three aspects: 1) they assess a limited range of mental states such as beliefs, 2) false beliefs are not comprehensively explored, and 3) the diverse personality traits of characters are overlooked. To address these challenges, we introduce T... | {
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2501.08841 | Exploring Task-Level Optimal Prompts for Visual In-Context Learning | [
"cs.AI",
"cs.CV"
] | With the development of Vision Foundation Models (VFMs) in recent years, Visual In-Context Learning (VICL) has become a better choice compared to modifying models in most scenarios. Different from retraining or fine-tuning model, VICL does not require modifications to the model's weights or architecture, and only needs... | {
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2501.08847 | Automatic tuning of communication protocols for vehicular ad hoc
networks using metaheuristics | [
"cs.NE",
"cs.AI",
"cs.NI"
] | The emerging field of vehicular ad hoc networks (VANETs) deals with a set of communicating vehicles which are able to spontaneously interconnect without any pre-existing infrastructure. In such kind of networks, it is crucial to make an optimal configuration of the communication protocols previously to the final networ... | {
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2501.08848 | RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learning | [
"cs.NI",
"cs.AI",
"cs.LG"
] | Network simulation is pivotal in network modeling, assisting with tasks ranging from capacity planning to performance estimation. Traditional approaches such as Discrete Event Simulation (DES) face limitations in terms of computational cost and accuracy. This paper introduces RouteNet-Gauss, a novel integration of a te... | {
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2501.08850 | Graph Counterfactual Explainable AI via Latent Space Traversal | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these models. Counterfactual explanations aim to explain predictions by finding the ''nearest'' in-distribution alternative input whose prediction c... | {
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2501.08851 | Digital Phenotyping for Adolescent Mental Health: A Feasibility Study
Employing Machine Learning to Predict Mental Health Risk From Active and
Passive Smartphone Data | [
"cs.LG",
"cs.AI"
] | Background: Adolescents are particularly vulnerable to mental disorders, with over 75% of cases manifesting before the age of 25. Research indicates that only 18 to 34% of young people experiencing high levels of depression or anxiety symptoms seek support. Digital tools leveraging smartphones offer scalable and early ... | {
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2501.08853 | Achieving Stability and Optimality: Control Strategy for a Wind Turbine
Supplying an Electrolyzer in the Islanded Storage-less Microgrid | [
"eess.SY",
"cs.SY"
] | Wind power generation supplying electrolyzers in islanded microgrids is an essential technical pathway for green hydrogen production, attracting growing attention in the transition towards net zero carbon emissions. Both academia and industry widely recognize that islanded AC microgrids normally rely on battery energy ... | {
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2501.08861 | Generative Planning with 3D-vision Language Pre-training for End-to-End
Autonomous Driving | [
"cs.CV"
] | Autonomous driving is a challenging task that requires perceiving and understanding the surrounding environment for safe trajectory planning. While existing vision-based end-to-end models have achieved promising results, these methods are still facing the challenges of vision understanding, decision reasoning and scene... | {
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2501.08862 | ARMOR: Shielding Unlearnable Examples against Data Augmentation | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Private data, when published online, may be collected by unauthorized parties to train deep neural networks (DNNs). To protect privacy, defensive noises can be added to original samples to degrade their learnability by DNNs. Recently, unlearnable examples are proposed to minimize the training loss such that the model l... | {
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2501.08865 | The geometry of moral decision making | [
"cs.IT",
"math.IT",
"physics.data-an"
] | We show how (resource) bounded rationality can be understood as the interplay of two fundamental moral principles: deontology and utilitarianism. In particular, we interpret deontology as a regularisation function in an optimal control problem, coupled with a free parameter, the inverse temperature, to shield the indiv... | {
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2501.08868 | Processing and Analyzing Real-World Driving Data: Insights on Trips,
Scenarios, and Human Driving Behaviors | [
"eess.SY",
"cs.HC",
"cs.SY"
] | Analyzing large volumes of real-world driving data is essential for providing meaningful and reliable insights into real-world trips, scenarios, and human driving behaviors. To this end, we developed a multi-level data processing approach that adds new information, segments data, and extracts desired parameters. Levera... | {
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2501.08869 | Silent Abandonment in Text-Based Contact Centers: Identifying,
Quantifying, and Mitigating its Operational Impacts | [
"cs.SI",
"cs.AI"
] | In the quest to improve services, companies offer customers the option to interact with agents via texting. Such contact centers face unique challenges compared to traditional call centers, as measuring customer experience proxies like abandonment and patience involves uncertainty. A key source of this uncertainty is s... | {
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2501.08871 | Joint Detection and Decoding: A Graph Neural Network Approach | [
"cs.IT",
"math.IT"
] | Narrowing the performance gap between optimal and feasible detection in inter-symbol interference (ISI) channels, this paper proposes to use graph neural networks (GNNs) for detection that can also be used to perform joint detection and decoding (JDD). For detection, the GNN is build upon the factor graph representatio... | {
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2501.08878 | Incrementally Learning Multiple Diverse Data Domains via Multi-Source
Dynamic Expansion Model | [
"cs.LG",
"cs.AI"
] | Continual Learning seeks to develop a model capable of incrementally assimilating new information while retaining prior knowledge. However, current research predominantly addresses a straightforward learning context, wherein all data samples originate from a singular data domain. This paper shifts focus to a more compl... | {
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2501.08880 | SLC$^2$-SLAM: Semantic-guided Loop Closure with Shared Latent Code for
NeRF SLAM | [
"cs.RO"
] | Targeting the notorious cumulative drift errors in NeRF SLAM, we propose a Semantic-guided Loop Closure with Shared Latent Code, dubbed SLC$^2$-SLAM. Especially, we argue that latent codes stored in many NeRF SLAM systems are not fully exploited, as they are only used for better reconstruction. In this paper, we propos... | {
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2501.08883 | Increasing Batch Size Improves Convergence of Stochastic Gradient
Descent with Momentum | [
"cs.LG"
] | Stochastic gradient descent with momentum (SGDM), which is defined by adding a momentum term to SGD, has been well studied in both theory and practice. Theoretically investigated results showed that the settings of the learning rate and momentum weight affect the convergence of SGDM. Meanwhile, practical results showed... | {
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2501.08884 | Improved Compression Bounds for Scenario Decision Making | [
"math.OC",
"cs.LG"
] | Scenario decision making offers a flexible way of making decision in an uncertain environment while obtaining probabilistic guarantees on the risk of failure of the decision. The idea of this approach is to draw samples of the uncertainty and make a decision based on the samples, called "scenarios". The probabilistic g... | {
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2501.08885 | Feature-based One-For-All: A Universal Framework for Heterogeneous
Knowledge Distillation | [
"cs.CV"
] | Knowledge distillation (KD) involves transferring knowledge from a pre-trained heavy teacher model to a lighter student model, thereby reducing the inference cost while maintaining comparable effectiveness. Prior KD techniques typically assume homogeneity between the teacher and student models. However, as technology a... | {
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2501.08887 | PAC Learnability of Scenario Decision-Making Algorithms: Necessary and
Sufficient Conditions | [
"cs.LG",
"math.OC"
] | We study the PAC property of scenario decision-making algorithms, that is, the ability to make a decision that has an arbitrarily low risk of violating an unknown safety constraint, provided sufficiently many realizations (called scenarios) of the safety constraint are sampled. Sufficient conditions for scenario decisi... | {
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2501.08888 | A Partial Initialization Strategy to Mitigate the Overfitting Problem in
CATE Estimation with Hidden Confounding | [
"cs.LG"
] | Estimating the conditional average treatment effect (CATE) from observational data plays a crucial role in areas such as e-commerce, healthcare, and economics. Existing studies mainly rely on the strong ignorability assumption that there are no hidden confounders, whose existence cannot be tested from observational dat... | {
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2501.08889 | Karatsuba Matrix Multiplication and its Efficient Custom Hardware
Implementations | [
"cs.AR",
"cs.AI",
"cs.PF"
] | While the Karatsuba algorithm reduces the complexity of large integer multiplication, the extra additions required minimize its benefits for smaller integers of more commonly-used bitwidths. In this work, we propose the extension of the scalar Karatsuba multiplication algorithm to matrix multiplication, showing how thi... | {
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2501.08896 | Parallel Query Processing with Heterogeneous Machines | [
"cs.DB"
] | We study the problem of computing a full Conjunctive Query in parallel using $p$ heterogeneous machines. Our computational model is similar to the MPC model, but each machine has its own cost function mapping from the number of bits it receives to a cost. An optimal algorithm should minimize the maximum cost across all... | {
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2501.08897 | Leveraging Large Language Models as Knowledge-Driven Agents for Reliable
Retrosynthesis Planning | [
"cs.AI"
] | Identifying reliable synthesis pathways in materials chemistry is a complex task, particularly in polymer science, due to the intricate and often non-unique nomenclature of macromolecules. To address this challenge, we propose an agent system that integrates large language models (LLMs) and knowledge graphs (KGs). By l... | {
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2501.08900 | Enhanced Multi-Scale Cross-Attention for Person Image Generation | [
"cs.CV"
] | In this paper, we propose a novel cross-attention-based generative adversarial network (GAN) for the challenging person image generation task. Cross-attention is a novel and intuitive multi-modal fusion method in which an attention/correlation matrix is calculated between two feature maps of different modalities. Speci... | {
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2501.08902 | Multi-View Transformers for Airway-To-Lung Ratio Inference on Cardiac CT
Scans: The C4R Study | [
"eess.IV",
"cs.CV",
"cs.LG"
] | The ratio of airway tree lumen to lung size (ALR), assessed at full inspiration on high resolution full-lung computed tomography (CT), is a major risk factor for chronic obstructive pulmonary disease (COPD). There is growing interest to infer ALR from cardiac CT images, which are widely available in epidemiological coh... | {
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2501.08905 | Computing Game Symmetries and Equilibria That Respect Them | [
"cs.GT",
"cs.AI",
"cs.CC",
"cs.MA"
] | Strategic interactions can be represented more concisely, and analyzed and solved more efficiently, if we are aware of the symmetries within the multiagent system. Symmetries also have conceptual implications, for example for equilibrium selection. We study the computational complexity of identifying and using symmetri... | {
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2501.08907 | Projection Implicit Q-Learning with Support Constraint for Offline
Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | Offline Reinforcement Learning (RL) faces a critical challenge of extrapolation errors caused by out-of-distribution (OOD) actions. Implicit Q-Learning (IQL) algorithm employs expectile regression to achieve in-sample learning, effectively mitigating the risks associated with OOD actions. However, the fixed hyperparame... | {
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2501.08908 | When Uncertainty Leads to Unsafety: Empirical Insights into the Role of
Uncertainty in Unmanned Aerial Vehicle Safety | [
"cs.SE",
"cs.RO"
] | Despite the recent developments in obstacle avoidance and other safety features, autonomous Unmanned Aerial Vehicles (UAVs) continue to face safety challenges. No previous work investigated the relationship between the behavioral uncertainty of a UAV and the unsafety of its flight. By quantifying uncertainty, it is pos... | {
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2501.08910 | Lights, Camera, Matching: The Role of Image Illumination in Fair Face
Recognition | [
"cs.CV"
] | Facial brightness is a key image quality factor impacting face recognition accuracy differentials across demographic groups. In this work, we aim to decrease the accuracy gap between the similarity score distributions for Caucasian and African American female mated image pairs, as measured by d' between distributions. ... | {
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2501.08912 | Empowering Agricultural Insights: RiceLeafBD -- A Novel Dataset and
Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer
Learning Technique | [
"cs.CV"
] | The number of people living in this agricultural nation of ours, which is surrounded by lush greenery, is growing on a daily basis. As a result of this, the level of arable land is decreasing, as well as residential houses and industrial factories. The food crisis is becoming the main threat for us in the upcoming days... | {
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2501.08913 | GenAI Content Detection Task 3: Cross-Domain Machine-Generated Text
Detection Challenge | [
"cs.CL",
"cs.LG"
] | Recently there have been many shared tasks targeting the detection of generated text from Large Language Models (LLMs). However, these shared tasks tend to focus either on cases where text is limited to one particular domain or cases where text can be from many domains, some of which may not be seen during test time. I... | {
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2501.08916 | Integrating Cybersecurity in Predictive Cost-Benefit Power Scheduling: A
DeepStack Model with Dynamic Defense Mechanism | [
"eess.SY",
"cs.SY"
] | This paper introduces a novel, deep learning-based predictive model tailored to address wind curtailment in contemporary power systems, while enhancing cybersecurity measures through the implementation of a Dynamic Defense Mechanism (DDM). The augmented BiLSTM architecture facilitates accurate short-term predictions fo... | {
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2501.08918 | Efficient Planning in Large-scale Systems Using Hierarchical Finite
State Machines | [
"eess.SY",
"cs.SY"
] | We consider optimal planning in a large-scale system formalised as a hierarchical finite state machine (HFSM). A planning algorithm is proposed computing an optimal plan between any two states in the HFSM, consisting of two steps: A pre-processing step that computes optimal exit costs of the machines in the HFSM, with ... | {
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2501.08922 | Discovery of Spatter Constitutive Models in Additive Manufacturing Using
Machine Learning | [
"cs.LG",
"cs.AI"
] | Additive manufacturing (AM) is a rapidly evolving technology that has attracted applications across a wide range of fields due to its ability to fabricate complex geometries. However, one of the key challenges in AM is achieving consistent print quality. This inconsistency is often attributed to uncontrolled melt pool ... | {
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2501.08924 | Learning Joint Denoising, Demosaicing, and Compression from the Raw
Natural Image Noise Dataset | [
"cs.CV",
"eess.IV"
] | This paper introduces the Raw Natural Image Noise Dataset (RawNIND), a diverse collection of paired raw images designed to support the development of denoising models that generalize across sensors, image development workflows, and styles. Two denoising methods are proposed: one operates directly on raw Bayer data, lev... | {
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2501.08925 | Disentangling Exploration of Large Language Models by Optimal
Exploitation | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Exploration is a crucial skill for self-improvement and open-ended problem-solving. However, it remains unclear if large language models can effectively explore the state-space within an unknown environment. This work isolates exploration as the sole objective, tasking the agent with delivering information that enhance... | {
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2501.08927 | Continuous Approach to Phase (Norm) Retrieval Frames | [
"math.FA",
"cs.IR",
"cs.NA",
"math-ph",
"math.MP",
"math.NA",
"physics.optics"
] | This paper investigates the properties of continuous frames, with a particular focus on phase retrieval and norm retrieval in the context of Hilbert spaces. We introduce the concept of continuous near-Riesz bases and prove their invariance under invertible operators. Some equivalent conditions for phase and norm retrie... | {
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2501.08931 | Visual WetlandBirds Dataset: Bird Species Identification and Behavior
Recognition in Videos | [
"cs.CV",
"cs.AI"
] | The current biodiversity loss crisis makes animal monitoring a relevant field of study. In light of this, data collected through monitoring can provide essential insights, and information for decision-making aimed at preserving global biodiversity. Despite the importance of such data, there is a notable scarcity of dat... | {
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2501.08933 | Separation Assurance in Urban Air Mobility Systems using Shared
Scheduling Protocols | [
"cs.MA"
] | Ensuring safe separation between aircraft is a critical challenge in air traffic management, particularly in urban air mobility (UAM) environments where high traffic density and low altitudes require precise control. In these environments, conflicts often arise at the intersections of flight corridors, posing significa... | {
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2501.08941 | A Reinforcement Learning Approach to Quiet and Safe UAM Traffic
Management | [
"cs.MA",
"cs.LG",
"cs.RO"
] | Urban air mobility (UAM) is a transformative system that operates various small aerial vehicles in urban environments to reshape urban transportation. However, integrating UAM into existing urban environments presents a variety of complex challenges. Recent analyses of UAM's operational constraints highlight aircraft n... | {
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2501.08943 | Neuromorphic Retina: An FPGA-based Emulator | [
"eess.IV",
"cs.NE"
] | Implementing accurate models of the retina is a challenging task, particularly in the context of creating visual prosthetics and devices. Notwithstanding the presence of diverse artificial renditions of the retina, the imperative task persists to pursue a more realistic model. In this work, we are emulating a neuromorp... | {
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2501.08944 | Physical AI Agents: Integrating Cognitive Intelligence with Real-World
Action | [
"cs.MA"
] | Vertical AI Agents are revolutionizing industries by delivering domain-specific intelligence and tailored solutions. However, many sectors, such as manufacturing, healthcare, and logistics, demand AI systems capable of extending their intelligence into the physical world, interacting directly with objects, environments... | {
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2501.08946 | Applying General Turn-taking Models to Conversational Human-Robot
Interaction | [
"cs.CL",
"cs.RO"
] | Turn-taking is a fundamental aspect of conversation, but current Human-Robot Interaction (HRI) systems often rely on simplistic, silence-based models, leading to unnatural pauses and interruptions. This paper investigates, for the first time, the application of general turn-taking models, specifically TurnGPT and Voice... | {
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2501.08950 | Computing Approximated Fixpoints via Dampened Mann Iteration | [
"cs.LO",
"cs.LG"
] | Fixpoints are ubiquitous in computer science and when dealing with quantitative semantics and verification one is commonly led to consider least fixpoints of (higher-dimensional) functions over the nonnegative reals. We show how to approximate the least fixpoint of such functions, focusing on the case in which they are... | {
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2501.08951 | Analyzing the Ethical Logic of Six Large Language Models | [
"cs.AI",
"cs.CY"
] | This study examines the ethical reasoning of six prominent generative large language models: OpenAI GPT-4o, Meta LLaMA 3.1, Perplexity, Anthropic Claude 3.5 Sonnet, Google Gemini, and Mistral 7B. The research explores how these models articulate and apply ethical logic, particularly in response to moral dilemmas such a... | {
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2501.08958 | Kolmogorov-Arnold Networks for Time Series Granger Causality Inference | [
"cs.LG",
"cs.AI"
] | We propose the Granger causality inference Kolmogorov-Arnold Networks (KANGCI), a novel architecture that extends the recently proposed Kolmogorov-Arnold Networks (KAN) to the domain of causal inference. By extracting base weights from KAN layers and incorporating the sparsity-inducing penalty and ridge regularization,... | {
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2501.08962 | An analysis of data variation and bias in image-based dermatological
datasets for machine learning classification | [
"cs.CV",
"cs.AI"
] | AI algorithms have become valuable in aiding professionals in healthcare. The increasing confidence obtained by these models is helpful in critical decision demands. In clinical dermatology, classification models can detect malignant lesions on patients' skin using only RGB images as input. However, most learning-based... | {
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2501.08963 | Training-Aware Risk Control for Intensity Modulated Radiation Therapies
Quality Assurance with Conformal Prediction | [
"cs.LG"
] | Measurement quality assurance (QA) practices play a key role in the safe use of Intensity Modulated Radiation Therapies (IMRT) for cancer treatment. These practices have reduced measurement-based IMRT QA failure below 1%. However, these practices are time and labor intensive which can lead to delays in patient care. In... | {
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2501.08970 | Trusted Machine Learning Models Unlock Private Inference for Problems
Currently Infeasible with Cryptography | [
"cs.CR",
"cs.AI",
"cs.LG"
] | We often interact with untrusted parties. Prioritization of privacy can limit the effectiveness of these interactions, as achieving certain goals necessitates sharing private data. Traditionally, addressing this challenge has involved either seeking trusted intermediaries or constructing cryptographic protocols that re... | {
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2501.08974 | Learning to Extract Cross-Domain Aspects and Understanding Sentiments
Using Large Language Models | [
"cs.CL"
] | Aspect-based sentiment analysis (ASBA) is a refined approach to sentiment analysis that aims to extract and classify sentiments based on specific aspects or features of a product, service, or entity. Unlike traditional sentiment analysis, which assigns a general sentiment score to entire reviews or texts, ABSA focuses ... | {
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2501.08977 | Development and Validation of the Provider Documentation Summarization
Quality Instrument for Large Language Models | [
"cs.AI"
] | As Large Language Models (LLMs) are integrated into electronic health record (EHR) workflows, validated instruments are essential to evaluate their performance before implementation. Existing instruments for provider documentation quality are often unsuitable for the complexities of LLM-generated text and lack validati... | {
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2501.08982 | CityLoc: 6DoF Pose Distributional Localization for Text Descriptions in
Large-Scale Scenes with Gaussian Representation | [
"cs.CV"
] | Localizing textual descriptions within large-scale 3D scenes presents inherent ambiguities, such as identifying all traffic lights in a city. Addressing this, we introduce a method to generate distributions of camera poses conditioned on textual descriptions, facilitating robust reasoning for broadly defined concepts. ... | {
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2501.08983 | CityDreamer4D: Compositional Generative Model of Unbounded 4D Cities | [
"cs.CV"
] | 3D scene generation has garnered growing attention in recent years and has made significant progress. Generating 4D cities is more challenging than 3D scenes due to the presence of structurally complex, visually diverse objects like buildings and vehicles, and heightened human sensitivity to distortions in urban enviro... | {
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2501.08985 | Personality Modeling for Persuasion of Misinformation using AI Agent | [
"cs.CL",
"cs.AI",
"cs.GT"
] | The proliferation of misinformation on social media platforms has highlighted the need to understand how individual personality traits influence susceptibility to and propagation of misinformation. This study employs an innovative agent-based modeling approach to investigate the relationship between personality traits ... | {
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2501.08987 | Degradedness Under Cooperation | [
"cs.IT",
"math.IT"
] | We study cooperation problems in broadcast and relay networks, where the receivers do not satisfy the classical physical degradedness assumptions. New notions of degradedness, strongly less noisy and strongly more capable are introduced. We show that under these conditions, decode and forward (D&F) is optimal for class... | {
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2501.08994 | RepVideo: Rethinking Cross-Layer Representation for Video Generation | [
"cs.CV"
] | Video generation has achieved remarkable progress with the introduction of diffusion models, which have significantly improved the quality of generated videos. However, recent research has primarily focused on scaling up model training, while offering limited insights into the direct impact of representations on the vi... | {
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2501.08995 | VECT-GAN: A variationally encoded generative model for overcoming data
scarcity in pharmaceutical science | [
"cs.LG"
] | Data scarcity in pharmaceutical research has led to reliance on labour-intensive trial-and-error approaches for development rather than data-driven methods. While Machine Learning offers a solution, existing datasets are often small and noisy, limiting their utility. To address this, we developed a Variationally Encode... | {
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2501.08998 | CrystalGRW: Generative Modeling of Crystal Structures with Targeted
Properties via Geodesic Random Walks | [
"cond-mat.mtrl-sci",
"cond-mat.stat-mech",
"cs.LG",
"physics.comp-ph"
] | Determining whether a candidate crystalline material is thermodynamically stable depends on identifying its true ground-state structure, a central challenge in computational materials science. We introduce CrystalGRW, a diffusion-based generative model on Riemannian manifolds that proposes novel crystal configurations ... | {
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2501.09001 | Vision Foundation Models for Computed Tomography | [
"eess.IV",
"cs.CV"
] | Foundation models (FMs) have shown transformative potential in radiology by performing diverse, complex tasks across imaging modalities. Here, we developed CT-FM, a large-scale 3D image-based pre-trained model designed explicitly for various radiological tasks. CT-FM was pre-trained using 148,000 computed tomography (C... | {
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2501.09004 | Aegis2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment
of LLM Guardrails | [
"cs.CL"
] | As Large Language Models (LLMs) and generative AI become increasingly widespread, concerns about content safety have grown in parallel. Currently, there is a clear lack of high-quality, human-annotated datasets that address the full spectrum of LLM-related safety risks and are usable for commercial applications. To bri... | {
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2501.09005 | Lightweight Security for Ambient-Powered Programmable Reflections with
Reconfigurable Intelligent Surfaces | [
"cs.IT",
"cs.ET",
"math.IT"
] | Ambient Internet-of-Things (AIoT) form a new class of emerging technology that promises to deliver pervasive wireless connectivity to previously disconnected devices and products, assisting dependent industries (for example, supply chain, clothing, remote surveillance, climate monitoring, and sensors) to obtain granula... | {
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2501.09006 | Improving Stability Estimates in Adversarial Explainable AI through
Alternate Search Methods | [
"cs.LG"
] | Advances in the effectiveness of machine learning models have come at the cost of enormous complexity resulting in a poor understanding of how they function. Local surrogate methods have been used to approximate the workings of these complex models, but recent work has revealed their vulnerability to adversarial attack... | {
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2501.09007 | AI-RAN: Transforming RAN with AI-driven Computing Infrastructure | [
"cs.AI",
"cs.NI",
"eess.SP"
] | The radio access network (RAN) landscape is undergoing a transformative shift from traditional, communication-centric infrastructures towards converged compute-communication platforms. This article introduces AI-RAN which integrates both RAN and artificial intelligence (AI) workloads on the same infrastructure. By doin... | {
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2501.09008 | SimGen: A Diffusion-Based Framework for Simultaneous Surgical Image and
Segmentation Mask Generation | [
"cs.CV"
] | Acquiring and annotating surgical data is often resource-intensive, ethical constraining, and requiring significant expert involvement. While generative AI models like text-to-image can alleviate data scarcity, incorporating spatial annotations, such as segmentation masks, is crucial for precision-driven surgical appli... | {
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2501.09009 | Towards Fast, Specialized Machine Learning Force Fields: Distilling
Foundation Models via Energy Hessians | [
"physics.chem-ph",
"cond-mat.mtrl-sci",
"cs.LG",
"physics.bio-ph"
] | The foundation model (FM) paradigm is transforming Machine Learning Force Fields (MLFFs), leveraging general-purpose representations and scalable training to perform a variety of computational chemistry tasks. Although MLFF FMs have begun to close the accuracy gap relative to first-principles methods, there is still a ... | {
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2501.09012 | Multimodal LLMs Can Reason about Aesthetics in Zero-Shot | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.MM"
] | We present the first study on how Multimodal LLMs' (MLLMs) reasoning ability shall be elicited to evaluate the aesthetics of artworks. To facilitate this investigation, we construct MM-StyleBench, a novel high-quality dataset for benchmarking artistic stylization. We then develop a principled method for human preferenc... | {
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2501.09014 | How Do Generative Models Draw a Software Engineer? A Case Study on
Stable Diffusion Bias | [
"cs.SE",
"cs.AI"
] | Generative models are nowadays widely used to generate graphical content used for multiple purposes, e.g. web, art, advertisement. However, it has been shown that the images generated by these models could reinforce societal biases already existing in specific contexts. In this paper, we focus on understanding if this ... | {
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2501.09019 | Ouroboros-Diffusion: Exploring Consistent Content Generation in
Tuning-free Long Video Diffusion | [
"cs.CV"
] | The first-in-first-out (FIFO) video diffusion, built on a pre-trained text-to-video model, has recently emerged as an effective approach for tuning-free long video generation. This technique maintains a queue of video frames with progressively increasing noise, continuously producing clean frames at the queue's head wh... | {
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2501.09021 | Navigating Ethical Challenges in Generative AI-Enhanced Research: The
ETHICAL Framework for Responsible Generative AI Use | [
"cs.CY",
"cs.AI"
] | The rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI tools can enhance research efficiency through automation of tasks such as literature review and data analysis, their use raises concerns abou... | {
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2501.09022 | Generative Models with ELBOs Converging to Entropy Sums | [
"stat.ML",
"cs.IT",
"cs.LG",
"math.IT",
"math.PR",
"math.ST",
"stat.TH"
] | The evidence lower bound (ELBO) is one of the most central objectives for probabilistic unsupervised learning. For the ELBOs of several generative models and model classes, we here prove convergence to entropy sums. As one result, we provide a list of generative models for which entropy convergence has been shown, so f... | {
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} |
2501.09024 | Social-LLaVA: Enhancing Robot Navigation through Human-Language
Reasoning in Social Spaces | [
"cs.CV",
"cs.HC",
"cs.RO"
] | Most existing social robot navigation techniques either leverage hand-crafted rules or human demonstrations to connect robot perception to socially compliant actions. However, there remains a significant gap in effectively translating perception into socially compliant actions, much like how human reasoning naturally o... | {
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} |
2501.09025 | Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy
Measures | [
"cs.CR",
"cs.AI",
"cs.CY",
"econ.GN",
"q-fin.EC"
] | The digital age, driven by the AI revolution, brings significant opportunities but also conceals security threats, which we refer to as cyber shadows. These threats pose risks at individual, organizational, and societal levels. This paper examines the systemic impact of these cyber threats and proposes a comprehensive ... | {
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2501.09026 | Intelligent Anti-Money Laundering Solution Based upon Novel Community
Detection in Massive Transaction Networks on Spark | [
"cs.SI",
"cs.AI",
"cs.CY"
] | Criminals are using every means available to launder the profits from their illegal activities into ostensibly legitimate assets. Meanwhile, most commercial anti-money laundering systems are still rule-based, which cannot adapt to the ever-changing tricks. Although some machine learning methods have been proposed, they... | {
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2501.09027 | Unveiling Behavioral Differences in Bilingual Information Operations: A
Network-Based Approach | [
"cs.SI"
] | Twitter has become a pivotal platform for conducting information operations (IOs), particularly during high-stakes political events. In this study, we analyze over a million tweets about the 2024 U.S. presidential election to explore an under-studied area: the behavioral differences of IO drivers from English- and Span... | {
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2501.09028 | Emergence of the Traffic Autonomous Zone (TAZ) for Telecommunication
Operations from Spatial Heterogeneity in Cellular Networks | [
"cs.SI"
] | In the field of telecommunications, various operations are driven by different physical quantities. Each has its own patterns in time and space, but all show some clustered structures in their spatial distribution. This reflects a unified rule of human mobility, suggesting the consistency among different telecommunicat... | {
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2501.09029 | Enhancing Data Integrity through Provenance Tracking in Semantic Web
Frameworks | [
"cs.CR",
"cs.AI"
] | This paper explores the integration of provenance tracking systems within the context of Semantic Web technologies to enhance data integrity in diverse operational environments. SURROUND Australia Pty Ltd demonstrates innovative applica-tions of the PROV Data Model (PROV-DM) and its Semantic Web variant, PROV-O, to sys... | {
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2501.09031 | Synthetic Data and Health Privacy | [
"cs.CR",
"cs.AI",
"cs.CY"
] | This Viewpoint discusses generative artificial intelligence and safeguarding privacy by using synthetic data as a substitute for private health data. | {
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2501.09034 | Physics-Informed Machine Learning for Microscale Drying of Plant-Based
Foods: A Systematic Review of Computational Models and Experimental Insights | [
"cs.LG",
"physics.bio-ph",
"physics.comp-ph"
] | This review examines the current state of research on microscale cellular changes during the drying of plant-based food materials (PBFM), with particular emphasis on computational modelling approaches. The review addresses the critical need for advanced computational methods in microscale investigations. We systematica... | {
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2501.09035 | DomainDemo: a dataset of domain-sharing activities among different
demographic groups on Twitter | [
"cs.SI",
"cs.CY"
] | Social media play a pivotal role in disseminating web content, particularly during elections, yet our understanding of the association between demographic factors and political discourse online remains limited. Here, we introduce a unique dataset, DomainDemo, linking domains shared on Twitter (X) with the demographic c... | {
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} |
2501.09038 | Do generative video models learn physical principles from watching
videos? | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | AI video generation is undergoing a revolution, with quality and realism advancing rapidly. These advances have led to a passionate scientific debate: Do video models learn "world models" that discover laws of physics -- or, alternatively, are they merely sophisticated pixel predictors that achieve visual realism witho... | {
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} |
2501.09039 | Playing Devil's Advocate: Unmasking Toxicity and Vulnerabilities in
Large Vision-Language Models | [
"cs.CR",
"cs.AI",
"cs.CY"
] | The rapid advancement of Large Vision-Language Models (LVLMs) has enhanced capabilities offering potential applications from content creation to productivity enhancement. Despite their innovative potential, LVLMs exhibit vulnerabilities, especially in generating potentially toxic or unsafe responses. Malicious actors c... | {
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} |
2501.09040 | Pseudolabel guided pixels contrast for domain adaptive semantic
segmentation | [
"cs.CV",
"cs.LG"
] | Semantic segmentation is essential for comprehending images, but the process necessitates a substantial amount of detailed annotations at the pixel level. Acquiring such annotations can be costly in the real-world. Unsupervised domain adaptation (UDA) for semantic segmentation is a technique that uses virtual data with... | {
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} |
2501.09041 | Generative Visual Commonsense Answering and Explaining with Generative
Scene Graph Constructing | [
"cs.CV",
"cs.CL"
] | Visual Commonsense Reasoning, which is regarded as one challenging task to pursue advanced visual scene comprehension, has been used to diagnose the reasoning ability of AI systems. However, reliable reasoning requires a good grasp of the scene's details. Existing work fails to effectively exploit the real-world object... | {
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} |
2501.09042 | CookingDiffusion: Cooking Procedural Image Generation with Stable
Diffusion | [
"cs.CV",
"cs.GR",
"cs.LG"
] | Recent advancements in text-to-image generation models have excelled in creating diverse and realistic images. This success extends to food imagery, where various conditional inputs like cooking styles, ingredients, and recipes are utilized. However, a yet-unexplored challenge is generating a sequence of procedural ima... | {
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} |
2501.09044 | TCMM: Token Constraint and Multi-Scale Memory Bank of Contrastive
Learning for Unsupervised Person Re-identification | [
"cs.CV",
"cs.AI"
] | This paper proposes the ViT Token Constraint and Multi-scale Memory bank (TCMM) method to address the patch noises and feature inconsistency in unsupervised person re-identification works. Many excellent methods use ViT features to obtain pseudo labels and clustering prototypes, then train the model with contrastive le... | {
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} |
2501.09045 | Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities | [
"cs.CV",
"cs.AI",
"cs.ET"
] | Foundation models have revolutionized artificial intelligence, setting new benchmarks in performance and enabling transformative capabilities across a wide range of vision and language tasks. However, despite the prevalence of spatio-temporal data in critical domains such as transportation, public health, and environme... | {
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} |
2501.09046 | Learning Hemodynamic Scalar Fields on Coronary Artery Meshes: A
Benchmark of Geometric Deep Learning Models | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Coronary artery disease, caused by the narrowing of coronary vessels due to atherosclerosis, is the leading cause of death worldwide. The diagnostic gold standard, fractional flow reserve (FFR), measures the trans-stenotic pressure ratio during maximal vasodilation but is invasive and costly. This has driven the develo... | {
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} |
2501.09048 | Anthropomorphic Features for On-Line Signatures | [
"cs.CV",
"cs.LG"
] | Many features have been proposed in on-line signature verification. Generally, these features rely on the position of the on-line signature samples and their dynamic properties, as recorded by a tablet. This paper proposes a novel feature space to describe efficiently on-line signatures. Since producing a signature req... | {
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} |
2501.09049 | Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI
Reconstruction | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Dynamic MRI reconstruction, one of inverse problems, has seen a surge by the use of deep learning techniques. Especially, the practical difficulty of obtaining ground truth data has led to the emergence of unsupervised learning approaches. A recent promising method among them is implicit neural representation (INR), wh... | {
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} |
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