February 2025 arXiv papers — page 36

Anthony Genevois

For every $n\geq 1$, the flat braid group $\mathrm{FB}_n$ is an analogue of the braid group $B_n$ that can be described as the fundamental group of the configuration space $$\left\{ \{x_1, \ldots, x_n \} \in \mathbb{R}^n / \mathrm{Sym}(n) \mid \text{there exist at most two indices $i,j$ such that } x_i=x_j \right\}.$$ Alternatively, $\mathrm{FB}_n$ can also

Seon-Geun Jeong, Pham Dang Anh Duc, Quang Vinh Do, Dae-Il Noh

In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantum physics was introduced as a key enabler for solving optimization problems quickly. However, only some studies consider quantum-based approaches in wireless communications. Theref

Jianjun Gao, Chengneng Jin, Yun Shi, Xiangyu Cui

Motivated by practical applications, we explore the constrained multi-period mean-variance portfolio selection problem within a market characterized by a dynamic factor model. This model captures predictability in asset returns driven by state variables and incorporates cone-type portfolio constraints that are crucial in practice. The model is broad enough t

Ahmad Bazzi, Roberto Bomfin, Marco Mezzavilla, Sundeep Rangan

Driven by the pursuit of gigabit-per-second data speeds for future 6G mobile networks, in addition to the support of sensing and artificial intelligence applications, the industry is expanding beyond crowded sub-6 GHz bands with innovative new spectrum allocations. In this paper, we chart a compelling vision for 6G within the frequency range 3 (FR3) spectrum

Joris Dannemann, Gero Junike

Batch normalization is one of the most important regularization techniques for neural networks, significantly improving training by centering the layers of the neural network. There have been several attempts to provide a theoretical justification for batch ormalization. Santurkar and Tsipras (2018) [How does batch normalization help optimization? Advances i

Yuhan Chen, Yihong Luo, Yifan Song, Pengwen Dai

Despite extensive research efforts focused on OOD detection on images, OOD detection on nodes in graph learning remains underexplored. The dependence among graph nodes hinders the trivial adaptation of existing approaches on images that assume inputs to be i.i.d. sampled, since many unique features and challenges specific to graphs are not considered, such a

Souliman Alghnam, Mohammad Alhussien, Khaled Shaheen

Speech enhancement plays an essential role in improving the quality of speech signals in noisy environments. This paper investigates the efficacy of integrating Bidirectional Gated Recurrent Units (BGRU) and Transformer models for speech enhancement tasks. Through a comprehensive experimental evaluation, our study demonstrates the superiority of this hybrid

Fangyuan Yu

Modern language models rely on static vocabularies, fixed before pretraining, in contrast to the adaptive vocabulary acquisition observed in human language learning. To bridge this gap, we introduce vocabulary curriculum learning, an approach that improves pretraining efficiency with log-linear scaling gains relative to vocabulary size. Our method alternates

Phillip Kingston, Théo Fagnoni, Mahsun Altin

This paper introduces Workflow Intention, a novel framework for identifying and encoding process objectives within complex business environments. Workflow Intention is the alignment of Input, Process and Output elements defining a Workflow's transformation objective interpreted from Workflow Signal inside Business Artefacts. It specifies how Input is process

Minh Duc Vu, Jieshan Chen, Zhenchang Xing, Qinghua Lu

With the proliferation of data across various domains, there is a critical demand for tools that enable non-experts to derive meaningful insights without deep data analysis skills. To address this need, existing automatic fact sheet generation tools offer heuristic-based solutions to extract facts and generate stories. However, they inadequately grasp the se

Hiroto Sugimori, Shinpei Hayashi

To improve the efficiency of software maintenance, change prediction techniques have been proposed to predict frequently changing modules. Whereas existing techniques focus primarily on class-level prediction, method-level prediction allows for more direct identification of change locations. Method-level prediction can be useful, but it may also negatively a

Syed Jubayer Jaman, Md. Zahurul Haque, Md Robiul Islam, Usama Abdun Noor

Currency recognition plays a vital role in banking, commerce, and assistive technology for visually impaired individuals. Traditional methods, such as manual verification and optical scanning, often suffer from limitations in accuracy and efficiency. This study introduces an advanced currency recognition system utilizing Convolutional Neural Networks (CNNs)

Yuki Sato, Kiyoshi Kanazawa

In financial market microstructure, there are two enigmatic empirical laws: (i) the market-order flow has predictable persistence due to metaorder splitters by institutional investors, well formulated as the Lillo-Mike-Farmer model. However, this phenomenon seems paradoxical given the diffusive and unpredictable price dynamics; (ii) the price impact $I(Q)$ o

Lipeng Zhu, Wenyan Ma, Weidong Mei, Yong Zeng

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless

Junzheng Peng, Jiahao Xian, Xi Lin, Manhong Yao

Super-resolution structured illumination microscopy (SR-SIM) is a widely used technique for enhancing the resolution of fluorescence imaging beyond the diffraction limit. Most existing SR-SIM methods rely on Moir\'e effect-based physical imaging models, which require the estimation of structured illumination parameters during image reconstruction. However, p

Lars Krupp, Daniel Geißler, Paul Lukowicz, Jakob Karolus

Improvements in the area of large language models have shifted towards the construction of models capable of using external tools and interpreting their outputs. These so-called web agents have the ability to interact autonomously with the internet. This allows them to become powerful daily assistants handling time-consuming, repetitive tasks while supportin

S. Peirani, Y. Suto, S. Han, S. K. Yi

(Reduced)Using the NewHorizon simulation, we have studied ten gas-versus-star counter-rotating galaxies in field environments with a stellar mass of M*~[1-5]x10^10 Msun. For all of them, the retrograde accretion of gas either from gas stripping from a nearby companion or from the circumgalactic medium is the starting point of the formation process. This is f

Yusuke Tominaga, Shotaro Shirai, Yuji Hishida, Hirotaka Terai

This study investigates the use of spiral geometry in superconducting resonators to achieve high intrinsic quality factors, crucial for applications in quantum computation and quantum sensing. We fabricated Archimedean Spiral Resonators (ASRs) using domain-matched epitaxially grown titanium nitride (TiN) on silicon wafers, achieving intrinsic quality factors

Che Liu, Cheng Ouyang, Zhongwei Wan, Haozhe Wang

Recent advances in multimodal ECG representation learning center on aligning ECG signals with paired free-text reports. However, suboptimal alignment persists due to the complexity of medical language and the reliance on a full 12-lead setup, which is often unavailable in under-resourced settings. To tackle these issues, we propose **K-MERL**, a knowledge-en

Tong Li, Shu Yang, Junchao Wu, Jiyao Wei

We present a comprehensive evaluation framework for assessing Large Language Models' (LLMs) capabilities in suicide prevention, focusing on two critical aspects: the Identification of Implicit Suicidal ideation (IIS) and the Provision of Appropriate Supportive responses (PAS). We introduce \ourdata, a novel dataset of 1,308 test cases built upon psychologica

Christine Lee, David Porfirio, Xinyu Jessica Wang, Kevin Zhao

Automated planning is traditionally the domain of experts, utilized in fields like manufacturing and healthcare with the aid of expert planning tools. Recent advancements in LLMs have made planning more accessible to everyday users due to their potential to assist users with complex planning tasks. However, LLMs face several application challenges within end

Wei Zhao, Zhe Li, Yige Li, Jun Sun

Large Vision-Language Models (LVLMs) have made significant strides in multimodal comprehension, thanks to extensive pre-training and fine-tuning on large-scale visual datasets. However, despite their robust textual safety mechanisms, they remain vulnerable to harmful visual inputs. Existing safeguards-typically relying on pre-filtering or fine-tuning-incur h

Kexin Huang, Junkang Wu, Ziqian Chen, Xue Wang

Preference learning is critical for aligning large language models (LLMs) with human values, with the quality of preference datasets playing a crucial role in this process. While existing metrics primarily assess data quality based on either explicit or implicit reward margins, they often provide contradictory evaluations for the same data. To address this i

Mu Jia, Junting Chen, Ying-Chang Liang, Pooi-Yuen Kam

Constant-envelope signals are widely used in mobile edge applications and wireless communication systems for their hardware-friendly design, energy efficiency, and reliability. However, reliable detection with simple, power-efficient receivers remains challenging. Coherent methods offer superior performance but require complex synchronization, increasing com

Ben Andrews, Glen Wheeler

We consider an invariant gradient flow for the invariant length functional for co-compact curves in inversive geometry, and prove that solutions exist for all time and converge to loxodromic curves, provided the initial curve is admissible (so that the invariant length element is well defined).

Scott T. Chapman, Pedro García-Sánchez, Christopher O'Neill, Vadim Ponomarenko

Several papers in the recent literature have studied factorization properties of affine monoids using the monoid's Betti elements. In this paper, we extend this study to more general rings and monoids. We open by demonstrating the issues with computing the complete set of Betti elements of a general commutative cancellative monoid, and as an example compute

Weiheng Liu, Yuxuan Wan, Jilong Wang, Yuxuan Kuang

Generalizable object fetching in cluttered scenes remains a fundamental and application-critical challenge in embodied AI. Closely packed objects cause inevitable occlusions, making safe action generation particularly difficult. Under such partial observability, effective policies must not only generalize across diverse objects and layouts but also reason ab

Hongyi Chen, Jingtao Ding, Jianhai Shu, Xinchun Yu

Controlling complex physics systems is important in diverse domains. While diffusion-based methods have demonstrated advantages over classical model-based approaches and myopic sequential learning methods in achieving global trajectory consistency, they are limited by sample efficiency.This paper presents SEDC (Sample-Efficient Diffusion-based Control), a no

Faisal Farooq, Irfan Ahmad Dar, Muzaffar Qadir Lone

We analyze the steady-state characteristics of a damped harmonic oscillator (system) in presence of a non-Markovian bath characterized by Lorentzian spectral density. Although Markovian baths presume memoryless dynamics, the introduction of complex temporal connections by a non-Markovian environment radically modifies the dynamics of the system and its stead

Yuanyun Zhang, Shi Li

The field of Artificial Intelligence in healthcare is evolving at an unprecedented pace, driven by rapid advancements in machine learning and the recent breakthroughs in large language models. While these innovations hold immense potential to transform clinical decision making, diagnostics, and patient care, the accelerating speed of AI development has outpa

R. Abbasi, M. Ackermann, J. Adams, S. K. Agarwalla

This study presents an energy-dependent analysis of seasonal variations in the atmospheric muon neutrino spectrum, using 11.3 years of data from the IceCube Neutrino Observatory. By leveraging a novel spectral unfolding method, we explore the energy range from 125 GeV to 10 TeV for zenith angles between 90{\deg} to 110{\deg}, corresponding to the Antarctic a

Leslie Howe, Tharindu D. Rajapaksha, Kalani H. Ellepola, Vinh X. Ho

The emergence of planar meta-lenses on flexible materials has profoundly impacted the long-standing perception of diffractive optics. Despite their advantages, these lenses still face challenges in design and fabrication to obtain high focusing efficiency and resolving power. A nanofabrication technique is demonstrated based on photolithography and polyimide

Mingyan Wu, Zhenghao Liu, Yukun Yan, Xinze Li

Retrieval-Augmented Generation (RAG) enhances the performance of Large Language Models (LLMs) by incorporating external knowledge. However, LLMs still encounter challenges in effectively utilizing the knowledge from retrieved documents, often being misled by irrelevant or noisy information. To address this issue, we introduce RankCoT, a knowledge refinement

Andrei Apostol, Maria Nutu

In Romania, cardiovascular problems are the leading cause of death, accounting for nearly one-third of annual fatalities. The severity of this situation calls for innovative diagnosis method for cardiovascular diseases. This article aims to explore efficient, light-weight and rapid methods for arrhythmia diagnosis, in resource-constrained healthcare settings

Qinghua He, Hongyang Jiang, Danqi Fang, Dawei Yang

Diabetic macular ischemia (DMI), marked by the loss of retinal capillaries in the macular area, contributes to vision impairment in patients with diabetes. Although color fundus photographs (CFPs), combined with artificial intelligence (AI), have been extensively applied in detecting various eye diseases, including diabetic retinopathy (DR), their applicatio

Shuji Shinohara, Daiki Morita, Hayato Hirai, Ryosuke Kuribayashi

Identifying statistical patterns characterizing human trajectories is crucial for public health, traffic engineering, city planning, and epidemic modeling. Recent developments in global positioning systems and mobile phone networks have enabled the collection of substantial information on human movement. Analyses of these data have revealed various power law

Jean-Marc Deragon, Rémy Merret

Tight regulation of messenger RNA (mRNA) stability is essential to ensure accurate gene expression in response to developmental and environmental cues. mRNA stability is controlled by mRNA decay pathways, which have traditionally been proposed to occur independently of translation. However, the recent discovery of a co-translational mRNA decay pathway (also

Yanping Chen, Xueting Han, Liangchuan Wu

In this paper, we study the boundedness of the fractional Riesz transforms in the Dunkl setting. Moreover, we establish the necessary and sufficient conditions for the boundedness of their commutator with respect to the central BMO space associated with Euclidean metric and the BMO space associated with Dunkl metric, respectively. Based on this, we further c

Matteo Contini, Victor Illien, Julien Barde, Sylvain Poulain

Drone-based remote sensing combined with AI-driven methodologies has shown great potential for accurate mapping and monitoring of coral reef ecosystems. This study presents a novel multi-scale approach to coral reef monitoring, integrating fine-scale underwater imagery with medium-scale aerial imagery. Underwater images are captured using an Autonomous Surfa

Hannah Calzi Kleidermacher, James Zou

Scientific research is inherently global. However, the vast majority of academic journals are published exclusively in English, creating barriers for non-native-English-speaking researchers. In this study, we leverage large language models (LLMs) to translate published scientific articles while preserving their native JATS XML formatting, thereby developing

Kai-Qi Wang, Jun-Ding Zheng, Wen-Yi Tong, Chun-Gang Duan

In recent years, kagome lattices have garnered significant attention for their diverse properties in topology, magnetism, and electron correlations. However, the exploration of breathing kagome lattices, which exhibit dynamic breathing behavior, remains relatively scarce. Structural breathing introduces an additional degree of freedom that is anticipated to

Rui Lu, Bihai Zhang, Dan Wang

With the popularity of 3D volumetric video applications, such as Autonomous Driving, Virtual Reality, and Mixed Reality, current developers have turned to deep learning for compressing volumetric video frames, i.e., point clouds for video upstreaming. The latest deep learning-based solutions offer higher efficiency, lower distortion, and better hardware supp

Rong Liu, Junye Liang, Jiaqi Yang, Jiang He

Due to the difficulty of obtaining labeled data for hyperspectral images (HSIs), cross-scene classification has emerged as a widely adopted approach in the remote sensing community. It involves training a model using labeled data from a source domain (SD) and unlabeled data from a target domain (TD), followed by inferencing on the TD. However, variations in

Nobin Sarwar

Visual Question Answering requires models to generate accurate answers by integrating visual and textual understanding. However, VQA models still struggle with hallucinations, producing convincing but incorrect answers, particularly in knowledge-driven and Out-of-Distribution scenarios. We introduce FilterRAG, a retrieval-augmented framework that combines BL

Hongqiu Wu, Weiqi Wu, Tianyang Xu, Jiameng Zhang

LLM-based Interactive Drama is a novel AI-based dialogue scenario, where the user (i.e. the player) plays the role of a character in the story, has conversations with characters played by LLM agents, and experiences an unfolding story. This paper begins with understanding interactive drama from two aspects: Immersion, the player's feeling of being present in

Jyh-An Lee, Peng Zhou

This paper first introduces China's legal framework regulating facial recognition technology (FRT) and analyzes the underlying problems. Although current laws and regulations have restricted the development of FRT under some circumstances, these restrictions may function poorly when the technology is installed by the government or when it is deployed for the

Hai-Tao Hu, Yang Chen, Xiaoshui Lin, Ai-Min Guo

Quasiperiodic models are important physical platforms to explore Anderson transitions in low dimensional systems, yet the exact mobility edges (MEs) are generally hard to be determined analytically. To date, the MEs in only a few models can be determined exactly. In this manuscript, we propose a new class of network models characterized by quasiperiodic slow

Sijia Li, Sergio Vicenzo, Bing Xu

In recent years, the fifth-generation (5G) new radio (NR) signals have emerged as a promising supplementary resource for urban navigation. However, a major challenge in utilizing 5G signals lies in their vulnerability to non-line-of-sight (NLoS) propagation effects, which are especially prevalent in urban street canyons. This paper applies the direct positio

Runzhong Wang, Rui-Xi Wang, Mrunali Manjrekar, Connor W. Coley

Molecular machine learning has gained popularity with the advancements of geometric deep learning. In parallel, retrieval-augmented generation has become a principled approach commonly used with language models. However, the optimal integration of retrieval augmentation into molecular machine learning remains unclear. Graph neural networks stand to benefit f

Jiazhen Hong, Geoffrey Mackellar, Soheila Ghane

Electroencephalogram (EEG) signals generally exhibit low signal-to-noise ratio (SNR) and high inter-subject variability, making generalization across subjects and domains challenging. Recent advances in deep learning, particularly self-supervised learning with Transformer-based architectures, have shown promise in EEG representation learning. However, their

Ziruo Zhao

Contrastive learning has emerged as a powerful paradigm for self-supervised representation learning. This work analyzes the theoretical limits of contrastive learning under nasty noise, where an adversary modifies or replaces training samples. Using PAC learning and VC-dimension analysis, lower and upper bounds on sample complexity in adversarial settings ar

Yu Lei, Basang Tsering-xiao

This research examines the regularity of weak solutions to the Div-Curl system with low regularity anisotropic coefficients. The Hölder regularity of the Div-Curl system with one anisotropic coefficient was an unresolved problem raised by Yin in 2016. We have addressed the open problem, and the findings extend to the scenario involving two anisotropic coeffi

Junwei Zhao, Tongjiang Wang, Ruizhu Chen

Recurrent and propagating intensity perturbations are frequently observed in extreme ultraviolet (EUV) channels along coronal fan loops above sunspots, and these perturbations are suggested to be slow magnetoacoustic waves. Numerous studies have been conducted to investigate their propagation speeds, damping, and excitation sources; however, there have been

Haris Aziz, Shivika Narang, Mashbat Suzuki

We propose a new model for aggregating preferences over a set of indivisible items based on a quantile value. In this model, each agent is endowed with a specific quantile, and the value of a given bundle is defined by the corresponding quantile of the individual values of the items within it. Our model captures the diverse ways in which agents may perceive

Fouad Naderi

A quantum expectation is a positive linear functional of norm one on a non-commutative probability space (i.e., a C*-algebra). For a given pair of quantum expectations $\mu$ and $\lambda$ on a non-commutative probability space $A$, we propose a definition for weak* continuity and weak* singularity of $\mu$ with respect to $\lambda$. Then, using the theory of

Changyo Han, Yosuke Nakagawa, Takeshi Naemura

Swarm User Interfaces allow dynamic arrangement of user environments through the use of multiple mobile robots, but their operational range is typically confined to a single plane due to constraints imposed by their two-wheel propulsion systems. We present corobos, a proof-of-concept design that enables these robots to cooperatively transition between table

Zhaocong Liu, Jie Huang

The property of the communication network and the constraints on the strategic space are two factors that determine the complexity of the distributed Nash equilibrium (DNE) seeking problem. The DNE seeking problem of aggregative games has been studied for unconstrained case over all types of communication networks and for various types of constrained games o

Chia-Tung Ho, Jing Gong, Yunsheng Bai, Chenhui Deng

Hardware design presents numerous challenges stemming from its complexity and advancing technologies. These challenges result in longer turn-around-time (TAT) for optimizing performance, power, area, and cost (PPAC) during synthesis, verification, physical design, and reliability loops. Large Language Models (LLMs) have shown remarkable capacity to comprehen

Harrison Jesse Smith, Nicky He, Yuting Ye

Drawing is a fun and intuitive way to create a character, accessible even to small children. However, animating 2D figure drawings is a much more challenging task, requiring specialized tools and skills. Bringing 2D figures to 3D so they can be animated and consumed in immersive media poses an even greater challenge. Moreover, it is desirable to preserve the

Naveen Edapurath Vijayan

This paper presents a novel data-driven approach to mitigating employee attrition using machine learning and data engineering techniques. The proposed framework integrates data from various human resources systems and leverages advanced feature engineering to capture a comprehensive set of factors influencing attrition. The study outlines a robust modeling a

Nitish Vikas Deshpande, Miguel Rodrigo Castellanos, Saeed R. Khosravirad, Jinfeng Du

A parasitic reconfigurable antenna array is a low-power approach for beamforming using passive tunable elements. Prior work on reconfigurable antennas in communication theory is based on ideal radiation pattern abstractions. It does not address the problem of physical realizability. Beamforming with parasitic elements is inherently difficult because mutual c

Zhiyu Yin, Kehai Chen, Xuefeng Bai, Ruili Jiang

Video generation aims to produce temporally coherent sequences of visual frames, representing a pivotal advancement in Artificial Intelligence Generated Content (AIGC). Compared to static image generation, video generation poses unique challenges: it demands not only high-quality individual frames but also strong temporal coherence to ensure consistency thro

Peipei Yuan, Zijing Xie, Shuo Ye, Hong Chen

Generative artificial intelligence holds significant potential for abuse, and generative image detection has become a key focus of research. However, existing methods primarily focused on detecting a specific generative model and emphasizing the localization of synthetic regions, while neglecting the interference caused by image size and style on model learn

Nan Cui, Tinghe Yun, Bohan Wei, Yang Li

Two-dimensional (2D) materials with extraordinary electrical properties, hold promising for large-scale, flexible electronics. However, their device performance could be hindered due to the excessive defects introduced via traditional electrode integration processes. Transfer printing techniques have been developed for van der Waals contacts integration, whi

Haoyuan Li, Yanpeng Zhou, Tao Tang, Jifei Song

Recent advancements in multi-modal 3D pre-training methods have shown promising efficacy in learning joint representations of text, images, and point clouds. However, adopting point clouds as 3D representation fails to fully capture the intricacies of the 3D world and exhibits a noticeable gap between the discrete points and the dense 2D pixels of images. To

Wen-Li Yuan, Bikai Gao, Yan Yan, Renxin Xu

Using the parity doublet model (PDM) for hadronic matter and a modified Nambu-Jona-Lasinio (NJL) model for quark matter, we investigate the potential existence of two- and three-flavor quark matter in neutron star cores. Both models respect chiral symmetry, and a sharp first-order phase transition is implemented via Maxwell construction. We find stable neutr

Tomohiro Nabika, Kenji Nagata, Shun Katakami, Masaichiro Mizumaki

Bayesian data analysis is widely used across many disciplines, and representative examples in materials science include spectral analysis and sparse modeling. In such applications, the underlying models often become complex and yield multimodal posterior distributions, making efficient sampling from multimodal distributions essential. Replica exchange Monte

Run Chen, Jun Shin, Julia Hirschberg

Previous research has shown that humans are more receptive towards language models that that exhibit empathetic behavior. While empathy is essential for developing helpful dialogue agents, very few large corpora containing empathetic dialogues are available for fine-tune LLMs. The few existing corpora have largely relied on crowdsourcing to simulate empathet

Dakyeom Ahn, Seora Park, Seolhee Lee, Jieun Cho

Virtual YouTubers (VTubers) have recently gained popularity as streamers using computer-generated avatars and real-time motion capture to create distinct virtual identities. While prior research has explored how VTubers construct virtual personas and engage audiences, little attention has been given to viewers' reactions when virtual and real identities blur

Dakyeom Ahn, Hajin Lim

While AI's potential in education and professional sports is widely recognized, its application in K-12 physical education (PE) remains underexplored with significant opportunities for innovation. This study aims to address this gap by engaging 17 in-service secondary school PE teachers in group ideation workshops to explore potential AI applications and cha

Adrian Vazquez-Marquez, Jenylin Zuniga-Apipilhuasco

In this article the 2-adjunction that relates universal arrows and extensive monads is constructed explicitly. This 2-adjunction resembles the one that relates adjunctions and monads since the 2-category of universal arrows is isomorphic to the 2-category of adjunctions and the 2-category of extensive monads is isomorphic to the 2-category of monads. This ar

Qiao Huang, Nicolas Privault

Stochastic branching algorithms provide a useful alternative to grid-based schemes for the numerical solution of partial differential equations, particularly in high-dimensional settings. However, they require a strict control of the integrability of random functionals of branching processes in order to ensure the non-explosion of solutions. In this paper, w

Liting Wen, Zimo Yang, Xianlin Zhang, Chi Ding

3D face reconstruction from a single sketch is a critical yet underexplored task with significant practical applications. The primary challenges stem from the substantial modality gap between 2D sketches and 3D facial structures, including: (1) accurately extracting facial keypoints from 2D sketches; (2) preserving diverse facial expressions and fine-grained

Adnan Shaout, Jiho Han

The vascular structure in retinal images plays a crucial role in ophthalmic diagnostics, and its accuracies are directly influenced by the quality of the retinal image. Contrast enhancement is one of the crucial steps in any segmentation algorithm - the more so since the retinal images are related to medical diagnosis. Contrast enhancement is a vital step th

G. S. Kocheril, C. Zagorec-Marks, H. J. Lewandowski

The aromatic molecule benzene is considered the essential building block for larger polycyclic aromatic hydrocarbons (PAHs) in space. Despite benzene's importance in the formation of PAHs, the formation mechanisms of interstellar benzene are not well understood. A single ion-molecule reaction sequence is considered when modeling the formation of benzene in t

Jianghao Chen, Zhenlin Wei, Zhenjiang Ren, Ziyong Li

Recent progress in Large Reasoning Models (LRMs) has significantly enhanced the reasoning abilities of Large Language Models (LLMs), empowering them to tackle increasingly complex tasks through reflection capabilities, such as making assumptions, backtracking, and self-refinement. However, effectively evaluating such reflection capabilities remains challengi

Shaun D. Hampton

Using the D1D5 CFT we investigate transitions involving a member of a certain class of states called superstrata states, which are holographically dual to certain smooth, horizonless, $1/8$-BPS, three-charge black hole microstates known as superstrata. We study these transitions by deforming the CFT away from the free orbifold point using a marginal deformat

Roger Waleffe, Devesh Sarda, Jason Mohoney, Emmanouil-Vasileios Vlatakis-Gkaragkounis

We study distributed training of Graph Neural Networks (GNNs) on billion-scale graphs that are partitioned across machines. Efficient training in this setting relies on min-edge-cut partitioning algorithms, which minimize cross-machine communication due to GNN neighborhood sampling. Yet, min-edge-cut partitioning over large graphs remains a challenge: State-

Robert R. Petro, Connor M. Phillips

If $\Gamma$ is a graph for which every edge is in exactly one clique of order $\omega$, then one can form a new graph with vertex set equal to these cliques. This is a generalization of the line graph of $\Gamma$. We discover many general results and classifications related to these clique graph that will be useful to researchers studying these objects. In p

Benjamin C. Koenig, Suyong Kim, Sili Deng

The recently proposed Kolmogorov-Arnold network (KAN) is a promising alternative to multi-layer perceptrons (MLPs) for data-driven modeling. While original KAN layers were only capable of representing the addition operator, the recently-proposed MultKAN layer combines addition and multiplication subnodes in an effort to improve representation performance. He

Yu-Chieh Chao, Yubei Chen, Weiwei Wang, Achintha Wijesinghe

Semantic communication marks a new paradigm shift from bit-wise data transmission to semantic information delivery for the purpose of bandwidth reduction. To more effectively carry out specialized downstream tasks at the receiver end, it is crucial to define the most critical semantic message in the data based on the task or goal-oriented features. In this w

Gahyoun Gim, Jinhyuk Yun, Sang Hoon Lee

We propose a framework to quantify and utilize interdisciplinarity in science and engineering curricula at the university-level higher education. We analyze interdisciplinary relations by standardizing large-scale official educational data in Korea using a cutting-edge large language model and constructing knowledge maps for disciplines of scientific educati

Beibei Xiong, Hangyu Lv, Haojia Shan, Jianlin Wang

Large language models (LLMs) have significantly advanced formal theorem proving, yet the scarcity of high-quality training data constrains their capabilities in complex mathematical domains. Combinatorics, a cornerstone of mathematics, provides essential tools for analyzing discrete structures and solving optimization problems. However, its inherent complexi

Haris Riaz, Ellen Riloff, Mihai Surdeanu

We propose a simple, unsupervised method that injects pragmatic principles in retrieval-augmented generation (RAG) frameworks such as Dense Passage Retrieval to enhance the utility of retrieved contexts. Our approach first identifies which sentences in a pool of documents retrieved by RAG are most relevant to the question at hand, cover all the topics addres

Xinyi Wang, Jiawei Li, Ao Feng, Didier Sornette

Without rigorous attention to the completeness of earthquake catalogs, claims of new discoveries or forecasting skills cannot be deemed credible. Therefore, estimating the completeness magnitude (Mc) is a critical step. Among various approaches, catalog-based methods are the simplest, most straightforward, and most commonly used. However, current evaluation

Miao Ye, Zhibang Jiang, Xingsi Xue, Xingwang Li

Attention-based transformers have played an important role in wireless sensor network (WSN) timing anomaly detection due to their ability to capture long-term dependencies. However, there are several issues that must be addressed, such as the fact that their ability to capture long-term dependencies is not completely reliable, their computational complexity

Jun Gao, XiaoMin Shen, Hongxi Xing, Yuxiang Zhao

We present the first global analysis of fragmentation functions (FFs) for light charged hadrons ($\pi^{\pm}$, $K^{\pm}$) at full next-to-next-to-leading order in Quantum Chromodynamics (QCD), incorporating world data from both single-inclusive electron-positron annihilation and semi-inclusive deep-inelastic scattering. The collinear factorization has been te

Muhammad Nawaz, Basma Nasir, Tehseen Zia, Zawar Hussain

Precise identification and localization of disease-specific features at the pixel-level are particularly important for early diagnosis, disease progression monitoring, and effective treatment in medical image analysis. However, conventional diagnostic AI systems lack decision transparency and cannot operate well in environments where there is a lack of pixel

Gefei Zhang, Shenming Ji, Yicao Li, Jingwei Tang

As programming education becomes more widespread, many college students from non-computer science backgrounds begin learning programming. Collaborative programming emerges as an effective method for instructors to support novice students in developing coding and teamwork abilities. However, due to limited class time and attention, instructors face challenges

Parag Khanna, Mårten Björkman, Christian Smith

This work explores the effect of object weight on human motion and grip release during handovers to enhance the naturalness, safety, and efficiency of robot-human interactions. We introduce adaptive robotic strategies based on the analysis of human handover behavior with varying object weights. The key contributions of this work includes the development of a

Yoshihiko Ihara, Masakazu Shimohashi, Markus Kriener

Several elements in the periodic table exhibit an interesting and often overlooked feature: They skip certain valence states which is discussed in the field of superconductivity to be in favor of fostering higher transition temperatures $T_c$. However, from the experimental point of view, it is often deemed difficult to probe changes in the valence state. He

Isaiah Andrews, Jiafeng Chen

Hypothesis tests and confidence intervals are ubiquitous in empirical research, yet their connection to subsequent decision-making is often unclear. We develop a theory of certified decisions that pairs recommended decisions with inferential guarantees. Specifically, we attach P-certificates -- upper bounds on loss that hold with probability at least $1-\alp

Yudong Xie, Zhifeng Han, Qinfan Xiao, Liwei Liang

Silent speech interfaces (SSI) are being actively developed to assist individuals with communication impairments who have long suffered from daily hardships and a reduced quality of life. However, silent sentences are difficult to segment and recognize due to elision and linking. A novel silent speech sentence recognition method is proposed to convert the fa

Jadon Y. Lin, C. Martijn de Sterke, Ognjen Ilic, Boris T. Kuhlmey

Lightsails are a highly promising spacecraft concept that has attracted interest in recent years due to its potential to travel at near-relativistic speeds. Such speeds, which current conventional crafts cannot reach, offer tantalizing opportunities to probe nearby stellar systems within a human lifetime. Recent advancements in photonics and metamaterials ha

Entejar Alam, Paul J. Rathouz, Peter Mueller

We introduce a novel varying-weight dependent Dirichlet process (DDP) model that extends a recently developed semi-parametric generalized linear model (SPGLM) by adding a nonparametric Bayesian prior on the baseline distribution of the GLM. We show that the resulting model takes the form of an inhomogeneous completely random measure that arises from exponent

Jingbo Liu, Jiacheng Chen, Zeyu Sun, Bo Qian

Flexible cooperation among base stations (BSs) is critical to improve resource utilization efficiency and meet personalized user demands. However, its practical implementation is hindered by the current radio access network (RAN), which relies on the coupling of uplink and downlink transmissions and channel state information feedback with inherent issues suc

Marco De Corato, Kun Zhang, Lailai Zhu

Rotating particles moving in fluids undergo a transverse migration via the inertia-induced Magnus effect. This phenomenon vanishes at colloidal scales because inertia is negligible and the fluid flow is time reversible. Yet, recent experiments discovered an inverse Magnus effect of colloids in polymeric and micellar solutions supposedly because their viscoel

Zachary McBride Lazri, Anirudh Nakra, Ivan Brugere, Danial Dervovic

Algorithmic fairness is often studied in static or single-agent settings, yet many real-world decision-making systems involve multiple interacting entities whose multi-stage actions jointly influence long-term outcomes. Existing fairness methods applied at isolated decision points frequently fail to mitigate disparities that accumulate over time. Although re

Basma Nasir, Tehseen Zia, Muhammad Nawaz, Catarina Moreira

Medical image annotation is essential for diagnosing diseases, yet manual annotation is time-consuming, costly, and prone to variability among experts. To address these challenges, we propose an automated explainable annotation system that integrates ensemble learning, visual explainability, and uncertainty quantification. Our approach combines three pre-tra

Jie Ren, Zhenwei Dai, Xianfeng Tang, Hui Liu

Unlearning has been proposed to remove copyrighted and privacy-sensitive data from Large Language Models (LLMs). Existing approaches primarily rely on fine-tuning-based methods, which can be categorized into gradient ascent-based (GA-based) and suppression-based methods. However, they often degrade model utility (the ability to respond to normal prompts). In