March 2025 arXiv papers — page 17

Tiago Machado, Sara E. Berger, Cassia Sanctos, Vagner Figueiredo de Santana

Computer Science and Design practitioners have been researching and proposing alternatives for a dearth of recommendations, standards, or best practices in user interfaces for decades. Now, with the advent of generative Artificial Intelligence (GenAI), we have yet again an emerging, powerful technology that lacks sufficient guidance in terms of possible inte

Hasan Moughnieh, Mohamad Chalhoub, Hasan Nasrallah, Cristiano Nattero

Adapting pre-trained models has become an effective strategy in artificial intelligence, offering a scalable and efficient alternative to training models from scratch. In the context of remote sensing (RS), where visual grounding(VG) remains underexplored, this approach enables the deployment of powerful vision-language models to achieve robust cross-modal u

Junlin Li, Yang Zhang, Bo Pang, Junqiang Bai

The precision of shape representation and the dimensionality of the design space significantly influence the cost and outcomes of aerodynamic optimization. The design space can be represented more compactly by maintaining geometric precision while reducing dimensions, hence enhancing the cost-effectiveness of the optimization process. This research presents

Anastasiia Fadeeva, Vincent Coriou, Diego Antognini, Claudiu Musat

Tablets and styluses are increasingly popular for taking notes. To optimize this experience and ensure a smooth and efficient workflow, it's important to develop methods for accurately interpreting and understanding the content of handwritten digital notes. We introduce a foundational model called InkFM for analyzing full pages of handwritten content. Traine

Ravshanjon Nazarov, Denis Khanabiev, Elizaveta Chernysheva, Alexandra Dudnikova

We study the impact of structural disorder on a radiative lifetime of symmetry-protected bound state in the continuum (BIC) and a bright mode in a one-dimensional periodic chain of coupled Mie resonators. Through experimental, simulation, and theoretical approach, we reveal an unusual linear decay in the radiative quality factor of the BIC with the increase

Justin Thorpe, Thomas Wanner

We discuss the identification of attracting sets using combinatorial multivector fields (CMVF) from Conley-Morse-Forman theory. A CMVF is a dynamical system induced by the action of a continuous dynamical system on a phase space discretization that can be represented as a Lefschetz complex. There is a rich theory under development establishing the connection

Zhengyi Zhao, Shubo Zhang, Yiming Du, Bin Liang

Large language models have improved dialogue systems, but often process conversational turns in isolation, overlooking the event structures that guide natural interactions. Hence we introduce EventWeave, a framework that explicitly models relationships between conversational events to generate more contextually appropriate dialogue responses. EventWeave cons

Yue Liu, Jiaying Wu, Yufei He, Ruihan Gong

Large Reasoning Models (LRMs) significantly improve the reasoning ability of Large Language Models (LLMs) by learning to reason, exhibiting promising performance in solving complex tasks. However, their deliberative reasoning process leads to inefficiencies in token usage, memory consumption, and inference time. Thus, this survey provides a review of efficie

Arash Nasr-Esfahany, Mohammad Alizadeh, Victor Lee, Hanna Alam

Cycle-level simulators such as gem5 are widely used in microarchitecture design, but they are prohibitively slow for large-scale design space explorations. We present Concorde, a new methodology for learning fast and accurate performance models of microarchitectures. Unlike existing simulators and learning approaches that emulate each instruction, Concorde p

Xiaodan Lyu, Leevi Kallioniemi, Hongbing Cai, Liheng An

Understanding and controlling nonlinear processes is crucial for engineering light-matter interaction and generating non-classical light. A significant challenge in ultra-thin nonlinear materials is the marked diminution of the nonlinear conversion efficiency due to the reduced light-matter interaction length and, in many cases, the centrosymmetric crystalli

Nan Jiang, Dae-Sik Moon, Yuan Qi Ni, Maria R. Drout

We present $BVi$ multi-band high-cadence observations of a Type II supernova (SN) KSP-SN-2022c from a star-forming galaxy at $z$ $\simeq$ 0.041 from its infant to nebular phase. Early light curve fitting with a single power-law is consistent with the first detection of roughly 15 minutes after shock breakout. The SN light curves feature a rapid rise and decl

Cai-Hong Wang, Jiang-Tao Yuan, Mao-Sheng Li, Ying-Hui Yang

Two sets of quantum entangled states that are equivalent under local unitary transformations may exhibit identical effectiveness and versatility in various quantum information processing tasks. Consequently, classification under local unitary transformations has become a fundamental issue in the theory of quantum entanglement. The primary objective of this w

Yuyang Liang, Yankai Chen, Yixiang Fang, Laks V. S. Lakshmanan

Electronic Health Records (EHR) have become a valuable resource for a wide range of predictive tasks in healthcare. However, existing approaches have largely focused on inter-visit event predictions, overlooking the importance of intra-visit nowcasting, which provides prompt clinical insights during an ongoing patient visit. To address this gap, we introduce

Katarzyna Olkowska-Pucko, Tomasz Woźniak, Elena Blundo, Natalia Zawadzka

Monolayers (MLs) of semiconducting transition metal dichalcogenides (\mbox{S-TMDs}) emit light very efficiently and display rich spin-valley physics, with gyromagnetic ($g$-) factors of about -4. Here, we investigate how these properties can be tailored by alloying. Magneto-optical spectroscopy is used to reveal the peculiar properties of excitonic complexes

Manisha Dhillon, Kuldeep Kumar Kataria

We introduce and study a multiparameter version of the generalized counting process (GCP), where there is a possibility of finitely many arrivals simultaneously. We call it the multiparameter GCP. In a particular case, it is uniquely represented as a weighted sum of independent multiparameter Poisson processes. For a specific case, we establish a relationshi

Václav Klika, Mohit P. Dalwadi, Andrew L. Krause, Eamonn A. Gaffney

Integral asymptotics play an important role in the analysis of differential equations and in a variety of other settings. In this work, we apply an integral asymptotics approach to study spatially localized solutions of a heterogeneous generalised Swift-Hohenberg equation. The outer solution is obtained via WKBJ asymptotics, while the inner solution requires

Rohit J Nandha, Ronak D Patel

Despite its technical superiority and flexibility, Linux remains a niche OS in the consumer markets. Because fragmentation stems from diverse distributions, it lacks the standardized experience, which discourages mainstream adoption. This foundational paper explores whether a balanced approach to standardization can bridge this gap without compromising Linux

Mingxu Sun, Bingqiu Chen, Baokun Sun, Tao Wang

The availability of large datasets containing stellar parameters, distances, and extinctions for stars in the Milky Way, particularly within the Galactic disk, is essential for advancing our understanding of the Galaxy's stellar populations, structure, kinematics, and chemical evolution. In this study, we present a catalog of stellar parameters, including ef

Naoum Karchev

In the present paper, we study the superconducting wire. It is known from Maxwell equations that the current creates magnetic field that suppresses superconductivity and wire starts to conduct with resistance. We consider the time dependent Ginzburg-Landau theory to resolve the problem. The solutions of the system of equations show that applied electric fiel

Phongpichit Channuie, Allah Ditta, Narakorn Kaewkhao, Ali Övgün

In this study, we investigate traversable wormholes within the framework of Einstein-Euler-Heisenberg (EEH) nonlinear electrodynamics. By employing the Einstein field equations with quantum corrections from the Euler-Heisenberg Lagrangian, we derive wormhole solutions and examine their geometric, physical, and gravitational properties. Two redshift function

Yufan Ren, Konstantinos Tertikas, Shalini Maiti, Junlin Han

Large Vision-Language Models (LVLMs) struggle with puzzles, which require precise perception, rule comprehension, and logical reasoning. Assessing and enhancing their performance in this domain is crucial, as it reflects their ability to engage in structured reasoning - an essential skill for real-world problem-solving. However, existing benchmarks primarily

Jens R. Larsen, Jakob L. Rørsted, Victor Aguirre Børsen-Koch, Mia S. Lundkvist

Metal-poor stars are key to understanding the first stellar generation in the Galaxy. Asteroseismic characterisation of red giants has traditionally relied on global seismic parameters, not the full spectrum of individual oscillation modes. Here, we present the first characterisation of two evolved very metal-poor stars, including the detailed mixed-mode pat

Yun Wan, Yoram M Kalman

Recent studies suggest that while generative AI (GenAI) can enhance individual creativity, it often reduces the diversity of collective outputs. A well-known example of this homogenization effect is by Doshi and Hauser (2024) who found that GenAI-generated plot ideas improved story writing creativity but led to convergence across writers' outputs. This study

Sagi Eppel, Mor Bismut, Alona Faktor-Strugatski

Shapes and textures are the basic building blocks of visual perception. The ability to identify shapes regardless of orientation, texture, or context, and to recognize textures and materials independently of their associated objects, is essential for a general visual understanding of the world. This work introduces the Large Shapes and Textures dataset (LAS&

Rajeev Singh

We build upon the recently formulated guiding-center kinetic theory for guiding-center plasma by incorporating spin degrees of freedom in the presence of electromagnetic fields. This approach yields a streamlined set of equations for guiding-center ideal hydrodynamics with spin--fewer than those in traditional spin hydrodynamics--owing to a restriction on mo

Vincent Jacob, Yanlei Diao

The widespread adoption of digital services, along with the scale and complexity at which they operate, has made incidents in IT operations increasingly more likely, diverse, and impactful. This has led to the rapid development of a central aspect of "Artificial Intelligence for IT Operations" (AIOps), focusing on detecting anomalies in vast amounts of multi

Sachin Sampath, B. Sundar Rajan

Function-Correcting Codes (FCCs) is a novel paradigm in Error Control Coding introduced by Lenz et. al. 2023 for the binary substitution channel \cite{FCC}. FCCs aim to protect the function evaluation of data against errors instead of the data itself, thereby relaxing the redundancy requirements of the code. Later R. Premlal et. al. \cite{LFCC} gave new boun

Jan Žižka

Software systems are expansive, exhibiting behaviors characteristic of complex systems, such as self-organization and emergence. These systems, highlighted by advancements in Large Language Models (LLMs) and other AI applications developed by entities like DeepMind and OpenAI showcase remarkable properties. Despite these advancements, there is a notable abse

Tomáš Kaiser, On-Hei Solomon Lo, Atsuhiro Nakamoto, Yuta Nozaki

Youngs proved that every non-bipartite quadrangulation of the projective plane $\mathbb{R}\mathrm{P}^2$ is 4-chromatic. Kaiser and Stehl\'{\i}k [J. Combin. Theory Ser. B 113 (2015), 1-17] generalised the notion of a quadrangulation to higher dimensions and extended Youngs' theorem by proving that every non-bipartite quadrangulation of the $d$-dimensional pro

Arjun Roy, Stavroula Rizou, Symeon Papadopoulos, Eirini Ntoutsi

Unfair treatment and discrimination are critical ethical concerns in AI systems, particularly as their adoption expands across diverse domains. Addressing these challenges, the recent introduction of the EU AI Act establishes a unified legal framework to ensure legal certainty for AI innovation and investment while safeguarding public interests, such as heal

Tianyu Hu, Shuai Wang, Yunhang Xie, Lingxiang Li

Integrated sensing and communication (ISAC) in the terahertz (THz) band enables obstacle detection, which in turn facilitates efficient beam management to mitigate THz signal blockage. Simultaneously, a THz radio map, which captures signal propagation characteristics through the distribution of received signal strength (RSS), is well-suited for sensing, as i

Jairo Bochi

We construct a continuous linear cocycle over an expanding base dynamics for which the Lyapunov exponents of all ergodic invariant probability measures are small, except for one measure whose Lyapunov exponents are away from zero. The support of this distinguished measure is not a periodic orbit. In particular, our example violates the periodic approximation

Danilo S. Carvalho, Yingji Zhang, Harriet Unsworth, André Freitas

We present LangVAE, a novel framework for modular construction of variational autoencoders (VAEs) on top of pre-trained large language models (LLMs). Such language model VAEs can encode the knowledge of their pre-trained components into more compact and semantically disentangled representations. The representations obtained in this way can be analysed with t

Hongjia Liu, Jinlong Li

Large language models (LLMs) have shown remarkable capabilities in solving complex tasks. Recent work has explored decomposing such tasks into subtasks with independent contexts. However, some contextually related subtasks may encounter information loss during execution, leading to redundant operations or execution failures. To address this issue, we propose

Youneng Bao, Wen Tan, Chuanmin Jia, Mu Li

Learned Image Compression (LIC) has attracted considerable attention due to their outstanding rate-distortion (R-D) performance and flexibility. However, the substantial computational cost poses challenges for practical deployment. The issue of feature redundancy in LIC is rarely addressed. Our findings indicate that many features within the LIC backbone net

Yichen Li, Yulun Wu, Jinyang Liu, Zhihan Jiang

Runtime failures are commonplace in modern distributed systems. When such issues arise, users often turn to platforms such as Github or JIRA to report them and request assistance. Automatically identifying the root cause of these failures is critical for ensuring high reliability and availability. However, prevailing automatic root cause analysis (RCA) appro

Tiago Almeida, Plinio Moreno, Catarina Barata

High hospital readmission rates are associated with significant costs and health risks for patients. Therefore, it is critical to develop predictive models that can support clinicians to determine whether or not a patient will return to the hospital in a relatively short period of time (e.g, 30-days). Nowadays, it is possible to collect both structured (elec

Rishi Kumar, K. Muralidhar, Indranil Saha Dalal

This study investigates the effects of geometric model reduction on blood flow simulations in the patient-specific descending aorta, followed by speed-accuracy trade-off analysis using 3D simulations. We demonstrate how wall shear stresses (WSS) can be reliably estimated for such realistic arteries using significantly faster simulations of highly idealized e

L. Bottura, B. Bordini

HTS has the potential of a game changer for many applications of superconductivity, not last in the field of particle accelerators and detectors. This paper explores the potential of HTS, with a focus on REBCO-coated conductors, in relation to the evolving demands of superconducting magnets for accelerators. HTS already have a spectacular current carrying ab

Haibo Hu, Jiacheng Zuo, Yang Lou, Yufei Cui

With the widespread adoption and deployment of autonomous driving, handling complex environments has become an unavoidable challenge. Due to the scarcity and diversity of extreme scenario datasets, current autonomous driving models struggle to effectively manage corner cases. This limitation poses a significant safety risk, according to the National Highway

Joonas Lahtinen

This article focuses on the measurement and evolution modeling of Standardized Kalman filtering for brain activity estimation using non-invasive electroencephalography data. Here, we propose new parameter tuning and a model that uses the rate of change in the brain activity distribution to improve the stability of otherwise accurate estimates. Namely, we pro

Shuntuo Xu, Zhou Yu, Jian Huang

The density ratio is an important metric for evaluating the relative likelihood of two probability distributions, with extensive applications in statistics and machine learning. However, existing estimation theories for density ratios often depend on stringent regularity conditions, mainly focusing on density ratio functions with bounded domains and ranges.

Miguel Sanz-Novo, Germán Molpeceres, Víctor M. Rivilla, Izaskun Jiménez-Serra

In recent astrochemical studies it has become crucial to study all the complete conformational panorama of the molecule, some of which are potentially detectable in the interstellar medium (ISM). In this context, the isomeric ratio can be used as a powerful tool to distinguish between different formation routes of molecules with increasing levels of complexi

Yuanyuan Gao, Hao Li, Jiaqi Chen, Zhengyu Zou

Despite its significant achievements in large-scale scene reconstruction, 3D Gaussian Splatting still faces substantial challenges, including slow processing, high computational costs, and limited geometric accuracy. These core issues arise from its inherently unstructured design and the absence of efficient parallelization. To overcome these challenges simu

Daniel Sabi Takou, Assimiou Yarou Mora, Ibrahim Nonkané, Latévi M. Lawson

In this paper, we study the dynamic of position-dependent mass system confined in harmonic oscillator potential. We derive the eigensystems by solving the Schr\''odinger-like equation which describes this system. We construct coherent states a Gazeau-Klauder for this system. We show that these states satisfy the Klauder's mathematical condition to build cohe

M Rita Verdelho, Alexandre Bernardino, Catarina Barata

Oncologists often rely on a multitude of data, including whole-slide images (WSIs), to guide therapeutic decisions, aiming for the best patient outcome. However, predicting the prognosis of cancer patients can be a challenging task due to tumor heterogeneity and intra-patient variability, and the complexity of analyzing WSIs. These images are extremely large

Paweł Borówka, Anatoli Shatsila

We study unramified Galois $\mathbb{Z}_3 \times \mathbb{Z}_3$ coverings of genus 2 curves and the corresponding Prym varieties and Prym maps. In particular, we prove that any such covering can be reconstructed from its Prym variety, that is, the Prym-Torelli theorem holds for these coverings. We also investigate the Prym map of unramified $G$-coverings of ge

Amitabh Sharma, Saksham Gupta, Shivansh Pratap Singh, Rishabh Dev Yadav

Stable aerial manipulation during dynamic tasks such as object catching, perching, or contact with rigid surfaces necessarily requires compliant behavior, which is often achieved via impedance control. Successful manipulation depends on how effectively the impedance control can tackle the unavoidable coupling forces between the aerial vehicle and the manipul

Yixiu Liu, Zehui He, Yuyuan Li, Zhongxuan Han

In this paper, we reproduce experimental results presented in our earlier work titled "In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems" that was presented in the proceeding of the 31st ACM International Conference on Multimedia.This work aims to verify the effectiveness of our previously proposed method and prov

Zijun Ding, Mingdie Xiong, Congcong Zhu, Jingrun Chen

Existing audio-driven visual dubbing methods have achieved great success. Despite this, we observe that the semantic ambiguity between spatial and temporal domains significantly degrades the synthesis stability for the dynamic faces. We argue that aligning the semantic features from spatial and temporal domains is a promising approach to stabilizing facial m

Jianpeng Liu, Qizhi Pan

This paper proposes a unified theoretical framework based on the Kolmogorov-Arnold representation theorem and kernel methods. By analyzing the mathematical relationship among kernels, B-spline basis functions in Kolmogorov-Arnold Networks (KANs) and the inner product operation in self-attention mechanisms, we establish a kernel-based feature fitting framewor

Aske Plaat, Max van Duijn, Niki van Stein, Mike Preuss

Background: There is great interest in agentic LLMs, large language models that act as agents. Objectives: We review the growing body of work in this area and provide a research agenda. Methods: Agentic LLMs are LLMs that (1) reason, (2) act, and (3) interact. We organize the literature according to these three categories. Results: The research in the first

San-Dong Guo

Net-zero-magnetization magnets are attracting significant research interest, driven by their potential for ultrahigh density and ultrafast performance. Among these materials, the altermagnets possess alternating spin-splitting band structures and exhibit a range of phenomena previously thought to be exclusive to ferromagnets, including the anomalous Hall and

Yuxiang Bao, Huijie Liu, Xun Gao, Huan Fu

Naive DDIM inversion process usually suffers from a trajectory deviation issue, i.e., the latent trajectory during reconstruction deviates from the one during inversion. To alleviate this issue, previous methods either learn to mitigate the deviation or design cumbersome compensation strategy to reduce the mismatch error, exhibiting substantial time and comp

Susi Lehtola, Lori A. Burns

According to the modern paradigms of software engineering, standard tasks are best accomplished by reusable open source libraries. We describe OpenOrbitalOptimizer: a reusable open source C++ library for the iterative solution of coupled self-consistent field (SCF) equations $\boldsymbol{F}_{p}^{\sigma}(\{\boldsymbol{C}_{p}^{\sigma}\})\boldsymbol{C}_{p}^{\si

Ryan Marinelli, Magnus Eckhoff

To effectively deploy Large Language Models (LLMs) in application-specific settings, fine-tuning techniques are applied to enhance performance on specialized tasks. This process often involves fine-tuning on user data data, which may contain sensitive information. Although not recommended, it is not uncommon for users to send passwords in messages, and fine-

Xueyu Zhou, Chun Yin IP, Jian Huang

The main objective of fair statistical modeling and machine learning is to minimize or eliminate biases that may arise from the data or the model itself, ensuring that predictions and decisions are not unjustly influenced by sensitive attributes such as race, gender, age, or other protected characteristics. In this paper, we introduce a Fair Sufficient Repre

Sangam Lee, Ryang Heo, SeongKu Kang, Dongha Lee

Existing dense retrieval models struggle with reasoning-intensive retrieval task as they fail to capture implicit relevance that requires reasoning beyond surface-level semantic information. To address these challenges, we propose Scenario-Profiled Indexing with Knowledge Expansion (SPIKE), a dense retrieval framework that explicitly indexes implicit relevan

Yuyuan Li, Junjie Fang, Chaochao Chen, Xiaolin Zheng

In this paper, we reproduce the experimental results presented in our previous work titled "Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems," which was published in the proceedings of the 31st ACM International Conference on Multimedia. This paper aims to validate the effectiveness of our proposed method and help others repro

Senkang Hu, Yanan Ma, Yihang Tao, Zhengru Fang

Large language models (LLMs) have achieved remarkable success in various tasks, such as decision-making, reasoning, and question answering. They have been widely used in edge devices. However, fine-tuning LLMs to specific tasks at the edge is challenging due to the high computational cost and the limited storage and energy resources at the edge. To address t

Elliot Benjamin, Franz Lemmermeyer, Chip Snyder

We determine the Galois group of the 2-class field tower for two particular families of imaginary quadratic number fields $k$ with $2$-class field tower of length $2$.

Huajie Jiang, Zhengxian Li, Xiaohan Yu, Yongli Hu

Generalized zero-shot learning aims to recognize both seen and unseen classes with the help of semantic information that is shared among different classes. It inevitably requires consistent visual-semantic alignment. Existing approaches fine-tune the visual backbone by seen-class data to obtain semantic-related visual features, which may cause overfitting on

Yichun Feng, Jiawei Wang, Ruikun He, Lu Zhou

Knowledge graphs and large language models (LLMs) are key tools for biomedical knowledge integration and reasoning, facilitating structured organization of scientific articles and discovery of complex semantic relationships. However, current methods face challenges: knowledge graph construction is limited by complex terminology, data heterogeneity, and rapid

Luca Passarella, Jerome Margueron, Giuseppe Pagliara

Relativistic mean-field models (RMF) based on the exchange of $\sigma$, $\omega$, and $\rho$ mesons including non-linear nucleon-$\sigma$ couplings and density-dependent $\rho$ coupling, are considered. A large set of models is generated using the Markov chain Monte Carlo approach and Bayesian statistics to reproduce nuclear physics knowledge encoded in term

Elliot Benjamin, Franz Lemmermeyer, Chip Snyder

We study normal extensions with Galois group Hol($C_8$) that are unramified over a complex quadratic subfield. The Galois group is either the semi-dihedral group or the modular group of order $16$. We present an explicit construction of such fields.

Ziang Lu, Lei Guo, Xu Yu, Zhiyong Cheng

In the evolving landscape of recommender systems, the challenge of effectively conducting privacy-preserving Cross-Domain Recommendation (CDR), especially under strict non-overlapping constraints, has emerged as a key focus. Despite extensive research has made significant progress, several limitations still exist: 1) Previous semantic-based methods fail to d

Siu-Wing Cheng, Haoqiang Huang, Le Jiang

While there are software systems that simplify trajectory streams on the fly, few curve simplification algorithms with quality guarantees fit the streaming requirements. We present streaming algorithms for two such problems under the Fr\'{e}chet distance $d_F$ in $\mathbb{R}^d$ for some constant $d \geq 2$. Consider a polygonal curve $\tau$ in $\mathbb{R}^d$

Zhihao Yuan, Yibo Peng, Jinke Ren, Yinghong Liao

Driven by the great success of Large Language Models (LLMs) in the 2D image domain, their applications in 3D scene understanding has emerged as a new trend. A key difference between 3D and 2D is that the situation of an egocentric observer in 3D scenes can change, resulting in different descriptions (e.g., ''left" or ''right"). However, current LLM-based met

Shoji Hashimoto

A strategy to compute inclusive hadronic processes in lattice QCD is discussed. The key idea is to view the inclusive decay or scattering rate as a smeared spectrum. The Euclidean time dependence of correlators obtained on the lattice can be used to approximate them. The method induces its own systematic errors in addition to the standard discretization effe

Xianglong He, Junyi Chen, Di Huang, Zexiang Liu

In the domain of 3D content creation, achieving optimal mesh topology through AI models has long been a pursuit for 3D artists. Previous methods, such as MeshGPT, have explored the generation of ready-to-use 3D objects via mesh auto-regressive techniques. While these methods produce visually impressive results, their reliance on token-by-token predictions in

Sol Erika Boman, Nita Mulliqi, Anders Blilie, Xiaoyi Ji

Tissue detection is a crucial first step in most digital pathology applications. Details of the segmentation algorithm are rarely reported, and there is a lack of studies investigating the downstream effects of a poor segmentation algorithm. Disregarding tissue detection quality could create a bottleneck for downstream performance and jeopardize patient safe

Tatsuya Yoshida, Eric Gaidos

The demographics of sub-Jovian planets around low-mass stars is dominated by populations of ``sub-Neptunes" and ``super-Earths", distinguished by the presence or absence of envelopes of low-molecular weight volatiles, i.e., H2, He, and H2O. The current paradigm is that sub-Neptunes on close-in orbits evolve into super-Earths via atmospheric escape driven by

Subhayan De, Reza Farzad, Patrick T. Brewick, Erik A. Johnson

In computational mechanics, multiple models are often present to describe a physical system. While Bayesian model selection is a helpful tool to compare these models using measurement data, it requires the computationally expensive estimation of a multidimensional integral -- known as the marginal likelihood or as the model evidence (\textit{i.e.}, the proba

Jiming Yu, Zhen Pan

The recent evidence of nanohertz (nHz) gravitational wave (GW) background by pulsar timing array (PTA) collaborations has sparked considerable interest in understanding its astrophysical origins, particularly regarding supermassive black hole binaries (SMBHBs). In this work, we focus on individual SMBHBs that will be hopefully detected in upcoming PTA observ

Binchuan Qi, Wei Gong, Li Li

In this paper, we adopt a probability distribution estimation perspective to explore the optimization mechanisms of supervised classification using deep neural networks. We demonstrate that, when employing the Fenchel-Young loss, despite the non-convex nature of the fitting error with respect to the model's parameters, global optimal solutions can be approxi

Randall R. Correll, Simon P. Worden

The colonization of Mars presents extraordinary challenges, including radiation exposure, low atmospheric pressure, and toxic regolith. Recent advancements in synthetic biology and genetic engineering offer unprecedented opportunities to address these obstacles by utilizing terrestrial extremophiles and engineered organisms. This paper examines the potential

Beibei Wang, Boyue Cui, Shiqu Chen, Xuan Wang

Motivation: In recent years, protein function prediction has broken through the bottleneck of sequence features, significantly improving prediction accuracy using high-precision protein structures predicted by AlphaFold2. While single-species protein function prediction methods have achieved remarkable success, multi-species protein function prediction metho

Hsin-Ling Hsu, Jengnan Tzeng

Hybrid retrieval techniques in Retrieval-Augmented Generation (RAG) systems enhance information retrieval by combining dense and sparse (e.g., BM25-based) retrieval methods. However, existing approaches struggle with adaptability, as fixed weighting schemes fail to adjust to different queries. To address this, we propose DAT (Dynamic Alpha Tuning), a novel h

Xinlei Shao, Hongruixuan Chen, Fan Zhao, Kirsty Magson

Coral reef ecosystems provide essential ecosystem services, but face significant threats from climate change and human activities. Although advances in deep learning have enabled automatic classification of coral reef conditions, conventional deep models struggle to achieve high performance when processing complex underwater ecological images. Vision foundat

Hoigi Seo, Junseo Bang, Haechang Lee, Joohoon Lee

Text-to-image (T2I) models often suffer from text-image misalignment in complex scenes involving multiple objects and attributes. Semantic binding has attempted to associate the generated attributes and objects with their corresponding noun phrases (NPs) by text or latent optimizations with the modulation of cross-attention (CA) maps; yet, the factors that i

Mingqing Liu, Hossein Kazemi, Majid Safari, Iman Tavakkolnia

This paper presents a comprehensive quantitative comparison between Terahertz (THz) communication (TeraCom) and optical wireless communication (OWC) technologies, focusing on both indoor and outdoor environments. We propose a comparison method for TeraCom and vertical-cavity surface-emitting laser (VCSEL)-based OWC in indoor scenarios, incorporating misalign

Punit Dubey, Mahima Upadhyay, Mahesh Choudhary, Namrata Singh

The pre and post-scission neutron multiplicities have been determined for the fission of the compound nucleus (CN) 206Rn, induced by the reaction 28Si+178Hf within the excitation energy interval of 61.0-90.0 MeV. We intentionally formed CN 206 Rn, which is below the shell closure CN, to examine the variation in N/Z with total neutron multiplicity, as data fo

Hyun-Chul Kim, June-Young Kim, Ho-Yeon Won

We present a series of recent works on the gravitational form factors (GFFs) of the nucleon within a pion mean-field approach, which is also called the chiral quark-soliton model. We investigate the flavor structure of the mass, angular momentum, and $D$-term form factors of the nucleon. The main findings of the present work are given as follows: the contrib

Giang Do, Hung Le, Truyen Tran

Sparse Mixture of Experts (SMoE) enables efficient training of large language models by routing input tokens to a select number of experts. However, training SMoE remains challenging due to the issue of representation collapse. Recent studies have focused on improving the router to mitigate this problem, but existing approaches face two key limitations: (1)

Sana Ullah, Peng Wu, Ting Peng, Zujin Fan

Mass concrete plays a crucial role in large-scale projects such as water conservancy hubs and transportation infrastructure. Due to its substantial volume and poor thermal conductivity, the accumulation of hydration heat during the curing process can lead to uneven temperature gradients and stress field distribution, which may cause structural cracking. This

Hyun-Chul Kim

In this talk, we present a series of recent works on the electromagnetic and axial-vector structures of low-lying singly heavy baryons. We first explain the pion mean-field approach, in which light and singly heavy baryons can be considered on an equal footing. We then discuss the results for the electromagnetic and radiative transition form factors of the s

Stefano Damiano, Kathleen MacWilliam, Valerio Lorenzoni, Thomas Dietzen

Data availability is essential in the development of acoustic signal processing algorithms, especially when it comes to data-driven approaches that demand large and diverse training datasets. For this reason, an increasing number of databases have been published in recent years, including either room impulse responses (RIRs) or audio recordings during motion

Kazumi Okuyama

We construct a holographic tensor network for the double-scaled SYK model (DSSYK). The moment of the transfer matrix of DSSYK can be mapped to the matrix product state (MPS) of a spin chain. By adding the height direction as a holographic direction, we recast the MPS for DSSYK into the holographic tensor network whose building block is a 4-index tensor with

Qingmei Wang, Fanmeng Wang, Bing Su, Hongteng Xu

Real-world event sequences are often generated by different temporal point processes (TPPs) and thus have clustering structures. Nonetheless, in the modeling and prediction of event sequences, most existing TPPs ignore the inherent clustering structures of the event sequences, leading to the models with unsatisfactory interpretability. In this study, we lear

Bin Han, Di Feng, Zexin Fang, Jie Wang

The deployment of large language models (LLMs) for next-generation network optimization introduces novel data governance challenges. mobile network operators (MNOs) increasingly leverage generative artificial intelligence (AI) for traffic prediction, anomaly detection, and service personalization, requiring access to users' sensitive network usage data-inclu

Tamizhelakkiya K, Dibakar Das, Jyotsna Bapat, Debabrata Das

As networks advance toward the Sixth Generation (6G), management of high-speed and ubiquitous connectivity poses major challenges in meeting diverse Service Level Agreements (SLAs). The Zero Touch Network (ZTN) framework has been proposed to automate and optimize network management tasks. It ensures SLAs are met effectively even during dynamic network condit

Heinz-Jürgen Schmidt, Thomas Bröcker

In order to reconcile the entropy reduction of a system through external interventions that are linked to a measurement with the second law of thermodynamics, there are two main proposals: (i) The entropy reduction is compensated by the entropy increase as a result of the measurement on the system (``Szilard principle"). (ii) The entropy reduction is compens

Nikita Ivanov, Alexander Rubtsov, Michael Vyalyi

A recently introduced measure of Boolean functions complexity--disjunc\-tive complexity (DC)--is compared with other complexity measures: the space complexity of streaming algorithms and the complexity of nondeterministic branching programs (NBP). We show that DC is incomparable with NBP. Specifically, we present a function that has low NBP but has subexpone

Mamtaj Akter, Jinkyung Katie Park, Pamela J. Wisniewski

In this position paper, we discuss the paradigm shift that moves away from parental mediation approaches toward collaborative approaches to promote adolescents' online safety. We present empirical studies that highlight the limitations of traditional parental control models and advocate for collaborative, community-driven solutions that prioritize teen empow

Hoang Thanh Nguyen, Yulan Qing

The quasi-redirecting (QR) boundary is a close generalization of the Gromov boundary to all finitely generated groups. In this paper, we establish that the QR boundary exists as a topological space for several well-studied classes of groups. These include fundamental groups of irreducible non-geometric 3-manifolds, groups that are hyperbolic relative to subg

Mamtaj Akter, Jinkyung Katie Park, Campbell Headrick, Xinru Page

Youth, while tech-savvy and highly active on social media, are still vulnerable to online privacy and security risks. Therefore, it is critical to understand how they negotiate and manage social connections versus protecting themselves in online contexts. In this work, we conducted a thematic analysis of 1,318 private conversations on Instagram from 149 yout

Ole Hans, Benedikt Walter, Jürgen Adamy

Remote driving has emerged as a solution for enabling human intervention in scenarios where Automated Driving Systems (ADS) face challenges, particularly in urban Operational Design Domains (ODDs). This study evaluates the performance of Remote Drivers (RDs) of passenger cars in a representative urban ODD in Las Vegas, focusing on the influence of cumulative

Ryosuke Yanagihara

Let $\ell$ be an odd prime, $N \geq 1$ be an integer, and $\delta \geq 1$ be a $\ell^N$-th power free integer such that ${\rm ord}_{\ell}(\delta) = 0$ or $\ell \nmid {\rm ord}_{\ell}(\delta)$. In this paper, we give an explicit formula for the root number of the Hecke character associated with a certain quotient curve of the twisted Fermat curve $X^{\ell^N}

Nikku Madhusudhan

The search for life beyond the solar system is a central goal in exoplanetary science. Exoplanet surveys are increasingly detecting potentially habitable exoplanets and large telescopes in space and on ground are aiming to detect possible biosignatures in their atmospheres. At the same time, theoretical studies are expanding the range of habitable environmen

Gabriel Recchia, Chatrik Singh Mangat, Issac Li, Gayatri Krishnakumar

As AI models tackle increasingly complex problems, ensuring reliable human oversight becomes more challenging due to the difficulty of verifying solutions. Approaches to scaling AI supervision include debate, in which two agents engage in structured dialogue to help a judge evaluate claims; critique, in which models identify potential flaws in proposed solut