November 2025 arXiv papers — page 187

Jia Liu, Jiawen Zhang, Xiyu Liu, Qi Meng

Real-time and accurate monitoring of humidity and pH is of great significance in daily life and industrial production. Existing humidity and pH measurement suffer from limitations such as low sensitivity, signal crosstalk, complex system structures, and inability to achieve real-time monitoring. In this work, the surface of a polarization maintaining fiber (

Ji-Shun Lian, Ze-Rui Wang, Jin Zhang

The spectral energy distributions (SEDs) of certain BL Lac objects (BL Lacs) exhibit an additional hard $\gamma$-ray component in the TeV energy range that surpasses the predictions of the one-zone leptonic jet model. The origin of this excess emission remains unclear. In this study, we selected five BL Lacs whose SEDs display a very hard intrinsic spectrum

Matteo Mio

We introduce the concept of compact quantitative equational theory. A quantitative equational theory is defined to be compact if all its consequences are derivable by means of finite proofs. We prove that the theory of interpolative barycentric (also known as convex) quantitative algebras of Mardare et. al. is compact. This serves as a paradigmatic example,

Koustav Mallik

We develop a symmetry-aware toolkit for finite mixtures whose components are only identifiable up to a finite \emph{folding} group action. The correct estimand is the multiset of parameter orbits in the quotient space, not an ordered list of raw parameters. We design invariant tensor summaries via the Reynolds projector, show that mixtures become convex comb

Yuanhan Ni, Fan Liu, Haoran Yin, Yanqun Tang

This paper investigates the ambiguity function (AF) of the emerging affine frequency division multiplexing (AFDM) waveform for Integrated Sensing and Communication (ISAC) signaling under a pulse shaping regime. Specifically, we first derive the closed-form expression of the average squared discrete period AF (DPAF) for AFDM waveform without pulse shaping, re

Shenglin Wang, Mingtong Dai, Jingxuan Su, Lingbo Liu

Robotic grasping is a fundamental capability for autonomous manipulation, yet remains highly challenging in cluttered environments where occlusion, poor perception quality, and inconsistent 3D reconstructions often lead to unstable or failed grasps. Conventional pipelines have widely relied on RGB-D cameras to provide geometric information, which fail on tra

Philipp C. Hornung

The calculation of the insurance liabilities of a cohort of dependent individuals in general requires the solution of a high-dimensional system of coupled linear forward integro-differential equations, which is infeasible for a larger cohort. However, by using a mean-field model, the high dimensional system of linear forward equations can be replaced by a lo

Andrew R. Winters, David A. Kopriva, Jan Nordström

We present a strategy for interpreting nonlinear, characteristic-type penalty terms as numerical boundary flux functions that provide provable bounds for solutions to nonlinear hyperbolic initial boundary value problems with open boundaries. This approach is enabled by recent work that found how to express the entropy flux as a quadratic form defined by a sy

Marianna Chatzakou, Aidyn Kassymov, Michael Ruzhansky

In this paper, we show global existence and non-existence results for the heat equation with some of the squares of smooth vector fields on $\Rn$ satisfying H\"{o}rmander's rank condition with a non-linearity of the form $f(u)$, where $f$ is a suitable function and $u$ is the solution. In particular, when $f(u)=u^p$, we calculate the critical Fujita exponent

Yue Xun, Jiaxing Xu, Wenbo Gao, Chen Yang

Resting-state fMRI has become a valuable tool for classifying brain disorders and constructing brain functional connectivity networks by tracking BOLD signals across brain regions. However, existing mod els largely neglect the multi-frequency nature of neuronal oscillations, treating BOLD signals as monolithic time series. This overlooks the cru cial fact th

Nicolò Pagan, Petter Törnberg, Christopher A. Bail, Anikó Hannák

Large language models (LLMs) are increasingly used in the social sciences to simulate human behavior, based on the assumption that they can generate realistic, human-like text. Yet this assumption remains largely untested. Existing validation efforts rely heavily on human-judgment-based evaluations -- testing whether humans can distinguish AI from human outp

S. Soundar Rajan, J. Baskar Babujee

In this paper, we look into Signed Product Cordial Labeling for Splitting Graphs of Bull graph and Splitting graph of Star graph , Square of Path graph, Coronaand also for the graph obtained by joining two copies of Helm by a Path of arbitrary length.

Ahmad Salmanogli, Hesam Zandi

This study introduces a superconducting quantum chip architecture designed to simultaneously preserve entanglement and readout fidelity, addressing one of the key trade-offs in the development of scalable quantum hardware. In conventional quantum circuits, strong qubit qubit coupling enhances entanglement but often leads to undesired crosstalk, dephasing, an

Daniele Bartoli, Marco Calderini, Giuseppe Marino, Francesco Pavese

In 2021, Calderini et al. introduced a construction for APN functions on $\mathbb{F}_{2^{2m}}$ in bivariate form $$ f(x,y)=\big(xy,\, x^{2^r+1} + x^{2^{r+m/2}} y^{2^{m/2}} + bxy^{2^r} + cy^{2^r+1}\big),\quad r < m/2,\quad \gcd(r, m) = 1. $$ They showed that this family exists provided the existence of a polynomial $$ P_{c,b}(X)=(cX^{2^r +1} + b X^{2^r}+1)^{2

Hanmo Chen, Chenghao Xu, Jiexi Yan, Cheng Deng

Human motion style transfer allows characters to appear less rigidity and more realism with specific style. Traditional arbitrary image style transfer typically process mean and variance which is proved effective. Meanwhile, similar methods have been adapted for motion style transfer. However, due to the fundamental differences between images and motion, rel

Arvid Siqveland

In the article Categorical Construction of Schemes, arXiv:2511.03433 we gave a natural definition of ordinary schemes based on the fact that the localization of a ring in a maximal ideal is a local representation of the corresponding function field. In this text, we replace the category of rings with a general locally small category $\cat C$, we consider a s

Josef Mayr, Anna Reithmeir, Maxime Di Folco, Julia A. Schnabel

Covariance descriptors capture second-order statistics of image features. They have shown strong performance in general computer vision tasks, but remain underexplored in medical imaging. We investigate their effectiveness for both conventional and learning-based medical image classification, with a particular focus on SPDNet, a classification network specif

S. Soundar Rajan, J. Baskar Babujee

Graph-theoretical labeling provides a rigorous mathematical framework for characterizing the structural and functional organization of complex networks. This paper investigates the application of cordial labeling and signed product cordial labeling to brain connectivity graphs, emphasizing their relevance to small-world network models in neuroscience. The co

Pengyu Yu, Fridtjof Brauns, M. Cristina Marchetti

Biological tissues exhibit diverse mechanical and rheological behaviors during morphogenesis. While much is known about tissue phase transitions controlled by structural order and cell mechanics, key questions regarding how tissue-scale nematic order emerges from cell-scale processes and influences tissue rheology remain unclear. Here, we develop a minimal v

Amir Yona, Yaron Oz

We demonstrate that charge teleportation serves as a superior observable for Quantum Energy Teleportation (QET)-based cryptographic primitives. While following the LOCC protocol structure of earlier proposals, we show that decoding key bits via local charge rather than energy provides exact bit symmetry and enhanced robustness: by Local Operations and Classi

Josh Kline, Panu Lahti, Jiang Li, Xiaodan Zhou

In the setting of a complete, doubling metric measure space $(X,d,\mu)$ supporting a $(1,1)$-Poincar\'e inequality, we show that for all $0<\theta<1$, the following fractional Poincar\'e inequality holds for all balls $B$ and locally integrable functions $u$, $$ \int_{B}|u-u_B|d\mu\le C(1-\theta)\,\text{rad}(B)^\theta\int_{\tau B}\int_{\tau B}\frac{|u(x)-u(y

Yoshiyasu Ozeki

The notion of a Kummer-faithful field, defined by Mochizuki, is expected as one of suitable base fields for anabelian geometry. In this paper, we study Kummer-faithfulness for algebraic extension fields of $p$-adic fields. We show that Kummer-faithfulness for such fields are deeply related with various finiteness properties on torsion points of (semi-)abelia

Francesco Giacosa, Anna Kolbus, Krzysztof Kyziol, Magdalena Plodowska

In this work, we experimentally study the fluorescence decay of Acridine Orange at late times, in order to test whether a late-time power-law behaviour emerges, a feature expected to be very small but consistent with quantum mechanical and quantum field theoretical predictions. Using two distinct photon detectors, we find that the data are well described by

Mohammed Musthafa Rafi, Adarsh Krishnamurthy, Aditya Balu

The proliferation of AI-generated content has created an absurd communication theater where senders use LLMs to inflate simple ideas into verbose content, recipients use LLMs to compress them back into summaries, and as a consequence neither party engage with authentic content. LAAC (LLM as a Communicator) proposes a paradigm shift - positioning LLMs as inte

Leszek Sliwko

This paper presents a reinforced genetic approach to a defined d-resource system optimization problem. The classical evolution schema was ineffective due to a very strict feasibility function in the studied problem. Hence, the presented strategy has introduced several modifications and adaptations to standard genetic routines, e.g.: a migration operator whic

Eun-Su Cho, Jongin Choi, Jeongmin Jin, Jae-Jin Lee

Machine unlearning, driven by privacy regulations and the "right to be forgotten", is increasingly needed at the edge, yet server-centric or retraining-heavy methods are impractical under tight computation and energy budgets. We present FiCABU (Fisher-based Context-Adaptive Balanced Unlearning), a software-hardware co-design that brings unlearning to edge AI

Christos Tranoris

This paper introduces a lightweight and auditable method for asynchronous information exchange between distributed entities using Git as the coordination medium. The proposed approach replaces traditional APIs and message brokers with a Git-based communication model built on the principles of Kubernetes Operators and Custom Resources (CRs). Each participatin

Chenxi Liu, Ding Cao, Siyu Liu, Yilin Wu

Membraneless droplets or liquid condensates formed via liquid-liquid phase separation (LLPS) play a pivotal role in cell biology and hold potential for biomedical engineering. While membraneless droplets are often studied in the context of interactions between passive components, it is increasingly recognized that active matter inclusions, such as molecular

Yizhen Yin, Dapeng Feng, Hongbo Chen, Yuhua Qi

Existing Active SLAM methodologies face issues such as slow exploration speed and suboptimal paths. To address these limitations, we propose a hybrid framework combining a Path-Uncertainty Co-Optimization Deep Reinforcement Learning framework and a Lightweight Stagnation Detection mechanism. The Path-Uncertainty Co-Optimization framework jointly optimizes tr

Oshando Johnson, Alexandra Fomina, Ranjith Krishnamurthy, Vaibhav Chaudhari

The prevalence of security vulnerabilities has prompted companies to adopt static application security testing (SAST) tools for vulnerability detection. Nevertheless, these tools frequently exhibit usability limitations, as their generic warning messages do not sufficiently communicate important information to developers, resulting in misunderstandings or ov

Rajdip Biswas, Bijita Biswas, Arindam Bhattacharyya

In this paper, we investigate the geometric structure of {\delta}- almost Yamabe solitons on paracontact metric manifolds endowed with a quarter-symmetric non-metric connection {\nabla}. We establish a series of classification results under specific assumptions, including collinearity with the Reeb vector fields, infinitesimal contact transformations, torse-

Mengkun Zhu, Siqi Chen, Xuhao Zhang

In this paper, we focus on the relationship between the d-P$\left(A_{3}^{(1)}/D_{5}^{(1)}\right)$ equations and a time-evolved Jacobi weight, $w(x)=x^{\alpha}(1-x)^{\beta}\mathrm{e}^{-sx}$, $x\in[0,1]$, $\alpha,\beta > -1$, $s>0$. From the perspective of Sakai's geometric theory of Painlev\'e equations, we derive that a recurrence relation closely related to

Y. Tampo, N. Kojiguchi, K. Isogai, D. Nogami

Dwarf nova (DN) superoutbursts are accompanied by superhumps, which change their periods and profiles over a superoutburst. We present the TESS and ground-based observations of nine WZ Sge-type DNe and candidates in superoutburst. In TCP J23580961$+$5502508, ASASSN-23ba, PNV J19030433$-$3102187, V748 Hya, and ASASSN-25ci, we confirmed double-peaked oscillati

Guylaine Hoffner, Philippe Djian

The presence of an expanded polyglutamine produces a toxic gain of function in huntingtin. Protein aggregation resulting from this gain of function is likely to be the cause of neuronal death. Two main mechanisms of aggregation have been proposed: hydrogen bonding by polar-zipper formation and covalent bonding by transglutaminase-catalyzed cross-linking. In

Maryam Tariq, Omar Alhussein, Raneem Abdelraheem, Abdullah Quran

Wideband and low-latency requirements in sixth-generation (6G) networks demand detectors that approach maximum-likelihood (ML) performance without incurring exponential complexity. This work develops a hybrid quantum-classical detection framework for reconfigurable intelligent surface (RIS)-assisted single-carrier (SC) frequency-domain equalization (FDE) ove

Mashrur Rahman, Mantaqa abedin, Monowar Zamil Abir, Faizul Islam Ansari

University students face immense challenges during their undergraduate lives, often being deprived of personalized on-demand guidance that mentors fail to provide at scale. Digital tools exist, but there is a serious lack of customized coaching for newcomers. This paper presents an AI-powered chatbot that will serve as a mentor for the students of BRAC Unive

Fatemehzahra Darzi, Rodrigo Escobar Diaz Guerrero, Thomas Bocklitz

Image registration refers to the process of spatially aligning two or more images by mapping them into a common coordinate system, so that corresponding anatomical or tissue structures are matched across images. In digital pathology, registration enables direct comparison and integration of information from different stains or imaging modalities, sup-porting

Tao Huang

We propose a framework based on the postulate of quantum vacuum self-consistency, where classical backgrounds -- spacetime, gauge fields, and the Higgs condensate -- are macroscopic order parameters of a single quantum state, sustained by the vacuum expectation values of the fluctuations they support. Using a background-field, heat-kernel approach, we derive

Dimitri N. Laikov

A simplification of the VV10 van der Waals density functional [J. Chem. Phys. 133, 244103 (2010)] is made by an approximation of the integrand of the six-dimentional integral in terms of a few products of three-dimensional density-like distributions and potential-like functions of the interelectronic distance only, opening the way for its straightforward com

Siqi Chen, Mengkun Zhu

In this paper, we use Sakai's geometric framework to explore the profound interconnection between recurrence coefficients of the semiclassical Laguerre weight $w(x)=x^{\lambda}\mathrm{e}^{-x^2+sx}$, $x\in\mathbb{R}^+$, $\lambda>-1$, $s\in\mathbb{R}$, and Painlev\'e equations. Specifically, we introduce a new transformation for the expressions obtained by Fil

Ion Mitxelena, Juan Felipe Huan Lew-Yee, Mario Piris

The Global Natural Orbital Functional (GNOF) provides a straightforward approach to capture most electron correlation effects without needing perturbative corrections or limited active spaces selection. In this work, we evaluate both the original GNOF and its modified variant GNOFm on a set of twelve 5- and 6-membered molecular rings, systems characterized p

Rui Liu, Runsheng Zhang, Shixiao Wang

This study focuses on the problem of user satisfaction classification and proposes a framework based on graph neural networks to address the limitations of traditional methods in handling complex interaction relationships and multidimensional features. User behaviors, interface elements, and their potential connections are abstracted into a graph structure,

Rajdip Biswas, Santu Dey, Arindam Bhattacharyya

In this article, we studied {\delta}-almost Yamabe solitons within the framework of para- contact metric manifolds. First, we proved that for a paracontact metric manifold {M}, if a paracontact metric g represents a {\delta}-almost Yamabe soliton associated with the potential vector field {Z} being an infinitesimal contact transformation, then {Z} is Killing

Zoltán M. Balogh, Károly J. Böröczky, Ágnes Mester

We establish quantitative stability results for classical distortion minimization problems in the theory of quasiconformal mappings. We consider the mean distortion functional and prove sharp stability estimates for the minimization problems regarding the linear stretch and spiral stretch maps, which arise as extremals in the class of mappins with finite dis

Xiaokai Wang, Shaoyuan Huang, Yuting Li, Xiaofei Wang

Deep neural networks (DNNs) form the cornerstone of modern AI services, supporting a wide range of applications, including autonomous driving, chatbots, and recommendation systems. As models increase in size and complexity, DNN workloads such as training and inference tasks impose unprecedented demands on distributed computing resources, making accurate runt

Suranjan Goswami, Abhinav Ravi, Raja Kolla, Ali Faraz

Despite significant advances in document understanding, determining the correct orientation of scanned or photographed documents remains a critical pre-processing step in the real world settings. Accurate rotation correction is essential for enhancing the performance of downstream tasks such as Optical Character Recognition (OCR) where misalignment commonly

Laurits Fredsgaard, Mikkel N. Schmidt

Deep ensembles are a powerful tool in machine learning, improving both model performance and uncertainty calibration. While ensembles are typically formed by training and tuning models individually, evidence suggests that jointly tuning the ensemble can lead to better performance. This paper investigates the impact of jointly tuning weight decay, temperature

Su Ho Cheong, Arnold Ngapasare, Vassos Achilleos, Georgios Theocharis

We investigate energy propagation in a one-dimensional stub lattice in the presence of both disorder and nonlinearity. In the periodic case, the stub lattice hosts two dispersive bands separated by a flat band; however, we show that sufficiently strong disorder fills all intermediate band gaps. By mapping the two-dimensional parameter space of disorder and n

Anzhuo Xie, Wei-Chen Chang

This study proposes a Transformer-based longitudinal modeling method to address challenges in clinical risk classification with heterogeneous Electronic Health Record (EHR) data, including irregular temporal patterns, large modality differences, and complex semantic structures. The method takes multi-source medical features as input and employs a feature emb

Asma Yamani, Malak Baslyman, Moataz Ahmed

Large Language Models (LLMs) are increasingly employed in software engineering tasks such as requirements elicitation, design, and evaluation, raising critical questions regarding their alignment with human judgments on responsible AI values. This study investigates how closely LLMs' value preferences align with those of two human groups: a US-representative

Yihao Chen, Yue Yang

We develop a deep reinforcement learning framework for controlling a bio-inspired jellyfish swimmer to navigate complex fluid environments with obstacles. While existing methods often rely on kinematic and geometric states, a key challenge remains in achieving efficient obstacle avoidance under strong fluid-structure interactions and near-wall effects. We au

Olav Finne Praesteng Larsen, Massimiliano Ruocco, Michail Spitieris, Abdulmajid Murad

Access to trajectory data is a key requirement for developing and validating Air Traffic Management (ATM) solutions, yet many secondary and regional airports face severe data scarcity. This limits the applicability of machine learning methods and the ability to perform large-scale simulations or "what-if" analyses. In this paper, we investigate whether gener

Yanting Wang

By comparing Schr\"odinger's cat with its classical counterpart, I show that a quantum superposition should be understood as an expectation over possible eigenstates weighted by wave-like probabilities. Upon the occurrence of a certain event, the quantum system is randomly realized into one of the possible eigenstates due to its intrinsic stochasticity. Whil

Fahim Ahmed, Md Mubtasim Ahasan, Jahir Sadik Monon, Muntasir Wahed

Text-to-SQL systems provide a natural language interface that can enable even laymen to access information stored in databases. However, existing Large Language Models (LLM) struggle with SQL generation from natural instructions due to large schema sizes and complex reasoning. Prior work often focuses on complex, somewhat impractical pipelines using flagship

Jason Block, Russell Miller

We formalize an existing computability-theoretic method of presenting first-order structures whose domains have the cardinality of the continuum. Work using these methods until now has emphasized their topological properties. We shift the focus to first-order properties, using computable structure theory (on countable structures) as a guide. We present three

Amitayu Banerjee

Let $G$ be a finite group and let $S$ be an inverse-closed subset of $G$ not containing the identity. The Cayley graph $\mathrm{Cay}(G,S)$ has vertex set $G$, where two vertices $x$ and $y$ are adjacent if and only if $x^{-1}y \in S$. Kaseasbeh and Erfanian (2021) determined the structure of all Cayley graphs on the dihedral group of order $2n$ for subsets $

Kun Zhang, Qicheng Zhang, Shuaishuai Tong, Wenquan Wu

Multistability, the coexistence of multiple stable states, is a cornerstone of nonlinear dynamical systems, governing their equilibrium, tunability, and emergent complexity. Recently, the concept of hidden multistability, where certain stable states evade detection via conventional continuous parameter sweeping, has garnered increasing attention due to its e

Jeong Jinwon, Chang Bin, Noh Han-Jin, Lee Seongsu

We have investigated the temperature dependence of the crystal structure of quasi-one-dimensional monoclinic BaIrO$_3$ using X-ray diffraction. Diffraction patterns were measured across a temperature range from 13 K to 300 K, with 5-degree steps, and Rietveld refinements were performed to extract the relevant lattice parameters. The resulting cell volumes ex

Xiaobo Zhao, Daniel E. Lucani

Massive data streams from IoT and cyber-physical systems must be processed under strict bandwidth, latency, and resource constraints. Generalized Deduplication (GD) is a promising lossless compression framework, as it supports random access and direct analytics on compressed data. However, existing GD algorithms exhibit quadratic complexity $\mathcal{O}(nd^{

Nivedan Yakolli, Avinash Gautam, Abhijit Das, Yuankai Qi

Vision-and-Language Navigation (VLN) is a multi-modal, cooperative task requiring agents to interpret human instructions, navigate 3D environments, and communicate effectively under ambiguity. This paper presents a comprehensive review of recent VLN advancements in robotics and outlines promising directions to improve multi-robot coordination. Despite progre

Jiaming Zhang, Yujie Yang, Haoning Wang, Liping Zhang

Safe reinforcement learning (RL) aims to optimize long-term performance while adhering to safety requirements. However, many practical applications involve an infinite number of constraints, forming semi-infinite safe RL (SI-safe RL). Such scenarios typically appear when safety conditions must be enforced across an entire continuous parameter space, such as

Xiumin Huang, Dong Lai, Bin Liu

A hierarchical three-body model can be widely applied to diverse astrophysical settings, from satellite-planet-star systems to binaries around supermassive black holes. The octupole-order perturbation on the inner binary from the tertiary can induce extreme eccentricities and cause orbital flips of the binary, but short-range forces such as those due to Gene

Boxuan Ma, Huiyong Li, Gen Li, Li Chen

Generative AI tools such as ChatGPT now provide novice programmers with unprecedented access to instant, personalized support. While this holds clear promise, their influence on students' metacognitive processes remains underexplored. Existing work has largely focused on correctness and usability, with limited attention to whether and how students' use of AI

Luca Quaroni

The turn of the millennium has seen a growing interest in the study of live cells by infrared (IR) spectroscopy, driven by the versatility, wealth of molecular information, and potential for high-throughput screening of the technique. Measurements on individual cells, either isolated or within a multi-cellular structure, provide information that is not avail

Sai Praneeth Donthu, Souvik Roy, Soumyarup Sadhukhan, Gogulapati Sreedurga

We study the classical probabilistic assignment problem, where finitely many indivisible objects are to be probabilistically or proportionally assigned among an equal number of agents. Each agent has an initial deterministic endowment and a strict preference over the objects. While the deterministic version of this problem is well understood, most notably th

Zheng Li, Weiyan Wang, Ruiyuan Li, Chao Chen

The torrential influx of floating-point data from domains like IoT and HPC necessitates high-performance lossless compression to mitigate storage costs while preserving absolute data fidelity. Leveraging GPU parallelism for this task presents significant challenges, including bottlenecks in heterogeneous data movement, complexities in executing precision-pre

Dazhong Chen, Yi-Cheng Lin, Yuchen Huang, Ziwei Gong

Automatic speech recognition (ASR) is critical for language accessibility, yet low-resource Cantonese remains challenging due to limited annotated data, six lexical tones, tone sandhi, and accent variation. Existing ASR models, such as Whisper, often suffer from high word error rates. Large audio-language models (LALMs), in contrast, can leverage broader con

Jahnabi Chakraborti, Anandateertha Mangasuli

We investigate Yamabe gradient solitons, which are warped product manifolds. We show that the fiber of a nontrivial warped product Yamabe gradient soliton has constant scalar curvature. Based on this result, we obtain a specific class of warped products that cannot be nontrivial solitons. Furthermore, we derive estimates of the scalar curvature and the solit

Mustafa Sencer Aydın, Fanhui Xu

We study the Navier-Stokes equations with transport noise in critical function spaces. Assuming the initial data belongs to $H^{1/2}$ almost surely, we establish the existence and uniqueness of a local-in-time probabilistically strong solution. Moreover, we show that the probability of global existence can be made arbitrarily close to $1$ by choosing the ini

Yujian Liu, Ze Wang, Hao Chen, Ximeng Sun

Computer-use agents can operate computers and automate laborious tasks, but despite recent rapid progress, they still lag behind human users, especially when tasks require domain-specific procedural knowledge about particular applications, platforms, and multi-step workflows. Humans can bridge this gap by watching video tutorials: we search, skim, and select

Neil Na, Chih-Hao Cheng, Shou-Chen Hsu, Che-Fu Liang

The recent rapid deployment of datacenter infrastructures for performing large language models (LLMs) and related artificial intelligence (AI) applications in the clouds is predicted to incur an exponentially growing energy consumption in the near-term future. In this paper, we propose and analyze the implementation of the transformer model, which is the cor

Hisashi Kasuya, Valto Purho

We can show that the Kuranishi space of a pair $(M,E)$ of a compact K\"ahler manifold $M$ and its flat Hermitian vector bundle $E$ is isomorphic to the direct product of the Kuranishi space of $M$ and the Kuranishi space of $E$. We study non-K\"ahler case. We show that the Kuranishi space of a pair $(M,E)$ of a complex parallelizable nilmanifold $M$ and its

Miguel E. Andres, Vadim Fedorov, Rida Sadek, Enric Spagnolo-Arrizabalaga

Voice AI agents are rapidly transitioning to production deployments, yet systematic methods for ensuring testing reliability remain underdeveloped. Organizations cannot objectively assess whether their testing approaches (internal tools or external platforms) actually work, creating a critical measurement gap as voice AI scales to billions of daily interacti

Hongbin Zhang, Shihao Gao, Yang Liu, Mingjie Xing

In recent years, end-to-end Large Language Model (LLM) technology has shown substantial advantages across various domains. As critical system software and infrastructure, compilers are responsible for transforming source code into target code. While LLMs have been leveraged to assist in compiler development and maintenance, their potential as an end-to-end c

Yitang Li, Zhengyi Luo, Tonghe Zhang, Cunxi Dai

Building Behavioral Foundation Models (BFMs) for humanoid robots has the potential to unify diverse control tasks under a single, promptable generalist policy. However, existing approaches are either exclusively deployed on simulated humanoid characters, or specialized to specific tasks such as tracking. We propose BFM-Zero, a framework that learns an effect

Monitirtha Dey, Trambak Banerjee, Prajamitra Bhuyan, Arunabha Majumdar

Replicability is central to scientific progress, and the partial conjunction (PC) hypothesis testing framework provides an objective tool to quantify it across disciplines. Existing PC methods assume independent studies. Yet many modern applications, such as genome-wide association studies (GWAS) with sample overlap, violate this assumption, leading to depen

Artemis Chaleplioglou, Efstathia Selinopoulou, Konstantinos Kyprianos, Alexandros Koulouris

The COVID-19 outbreak rapidly became a pandemic in the first quarter of 2020, posing an unprecedented threat and challenge to healthcare systems and the public. Governments in nearly every country focused on immunization programs for the general population using mRNA vaccines against this disease, marking the first large-scale use of this technology. Previou

Shengyu Tang, Zeyuan Lu, Jiazhi Dong, Changdong Yu

Accurate perception of the marine environment through robust multi-object tracking (MOT) is essential for ensuring safe vessel navigation and effective maritime surveillance. However, the complicated maritime environment often causes camera motion and subsequent visual degradation, posing significant challenges to MOT. To address this challenge, we propose a

Xiaojun Song, Jichao Yuan

In this paper, we propose new specification tests for regression models with measurement errors in the explanatory variables. Inspired by the integrated conditional moment (ICM) approach, we use a deconvoluted residual-marked empirical process and construct ICM-type test statistics based on it. The issue of measurement errors is addressed by applying a decon

Venkata Manikanta Desu, Syed Fawaz Ali

This study presents a complete pipeline for automated tennis match analysis. Our framework integrates multiple deep learning models to detect and track players and the tennis ball in real time, while also identifying court keypoints for spatial reference. Using YOLOv8 for player detection, a custom-trained YOLOv5 model for ball tracking, and a ResNet50-based

Isaac M. Hair, Amit Sahai

We prove that SVP$_p$ is NP-hard to approximate within a factor of $2^{\log^{1 - \varepsilon} n}$, for all constants $\varepsilon > 0$ and $p > 2$, under standard deterministic Karp reductions. This result is also the first proof that \emph{exact} SVP$_p$ is NP-hard in a finite $\ell_p$ norm. Hardness for SVP$_p$ with $p$ finite was previously only known if

Tengjie Li, Shikui Tu, Lei Xu

Recent advances in vision-language models have facilitated progress in sketch generation. However, existing specialized methods primarily focus on generic synthesis and lack mechanisms for precise control over sketch styles. In this work, we propose a training-free framework based on diffusion models that enables explicit style guidance via textual prompts a

Adiba Amira Siddiqa, Sayed Shafaat Mahmud, Cosmin Ilie

Some of the first stars in the Universe might be powered by Dark Matter (DM) annihilations, rather than nuclear fusion. Those objects, i.e. Dark stars (DS), offer a unique window into understanding DM via the observational study of the formation and evolution of the first stars and their Black Hole (BH) remnants. In \cite{NNSMDSPhot} (Paper~I) we introduced

Sayed Shafaat Mahmud, Adiba Amira Siddiqa, Cosmin Ilie

The formation of the first stars in the universe could be significantly impacted by the effects of Dark Matter (DM). Namely, if DM is in the form of Weakly Interacting Massive Particles (WIMPs), it could lead to the formation (at $z\sim 25-10$) of stars that are powered by DM annihilations alone, i.e. Dark Stars (DSs). Those objects can grow to become superm

Xinyuan Li, Murong Xu, Wenbiao Tao, Hanlun Zhu

Large language models (LLMs) achieve high performance on mathematical reasoning, but these results can be inflated by training data leakage or superficial pattern matching rather than genuine reasoning. To this end, an adversarial perturbation-based evaluation is needed to measure true mathematical reasoning ability. Current rule-based perturbation methods o

Jiyao Zhang, Yue Hu

Magnetohydrodynamic (MHD) turbulence plays a central role in many astrophysical processes in the interstellar medium (ISM), including star formation and cosmic-ray transport and acceleration. MHD turbulence can be decomposed into three fundamental modes-fast, slow, and Alfv\'en-each contributing differently to the dynamics of the medium. However, characteriz

Xiaochen Liu, Ken-Tye Yong

Predicting phenomena that mix few-photon quantum optics with strong field nonlinear optics is hindered by the use of separate theoretical formalisms for each regime. We close this gap with a unified effective field theory valid for frequencies lower than the material-dependent cutoff set by the band gap, plasma frequency, or similar scale. The action couples

Yunghee Lee, Byeonghyun Pak, Junwha Hong, Hoseong Kim

In this paper, we propose Tortoise and Hare Guidance (THG), a training-free strategy that accelerates diffusion sampling while maintaining high-fidelity generation. We demonstrate that the noise estimate and the additional guidance term exhibit markedly different sensitivity to numerical error by reformulating the classifier-free guidance (CFG) ODE as a mult

Esha Jain, Sankha S. Basu

In this article, we have introduced a Logic of Formal Inconsistency (LFI) that we call $\vd$. This logic is non-self-extensional, i.e., the replacement property, or the rule for substitution of equivalents, does not hold. A Hilbert-style presentation for the logic has been provided. Then, a topological semantics for $\vd$ has been described, subsequent to wh

Ruksit Rojpaisarnkit, Youmei Fan, Kenichi Matsumoto, Raula Gaikovina Kula

With the widespread adoption of Foundation Model (FM)-powered tools in software engineering, the natural language prompt has become a critical interface between developers and Large Language Models (LLMs). While much research has focused on prompt structure, the natural language proficiency is an underexplored factor that can influence the quality of generat

Paul Badu Yakubu, Lesther Santana, Mohamed Rahouti, Yufeng Xin

With the increasing frequency and sophistication of Distributed Denial of Service (DDoS) attacks, it has become critical to develop more efficient and interpretable detection methods. Traditional detection systems often struggle with scalability and transparency, hindering real-time response and understanding of attack vectors. This paper presents an automat

Biao Liu, Yuanzhi Liang

Text-to-Image (T2I) synthesis has made significant advancements in recent years, driving applications such as generating datasets automatically. However, precise control over object localization in generated images remains a challenge. Existing methods fail to fully utilize positional information, leading to an inadequate understanding of object spatial layo

Debamita Chatterjee, Himanshu Lekharu, Riddhi Shah

For a connected Lie group $G$, we study the dynamics of actions of automorphisms of $G$ on certain compact invariant subspaces of closed subgroups of $G$ in terms of distality and expansivity. We show that only the finite order automorphisms of $G$ act distally on Sub$^p_G$, the smallest compact space containing all closed one-parameter subgroups of $G$, whe

Ke-Xiong Yan, Zhi-Cheng Shi, Ye-Hong Chen, Yan Xia

Pure dephasing is the dominant leak mechanism in photonic cat qubits because its phase errors disrupt the parity protection, rendering the qubit vulnerable to energy relaxation. In this manuscript, we reveal that this dephasing mechanism conceals an interesting physical phenomenon: it induces \textit{asymmetric leakage} from the cat-state subspace, where eve

Yanbo Pang, Qingkai Li, Mingguo Zhao

As robotic arm applications expand beyond traditional industrial settings into service-oriented domains such as catering, household and retail, existing control algorithms struggle to achieve the level of agile manipulation required in unstructured environments characterized by dynamic trajectories, unpredictable interactions, and diverse objects. This paper

Saurabh Srivastava, Janit Bidhan, Hao Yan, Abhishek Dey

Large Reasoning Models (LRMs) achieve strong performance through explicit chain-of-thought reasoning but suffer from \textit{overthinking}: generating excessive reasoning tokens even for trivial queries. {Beyond inflating cost, overthinking can be self-defeating: models enter recursive self-doubt loops that exhaust token budgets without producing an answer,

Chengu Wang

We establish new depth upper bounds for sorting networks on 27 and 28 channels, improving the previous best bound of 14 to 13. Our 28-channel network is constructed with reflectional symmetry by combining high-quality prefixes of 16- and 12-channel networks, extending them greedily one comparator at a time, and using a SAT solver to complete the remaining la

Michael Heron, Pauline Belford, Klara Aune

Psychogeography -- the study of how environments shape emotion and behaviour -- has long concerned itself with the emotional resonance of the physical, often through the idea of the derive through the city. Its philosophical core, however, is primarily concerned with identifying affective relationships between the personal and the environmental, and this doe

Chao Guo, Jiahe Xu, Moshe Zukerman

Hardware disaggregation seeks to transform Data Center (DC) resources from traditional server fleets into unified resource pools. Despite existing challenges that may hinder its full realization, significant progress has been made in both industry and academia. In this article, we provide an overview of the motivations and recent advancements in hardware dis

Moses Charikar, Chirag Pabbaraju, Ambuj Tewari

We study the problem of language identification in the limit, where given a sequence of examples from a target language, the goal of the learner is to output a sequence of guesses for the target language such that all the guesses beyond some finite time are correct. Classical results of Gold showed that language identification in the limit is impossible for