December 2025 arXiv papers — page 117

Jesse Ponnock

Domain-adaptive pretraining (DAPT) offers a practical path to specializing large language models for high-value domains without full retraining. We conduct an early-stage scaling-law analysis of continued pretraining on U.S. SEC filings, training 1B and 3B-parameter Llama-3.2 models on a 400M-token financial corpus with validation checkpoints at 50M, 100M, 2

Thuy-Linh Le, Hardit Singh, Julia M. Boyle, Matthew Zimmermann

Programmable photonic integrated circuits (PICs) have recently emerged as an important technology for quantum information science and artificial neural networks. In particular, PICs with MEMS-based modulators have the advantages of voltage-based control, ultra-low-energy consumption, cryogenic compatibility, and CMOS-foundry support. Here we report a cantile

Truong Xuan Khanh, Truong Quynh Hoa

A foundational assumption in complex-system collapse studies is that critical transitions are second-order, preceded by early-warning signals like rising autocorrelation, variance, and critical slowing down (Scheffer, 2009). We show this fails for feedback-amplified adaptive systems. We prove entropy collapse - the irreversible contraction of effective state

Chiara Boiti, Renato Manfrin

We prove that the special Kirchhoff equation studied by Pokhozhaev admits a third-order conservation law. We further show that if the energy of the solution is sufficiently small, then the $L^2$-norms of the derivatives up to third order of the solution remain uniformly bounded with respect to time.

Hans Fischer

The designation ``Bernstein-von Mises theorem'' is apparently due to Lucien Le Cam. Roughly, the assertion of this theorem states that the posterior distribution of a parameter, conditioned on a large sample, is approximately normal, independent of a particular prior. The present paper discusses important steps in the development of this theorem and its appl

Junqiao Fan, Yunjiao Zhou, Yizhuo Yang, Xinyuan Cui

Human mesh reconstruction (HMR) provides direct insights into body-environment interaction, which enables various immersive applications. While existing large-scale HMR datasets rely heavily on line-of-sight RGB input, vision-based sensing is limited by occlusion, lighting variation, and privacy concerns. To overcome these limitations, recent efforts have ex

Haichuan Li, Changda Tian, Panos Trahanias, Tomi Westerlund

We present INDOOR-LIDAR, a comprehensive hybrid dataset of indoor 3D LiDAR point clouds designed to advance research in robot perception. Existing indoor LiDAR datasets often suffer from limited scale, inconsistent annotation formats, and human-induced variability during data collection. INDOOR-LIDAR addresses these limitations by integrating simulated envir

Torsten Enßlin, Vincent Eberle, Matteo Guardiani, Margret Westerkamp

Bayesian imaging of astrophysical measurement data shares universal properties across the electromagnetic spectrum: it requires probabilistic descriptions of possible images and spectra, and instrument responses. To unify Bayesian imaging, we present the Universal Bayesian Imaging Kit (UBIK). Currently, UBIK images data from Chandra, eROSITA, JWST, and ALMA.

Hyunkoo Lee, Wooseok Jang, Jini Yang, Taehwan Kim

Video personalization aims to generate videos that faithfully reflect a user-provided subject while following a text prompt. However, existing approaches often rely on heavy video-based finetuning or large-scale video datasets, which impose substantial computational cost and are difficult to scale. Furthermore, they still struggle to maintain fine-grained ap

Giacomo Micheli

In this paper we provide a short proof of the Riemann Hypothesis for Drinfeld modules which uses only basic notions from the theory of global function fields and of Drinfeld modules.

Joel Torres, Rodrigo C. V. Coelho, Patrick Oswald, Francesc Sagés

Quasiparticles in liquid crystals, such as torons and skyrmions, represent a new class of topologically protected solitonic excitations, offering a promising route toward soft microrobotics. Here we demonstrate that torons can be propelled by modulated electric fields and magnetically steered with full directional control, thus achieving programmable traject

Shrinivass Arunachalam Balasubramanian

User Interface (UI) optimization is essential in the digital era to enhance user satisfaction in web environments. Nevertheless, the existing UI optimization models had overlooked the Cross-Responsiveness (CR) assessment, affecting the user interaction efficiency. Consequently, this article proposes a dynamic web UI optimization through CR assessment using F

Peixuan Zhang, Zijian Jia, Kaiqi Liu, Shuchen Weng

While recent advancements in generative models have achieved remarkable visual fidelity in video synthesis, creating coherent multi-shot narratives remains a significant challenge. To address this, keyframe-based approaches have emerged as a promising alternative to computationally intensive end-to-end methods, offering the advantages of fine-grained control

David M. Berry

The emergence of Large Language Models presents a remarkable opportunity for humanities and social science research. I argue these technologies instantiate what I have called the algorithmic condition, whereby computational systems increasingly mediate not just our analytical tools but how we understand nature and society more generally. This article introdu

Nicolai A. Weinreich, Marco Muñiz, Marius Mikučionis, Kim G. Larsen

In this paper, we propose a data-efficient online battery identification method which targets highly informative battery cell data segments based on the driving pattern of the vehicle. We consider the case of a vehicle driving on/off a motorway and construct an Extended Time Regular Expression (ETRE) to detect data segments fitting these driving patterns. Si

Muhammad Ahsen, Boyan Yanakiev, Claudio Rosa, Ramoni Adeogun

Extended Reality (XR) applications have limited capacity in 5th generation-advanced (5G-A) cellular networks due to high throughput requirements coupled with strict latency and high reliability constraints. To enhance XR capacity in the downlink (DL), this paper investigates multi-connected XR tethering groups (TGrs), comprising an XR device and a cooperatin

Shuyang Xie, Jie Zhou, Jun Wang, Renjing Xu

Computer-generated holography (CGH) presents a transformative solution for near-eye displays in augmented and virtual reality. Recent advances in deep learning have greatly improved CGH in reconstructed quality and computational efficiency. However, deploying neural CGH pipelines directly on compact, eyeglass-style devices is hindered by stringent constraint

Babak Badnava, Jacob Chakareski, Morteza Hashemi

Diverse emerging VR applications integrate streaming of high fidelity 360 video content that requires ample amounts of computation and data rate. Scalable 360 video tiling enables having elastic VR computational tasks that can be scaled adaptively in computation and data rate based on the available user and system resources. We integrate scalable 360 video t

Thai-Duy Dinh, Minh-Luan Vo, Cuong Tuan Nguyen, Bich-Hien Vo

Mosquito-borne diseases pose a serious global health threat, causing over 700,000 deaths annually. This work introduces a proof-of-concept Synthetic Swarm Mosquito Dataset for Acoustic Classification, created to simulate realistic multi-species and noisy swarm conditions. Unlike conventional datasets that require labor-intensive recording of individual mosqu

Chenxi Huang, Tao Chen, Qian Liang, Matthew A. Krebs

Topology can imbue lattice systems with special properties, notably the presence of robust eigenstates living at their boundary. Through dimensional reduction, the robust bulk band topology of, e.g., the integer quantum Hall system can be mapped onto similarly robust charge-pumping dynamics of a topological pump living in one lower dimension. Recent studies

Yan Yang, George Bebis, Mircea Nicolescu

The absence of large-scale masked face datasets challenges masked face detection and recognition. We propose a two-step generative data augmentation framework combining rule-based mask warping with unpaired image-to-image translation via GANs, producing masked face samples that go beyond rule-based overlays. Trained on about 19,100 images in the target domai

Zeusu Sato

We provide an epsilon-delta interpretation of Chatterjee's rank correlation by tracing its origin to a notion of local dependence between random variables. Starting from a primitive epsilon-delta construction, we show that rank-based dependence measures arise naturally as epsilon to zero limits of local averaging procedures. Within this framework, Chatterjee

Antoine Barbieri, Angelo Alcaraz, Mouna Abed, Hugues de Courson

The systematic collection of longitudinal data is very common in practice, making mixed models widely used. Most developments around these models focus on modeling the mean trajectory of repeated measurements, typically under the assumption of homoskedasticity. However, as data become increasingly rich through intensive collection over time, these models can

Parveen Kumar, Ankit Kumar, Manu Rohilla

In this paper, we introduce the concept of cyclic orbital contraction mappings which generalizes the concept of cyclic contraction mappings. We establish the existence of best proximity point of these mappings in the framework of $CAT_p(0)$ metric spaces. Also, we study the existence of best proximity point theorems for cyclic orbital contraction mappings in

Yufei Yin, Qianke Meng, Minghao Chen, Jiajun Ding

Long-form video understanding remains challenging due to the extended temporal structure and dense multimodal cues. Despite recent progress, many existing approaches still rely on hand-crafted reasoning pipelines or employ token-consuming video preprocessing to guide MLLMs in autonomous reasoning. To overcome these limitations, we introduce VideoARM, an Agen

Antton Goïcoechea, François Sarrazin, Theodosios Karamanos, Mathias Fink

Modern imaging and sensing in complex environments, ranging from biomedical diagnostics to wireless communication, relies on accurately measuring and then controlling the wave propagation. Conventional approaches demand large arrays of antennas or transducers to reconstruct the full reflection or transmission matrix, enabling advanced protocols such as selec

Stéphanie M. van den Berg, Ulrich Halekoh, Sören Möller, Andreas Kryger Jensen

We develop a supervised deep-learning approach to estimate mutual information between two continuous random variables. As labels, we use the Linfoot informational correlation, a transformation of mutual information that has many important properties. Our method is based on ground truth labels for Gaussian and Clayton copulas. We compare our method with estim

Zishen Song, Yongjian Zhu, Dong Wang, Hongzhan Liu

Timely and accurate detection of foliar diseases is vital for safeguarding crop growth and reducing yield losses. Yet, in real-field conditions, cluttered backgrounds, domain shifts, and limited lesion-level datasets hinder robust modeling. To address these challenges, we release Daylily-Leaf, a paired lesion-level dataset comprising 1,746 RGB images and 7,8

Yueshen Li, Krishnaveni Unnikrishnan, Aadya Agrawal

Research on relationship quality often relies on lengthy questionnaires or invasive textual corpora, limiting ecological validity and user privacy. We ask whether a sequence of single-word choices made in a playful setting can reveal personality and predict interpersonal compatibility. We introduce the Tacit Understanding Game (TUG), a two-player online word

H. C. W. Price, T. S. Evans

Main path analysis has long been used to trace knowledge trajectories in citation networks, yet it lacks solid theoretical foundations. To understand when and why this approach succeeds, we analyse directed acyclic graphs created from two types of artificial models and by looking at over twenty networks derived from real data. We show that entropy-based vari

Yutian Wu, Xiaojing Zhang, Chenyang Huang, Yang Yu

Context. Rotational instability of rubble-pile asteroids can trigger mass shedding, forming transient debris clouds that may provide the initial conditions for secondary formation in binary systems. Aims. We investigate the dynamical and collisional evolution of a debris cloud numerically generated around a Didymos-like progenitor, as a representative case f

Mohamed Chaouch, Thanasis Stengos

This paper investigates the nexus between subjective well-being and sustainability, proxied by the Sustainable Development Goals (SDG) Index, using cross-country data from 126 nations in 2022. While prior research has highlighted a positive association between happiness and sustainable development, existing approaches largely rely on linear regressions or co

Alfons Van Daele

Discrete quantum groups were introduced as duals of compact quantum groups by Podle\'s and Woronowicz in 1990. Shortly after, they were defined and studied intrinsically by Effros and Ruan, and by this author. In 1998, with the introduction of the multiplier Hopf algebras with integrals (also called algebraic quantum groups), the duality between discrete and

Yidong Zhou, Jorik Jooken, Baoyuan Shan, Jan Goedgebeur

A graph $G$ is $k$-vertex-critical if $\chi(G)=k$, but $\chi(G')<k$ for every proper induced subgraph $G'$ of $G$. For a family of graphs $\mathcal{F}$, $G$ is $\mathcal{F}$-free if no graph $F \in \mathcal{F}$ is an induced subgraph of $G$. We show that there are exactly three 4-vertex-critical $\{P_7,C_3\}$-free graphs containing an induced $C_7$, thereby

Xin Sun, Rongjun Ma, Shu Wei, Pablo Cesar

As AI-generated health information proliferates online and becomes increasingly indistinguishable from human-sourced information, it becomes critical to understand how people trust and label such content, especially when the information is inaccurate. We conducted two complementary studies: (1) a mixed-methods survey (N=142) employing a 2 (source: Human vs.

Shi-Bei Kong, Ying Wang, Yu-Ke Wang

In this paper, we study the equation of state and its properties of the perfect fluid in the $D$-dimensional FRW universe under Einstein gravity, Gauss-Bonnet gravity and Lovelock gravity. In Einstein gravity, we get the equation of state and find that it has no critical point in the $P$-$V$ diagram, but its isothermal lines have minima in the $4$-dimensiona

George E. Andrews, Aritram Dhar

Glaisher's theorem states that the number of partitions of $n$ into parts which repeat at most $m-1$ times is equal to the number of partitions of $n$ into parts which are not divisible by $m$. The $m=2$ case is Euler's famous partition theorem. Recently, Andrews, Kumar, and Yee gave two new partition functions $C(n)$ and $D(n)$ related to Euler's theorem. L

Anisha Sen, S. P. Rajaguru, Abhinav Govindan Iyer, Ruizhu Chen

Using solar-cycle long helioseismic measurements of meridional and zonal flows in the near-surface shear layer (NSSL) of the Sun, we study their spatio-temporal variations and connections to active regions. We find that near-surface inflows towards active latitudes are part of a local circulation with an outflow away from them at depths around 0.97 R, which

Olusola Odeyomi, Tokunbo Ogunfunmi, Adjovi Laba

This paper investigates online distributed aggregative games with time-varying cost functions, where agents are interconnected through an unbalanced communication graph. Due to the distributed and noncooperative nature of the game, some curious agents may wish to steal sensitive information from neighboring agents during parameter exchanges. Additionally, co

Fernando Parisio

We investigate how quantum coherence can be distributed among the several off-diagonal elements of an arbitrary density matrix. An easily computable quantity that captures this variability notion is proposed and it is argued that it presents marked features of complexity quantifiers. It turns out that this coherence dispersion ($\Delta_{\rm c}$) is maximized

Paul Hofman, Yusuf Sale, Eyke Hüllermeier

Proper quantification of predictive uncertainty is essential for the use of machine learning in safety-critical applications. Various uncertainty measures have been proposed for this purpose, typically claiming superiority over other measures. In this paper, we argue that there is no single best measure. Instead, uncertainty quantification should be tailored

Wenwu Gao, Dongyi Zheng, Hanbing Zhu

Quantile regression (QR) is now widely used to analyze the effect of covariates on the conditional distribution of a response variable. It provides a more comprehensive picture of the relationship between a response and covariates compared with classical least squares regression. However, the non-differentiability of the check loss function precludes the use

Maurya Goyal, Anuj Singh, Hadi Jamali-Rad

Aligning diffusion model outputs with downstream objectives is essential for improving task-specific performance. Broadly, inference-time training-free approaches for aligning diffusion models can be categorized into two main strategies: sampling-based methods, which explore multiple candidate outputs and select those with higher reward signals, and gradient

S. Özüm, T. Akkurt, R. Erdem, N. Güçlü

We analyze the equilibrium states of quantum lattice model with local multi-well potentials for Sn$_2$P$_2$S$_6$ ferroelectric crystals using the mean and Gaussian curvatures ($H$, $K$), curvedness ($C$) and shape index ($S$). From the energy gap, pressure and temperature variations of $H$, $K$, $C$ and $S$, we have reported the geometric construction of the

Yushen Fang, Jianjun Li, Mingqian Ding, Chang Liu

Although Large language Model (LLM)-powered information extraction (IE) systems have shown impressive capabilities, current fine-tuning paradigms face two major limitations: high training costs and difficulties in aligning with LLM preferences. To address these issues, we propose a novel universal IE paradigm, the Self-Correcting Iterative Refinement (SCIR)

R. Yu. Korolkov, R. O. Malysh, A. V. Korotun, R. A. Kulykovskyi

The optical and plasmonic properties of metal-dielectric nanoeggs were investigated in this study. Frequency dependencies of polarizability, absorption and scattering cross-sections, and radiation efficiency were determined. Expressions describing the size-dependent behavior of surface plasmon resonance frequencies were derived. The causes of blue and red sh

Anup Kushwaha, Om Prakash

This paper investigates the hull codes of free linear codes over a non-unital ring $ E= \langle \kappa,\tau \mid 2 \kappa =2 \tau=0,~ \kappa^2=\kappa,~ \tau^2=\tau,~ \kappa \tau=\kappa,~ \tau \kappa=\tau \rangle$. Initially, we examine the residue and torsion codes of various hulls of $E$-linear codes and obtain an explicit form of the generator matrix of th

Eden Gross, Ryan Kruger, Francois Toerien

In the last five years, expected shortfall (ES) and stressed ES (SES) have become key required regulatory measures of market risk in the banking sector, especially following events such as the global financial crisis. Thus, finding ways to optimize their estimation is of great importance. We extend the application of dynamic Bayesian networks (DBNs) to the e

Nina Stankovic, Huw Morgan, Marilena Mierla, Nancy Narang

Small-scale propagating disturbances (PD) are ubiquitous in the solar corona. Time-Normalised Optical Flow (TNOF) is a method developed for mapping PD velocity fields in time series of Extreme-Ultraviolet (EUV) images. We show PD velocity fields of a quiet Sun (QS) region containing a small coronal hole (CH) and filament channel (FC) jointly observed by Extr

Saad Alqithami

The purpose of this study is to investigate how homophily, memory constraints, and adversarial disruptions collectively shape the resilience and adaptability of complex networks. To achieve this, we develop a new framework that integrates explicit memory decay mechanisms into homophily-based models and systematically evaluate their performance across diverse

Nico Unglert, Michael Ketter, Georg K. H. Madsen

Accurate prediction of materials phase diagrams from first principles remains a central challenge in computational materials science. Machine-learning interatomic potentials can provide near-DFT accuracy at a fraction of the cost, but their reliability crucially depends on the availability of representative training data that span all relevant regions of the

Guoxing Ji

Let $\mathfrak A$ be a subdiagonal algebra with diagonal $\mathfrak D$ in a $\sigma$-finite von Neumann algebra $\mathcal M$ with respect to a faithful normal conditional expectation $\Phi$. We mainly consider the interpolation problem in $\mathfrak A$ with the universal factorization property. We determine when a finitely generated left ideal in $\mathfrak

Maria Chudnovsky, J. Pascal Gollin, Matjaž Krnc, Martin Milanič

Gartland and Lokshtanov conjectured that every graph that excludes some planar graph as an induced minor has a balanced separator, that is, a separator whose deletion leaves every component with no more than half of the vertices of the graph, which is dominated by a bounded number of vertices. We confirm this conjecture for excluding any fixed wheel, that is

Anisha Sen, S. P. Rajaguru, Ruizhu Chen, Junwei Zhao

Using time-distance helioseismic measurements of meridional flow in the near-surface shear layer over a period of 14 years, starting from May 2010, we probe the depth structure and evolution of its cross-equatorial part. We confirm that the hemispheric magnetic asymmetry determines the amplitude and direction of such flows. Additionally, we find that these f

J. Alexander Curtis, Nasir U. Eisty

Penetration testing is a cornerstone of cybersecurity, traditionally driven by manual, time-intensive processes. As systems grow in complexity, there is a pressing need for more scalable and efficient testing methodologies. This systematic literature review examines how Artificial Intelligence (AI) is reshaping penetration testing, analyzing 58 peer-reviewed

Loc Hoang Tran, Bao Nguyen Tran, Luong Anh Tuan Nguyen

The problem that we would like to solve in this paper is to compute the edge p-Laplacian centrality for the air traffic network. In this problem, instead of computing the edge p-Laplacian centrality directly which is the very hard problem, we convert the air traffic network to the line graph. Finally, we will compute the node p-Laplacian centrality of the li

Shubhada Agrawal, Aaditya Ramdas

We prove that a classic sub-Gaussian mixture proposed by Robbins in a stochastic setting actually satisfies a path-wise (deterministic) regret bound. For every path in a natural ``Ville event'' $\mathcal E_\alpha$, this regret till time $T$ is bounded by $\ln^2(1/\alpha)/V_T + \ln (1/\alpha) + \ln \ln V_T$ up to universal constants, where $V_T$ is a nonnegat

Meilin Li, Ji He, Yi Yu, Jia Xu

The rapid proliferation of Artificial Intelligence Generated Content has precipitated a crisis of trust and urgent regulatory demands. However, existing identification tools suffer from fragmentation and a lack of support for visible compliance marking. To address these gaps, we introduce the \textbf{UniMark}, an open-source, unified framework for multimodal

Zhiqi Peng, Youzhou Zhou

In this paper, we derive an integral representation for the distribution of the number of types $K_n$ in the Ewens-Pitman model. Based on this representation, we also establish precise large deviations and precise moderate deviations for $K_n$. After careful examination, we find that the rate function exhibits a second-order phase transition and the critical

Dongyeong Ko

We prove the regularity of cohomogeneity two equivariant isotopy minimization problems. Based on this, we develop cohomogeneity two equivariant min-max theory for minimal hypersurfaces proposed by Pitts and Rubinstein in 1988. As an application, for $g \ge 1$ and $4 \le n+1 \le 7$, we construct minimal hypersurfaces $\Sigma_{g}^{n}$ on round spheres $\mathbb

Yuan Li, Shuaijie Wang, Xiaoming Xu

Let $\mathcal{H}$ be a complex finite-dimensional or infinite-dimensional separable Hilbert space, $\mathcal{B(H)}$ and $\mathcal{T(H)}$ be the Banach spaces of all bounded linear operators and of all trace class operators on $\mathcal{H},$ respectively. In this paper, we give a concrete description of the linear maps $\Phi:\mathcal{T(H)}\rightarrow \mathcal

A. Hammad, S. Moretti, A. P. Przybyl, H. Waltari

The final state with four $b$-quarks has generally the largest event rate in Standard Model (SM)-like Higgs ($h_{\rm SM}$) pair production, but also the largest backgrounds. We study such a final state using the $gg\to h_{\rm SM}h_{\rm SM}$ production mechanism and Benchmarks Points (BPs) derived from the Next-to-Minimal Supersymmetric SM (NMSSM) in the boos

Emanuele Martelli, Vincent Jaunet, Giacomo Della Posta, Matteo Bernardini

The present work reports an investigation into the statistical properties of wall-pressure fluctuations in a highly over-expanded nozzle flow, characterized by significant shock-induced flow separation. This regime is extremely hazardous to rocket nozzles, as it leads to very high off-axis loads. The database under investigation has been obtained both experi

Edoardo Sernesi

Let $\V_{d,n}$ be the Severi variety of irreducible plane curves of degree $d\ge 4$ having $n$ nodes, with $0\le n \le \binom{d-1}{2}-1$. We prove that for every $[\ol C]\in \V_{d,n}$, the infinitesimal variation of the Hodge structure of the normalization $C$ of $\ol C$ is maximal as $[\ol C]$ moves in $\V_{d,n}$. As a preliminary result, we also prove that

Anatoly A. Krasnovsky

While distributed tracing and chaos engineering are becoming standard for microservices, resilience models remain largely manual and bespoke. We revisit a trace-discovered connectivity model that derives a service dependency graph from traces and uses Monte Carlo simulation to estimate endpoint availability under fail-stop service failures. Compared to earli

Mahdieh Shojaei Baghini, Adam Armada-Moreira, Alessio Di Clemente, Dibyajyoti Mukherjee

Implantable and wearable devices require antennas that are both miniaturized and efficient, yet conventional designs are constrained by narrow bandwidth and orientation sensitivity. We report overtone ultra-wideband magnetoelectric (OUWB-ME) antennas that exploit higher order acoustic modes in polished silicon substrates to achieve a 22.6 GHz bandwidth in th

Bing-Dong Wan, Ming-Yang Yuan, Jun-Hao Zhang, Yan Zhang

We investigate gluonic hidden-charm tetraquark states composed of two valence quarks, two valence antiquarks and an explicit valence gluon. In the color configuration $[\bar{3}_c]_{c q}\otimes[8_c]_{G}\otimes[3_c]_{\bar{c}\bar{q}}$, a complete set of eight interpolating currents is constructed for states with quantum numbers $^{PC}=0^{++}$, $0^{-+},$ $0^{--}

Shenghao Fu, Yukun Su, Fengyun Rao, Jing Lyu

Open-vocabulary object detection aims to detect arbitrary classes via text prompts. Methods without cross-modal fusion layers (non-fusion) offer faster inference by treating recognition as a retrieval problem, \ie, matching regions to text queries in a shared embedding space. In this work, we fully explore this retrieval philosophy and demonstrate its unique

Shaohan Xu, Kexiang Xu, Ivan Damnjanović

The number of spanning trees in a graph $G$ is the total number of distinct spanning subgraphs of $G$ that are trees. In this paper we characterize the unique graph with a prescribed vertex (resp. edge) connectivity, minimum degree and order that attains the maximum number of spanning trees. Moreover, all the bipartite graphs are determined with a given vert

Amir Yunus, Peng Rend Gay, Oon Teng Lee

This study examines the development and deployment of a Generative AI proof-of-concept (POC) designed to support lecturers in a vocational education setting in Singapore. Employing a user-centred, mixed-methods design process, we co-developed an AI chatbot with lecturers to address recurring instructional challenges during exam preparation, specifically mana

Yang Ou, Xiongwei Zhao, Xinye Yang, Yihan Wang

Unsupervised domain adaptation (UDA) enables semantic segmentation models to generalize from a labeled source domain to an unlabeled target domain. However, existing UDA methods still struggle to bridge the domain gap due to cross-domain contextual ambiguity, inconsistent feature representations, and class-wise pseudo-label noise. To address these challenges

Huan Zheng, Yucheng Zhou, Tianyi Yan, Jiayi Su

While end-to-end autonomous driving has achieved remarkable progress in geometric control, current systems remain constrained by a command-following paradigm that relies on simple navigational instructions. Transitioning to genuinely intelligent agents requires the capability to interpret and fulfill high-level, abstract human intentions. However, this advan

Mahima Kumavat, Aditya Maheshwari

TwinFormer is a hierarchical Transformer for long-sequence time-series forecasting. It divides the input into non-overlapping temporal patches and processes them in two stages: (1) a Local Informer with top-$k$ Sparse Attention models intra-patch dynamics, followed by mean pooling; (2) a Global Informer captures long-range inter-patch dependencies using the

Kartik Gaur, Avijit Barua, Sarthak Tripathi, Léo J. Roche

The scalable integration of solid-state quantum emitters into photonic nanostructures remains a central challenge for quantum photonic technologies. Here, we demonstrate a robust and streamlined integration strategy that tackles the long-standing issue of deterministic fabrication on randomly positioned self-assembled quantum dots (QDs), leveraging a buried-

Vlad Popescu-Vifor, Ilir Murturi, Praveen Kumar Donta, Schahram Dustdar

The increasing device heterogeneity and decentralization requirements in the computing continuum (i.e., spanning edge, fog, and cloud) introduce new challenges in resource orchestration. In such environments, agents are often responsible for optimizing resource usage across deployed services. However, agent decisions can lead to persistent conflict loops, in

Andreas Konstantinou

This paper investigates how a self bound equation of state (EOS), which describes strange quark stars, affects the rotational properties of compact stars, focusing on deviations from universal relations governing gravitational mass and radius changes due to rotation. The analysis reveals significant deviations in stars with higher surface-to-center total ene

Radu-Gabriel Chivereanu, Tiberiu Boros

This work introduces a lightweight input-level adapter for the F5-TTS model that enables Romanian Language support. To preserve the existing capabilities of the model (voice cloning, English and Chinese support), we keep the original weights frozen, append a sub-network to the model and train it as an extension for the textual embedding matrix of the text en

Hyunju Lee, Youngmin Oh, Jeimin Jeon, Donghyeon Baek

Transformer architecture search (TAS) aims to automatically discover efficient vision transformers (ViTs), reducing the need for manual design. Existing TAS methods typically train an over-parameterized network (i.e., a supernet) that encompasses all candidate architectures (i.e., subnets). However, all subnets share the same set of weights, which leads to i

Wenjun Yu, Sitian Chen, Cheng Chen, Amelie Chi Zhou

Deep Learning Recommendation Models (DLRMs) underpin personalized services but face a critical freshness-accuracy tradeoff due to massive parameter synchronization overheads. Production DLRMs deploy decoupled training/inference clusters, where synchronizing petabyte-scale embedding tables (EMTs) causes multi-minute staleness, degrading recommendation quality

Masaru Nagaoka

This paper reviews Lacini's classification [Lac24] of log del Pezzo surfaces of rank one in characteristics different from two and three, with a focus on where and how Lacini enhanced the techniques of Keel and McKernan [KM99]. We point out that there is at most one log del Pezzo surface that may have been erroneously omitted from the list in [Lac24, \S 6.1]

Wei Wang, Ji Xu, Ya-Teng Zhang, Xiao-Rong Zhou

Short-range correlation pairs (SRCs) -- core of nuclear structure, composed of highly off-shell nucleons -- are mostly studied via electron-nucleon scattering, leaving a gap in meson-based probes. We propose probing SRC off-shell nucleons via quasielastic $\pi^+$-bound proton scattering ($\pi^+ p \to \pi^+ p$) at electron-positron colliders, of which the ber

Arti Pandey, Kaustav Paul, Kamal Santra

Given a simple undirected graph $G = (V, E)$, the open neighbourhood of a vertex $v \in V$ is defined as $N_G(v) = \{u \in V \mid uv \in E\}$, and the closed neighbourhood as $N_G[v] = N_G(v) \cup \{v\}$. A subset $D \subseteq V$ is called a vertex-edge dominating set if, for every edge $uv \in E$, at least one vertex from $D$ appears in $N_G[u] \cup N_G[v]$

Che-Yen Chu, Chin-Ping Hu, Teruaki Enoto, George A. Younes

In this paper, we present a comprehensive catalog of short bursts from magnetars based on eight years of NICER observations. A total of 1130 bursts were identified from 14 sources, with the sample dominated by SGR 1935+2154, which accounts for 76% of all detected bursts. We analyzed burst durations, spectral properties, and their correlations across multiple

Krystian Ilkiewicz, Henryka Netzel

We present a search for X-ray counterparts to RR Lyrae and Cepheid variables using data from the first eROSITA all-sky survey. We identify seven RR Lyrae and eight Cepheid variables with positional matches to X-ray sources. While most Cepheid associations appear reliable, the RR Lyrae matches are predominantly spurious. Only one source, OGLE-BLG-RRLYR-00252,

Bingbing Wang, Shengyan Sun, Jiaqi Wang, Yu Tang

Outlier detection and concept drift detection represent two challenges in data analysis. Most studies address these issues separately. However, joint detection mechanisms in regression remain underexplored, where the continuous nature of output spaces makes distinguishing drifts from outliers inherently challenging. To address this, we propose a novel robust

Mahule Roy

Conventional generative models for materials discovery are predominantly trained and validated using data from Density Functional Theory (DFT) with approximate exchange-correlation functionals. This creates a fundamental bottleneck: these models inherit DFT's systematic failures for strongly correlated systems, leading to exploration biases and an inability

Ahmad Zafarani, Zahra Dehghanian, Mohammadreza Davoodi, Mohsen Shadroo

The evaluation of drag based image editing models is unreliable due to a lack of standardized benchmarks and metrics. This ambiguity stems from inconsistent evaluation protocols and, critically, the absence of datasets containing ground truth target images, making objective comparisons between competing methods difficult. To address this, we introduce \textb

Lujuan Dang, Zilai Wang

Accurate estimation of the State of Charge (SOC) is critical for ensuring the safety, reliability, and performance optimization of lithium-ion battery systems. Conventional data-driven neural network models often struggle to fully characterize the inherent complex nonlinearities and memory-dependent dynamics of electrochemical processes, significantly limiti

Donghyuk Kim, Sejeong Yang, Wonjin Shin, Joo-Young Kim

Streaming video large language models (LLMs) are increasingly used for real-time multimodal tasks such as video captioning, question answering, conversational agents, and augmented reality. However, these models face fundamental memory and computational challenges because their key-value (KV) caches grow substantially with continuous streaming video input. T

Junjie Xu, Xingjiao Wu, Luwei Xiao, Yuzhe Yang

As large language models (LLMs) move into persistent, user-facing roles, their behavior must be understood not as isolated responses but as a trajectory unfolding over sustained interaction. We introduce the concept of the chain-of-affect (CoA), a temporally extended affective process through which LLMs develop state-like behavioral tendencies that shape gen

Ivan Ruiz Manuel, Meijun Chen, Francesco Lombardi, Stefan Pfenninger-Lee

Pathways that describe the optimal evolution of energy systems across multiple decades are important in energy system research and policy literature, with net-zero and similar climate policies being common drivers behind them. While there are many studies on aspects such as spatial and operational resolution, model features, and model transparency, there has

Huizheng Wang, Zichuan Wang, Hongbin Wang, Jingxiang Hou

Training large language models (LLMs) imposes extreme demands on computation, memory capacity, and interconnect bandwidth, driven by their ever-increasing parameter scales and intensive data movement. Wafer-scale integration offers a promising solution by densely integrating multiple single-die chips with high-speed die-to-die (D2D) interconnects. However, t

Yuuga Takasu, Satoru Hayami

Magnetic octupole (MO) currents have recently attracted significant attention as a driving force for the Neel vector dynamics in d-wave altermagnets, a new class of antiferromagnets that exhibit nonrelativistic spin-split band structures. From a symmetry perspective, the MO includes an axial-dipole component analogous to that of the spin, making it essential

A. A. Shiryaev, E. A. Vasilev, A. L. Vasilev, V. V. Artemov

The paper presents results of investigation of a natural Ib-IaA diamond containing Y-defects from Yubileinaya kimberlite pipe, Yakutia. Analysis of spatial distribution of nitrogen-related A and C centers and intensity of Infra-red absorption at Raman frequency (1332 cm-1) reveals anticorrelation between these defects. Transmission electron microscopy of a z

Thibault Geoffroy, Myriam Maumy, Lionel Prevost

As artificial intelligence (AI) systems become increasingly embedded in our daily life, the ability to recognize and adapt to human emotions is essential for effective human-computer interaction. Facial expression recognition (FER) provides a primary channel for inferring affective states, but the dynamic and culturally nuanced nature of emotions requires mo

Jeffrey van der Voort, Martin Verlaan, Hanne Kekkonen

Currently, more and more machine learning (ML) surrogates are being developed for computationally expensive physical models. In this work we investigate the use of a Multi-Fidelity Ensemble Kalman Filter (MF-EnKF) in which the low-fidelity model is such a machine learning surrogate model, instead of a traditional low-resolution or reduced-order model. The id

Qiongqiong Pan, Yunze Wang, Jiang Zeng

The generating polynomial of permutations of size $n$, counted by the number of alternating runs, has a root at $-1$ of multiplicity $\lfloor (n-2)/2 \rfloor$ for all $n \ge 2$. This result can be derived by combining the David--Barton formula for Eulerian polynomials with the Foata--Sch\"utzenberger $\gamma$--decomposition. More recently, B\'ona gave a grou

Eshwar Srinivasan, Ramesh Hariharasubramanian

A graph $G$ with vertex set $V(G)$ and edge set $E(G)$ is said to be word-representable if there exists a word $w$ over the alphabet $V(G)$ such that, for any two distinct letters $x,y \in V(G)$, the letters $x$ and $y$ alternate in $w$ if and only if $(x,y) \in E(G)$. Equivalently, a graph is word-representable if and only if it admits a semi-transitive ori

Bihao You, Jiping Cui

Prediction of epilepsy based on electroencephalogram (EEG) signals is a rapidly evolving field. Previous studies have traditionally applied 1D processing to the entire EEG signal. However, we have adopted the Gram Matrix method to transform the signals into a 3D representation, enabling modeling of signal relationships across dimensions while preserving the

Yuhan Chen, Shang Qu, Zhiqiang Gao, Yuejin Yang

Post-translational modifications (PTMs) serve as a dynamic chemical language regulating protein function, yet current proteomic methods remain blind to a vast portion of the modified proteome. Standard database search algorithms suffer from a combinatorial explosion of search spaces, limiting the identification of uncharacterized or complex modifications. He