November 2025 arXiv papers — page 88

Zahra Teimouri, Mahtab Masouminia, Ashkan Irannezhad, Reza Eslami

Electrochemical CO2 reduction (CO2R) offers a promising approach to decarbonize chemical manufacturing through production of carbon-neutral fuels. However, insufficient performance and instability of membrane electrode assembly (MEA) reactors limit commercial viability, with both metrics directly impacted by the CO2R catalysts. Here we develop an atomically

Xinran Zhu, Cong Wang, Duane Searsmith

The growing integration of generative AI in higher education is transforming how students write, learn, and engage with knowledge. As AI tools become more integrated into classrooms, there is an urgent need for pedagogical approaches that help students use them critically and reflectively. This study proposes a pedagogical design that integrates AI and peer

Jingchen Peng, Chaowen Deng, Yili Deng, Boxiang Ren

This paper presents Hyper-VIB, a hypernetwork-enhanced information bottleneck (IB) approach designed to enable efficient task-oriented communications in 6G collaborative intelligent systems. Leveraging IB theory, our approach enables an optimal end-to-end joint training of device and network models, in terms of the maximal task execution accuracy as well as

Yanxuan Yu, Michael S. Hughes, Julien Lee, Jiacheng Zhou

We study classification under extreme class imbalance where recall and calibration are both critical, for example in medical diagnosis scenarios. We propose AF-SMOTE, a mathematically motivated augmentation framework that first synthesizes minority points and then filters them by an adversarial discriminator and a boundary utility model. We prove that, under

Tsunenori Inakura, Wataru Horiuchi, Shin'ichiro Michimasa, Masaomi Tanaka

We present a systematic survey of even-even nuclei with $Z>50$ to identify where a very large prolate configuration driven by the second intruder orbit emerges. Within the energy density functional theory framework, we find in representative cases a pronounced prolate minimum at quadrupole deformation $\beta_2\approx$ 0.3--0.4. A characteristic feature of th

Kousuke Kuto, Yaping Wu

We investigate the existence and instability of a class of blowing-up positive steady states arising in a shadow system of the Shigesada-Kawasaki-Teramoto (SKT) two-species competition model, as well as in the corresponding perturbed SKT model with a sufficiently large cross-diffusion coefficient and bounded random diffusion parameters. In their classical wo

Dorien Herremans, Abhinaba Roy

Recent advances in generative AI for music have achieved remarkable fidelity and stylistic diversity, yet these systems often fail to align with nuanced human preferences due to the specific loss functions they use. This paper advocates for the systematic application of preference alignment techniques to music generation, addressing the fundamental gap betwe

Jian Zhai, Kelvin Shuangjian Zhang

We consider the problem of recovering the Riemannian metric on a compact closed manifold from the optimal transport maps when the underlying cost function is the squared Riemann distance. We show that the metric can be uniquely determined up to a multiplicative constant.

Kamal Mammadov, Damith C. Ranasinghe

This paper presents a novel strategy for a multi-agent pursuit-evasion game involving multiple faster pursuers with heterogenous speeds and a single slower evader. We define a geometric region, the evader's safe-reachable set, as the intersection of Apollonius circles derived from each pursuer-evader pair. The capture strategy is formulated as a zero-sum gam

Yunpeng Fan, Xinbao Zhao, Yu Zhou, Quanzhao Yue

Conventional strategies for enhancing creep resistance often rely on grain boundary strengthening, yet this approach can inadvertently promote premature grain boundary fracture. This study presents a subtractive alloy design strategy for nickel-based directionally solidified superalloys (DS superalloy) through elimination of conventional grain boundary stren

Fanbo Ju, Haiyuan Shi, Qingjian Ni

Precipitation nowcasting, which aims to provide high spatio-temporal resolution precipitation forecasts by leveraging current radar observations, is a core task in regional weather forecasting. Recently, the cascaded architecture has emerged as the mainstream paradigm for deep learning-based precipitation nowcasting. This paradigm involves a deterministic mo

Kaixin Lu, Ziliang Lyu, Haoyong Yu

This work studies the inverse optimality of input-to-state stabilizing controllers with input-output stability guarantees for nonlinear homogeneous systems. We formulate a new inverse optimal control problem, where the cost functional incorporates penalties on the output, in addition to the state, control and disturbance as in current related works. One bene

Thanh-Cong Nguyen, Ngoc-Thanh Nguyen, Van-Giau Ung, Duc-Ly Vu

Recently, the number of malicious open-source packages in package repositories has been increasing dramatically. While major security scanners focus on identifying known Common Vulnerabilities and Exposures (CVEs) in open-source packages, there are very few studies on detecting malicious packages. Malicious open-source package detection typically requires st

S. Preethi, Ankush Kamboj, Ramkarn Patne, P. A. L. Narayana

We examine the linear stability of a shear flow driven by wind stress at the free surface and rotation at the lower boundary, mimicking oceanic flows influenced by surface winds and rotation of Earth. The linearised eigenvalue problem is solved using the Chebyshev spectral collocation method and a longwave asymptotic analysis. Our results reveal new longwave

Jeffrey Jiang, Kevin Hong, Emily Kuczynski, Gregory Pottie

While intelligent tutoring systems (ITSs) can use information from past students to personalize instruction, each new student is unique. Moreover, the education problem is inherently difficult because the learning process is only partially observable. We therefore develop a dynamic, time-series environment to simulate a classroom setting, with student-teache

Yifan Cai, Linh Thi Xuan Phan

Large-scale cyber-physical systems (CPS), such as railway control systems and smart grids, consist of geographically distributed subsystems that are connected via unreliable, asynchronous inter-region networks. Their scale and distribution make them especially vulnerable to faults and attacks. Unfortunately, existing fault-tolerant methods either consume exc

Mingran Sun, Lu Bai, Ziwei Huang, Xuesong Cai

A wireless channel foundation model for pathloss map generation (WiCo-PG) via Synesthesia of Machines (SoM) is developed for the first time. Considering sixth-generation (6G) uncrewed aerial vehicle (UAV)-to-ground (U2G) scenarios, a new multi-modal sensing-communication dataset is constructed for WiCo-PG pre-training, including multiple U2G scenarios, diver

David Senjaya

The claim in \cite{Al-Badawi:2025ipr} that *"the errors in the foundational components (3) and (5) of Ref. [1] invalidate all subsequent analyses, numerical results, and physical interpretations that depend on them"* is **entirely unfounded**. This statement reflects a fundamental misunderstanding of the typographical nature of the error and appears to be a

Zekun Wang, Sashank Varma

Mathematical thinking is a fundamental aspect of human cognition. Cognitive scientists have investigated the mechanisms that underlie our ability to thinking geometrically and numerically, to take two prominent examples, and developmental scientists have documented the trajectories of these abilities over the lifespan. Prior research has shown that computer

Christophe Gyurgyik, Alexander J Root, Fredrik Kjolstad

Bounding volume hierarchies are ubiquitous acceleration structures in graphics, scientific computing, and data analytics. Their performance depends critically on data layout choices that affect cache utilization, memory bandwidth, and vectorization -- increasingly dominant factors in modern computing. Yet, in most programming systems, these layout choices ar

Zunyi Deng, Wenwen Xuan, Bin Wei, Yongheng Li

Bi2O2Se is an emerging semiconductor with intrinsically low thermal conductivity, making it a promising material for thermoelectric applications. Hydrostatic pressure can effectively tunes the thermal conductivity, with various pressure-dependent trends reported. However, its impact on thermal anisotropy, particularly in the highly anisotropic Bi2O2Se, remai

Zengrui Han, Lu Bai, Xuesong Cai, Xiang Cheng

Precise modeling of channel multipath is essential for understanding wireless propagation environments and optimizing communication systems. In particular, sixth-generation (6G) artificial intelligence (AI)-native communication systems demand massive and high-quality multipath channel data to enable intelligent model training and performance optimization. In

M. E. Pietrzyk, V. A. Kholodnyi, I. V. Kanattšikov, J. Kozicki

Building upon previous work that derived an alternative to (galactic) dark matter in the form of Modified Newtonian Dynamics (MOND), with a specific theoretical interpolating function, from the motion of a non-relativistic test particle in the gravitational field of a point mass immersed in the non-relativistic static limit of the spin connection foam -- whi

T. Wu, P. H. Diamond, L. Nie, R. Ke

This paper investigates the impact of edge turbulence spreading on broadening the heat flux width in Ohmic-plasma approaching the density limit of the J-TEXT tokamak. At the plasma edge, the EXB shear flow collapses while turbulence transport and spreading enhances significantly when approaching the density limit. The heat flux width increases with normalize

Donald Goldfarb, Lexiao Lai, Tianyi Lin, Jiayu Zhang

We extend the standard notion of self-concordance to non-convex optimization and develop a family of second-order algorithms with global convergence guarantees. In particular, two function classes -- \textit{weakly self-concordant} functions and \textit{$F$-based self-concordant} functions -- generalize the self-concordant framework beyond convexity, without

Nick-Marios T. Kokolakis, Shanqing Liu, Jerome Darbon, Rahul Mangharam

We develop a game-theoretic framework for adversarially robust optimal safe predefined-time stabilization of parameter-dependent nonlinear dynamical systems with nonquadratic cost functionals. Our approach ensures that all system trajectories remain within a specified admissible set and converge to equilibrium in a predefined time despite adversarial disturb

Yiping Wang, Peiren Wang, Zhenye Li, Fang Liu

Complex spatial connectivity patterns, such as interictal suppression and ictal propagation, complicate accurate drug-resistant epilepsy (DRE) seizure detection using stereotactic electroencephalography (SEEG) and traditional machine learning methods. Two critical challenges remain:(1)a low signal-to-noise ratio in functional connectivity estimates, making i

Zhenyu Cui, Jiahuan Zhou, Yuxin Peng

Lifelong person Re-IDentification (LReID) aims to match the same person employing continuously collected individual data from different scenarios. To achieve continuous all-day person matching across day and night, Visible-Infrared Lifelong person Re-IDentification (VI-LReID) focuses on sequential training on data from visible and infrared modalities and pur

Davood Momeni

We investigate a four-parameter entropic dark energy model in a spatially curved FLRW universe, based on a generalized entropy-area relation at the apparent horizon. While the proposed entropy function captures a broad class of gravitational entropy corrections, including Bekenstein-Hawking, Tsallis, and power-law forms, it does not encompass information-the

Kexin Chu, Dawei Xiang, Zixu Shen, Yiwei Yang

Mixture-of-Experts (MoE) has become a practical architecture for scaling LLM capacity while keeping per-token compute modest, but deploying MoE models on a single, memory-limited GPU remains difficult because expert weights dominate the HBM footprint. Existing expert offloading and prefetching systems reduce the resident set, yet they often pay expert-loadin

Ibrahim Shahbaz, Eman Hammad, Abdallah Farraj

Power systems remain highly vulnerable to disturbances and cyber-attacks, underscoring the need for resilient and adaptive control strategies. In this work, we investigate a data-driven Federated Learning Control (FLC) framework for transient stability resilience under cyber-physical disturbances. The FLC employs interpretable neural controllers based on the

Gi-Hwan Shin, Young-Seok Kweon, Seungwon Oh, Seong-Whan Lee

Sleep is crucial for memory consolidation, underpinning effective learning. Targeted memory reactivation (TMR) can strengthen neural representations by re-engaging learning circuits during sleep. However, TMR protocols overlook individual differences in learning capacity and memory trace strength, limiting efficacy for difficult-to-recall memories. Here, we

Gi-Hwan Shin

Modern lifestyles contribute to insufficient sleep, impairing cognitive function and weakening the immune system. Sleep quality (SQ) is vital for physiological and mental health, making its understanding and accurate assessment critical. However, its multifaceted nature, shaped by neurological and environmental factors, makes precise quantification challengi

Rishikesh G. Jha, K. Sasikumar Raja, R. Ramesh, C. Kathiravan

Solar radio type II bursts are slow-drifting bursts that exhibit various distinct features such as Fundamental (F) and Harmonic (H) emissions, band-splitting, and discrete fine structures in the dynamic spectra. Observationally, it has been found that in some cases the F emission is stronger than the H emission, and vice versa. The reason for such behavior h

Katie Rainey, Erin Hausmann, Donald Waagen, David Gray

Understanding the relationships between data points in the latent decision space derived by the deep learning system is critical to evaluating and interpreting the performance of the system on real world data. Detecting \textit{out-of-distribution} (OOD) data for deep learning systems continues to be an active research topic. We investigate the connection be

Yuan Liu, Xurui Li, Jianxiang Tian, Xunwang Yan

We study how finite-window sampling (random spatial truncation) and reciprocal-space radial binning influence the detection of hyperuniformity in disordered systems. Using thirteen representative two-dimensional simulation systems (two stealthy hyperuniform systems with distinct constraint parameters and ; hyperuniform Gaussian pair statistics system; six hy

Michael K. -H. Kiessling, David J. Wales

Good a-priori bounds on the smallest pairwise distance $r_{\rm{{min}}}(\mbox{LJ}_N^{\rm{gmin}})$ for a three-dimensional (3D) Lennard-Jones $N$-body cluster of globally minimal energy can significantly reduce the computational search space in the NP-hard problem to find this configuration. In this contribution the virial theorem is exploited for this purpose

Shehan Irteza Pranto, Brett Fassler, Md Rafi Islam, Ashley Schenkel

Understanding puffing topography (PT), which includes puff duration, intra-puff interval, and puff count per session, is critical for evaluating Electronic Nicotine Delivery Systems (ENDS) use, toxicant exposure, and informing regulatory decisions. We developed FRIENDS (Flexible Robust Instrumentation of ENDS), an open-source device that can be attached to E

Aimee Schechter, Aleksandra Ciprijanovic, Rebecca Nevin, Julie Comerford

As we enter the era of large imaging surveys such as $\textit{Roman}$, Rubin, and $\textit{Euclid}$, a deeper understanding of potential biases and selection effects in optical astronomical catalogs created with the use of ML-based methods is paramount. This work focuses on a deeper understanding of the performance and limitations of deep learning-based clas

Moses Kiprono

Large Language Models (LLMs) are powerful linguistic engines but remain susceptible to hallucinations: plausible-sounding outputs that are factually incorrect or unsupported. In this work, we present a mathematically grounded framework to understand, measure, and mitigate these hallucinations. Drawing on probabilistic modeling, information theory, trigonomet

Halil S. Kelebek, Linnea M. Wolniewicz, Michael D. Vergalla, Simone Mestici

The ionosphere is a critical component of near-Earth space, shaping GNSS accuracy, high-frequency communications, and aviation operations. For these reasons, accurate forecasting and modeling of ionospheric variability has become increasingly relevant. To address this gap, we present IonCast, a suite of deep learning models that include a GraphCast-inspired

Reza Mirjalili, Behrad Braghi, Shahram Shadrokh Sikari

Accurate prediction of project duration and cost remains one of the most challenging aspects of project management, particularly in resource-constrained and interdependent task networks. Traditional analytical techniques such as the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) rely on simplified and often static assumptions r

Fatemeh Lotfi, Hossein Rajoli, Fatemeh Afghah

Next-generation networks utilize the Open Radio Access Network (O-RAN) architecture to enable dynamic resource management, facilitated by the RAN Intelligent Controller (RIC). While deep reinforcement learning (DRL) models show promise in optimizing network resources, they often struggle with robustness and generalizability in dynamic environments. This pape

Ernesto F. Eiroa, Emilio Rubín de Celis, Claudio Simeone

In this article we study spherical thin-shell wormholes in five-dimensional Einstein-Gauss-Bonnet gravity. We show that configurations supported by non-exotic matter, that is matter satisfying the weak energy condition, are possible at the same time that traversability problems associated with strong radial tides at the throat can be avoided when suitable va

Singon Kim

Abstractive compression utilizes smaller langauge models to condense query-relevant context, reducing computational costs in retrieval-augmented generation (RAG). However, retrieved documents often include information that is either irrelevant to answering the query or misleading due to factual incorrect content, despite having high relevance scores. This be

Fahad Alrasheedi, Hesham Ali

Electric vehicle (EV) adoption is essential for reducing carbon dioxide (CO2) emissions from internal combustion engine vehicles (ICEVs), which account for nearly half of transportation-related emissions in the United States. Yet regional EV adoption varies widely, and prior studies often overlook county-level heterogeneity by relying on broad state-level an

Yueru He, Xueqing Peng, Yupeng Cao, Yan Wang

Recent progress in multimodal large language models (MLLMs) has substantially improved document understanding, yet strong optical character recognition (OCR) performance on surface metrics does not guarantee faithful preservation of decision-critical evidence. This limitation is especially consequential in financial documents, where small visual errors can i

Pratyusava Baral, Cody Messick, Patrick Brady

The LIGO-Virgo-KAGRA collaboration uses multiple independent search pipelines to detect gravitational waves, often resulting in multiple triggers (g-events) for a single astrophysical source. These triggers are grouped into superevents, raising a critical question for multimessenger astronomy: which g-event provides the most accurate sky localization for ele

Elizabeth Shen, Huiyang Zhou

Lengthening a computer memory's lifespan is important for e-waste and sustainability. Uneven wear of memory is a major barrier. The problem is becoming even more urgent as emerging memory such as phase-change memory is subject to even shorter lifespan. Various solutions have been proposed, but they either require complicated hardware extensions or apply only

Jonas M. Mikhaeil, Donald P. Green, David Blei

Scientific progress is inherently sequential: collective knowledge is updated as new studies enter the literature. We propose the sequential meta-analysis research trace (SMART), which quantifies the influence of each study at the time it enters the literature. In contrast to classical meta-analysis, our method can capture how new studies may cast doubt on p

Naonori Kakimura, Yoshihiko Terai

In this paper, we propose fast pseudo-polynomial-time algorithms for computing power indices in weighted majority games. We show that we can compute the Banzhaf index for all players in $O(n+q\log (q))$ time, where $n$ is the number of players and $q$ is a given quota. Moreover, we prove that the Shapley--Shubik index for all players can be computed in $O(nq

Yue Yu, Xiaobo Zheng, Shaoming He

Distributed optimization offers a promising paradigm for trajectory planning in Unmanned Aerial Vehicle (UAV) swarms, yet its deployment in communication-constrained environments remains challenging due to unreliable links and limited data exchange. This paper addresses this issue via a two-tier architecture explicitly designed for operation under communicat

Arkadiy Aliev

Let $ K $ be a convex body in $ \mathbb{R}^n $. We denote the volume of $ K $ by $ \vert K\vert $, and the polar body of its difference body $ K - K $ by $ (K - K)^{\circ} $. We provide a new proof of the well-known estimate \[ |K||(K - K)^{\circ}| \geq \frac{3}{2} \] for $ K \subset \mathbb{R}^2 $, with equality attained for a triangle. For $ K \subset \mat

Zhuolun Jiang, Songyue Wang, Xiaokun Pei, Tianyue Lu

Modern data-intensive applications face memory latency challenges exacerbated by disaggregated memory systems. Recent work shows that coroutines are promising in effectively interleaving tasks and hiding memory latency, but they struggle to balance latency-hiding efficiency with runtime overhead. We present CoroAMU, a hardware-software co-designed system for

Alex Cuellar, Christopher K Fourie, Julie A Shah

Learning from Demonstration (LfD) has shown to provide robots with fundamental motion skills for a variety of domains. Various branches of LfD research (e.g., learned dynamical systems and movement primitives) can generally be classified into ''time-dependent'' or ''time-independent'' systems. Each provides fundamental benefits and drawbacks -- time-independ

S. A. K. Wijethunga, A. Sukhanov, O. Napoly, K. McGee

Field emission (FE) remains a significant hurdle for achieving optimal performance and reliability in super-conducting radiofrequency (SRF) cavities used in accelerator cryomodules. A thorough understanding of the generation and propagation of FE-induced radiation is therefore essential to mitigate this problem. The absence of standardized measurement protoc

Jade Pinkenburg, Changuk Lee, Mohammad Meraj Ghanbari, Cem Yalcin

Spatially distributed peripheral nerve recordings can be used to reconstruct motor intention and improve natural control of prosthetics However, many existing clinical solutions rely on percutaneous wires to access peripheral nerves; these sites are prone to infection and motion-induced electrode degradation, preventing chronic use. To address the need for f

Francisco González-Acuña, Araceli Guzmán-Tristán, Jesús Rodríguez-Viorato, José Andrés Rodríguez Migueles

We study the existence of branched coverings between closed $3$-manifolds, with emphasis on universal knots and links. We prove that the only closed $3$-manifolds that admit a universal link are spherical. Furthermore, we distinguish between universal links and complement universal links and show that these notions do not coincide in general, by exhibiting i

Ye Tian, Chengcheng Wang, Jing Han, Yehui Tang

As Large Language Models (LLMs) continue to grow in size, storing and transmitting them on edge devices becomes increasingly challenging. Traditional methods like quantization and pruning struggle to achieve extreme compression of LLMs without sacrificing accuracy. In this paper, we introduce PocketLLM, a novel approach to compress LLMs in a latent space via

William Baumgartner, Gizem Kayar-Ceylan

Haptic feedback enhances immersion in virtual environments by allowing users to physically interact with simulated objects. Supporting accurate force responses in multiphysics systems is challenging because physically based simulation of fluid, rigid, and deformable materials is computationally demanding, especially when interaction must occur in real time.

C. Weeks, P. Strange, P. Drmota, J. Quintanilla

We study quantum advantage in the 1-step graph domination game on cycle graphs numerically, analytically and through the use of Noisy intermediate scale quantum (NISQ) processors. We find explicit strategies that realise the recently found upper bounds for small graphs and generalise them to larger cycles. We demonstrate that NISQ computers realise the predi

Maggie Williams, Emery Doucet, Sebastian Deffner

In introductory biology, aging is typically explained as a result of mutations during the DNA replication process within cells. Upon abstraction, we recognize that cellular aging can be understood as the gradual decay in fidelity of information transcription. Since cellular processes are microscopic and inherently stochastic, the abstracted process of inform

Marwan Ait Haddou, Mohamed Belfkir, Salah Eddine El Harrauss

In this paper, we propose a new Hybrid Quantum Machine Learning (HyQML) framework to improve the sensitivity of double Higgs boson searches in the $HH \to b\bar{b}γγ$ final state at $\sqrt{s}$ = 13.6 TeV. The proposed model combines parameterized quantum circuits with a classical neural network meta-model, enabling event-level features to be embedded in a qu

Jorge C. Romão, Rafael Boto, Pedro N. Figueiredo, João P. Silva

We investigate the constraints and phenomenology of a three Higgs doublet model (3HDM) with a $\Z2\times\Z2$ symmetry, featuring two inert scalar doublets that give rise to a two-component dark matter (DM) scenario. We analyze the model's vacuum structure, exploring the competition between different symmetry-breaking minima, and subject it to comprehensi

José A. Tirado-Domínguez, Eladio Gutiérrez, Oscar Plata

Task scheduling with constrained time intervals and limited resources remains a fundamental challenge across domains such as manufacturing, logistics, cloud computing, and healthcare. This study presents a novel variant of the Quantum Approximate Optimization Algorithm (QAOA) designed to address the task scheduling problem formulated as a Quadratic Unconstra

Zsófia Simon, Jari Saramäki

Temporal-network models have provided key insights into how time-varying connectivity shapes dynamical processes such as spreading. Among them, the activity-driven model is a widely used, analytically tractable benchmark. Yet many temporal networks, such as those of physical proximity, are also embedded in space, and spatial constraints are known to affect d

Simon Allzén

Philosophers increasingly treat semantics as decisive for realism about dark matter. In this paper, I consider a recent proposal from Vaynberg (2024) anchored in the causal-descriptive theory of reference from Psillos (1999, 2012). I argue that the application of Psillos' general scientific realist framework in the local context of dark matter is misguid

Xabier Lekunberri, Ahmad Kamal, Izaro Goienetxea, Jon Ruiz

Purse seiners play a crucial role in tuna fishing, as approximately 69% of the world's tropical tuna is caught using this gear. All tuna Regional Fisheries Management Organizations have established minimum standards to use electronic monitoring (EM) in fisheries in addition to traditional observers. The EM systems produce a massive amount of video data t

Virgile Dandoy, Christian Döring, Gaétan Facchinetti, Laura Lopez-Honorez

The $21\,$cm signal originating from Cosmic Dawn to the Epoch of Reionisation is highly sensitive to the processes governing star formation in the early universe as well as new physics. In this work, we focus on the imprint of non-cold dark matter (DM), which impacts the formation of the smallest halos. Our goal in particular is to clarify whether near-futur

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We present a search for the lepton number violating decay $Ξ^-\rightarrowΣ^+e^-e^- +c.c.$ with $(10087\pm44)\times10^6$ $J/ψ$ events collected by the BESIII detector at the BEPCII collider. Employing a blind analysis strategy, no significant signal is observed above the expected background yield. The upper limit on the branching fraction is determined to be

Yulong He, Anton V. Proskurnikov, Artem Sedakov

Online social media platforms enable influencers to distribute content and quickly capture audience reactions, significantly shaping their promotional strategies and advertising agreements. Understanding how sentiment dynamics and emotional contagion unfold among followers is vital for influencers and marketers, as these processes shape engagement, brand per

B. Cvetković, D. Rakonjac

We consider the general construction of near-horizon limit for extremal black hole solutions with non-trivial torsion, and derive the covariant geometric conditions for the existence this limit. A near-horizon solution with torsion is constructed in the case of extremal Kerr-AdS black hole, starting from an ansatz compatible with near-horizon symmetry. We de

Dimitria Silveria, Kleber Cabral, Peter Jardine, Sidney Givigi

This paper presents the integration and experimental validation of advanced control strategies for quadcopters based on Lie groups. We build upon recent theoretical developments on SE2(3)-based controllers and introduce a novel SE2(3) model predictive controller (MPC) that combines the predictive capabilities and constraint-handling of optimal control with t

Theodoulos Garefalakis, Giorgos Kapetanakis

Denote by $\mathbb F_q$ the finite field of order $q$ and by $\mathbb F_{q^n}$ its extension of degree $n$. Some $a\in\mathbb F_{q^n}$ is called primitive if it generates the multiplicative group $\mathbb F_{q^n}^*$ and it is called $q^n/q$-normal if its $\mathbb F_q$-conjugates form an $\mathbb F_q$-basis of $\mathbb F_{q^n}$ if the latter is viewed as an $

Aditya Chincholi, Sylvain Capponi, Fabien Alet

We present a quantum Monte Carlo-based approach to detect and compute the most dominant correlations for many-body systems without prior knowledge. It is based on the measurement and analysis of the correlation density matrix between two (small) subsystems embedded in the full (large) sample. In order to benchmark this procedure, we investigate zero-temperat

Katlego Machethe, Pramod Sharma, Mukesh Kumar, Rafiqul Rahaman

The proposed Large Hadron Electron Collider (LHeC), with center-of-mass energy of $\sqrt{s}\approx 1.3$ TeV, provides a clean and sensitive environment to probe the top quark's neutral current interactions with the $Z$ boson via the process $e^- p \to e^- t \bar{t}$. We investigate the precision with which the Standard Model (SM) $t\bar{t}Z$ couplings-th

Rupert Li, Lampros Gavalakis, Ioannis Kontoyiannis

Following a growing number of studies that, over the past 15 years, have established entropy inequalities via ideas and tools from additive combinatorics, in this work we obtain a number of new bounds for the differential entropy of sums, products, and sum-product combinations of continuous random variables. Partly motivated by recent work by Goh on the disc

Christopher Gaul, Eduardo Fidalgo, Enrique Alegre, Rocío Alaiz Rodríguez

Accurate automatic screening of minors in unconstrained images requires models robust to distribution shift and resilient to the under-representation of children in public datasets. To address these issues, we propose a multi-task architecture with dedicated under/over-age discrimination tasks based on a frozen FaRL vision-language backbone joined with a com

Luyang Zhang, Cathy Jiao, Beibei Li, Chenyan Xiong

Training data is the backbone of large language models (LLMs), yet today's data markets often operate under exploitative pricing -- sourcing data from marginalized groups with little pay or recognition. This paper introduces a theoretical framework for LLM data markets, modeling the strategic interactions between buyers (LLM builders) and sellers (human

Denys Bulavka, Russ Woodroofe

We give a short and relatively elementary proof of the Hilton-Milner Theorem.

Hari Dahal, Wei Liu, Yangyang Xu

We give a damped proximal augmented Lagrangian method (DPALM) for solving problems with a weakly-convex objective and convex linear/nonlinear constraints. Instead of taking a full stepsize, DPALM adopts a damped dual stepsize to ensure the boundedness of dual iterates. We show that DPALM can produce a (near) $\vareps$-KKT point within $O(\vareps^{-2})$ outer

Gary Martinez-Nunez

We give a decomposition of the jacobian variety of a generalized Fermat curve. This extends a result obtained by Auffarth, Lucchini-Arteche and Rojas on Humbert-Edge curves, which are a particular case of generalized Fermat curves. (A counting on the number of factor has been added)

Yuly Billig, Henrique Rocha

We study differentiable holonomic sheaves of $AV$-modules on a smooth quasi-projective variety. We show that a simple differentiable holonomic sheaf $M$ of $AV$-modules is locally the tensor product of a simple holonomic $D$-module and a simple finite-dimensional $gl_n$-module $W$. In particular, in the case when $W$ is integrable, $M$ is the tensor product

Kai Yang, Ruoxia Chen, Anders K. R. Christensen, Mathieu Bauchy

The simulation of borosilicate glasses is challenging due to the composition and temperature dependent coordination state of boron atoms. Here, we present a newly developed machine learning optimized classical potential for molecular dynamics simulations that achieves transferability across diverse borosilicate glass compositions. Our potential accurately pr

Nicholas Cooper, Lijun Chen, Sailesh Dwivedy, Danna Gurari

Knowledge distillation (KD) methods can transfer knowledge of a parameter-heavy teacher model to a light-weight student model. The status quo for feature KD methods is to utilize loss functions based on logits (i.e., pre-softmax class scores) and intermediate layer features (i.e., latent representations). Unlike previous approaches, we propose a feature KD f

Ahmet Umur Özsoy

Calibration of option pricing models is routinely repeated as markets evolve, yet modern systems lack an operator for removing data from a calibrated model without full retraining. When quotes become stale, corrupted, or subject to deletion requirements, existing calibration pipelines must rebuild the entire nonlinear least-squares problem, even if only a sm

Soma Hirai, Ryoto Watanabe, Yuki Nishida, Masashi Iwasaki

This paper investigates the eigenvalue problem of integral operators whose kernels can be expressed as a finite sum of pairwise products of single-variable functions, making them separable. By consdiering the matrix form of the separable kernel in the integral operator, we establish the relationship between the eigenvalues and eigenfunctions of the integral

Andrea Bianchi

We introduce a symmetric monoidal $\infty$-category $\mathrm{GrCob}$ of graph cobordisms between spaces, and use the homology of its morphism spaces to define string operations. Precisely, for an $E_\infty$-ring spectrum $R$ and an oriented $d$-dimensional $R$-Poincar\'e duality space $M$, we construct a "graph field theory" $\mathrm{GFT}_M$, i.e. a symmetri

Xiangyu Li, Tianyi Wang, Junfeng Jiao, Christian Claudel

As autonomous vehicles (AVs) are increasingly deployed on public roads, understanding their real-world behaviors is critical for traffic safety analysis and regulatory oversight. However, many data-driven methods lack interpretability and cannot provide verifiable explanations of AV behavior in mixed traffic. This paper proposes SVBRD-LLM, a self-verifying b

Adam R. Smith

End-periodic homotopy equivalences of infinite, locally finite graphs serve as dimension-one analogs of the end-periodic automorphisms traditionally defined on infinite-type surfaces. We demonstrate that if $\Gamma$ is an infinite graph with finitely many ends, and $g \colon \Gamma \to \Gamma$ is end-periodic, then its mapping torus $Z_g$ admits a flowline-p

Sergio Cabello, Alexander Dobler, Gašper Fijavž, Thekla Hamm

A drawing of a graph is 1-planar if each edge participates in at most one crossing and adjacent edges do not cross. Up to symmetry, each crossing in a 1-planar drawing belongs to one out of six possible crossing types, where a type characterizes the subgraph induced by the four vertices of the crossing edges. Each of the 63 possible nonempty subsets $\mathca

Fanghao Zhang, Guanchun Rui, Yujie Quan, Shantal Adajian

Active thermal management requires advances in thermal switching materials, whose thermal conductivity responds to external stimuli. The electric field, as one of the most convenient and effective stimuli, has shown great potential in tuning the thermal conductivity of ferroelectric materials. While previous studies on electric-field-induced ferroelectric th

Andréia M. Basso, Wesley B. Cardoso

This work presents a dimensional reduction of Bose-Einstein condensates confined by generalized transverse potentials, parametrized by an exponent $n$. Starting from the three-dimensional Gross-Pitaevskii equation, we employ a variational ansatz to derive an effective one-dimensional nonpolynomial Schr\"odinger equation, which self-consistently determines th

W. Bradley Knox, Katie Bradford, Samanta Varela Castro, Desmond C. Ong

Amid the growing prevalence of human-AI interaction, large language models and other AI-based entities increasingly provide forms of companionship to human users. Such AI companionship -- i.e., bonded relationships between humans and AI systems that resemble the relationships people have with family members, friends, and romantic partners -- might substantia

Iván Felipe Benavides-Martínez, Cristiam Victoriano Portilla-Cabrera, Katherine E. Mills, Claire Enterline

Benthic habitat is challenging due to the environmental complexity of the seafloor, technological limitations, and elevated operational costs, especially in under-explored regions. This generates knowledge gaps for the sustainable management of hydrobiological resources and their nexus with society. We developed ECOSAIC (Ecological Compression via Orthogonal

Christian V. Guthier, Christopher E Kehayias, Cosmin Ciausu, Jordan O. Gasho

Importance: Coronary algorithm for cardiac sub structures and prospective real-time surveillance of cardiac dose exposure. Methods: Retro and prospective study to validate AI auto-segmentation. A 3D UNet was trained on 560 thoracic CT scans from a single institution (2003-2014) and validated internally (n=70). External validation was performed in 283 patient

Gbenga Omotara, Ramy Farag, Seyed Mohamad Ali Tousi, G. N. DeSouza

Transparent object perception remains a major challenge in computer vision research, as transparency confounds both depth estimation and semantic segmentation. Recent work has explored multi-task learning frameworks to improve robustness, yet negative cross-task interactions often hinder performance. In this work, we introduce Edge-Guided Spatial Attention (

Zanxu Wang, Homayoon Beigi

This paper addresses data quality issues in multimodal emotion recognition in conversation (MERC) through systematic quality control and multi-stage transfer learning. We implement a quality control pipeline for MELD and IEMOCAP datasets that validates speaker identity, audio-text alignment, and face detection. We leverage transfer learning from speaker and

Paul J Dauenhauer

The illumination of catalytic surfaces with a continuous or pulsed stream of photons dynamically modulates surface chemistry for faster rates, higher conversion, or product selectivity control. To establish fundamental principles of dynamic photon-modulated catalysis, the photocatalytic conversion of a generic surface reaction was simulated to understand the

Swagata Acharya, Dimitar Pashov, Daphne Lubert-Perquel, Mark van Schilfgaarde

A functional heterostructure is central to integrated circuitry in quantum photonics, optoelectronics, neuromorphic computing, spintronics, and straintronics. Recently, heterostructures combining 2D magnets and nonmagnetic transition metal dichalcogenides (TMDs) have been explored. In these, electron and hole wavefunctions are localized in 2D magnets but del