November 2025 arXiv papers — page 108

Wei Xu, Fan Yang, Qinyuan Cui, Zhi Chen

This paper investigates a project with stochastic activity durations and cash flows under discrete scenarios, where activities must satisfy precedence constraints generating cash inflows and outflows. The objective is to maximize expected net present value (NPV) by accelerating inflows and deferring outflows. We formulate the problem as a discrete-time Marko

S. P. de Alwis

We first note that, at least in perturbation theory, there is a well-defined (subject to regularization) Lorentzian definition of the quantum effective action in both flat and curved space including (perturbative) gravity. The advantage of the latter is that we do not need to deal with the conformal factor problems of Euclidean quantum gravity. We then make

Xi Zheng, Yinghui Huang, Xiangyu Chang, Ruoxi Jia

Rigorous valuation of individual data sources is critical for fair compensation in data markets, informed data acquisition, and transparent development of ML/AI models. Classical Data Shapley (DS) provides a essential axiomatic framework for data valuation but is constrained by its symmetry axiom that assumes interchangeability of data sources. This assumpti

Ke Wang, Qiang Zhang, Xuezhi Zhao

In this paper, we primarily investigate the following symmetric presentation of the surface group $\pi_1(\Sigma_g)=\left\langle c_1,\dots, c_{2g}\mid c_1\cdots c_{2g}c_1^{-1}\cdots c_{2g}^{-1}\right\rangle$. For every nontrivial element $x\in \pi_1(\Sigma_g)$, we obtain a uniform representation of the normal forms of $x^k$ under the length-lexicographical or

Wenxin Zhu, Andong Chen, Yuchen Song, Kehai Chen

With the remarkable success of Multimodal Large Language Models (MLLMs) in perception tasks, enhancing their complex reasoning capabilities has emerged as a critical research focus. Existing models still suffer from challenges such as opaque reasoning paths and insufficient generalization ability. Chain-of-Thought (CoT) reasoning, which has demonstrated sign

Yongjoo Jang, Sangwoo Hwang, Hojin Lee, Sangwoo Jung

The advancement of large language models has led to models with billions of parameters, significantly increasing memory and compute demands. Serving such models on conventional hardware is challenging due to limited DRAM capacity and high GPU costs. Thus, in this work, we propose offloading the single-batch token generation to a 3D NAND flash processing-in-m

S. V. Bolokhov

We study quasinormal modes of test scalar, electromagnetic, and Dirac fields in the background of a new analytic regular black-hole solution obtained as an exact solution of the Einstein equations sourced by a Dehnen-type matter distribution in [R. A. Konoplya, A. Zhidenko, arXiv:2511.03066]. The metric is asymptotically flat and characterized by a simple la

Hao-Song Li

We present a systematic calculation of the axial charges and magnetic moments for the decuplet of hidden-charm molecular pentaquarks within the framework of the constituent quark model. Our findings reveal that the axial charges of these states are comparable in magnitude to that of the nucleon. Furthermore, we find that their magnetic moments obey a set of

Noah Siekierski, Kausthubh Chandramouli, Christian Kümmerle, Bojko N. Bakalov

Quantum state tomography (QST) is an indispensable tool for characterizing many-body quantum systems. However, due to the exponential scaling of the cost of the protocol with system size, many approaches have been developed for quantum states with specific structure, such as low-rank states. In this paper, we show how approximate message passing (AMP), an al

Marc Stromberg

Explicit details are presented for calculation of $A^+B$, $A^+AB$ and $AA^+B$ where $A_{m\times n}$ is any nonzero matrix, $A^+$ is the Moore-Penrose pseudoinverse of $A$ and $B$ is any matrix of appropriate dimensions, where the quantities in question are found using only the storage originally allocated to the matrices $A$ and $B$ (together with some simpl

Jihoon Moon

Deep neural networks achieve state of the art performance but remain difficult to interpret mechanistically. In this work, we propose a control theoretic framework that treats a trained neural network as a nonlinear state space system and uses local linearization, controllability and observability Gramians, and Hankel singular values to analyze its internal

Kang Yin, Hye-Bin Shin

Clinical electroencephalogram (EEG) reports encode domain-specific linguistic conventions that general-purpose language models (LMs) fail to capture. We introduce NeuroLex, a lightweight domain-adaptive language model trained purely on EEG report text from the Harvard Electroencephalography Database. Unlike existing biomedical LMs, NeuroLex is tailored to th

Saranzaya Magsarjav, Jonathan Tuke, Lewis Mitchell, Melissa Humphries

Topic models are often judged by the consistency of their outputs across repeated runs, implicitly assuming that repeatable topic output is a successful recovery of the underlying topics. We show that this assumption is false: repeatability is not recovery. We introduce a stability framework that jointly measures consistency among repeated runs and accuracy

Di. Wang, Yongjin. Li

The criterion for a point in the unit ball to be a strongly exposed point is given. The necessity and sufficiency conditions for Orlicz-Lorentz spaces to possess strongly exposed property are given. Besides, some useful methods are obtained to handle issues related to decreasing rearrangement.

Max M. Sun, Todd Murphey

Generative model-based imitation learning methods have recently achieved strong results in learning high-complexity motor skills from human demonstrations. However, imitation learning of interactive policies that coordinate with humans in shared spaces without explicit communication remains challenging, due to the significantly higher behavioral complexity i

Toru Kitagawa, Yizhou Kuang

Leaving posterior sensitivity concerns aside, non-identifiability of the parameters does not raise a difficulty for Bayesian inference as far as the posterior is proper, but multi-modality or flat regions of the posterior induced by the lack of identification leaves a challenge for modern Bayesian computation. Sampling methods often struggle with slow or non

Zelin Zhu, Yancheng Huang, Kai Yang

Online change detection (OCD) aims to rapidly identify change points in streaming data and is critical in applications such as power system monitoring, wireless network sensing, and financial anomaly detection. Existing OCD methods typically assume precise system knowledge, which is unrealistic due to estimation errors and environmental variations. Moreover,

Julia Santaniello, Matthew Russell, Benson Jiang, Donatello Sassaroli

Reinforcement Learning from Human Feedback (RLHF) is a methodology that aligns agent behavior with human preferences by integrating user feedback into the agent's training process. This paper introduces a framework that guides agent training through implicit neural signals, with a focus on the neural classification problem. Our work presents and releases a n

Zhang Junye, Zheng Hongyu, Cheng Jingran, Zhang Shengli

This study introduces a novel iron-based gas diffusion electrode-photocatalytic system aimed at enhancing the degradation of phenolic compounds in wastewater. Phenolic compounds are toxic environmental pollutants with significant resistance to biodegradation. The traditional methods for treating phenol wastewater, including biological treatments and adsorpti

Mengyi Chen, Pengru Huang, Kostya S. Novoselov, Qianxiao Li

Macroscopic dynamical descriptions of complex physical systems are crucial for understanding and controlling material behavior. With the growing availability of data and compute, machine learning has become a promising alternative to first-principles methods to build accurate macroscopic models from microscopic trajectory simulations. However, for spatially

Shuning Zhang, Yijing Liu, Yuyu Liu, Ying Ma

Pervasive data collection by Smart Home Devices (SHDs) demands robust Privacy Protection Mechanisms (PPMs). The effectiveness of many PPMs, particularly user-facing controls, depends on user awareness and adoption, which are shaped by manufacturers' public documentations. However, the landscape of academic proposals and commercial disclosures remains underex

S. Al Kharusi, G. Anton, I. Badhrees, P. S. Barbeau

EXO-200 was a leading double beta decay experiment consisting of a single-phase, enriched liquid xenon time projection chamber filled with an admixture of 80.672% $^{136}$Xe and 19.098% $^{134}$Xe. The detector operated at WIPP between 2010 and 2018 and was designed to search for double beta decay of $^{136}$Xe. Data was acquired in two phases separated by a

Yating Zou, Batuhan Keskin, Gregor G. Taylor, Zenghui Li

Quantum technologies offer unprecedented capabilities in computation and secure information transfer. Their implementation requires qubits to operate at cryogenic temperatures (CT) while control and readout electronics typically still remains at room temperature (RT). As systems scale to millions of qubits, the electronics should also operate at CT to avoid

A. Toksumakov, G. Ermolaev, D. Grudinin, A. Slavich

Graphite is a cornerstone material for revolutionary technologies, from energy storage to the entire field of two-dimensional materials. Despite its foundational role, the predictive power required for engineering emergent optical behavior in van der Waals heterostructures is severely constrained by persistent discrepancies in reported optical constants. We

Jiangyifei Zhu, Yuzhe Wang, Tao Qiang, Vu Phan

In this paper, our goal is to enable quantitative feedback on muscle fatigue during exercise to optimize exercise effectiveness while minimizing injury risk. We seek to capture fatigue by monitoring surface vibrations that muscle exertion induces. Muscle vibrations are unique as they arise from the asynchronous firing of motor units, producing surface micro-

Gabriel Brandão de Gracia, Rodolfo José Bueno Rogerio

Throughout this paper, we conduct our discussion by a partial review of Elko's Hermiticity, introducing the Hermitian formulation for interacting mass dimension one fermions based on Elko spinor. It includes pivotal observations about renormalizability and the study of some allowed interactions. Beyond these points, since dark-matter phenomenology is mai

V. K. Oikonomou

In this work we introduce the concept of self-interacting dark matter with scale-dependent equation of state, in the context of which dark matter is collisional and its equation of state is radius-dependent and has the form $P(r)=K(r)\left(\frac{ρ(r)}{ρ_{\star}}\right)^{γ(r)}$. We confronted the effectively 2-parameter model with 174 galaxies from the SPARC

Ariel G. Sanchez, Andrés N. Ruiz, Facundo Rodriguez, Carlos Correa

We present Aletheia, a new emulator of the non-linear matter power spectrum, $P(k)$, built upon the evolution mapping framework. This framework addresses the limitations of traditional emulation by focusing on $h$-independent cosmological parameters, which can be separated into those defining the linear power spectrum shape ($\mathbfΘ_{\mathrm{s}}$) and thos

Nitish Kumar Chandra, Eneet Kaur, Kaushik P. Seshadreesan

Fault tolerant quantum computation over distributed quantum computing (DQC) platforms requires careful evaluation of resource requirements and noise thresholds. As quantum hardware advances toward modular and networked architectures, various fault tolerant DQC schemes have been proposed, which can be broadly categorized into three architectural types. Type 1

Torec T. Luik, Joost de Folter, Rodrigo Rosas-Bertolini, Eric A. J. Reits

We present BIOMERO 2.0, a major evolution of the BIOMERO framework that transforms OMERO into a FAIR-compliant (findable, accessible, interoperable, and reusable), provenance-aware bioimaging platform. BIOMERO 2.0 integrates data import, preprocessing, analysis, and workflow monitoring through an OMERO.web plugin and containerized components. The importer su

Siyang Cheng, Gaotian Liu, Rui Mei, Yilin Wang

The rapid adoption of large language models (LLMs) has brought both transformative applications and new security risks, including jailbreak attacks that bypass alignment safeguards to elicit harmful outputs. Existing automated jailbreak generation approaches e.g. AutoDAN, suffer from limited mutation diversity, shallow fitness evaluation, and fragile keyword

Alice P. G. Hall, Carlos H. S. Vieira, Jonas F. G. Santos

Gaussian quantum states and channels are pivotal across many branches of quantum science and their applications, including the processing and storage of quantum information, the investigation of thermodynamics in the quantum regime, and quantum computation. The great advantage is that Gaussian states are experimentally accessible via their first and second s

Nina Codreanu, Tim Turan, Daniel Bedialauneta Rodriguez, Matteo Pasini

The tin-vacancy center in diamond (SnV) has emerged as a compelling building block for realizing next-generation quantum networks thanks to its excellent optical and spin properties. Coupling to photonic crystal cavities (PCCs) promises to further enhance the SnV light-matter interface and unlock a diverse range of entanglement generation protocols. Recent p

M. Molero, A. Arcones, F. Montes, C. J. Hansen

The astrophysical site(s) of the r-process are uncertain, with candidates such as neutron star mergers and magneto-rotational supernovae predicting different event rates, delay times, and heavy-element yields. Galactic chemical evolution models constrain these properties by comparing model predictions with observed abundances. We explore, in a systematic and

Xiaobin Rong, Qinwen Hu, Mansur Yesilbursa, Kamil Wojcicki

Generative models have shown remarkable performance in speech enhancement (SE), achieving superior perceptual quality over traditional discriminative approaches. However, existing generative SE approaches often overlook the risk of hallucination under severe noise, leading to incorrect spoken content or inconsistent speaker characteristics, which we term lin

Jitendra Dhakar, Ram Prakash Bharti

Electrokinetic flows in microchannels with opposite charge asymmetry, i.e., unequal and contrasting surface charges on opposing channel walls, significantly influence microfluidic hydrodynamics and can be exploited for enhanced control of mass transfer, mixing, and heat transport in microfluidic applications. This study numerically investigates electroviscou

Tatsuya Shirato, Ryota Yambe, Satoru Hayami

We theoretically investigate the effects of time-dependent electromagnetic fields on frustrated magnets with the spatial inversion symmetry. Two types of external-field setups are considered: One is a circularly polarized electromagnetic field and the other is a combination of a circularly polarized electric field and a static magnetic field. The system is m

Michal Levin, Itzik Klein

Low-cost micro-electromechanical accelerometers are widely used in navigation, robotics, and consumer devices for motion sensing and position estimation. However, their performance is often degraded by bias errors. To eliminate deterministic bias terms a calibration procedure is applied under stationary conditions. It requires accelerom- eter leveling or com

Rosario Napoli, Giovanni Lonia, Antonio Celesti, Massimo Villari

Traditional Machine Learning (ML) methods require large amounts of data to perform well, limiting their applicability in sparse or incomplete scenarios and forcing the usage of additional synthetic data to improve the model training. To overcome this challenge, the research community is looking more and more at Graph Machine Learning (GML) as it offers a pow

Lekhraj Malviya, Ram Prakash Bharti, Abhishek Kumar Lal

This study numerically examines the steady unconfined laminar flow of incompressible non-Newtonian power-law fluids past a pair of side-by-side counter-rotating circular cylinders using the finite element method. The cylinders simultaneously rotate at equal angular speeds in opposite directions, with the upper cylinder (UC) rotating clockwise and the lower c

Hanxu Wang, Tao Yang, Yicheng Chi, Zhenyu Zhang

The triple-flame system serves as the fundamental unit for understanding multi-flame interactions, revealing critical coupling mechanisms that scale to complex burner arrays. In this study, we investigated triple flame oscillators, consisting of three flickering laminar buoyant diffusion flames arranged in an isosceles triangular configuration, to construct

H. P. Veiga, D. R. Pinheiro, J. P. Santos Pires, J. M. Viana Parente Lopes

We introduce a linear-scaling stochastic method to compute real-space maps of any positive local spectral operator in a tight-binding model. By employing positive-definite estimators, the sampling error at each site can be rigorously bounded relative to the mean via the Markov inequality, overcoming the lack of self-averaging and enabling accurate estimates

A. V. Trifonov, M. O. Nestoklon, M. -A. Hollberg, S. Grisard

Exciton-phonon interactions govern the energy level spectrum and thus the optical response in semiconductors. In this respect, lead-halide perovskite nanocrystals represent a unique system, for which the interaction with optical phonons is particularly strong, giving rise to a ladder of multiple exciton states which can be optically excited with femtosecond

Moritz Blumenthal, Martin Uecker

Purpose: Phase singularities are a common problem in image reconstruction with auto-calibrated sensitivities due to an inherent ambiguity of the estimation problem. The purpose of this work is to develop a method for detecting and correcting phase poles in non-linear inverse (NLINV) reconstruction of MR images and coil sensitivity maps. Methods: Phase poles

A. A. Araújo Filho, N. Heidari, Iarley P. Lobo, V. B. Bezerra

In this paper, we examine the physical consequences of a recently introduced black hole solution in bumblebee gravity [1]. The geometry is first presented and then reformulated through suitable coordinate adjustments, which make its global conical character evident. We then study the propagation of particles by solving the geodesic equations for null and tim

Rongqin Chen, Fan Mo, Pak Lon Ip, Shenghui Zhang

Higher-order Graph Neural Networks (HOGNNs) based on the 2-FWL test achieve superior expressivity by modeling 2- and 3-node interactions, but at $\mathcal{O}(n^3)$ computational cost. However, this computational burden is typically mitigated by existing efficiency methods at the cost of reduced expressivity. We propose \textbf{Co-Sparsify}, a connectivity-aw

Laszlo B. Kish

Exponential parallelism, a defining principle of advanced computational systems, holds promise for transformative impacts across several scientific and industrial domains. This feature paper provides a comparative overview of Quantum Computing (QC) and Instantaneous Noise-based Logic (INBL), focusing on their practical strengths, limitations, and application

Waheed U. Bajwa, Mert Gurbuzbalaban, Mustafa Ali Kutbay, Lingjiong Zhu

Sampling from a target distribution induced by training data is central to Bayesian learning, with Stochastic Gradient Langevin Dynamics (SGLD) serving as a key tool for scalable posterior sampling and decentralized variants enabling learning when data are distributed across a network of agents. This paper introduces DIGing-SGLD, a decentralized SGLD algorit

Thomas A. Schmidt

We prove a conjecture of Calta, Kraaikamp and the author: For all $n\ge 3$, each member of their one-parameter family of interval maps, denoted $T_{3,n,\alpha}$, has its `first expansive return map' of natural extension given by the first return map under the geodesic flow to a section of the unit tangent bundle of the hyperbolic surface uniformized by the u

Rohit Kundu, Vishal Mohanty, Hao Xiong, Shan Jia

The proliferation of generative AI has led to hyper-realistic synthetic videos, escalating misuse risks and outstripping binary real/fake detectors. We introduce SAGA (Source Attribution of Generative AI videos), the first comprehensive framework to address the urgent need for AI-generated video source attribution at a large scale. Unlike traditional detecti

Niranjan Chebrolu, Kokil Jaidka, Gerard Christopher Yeo

Complex social behaviors, such as empathy and strategic politeness, are widely assumed to resist the directional decomposition that makes activation steering effective for coarse attributes like sentiment or toxicity. We present STAR: Steering via Attribution and Representation, which tests this assumption by using attribution patching to identify the layer-

John Drummond, Wayne Orchiston, Carolyn Brown, Jonathan Horner

C/1910 A1 was one of the Great Comets of the twentieth century. Although it was widely observed from the Northern Hemisphere, it was first discovered by observers south of the Equator. The comet arrived just months before the widely anticipated apparition of Comet 1P/Halley and was significantly more spectacular. As a result, the two comets were confused, an

Joanna Klauser, Bruno Albert, Christian Lindenmeier, Andreas Hammer

Personal communication using technical means is protected by telecommunications secrecy. Any interference with this fundamental right requires a legal basis, which has existed for many years for traditional communication services in the form of telecommunications surveillance (TK\"U, {\S} 100a StPO) and appears to be widely accepted by society. The basis for

Michael Chen, Raghav Kansal, Abhijith Gandrakota, Zichun Hao

We present a systematic evaluation of representation learning objectives for particle physics within a unified framework. Our study employs a shared transformer-based particle-cloud encoder with standardized preprocessing, matched sampling, and a consistent evaluation protocol on a jet classification dataset. We compare contrastive (supervised and self-super

Mohammad Marufur Rahman, Guanchu Wang, Kaixiong Zhou, Minghan Chen

Catastrophic forgetting is a longstanding challenge in continual learning, where models lose knowledge from earlier tasks when learning new ones. While various mitigation strategies have been proposed for Multi-Layer Perceptrons (MLPs), recent architectural advances like Kolmogorov-Arnold Networks (KANs) have been suggested to offer intrinsic resistance to f

Ayush Chaudhary, Sisir Doppalpudi

The deployment of robust malware detection systems in big data environments requires careful consideration of both security effectiveness and computational efficiency. While recent advances in adversarial defenses have demonstrated strong robustness improvements, they often introduce computational overhead ranging from 4x to 22x, which presents significant c

Sahel Vahedi Noori, Bin Hu, Geir Dullerud, Peter Seiler

This paper analyzes internal stability of a discrete-time feedback system with a ReLU nonlinearity. This feedback system is motivated by recurrent neural networks. We first review existing static quadratic constraints (QCs) for slope-restricted nonlinearities. Next, we derive hard integral quadratic constraints (IQCs) for scalar ReLU by using finite impulse

Yuan Zhong, Gang Chen, Paul A. Taylor, Jian Kang

Bayesian spatial modeling provides a flexible framework for whole-brain fMRI analysis by explicitly incorporating spatial dependencies, overcoming the limitations of traditional massive univariate approaches that lead to information waste. In this work, we introduce SIMBA, a Scalable Image Modeling using a Bayesian Approach, for group-level fMRI analysis, wh

Youhei Masada, Tomoya Takiwaki, Nobumitsu Yoko

We perform three-dimensional hydrodynamic simulations of two idealized regimes of stellar convection: a cooling-driven model (Model C) and an entropy-gradient-driven model (Model S). The two regimes exhibit striking contrasts: while Model S develops large, relatively stationary eddies excited at depth, Model C is dominated near the surface by intermittent pl

Sajed Jalil, Shuvo Saha, Hossain Mohammad Seym

Over the past few years, improving LLM code generation capabilities has been a key focus in NLP research. Despite Bengali having 242 million native speakers worldwide, it receives little attention when it comes to training LLMs. More recently, various fine-tuning and augmented generation techniques have been employed to significantly enhance code generation

Euzeli C. dos Santos, Tracey Birdwell

AI has redefined the boundaries of assistance in education, often blurring the line between guided learning and dependency. This paper revisits Vygotsky's Zone of Proximal Development (ZPD) through the lens of the P2P Teaching framework. By contrasting temporary scaffolding with the emerging phenomenon of permanent digital mediation, the study introduces the

Ruiyu Wang, Yuzhang Xie, Xiao Hu, Carl Yang

Assessing journal impact is central to scholarly communication, yet existing open resources rarely capture how collaboration structures and artificial intelligence (AI) research jointly shape venue prestige in biomedicine. We present BioMedJImpact, a large-scale, biomedical-oriented dataset designed to advance journal-level analysis of scientific impact and

Shota Inagawa, Hiroki Hata, Shigefumi Hata

This study investigates the synchronization dynamics of coupled-oscillator systems in which some of the oscillators are damaged and lose their autonomous oscillations. The damaged elements are modeled using damped oscillators; thus, the system is composed of both limit-cycle oscillators and damped oscillators. In this system, as is commonly observed in conve

Aditya Kumar, Arash Yavari

In this paper, we formulate a geometric theory of the mechanics of arterial growth. An artery is modeled as a finite-length thick shell that is made of an incompressible nonlinear anisotropic solid. An initial radially-symmetric distribution of finite radial and circumferential eigenstrains is assumed. Bulk growth is assumed to be isotropic. A novel framewor

Brianna N Ryan, Harold Douglas Pinckney D Pinckney, Michael P Short, Joseph A Formaggio

The potential to use neutrinos for nuclear non-proliferation has been heavily debated due to the tension between production abundance and low interaction rate. A newly detected neutrino interaction channel, coherent elastic neutrino-nucleus scattering (CE$\nu$NS), could potentially end this debate due to its improved cross-section compared to other neutrino

Shasha Zhou, Mingyu Huang, Jack Cole, Charles Britton

The recent proliferation of large language models (LLMs) holds the potential to revolutionize healthcare, with strong capabilities in diverse medical tasks. Yet, deploying LLMs in high-stakes healthcare settings requires rigorous verification and validation to understand any potential harm. This paper investigates the reliability and viability of using medic

Gregory J. Gilbert, Judah Van Zandt, Erik A. Petigura, Steven Giacalone

To date, hundreds of sub-stellar objects with masses between $1-80\ M_{\rm Jup}$ have been detected orbiting main-sequence stars. The current convention uses the deuterium-burning limit, $M_c \approx 13 M_{\rm Jup}$ to divide this population between giant planets and brown dwarfs. However, this classification heuristic is largely divorced from any formation

Snehinh Sen

In this paper, we explore semirings in which all congruences are finitely generated. Such semirings are dubbed \textit{Congruence Noetherian}. After developing sufficient background and examples, we focus on the canonical positive models of a real order and show that this obvious choice, though not finitely generated as an $\N$-module, is both Congruence Noe

Yuejun Shen, Zhiqiao Jiang, Yunfan Huang, Brittany M. Cleary

In a finite-time continuous phase transition, topological defects emerge as the system undergoes spontaneous symmetry breaking. The Kibble-Zurek mechanism predicts how the defect density scales with the quench rate. During such processes, dissipation also arises as the system fails to adiabatically follow the control protocol near the critical point. Quantif

Sanjay Bhandarkar, Debarshi Mitra, Jürgen Horbach, Apratim Chatterji

Under high cylindrical confinement, segments of ring polymers can be localized along the long axis of the cylinder by introducing internal loops within the ring polymer. The emergent organization of the polymer segments occurs because of the entropic repulsion between internal loops. These principles were used to identify the underlying mechanism of bacteria

Min Shi

Cases of Deligne's companion conjecture for normal schemes over finite fields have been proven by L. Lafforgue, Drinfeld, and Zheng in recent years: L. Lafforgue proved the conjecture for curves, Drinfeld proved the conjecture for all smooth schemes and later also for representations valued in a reductive group, and Zheng proved Deligne's conjecture for smoo

Kensuke Arakawa, Daniel Carranza, Chris Kapulkin

We construct the covariant and the cocartesian model structures on the slice categories of cubical sets and marked cubical sets, respectively. As an application, we derive a version of the Bousfield-Kan formula for arbitrary cofibrantly generated monoidal model categories satisfying Muro's axiom.

Omar Adalat, Francesco Belardinelli

Specifying informative and dense reward functions remains a pivotal challenge in Reinforcement Learning, as it directly affects the efficiency of agent training. In this work, we harness the expressive power of quantitative Linear Temporal Logic on finite traces (($\text{LTL}_f[\mathcal{F}]$)) to synthesize reward monitors that generate a dense stream of rew

Giuseppina Nigro, Francesco Berrilli, Giuseppe Bono, Dario Del Moro

Rapidly rotating late M dwarfs are observed in two different branches of magnetic activity, although they operate in the same stellar parameter range. Current empirical evidence indicates that M dwarfs with spectral types ranging from M3 / M4 to late-type M dwarfs, stellar masses smaller than 0.15 M$_\odot$, and rotational period shorter than four days displ

Haoxi Hu

This article gives a new upper bound for the resurgence number of symbolic powers of matroidal configuration in the following situations: the height of the matroidal configuration is big, or the height is small, and the corresponding simplicial complex of the matroidal configuration is peaked. The Peaked simplicial complex is a generalization of bipartite gr

Noriaki Sato, Marco Scutari, Shuichi Kawano, Rui Yamaguchi

Estimating causal networks from biological data is a critical step in systems biology. When evaluating the inferred network, assessing the networks based on their intervention effects is particularly important for downstream probabilistic reasoning and the identification of potential drug targets. In the context of gene regulatory network inference, biologic

Ali Falahati, Mohammad Mohammadi Amiri, Kate Larson, Lukasz Golab

In self-consuming generative models that train on their own outputs, alignment with user preferences becomes a recursive rather than one-time process. We provide the first formal foundation for analyzing the long-term effects of such recursive retraining on alignment. Under a two-stage curation mechanism based on the Bradley-Terry (BT) model, we model alignm

Yu-Han Huang, Venugopal V. Veeravalli

A finite-horizon variant of the quickest change detection (QCD) problem that is of relevance to learning in non-stationary environments is studied. The metric characterizing false alarms is the probability of a false alarm occurring before the horizon ends. The metric that characterizes the delay is \emph{latency}, which is the smallest value such that the p

John M. Mango, Ronald Katende

We study when a single linear sketch can control the largest and smallest nonzero singular values of every rank-$r$ matrix. Classical oblivious embeddings require $s=\Theta(r/\varepsilon^{2})$ for $(1\pm\varepsilon)$ distortion, but this does not yield constant-factor control of extreme singular values or condition numbers. We formalize a conjecture that $s=

Andrew Zhou

Accurate segmentation of brain tumors is vital for diagnosis, surgical planning, and treatment monitoring. Deep learning has advanced on benchmarks, but two issues limit clinical use: no uncertainty estimates for errors and no segmentation of healthy brain structures around tumors for surgery. Current methods fail to unify tumor localization with anatomical

B. Fazekas, I. Fazekas

In this paper, we introduce a convergence notion for ordered selections. Our convergence notion is based on subpermutation densities and convergences of the marginal distributions. A particular case of this convergence is the well-known convergence of permutation sequences. We also introduce a family of probability measures called generalized permutons. We s

Rylan Malarchick

Numerical optimal control (GRAPE) can in principle discover pulse shapes that suppress all coherent gate error to machine precision. But when does that capability actually matter? We present a systematic comparison of Gaussian, DRAG, and GRAPE pulses for single-qubit gates on a three-level transmon model parameterized by IQM Garnet hardware ($T_1 = 37\,\mu$s

Juan C. Rocha, Caroline Schil, Emilie A. L. Lindkvist, Reinette Biggs

Current research challenges in sustainability science require us to consider nonlinear changes e.g. shifts that do not happen gradually but can be sudden and difficult to predict. Central questions are therefore how we can prevent harmful shifts, promote desirable ones, and better anticipate both. The regime shifts and transformations literature is well-equi

Nathan Breslow, Aayush Mishra, Mahler Revsine, Michael C. Schatz

In-context learning (ICL) -- the capacity of a model to infer and apply abstract patterns from examples provided within its input -- has been extensively studied in large language models trained for next-token prediction on human text. In fact, prior work often attributes this emergent behavior to distinctive statistical properties in human language. This ra

Koka Sathwik, Werner Krauth

We reconsider real-space renormalization for the two-dimensional Ising model, following the path traced out by Wilson in Sect. VI of his 1975 Reviews of Modern Physics. In that reference, Wilson considerably extended the Kadanoff decimation procedure towards a possibly rigorous construction of a real-space scale-invariant hamiltonian. Wilson's construction h

Bowen He, Xiaoan Xu, Alper Kamil Bozkurt, Vahid Tarokh

Solving Inductive Logic Programming (ILP) problems with neural networks is a key challenge in Neural-Symbolic Ar- tificial Intelligence (AI). While most research has focused on designing novel network architectures for individual prob- lems, less effort has been devoted to exploring new learning paradigms involving a sequence of problems. In this work, we in

Mohamad A. Hady, Siyi Hu, Mahardhika Pratama, Zehong Cao

This work investigates resource optimization in heterogeneous satellite clusters performing autonomous Earth Observation (EO) missions using Reinforcement Learning (RL). In the proposed setting, two optical satellites and one Synthetic Aperture Radar (SAR) satellite operate cooperatively in low Earth orbit to capture ground targets and manage their limited o

Dahao Tang, Nan Yang, Yanli Li, Zhiyu Zhu

Selecting an appropriate look-back horizon remains a fundamental challenge in time series forecasting (TSF), particularly in the federated learning scenarios where data is decentralized, heterogeneous, and often non-independent. While recent work has explored horizon selection by preserving forecasting-relevant information in an intrinsic space, these approa

Yi Xia

The breakdown of the quasiparticle approximation (QPA) for phonons in strongly anharmonic materials necessitates advanced first-principles frameworks for accurate lattice dynamics and thermal transport predictions. We develop a comprehensive beyond-quasiparticle approximation (BQPA) approach incorporating both three- (3ph) and four-phonon (4ph) interactions

Rubén Darío Guerrero

The semiconductor industry faces a computational crisis in extreme ultraviolet (EUV) lithography optimization, where traditional methods consume billions of CPU hours while failing to achieve sub-nanometer precision. We present a physics-constrained adaptive learning framework that automatically calibrates electromagnetic approximations through learnable par

José Carlos M. Silva, Diogo H. Silva, Wesley Cota, Francisco A. Rodrigues

Bridging nodes, which connect critical components of a network, play an important role in maintaining structural integrity and facilitating communication within the network, representing indirect yet relevant connections. Epidemic triggering mechanisms in networks often involve long-range mutual activation of hubs, mediated by paths composed of low-degree no

Kristina Bukina, Dima L. Shepelyansky

We study the process of opinion formation in an Ising social network of scientific collaborations. The network is undirected. An Ising spin is associated with each network node being oriented up (red) or down (blue). Certain nodes carry fixed, opposite opinions whose influence propagates over the other spins, which are flipped according to the majority-influ

Maria Larchenko, Dmitry Guskov, Alexander Lobashev, Georgy Derevyanko

Color harmonization adjusts the colors of an inserted object so that it perceptually matches the surrounding image, resulting in a seamless composite. The harmonization problem naturally arises in augmented reality (AR), yet harmonization algorithms are not currently integrated into AR pipelines because real-time solutions are scarce. In this work, we addres

Jingru Huang, Haijie Xu, Manrui Jiang, Chen Zhang

Bayesian optimization (BO) has been widely used to optimize expensive and gradient-free objective functions across various domains. However, existing BO methods have not addressed the objective where both inputs and outputs are functions, which increasingly arise in complex systems as advanced sensing technologies. To fill this gap, we propose a novel functi

Thomas Rivasseau

Current Large Language Model alignment research mostly focuses on improving model robustness against adversarial attacks and misbehavior by training on examples and prompting. Research has shown that LLM jailbreak probability increases with the size of the user input or conversation length. There is a lack of appropriate research into means of strengthening

Cristina Fernandes, Carlos Hoppen, George Kontogeorgiou, Guilherme Oliveira Mota

A strongly separating path system in a graph $G$ is a collection $\mathcal{P}$ of paths in $G$ such that, for every two edges $e$ and $f$ of $G$, there is a paths in $\mathcal{P}$ with $e$ and not $f$, and vice-versa. The minimum number of such a system is the so called strong separation number of $G$. We prove that the strong separation number of every $2$-

Matthew W. Kenaston, Umair Ayub, Mihir Parmar, Muhammad Umair Anjum

Despite high performance on clinical benchmarks, large language models may reach correct conclusions through faulty reasoning, a failure mode with safety implications for oncology decision support that is not captured by accuracy-based evaluation. In this two-cohort retrospective study, we developed a hierarchical taxonomy of reasoning errors from GPT-4 chai

Thomas Sales, Iain Smears

We analyse fully nonlinear second-order mean field games (MFG) with nondifferentiable Hamiltonians, which take the form of a coupled system of a fully nonlinear Hamilton-Jacobi-Bellman equation and a Kolmogorov-Fokker-Planck partial differential inclusion (PDI) featuring the set-valued subdifferential of the Hamiltonian. We show the existence of solutions of

Zhenshuo Zhang, Minxuan Duan, Youran Ye, Hongyang R. Zhang

We study the problem of efficiently estimating policies that simultaneously optimize multiple objectives in reinforcement learning (RL). Given $n$ objectives (or tasks), we seek the optimal partition of these objectives into $k \ll n$ groups, where each group comprises related objectives that can be trained together. This problem arises in applications such

Vignesh Rajagopal, Kasun Weerakoon Kulathun Mudiyanselage, Gershom Devake Seneviratne, Pon Aswin Sankaralingam

We present DR. Nav (Dead-End Recovery-aware Navigation), a novel approach to autonomous navigation in scenarios where dead-end detection and recovery are critical, particularly in unstructured environments where robots must handle corners, vegetation occlusions, and blocked junctions. DR. Nav introduces a proactive strategy for navigation in unmapped environ