October 2025 arXiv papers — page 18

Zhifei Luo, Lin Li, Xiaohui Tao, Kaize Shi

The proliferation of signed networks in contemporary social media platforms necessitates robust privacy-preserving mechanisms. Graph unlearning, which aims to eliminate the influence of specific data points from trained models without full retraining, becomes particularly critical in these scenarios where user interactions are sensitive and dynamic. Existing

Alex Shamis, Matt Stephenson, Linfeng Zhou

Blockchains face inherent limitations when communicating outside their own ecosystem, largely due to the Byzantine Fault Tolerant (BFT) 3f+1 security model. Trusted Execution Environments (TEEs) are a promising mitigation because they allow a single trusted broker to interface securely with external systems. This paper develops a cost-of-collusion principal-

Alessandro Zito, Giovanni Parmigiani, Jeffrey W. Miller

Mutational signatures are powerful summaries of the mutational processes altering the DNA of cancer cells. The usual approach to mutational signature analysis consists of decomposing the matrix of mutation counts from a sample of patients using non-negative matrix factorization (NMF). However, this ignores the heterogeneous patterns of mutation rates along t

Fazel Arasteh, Arian Haghparast, Manos Papagelis

Traffic congestion in urban road networks leads to longer trip times and higher emissions, especially during peak periods. While the Shortest Path First (SPF) algorithm is optimal for a single vehicle in a static network, it performs poorly in dynamic, multi-vehicle settings, often worsening congestion by routing all vehicles along identical paths. We addres

Kensho Araya, Kazuhiro Ishige

We study the one-dimensional one-phase Stefan problem for the heat equation with a nonlinear boundary condition. We show that all solutions fall into one of three distinct types: global-in-time solutions with exponential decay, global-in-time solutions with non-exponential decay, and finite-time blow-up solutions. The classification depends on the size of th

V. Sanchez Padilla, Albert Espinal, Jennifer M. Case, Jose Cordova-Garcia

ABET accreditation is an increasingly prominent system of global accreditation of engineering programs, and the assessment requires programs to demonstrate that they meet the needs of the program's stakeholders, typically industrial potential employers of graduates. To obtain these inputs, programs are required to assemble an advisory committee board. The vi

Zheng Zhang, Haonan Li, Xingyu Li, Hang Zhang

Bug bisection has been an important security task that aims to understand the range of software versions impacted by a bug, i.e., identifying the commit that introduced the bug. However, traditional patch-based bisection methods are faced with several significant barriers: For example, they assume that the bug-inducing commit (BIC) and the patch commit modif

Di-Chang Chen, Ji-Wei Xie, Ji-Lin Zhou, Fei Dai

The discovery of hot Jupiters has challenged the classical planet formation theory. Although various formation mechanisms have been proposed, the dominant channel and relative contributions remain unclear. Furthermore, hot Jupiters offer a unique opportunity to test tidal theory and measure the fundamental tidal quality factor, which is yet to be well-constr

Yuhua Jiang, Shuang Cheng, Yihao Liu, Ermo Hua

Large Language Models (LLMs) excel at general language tasks but struggle in specialized domains. Specialized Generalist Models (SGMs) address this by preserving broad capabilities while adapting to target domains. However, existing architectures provide limited support for task-guided specialized memory mechanisms. In this work, we introduce Nirvana, an SGM

Fan Yang, Lingyao Li, Yaxin Hu, Michael Rodgers

Robots with anthropomorphic features are increasingly shaping how humans perceive and morally engage with them. Our research investigates how different levels of anthropomorphism influence protective responses to robot abuse, extending the Computers as Social Actors (CASA) and uncanny valley theories into a moral domain. In an experiment, we invite 201 parti

Fan Yang, Renkai Ma, Yaxin Hu, Michael Rodgers

Social robots like Moxie are designed to form strong emotional bonds with children, but their abrupt discontinuation can cause significant struggles and distress to children. When these services end, the resulting harm raises complex questions of who bears responsibility when children's emotional bonds are broken. Using the Moxie shutdown as a case study thr

Rocky Klopfenstein, Yang He, Andrew Tremante, Yuepeng Wang

Community-driven Text-to-SQL evaluation platforms play a pivotal role in tracking the state of the art of Text-to-SQL performance. The reliability of the evaluation process is critical for driving progress in the field. Current evaluation methods are largely test-based, which involves comparing the execution results of a generated SQL query and a human-label

Daniele Celoria

The purpose of this short note is twofold: First to elucidate some connections between the ``building block'' of Dimofte--Gaiotto--Gukov's $3$D index, known as the tetrahedral index $I_\Delta (m,e)$, and Hahn--Exton's $q$-analogue of the Bessel function $J_\nu (z;q)$. The correspondence between $I_\Delta$ and $J_\nu$ will allow us to translate useful relatio

Sam McArdle, Alexander M. Dalzell, Aleksander Kubica, Fernando G. S. L. Brandão

We evaluate strategies for reducing the run time of fault-tolerant quantum computations, targeting practical utility in scientific or industrial workflows. Delivering a technology with broad impact requires scaling devices, while also maintaining acceptable run times for computations. Optimizing logical clock speed may require moving beyond current strategie

James Meldrum, Basem Suleiman, Fethi Rabhi, Muhammad Johan Alibasa

Synthetic data generation has emerged as a promising approach to address the challenges of using sensitive financial data in machine learning applications. By leveraging generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), it is possible to create artificial datasets that preserve the statistical properties o

Thanh Le, Hai Duong, Yusheng Ji, ThanhVu Nguyen

In 5G mobile communication systems, MU-MIMO has been applied to enhance spectral efficiency and support high data rates. To maximize spectral efficiency while providing fairness among users, the base station (BS) needs to selects a subset of users for data transmission. Given that this problem is NP-hard, DRL-based methods have been proposed to infer the nea

Vladimir Calvera, Naren Manjunath, Maissam Barkeshli

We study bosonic symmetry-protected topological (SPT) phases in (2+1) dimensions with symmetry $G = G_{\text{space}}\times K$, where $G_{\text{space}}$ is a general wallpaper group and $K=\text{U}(1),\mathbb{Z}_N, \text{SO}(3)$ is an internal symmetry. In each case we propose a set of many-body invariants that can detect all the different phases predicted fr

Lvzhou Chen, Yash Lodha

A group has normal rank (or weight) greater than one if no single element normally generates the group. The Wiegold problem from 1976 asks about the existence of a finitely generated perfect group of normal rank greater than one. We show that any free product of nontrivial left-orderable groups has normal rank greater than one. This solves the Wiegold proble

Ning Wang, Yi-Jie Duan, Hong Yao, Hui-Ming Jia

The influence of positive $Q$-value four-neutron transfer (PQ4NT) effects on the sub-barrier capture cross sections is systematically investigated using the empirical barrier distribution (EBD2) method. For 13 fusion reactions with $Q_{4n}>0$, sustained neutron-pair transfer is found to reduce barrier heights and enhance capture cross sections at sub-barrier

Shenshen Luan, Yumo Tian, Xinyu Zhang, Qingwen Zhang

The proliferation of large-scale distributed systems, such as satellite constellations and high-performance computing clusters, demands robust communication primitives that maintain coordination under unreliable links. The torus topology, with its inherent rotational and reflection symmetries, is a prevalent architecture in these domains. However, convention

Akifumi Mine, Takeshi Suzuki, Yigui Zhong, Sahand Najafzadeh

\beta-PdBi2 is one of the candidates for topological superconductors with a superconducting (SC) transition temperature (Tc) of 5.3 K, in which parity mixing of spin singlet and spin triplet has been anticipated, being crucial for the further understanding of relationship with inversion symmetry and parity mixing in the superconductivity. In this work, we me

Leixian Shen, Yifang Wang, Huamin Qu, Xing Xie

Text prompt is the most common way for human-generative AI (GenAI) communication. Though convenient, it is challenging to convey fine-grained and referential intent. One promising solution is to combine text prompts with precise GUI interactions, like brushing and clicking. However, there lacks a formal model to capture synergistic designs between prompts an

Di Zhang

This paper proposes a novel paradigm for machine learning that moves beyond traditional parameter optimization. Unlike conventional approaches that search for optimal parameters within a fixed geometric space, our core idea is to treat the model itself as a malleable geometric entity. Specifically, we optimize the metric tensor field on a manifold with a pre

Chi Zhang, Mingrui Li, Wenzhe Tong, Xiaonan Huang

Tensegrity robots combine rigid rods and elastic cables, offering high resilience and deployability but at the same time posing major challenges for locomotion control due to their underactuated and highly coupled dynamics. This paper introduces a morphology-aware reinforcement learning framework that integrates a graph neural network (GNN) into the Soft Act

Xinpeng Li, Miao Yu

This paper extends the asymmetric Kullback-Leibler divergence and symmetric Jensen-Shannon divergence from two probability measures to the case of two sets of probability measures. We establish some fundamental properties of these generalized divergences, including a duality formula and a Pinsker-type inequality. Furthermore, convergence results are derived

Andrés Navas

We present two parametric families of area-rearrangement proofs of the Pythagorean theorem. Our contribution is primarily constructive and expository: we organize the geometry through a single angular parameter. For certain appropriate parameters, we recover several known motifs as special instances. For less regular parameters, the argument becomes more com

David Otte, Jörg K. H. Franke, Arbër Zela, Fábio Ferreira

Symbolic regression (SR) aims to discover the underlying mathematical expressions that explain observed data. This holds promise for both gaining scientific insight and for producing inherently interpretable and generalizable models for tabular data. In this work we focus on the basics of SR. Deep learning-based SR has recently become competitive with geneti

Alireza Arastou, Algo Carè, Ye Wang, Marco Campi

This paper presents a novel approach to stochastic economic model predictive control (SEMPC) that minimizes average economic cost while satisfying an empirical expected shortfall (EES) constraint to manage risk. A new scenario-based problem formulation ensuring controlled risk with high confidence while minimizing the average cost is introduced. The probabil

Adam Rudnik

In this paper we consider noncompact smooth metric measure spaces $(M, g,e^{-f}dvol_{g})$ of nonnegative Bakry-\'Emery Ricci curvature, i.e. $Ric + D^{2}f - \frac{1}{N}df \otimes df \geq 0$, for $0< N \leq \infty$, in order to obtain geometric inequalities for the boundary of a given open and bounded set $\Omega\subset M$, with regular boundary $\partial \Om

Tomoharu Iwata, Futoshi Futami

We propose a data-driven framework for efficiently solving quadratic programming (QP) problems by reducing the number of variables in high-dimensional QPs using instance-specific projection. A graph neural network-based model is designed to generate projections tailored to each QP instance, enabling us to produce high-quality solutions even for previously un

Sina Keshvadi

Path-aware networking (PAN) architectures, such as SCION and emerging LEO constellations, expose tens to hundreds of verifiable paths to endpoints. When multipath protocols like MPTCP and MPQUIC greedily exploit this diversity, uncoordinated migration can induce persistent, high-amplitude load oscillations. Although this instability is well-known, its quanti

NVIDIA, :, Yan Wang, Wenjie Luo

End-to-end architectures trained via imitation learning have advanced autonomous driving by scaling model size and data, yet performance remains brittle in safety-critical long-tail scenarios where supervision is sparse and causal understanding is limited. We introduce Alpamayo-R1 (AR1), a vision-language-action model (VLA) that integrates Chain of Causation

Qiuye Jia, Junyong Zhang, Jiqiang Zheng

We prove a sharp $L^p$-boundedness criterion for Bochner-Riesz multipliers on flat cones $X = (0,\infty) \times \mathbb{S}_\sigma^1$. The operator $S_\lambda^\delta(\Delta_X)$ is bounded on $L^p(X)$ for $1 \leq p \leq \infty$, $p \neq 2$, if and only if $\delta > \delta_c(p,2) = \max\left\{ 0, 2\left| 1/2 - 1/p \right| - 1/2 \right\}$. This result is also ap

Ziqi Guo, Daniel Carne, Krutarth Khot, Dudong Feng

Heat conduction and radiation are two of the three fundamental modes of heat transfer, playing a critical role in a wide range of scientific and engineering applications ranging from energy systems to materials science. However, traditional physics-based simulation methods for modeling these processes often suffer from prohibitive computational costs. In rec

Hari Lee

We introduce Text-based Explainable Video Anomaly Detection (TbVAD), a language-driven framework for weakly supervised video anomaly detection that performs anomaly detection and explanation entirely within the textual domain. Unlike conventional WSVAD models that rely on explicit visual features, TbVAD represents video semantics through language, enabling i

Andrew L. Kun

The AI we use is powerful, and its power is increasing rapidly. If this powerful AI is to serve the needs of consumers, voters, and decision makers, then it is imperative that the AI is accountable. In general, an agent is accountable to a forum if the forum can request information from the agent about its actions, if the forum and the agent can discuss this

Chijul B. Tripathy

We revisit the Strong Birthday Problem (SBP) introduced by DasGupta'05, which asks for the minimum population n required such that, with a probability of at least 1/2, every individual in the group shares a birthday with at least one other person. Formally, we develop and analyze computational frameworks to determine the probability that in a group of n peop

Alon Bebchuk

We study the problem of allocating a set of indivisible items to agents with supermodular utilities to maximize the Nash social welfare. We show that the problem is NP-hard for any approximation factor.

Hyowon Park, Ivar Martin

A triangular Co-ion lattice intercalated between 1-H NbS$_2$ layers can exhibit a large anomalous Hall effect (AHE) due to the finite scalar spin chirality originating from the non-coplanar $3q$ ordering of Co spins. This large AHE occurs when the scalar spin chirality is uniform in all Co layers, as indeed found in the Co$_{1/3}$NbS$_2$ case [Phys. Rev. Mat

Le Wang, Xin Xing, Youhui Ye

This paper reinterprets the Synthetic Control (SC) framework through the lens of weighting philosophy, arguing that the contrast between traditional SC and Difference-in-Differences (DID) reflects two distinct modeling mindsets: sparse versus dense weighting schemes. Rather than viewing sparsity as inherently superior, we treat it as a modeling choice simple

Hoyeon Chang, Seungjin Kim, Yoonseok Choi

We propose a novel approach for dynamic negative prompting in diffusion models that leverages Vision-Language Models (VLMs) to adaptively generate negative prompts during the denoising process. Unlike traditional Negative Prompting methods that use fixed negative prompts, our method generates intermediate image predictions at specific denoising steps and que

Anshu Dubey, Akash Dhruv

With the emergence and rapid evolution of large language models (LLM), automating coding tasks has become an important research topic. Many efforts are underway and literature abounds about the efficacy of models and their ability to generate code. A less explored aspect of code generation is for new algorithms, where the training dataset would not have incl

Xiachong Lin, Arian Prabowo, Imran Razzak, Hao Xue

The evolution of electric vehicles (EVs) is reshaping the automotive industry, advocating for more sustainable transportation practices. Accurately predicting EV charging behavior is essential for effective infrastructure planning and optimization. However, the charging load of EVs is significantly influenced by uncertainties and randomness, posing challenge

S. Abubakar, M. Acsencio-Sosa, D. Ajana, M. A. Aman

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) was designed to reconstruct neutrino events from the Fermilab Booster Neutrino Beam (BNB) with the parallel goals of measuring neutron production in interactions with oxygen and serving as a testbed for new technology. The ANNIE detector consists of a 26-ton water Cherenkov target tank instrumen

Yuyue Zhou, Jessica Knight, Shrimanti Ghosh, Banafshe Felfeliyan

Elbow and wrist fractures are the most common fractures in pediatric populations. Automatic segmentation of musculoskeletal structures in ultrasound (US) can improve diagnostic accuracy and treatment planning. Fractures appear as cortical defects but require expert interpretation. Deep learning (DL) can provide real-time feedback and highlight key structures

David Chan, Myungsin Cho, David Mehrle, Pablo S. Ocal

We investigate how the notions of pairings of operads of May and compatible pairs of indexing systems of Blumberg--Hill relate via the correspondence between indexing systems and $N_{\infty}$-operads. We show that a pairing of operads induces a pairing on the associated indexing systems. Conversely, we show that in many cases, compatible pairs of indexing sy

Pengfei Lyu, Zhengchi Ma, Linjun Zhang, Anru R. Zhang

Imbalanced data, where the positive samples represent only a small proportion compared to the negative samples, makes it challenging for classification problems to balance the false positive and false negative rates. A common approach to addressing the challenge involves generating synthetic data for the minority group and then training classification models

Varun Kotharkar, Michael L. Stein

We establish a rigorous asymptotic theory for the joint estimation of roughness and scale parameters in two-dimensional Gaussian random fields with power-law generalized covariances \cite{Matheron1973, Stein1999, Yaglom1987}. Our main results are bivariate central limit theorems for a class of method-of-moments estimators under increasing-domain and fixed-do

Feiyang Lin

Splitting loci are certain natural closed substacks of the stack of vector bundles on $\mathbb{P}^1$, which have found interesting applications in the Brill-Noether theory of $k$-gonal curves. In this paper, we completely characterize when splitting loci, as algebraic stacks, are Gorenstein or $\mathbb{Q}$-Gorenstein. The main ingredients of the proof are a

Shaoxin Wang, Hanjing Yao

Kernel logistic regression (KLR) is a powerful classification method widely applied across diverse domains. In many real-world scenarios, indefinite kernels capture more domain-specific structural information than positive definite kernels. This paper proposes a novel $L_1$-norm regularized indefinite kernel logistic regression (RIKLR) model, which extends t

Victor Guedes, Siddarth Ajith, Shu Yan Lau, Kent Yagi

Neutron stars may exhibit pressure anisotropy arising from various physical mechanisms, such as elasticity, magnetic fields, viscosity, and superfluidity. We compute the tidal deformability and the $f$-mode oscillation frequency of anisotropic neutron stars using a phenomenological quasi-local model characterized by a single dimensionless anisotropy paramete

Jamie Ngoc Dinh, You-Jin Kim, Myungin Lee

Mindfulness has been studied and practiced in enhancing psychological well-being while reducing neuroticism and psychopathological indicators. However, practicing mindfulness with continuous attention is challenging, especially for beginners. In the proposed system, FractalBrain, we utilize an interactive audiovisual fractal with a geometric repetitive patte

Emily Steiner, Daniel van der Spuy, Futian Zhou, Afereti Pama

While autonomous racing performance in Time-Trial scenarios has seen significant progress and development, autonomous wheel-to-wheel racing and overtaking are still severely limited. These limitations are particularly apparent in real-life driving scenarios where state-of-the-art algorithms struggle to safely or reliably complete overtaking manoeuvres. This

José Luis Pérez, Kazutoshi Yamazaki, Qingyuan Zhang

This study considers a continuous-review inventory model for a single item with two replenishment modes. Replenishments may occur continuously at any time with a higher unit cost, or at discrete times governed by Poisson arrivals with a lower cost. From a practical standpoint, the model represents an inventory system with random deal offerings. Demand is mod

Jiali Cheng, Chirag Agarwal, Hadi Amiri

Knowledge distillation (KD) is an effective method for model compression and transferring knowledge between models. However, its effect on model's robustness against spurious correlations that degrade performance on out-of-distribution data remains underexplored. This study investigates the effect of knowledge distillation on the transferability of ``debiasi

Kaiwen Zhou, Ahmed Elgohary, A S M Iftekhar, Amin Saied

The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring their safe deployment. We present SIRAJ: a generic red-teaming framework for arbitrary black-box LLM agents. We employ a dynamic two-step process that starts with an agent definiti

Nan Gu, Junjie Qin

Electric vehicle charging and geo-distributed datacenters introduce spatially flexible loads (FLs) that couple power, transportation, and datacenter networks. These couplings create a closed-loop feedback between locational marginal prices (LMPs) and decisions of the FL systems, challenging the foundations of conventional competitive equilibrium (CE) in elec

Matt Salehi

This study explored how advanced budgeting techniques and economic indicators influence funding levels and strategic alignment in California Community Colleges (CCCs). Despite widespread implementation of budgeting reforms, many CCCs continue to face challenges aligning financial planning with institutional missions, particularly in supporting diversity, equ

Matthew L. Stevens, Noah I. Wasserbeck, Zachary Goisman, Arefur Rahman

The active stabilization of polarization channels is a task of growing importance as quantum networks move to deployed demonstrations over existing fiber infrastructure. However, the uniquely strict requirements for high-fidelity qubit transmission complicate the extent to which classical solutions may apply to future quantum networks, particularly in terms

David Smith, Jie Dong, Yizhou Yang

In this paper, we study decentralized decision-making where agents optimize private objectives under incomplete information and imperfect public monitoring, in a non-cooperative setting. By shaping utilities-embedding shadow prices or Karush-Kuhn-Tucker(KKT)-aligned penalties-we make the stage game an exact-potential game whose unique equilibrium equals the

Felipe Larios, Mariana Borras-Osorio, Yuqi Wu, Ana Gabriela Claros

Importance Incidental thyroid findings (ITFs) are increasingly detected on imaging performed for non-thyroid indications. Their prevalence, features, and clinical consequences remain undefined. Objective To develop, validate, and deploy a natural language processing (NLP) pipeline to identify ITFs in radiology reports and assess their prevalence, features, a

Zhaoqing Ding, Xuejiao Chen, Lei Liao, Zhen Wang

SrRuO3 is a canonical itinerant ferromagnet, yet its properties in the extreme two-dimensional limit on a (111) crystal plane remain largely unexplored. Here, we demonstrate a complete transformation of its ground state driven by dimensional reduction. As the thickness of (111)-oriented SrRuO3 films is reduced to a few unit cells, the system transitions from

J. D. A. Islas-García, M. del Castillo-Mussot, Marcelo B. Ribeiro

The evolution of global income distribution from 1988 to 2018 is analyzed using purchasing power parity exchange rates and well-established statistical distributions. This research proposes the use of two separate distributions to more accurately represent the overall data, rather than relying on a single distribution. The global income distribution was fitt

Thiago Puccinelli, Lucas Martin Farigliano, Gustavo Martini Dalpian

Tin-based halide perovskites have emerged as promising lead-free alternatives for optoelectronic applications, yet their structural stability and phase behavior at finite temperatures remain challenging to predict. Here, we assess the predictive capabilities of the foundational machine learning model MACE-MP-0 - trained on a broad chemical space and applied

Kehao Zhuang, Huanhai Xin, Hangyu Chen, Linbin Huang

With the development of renewable energy sources, power systems are gradually evolving into a system comprising both grid-forming (GFM) and grid-following (GFL) converters. However, the dynamic interaction between the two types of converters, especially low-inertia GFM converters and GFL converters, remains unclear due to the substantial differences in their

H. Andréka, J. Madarász, J. Manchak, I. Németi

By removing a fractal from time-rolled Minkowski spacetime, we construct an extendible spacetime without closed timelike curves whose every extension contains closed timelike curves. This settles a question posed by Geroch.

Fahad Aziz, Zhengqi Niu, Tzu-Yen Hsieh, Kuan Ting Lin

We investigate the power spectral density emitted by a superconducting artificial atom coupled to the end of a semi-infinite transmission line and driven by two continuous radio-frequency fields. In this setup, we observe the generation of multiple frequency peaks and the formation of frequency combs with equal detuning between those peaks. The frequency pea

Steffen Bollmann, Andreas Haller, Jukka I. Väyrynen, Thomas L. Schmidt

Motivated by recent observations of fractional Chern insulators (FCIs) in the vicinity of superconducting (SC) phases, we study fractional quantum (anomalous) Hall-superconductor heterostructures in the presence of $U(1)$ order-parameter fluctuations and particularly focus on the case of $ν= 2/3$ quantum Hall states leading to $\mathbb Z_3$ parafermions. We

Noah Kubli, Alessia Franchini, Eric R. Coughlin, C. J. Nixon

In a tidal disruption event (TDE), a star is disrupted by the tidal field of a massive black hole, creating a debris stream that returns to the black hole, forms an accretion flow, and powers a luminous flare. Over the last few decades, several numerical studies have concluded that shock-induced dissipation occurs as the stream returns to pericentre (i.e., p

Spencer Teetaert, Sven Lilge, Jessica Burgner-Kahrs, Timothy D. Barfoot

Stochastic state estimation methods for continuum robots (CRs) often struggle to balance accuracy and computational efficiency. While several recent works have explored sliding-window formulations for CRs, these methods are limited to simplified, discrete-time approximations and do not provide stochastic representations. In contrast, current stochastic filte

Marija Bliznac Trebješanin

A general construction yielding infinitely many families of $D(m^2)$-triples of triangular numbers is presented. Moreover, each triple obtained from this construction contains the same triangular number $T_n$.

Matteo Crispino, Niklas Witt, Tommaso Gorni, Giorgio Sangiovanni

We report a comprehensive study of electronic-correlation effects in Manganese-based antiferromagnetic pnictides BaMn$_2$Pn$_2$ (Pn=P,As,Sb,Bi). Our density functional theory plus slave-spin mean-field simulations indicate that all the compounds lie on the strong-coupling side of an itinerant-to-localized moment crossover, corresponding to the critical inter

David Leprich, Mario Rosenfelder, Markus Herrmann-Wicklmayr, Kathrin Flaßkamp

This article proposes a modular optimal control framework for local three-dimensional ellipsoidal obstacle avoidance, exemplarily applied to model predictive path-following control. Static as well as moving obstacles are considered. Central to the approach is a computationally efficient and continuously differentiable condition for detecting collisions with

Dong Xie, Chunling Xu

Super-Heisenberg scaling, which scales as $N^{-β}$ with $β>1$ in terms of the number of particles $N$ or $T^{-β}$ in terms of the evolution time $T$, is better than Heisenberg scaling in quantum metrology. It has been proven that super-Heisenberg scaling can be achieved when the Hamiltonian of the system involves many-body interactions or the time-dependent

Sattwik Sadhu, Nitin Kriplani, Anirban Sain, Raghunath Chelakkot

Follower activity results in a large variety of conformational and dynamical states in active chains and filaments. These states are formed due to the coupling between chain geometry and the local activity. We study the origin and emergence of such patterns in noiseless, flexible active chains. In the overdamped limit, we observed a range of dynamical steady

Shih-Kai Chou, Mengran Zhao, Cheng-Nan Hu, Kuang-Chung Chou

Accurate Angle-of-arrival (AoA) estimation is essential for next-generation wireless communication systems to enable reliable beamforming, high-precision localization, and integrated sensing. Unfortunately, classical high-resolution techniques require multi-element arrays and extensive snapshot collection, while generic Machine Learning (ML) approaches often

Luis A. Anchordoqui, John K. Anders, Akitaka Ariga, Tomoko Ariga

The Forward Physics Facility (FPF) is a proposed extension of the HL-LHC program designed to exploit the unique scientific opportunities offered by the intense flux of high energy neutrinos, and possibly new particles, in the far-forward direction. Located in a well-shielded cavern 627 m downstream of one of the LHC interaction points, the facility will supp

Kunlin Li, Chenlin Guo, Zhaofan Zhu, Haiou Wang

Reactive flows in confined spaces involve complex flame-wall interaction (FWI). This work aims to gain more insights into the physics of the premixed near-wall flame and the wall heat flux as an important engineering relevant quantity. Two different flame configurations have been studied, including the normal flushing flame and inclined sweeping flame. By in

Buddhi Wijenayake, Athulya Ratnayake, Lelumi Edirisinghe, Uditha Wijeratne

Understanding how population age structure shapes COVID-19 burden is crucial for pandemic preparedness, yet common summary measures such as median age ignore key distributional features like skewness, bimodality, and the proportional weight of high-risk cohorts. We extend the PoPStat framework, originally devised to link entire population pyramids with cause

Songyang Liu, Chaozhuo Li, Jiameng Qiu, Xi Zhang

With the rapid advancement of artificial intelligence, Large Language Models (LLMs) have shown remarkable capabilities in Natural Language Processing (NLP), including content generation, human-computer interaction, machine translation, and code generation. However, their widespread deployment has also raised significant safety concerns. In particular, LLM-ge

C. A. Cremonini, P. A. Grassi, R. Noris, L. Ravera

We combine the theory of Cartan-Tanaka prolongations with the Molien-Weyl integral formula and Hilbert-Poincaré series to compute the Spencer cohomology groups of the $D=11$ Poincaré superalgebra $\mathfrak p$, relevant for superspace formulations of $11$-dimensional supergravity in terms of nonholonomic superstructures. This includes novel fermionic Spencer

Mara Daniels, Cédric Gerbelot, Florent Krzakala, Lenka Zdeborová

Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstruction algorithms under generative priors is, however, challenging. For generative priors with fully connected layers and Gaussian i.i.d. weights, this was achieved by the multi-layer

Michael Lau, Filippo Pecci, Jesse D. Jenkins

Contemporary macro energy systems modelling is characterized by the need to represent strategic and operational decisions with high temporal and spatial resolution and represent discrete investment and retirement decisions. This drive towards greater fidelity, however, conflicts with a simultaneous push towards greater model representation of inherent comple

Qile Wu, Seamus D. O Hara, Joseph B. Costello, Loren N. Pfeiffer

Reconstructing effective Hamiltonians of condensed matter systems directly from experimental data is challenging because of the intricate relationship between Hamiltonian parameters and observables. Here, we reconstruct an effective three-band electron-hole (e-h) Hamiltonian in bulk GaAs based on high-order sideband generation (HSG) induced by quasi-continuo

Jonathan Chan

Many proof assistants allow the use of features and axioms that increase their expressive power. However, these extensions must be used with care, as some combinations are known to lead to logical inconsistencies. Therefore, proof assistants include mechanisms that track which extensions are used in a proof development or module, ensuring that incompatible e

Ali Rasekh, Erfan Bagheri Soula, Omid Daliran, Simon Gottschalk

Despite significant advances in Multimodal Large Language Models (MLLMs), understanding complex temporal dynamics in videos remains a major challenge. Our experiments show that current Video Large Language Model (Video-LLM) architectures have critical limitations in temporal understanding, struggling with tasks that require detailed comprehension of action s

Feichen Gan, Youcun Lu, Yingying Zhang, Yukun Liu

Reliable uncertainty quantification is crucial for reinforcement learning (RL) in high-stakes settings. We propose a unified conformal prediction framework for infinite-horizon policy evaluation that constructs distribution-free prediction intervals {for returns} in both on-policy and off-policy settings. Our method integrates distributional RL with conforma

Semyon Lomasov, Judah Goldfeder, Mehmet Hamza Erol, Matthew So

Do AI systems truly understand human concepts or merely mimic surface patterns? We investigate this through chess, where human creativity meets precise strategic concepts. Analyzing a 270M-parameter transformer that achieves grandmaster-level play, we uncover a striking paradox: while early layers encode human concepts like center control and knight outposts

HyoJung Han, Sweta Agrawal, Eleftheria Briakou

Cross-lingual alignment (CLA) aims to align multilingual representations, enabling Large Language Models (LLMs) to seamlessly transfer knowledge across languages. While intuitive, we hypothesize, this pursuit of representational convergence can inadvertently cause "cultural erasure", the functional loss of providing culturally-situated responses that should

Zhipeng Bao, Qianwen Li

Despite significant advancements in recent decades, autonomous vehicles (AVs) continue to face challenges in navigating certain traffic scenarios where human drivers excel. In such situations, AVs often become immobilized, disrupting overall traffic flow. Current recovery solutions, such as remote intervention (which is costly and inefficient) and manual tak

Xinqi Li, Yi Zhang, Li-Ting Huang, Hsiao-Huang Chang

Multiparametric mapping MRI has become a viable tool for myocardial tissue characterization. However, misalignment between multiparametric maps makes pixel-wise analysis challenging. To address this challenge, we developed a generalizable physics-informed deep-learning model using test-time adaptation to enable group image registration across contrast weight

Feijie Wu, Weiwu Zhu, Yuxiang Zhang, Soumya Chatterjee

Multi-tool-integrated reasoning enables LLM-empowered tool-use agents to solve complex tasks by interleaving natural-language reasoning with calls to external tools. However, training such agents from outcome-only rewards suffers from credit-assignment ambiguity, obscuring which intermediate tool-use decisions drive success or failure. In this paper, we prop

Jonathan Gorard, James Juno, Ammar Hakim

The equations of general relativistic magnetohydrodynamics (GRMHD) have become the standard mathematical framework for modeling high-energy plasmas in curved spacetimes. However, the fragility of the primitive variable reconstruction operation in GRMHD, as well as the difficulties in maintaining strong hyperbolicity of the equations, sharply limit the applic

Bilal Hassan, Areg Karapetyan, Aaron Chung Hin Chow, Samer Madanat

Climate change and sea-level rise (SLR) pose escalating threats to coastal cities, intensifying the need for efficient and accurate methods to predict potential flood hazards. Traditional physics-based hydrodynamic simulators, although precise, are computationally expensive and impractical for city-scale coastal planning applications. Deep Learning (DL) tech

Michael Arntzenius

miniKanren's key semantic advance over Prolog is to implement a complete yet efficient search strategy, fairly interleaving execution between disjuncts. This fairness is accomplished by bounding how much work is done exploring one disjunct before switching to the next. We show that the same idea -- fairness via bounded work -- underlies an elegant compositio

Zhengtao Ma, Rafael Gomez, Togtokhtur Batbold, Zishuo Zhu

Fully automated vehicles (FAVs) hold promise for enhancing the mobility of blind and low-vision (BLV) individuals. To understand the situated interaction needs of BLV passengers, we conducted six on-road, and in-lab focus groups with 16 participants, immersing them in real-world driving conditions. Our thematic analysis reveals that BLV participants express

Hyeonjun Lee, Hyungseob Shin, Gunhee Nam, Hyeonsoo Lee

Survival analysis is a task to model the time until an event of interest occurs, widely used in clinical and biomedical research. A key challenge is to model patient heterogeneity while also adapting risk predictions to both individual characteristics and temporal dynamics. We propose a dual mixture-of-experts (MoE) framework for discrete-time survival analy

Tomohiro Kawakami, Hiroshi Tanaka

Every bounded definable open set is a union of finitely many open strong cells in a weakly o-minimal expansion of a real closed field. We prove this fact and another theorem similar to it.

Siyi Wu, Chiaxin Liang, Ziqian Bi, Leyi Zhao

The rapid growth of research literature, particularly in large language models (LLMs), has made producing comprehensive and current survey papers increasingly difficult. This paper introduces autosurvey2, a multi-stage pipeline that automates survey generation through retrieval-augmented synthesis and structured evaluation. The system integrates parallel sec

Amine Razig, Youssef Soulaymani, Loubna Benabbou, Pierre Cauchy

Automated monitoring of marine mammals in the St. Lawrence Estuary faces extreme challenges: calls span low-frequency moans to ultrasonic clicks, often overlap, and are embedded in variable anthropogenic and environmental noise. We introduce a multi-step, attention-guided framework that first segments spectrograms to generate soft masks of biologically relev