October 2025 arXiv papers — page 146

Yong Liao, Yuping Yang, Houyi Yu

We study the combinatorial equivalence of separable elements in types $A$ and $B$. A bijection is constructed from the set of separable permutations in the symmetric group $S_{n+1}$ to the set of separable signed permutations in the hyperoctahedral group $B_n$. This bijection preserves descent statistics and induces a poset isomorphism under the left weak or

Baigang Chen, Dongfang Zhao

Modern FFT/NTT analytics, coded computation, and privacy-preserving ML interface routinely move polynomial frames across NICs, storage, and accelerators. However, even rare silent data corruption (SDC) can flip a few ring coefficients and cascade through downstream arithmetic. Conventional defenses are ill-matched to current low-latency pipelines: detect-and

Onur Eren Arpaci, Raouf Boutaba, Florian Kerschbaum

An important function of collaborative network intrusion detection is to analyze the network logs of the collaborators for joint IP addresses. However, sharing IP addresses in plain is sensitive and may be even subject to privacy legislation as it is personally identifiable information. In this paper, we present the privacy-preserving collection of IP addres

Yukun Zhang, Qi Dong

Existing alignment techniques for Large Language Models (LLMs), such as Direct Preference Optimization (DPO), typically treat the model as a monolithic entity, applying uniform optimization pressure across all layers. This approach overlooks the functional specialization within the Transformer architecture, where different layers are known to handle distinct

Jirô Akahori, Yusuke Ide, Tomoki Kato, Norio Konno

In this paper, we introduce hierarchical random walks at first. In this model, we use two types of random walkers, {global and local} walkers. The global walker chooses a local walker at every step, then the chosen local walker moves a single step. After that we construct the corresponding continuous-time quantum walks and discuss its spectral structures. Th

Wanying Ge, Xin Wang, Junichi Yamagishi

Deepfake speech attribution remains challenging for existing solutions. Classifier-based solutions often fail to generalize to domain-shifted samples, and watermarking-based solutions are easily compromised by distortions like codec compression or malicious removal attacks. To address these issues, we propose FakeMark, a novel watermarking framework that inj

Ruibo Chen, Jiacheng Pan, Heng Huang, Zhenheng Yang

Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecified prompts, leading to suboptimal image-text alignment, aesthetics, and quality. We propose a prompt rewriting framework that leverages large language models (LLMs) to refine user inputs before feeding them into T2

Sungmin Kang, Yavuz Faruk Bakman, Duygu Nur Yaldiz, Baturalp Buyukates

The rapid advancement of large language models (LLMs) has transformed the landscape of natural language processing, enabling breakthroughs across a wide range of areas including question answering, machine translation, and text summarization. Yet, their deployment in real-world applications has raised concerns over reliability and trustworthiness, as LLMs re

Jit Wu Yap

Let $K$ be a number field and $f: \mathbb{P}^1 \to \mathbb{P}^1$ a rational map of degree $d \geq 2$ with at most $s$ places of bad reduction, where we include all archimedean places. We prove that there exists constants $c_1,c_2 > 0$, depending only on $d$ and not on $f$ or $K$, such that $$ \# \left\{ x \in \mathbb{P}^1(K) \mid \widehat{h}_f(x) \leq \frac{

Jean-Pierre Fouque, Tomoyuki Ichiba, Ka Lok Lam

We explicitly connect (discrete-time) quantum walks on Z with a four-state Markov additive process via a Feynman-type formula (2.5). Using this representation, we derive a relation between the spectral decomposition of the Markov additive process and the limiting density of the homogeneous quantum walk. In addition, we consider a space-time rescaling of quan

Mat Langford, James McCoy

In a recent article, a localization of the Huisken--Stampacchia iteration method was developed, and used to establish localizations of the well-known "umbilic", "convexity" and "cylindrical" estimates for hypersurfaces evolving in Euclidean space by mean curvature flow. Here, we adapt the methods developed there to treat more general (fully nonlinear) flows,

Kemal Kurniawan, Meladel Mistica, Timothy Baldwin, Jey Han Lau

The impact of human label variation (HLV) on model fairness is an unexplored topic. This paper examines the interplay by comparing training on majority-vote labels with a range of HLV methods. Our experiments show that without explicit debiasing, HLV training methods have a positive impact on fairness under certain configurations.

Thomas Lehéricy

We study the critical centered branching random walk with offspring and displacement distributions having finite variance, under minimal assumptions on its structure. We show that the probability that the position of the right-most particle is larger than $r$ decays like an explicit constant times $r^{-2}$; this generalizes an earlier result by Lalley and Sh

Kangqi Ni, Zhen Tan, Zijie Liu, Pingzhi Li

Reinforcement Learning with Verifiable Rewards (RLVR), primarily driven by the Group Relative Policy Optimization (GRPO) algorithm, is a leading approach for enhancing the reasoning abilities of Large Language Models (LLMs). Despite its wide adoption, GRPO's gains are often inconsistent; for instance, a model may show significant improvement in one reasoning

Chathurangi Shyalika, Aryaman Sharma, Fadi El Kalach, Utkarshani Jaimini

Modern manufacturing environments demand not only accurate predictions but also interpretable insights to process anomalies, root causes, and potential interventions. Existing AI systems often function as isolated black boxes, lacking the seamless integration of prediction, explanation, and causal reasoning required for a unified decision-support solution. T

Jung-Woo Shim, Yeong-Joon Ju, Ji-Hoon Park, Seong-Whan Lee

Recent advancements in large language models (LLMs) have shown strong performance in natural language understanding and generation tasks. However, LLMs continue to encounter challenges with hallucinations, where models generate plausible but incorrect information. While several factors contribute to hallucinations, the impact of ill-formed prompts, prompts w

Urvashi Kishnani, Sanchari Das

E-commerce mobile applications are central to global financial transactions, making their security and privacy crucial. In this study, we analyze 92 top-grossing Android e-commerce apps (58 U.S.-based and 34 international) using MobSF, AndroBugs, and RiskInDroid. Our analysis shows widespread SSL and certificate weaknesses, with approximately 92% using unsec

David E. Kaplan, Surjeet Rajendran

We argue that the hierarchy problem of the standard model of particle physics can be solved by adding a state-dependent term to the Higgs sector. We present an example of a scalar field with a Higgs-like potential with an additional term proportional to the expectation value of the squared Higgs field operator. We show that the mass can be parametrically lig

Jung-Woo Shim, Yeong-Joon Ju, Ji-Hoon Park, Seong-Whan Lee

Recent advancements in large language models (LLMs) highlight their fluency in generating responses to diverse prompts. However, these models sometimes generate plausible yet incorrect ``hallucinated" facts, undermining trust. A frequent but often overlooked cause of such errors is the use of poorly structured or vague prompts by users, leading LLMs to base

Zhengjie Sun, Mengyuan Lv, Xingping Sun

We establish a deterministic and stochastic spherical quasi-interpolation framework featuring scaled zonal kernels derived from radial basis functions on the ambient Euclidean space. The method incorporates both quasi-Monte Carlo and Monte Carlo quadrature rules to construct easily computable quasi-interpolants, which provide efficient approximation to Sobol

Junsoo Oh, Wei Huang, Taiji Suzuki

Mamba, a recently proposed linear-time sequence model, has attracted significant attention for its computational efficiency and strong empirical performance. However, a rigorous theoretical understanding of its underlying mechanisms remains limited. In this work, we provide a theoretical analysis of Mamba's in-context learning (ICL) capability by focusing on

Juan D. García-Muñoz, A. Contreras-Astorga, L. M. Nieto

We developed a low-energy model that can be used at any time to describe the dynamics of DNA bubbles at temperatures below the melting point. The Schr\"odinger equation associated with this problem is solved in imaginary time with a quantum Coulomb potential, and we obtain an approximate expression for its more general physical solution as a linear combinati

William Grimble, Joel Kastner, B. Sargent, Keivan Stassun

In order to understand the mineralogy and structure of protoplanetary disks, it is important to analyze them from both an empirical spectrum-based perspective and a radiative transfer image-based perspective. In a prior paper, we set forth an empirical mineralogy mid-IR spectral model that conveyed spatial information and worked in tandem with a radiative tr

Adam Bradley, Bradford Saad

A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding both of these d

Sarah Lindley, Chandralekha Singh

In this investigation, we analyzed individual interviews with six female undergraduate physics majors at a large, public, research university in the US to understand their progression at different transition points to becoming physicists. Following the frameworks of standpoint theory, Schlossberg's transition theory, and domains of power, we focused our anal

Alice Saebom Kwak, Maria Alexeeva, Gus Hahn-Powell, Keith Alcock

The current trend in information extraction (IE) is to rely extensively on large language models, effectively discarding decades of experience in building symbolic or statistical IE systems. This paper compares a neuro-symbolic (NS) and an LLM-based IE system in the agricultural domain, evaluating them on nine interviews across pork, dairy, and crop subdomai

Liang-Liang Sun, Xiang Zhou, Chengjie Zhang, Zizhu Wang

Quantum correlations in Bell and prepare-and-measure experiments are central resources for probing nonclassicality and enabling device-based quantum information protocols. In the absence of shared public randomness (i.e., without run-to-run mixing), even qubit correlation sets are typically non-convex, making standard convex characterizations inadequate. Her

Misam Abbas

Attributing authorship in the era of large language models (LLMs) is increasingly challenging as machine-generated prose rivals human writing. We benchmark two complementary attribution mechanisms , fixed Style Embeddings and an instruction-tuned LLM judge (GPT-4o) on the Human AI Parallel Corpus, an open dataset of 600 balanced instances spanning six domain

Naomi Fridman, Anat Goldstein

Breast cancer is the most diagnosed cancer in women, with HER2 status critically guiding treatment decisions. Noninvasive prediction of HER2 status from dynamic contrast-enhanced MRI (DCE-MRI) could streamline diagnostics and reduce reliance on biopsy. However, preprocessing high-dynamic-range DCE-MRI into standardized 8-bit RGB format for pretrained neural

Abderrahmane Benfanich, Yves Bourgault, Abdelaziz Beljadid

The Richards equation, a nonlinear elliptic parabolic equation, is widely used to model infiltration in porous media. We develop a finite element method for solving the Richards equation by introducing a new bounded auxiliary variable to eliminate unbounded terms in the weak formulation of the method. This formulation is discretized using a semi-implicit sch

Michelangelo Tagliavini, Fabian Wenzel, Maurits W. Haverkort

Resonant Inelastic X-Ray Scattering (RIXS) is a well-established tool for probing excitations in a wide range of materials. The measured spectra strongly depend on the scattering geometry, via its influence on the polarization of the incoming and outgoing light. By employing a tensor representation of the 4-point response function that governs the RIXS inten

Arthur Bril, Nai Chao Hu, Nick Bultinck

We report on a study of the interacting phase diagram of $3.65^\circ$-twisted WSe$_2$ at moiré hole filling $ν=1$, in which we find previously-overlooked types of magnetism. Specifically, in part of the phase diagram we obtain a magnetic order parameter which modulates in space with four different non-zero wave vectors, corresponding to the three $M$-points

Theodore Andronikos, Constantinos Bitsakos, Konstantinos Nikas, Georgios I. Goumas

This article investigates the probabilistic relationship between quantum classification of Boolean functions and their Hamming distance. By integrating concepts from quantum computing, information theory, and combinatorics, we explore how Hamming distance serves as a metric for analyzing deviations in function classification. Our extensive experimental resul

M. S. Cagliari, A. Bairagi, B. Wandelt

The advent of Stage IV galaxy redshift surveys such as DESI and Euclid marks the beginning of an era of precision cosmology, with one key objective being the detection of primordial non-Gaussianities (PNG), potential signatures of inflationary physics. In particular, constraining the amplitude of local-type PNG, parameterised by $f_{\rm NL}$, with $σ_{f_{\rm

James A. A. Trussler, Alex J. Cameron, Daniel J. Eisenstein, Harley Katz

The discovery of high-redshift galaxies exhibiting a steep spectral UV downturn potentially indicative of two-photon continuum emission marks a turning point in our search for signatures of top-heavy star formation in the early Universe. We develop a photometric search method for identifying further nebular-dominated galaxy candidates, whose nebular continuu

Sebastian Deffner

What are the resources that can be leveraged for a thermodynamic device to exhibit genuine quantum advantage? Typically, the answer to this question is sought in quantum correlations. In the present work, we show that quantum Otto engines that operate with nonlinear qubits significantly outperform linear engines. To this end, we develop a comprehensive therm

Mrinal Manna, Sourav Mukherjee, Soumen Giri, Pramod Bhakuni

Universal behavior in far-from-equilibrium systems is driven by interactions between transport processes and noise structure. The Kardar-Parisi-Zhang (KPZ) framework predicts that extensions incorporating conserved currents or temporally correlated noise give rise to distinct growth morphologies and universality classes, yet direct experimental realization h

Arpad Benyi, Ioan Casu

We present an elementary derivation of the period-three cycles for the real quadratic map $x\mapsto x^2+c$, a fundamental model in one-dimensional discrete dynamics. Using symmetric polynomials, we obtain a complete algebraic characterization of 3-cycles and determine explicit conditions for their existence and stability, without reliance on computer algebra

Zehua Dou, Laura Tropf, Tobias Lappan, Hannes Rox

Low temperature water electrolyzers (LTWEs) and low temperature hydrogen fuel cells (LTFCs) present a promising technological strategy for the productions and usages of green hydrogen energy towards a net-zero world. However, the interactions of gas/liquid (fluid) transport and the intrinsic reaction kinetics in LTWEs/LTFCs present one of the key hurdles hin

Yixuan Y Zheng, Hideki Takayasu, Misako Takayasu

In urban railway systems, if every commuter were perfectly rational, concentrated demand on optimal routes would undermine system performance. Understanding the actual degree of collective route choice determinism, and whether it remains stable over time, is essential for transportation policy. Smartphone GPS data offers both the scale and complete trajector

Zhengyang Qiu, Junfeng Chen, Dmitrii V. Semenok, Qingyi Zhong

Systematically controlling the superconducting transition temperature ($T_\text{c}$) in the bilayer Ruddlesden-Popper nickelate La$_3$Ni$_2$O$_7$ remains a significant challenge. Here, we address this by synthesizing high-quality polycrystalline La$_{3-x}$Nd$_x$Ni$_2$O$_7$ ($0 \leq x \leq 2.4$) with record-level rare-earth substitution. Nd doping compresses

Daniel Pulido-Cortázar, Daniel Gibert, Felip Manyà

Over the last decade, machine learning has been extensively applied to identify malicious Android applications. However, such approaches remain vulnerable against adversarial examples, i.e., examples that are subtly manipulated to fool a machine learning model into making incorrect predictions. This research presents DeepTrust, a novel metaheuristic that arr

Chaoxin Huang, Jingyuan Li, Xing Huang, Hengyuan Zhang

The discovery of 80 K superconductivity in pressurized bilayer Ruddlesden-Popper (RP) nickelate La$_3$Ni$_2$O$_7$ has established a new high-temperature superconductor family. The quest to understand the governing principles of RP nickelate superconductivity has become a central focus in condensed matter physics. Here, we report a critical advance by synthes

Ziyue Qi, Hanqi Pi, Yan Zhang, Jiaxuan Liu

The interplay between moiré flat bands originating from different valleys can give rise to a variety of exotic quantum phases. In this work, we investigate the electronic properties of twisted mono-bilayer (A-AB) and mono-trilayer (A-ABA) MoTe$_2$ using first-principles calculations and continuum models. Unlike previous studies on twisted bilayer systems, in

Valentino Tosatti

We study the space of Ricci-flat Kahler metrics on a given Calabi-Yau manifold, pose a number of questions about their possible degenerations, and survey some recent results on these questions.

Daniel Scalena, Gabriele Sarti, Arianna Bisazza, Elisabetta Fersini

Large language models have simplified the production of personalized translations reflecting predefined stylistic constraints. However, these systems still struggle when stylistic requirements are implicitly represented by a set of examples, such as texts produced by a specific human translator. In this work, we explore various strategies for personalizing a

Phoenix M. M. Paing, Daniel F. V. James

We analyze the time-dependence of N-level systems under the Rotating Wave Approximation and dipole selection rules. Such systems can be solved straightforwardly if the Hamiltonian can be transformed into a time-independent form. The conditions under which a unitary transformation can be used to render time-dependent Hamiltonians into a time-independent form,

Ryosuke Ooe

We prove the rationality of the characteristic form for a degree one character of the Galois group of an abelian extension of henselian discrete valuation fields. We prove the integrality of the characteristic form for a rank one sheaf on a regular excellent scheme. These properties are shown by reducing to the corresponding properties of the refined Swan co

Leili Barekatain, Ben Glocker

Understanding model decisions is crucial in medical imaging, where interpretability directly impacts clinical trust and adoption. Vision Transformers (ViTs) have demonstrated state-of-the-art performance in diagnostic imaging; however, their complex attention mechanisms pose challenges to explainability. This study evaluates the explainability of different V

Ivan R. Vasquez, A. Oliveros

We present a cosmological analysis of an exponential $f(Q)$ gravity model, within the dynamical systems formalism. Following the method introduced by B\"ohmer \textit{et al} [Universe \textbf{9} no.4, 166 (2023)], the modified Friedmann modified equations are successfully reduced to an autonomous system. Given the exponential form of $f(Q)$, the equilibrium

Slimane Adjerid, Tao Lin, Haroun Meghaichi

The Frenet apparatus is a new framework for constructing high order geometry-conforming immersed finite element functions for interface problems. In this report, we present a procedure for constructing the local IFE bases in some detail as well as a new approach for constructing orthonormal bases using the singular value decomposition of the local generalize

L. F. Alves da Silva, H. Sanchez, M. A. Ponte, M. H. Y. Moussa

We present a thermal engine that exploits the \emph{cooperative superradiance} and \emph{superabsorption} of a sample of \(N\) two-level atoms. This engine operates using a single cold reservoir via cycles of collective pumping followed by decay. Using an effective mean-field Hamiltonian to describe the many-body dynamics, we design optimized drive pulses th

Rochish M. Thaokar, Rupesh Kumar, Nalinikanta Behera, Mohammad Maoyafikuddin

This work attempts to understand the mechanism of simultaneous electrodeformation and electroporation in Giant Unilamellar Vesicles (GUVs) using a minimal analytical model. In the small deformation limit, the coupled electroporation, electrohydrodynamics and membrane mechanics are solved. The excess membrane area generated by electroporation manifests as amp

Ali Montazeralghaem, Guy Tennenholtz, Craig Boutilier, Ofer Meshi

Large Language Models (LLMs) have made it possible for recommendation systems to interact with users in open-ended conversational interfaces. In order to personalize LLM responses, it is crucial to elicit user preferences, especially when there is limited user history. One way to get more information is to present clarifying questions to the user. However, g

Ansh Tiwari, Ayush Chauhan

Spiking neural networks (SNNs) promise energy-efficient artificial intelligence on neuromorphic hardware but struggle with tasks requiring both fast adaptation and long-term memory, especially in continual learning. We propose Local Timescale Gating (LT-Gate), a neuron model that combines dual time-constant dynamics with an adaptive gating mechanism. Each sp

Eric He, Akash Gupta, Adian Liusie, Vatsal Raina

Text--image retrieval is necessary for applications such as product recommendation. Embedding-based approaches like CLIP enable efficient large-scale retrieval via vector similarity search, but they are primarily trained on literal caption-like text--image pairs and often fail to capture abstract or persona-driven attributes common in product recommendation

Jiaqi Li, Zhipeng Lou, Johannes Schmidt-Hieber, Wei Biao Wu

Stochastic Gradient Descent (SGD) and its Ruppert-Polyak averaged variant (ASGD) lie at the heart of modern large-scale learning, yet their theoretical properties in high-dimensional settings are rarely understood. In this paper, we provide rigorous statistical guarantees for constant learning-rate SGD and ASGD in high-dimensional regimes. Our key innovation

Keshav Sood, Sanjay Selvaraj, Youyang Qu

The use of Immersive Technologies has shown its potential to revolutionize many sectors such as health, entertainment, education, and industrial sectors. Immersive technologies such as Virtual Reality (VR), Augmented reality (AR), and Mixed Reality (MR) have redefined user interaction through real-time biometric and behavioral tracking. Although Immersive Te

Rulla Al-Haideri, Changhe Liu, Karim Ismail, Bilal Farooq

Crash data objectively characterize road safety but are rare and often unsuitable for proactive safety management. Traffic conflict indicators such as time-to-collision (TTC) provide continuous measures of collision proximity but require thresholds to distinguish routine from safety-critical interactions. Extreme Value Theory (EVT) offers statistically defin

Ashlae Blum'e

In audio signal processing, the interpretation of complex information using visual representation enhances pattern recognition through its alignment with human perceptual systems. Software tools that carry hidden assumptions inherited from their historical contexts risk misalignment with modern workflows as design origins become obscured. We argue that creat

Bei Zhou, Maximilian Balmus, Cesare Corrado, Ludovica Cicci

Cardiac electrophysiology (CEP) simulations are increasingly used for understanding cardiac arrhythmias and guiding clinical decisions. However, these simulations typically require high-performance computing resources with numerous CPU cores, which are often inaccessible to many research groups and clinicians. To address this, we present TorchCor, a high-per

Stephen Zhou

We study positive singular solutions of the Loewner-Nirenberg problem on conical domains and establish the existence of solutions that admit prescribed asymptotic expansions near vertices, valid to arbitrarily high order of approximation.

Cody Melcher, Zeinab Alizadeh, Lindsey Hiett, Afrooz Jalilzadeh

Semi-Infinite Programming (SIP) has emerged as a powerful framework for modeling problems with infinite constraints, however, its theoretical development in the context of nonconvex and large-scale optimization remains limited. In this paper, we investigate a class of nonconvex min-max optimization problems with nonconvex infinite constraints, motivated by a

Riku Yamamoto, Sejun Park, Zachary W. Riedel, Phurba Sherpa

We report $^{73}$Ge nuclear quadrupole resonance (NQR) and magnetic resonance (NMR) spectroscopy in the heavy-fermion quantum-critical ferromagnet CeRh$_6$Ge$_4$. NQR and NMR spectral measurements at the two non-equivalent Ge sites reveal electric field gradient tensors and the directions of their principal axes relative to the hexagonal basal plane. The spi

James Cook, Surendra Ghentiyala, Ian Mertz, Edward Pyne

In the standard model of computing multi-output functions in logspace ($\mathsf{FL}$), we are given a read-only tape holding $x$ and a logarithmic length worktape, and must print $f(x)$ to a dedicated write-only tape. However, there has been extensive work (both in theory and in practice) on algorithms that transform $x$ into $f(x)$ in-place on a single read

Louis Wai-Tong Fan, Ali Pakzad

Due to the chaotic nature of turbulence, statistical quantities are often more informative than pointwise characterizations. In this work, we consider the stochastic Ladyzhenskaya-Smagorinsky equation driven by space-time Gaussian noise on a three-dimensional periodic domain. We derive a rigorous upper bound on the first moment of the energy dissipation rate

William Y. Chen

In this survey article we give an overview of how noncongruence modular curves can be viewed as Hurwitz moduli spaces of covers of elliptic curves at most branched above the origin. We describe some natural questions that arise, and applications of these ideas to the Inverse Galois Problem, Markoff triples and the arithmetic of Fourier coefficients for nonco

Abbas Shiri, Kenneth L. Schepler, Ayman F. Abouraddy

High-finesse planar Fabry-P{\'e}rot (FP) cavities spectrally filter the incident field at discrete resonances, and thus cannot be utilized to resonantly enhance the field of ultrashort pulses. Introducing judicious angular dispersion into a pulse can give rise to `omni-resonance', whereby the entire bandwidth of a spatiotemporally structured ultrafast pulse

Xinyu Wang, Haoming Yu, Yicheng Yang, Zhiyuan Li

Academic dishonesty is met with zero tolerance in higher education, yet plagiarism has become increasingly prevalent in the era of online teaching and learning. Automatic Question Generation (AQG) presents a potential solution to mitigate copying by creating unique questions for each student. Additionally, AQG can provide a vast array of practice questions.

Jinchuan Tian, Sang-gil Lee, Zhifeng Kong, Sreyan Ghosh

Recent advances in the audio language modeling (ALM) domain tackle audio understanding and text-to-audio generation as separate tasks. Very few studies attempt to unify these tasks -- an essential step toward advanced multimodal reasoning. This paper introduces U}nified Audio Language Model (UALM), which aims to unify audio understanding, text-to-audio gener

Skyler Oakeson, David H. Smith, Jaxton Winder, Seth Poulsen

This paper extends the functionality of block ordering problems (such as Parsons problems and Proof Blocks) to include optional blocks. We detail the algorithms used to implement the optional block feature and present usage experiences from instructors who have integrated it into their curriculum. The optional blocks feature enables instructors to create mor

Ryan Shea, Yunan Lu, Liang Qiu, Zhou Yu

Evaluating multi-turn interactive agents is challenging due to the need for human assessment. Evaluation with simulated users has been introduced as an alternative, however existing approaches typically model generic users and overlook the domain-specific principles required to capture realistic behavior. We propose SAGE, a novel user Simulation framework fo

Tanner Muturi, Blessing Agyei Kyem, Joshua Kofi Asamoah, Neema Jakisa Owor

Spatial reasoning in large-scale 3D environments such as warehouses remains a significant challenge for vision-language systems due to scene clutter, occlusions, and the need for precise spatial understanding. Existing models often struggle with generalization in such settings, as they rely heavily on local appearance and lack explicit spatial grounding. In

Gökhan Kara, Lorenzo J. A. Ferraresi, Dmitry N. Dirin, Roman Furrer

The simplicity in the fabrication of photoconductors makes them a valuable choice to investigate optoelectronic properties of colloidal quantum dot (cQD) films. Lateral photoconductors generally require a large size, in the mm2, and are limited in operation speed due to the presence of trapping sites. In contrast, hybrid phototransistors are fabricated in th

Yinan Wang, Byeongjin Kim, Nishanth Ravi, Kapil Saha

We demonstrate a record-high 62.6 GHz solidly mounted acoustic resonator (SMR) incorporating a 67.6 nm scandium aluminum nitride (Sc0.3Al0.7N) piezoelectric layer on a 40 nm buried platinum (Pt) bottom electrode, positioned above an acoustic Bragg reflector composed of alternating SiO2 (28.2 nm) and Ta2O5 (24.3 nm) layers in 8.5 pairs. The Bragg reflector an

Supravat Sarkar

Given varieties $X, Y, W$ and dominant morphisms $\phi:X\to Y$ and $f:X\to W$ such that $f$ is constant on fibres of $\phi$ , we give sufficient conditions to guarantee that $f$ descends to a rational map or a morphism $Y\to W.$ We pay special attention to the case that the ground field has positive characteristic. This extends previous works of Aichinger an

Hatem Ibrahem, Ahmed Salem, Qinmin Vivian Hu, Guanghui Wang

Accurately estimating the 3D layout of rooms is a crucial task in computer vision, with potential applications in robotics, augmented reality, and interior design. This paper proposes a novel model, PanoTPS-Net, to estimate room layout from a single panorama image. Leveraging a Convolutional Neural Network (CNN) and incorporating a Thin Plate Spline (TPS) sp

Cody Melcher, Afrooz Jalilzadeh, Erfan Yazdandoost Hamedani

In this paper, we study saddle point (SP) problems, focusing on convex-concave optimization involving functions that satisfy either two-sided quadratic functional growth (QFG) or two-sided quadratic gradient growth (QGG)--novel conditions tailored specifically for SP problems as extensions of quadratic growth conditions in minimization. These conditions rela

Catalina Freijo, Fabio Tal

We study cocycles of homeomorphisms of $\T$ in the isotopy class of the identity over shift spaces, using as a tool a novel definition of rotation sets inspired in the classical work of Miziurewicz and Zieman. We discuss different notions of rotation sets, for the full cocyle as well as for measures invariant by the shift dynamics on the base. We present som

Keito Watanabe, Stefanie Walch, Tim-Eric Rathjen, Jonathan Mackey

Bow shocks generated from the interaction of winds from massive runaway stars with the interstellar medium have been shown to be prominent particle accelerators through recent $\gamma$-ray and radio synchrotron observations. Here, we study particle acceleration from bow shocks by conducting 3D ideal cosmic ray magnetohydrodynamic simulations in the advection

Conor Rowan

Second-order methods are emerging as promising alternatives to standard first-order optimizers such as gradient descent and ADAM for training neural networks. Though the advantages of including curvature information in computing optimization steps have been celebrated in the scientific machine learning literature, the only second-order methods that have been

Jasivan Alex Sivakumar, Philipp Borchert, Ronald Cardenas, Gerasimos Lampouras

Autoformalisation, the task of expressing informal mathematical statements in formal language, is often viewed as a direct translation process. This, however, disregards a critical preceding step: conjecturing. Many mathematical problems cannot be formalised directly without first conjecturing a conclusion such as an explicit answer, or a specific bound. Sin

Owen Queen, Harrison G. Zhang, James Zou

Variant and gene interpretation are fundamental to personalized medicine and translational biomedicine. However, traditional approaches are manual and labor-intensive. Generative language models (LMs) can facilitate this process, accelerating the translation of fundamental research into clinically-actionable insights. While existing benchmarks have attempted

Mattia Scardecchia

Despite the striking successes of deep neural networks trained with gradient-based optimization, these methods differ fundamentally from their biological counterparts. This gap raises key questions about how nature achieves robust, sample-efficient learning at minimal energy costs and solves the credit-assignment problem without backpropagation. We take a st

David Sabin-Miller, Christopher Harding

The dynamics of political opinion are a critical component of modern society with large-scale implications for the evolution of intra- and international political discourse and policy. Here we utilize recent high-resolution survey data to quantitatively capture leading-order psychological and information-environmental patterns. We then inform simulations of

Pratyusa Datta, Philippe Lemey, Marc A. Suchard

Reconstructing evolutionary histories and estimating the rate of evolution from molecular sequence data is of central importance in evolutionary biology and infectious disease research. We introduce a flexible Bayesian phylogenetic inference framework that accommodates changing evolutionary rates over time by modeling sequence character substitution processe

Yankai Zhang, Yoshitaka Tanimura

We investigate the quantum dynamics of Coulomb potential systems in thermal baths. We study these systems within the framework of open quantum dynamics theory, focusing on preserving the rotational symmetry of the entire system, including the baths. Thus, we employ a three-dimensional rotationally invariant system-bath (3D-RISB) model to derive numerically `

Andrew Pendleton

We introduce consecutive equi-$n$-squares, a variant of equi-$n$-squares in which at least one row or column forms a fixed permutation of $\{1,\dots,n\}$, taken for concreteness to be $(1,\dots,n)$. More generally, the enumeration and probabilistic arguments presented here extend to the occurrence of any prescribed permutation as a row or column of an equi-$

Jusheng Zhang, Kaitong Cai, Jing Yang, Keze Wang

Preference-based finetuning of vision--language models (VLMs) is brittle: trivially wrong negatives inject uninformative gradients that destabilize training. We recast alignment as \textbf{learning-dynamics--aware optimization} and introduce \textbf{Cooling-Weighted DPO (CW-DPO)}, a two-stage recipe that explicitly models and exploits the training trajectory

Sayash Kapoor, Benedikt Stroebl, Peter Kirgis, Nitya Nadgir

AI agents have been developed for complex real-world tasks from coding to customer service. But AI agent evaluations suffer from many challenges that undermine our understanding of how well agents really work. We introduce the Holistic Agent Leaderboard (HAL) to address these challenges. We make three main contributions. First, we provide a standardized eval

Andrew Pendleton

The Index Conjecture in zero-sum theory states that when $n$ is coprime to $6$ and $k$ equals $4$, every minimal zero-sum sequence of length $k$ modulo $n$ has index $1$. While other values of $(k,n)$ have been studied thoroughly in the last 30 years, it is only recently that the conjecture has been proven for $n>10^{20}$. In this paper, we prove that said u

Zouaoui Bekri, Nicola Fabiano

In this paper, we introduce a new contraction condition that combines the framework of Singh's extension with the classical Chatterjea contraction. This generalized form, called the Singh-Chatterjea contraction, is defined on the p-th iterate of a mapping. We establish fixed point theorems for such mappings in complete metric spaces and show that our results

Emilia Del Bono, Angus Holford, Tommaso Sartori

We study the effects of academic rank using data on the entire population of children enrolled in primary schools in Aberdeen, Scotland, in 1962. Exploiting quasi-random variation in peer group composition, we estimate the causal impact of rank on academic performance, noncognitive development, parental investment, and long-term outcomes. Higher rank improve

Yuxin Du, Xin Fu, Wenjia Jing

We study time-harmonic scattering by a periodic array of penetrable, high-contrast obstacles with small period, confined to a bounded Lipschitz domain. The strong contrast between the obstacles and the background induces subwavelength resonances. We derive a frequency-dependent effective model in the vanishing-period limit and prove quantitative convergence

Xing-Yao Wang, Jia-Sheng Li, Mi-Xiang Lan

Polarization is very sensitive to the configuration of the magnetic field in the radiation region. In addition to polarization curve and polarization spectrum, studies of polarization angle (PA) rotation spectrum is also crucial. In this paper, we use a simple parametric magnetic reconnection model with a large-scale aligned magnetic field in the radiation r

Oussama Hamza, Christian Maire, Ján Mináč, Nguyen Duy Tân

In this paper, we describe minimal presentations of maximal pro-$2$ quotients of absolute Galois groups of formally real Pythagorean fields of finite type. For this purpose, we introduce a new class of pro-$2$ groups: $\Delta$-Right Angled Artin groups. We show that maximal pro-$2$ quotients of absolute Galois groups of formally real Pythagorean fields of fi

Nishant Chandgotia, Silvère Gangloff, Benjamin Hellouin de Menibus, Piotr Oprocha

We study the cohomology of symbolic dynamical systems called homshifts: they are the nearest-neighbour $\mathbb{Z}^d$ shifts of finite type whose adjacency rules are the same in every direction. Building on the work of Klaus Schmidt (Pacific J. Math. 170 (1995), no.1, 237-269) we give a necessary and sufficient condition for homshifts to be cohomological tri

Jaya Shivangani Kashyap, Robert Devaty, Chandralekha Singh

The electrostatic potential in certain types of boundary value problems can be found by solving Laplace's Equation (LE). To develop students' ability for solving problems that can be solved effectively using Laplace's equation in an upper-level electricity and magnetism course, we developed and validated a tutorial focused on finding electrostatic potential

Weiwei Sun, Miao Lu, Zhan Ling, Kang Liu

Large language model (LLM) agents are fundamentally constrained by context length on long-horizon tasks. We introduce Context-Folding, a framework that empowers agents to actively manage their working context. An agent can procedurally branch into a sub-trajectory to handle a subtask and then fold it upon completion, collapsing the intermediate steps while r

Á. Martínez Arranz, R. Schödel, H. Bouy, F. Nogueras-Lara

The center of the Milky Way is a unique environment of great astrophysical interest, but its extreme crowding and extinction make it difficult to study. The GALACTICNUCLEUS survey, a high-angular-resolution near-infrared imaging program, was designed to overcome these challenges. We present the methodology and first results of its second data release, which