October 2025 arXiv papers — page 28

Nourah M Salem, Elizabeth White, Michael Bada, Lawrence Hunter

Scientific progress is driven by the deliberate articulation of what remains unknown. This study investigates the ability of large language models (LLMs) to identify research knowledge gaps in the biomedical literature. We define two categories of knowledge gaps: explicit gaps, clear declarations of missing knowledge; and implicit gaps, context-inferred miss

Pedro Corrêa, João Lima, Victor Moreno, Lucas Ueda

Advancements in spoken language processing have driven the development of spoken language models (SLMs), designed to achieve universal audio understanding by jointly learning text and audio representations for a wide range of tasks. Although promising results have been achieved, there is growing discussion regarding these models' generalization capabilities

Hayato Idei, Tamon Miyake, Tetsuya Ogata, Yuichi Yamashita

The rapid aging of societies is intensifying demand for autonomous care robots; however, most existing systems are task-specific and rely on handcrafted preprocessing, limiting their ability to generalize across diverse scenarios. A prevailing theory in cognitive neuroscience proposes that the human brain operates through hierarchical predictive processing,

Valerie Odeh-Couvertier, Gabriel Zayas-Caban, Brian Patterson, Amy Cochran

Clinical risk prediction is a valuable tool for guiding healthcare interventions toward those most likely to benefit. Yet, evaluating the pairing of a risk prediction model with an intervention using randomized controlled trials presents substantial challenges, making quasi-experimental designs an attractive alternatives. Existing designs, however, assume th

Shunjie-Fabian Zheng, Hyeonjun Lee, Thijs Kooi, Ali Diba

Breast cancer remains the most commonly diagnosed malignancy among women in the developed world. Early detection through mammography screening plays a pivotal role in reducing mortality rates. While computer-aided diagnosis (CAD) systems have shown promise in assisting radiologists, existing approaches face critical limitations in clinical deployment - parti

Shereen Ismail, Eman Hammad, William Hatcher, Salah Dandan

This paper presents an initial longitudinal analysis of unsolicited Internet traffic collected between 2005 and 2025 by one of the largest and most persistent network telescopes in the United States, operated by Merit Network. The dataset provides a unique view into global threat activity as observed through scanning and backscatter traffic, key indicators o

Yaniv Yacoby

We present a new undergraduate ML course at our institution, a small liberal arts college serving students minoritized in STEM, designed to empower students to critically connect the mathematical foundations of ML with its sociotechnical implications. We propose a "framework-focused" approach, teaching students the language and formalism of probabilistic mod

Ivan Vican, Hugo De Moraes, Chongjun Liao, Nathnael H. Tsegaye

Hearing research requires a calibrated sound source, traditionally as lab equipment. Online research is quicker and more inclusive, but most participants lack calibration equipment and their sound sources are uncalibrated and diverse. This article explains how the open-source EasyEyes.app calibrates loudspeakers online. A library of smartphone-microphone pro

Allen H Boozer

The mathematical concepts of chaos, cartori, and turnstiles underlie a number of areas of tokamak and stellarator physics. Nevertheless, these concepts have seldom explicitly appeared in publications on fusion plasmas. The absence of physical intuition about these concepts is responsible for misunderstandings and slows developments in a number of areas: magn

Roger Clay

The 2025 June 01 Forbush Decrease in the terrestrial ground-level flux of cosmic ray secondaries was recorded by many cosmic ray systems. This was the deepest such decrease, from the quiescent value of the flux, which has been observed in the past two decades. It resulted from a complex series of solar events, none of which on its own reached the most extrem

Scott Mullane

We show that the pseudo-effective cone of divisors of $\overline{M}_{0,n}$ is not polyhedral for $n\geq 8$ by constructing an extremal non-polyhedral ray of the dual cone of moving curves via maps on meromorphic strata of differentials returning the residues at the poles of the parameterised differentials. An immediate corollary is that these spaces are not

Karthekeyan Chandrasekaran, Chandra Chekuri, Weihang Wang, Weihao Zhu

Graphs and hypergraphs combine expressive modeling power with algorithmic efficiency for a wide range of applications. Hedgegraphs generalize hypergraphs further by grouping hyperedges under a color/hedge. This allows hedgegraphs to model dependencies between hyperedges and leads to several applications. However, it poses algorithmic challenges. In particula

Zhifeng Wang, Longlong Li, Chunyan Zeng

Within the current sphere of deep learning research, despite the extensive application of optimization algorithms such as Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (Adam), there remains a pronounced inadequacy in their capability to address fluctuations in learning efficiency, meet the demands of complex models, and tackle non-convex o

Michael Ion, Alex McDonough

A celebrated result of Seymour is that all regular matroids are built up from graphic matroids, cographic matroids, and a specific 10 element rank 5 matroid called $R_{10}$. In this article, we give a simple description of chip-firing on $R_{10}$ using complex numbers on the vertices of a pentagon, and link to an app where readers can play around with the co

Petr Stibinger, Tomas Baca, Daniela Doubravova, Jan Rusnak

We present a novel approach to localizing radioactive material by cooperating Micro Aerial Vehicles (MAVs). Our approach utilizes a state-of-the-art single-detector Compton camera as a highly sensitive, yet miniature detector of ionizing radiation. The detector's exceptionally low weight (40 g) opens up new possibilities of radiation detection by a team

Marcos J. Hernández, Bogar Díaz, J. David Vergara

In this work, we analyze perturbative expansions of the quantum metric tensor (QMT) in anharmonic oscillators, focusing on quartic, sextic, and $d$-dimensional models. Using high-order perturbation theory, we show that the divergent QMT series exhibit factorial growth. Our analysis identifies universal non-perturbative scales, with coefficients displaying la

Arthur Plaud, Olivier Bénichou

Occupation times quantify how long a stochastic process remains in a region, and their single-time statistics are famously given by the arcsine law for Brownian and Lévy processes. By contrast, two-time occupation statistics, which directly probe temporal correlations and aging, have resisted exact characterization beyond renewal processes. In this Letter we

Xuyang Liang, Xiao-Chuan Wu, Zenan Liu, Zhe Wang

The disorder operator, as an easily measured nonlocal observable, displays great potential in detecting intrinsic information of field theories. It has been systematically studied in one- and two-dimensional (1D and 2D) quantum systems, while the knowledge of 3D is still limited. The disorder operator associated with U(1) global symmetry exhibits rich geomet

Piotr Marciniec, M. A. Wolfe, Tyler Kovach, J. Reily

A common method for reading out the state of a spin qubit is by latching one logical qubit state, either $|1\rangle$ or $|0\rangle$, onto a different, metastable charge state. Such a latched state can provide a superior charge sensing signal for qubit readout, and it can have a lifetime chosen to be long enough that the charge sensed readout can be high fide

Peng-Xiang Hao, Shunta Takahashi

We advance the study of flat space holography by computing the entanglement entropy of highly excited states in two-dimensional Carrollian/Galilean Conformal Field Theories (C/G CFTs). Our approach is centered on a novel, physically intuitive derivation of the heavy-light conformal block in the large central charge limit, where the backreaction of heavy oper

Kirill Boguslavski, Florian Lindenbauer, Aleksas Mazeliauskas, Adam Takacs

We apply weakly coupled QCD kinetic theory to investigate the thermalization of high-momentum on-shell partons (minijets) in a Quark-Gluon Plasma (QGP). Our approach incorporates isotropic hard thermal loop screening to model soft quark and gluon exchanges, allowing us to verify consistency with established analytic results of jet transport coefficients. We

Valdemar Frederiksen, Henrik Bruus

We show that by embedding liquid-filled acoustofluidic cavities in a metamaterial, the quality factor of the cavity at selected acoustic resonance modes can be enhanced by 2 to 3 orders of magnitude relative to a comparable conventional cavity by matching the coarse-grained elastic moduli of the metamaterial to the acoustic properties of the liquid.

Muhammad Khalil, Ke Wang, Jinho Choi

This paper presents a unified modeling, estimation, and feedback framework for reconfigurable intelligent surface RIS-assisted optical wireless links. The key modeling element is a long-exposure pixel gain that extends the classical diffraction-limited response by statistically averaging angular jitter and mispointing; it admits an exact real-integral form a

Yusuke Tsunoda, Seiya Yamamoto, Kazuki Ito, Runze Xiao

Multi-legged mobile robots possess high mobility performance in rough terrain environments, stemming from their high postural stability, joint flexibility, and the redundancy provided by multiple legs. In prior research on navigating between different environments such as land and water, the primary strategy employed involves switching to a controller that g

Ting-Hsuan Li, Po-Feng Wu

This study investigates the origin of secondary star formation, i.e., rejuvenation in nearby galaxies. From the MaNGA IFU survey, we use stellar absorption features, D$_n$4000 and EW(H$\mathrm{δ_A}$), to identify regions that started the rejuvenation within the last $\sim$200~Myr and use gas-phase metallicity as a primary tracer for accretion of pristine gas

Hamdy Arkoub, Jia-Hong Ke, Kaustubh Bawane, Miaomiao Jin

Recent experiments have shown that chemical ordering in NiCr alloys can significantly accelerate corrosion in molten salt environments. However, the underlying mechanisms remain poorly understood. Using reactive molecular dynamics and first-principles calculations, we show that long-range ordered Ni$_2$Cr in Ni-33at.%Cr alloys corrodes far more rapidly in FL

Daniel Perkins, Davis Hunter, Drake Brown, Trevor Garrity

Understanding the dynamics of the spread of diseases within populations is critical for effective public health interventions. We extend the classical SIR model by incorporating additional complexities such as the introduction of a cure and migration between cities. Our framework leverages a system of differential equations to simulate disease transmission a

Si-Jing Du, Ao Chen, Garnet Kin-Lic Chan

We describe a class of neuralized fermionic tensor network states (NN-fTNS) that introduce non-linearity into fermionic tensor networks through configuration-dependent neural network transformations of the local tensors. The construction uses the fTNS algebra to implement a natural fermionic sign structure and is compatible with standard tensor network algor

Marco Antonio Barroca, Tanvi Gujarati, Vidushi Sharma, Rodrigo Neumann Barros Ferreira

Quantum-chemical simulations are essential for predicting energies of chemical reactions. Accurately solving the many-body Schrödinger equation for reagent and product states of most relevant chemical process is, however, unfeasible. Quantum computing offers a pathway for predicting energies of correlated electronic systems with localized interactions. Here,

Kamillo Ferry, Francesco Nowell

Computing Gröbner bases is known to have a very high upper bound on computation time with respect to input length. Due to the connection between polyhedral geometry and Gröbner bases through the Gröbner fan, one can attempt an incremental approach to compute Gröbner bases. First computing a Gröbner basis with respect to an `easy' term order and transform

Can Yaylali

We define an $\infty$-category of rational motives for inverse limits of algebraic stacks, so-called pro-algebraic stacks. We show that it admits a $6$-functor formalism for certain classes of morphisms. On pro-schemes, we show that this $6$-functor formalism is in the sense of Liu-Zheng. This theory yields an approach to the theory of motives for non-repres

Caterina Campagnolo, Francesco Fournier-Facio, Yash Lodha, Marco Moraschini

We prove the vanishing of bounded cohomology with separable dual coefficients for many groups of interest in geometry, dynamics, and algebra. These include compactly supported structure-preserving diffeomorphism groups of certain manifolds; the group of interval exchange transformations of the half line; piecewise linear and piecewise projective groups of th

Dawei Chen, Samuel Grushevsky, David Holmes, Martin Möller

Multi-scale differentials were constructed by M.~Bainbridge, D.~Chen, Q.~Gendron, S.~Grushevsky, and M.~Möller, from the viewpoint of flat and complex geometry, for the purpose of compactifying moduli spaces of curves together with a differential with prescribed orders of zeros and poles. Logarithmic differentials were constructed by S.~Marcus and J.~Wise, a

Dániel István Németh, Kálmán Tornai

Electrical load classification is generally divided into intrusive and non-intrusive approaches, both having their limitations and advantages. With the non-intrusive approach, controlling appliances is not possible, but the installation cost of a single measurement device is cheap. In comparison, intrusive, smart plug-based solutions offer individual applian

Amanda Dsouza, Harit Vishwakarma, Zhengyang Qi, Justin Bauer

The rapid progress and widespread deployment of LLMs and LLM-powered agents has outpaced our ability to evaluate them. Hand-crafted, static benchmarks are the primary tool for assessing model capabilities, but these quickly become saturated. In contrast, dynamic benchmarks evolve alongside the models they evaluate, but are expensive to create and continuousl

G. Robalo Rei, C. P. Schmidt, J. Nitzler, M. Dinkel

Bayesian methods are particularly effective for addressing inverse problems due to their ability to manage uncertainties inherent in the inference process. However, employing these methods with costly forward models poses significant challenges, especially in the context of non-differentiable models, where the absence of likelihood model gradient information

Zhufeng Li, Niki Kilbertus

Causal discovery aims to recover graphs that represent causal relations among given variables from observations, and new methods are constantly being proposed. Increasingly, the community raises questions about how much progress is made, because properly evaluating discovered graphs remains notoriously difficult, particularly under latent confounding. We pro

Kellin N. Rumsey, Devin Francom, Graham C. Gibson, J. Derek Tucker

Polynomial chaos expansions (PCE) are widely used for uncertainty quantification (UQ) tasks, particularly in the applied mathematics community. However, PCE has received comparatively less attention in the statistics literature, and fully Bayesian formulations remain rare, especially with implementations in R. Motivated by the success of adaptive Bayesian ma

Qicheng Zhang, Karl Battams

Interstellar comet 3I/ATLAS has been approaching its 2025 October 29 perihelion while opposite the Sun from Earth, hindering ground-based optical observations over the preceding month. However, this geometry placed the comet within the fields of view of several space-based solar coronagraphs and heliospheric imagers, enabling its continued observation during

Farnoosh Namdarpour, Joseph Y. J. Chow

Ride-pooling, also known as ride-sharing, shared ride-hailing, or microtransit, is a service wherein passengers share rides. This service can reduce costs for both passengers and operators and reduce congestion and environmental impacts. A key limitation, however, is its myopic decision-making, which overlooks long-term effects of dispatch decisions. To addr

Zahra Ebrahimi Vargoorani, Amir Mohammad Ghoreyshi, Ching Yee Suen

Developing a highly accurate automatic license plate recognition system (ALPR) is challenging due to environmental factors such as lighting, rain, and dust. Additional difficulties include high vehicle speeds, varying camera angles, and low-quality or low-resolution images. ALPR is vital in traffic control, parking, vehicle tracking, toll collection, and law

Young Ho Kim, Yuri V. Lvov, Leslie M. Smith, Minh-Binh Tran

In this work, we study a three-wave kinetic equation with resonance broadening arising from the theory of stratified ocean flows. Unlike Gamba-Smith-Tran(On the wave turbulence theory for stratified flows in the ocean, Math. Models Methods Appl. Sci. 30 (2020), no.1, 105--137), we employ a different formulation of the resonance broadening, which makes the pr

Daoji Huang, June Huh, Daniel Soskin, Botong Wang

We study multiplicative inequalities among entries of Lorentzian matrices, referred to as bounded ratios. These inequalities can be viewed as generalizations of the classical Alexandrov--Fenchel inequalities for mixed volumes. Our main structural result identifies the cone of all bounded ratios on Lorentzian matrices with the dual of the cut cone, a finitely

Chen Gong

We analyze the behavior of multipliers of a degenerating sequence of complex rational maps. We show either most periodic points have uniformly bounded multipliers, or most of them have exploding multipliers at a common scale. We further explore the set of scales induced by the growth of multipliers. Using Ahbyankar's theorem, we prove that there can be at mo

Jie Zhou, Yi Lu, Chenyu Wang, Luke Suter

Ultrawide bandgap (UWBG) semiconductors exhibit exceptional electrical and thermal properties, offering strong potential for high power and high frequency electronics. However, efficient doping in UWBG materials is typically limited to either n type or p type, constraining their application to unipolar devices. The realization of pn junctions through heterog

Evan S. Gawlik, Jack McKee

We use moving frame techniques to derive a notion of curvature for a class of piecewise-smooth Riemannian metrics called Regge metrics, showing that it is a measure that simultaneously satisfies the (weak) Cartan structure equations and the appropriate gauge transformation law. It turns out that this distributional curvature is equivalent to existing notions

Sarmad Ahmad Khan, Simon Bernatz, Zahra Moslehi, Florian Buettner

Radiomics-based machine learning models show promise for clinical decision support but are vulnerable to distribution shifts caused by variations in imaging protocols, positioning, and segmentation. This study systematically investigates the robustness of radiomics-based machine learning models under distribution shifts across five MRI sequences. We evaluate

Zirui Cheng, Jikai Sun, Anjun Gao, Yueyang Quan

Large language models (LLMs) have transformed natural language processing (NLP), enabling applications from content generation to decision support. Retrieval-Augmented Generation (RAG) improves LLMs by incorporating external knowledge but also introduces security risks, particularly from data poisoning, where the attacker injects poisoned texts into the know

Ian McConachie, Anna de Graaff, Michael V. Maseda, Joel Leja

At low redshift, massive quiescent galaxies (MQGs) are most frequently found in massive, rich galaxy clusters, but at high redshift the trend is less clear. Here, we present spectroscopic evidence of the effects of environment on the formation and assembly of high-redshift MQGs. We identify 25 (5) $\log (M_*/\mathrm{M_\odot}\geq10.5$ ($10.0\leq\log (M_*/\mat

Rahil Soroushmojdehi, Sina Javadzadeh, Mehrnaz Asadi, Terence D. Sanger

Disentangling shared network-level dynamics from region-specific activity is a central challenge in modeling multi-region neural data. We introduce SPIRE (Shared-Private Inter-Regional Encoder), a deep multi-encoder autoencoder that factorizes recordings into shared and private latent subspaces with novel alignment and disentanglement losses. Trained solely

NVIDIA, :, Arslan Ali, Junjie Bai

We introduce [Cosmos-Predict2.5], the latest generation of the Cosmos World Foundation Models for Physical AI. Built on a flow-based architecture, [Cosmos-Predict2.5] unifies Text2World, Image2World, and Video2World generation in a single model and leverages [Cosmos-Reason1], a Physical AI vision-language model, to provide richer text grounding and finer con

Changkai Zhang, Jan von Delft

The two-dimensional (2D) Hubbard model has long attracted interest for its rich phase diagram and its relevance to high-$T_c$ superconductivity. However, reliable finite-temperature studies remain challenging due to the exponential complexity of many-body interactions. Here, we introduce an enhanced $1\text{s}^+$ eXponential Tensor Renormalization Group algo

Daniil Klyuev

Let $A=A_{G,N}^{\hbar=1}$ be a quantized Coulomb branch with an antilinear automorphism $\rho$. A map $T\colon A\to\mathbb{C}$ is called a positive trace if $T(a\rho(a))>0$ for all nonzero $a\in A$. Positive traces on Coulomb branches appear in the study of supersymmetric gauge theories. We classify positive traces on all abelian Coulomb branches, meaning $G

Minti Liu, Qinghua Guo, Cao Zeng, Yanguang Yu

This work addresses the problem of tracking maneuvering objects with complex motion patterns, a task in which conventional methods often struggle due to their reliance on predefined motion models. We integrate a data-driven liquid neural network (LNN) into the random finite set (RFS) framework, leading to two LNN-RFS filters. By learning continuous-time dyna

Rémy Mahfouf

We establish conformal invariance of Ising spin correlations on critical doubly periodic graphs, showing that their scaling limit coincides with that of the critical square lattice, as originally proved by Chelkak, Hongler and Izyurov. To overcome the absence of integrability and quantitative full plane constructions in the periodic setting, we combine discr

S. P. Cosentino, C. Inserra, M. L. Pumo

Supernova (SN) 1987A, with its long-rising ($\gtrsim$40 days) light curve, defines a rare subclass of type II SNe known as 1987A-like events. Representing only 1-3 percent of all core-collapse SNe and often found in low-metallicity environments, their large diversity suggests a wide range of progenitor and explosion properties. This study aims to improve the

Qi Lin, Zhenyu Zhang, Viraj Thakkar, Zhenjie Sun

Automatically configuring storage systems is hard: parameter spaces are large and conditions vary across workloads, deployments, and versions. Heuristic and ML tuners are often system specific, require manual glue, and degrade under changes. Recent LLM-based approaches help but usually treat tuning as a single-shot, system-specific task, which limits cross-s

Mateen Ahmed Abbasi, Petri Ihantola, Tommi Mikkonen, Niko Mäkitalo

The future of Requirements Engineering (RE) is increasingly driven by artificial intelligence (AI), reshaping how we elicit, analyze, and validate requirements. Traditional RE is based on labor-intensive manual processes prone to errors and complexity. AI-powered approaches, specifically large language models (LLMs), natural language processing (NLP), and ge

Chuyue Sun, Yican Sun, Daneshvar Amrollahi, Ethan Zhang

We introduce VeriStruct, a novel framework that extends AI-assisted automated verification from single functions to more complex data structure modules in Verus. VeriStruct employs a planner module to orchestrate the systematic generation of abstractions, type invariants, specifications, and proof code. To address the challenge that LLMs often misunderstand

Minkyung Kim, Junsik Kim, Woongcheol Yang, Sangdon Park

Large Language Models (LLMs) enable dynamic game interactions but fail to follow essential procedural flows in rule-governed trading systems, eroding player trust. This work resolves the core tension between the creative flexibility of LLMs and the procedural demands of in-game trading (browse-offer-review-confirm). To this end, Autoregressive State-Tracking

Anuj Kankani, Sean T. McWilliams

The Backwards-One-Body (BOB) model provides a fully analytical and physically motivated description of the merger-ringdown gravitational radiation emanating from a black hole binary merger. We perform a comprehensive validation of BOB for the dominant $(2,2)$ mode of quasi-circular and non-precessing systems, assessing its accuracy against numerical relativi

Anna B. T. Penzlin, Alexandros Ziampras, Nicolas T. Kurtovic, Marcelo Barraza-Alfaro

Observations of circumbinary discs reveal inner cavities, with their shape and size varying strongly between different systems. The structure of the cavity is determined by the complex interplay between spirals induced by tidal forcing from the binary and the viscous and radiative damping of the spirals at the cavity edge. To fully understand what determines

Afsah Sharaf Khan, Falong Fan, Doohwan DH Kim, Abdurrahman Alshareef

With the accelerating availability of multimodal surgical data and real-time computation, Surgical Digital Twins (SDTs) have emerged as virtual counterparts that mirror, predict, and inform decisions across pre-, intra-, and postoperative care. Despite promising demonstrations, SDTs face persistent challenges: fusing heterogeneous imaging, kinematics, and ph

Kate Brockman, Brian Colburn, Joseph Garza, Yidong Liao

A critical need exists for optimal therapeutic strategies for neuroblastoma, a prevalent and often fatal pediatric solid malignancy. To address the demand for quantitative models that can guide clinical decision-making, a novel mathematical framework was developed. Combination therapies involving immunotherapy, such as Interleukin-2 (IL-2), and chemotherapy,

Alejandro José Uría-Álvarez

Topological phases of matter have garnered significant interest over the past two decades for two main reasons: their identification, via topological invariants, relies on the quantum geometry of the Bloch states, bringing attention to an aspect of electronic band structure overlooked up to their discovery. Secondly, these classes of materials present electr

A. N. Anisimov, A. V. Mathews, K. Mavridou, U. Kentsch

We report the experimental realization and optical characterization of chlorine-vacancy (ClV) color centers in 4H-SiC emitting in the fiber-optic telecom bands. These defects are created via chlorine ion implantation followed by high-temperature annealing. Photoluminescence spectroscopy reveals four distinct ClV configurations with zero-phonon lines (ZPLs) l

James Zhang, Miles Kodama, Zongze Wu, Michael Chen

Chinese authorities are extending the country's four-phase emergency response framework (prevent, warn, respond, and recover) to address risks from advanced artificial intelligence (AI). Concrete mechanisms for the proactive prevention and warning phases, however, remain under development. This paper analyzes an implementation model inspired by international

Islam Nassar, Yang Lin, Yuan Jin, Rongxin Zhu

Evaluation and Management (E/M) coding, under the Current Procedural Terminology (CPT) taxonomy, documents medical services provided to patients by physicians. Used primarily for billing purposes, it is in physicians' best interest to provide accurate CPT E/M codes. %While important, it is an auxiliary task that adds to physicians' documentation burden. Auto

Christopher R. Schwarze, Anthony D. Manni, David S. Simon, Alexander V. Sergienko

We describe how weak phase modulations applied to classical coherent light in specially modified linear interferometers can be used to perform primitive computational tasks. Instead of encoding operations within a fixed unitary state, the operations are enacted by moving from one state to another. This harnesses the particular phase parametrization of an int

Saptarshi Saha, Dhruv Vansraj Rathore, Utpal Garain

Most counterfactual inference frameworks traditionally assume acyclic structural causal models (SCMs), i.e. directed acyclic graphs (DAGs). However, many real-world systems (e.g. biological systems) contain feedback loops or cyclic dependencies that violate acyclicity. In this work, we study counterfactual inference in cyclic SCMs under shift-scale intervent

Guifré Molera Calvés, Shinji Horiuchi, Edwin Peters, Ed Kruzins

This study presents a novel multi-static radar technique for space debris characterisation using micro-Doppler signatures, developed within the Southern Hemisphere Asteroid Radar Programme (SHARP). The method employs C-band continuous waveforms transmitted from NASA's Deep Space Network (DSN) Canberra station, with reflected signals captured by distributed g

Zhenyu Liu, Yi Ma, Rahim Tafazolli, Zhi Ding

Real-time transmission of video over wireless networks remains highly challenging, even with advanced deep models, particularly under severe channel conditions such as limited bandwidth and weak connectivity. In this paper, we propose Resi-VidTok, a Resilient Tokenization-Enabled framework designed for ultra-low-rate and lightweight video transmission that d

Riddhi Pratim Ghosh, Ian Barnett

This paper investigates two prominent probabilistic neural modeling paradigms: Bayesian Neural Networks (BNNs) and Mixture Density Networks (MDNs) for uncertainty-aware nonlinear regression. While BNNs incorporate epistemic uncertainty by placing prior distributions over network parameters, MDNs directly model the conditional output distribution, thereby cap

Kevin Frans, Pieter Abbeel, Sergey Levine

A range of recent optimizers have emerged that approximate the same "matrix-whitening" transformation in various ways. In this work, we systematically deconstruct such optimizers, aiming to disentangle the key components that explain performance. Across tuned hyperparameters across the board, all flavors of matrix-whitening methods reliably outperform elemen

Racchit Jain, Satya Lokam, Yehonathan Refael, Adam Hakim

Large Language Models (LLMs) represent valuable intellectual property (IP), reflecting significant investments in training data, compute, and expertise. Deploying these models on partially trusted or insecure devices introduces substantial risk of model theft, making it essential to design inference protocols with provable security guarantees. We present the

Chen-Te Ma

We examine the role of non-commutative geometry in D$p$-branes within large R-R field backgrounds. In this context, the background of a significant ($p-1$)-form R-R field can be effectively described using a ($p-1$)-bracket, similar to the method used in the NS-NS case. We begin by recalling how non-commutative geometry arises from the quantization of open s

Ismail Gemaledin, Iusuf Gemaledin

Metric spaces are a fundamental component of mathematics and have a paramount importance as a framework for measuring distance. They can be found in many different branches of mathematics, such as analysis and topology. This paper offers an elementary exposition of metric spaces and their associated topologies. We start by recalling the basic axioms through

J. A. Vera

We revisit the characterization of \emph{trivial} isochronous centers for planar polynomial Hamiltonian systems in degrees $5$ and $7$ obtained by Braun--Llibre--Mereu, and we formalize two conclusions suggested by their method. First, a \emph{triangular family} yields trivial (indeed global) isochronous centers in every odd degree $n=2k-1\geq3$. Second, a g

Manvendra Somvanshi, D. Jaffino Stargen

We quantize punctured plane, $X=\mathbb{R}^2-\{0\}$, employing Isham's group theoretic quantization procedure. After sketching out a brief review of group theoretic quantization procedure, we apply the quantization scheme to the phase space, $M=X \times \R^2$, corresponding to the punctured plane, $X$. Particularly, we find the canonical Lie group, $\mathscr

Siavash M. Alamouti, Fay Arjomandi

Modern multi-agent systems ranging from sensor networks monitoring critical infrastructure to crowdsourcing platforms aggregating human intelligence can suffer significant performance degradation due to systematic biases that vary with environmental conditions. Current approaches either ignore these biases, leading to suboptimal decisions, or require expensi

Emilio De Santis, Kádmo Laxa, Eva Löcherbach

In this article we introduce two new perfect simulation algorithms for chains with infinite memory. Both algorithms belong to the coupling of past procedures. The novelty of our approach is that it allows to include unknown states to the possible past symbols such that we can also deal with sparsely distributed past dependencies. In our first algorithm, spon

D. Kotschick

We show that in every even dimension there are closed manifolds that are doubles, but have no open book decomposition. In high dimensions, this contradicts the conclusions in Ranicki's book on high-dimensional knot theory. In all dimensions, examples arise from the non-multiplicativity of the signature in fibre bundles. We discuss many examples and applicati

Matsive Ali, Blake Gassen, Sen Liu

This paper presents an integrated robotic fused deposition modeling additive manufacturing system featuring closed-loop thermal control and intelligent in-situ defect correction using a 6-degree of freedom robotic arm and an Oak-D camera. The robot arm end effector was modified to mount an E3D hotend thermally regulated by an IoT microcontroller, enabling pr

Luke Serafin

We introduce Morita equivalence to the study of Kleene algebras and modules. Classical characterizations of Morita-equivalent semirings such as having equivalent categories of modules and one semiring being a full matrix algebra over the other carry over. We also observe that Morita equivalence can be applied to extending and restricting scalars in Lindenbau

Chin-Jou Li, Kalvin Chang, Shikhar Bharadwaj, Eunjung Yeo

Recent advances in spoken language processing have led to substantial progress in phonetic tasks such as automatic speech recognition (ASR), phone recognition (PR), grapheme-to-phoneme conversion (G2P), and phoneme-to-grapheme conversion (P2G). Despite their conceptual similarity, these tasks have largely been studied in isolation, each relying on task-speci

Zitong Feng, Giuseppe Marra, Irene Barbeito Edreira, Hesham Sakr

Ultra-stable lasers are fundamental to a growing range of applications, including optical frequency metrology, fundamental physics and quantum sensing. Their outstanding performance is achieved by stabilizing their frequency to Ultra-Low Expansion (ULE) optical cavities. However, the complexity of fabrication and assembly of these systems - even for compact

Hemanath Arumugam, Falong Fan, Bo Liu

Hierarchical Reinforcement Learning (HRL) enhances the scalability of decision-making in long-horizon tasks by introducing temporal abstraction through options-policies that span multiple timesteps. Despite its theoretical appeal, the practical implementation of HRL suffers from the challenge of autonomously discovering semantically meaningful subgoals and l

Stephen Alaba John, Joye Ahmed Shonubi, Patience Farida Azuikpe, Victor Oluwatosin Ologun

The inception of AI-based fraud detection systems has presented the banking sector across the globe the opportunity to enhance fraud prevention mechanisms. However, the extent of adoption in Nigeria has been slow, fragmented, and inconsistent due to high cost of implementation and lack of technical expertise. This study seeks to investigate extent of adoptio

Ria Jayanti, Tanish Jain

In recent years, machine learning has become an increasingly powerful tool for supporting seizure detection and monitoring in epilepsy care. Traditional approaches focus on identifying seizures only after they begin, which limits the opportunity for early intervention and proactive treatment. In this study, we propose a novel approach that integrates both re

Najmeh Nazari, Banafsheh Saber Latibari, Elahe Hosseini, Fatemeh Movafagh

Forget and Rewire (FaR) methodology has demonstrated strong resilience against Bit-Flip Attacks (BFAs) on Transformer-based models by obfuscating critical parameters through dynamic rewiring of linear layers. However, the application of FaR introduces non-negligible performance and memory overheads, primarily due to the runtime modification of activation pat

Ximan Sun, Xiang Cheng

Diffusion policies are competitive for offline reinforcement learning (RL) but are typically guided at sampling time by heuristics that lack a statistical notion of risk. We introduce LRT-Diffusion, a risk-aware sampling rule that treats each denoising step as a sequential hypothesis test between the unconditional prior and the state-conditional policy head.

Chi Zhao, Jing Liu, Elena Parilina

The exponential growth of data volumes has led to escalating computational costs in machine learning model training. However, many features fail to contribute positively to model performance while consuming substantial computational resources. This paper presents an end-to-end feature selection framework for tabular data based on game theory. We formulate fe

Reza Saadati Fard, Emmanuel Agu, Palawat Busaranuvong, Deepak Kumar

Pressure ulcers (PUs) are a serious and prevalent healthcare concern. Accurate classification of PU severity (Stages I-IV) is essential for proper treatment but remains challenging due to subtle visual distinctions and subjective interpretation, leading to variability among clinicians. Prior AI-based approaches using Convolutional Neural Networks (CNNs) and

Chenyu Tang, Mayank Prakash Pandey, Cheng Giuseppe Chen, Alberto Megías

Molecular transitions -- such as protein folding, allostery, and membrane transport -- are central to biology yet remain notoriously difficult to simulate. Their intrinsic rarity pushes them beyond reach of standard molecular dynamics, while enhanced-sampling methods are costly and often depend on arbitrary variables that bias outcomes. We introduce Gen-COMP

Chung-Jun Tsai, Mao-Pei Tsui, Jingbo Wan, Mu-Tao Wang

We introduce an evolving-plane ansatz for the explicit construction of entire minimal graphs of dimension $n$ ($n\geq 3$) and codimension $m$ ($m\geq 2$), for any odd integer $n$. Under this ansatz, the minimal surface system reduces to the geodesic equation on the Grassmannian in affine coordinates. Geometrically, this equation dictates how the slope of an

Kunle Adegoke, Robert Frontczak, Karol Gryszka

In this paper, we find an elementary approach for double sums where the inner sum is binomial but incomplete. We apply our core identity and its relatives to double sums involving famous numbers such as harmonic numbers, Fibonacci numbers, Stirling numbers and $r$-Stirling numbers of the second kind.

Boris Tsvelikhovskiy

Let $ G = \mathbb{Z}/r\mathbb{Z}$ be the cyclic group of order $r$, and let $\varpi = e^{2\pi i / r}$ denote a primitive $r$ th root of unity. Consider the action of $G$ on $\mathbb{C}^n$ via the embedding $$ \varphi : G \hookrightarrow GL_n(\mathbb{C}), \qquad \varphi(1) = \mathrm{diag}\!\bigl( \underbrace{\varpi, \dots, \varpi}_{s},\, \underbrace{\varpi^{-

Banafsheh Saber Latibari, Najmeh Nazari, Hossein Sayadi, Houman Homayoun

Vision Transformers (ViTs) have emerged as powerful architectures in medical image analysis, excelling in tasks such as disease detection, segmentation, and classification. However, their reliance on large, attention-driven models makes them vulnerable to hardware-level attacks. In this paper, we propose a novel threat model referred to as Med-Hammer that co

Aswin Undavalli, Kareem Rashed, Zhili Xiao, Arun Natarajan

In this paper, we demonstrate how the physics of entropy production, when combined with symmetry constraints, can be used for implementing high-performance and energy-efficient analog computing systems. At the core of the proposed framework is a generalized maximum-entropy principle that can describe the evolution of a mesoscopic physical system formed by an

Mia Adler, Carrie Liang, Brian Peng, Oleg Presnyakov

Machine Learning-assisted directed evolution (MLDE) is a powerful tool for efficiently navigating antibody fitness landscapes. Many structure-aware MLDE pipelines rely on a single conformation or a single committee across all conformations, limiting their ability to separate conformational uncertainty from epistemic uncertainty. Here, we introduce a rank -co