April 2026 arXiv papers — page 86

Uzay Aydin

Photometric redshift estimation is a key requirement for modern large-area surveys, where spectroscopic measurements are observationally prohibitive. Seyfert II galaxies provide a particularly challenging test case due to the combined effects of nuclear activity, host-galaxy emission, and dust attenuation. In this work, we develop a machine learning approach

Kangbo Bai, Zhantong Zhu, Yifan Ding, Tianyu Jia

In large-scale distributed LLM training, communication between devices becomes the key performance bottleneck. Chiplet technology can integrate multiple dies into a package to scale-up node performance with higher bandwidth. Meanwhile, optical interconnect (OI) technology offers long-reach, high-bandwidth links, making it well suited for scale-out networks.

Chao Li

Memristive crossbars store numerical weights needing aggregation and decoding; a single junction means nothing alone. This paper presents a fundamentally different use: each junction stores a complete, domain-scoped logical assertion (holds/negated/undefined). Ternary resistance states encode these values directly. We establish a structure-preserving mapping

Sida Cao, Matthew R. Edwards

High-energy terahertz pulses are challenging to produce due to the low conversion efficiency and limited optical damage threshold of nonlinear crystals. Here, we describe the high-efficiency generation of terahertz radiation pulses with tunable frequency and field strengths exceeding 500 GV/m by propagating two-color laser pulses through a strongly magnetize

Matthew V. Macfarlane, Clément Bonnet, Herke van Hoof, Levi H. S. Lelis

A central challenge in program induction has long been the trade-off between symbolic and neural approaches. Symbolic methods offer compositional generalisation and data efficiency, yet their scalability is constrained by formalisms such as domain-specific languages (DSLs), which are labour-intensive to create and may not transfer to new domains. In contrast

Moyan Liu, Qin Huang, Upmanu Lall

Advances in deep learning methods for weather forecasting are creating opportunities to computationally explore the potential for steering or control of extreme weather trajectories for societal risk reduction. We present initial investigations into the feasibility of redirecting extreme atmospheric rivers (ARs) through small, instability-aware perturbations

Shuai Li, Ton T. H. Duong, Damiano Zanotto

The collaboration between humans and robots is critical in many robotic applications, especially in those requiring physical human-robot interaction (pHRI). Previous research in pHRI has largely focused on robotic manipulators, employing impedance or admittance control to maintain operational safety. Conversely, research in human-quadrotor cooperative load t

Joshua J. Ziegler, Justin D. Finke, J. Martin Laming

The shock fronts of supernova remnants (SNRs) are believed to be significant sites of acceleration of cosmic ray particles. Previous researchers have shown that a particle distribution similar to a log-parabola can be generated when particles have an energy-dependent escape. We explore the acceleration of electrons at SNR shock fronts, and show that modeling

Thamer Hmidhi, Radhouane Fekih-Salem

We investigate the dynamics of the AM2 model in a serial configuration of two interconnected chemostats with distinct dilution rates. The system is described by nonlinear differential equations, for which the usual reduction from an eight-dimensional system to a four-dimensional one is no longer possible due to the distinct dilution rates. We provide a compl

Brandon B. Le, Seung-Hun Lee, Gia-Wei Chern

We investigate the energy landscape of the kagome Heisenberg antiferromagnet within its coplanar ground-state manifold. Although coplanar states are degenerate at harmonic order, transitions between them require collective weathervane-loop rotations whose barriers grow strongly with loop size. To characterize this structure, we construct disconnectivity grap

Sebastian Zając, Rafał Pracht

In a global derivatives market with notional values in the hundreds of trillions of dollars, the accuracy and efficiency of pricing models are of fundamental importance, with direct implications for risk management, capital allocation, and regulatory compliance. In this work, we employ the Black-Scholes-Merton (BSM) framework not as an end in itself, but as

Isaac Llorente-Saguer

Aligned language models refuse harmful instructions, but the representations through which they recognise such instructions are less well characterised than the behaviours they produce. Harmful intent is linearly separable from residual-stream activations across 12 models spanning four architectural families (Qwen2.5, Qwen3.5, Llama-3.2, Gemma-3) and three a

Luca Ciampaglia

Aerobat is a bat-inspired flapping-wing robot with a wing gait generate by the computational structure, a planar linkage of carbon fiber links driven by a single motor. This design minimizes weight but couples both wings to a shared input motor, eliminating independent thrust control and preventing asymmetric maneuvers. This thesis investigates thrust regula

Dirk Ekelschot

The main goal of this paper is to demonstrate the application of metric-based mesh adaptation to real gas problems and highlight the benefits particularly when complex geometries are considered. We use the Hessian of the temperature solution as an indicator to dictate where the mesh needs refinement or coarsening. In the context of hypersonic flow simulation

Yihao Tan, Marianthi Markatou, Saptarshi Chakraborty

Postmarketing safety surveillance relies on data from spontaneous reporting systems (SRS) such as FAERS, EudraVigilance and VigiBase, and commonly uses SRS data mining methods to assess the associations between drugs and adverse events (AEs). Traditionally, these analyses have focused on signal detection framed as a binary decision problem, whereas more rece

H. R. Fazlollahi

In this study, we propose an extension of general relativity inspired by the van der Waals equation of state, incorporating non-ideal thermodynamic effects into the gravitational sector. Our approach is based on the thermodynamic interpretation of gravity introduced by Jacobson, in which the field equations arise from the Clausius relation. Within this frame

Manuel Israel Cazares

We present a systematic empirical study of prompt engineering for formal mathematical reasoning in the context of the SAIR Equational Theories Stage 1 competition. The task requires deciding whether one equational law implies another over all magmas -- a problem that is undecidable in general but decidable for FALSE via finite model search. Over five weeks,

Mustafa Mert Özyılmaz

ARM-based and x86-64 laptop processors differ not only in instruction-set design, but also in memory hierarchy, core organization, system integration, and power-management mechanisms. This study presents a combined architectural and experimental comparison of an Apple M3 system and an AMD Ryzen 7 3750H system. The architectural analysis contrasts AArch64's f

Tobias Wenzel, Richard Bowman, Gemma S. Cairns, Benedict Diederich

Optical imaging technologies are central to discovery in the life and physical sciences, yet their impact depends on how readily they can be built, adapted, and sustained across laboratories. Digital fabrication, including desktop 3D printing, offers new ways to engineer imaging instruments by simplifying assembly, lowering replication barriers, and enabling

Tom Goertzen, Geordie Williamson

We describe a conjectural approach to obtaining canonical bases of the Hecke algebra at $q=1$ via continuous quadratic optimization. We focus on Specht modules $S^\lambda$ and proper cones inside $S^\lambda$ that are invariant under the action of $1+s$ for all simple reflections $s\in S$. We show that there are unique minimal and maximal cones invariant unde

Henry Fraser, Jessica M. Szczuka, Raffaele F. Ciriello

A series of high-profile tragedies involving companion chatbots has triggered an unusually rapid regulatory response. Several jurisdictions, including Australia, California, and New York, have introduced enforceable regulation, while regulators elsewhere have signaled growing concern about risks posed by companion chatbots, particularly to children. In paral

Mengzhao Jia, Zhihan Zhang, Meng Jiang

Reinforcement Learning with Verifiable Rewards (RLVR) improves multimodal reasoning by rewarding verifiable final answers. Yet answer-correct trajectories may still rely on incomplete derivations, weak evidence, or statements that contradict their conclusions. This gap between answer correctness and reasoning validity, which we call reasoning-answer inconsis

Masato Tsujii, Zhiyuan Zhang

Let $s > 1$ be a large integer, and let $f$ be a diffeomorphism sufficiently close in the $C^{s}$-topology to the time-1 map of a $C^{s}$ generic volume-preserving Anosov flow on a $3$-dimensional compact manifold. We show that for any probability measure $\mu$ with smooth density, $f^n_* \mu$ converges exponentially fast to a common limit measure with full

Yash M. Lokare, Matthew J. Coley-O'Rourke

Arrays of ultracold neutral atoms, also known as Rydberg atom arrays, are rapidly developing into a powerful and versatile platform for quantum simulation. However, theoretical predictions about the bulk quantum phases of matter present in these systems have often diverged from experimental realizations on finite-sized arrays due to the strong effects of the

Peter Racioppo

We present AC-SINDy, a compositional extension of the Sparse Identification of Nonlinear Dynamics (SINDy) framework that replaces explicit feature libraries with a structured representation based on arithmetic circuits. Rather than enumerating candidate basis functions, the proposed approach constructs nonlinear features through compositions of linear functi

Ali Mohammadiasl, Bita Akram, Seyyedali Hosseinalipour, Rajeev Sahay

Social learning networks (SLNs) are graphical representations that capture student interactions within educational settings (e.g., a classroom), with nodes representing students and edges denoting interactions. Accurately predicting future interactions in these networks (i.e., link prediction) is crucial for enabling effective collaborative learning, support

Blake Werner, Sergio A. Esteban, Massimiliano De Sa, Max H. Cohen

Reduced-order models are powerful for analyzing and controlling high-dimensional dynamical systems. Yet constructing these models for complex hybrid systems such as legged robots remains challenging. Classical approaches rely on hand-designed template models (e.g., LIP, SLIP), which, though insightful, only approximate the underlying dynamics. In contrast, d

Mengdi Wang, Yuchen Sun, Bo Zhu

We present a matrix-free GPU multigrid preconditioner with algebraically consistent coarsening for solving Poisson equations on adaptive octree grids with irregular domains. Within uniform-resolution regions, the coarsening satisfies the Galerkin principle. At T-junctions between refinement levels, we propose a flux-consistent coarse-grid correction that res

Oussama Korichi, Markus Hiekkamaki, Robert Fickler

Light with complex structures in polarization, phase and amplitude, has attracted a lot of attention in a broad range of applications and fundamental studies in classical and quantum optics. Along with the increased interest in structured light comes a need for efficient modulation platforms operating simultaneously for many modes. Multi plane light conversi

Akel Hashim

The quantum instrument (QI) formalism is required to model mid-circuit measurements (MCMs) and the dependence of the post-measurement state on the measurement outcome. Correctly modeling QIs is essential for applications using MCMs, such as adaptive circuits and quantum error correction. Although QIs yield a joint quantum-classical state after measurement, e

Vassilios Exarhakos, Jinghui Cheng, Jin L. C. Guo

Current AI-assisted programming tools are predominantly linear and chat-based, which deviates from the iterative and branching nature of programming itself. Our preliminary study with developers using AI assistants suggested that they often struggle to explore alternatives, manage prompting sequences, and trace changes. Informed by these insights, we created

George Koomullil

We present a formally verified framework for patent analysis as a hybrid AI + Lean 4 pipeline. The DAG-coverage core (Algorithm 1b) is fully machine-verified once bounded match scores are fixed. Freedom-to-operate, claim-construction sensitivity, cross-claim consistency, and doctrine-of-equivalents analyses are formalized at the specification level with kern

Zhongying Wang, Kevin Lane, Levi Cai, Morteza Karimzadeh

Supervised learning with Earth observation inputs is often limited by the sparsity of high-quality labeled or in-situ measured data to use as training labels. With the abundance of geographic data products, in many cases there are variables correlated with - but different from - the variable of interest that can be leveraged. We integrate such proxy variable

Yuefei Chen, Yihao Quan, Xiaodong Lin, Ruixiang Tang

LLMs frequently generate fictitious yet convincing citations, often expressing high confidence even when the underlying reference is wrong. We study this failure across 9 models and 108{,}000 generated references, and find that author names fail far more often than other fields across all models and settings. Citation style has no measurable effect, while re

Ondřej Červinka, Martin Hrtoň, Štěpán Venos, Jakub Lelek

Metasurfaces have emerged as a powerful platform for compact optical sensors by replacing bulky lenses with flat arrays of subwavelength nanostructures. In precision optical metrology, the simultaneous mapping of a beam's polarization state and wavefront is crucial for real-time diagnostics of stress-induced birefringence and surface flatness. To achieve thi

Pedro Barbosa de Carvalho Neto

We introduce LegalBench-BR, the first public benchmark for evaluating language models on Brazilian legal text classification. The dataset comprises 3,105 appellate proceedings from the Santa Catarina State Court (TJSC), collected via the DataJud API (CNJ) and annotated across five legal areas through LLM-assisted labeling with heuristic validation. On a clas

The GAPS Collaboration, Kazutaka Aoyama, Tsuguo Aramaki, Padrick Beggs

The General Antiparticle Spectrometer (GAPS) is an Antarctic stratospheric balloon mission designed to provide unmatched sensitivity to low-energy (<0.25 GeV/n) cosmic-ray antiprotons, antideuterons, and antihelium nuclei as signatures of dark matter. The distinctive GAPS particle identification technique relies on measuring the energy loss along the track o

Mychal Amoafo, Ilya Kolmanovsky, Anuradha Annaswamy

This paper proposes a control architecture integrating adaptation with Lyapunov-based Reference Governors (LRGs) to ensure state constraint satisfaction for first-order systems with parametric uncertainties. Adaptation combined with LRGs guarantees stability, ensures good control performance, and remains safe even with parametric uncertainties. Simulations o

Ben Blum-Smith, Sylvan Crane, Karla Guzman, Alexis Menenses

We investigate degree bounds for fields of rational invariants of representations of finite groups. We prove many cases of a bound for $\mathbb{Z}/p\mathbb{Z}$ conjectured by Blum-Smith, Garcia, Hidalgo, and Rodriguez. For arbitrary groups, we also prove a new bound on the minimum degree $d$ such that the polynomials of degree $\leq d$ span the field of rati

Khoa D. Nguyen

In 1963, Rapha\"el Salem concluded his highly influential book ``Algebraic Numbers and Fourier Analysis'' with a list of four unsolved problems. The first two problems remain wide open while the last problem on the absolute continuity of Bernoulli convolutions has seen significant progress over the years including recent results by Shmerkin and Varj\'u. In t

Zhonghao Zhan, Huichi Zhou, Zhenhao Li, Peiyuan Jing

Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack surface. Current evaluations benchmark capability in benign settings, asking "can the agent use tools correctly" but never "what if the tools lie". We identify this Trust Gap: agents are evaluated for perfor

Mina Gabriel, Pei Wang

Large language models (LLMs) are highly capable at language generation, but they remain unreliable when reasoning requires explicit symbolic structure, multi-step inference, and interpretable uncertainty. This paper presents a neuro-symbolic framework for translating natural-language reasoning problems into executable formal representations using first-order

Alex McAvoy

The expected meeting time of two random walkers on an undirected graph of size $N$, where at each time step one walker moves and the process stops when they collide, satisfies a system of $\binom{N}{2}$ linear equations. Na\"{i}vely, solving this system takes $O\left(N^{6}\right)$ operations. However, this system of linear equations has nice structure in tha

Ludovic Goudenège, Christian Olivera, Gabriela Planas, Alexandre Richard

This paper is concerned with a fluid-particle system given by the incompressible Navier-Stokes equations coupled with the Vlasov(-Fokker-Planck) equation through a drag force. Such a model arises naturally in the study of aerosols, sprays, and more generally two-phase flows. In dimensions $d\in \{2,3\}$, we establish a rate of convergence for a system of $N$

Adnan Khan, Abbas Akkasi, Majid Komeili

Tactile graphics require careful expert validation before reaching blind and visually impaired (BVI) learners, yet existing datasets provide only coarse holistic quality ratings that offer no actionable repair signal. We present TactileEval, a three-stage pipeline that takes a first step toward automating this process. Drawing on expert free-text comments fr

Troy I. Johnson, Justin T. Cole

An outstanding challenge in the field of topological insulators is the realization of nonlinear systems that support coherent traveling waves. Highly nonlinear lattices can suffer from significant radiation losses due to Peierls-Nabarro effects. In this work a nonlinear tight-binding model that supports robust traveling edge states is proposed and examined.

Wouter van Doorn, Pietro Monticone, Quanyu Tang

For a family $(A_q)_{q\in Q}$ of subsets of a semigroup, the product intersection set records those exponents $h \in \mathbb{N}$ for which the $h$-fold product set of the intersection, $(\bigcap_q A_q)^h$, is equal to $\bigcap_q A_q^h$, the intersection of the product sets. Nathanson recently asked which subsets of $\mathbb{N}$ can occur as a product interse

Lucie Charlotte Magister, Alexander Norcliffe, Iulia Duta, Pietro Lio

The reasoning process of Graph Neural Networks is complex and considered opaque, limiting trust in their predictions. To alleviate this issue, prior work has proposed concept-based explanations, extracted from clusters in the model's node embeddings. However, a limitation of concept-based explanations is that they only explain the node embedding space and ar

Junhao Dong, Yifei Zhang, Hao Zhu, Yew-Soon Ong

Vision-Language Models (VLMs) can perform zero-shot classification but are susceptible to adversarial attacks. While robust fine-tuning improves their robustness, existing approaches align fixed text embeddings with an image embedding, sacrificing natural performance and robustness. A robustness degradation also occurs when a model faces adversarial attacks

Kell Fremouw, Ryan A. DeCrescent, Xianfu Zhang, Yi Yang

Gradual reverse-bias breakdown in metal-halide perovskite diodes and solar cells is thought to originate from hole tunneling through steep bands in an ionic depletion region near the electron transport layer after positively charged iodine vacancies accumulate near the hole-transport layer (HTL). However, typical reported mobile ion concentrations near $1\ti

Nicolas Salvadé, Tim Hillel

Generalized Additive Models (GAMs) can be used to create non-linear glass-box (i.e. explicitly interpretable) models, where the predictive function is fully observable over the complete input space. However, glass-box interpretability itself does not allow for the incorporation of expert knowledge from the modeller. In this paper, we present ParamBoost, a no

Guoming Long, Shihai Wang, Hui Fang, Tao Chen

Bug reports, encompassing a wide range of bug types, are crucial for maintaining software quality. However, the increasing complexity and volume of bug reports pose a significant challenge in sole manual identification and assignment to the appropriate teams for resolution, as dealing with all the reports is time-consuming and resource-intensive. In this pap

Wenpeng Xu

GUI agents that control desktop computers via screenshot-and-click loops introduce a new class of vulnerability: the observation-to-action gap (mean 6.51 s on real OSWorld workloads) creates a Time-Of-Check, Time-Of-Use (TOCTOU) window during which an unprivileged attacker can manipulate the UI state. We formalize this as a Visual Atomicity Violation and cha

Jannik Truong, Greg Aldering, Saul Perlmutter, David Rubin

Combined cosmological probes currently indicate that best-fit values in the $w_0-w_a$ parametrization of dynamical dark energy deviate from $\Lambda$CDM by $\sim3\sigma$. In this work, we present a supernova survey capable of measuring dynamical dark energy at the $>5\sigma$ level with just one year of data, starting in 2027. We first show that with the pres

Nicolas F. Armijo, Yunier Bello Cruz, Gabriel Haeser

In this work we develop and analyze a semi-smooth Newton method for the general nonlinear conic programming problem. In particular, we study the problem with a generalized simplicial cone, i.e., the image of a symmetric cone under a linear mapping. We generalize Robinson's normal equations to a conic setting, yielding what we call the conic projection equati

Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger, William A. P. Smith

In continual learning, the primary challenge is to learn new information without forgetting old knowledge. A common solution addresses this trade-off through regularization, penalizing changes to parameters critical for previous tasks. In most cases, this regularization term is directly added to the training loss and optimized with standard gradient descent,

Mohammed Q. Alkhatib

Hyperspectral image (HSI) classification remains challenging due to high spectral dimensionality, redundancy, and limited labeled data. Although convolutional neural networks (CNNs) and Vision Transformers (ViTs) achieve strong performance by exploiting spectral-spatial information and long-range dependencies, they often incur high computational cost and lar

Eleonora Di Nezza, Alexander Rashkovskii

We study continuity, H\"older regularity, and $C^{1,1}$-regularity of geodesics between continuous plurisubharmonic functions on bounded domains of $\mathbb{C}^n$. We then derive regularity properties of rooftop envelopes.

Masaru Hitomi, Takuto Kawakami, Mikito Koshino

We develop a theoretical framework for Landau levels in quasi-periodic twisted bilayer graphene at a $30^\circ$ twist angle, a system without translational symmetry but possessing 12-fold rotational symmetry. Using a quasi-band formalism, we incorporate the magnetic field through a conventional momentum substitution in the zero-field Hamiltonian. This approa

Mohammed Q. Alkhatib

This paper presents DDF2Pol, a lightweight dual-domain convolutional neural network for PolSAR image classification. The proposed architecture integrates two parallel feature extraction streams, one real-valued and one complex-valued, designed to capture complementary spatial and polarimetric information from PolSAR data. To further refine the extracted feat

Kenneth Benício, André L. F. de Almeida, Fazal-E Asim, Bruno Sokal

We investigate the performance of beyond-diagonal reconfigurable intelligent surfaces (BD-RIS) for bistatic MIMO multi-target sensing using a two-stage tensor Doppler-delay-angle estimation (TenDAE). The first stage solves a Kronecker sum approximation (KSA) with a rank equal to the number of targets. The second stage employs a nested tensor factorization es

Mariia Kryvoruchko, Brian A. Camley

When cells collide, they often exhibit "contact inhibition of locomotion" (CIL), a behavior in which cells repolarize and migrate away from the site of contact. Experimental CIL outcomes are highly variable - why? Here, we develop a minimal stochastic model to quantify how intrinsic noise in cell polarity, arising from the finite number of signaling molecule

Katherine Wang, Nadia Berthouze, Aneesha Singh

AI systems are increasingly embedded in multi-user social environments, yet most alignment frameworks conceptualize interaction as a dyadic relationship between a single user and an AI system. Livestreaming platforms challenge this assumption: interaction unfolds among streamers and audiences in real time, producing dynamic affective and social feedback loop

Golo Henseke

This study examines who adopts generative AI and whether early adoption has begun to reshape the task content of jobs across 35 European countries. Adoption ranges from under 3% to 25%. Occupational exposure strongly predicts uptake, but AI does not diffuse passively along exposure lines. At the worker level, skills, abstract task content, and employee organ

Elisa Continelli, Jan Haskovec, Cristina Pignotti

We investigate consensus formation and flocking behavior in multi-agent systems subject to two distinct types of delays: a transmission delay accounting for information exchange between agents, and a reaction delay representing the processing time before agents adjust their states. For a simplified linear two-agent system, we provide explicit insight into ho

Gordon Ma, Xiufan Li

Barren-plateau results have established exponential gradient suppression as a widely cited obstacle to the scalability of variational quantum algorithms. When and whether these results extend to a given objective has been addressed through loss-specific arguments, but a general structural characterization has remained open. We show that the objective itself

Qingcheng Zeng, Puxuan Yu, Aman Mehta, Fuheng Zhao

Instruction-following information retrieval (IF-IR) studies retrieval systems that must not only find documents relevant to a query, but also obey explicit user constraints such as required attributes, exclusions, or output preferences. However, most retrievers are trained primarily for semantic relevance and often fail to distinguish documents that match th

Sui Chen, Emilie Parent, Nanda Rea, Francesco Topputo

Current space missions primarily depend on Earth-based Guidance, Navigation, and Control (GNC) systems involving human-in-the-loop operations. X-ray pulsar-based navigation offers a promising alternative by using the very precise periodic X-ray emissions from pulsars for fully autonomous state estimation. This study presents a comprehensive analysis of pulsa

Scott B. Lee, Stephanie R. Dulovic, Joseph W. Stiles, Xin Zhang

Polar metals and noncentrosymmetric superconductors are exceptionally rare, yet their broken inversion symmetry can give rise to emergent electronic phenomena including mixed singlet-triplet superconducting pairing. As only a few such materials have been found among known compounds, accessing new examples requires synthetic strategies that go beyond conventi

Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou, Oscar Mendez

Human perception generalizes well across different domains, but most vision models struggle beyond their training data. This gap motivates multi-dataset learning, where a single model is trained on diverse datasets to improve robustness under domain shifts. However, unified training remains challenging due to inconsistencies in data distributions and label s

Luke Miller, Yugyung Lee

We introduce QuIC, a training-free quantum graph embedding that maps graphs to sorted output distributions via a fixed parameterized circuit. In the ideal one-repetition setting, we prove that the resulting sorted distribution is permutation-invariant and injective on labeled graphs under an irrational-angle condition, yielding completeness on isomorphism cl

Ben Seiyon Lee, Reetam Majumder, Jordan Richards, Emma S. Simpson

Understanding and mapping extreme heat is critical for risk management and public health planning, particularly in regions with complex terrain and heterogeneous climate. We present a case study of extreme heat in the Four Corners region of the United States, using high-resolution surface skin temperature data from the North American Land Data Assimilation S

Chris Cameron, Wangzheng Wang, Nikita Ivanov, Ashmita Bhattacharyya

Looped transformers scale computational depth without increasing parameter count by repeatedly applying a shared transformer block and can be used for iterative refinement, where each loop rewrites a full fixed-size prediction in parallel. On difficult problems, such as those that require search-like computation, reaching a highly structured solution startin

Kanishk Bakshi, Kathiravan Srinivasan

This research investigates the performance and efficacy of machine learning models in stock prediction, comparing Artificial Neural Networks (ANNs), Quantum Qubit-based Neural Networks (QQBNs), and Quantum Qutrit-based Neural Networks (QQTNs). By outlining methodologies, architectures, and training procedures, the study highlights significant differences in

Siavash Kakavand, Christoph Strohmeyer, Michael Schlotter

Quantum kernel methods have been proposed as a promising approach for leveraging near-term quantum computers for supervised learning, yet rigorous benchmarks against strong classical baselines remain scarce. We present a comprehensive empirical study of quantum kernel support vector machines (QSVMs) across nine binary classification datasets, four quantum fe

Sinan G. Aksoy, Alexandra A. Sabrio, Erik VonKaenel, Lee Burke

We propose a scalable, multifactorial experimental framework that systematically probes LLM sensitivity to subtle semantic changes in pairwise document comparison. We analogize this as a needle-in-a-haystack problem: a single semantically altered sentence (the needle) is embedded within surrounding context (the hay), and we vary the perturbation type (negati

Dinithi Jayasuriya, Aravind Saravanan, Nilesh Ahuja, Amanda Rios

Large language models (LLMs) have enabled natural-language-driven automation of electronic design automation (EDA) workflows, but reliable execution of generated scripts remains a fundamental challenge. In LLM-based EDA tasks, failures arise not from syntax errors but from violations of implicit structural dependencies over design objects, including invalid

Priya Drashni, Hari P. Lamsal, Belle A. White, Noah A. Crum

We demonstrate the generation of sub-Poissonian twin fields via near-degenerate spontaneous four-wave mixing (SFWM) in warm $^{85}\mathrm{Rb}$ vapor at 795~nm. When seeded with a weak coherent field, the generated twin beams exhibit approximately $5.5~\mathrm{dB}$ of intensity-difference squeezing in free space and retain about $3~\mathrm{dB}$ after coupling

Haiyang Wu, Juan J. Gonzales Torres, George Vosselman, Ville Lehtola

Frame-wise semantic segmentation of indoor lidar scans is a fundamental step toward higher-level 3D scene understanding and mapping applications. However, acquiring frame-wise ground truth for training deep learning models is costly and time-consuming. This challenge is largely addressed, for imagery, by Visual Foundation Models (VFMs) which segment image fr

Lenny Jones

Let $f(x)=x^{12}+ax^{6}+b\in {\mathbb Z}[x]$, with $ab\ne 0$. We say that $f(x)$ is {\em monogenic} if $f(x)$ is irreducible over ${\mathbb Q}$ and $\{1,\theta,\theta^2,\ldots,\theta^{11}\}$ is a basis for the ring of integers of ${\mathbb Q}(\theta)$, where $f(\theta)=0$. For each possible Galois group $G$ of $f(x)$ over ${\mathbb Q}$, we give explicit desc

Abrar Majeedi, Zhiyuan Ruan, Ziyi Zhao, Hongcheng Wang

Multimodal large language models (MLLMs) have achieved impressive performance on visual perception and reasoning tasks with RGB imagery, yet they remain fragile under common degradations, such as fog, blur, or low-light conditions. Infrared (IR) imaging, a well-established complement to RGB, offers inherent robustness in these conditions, but its integration

Bryan Kaiser, Kyle Hickmann, Sharmistha Chakrabarti, Soumi De

Artificial Intelligence (AI) surrogate models provide a computationally efficient alternative to full-physics simulations, but no public datasets currently exist for training and validating models of high-explosive-driven, multi-material shock dynamics. Simulating shock propagation is challenging due to the need for material-specific equations of state (EOS)

Euihyeon Choi, Koki Ho

This paper studies the integrated spacecraft routing and trajectory optimization problem for satellite servicing missions involving partial en-route propellant replenishment. Unlike terrestrial routing problems, spacecraft operate in a dynamic environment, and we need to optimize the spacecraft routing over a network with nonlinear and time-dependent traject

Takayuki Hibi, Selvi Kara, Dalena Vien

Given $n \geq 1$, we study the existence of a tree on $n$ vertices whose independence polynomial is symmetric and unimodal as well as the existence of a symmetric and unimodal independence polynomial of degree $n$ of a tree.

Alessandro Fusta Moro, Antony Sikorski, Daniel McKenzie, Alessandro Fassò

Air quality monitoring in Italy relies on sparse, irregular, ground-based stations that provide high-quality but incomplete measurements of pollution. Chemical transport models (CTMs) offer full spatial and temporal coverage but smooth over local variability. We develop a spatial transfer-learning framework that integrates these two data sources to produce d

Yang Liu, Zhiyong Zhang

Real-time, high-fidelity 3D human reconstruction from RGB images is essential for interactive applications such as virtual reality and gaming, yet remains challenging due to the complex non-rigid deformations of dynamic human bodies. Although 3D Gaussian Splatting enables efficient rendering, existing methods struggle to capture fine geometric details and of

Fabricio Dos Santos, Christophe Hohlweg, Aleksandr Trufanov

In this article, we propose to initiate the general study of involution systems. An {\em involution system}, that is, a group $W$ generated by a set of involutions $S$, is naturally endowed with a {\em weak order} arising from orienting the Cayley graph of $(W,S)$. In the case of a Coxeter system $(W,S)$, Bj\"orner showed that the weak order is a complete me

Chang Liu

Institutional allocators often evaluate structured strategies on the basis of marketed backtests -- hypothetical track records constructed by applying a strategy's rules to historical data prior to any live trading, also referred to as pro-forma performance. It is unclear how much of that signal survives once the strategy is actually traded. Using 1,726 comm

Aoran Zhang, Tianyao Wei, Maria J. Guerrero, César A. Uribe

Recovering latent structure from count data has received considerable attention in network inference, particularly when one seeks both cross-group interactions and within-group similarity patterns in bipartite networks, which is widely used in ecology research. Such networks are often sparse and inherently imperfect in their detection. Existing models mainly

Sufian Al majmaie, Ghazal Ghajari, Niraj Prasad Bhatta, Fathi Amsaad

The integration of Fog Computing with Flying Ad-Hoc Networks (FANETs) offers promising capabilities for decentralized, low-latency intelligence in UAV-based applications. However, the distributed nature, mobility, and resource constraints of FANETs expose them to significant security and privacy challenges, particularly against quantum threats. To address th

Radhouane Fekih-Salem

We extend a three-stage anaerobic digestion model by incorporating microbial mortality into the hydrolysis of particulate organic matter. The model describes hydrolysis, acidogenesis, and methanogenesis, each with distinct dilution and decay rates, and accounts for non-monotonic growth in order to capture substrate inhibition. Two hydrolysis mechanisms are c

Jeffrey J. Donatelli, Miaoqi Chu, Zixi Hu, Zhang Jiang

Coherent surface scattering imaging (CSSI) is an emerging experimental technique uniquely suited to probing the structure of thin nanostructures. In these experiments, a specimen is placed on a substrate, and a series of X-ray diffraction patterns is collected at grazing incidence angles as the specimen is rotated. However, reconstructing the specimen's 3D s

Alexandre L. M. Levada

Principal Component Analysis (PCA) is a fundamental tool for representation learning, but its global linear formulation fails to capture the structure of data supported on curved manifolds. In contrast, manifold learning methods model nonlinearity but often sacrifice the spectral structure and stability of PCA. We propose \emph{Geodesic Tangent Space Aggrega

Elysia Wang

Let $\Sigma$ be a bounded surface. We prove the Dehn-Nielsen-Baer theorem for bounded surfaces to show that the mapping class group of $\Sigma$ is isomorphic to the automorphisms of the fundamental groupoid of $\Sigma$ that fix loops around the boundary.

Aleksandra Lekić, Predrag Pejović

An algorithm for simulation of switching converters is proposed in the paper. The algorithm is based on simulation of averaged circuit model applying "switching cell" concept, and construction of instantaneous values of the waveforms using quasi steady state and linear ripple approximation. Simulation covers converters operating both in the continuous and th

Yu-Wen Chen, Can Kizilkale, Murat Arcak

It is well known that mirror descent may diverge or cycle on merely monotone variational inequalities. In this paper, we propose \emph{Target Mirror Descent} (TMD), a unified framework that stabilizes monotone flows via a target point correction mechanism in the dual update. By appropriate design choices, TMD recovers the proximal point algorithm, extragradi

Yang Liu, Zhiyong Zhang

3D human mesh recovery from monocular RGB images aims to estimate anatomically plausible 3D human models for downstream applications, but remains challenging under partial or severe occlusions. Regression-based methods are efficient yet often produce implausible or inaccurate results in unconstrained scenarios, while diffusion-based methods provide strong ge

Priyam Dey, Aditya Sahdev, Sunny Bhati, Konda Reddy Mopuri

Despite the perceived success of large-scale dataset distillation (DD) methods, recent evidence finds that simple random image baselines perform on-par with state-of-theart DD methods like SRe2L due to the use of soft labels during downstream model training. This is in contrast with the findings in coreset literature, where high-quality coresets consistently

Aleksandra Lekić, Predrag Pejović

An algorithm for simulation of switching converters is proposed in the paper. The algorithm is based on simulation of averaged circuit model applying "switching cell" concept, and construction of instantaneous values of the waveforms using quasi steady state and linear ripple approximation. Simulation covers converters operating both in the continuous and th

Chongming Li, Tyler Meadows, Troy Day

Within many bacterial colonies, persister cells exist as a subpopulation that is tolerant to antibiotics and other stressors, yet not genetically distinct from the rest of the colony. A recent study has proposed epigenetic inheritance as a mechanism that leads to the presence of persister cells. We analyze a nonlocal PDE--ODE model introduced in that study t