arXiv papers from April 2026

Param Budhraja, Aditya Gangrade, Alex Olshevsky, Venkatesh Saligrama

Deploying reinforcement learning policies in the real world requires adapting to time-varying environments. We study this problem in the contextual Markov Decision Process (cMDP) framework, where a family of environments is indexed by a low-dimensional context unknown at test time. The standard approach decomposes the problem: train a so-called "universal po

Rebecca Saul, Jingzhi Jiang, Elliott Chia, David Wagner

Traditionally, machine learning methods for PE malware detection have relied on static features like byte histograms, string information, and PE header contents. One barrier to incorporating dynamic analysis features has been the semi-structured nature of sandbox behavior reports. We show that, using the latest generation of large language models with reason

Manho Park, Christopher V. Rackauckas, Christopher W. Tessum

Machine-learned surrogate modeling of advection may accelerate geoscientific models, but existing approaches have either achieved limited speedup or have sacrificed spatial resolution compared to the model they are trained to emulate. We developed a machine-learned solver that speeds up advection simulations without sacrificing spatial resolution through the

Alan Gomes, Anderson Gonçalves, Samuel Felipe dos Santos, Nathan Felipe Alves

Plant phenology-the study of recurrent life cycle events-is essential for understanding ecosystem dynamics and their responses to climate change impacts. While Unmanned Aerial Vehicles (UAVs) and near-surface cameras enable high-resolution monitoring, identifying plant species across time remains computationally challenging. State-of-the-art approaches, spec

Michael J. Parker, Maria G. Zavala-Cerna

This study presents a systematic approach to identifying and characterizing student misconceptions in online learning environments through a novel combination of quantitative performance analysis and large language model (LLM) assessment. We analyzed data from 9 course periods across 5 online biomedical science courses, encompassing 3,802 medical student enr

Artem Petrov, Guillermo A. Hernández-Mendoza, Alfredo Alexander-Katz

Block copolymers often create droplets when placed on a substrate. Such nanostructured droplets can be arranged into regular microstructured arrays, thereby forming hierarchically organized materials that can be used in microelectronics, plasmonics, sensing, photonics, metamaterials production, and even cryptography. However, it is unclear if such materials

Bingzheng Gan, Tianyi Zhang, Yusu Li, Jing Huang

The scalability of Large Language Models to long sequences is hindered by the quadratic cost of attention and the limitations of positional encodings. To address these, we introduce Caracal, a novel architecture that replaces attention with a parameter-efficient, O(L log(L)) Multi-Head Fourier (MHF) module. Our contributions are threefold: (1) We leverage th

Maryam Sadat Hosseini Azad, Shahriar Baradaran Shokouhi, Amir Abbas Hamidi Imani, Shahin Atakishiyev

The application of computer vision is gradually increasing across various domains. They employ deep learning models with a black-box nature. Without the ability to explain the behavior of neural networks, especially their decision-making processes, it is not possible to recognize their efficiency, predict system failures, or effectively implement them in rea

Gayathri Viswanath, Mickaël Bonnefoy, Catherine Dougados, Simon C. Ringqvist

High-resolution spectroscopic observations of helium emission lines provide a powerful probe of accretion geometry in classical T Tauri stars, revealing regions not well traced by hydrogen lines. Parallel studies in the planetary-mass regime are lacking. In this work, we investigate helium emission from the nearby (47 pc), wide-orbit (~84 au), ~13 $M_{Jup}$

Gun Woo Warren Park, Anthony Tang, Fanny Chevalier

In remote video meetings, visual non-verbal cues, such as facial expressions or head movements, are seen continuously but often only partially. This increases ambiguity compared to in-person settings and can cause misinterpretation or misalignment between intended and perceived meaning. Motivated by communication theories, we designed FaceValue, a technology

Koray Mentesoglu, Rahul Trivedi, Sara Mouradian

We present a protocol in which sequential weak measurements of a quantum harmonic oscillator enable simultaneous estimation of both quadratures of a displacement channel. Calculations of the quantum Fisher information show that the measurement backaction can increase the information gained for a range of measurement strengths. The protocol distributes inform

Mingrui Yuan, Nikolay V. Golubev

Time-resolved x-ray diffraction (TR-XRD) and ultrafast electron diffraction (TR-UED) are emerging tools for probing ultrafast quantum dynamics. From a theoretical perspective, they are commonly described within different frameworks and modeled using distinct approximations. Here, we present a unified quantum-field-based description of ultrafast diffraction i

Mauricio Corrêa, Pablo Perrella, Sebastián Velazquez

We study holomorphic foliations on normal crossings varieties arising as semistable degenerations. We do so by we exploring the notion of foliated d-semistability using the language of logarithmic structures in the sense of Fontaine-Illusie. First, we identify both local and global obstructions to d-semistability. In order to analyze the existence of smoothi

Matteo Raviola, Benjamin Peherstorfer

Dirac-Frenkel instantaneous residual minimization evolves nonlinear parametrizations of PDE solutions in time, but ill-conditioning can render the parameter dynamics non-unique. We interpret this non-uniqueness as a gauge freedom: nullspace directions that leave the time derivative unchanged can be used to select better-conditioned parameter velocities. Buil

Luis Rodríguez-Flores, Luciano García-Bañuelos, Abel Armas-Cervantes, Astrid Rivera-Partida

Conformance checking, one of the main process mining operations, aims to identify discrepancies between a process model and an event log. The model represents the expected behaviour, whereas the event log represents the actual process behaviour as captured in information systems records. Traditionally, the process model and the event log are both accessible

Pitch Sinlapanuntakul, Soyun Moon, Yuri Kawada, Yeha Chung

Early-stage concept envisioning is a critical juncture in AI design, shaping how designers frame problems and the decisions that follow. Yet values and potential harms are often too abstract or addressed too late to meaningfully shape design. Using a Research-through-Design (RtD) approach, we developed the AI Concept Envisioning Toolkit, comprising an AI Cap

Aleksandar Armacki, Haoyuan Cai, Ali H. Sayed

We study high-probability (HP) convergence guarantees in decentralized stochastic optimization, where multiple agents collaborate to jointly train a model over a network. Existing HP results in decentralized settings almost exclusively focus on the Decentralized Stochastic Gradient Descent ($\mathtt{DSGD}$) algorithm, which requires strong assumptions, such

Pitch Sinlapanuntakul, Aayushi Dangol, Xiaoyi Xue, Mark Zachry

As AI integrates into design practice, designers increasingly use generative AI tools to envision AI-enabled solutions, positioning AI as both design tool and design material. This dual role creates recursive value tensions distinct from traditional design work. We engaged 18 designers in a concept envisioning activity and interviews to understand how they n

Zawad Yalmie Sazid, Robert Abbas, Sasa Maric

AI-powered edge computing security is moving Intelligent Transportation Systems (ITS) from passive, rule-based protections to proactive, smart, zero-touch, self-sufficient safeguards that neutralize threats in milliseconds. As transportation becomes more connected with edge computing, massive IoT, and advanced 5G for vehicle-to-everything (V2X) connectivity,

Manav Batavia, Cheng Chen, Anna Natalie Chlopecki, Timothy Duff

We introduce the package \texttt{EliminationTemplates} for the Macaulay2 computer algebra system, which provides tools for constructing automatic solvers for families of zero-dimensional radical ideals depending on algebraically independent parameters. This article provides a self-contained description of how elimination templates are constructed for such fa

Shuchi Chawla, Kristin Sheridan

We consider the problem of finding the value of a maximum flow over time in a network with uniform edge lengths where the edge capacities change at specific time instants. To solve this problem, we show how to construct a condensed version of a Time Expanded Network (cTEN) whose standard max flow value is the same as the max flow over time on the original ne

Tiejin Chen, Ahmadreza Moradipari, Kyungtae Han, Hua Wei

Trip planning for intelligent vehicles increasingly requires selecting optimal routes rather than merely producing feasible itineraries, as interacting factors such as travel time, energy consumption, and traffic conditions directly affect plan quality. Yet existing systems are largely designed for feasibility-oriented planning, and current benchmarks provid

Viktor Berger, Nikolay Prokof'ev, Boris Svistunov

Landau theory of superfluidity associates low-temperature flow of the normal component with the phonon wind. This picture does not apply to superfluids in which Galilean invariance is broken either by disorder, porous media, or lattice potential, and the phonon wind is no longer solely responsible for depletion of the superfluid component. Based on Popov's h

Zheyu Wen, George Biros

We introduce LNODE, a mechanism-based phenomenological model for amyloid beta (A$\beta$) dynamics, calibrated using positron emission tomography (PET) imaging. A$\beta$ is a key biomarker of Alzheimer's disease. LNODE is designed to support the fusion, harmonization, quantitative analysis, and interpretation of Abeta PET scans. We evaluate LNODE on 1461 subj

Sheza Munir, Ahanaf Rodoshi, Sumin Lee, Feiran Chang

Standard methods for aggregating natural language judgments, such as majority voting, often fail to produce logically consistent results when applied to high-conflict domains, treating differing opinions as noise. We propose a neuro-symbolic aggregation framework that formalizes conflict resolution through Weighted Maximum Satisfiability (MaxSAT). Our pipeli

Hamidreza Saghir

Recent white-box OOD detection methods for LLMs -- including CED, RAUQ, and WildGuard confidence scores -- appear effective, but we show they are structurally confounded by sequence length (|r| >= 0.61) and collapse to near-chance under length-matched evaluation. Even raw attention entropy (mean H(alpha) across heads and layers), a natural baseline we includ

Daniel Zhu, Zihan Wang, Xuchan Bao, Jerry Wei

As language model safeguards become more robust, attackers are pushed toward developing increasingly complex jailbreaks. Prior work has found that this complexity imposes a "jailbreak tax" that degrades the target model's task performance. We show that this tax scales inversely with model capability and that the most advanced jailbreaks effectively yield no

Enrique Artal Bartolo, Santiago López de Medrano, María Teresa Lozano Imízcoz

The goal of this work is to continue the study the smoothings of 3-dimensional manifolds with singularities obtained as small covers of non simple right-angle Coxeter polyhedral orbifolds. They appear in the study of coaxial intersections of ellipsoids. In particular we introduce the concept of $n$-pyramitoid generalizing the $n$-pyramid.

Deidre A. Hunter, Bruce G. Elmegreen

We have examined the stellar structure of 10 nearby, low stellar mass (10^6 to 6 x 10^7 Msolar) dwarf irregular galaxies by fitting ellipses as a function of surface brightness on ultra-deep V images. These are compared to far ultraviolet images as tracers of the star formation. We find that the often asymmetrical distribution of large patches of star format

Kartikeya Singh, Christo Aluckal, Romeo Orsolino, Karthik Dantu

Legged robots maintain dynamic feasibility through multicontact interactions with terrain. Learned foothold prediction can provide feasibility-aware costs for motion planning and path selection, but accurately predicting future contacts from perceptual inputs such as height scans remains challenging on highly unstructured terrain, even with a repetitive gait

Bimol Nath Roy, Rahul Golder, MM Faruque Hasan

Nonlinear Parametric Optimization Network (NLPOpt-Net) is an unsupervised learning architecture to solve constrained nonlinear programs (NLP). Given the structure of an NLP, it learns the parametric solution maps with guaranteed constraint satisfaction. The architecture consists of a backbone neural network (NN) followed by a multilayer ($k$-layered) project

Bowen Li, Nikolaos Pappas

In this paper, we consider remote reconstruction over wireless networks when simultaneous accuracy at the legitimate receiver and confidentiality against eavesdropping are required. These two objectives are often treated separately, even though they arise from the same update process and are marginals of a joint reconstruction event. This paper introduces co

Jatin Gupta, Akhil Sharma, Saransh Singhania, Ali Imam Abidi

The inception of Large Language Models (LLMs) has catalyzed AI adoption in the finance sector, yet their reliability in complex, jurisdiction-specific tasks like Indian Chartered Accountancy (CA) remains limited. The models display difficulty in executing numerical tasks which require multiple steps while also needing advanced knowledge about legal regulatio

Osmar Luiz Ferreira de Carvalho, Osmar Abílio de Carvalho Júnior, Anesmar Olino de Albuquerque, Daniel Guerreiro e Silva

SAM2 produces high-quality zero-shot segmentation on natural images, but applying it to large remote sensing scenes exposes two problems: (1) its mask generator faces an inherent quality-coverage trade-off: strict thresholds yield precise masks but leave most of the image unsegmented, while relaxed thresholds increase coverage at the cost of mask quality; an

Junsun Choi, Sam Son, Sunjin Choi, Hansung Kim

Mixture-of-experts (MoE) architectures have turned LLM serving into a cluster-scale workload in which communication consumes a considerable portion of LLM serving runtime. This has prompted industry to invest heavily in expensive high-bandwidth scale-up networks. We question whether such costly infrastructure is strictly necessary. We present the first syste

Bhagyashree Wagh, Akash Singh

Mamba's recurrent state h_t is, by construction, a compressed summary of every token seen so far. This raises a tempting hypothesis: if we extract token-level outputs y_t at fixed patch boundaries, we obtain semantic sentence summaries for free, with no pooling head, no fine-tuning, and no [CLS] token. We test this hypothesis carefully. Across five benchmark

Andac Demir, Erik W. Anderson, Jeremy L. Jenkins, Srayanta Mukherjee

In this work, we introduce CellxPert, a scalable multimodal foundation model that unifies single-cell and spatial multi-omics within a common representation space. CellxPert jointly encodes transcriptomic (scRNA-seq), chromatin-accessibility (ATAC-seq), and surface-proteomic (CITE-seq) measurements, while directly incorporating MERFISH and imaging mass-cytom

Yi Zhu, Brahmi Dwivedi, Jayaram Raghuram, Surya Koppisetti

Existing voice deepfake detection and localization models rely heavily on representations extracted from speech foundation models (SFMs). However, downstream finetuning has now reached a state of diminishing returns. In this paper, we shift the focus to pretraining and propose a novel recipe that combines bottleneck masked embedding prediction with flow-matc

Yuhui Lu, Wenjing Liu, Kun Zhan

Standard diffusion models for graph generation typically rely on uniform time-stepping, an approach that overlooks the non-homogeneous dynamics of distributional evolution on complex manifolds. In this paper, we present an information-geometric framework that reinterprets the diffusion sampling trajectory as a parametric curve on a Riemannian manifold. Our k

Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, Emil Björnson, Sunwoo Kim

Orthogonal frequency-division multiplexing (OFDM) and its static sinusoidal subcarriers have underpinned the 4G and 5G eras, delivering high spectral efficiency and resilience to multipath fading through an efficient multicarrier architecture. However, as future systems move toward doubly dispersive environments driven by high-mobility applications and migra

Frederik Hytting Jørgensen, Sebastian Weichwald, Lewis Hammond

A key challenge for the safety of advanced AI systems is the possibility that multiple simpler agents might inadvertently form a collective agent with capabilities and goals distinct from those of any individual. More generally, determining when a group of agents can be viewed as a unified collective agent is a foundational question in the study of interacti

Felix Reichel

We place three algorithms for computing the unbiased sample covariance matrix in streaming and distributed settings on a common algebraic, numerical, and statistical foundation. The Gram algorithm, derived from the variance reformulation, maintains the running cross-product matrix $G_t = \sum_{i=1}^t x_i x_i^\top$ and the column-sum vector $s_t = \sum_{i=1}^

Anthony Gatti, Anoosha Fayyaz, Prashant Krishnamurthy, Kaushik P. Seshadreesan

Many quantum-network applications require end-to-end Bell pairs whose fidelity exceeds a request-specific threshold, but existing entanglement routing algorithms either optimize only throughput without regard for fidelity or enforce fidelity guarantees using centralized controllers with global link-state knowledge. We present Q-GUARD, an online entanglement

Sydney Johns, Heng Jin, Chaoyu Zhang, Y. Thomas Hou

Large language models (LLMs) are now being explored for defense applications that require reliable and legally compliant decision support. They also hold significant potential to enhance decision making, coordination, and operational efficiency in military contexts. These uses demand evaluation methods that reflect the doctrinal standards that guide real mil

Yajvan Ravan, Adam Rashid, Alan Yu, Kai McClennen

We introduce Lucid-XR, a generative data engine for creating diverse and realistic-looking multi-modal data to train real-world robotic systems. At the core of Lucid-XR is vuer, a web-based physics simulation environment that runs directly on the XR headset, enabling internet-scale access to immersive, latency-free virtual interactions without requiring spec

Nicholas DeFilippis, Oliver Bühler, K. Shafer Smith

Interactions between inertia-gravity waves and balanced flows lead to a spectral diffusion of wave action. Prior work has established that this diffusion is weak across constant frequency surfaces in three-dimensional settings, but can be significant in two dimensions with a non-stationary balanced flow. We investigate the two-dimensional setting through num

Jamal Elfakir

This thesis, explores the quantum entanglement and evolution through both a geometric and dynamical perspective. The first part focuses on classical phase space and its central role in Hamiltonian mechanics, emphasizing the importance of symplectic structures in describing mechanical states. The study highlights the formal analogy between classical phase spa

Wen Li, Rong Ni, Bozhi Tian, Pedro Lopes

Thermal referral enables thermal sensations in locations lacking thermal actuators--this is achieved using vibrotactile actuators to redirect a nearby thermal sensation to where a tactile sensation is applied. However, we found that its reliance on vibration introduces critical limitations: it struggles to produce cold referral, and the inherent strong tacti

Falk L Wiegmann, Nancy L Ford

The ramp filter kernel and cutoff frequency are fundamental parameters of the Feldkamp-Davis-Kress (FDK) algorithm that determine the resolution and noise characteristics of the reconstructed image. Despite their importance, systematic evaluations of their combined effect on task-based image quality in preclinical micro-CT are scarce, and many studies do not

Jesse Schneider, William J. Welch

Bayesian optimization is a sequential method for minimizing objective functions that are expensive to evaluate and about which few assumptions can be made. By using all gathered data to train a Gaussian process model for the function and adaptively employing a mixture of global exploration and local exploitation, this method has been used for optimization in

Aviral Srivastava, Sourav Panda

Safety-aligned large language models rely on RLHF and instruction tuning to refuse harmful requests, yet the internal mechanisms implementing safety behavior remain poorly understood. We introduce the Attention Redistribution Attack (ARA), a white-box adversarial attack that identifies safety-critical attention heads and crafts nonsemantic adversarial tokens

Vadim Prokofev, Anton Zabrodin

We consider the Schwarzian KP and Harry Dym hierarchies in the framework of the bilinear formalism which is well known for such integrable hierarchies as KP, modified KP, BKP, Toda lattice and other. We show that, similarly to the bilinear formulation of the modified KP hierarchy, the Schwarzian KP can be reformulated as an integral bilinear equation for a p

Domenico Capuani

A boundary integral equation (BIE) formulation for 2-D transient elastic wave propagation problems is presented. On the basis of the three-dimensional integral identity, the time-dependent kernels for the two-dimensional boundary integral equation are obtained. A linear time variation of displacements and tractions is assumed over each time step and an impli

Aishani Pathak, Hasti Seifi

Egocentric pose estimation for Augmented Reality (AR) and assistive devices requires not just accurate predictions but guaranteed uncertainty regions. Conformal prediction (CP) provides such guarantees without retraining, but we show that standard CP with a single fixed threshold achieves nominal 90% overall coverage while covering only ~60% of the hardest 2

Brían Ó Fearraigh

The KM3NeT research infrastructure instruments a large volume of seawater using photomultiplier tubes, which are sensitive to the Cherenkov radiation stimulated by the products of neutrino interactions in the water, as well as that stimulated by atmospheric muons which penetrate the sea depths. The KM3NeT/ARCA and KM3NeT/ORCA detectors are situated at differ

Hussein Suprême, Martin de Montigny, Kevin-R. Sorto-Ventura, Hind Chit Dirani

The increasing integration of distributed energy resources (DERs), variable renewable energy sources, and emerging technologies presents new challenges for transmission system expansion planning (TSEP). Traditional snapshot-based and deterministic approaches are inadequate for capturing the temporal dynamics and operational constraints of modern power system

Simon Mahler, Nikita Stroev, Mahmoud Abu Rmilah, Asher Friesem

Controlled experimental studies of percolation are challenging due to difficulties in tuning site connectivity, isolating local interactions, and mitigating finite-size effects. In this work, we experimentally investigate percolation with a platform of coupled lasers, where connectivity, interaction strength, and system size can be controlled. Using a square

Carles Domingo-Enrich, Yuanqi Du, Michael S. Albergo

We study the problem of training diffusion and flow generative models to sample from target distributions defined by an exponential tilting of a base density; a formulation that subsumes both sampling from unnormalized densities and reward fine-tuning of pre-trained models. This problem can be approached from a stochastic optimal control (SOC) perspective, u

Prerna Juneja, Lika Lomidze

There are growing concerns about the risks posed by AI companion applications designed for emotional engagement. Existing safety evaluations often rely on self-reported user data or interviews, offering limited insights into real-time dynamics. We present the first end-to-end scalable framework for controlled simulation and safety evaluation of multi-turn in

Jan Sobotka, Mustafa O. Karabag, Ufuk Topcu

Large language models (LLMs) are increasingly tasked with strategic decision-making under incomplete information, such as in negotiation and policymaking. While LLMs can excel at many such tasks, they also fail in ways that are poorly understood. We shed light on these failures by uncovering two fundamental gaps in the internal mechanisms underlying the deci

Christiaan M. Geldenhuys, Thomas R. Niesler

We show that pretrained acoustic embeddings classify elephant vocalisations at a level approaching that of end-to-end supervised neural networks, without any fine-tuning of the embedding model. This result is of practical importance because annotated bioacoustic data are scarce and costly to obtain, leaving conventional supervised approaches prone to overfit

Abdulhady Abas Abdullah, Fatemeh Daneshfar, Seyedali Mirjalili, Mourad Oussalah

Aligning large language models (LLMs) with human preferences is commonly done via reinforcement learning from human feedback (RLHF) with Proximal Policy Optimization (PPO) or, more simply, via Direct Preference Optimization (DPO). While DPO is stable and RL-free, it treats preferences as flat winner vs. loser signals and is sensitive to noisy or brittle pref

Á. Valenzuela Navarro, M. Zoccali, E. Valenti, R. Contreras Ramos

The Nuclear Bulge of the Milky Way harbors stellar populations that provide crucial insights into galaxy formation processes and serve as a nearby analog for understanding bulge formation in external galaxies. However, detailed studies of this region are severely hampered by extreme and highly variable interstellar extinction, which obscures the intrinsic st

Jiří Ryjáček, Leonard Hlodák, Jiří Liška, Jan Pinc

Dilute Mg-Zn wires are of great interest for biodegradable small-bone fixation, as magnesium degradation can support bone-related processes, while low zinc additions may provide biological benefits without compromising biocompatibility. In this work, the influence of Zn content below the room-temperature solubility limit was assessed in Mg-Zn wires intended

Jeffery Li, Jayson Lynch, Liva Olina, Cecilia Chen

In nearly every discipline, scientific computations are limited by the cost and speed of computation. For example, the best-known exact algorithms for the canonical Traveling Salesman Problem would take centuries to run on an instance of size 1 million. A natural response to such limits is to try to find new algorithms or to parallelize existing ones, but ma

Jingxiang Chen, Mohamed Ibrahim, Yang Liu

We present VkSplat, a high-performance, cross-vendor 3D Gaussian Splatting (3DGS) training pipeline implemented fully in Vulkan compute, addressing performance and compatibility limitation of existing training pipelines. With various optimizations, we achieve $3.3\times$ speed and $33\%$ VRAM reduction over CUDA+PyTorch baseline, maintaining quality, and dem

Haofei Yu, Yining Zhao, Lenore Blum, Manuel Blum

Despite remarkable advances, today's AI systems remain narrow in scope, falling short of the flexible, adaptive, and multisensory intelligence that characterizes human capabilities. This gap has fueled longstanding debates about whether AI might one day achieve human-like generality or even consciousness, and whether theories of consciousness can inspire new

Erkka Rantahalvari, Olli Silvén, Zinelabidine Boulkenafet, Constantino Álvarez Casado

Mobile remote identity verification (RIdV) systems are exposed to attacks that manipulate or replace the facial video stream, including presentation attacks, real-time deepfakes, and video injection. Recent European requirements, including ETSI TS 119 461 and CEN/TS 18099, motivate complementary evidence channels beyond camera-based presentation-attack detec

Kamtila Kari, Iskamlé Bruno, Diekouam Fotso Luc Éméry, Tcheka Calvin

In this paper, we show that for a given degenerate bivector $\pi= y^n\partial_x \wedge \partial_y$ with $n>1$, the classical Poisson cohomology group and the logarithmic Poisson cohomology group along the ideal $\mathcal{I}=y^n\mathbb{F}[x,y] $ are isomorphics in every d\'egr\'ee. This result follows from determination of the logarithmic Hamiltonian operator

Sandip Sinharay

Person-fit statistics are widely used to detect aberrant response patterns in educational and psychological measurement. Snijders (2001) suggested an asymptotically correct standardization for a broad class of such statistics. This paper presents an alternative derivation of this standardization. The derivation yields several advantages including a simpler f

Ahona Bhattacharyya, Tessa Haldes, Jeffrey A. Nanzer, Susan C. Hagness

We investigate multi-objective adaptive beamformer design strategies for non-invasive microwave hyperthermia. Our focus is to address the challenges of maintaining focused power deposition in desired locations while reducing unwanted heating elsewhere under conditions of changing dielectric properties. The process of heating the media causes changes in the d

Saniya Shinde, Maximilian A. Weissflog, Shaun Lung, Elkin A. Santos

Entangled photon pairs play a major role in various modern technologies such as quantum imaging, communication, and computing. Conventional photon-pair sources are often based on spontaneous parametric down-conversion in bulk nonlinear crystals. Recent advances have also shown entangled photon-pairs from transition metal dichalcogenide thin-films, however, t

Anirban Sen, Somdatta Barik, Kallol paul

We characterize bounded, compact, and Hilbert-Schmidt composition-differentiation operators on weighted Dirichlet spaces. The essential norm is estimated via the asymptotic behavior of a function that involves the generalized Nevanlinna counting function of the inducing map. Norm estimates for particular inducing maps are given, and examples are provided to

Harbir Antil, Yaw Owusu-Agyemang, Rohit Khandelwal, Jimmie Adriazola

We develop a structure-preserving solution framework for the optimal control of the time-dependent Maxwell's equations. Building on a well-posedness theory for a weak form of the forward problem, we first analyze a forward solver that couples N\'ed\'elec and Raviart--Thomas finite elements with Crank--Nicolson time stepping. The solver preserves the de~Rham

S. S. Onuchin, Ya. S. Lyakhova, L. D. Silakov, A. N. Rubtsov

Collective excitations in fermionic systems play a crucial role in determining their physical properties. An important challenge is to develop efficient theoretical approaches for describing these excitations and their coupling to fermionic degrees of freedom. In this work, we revisit the problem of quantifying the contributions of individual bosonic modes o

Giulio Fattore, Maria Elena Valcher, Rui Gao, Guang-Hong Yang

This paper addresses the problem of distributed state estimation for discrete-time linear time-invariant systems. Building on the framework proposed in Gao & Yang (2025), we exploit the Jordan canonical form of the system matrix to develop two distributed estimation schemes that ensure asymptotic convergence of local estimates to the true system state. In bo

Pál András Papp, Toni Böhnlein, A. N. Yzelman

The efficient parallel execution of complex computations requires balancing the workload across processors while minimizing the communication between them. This inherent trade-off is often captured by graph partitioning or DAG scheduling problems. For the sake of model simplicity, most works on these problems assume that nodes can be assigned to only a singl

Gilberto Aguilar-Pérez, Deryan Alvarado, Miguel Cruz, Estefany Ruíz-Ramos

We explore the theoretical viability of modeling a decaying dark matter sector through a unified scalar field approach. Using exact analytical solutions of the Friedmann constraints, we map the fluid phenomenology onto a scalar field potential. Our analysis reveals that physical viability, specifically the existence of a well-defined potential minimum; inevi

Christopher Hoffman, Tobias Johnson, Matthew Junge, Josh Meisel

To explain the ubiquity of power laws and fractals in nature, Bak, Tang, and Wiesenfeld formulated simple conditions for a system to self-organize into a critical state. Dickman, Mu\~noz, Vespignani, and Zapperi postulated that the self-organized critical state matches the critical state in corresponding fixed-energy models undergoing traditional phase trans

Thea Aviss

Current transformers discard their rich latent residual stream between positions, reconstructing latent reasoning context at each new position and leaving potential reasoning capacity untapped. The State Stream Transformer (SST) V2 enables parameter-efficient reasoning in continuous latent space through an FFN-driven nonlinear recurrence at each decoder laye

Nelson R. F. Braga, William S. Cunha

In recent years, many interesting works providing a topological description for black hole (BH) properties have appeared in the literature. In particular, in this framework BHs correspond to topological defects in an enlarged (off-shell) parameter space, with an associated total topological charge. In gauge/gravity duality the transition from the confined to

Servet Ozdemir, Mikhail Kashchenko, Kostya S. Novoselov

The small gap room temperature semiconductor a-RuCl3 which is known to undergo a Mott-Hubbard transition at low temperatures, is one of the most promising candidates for realisation of an exotic matter form, the quantum spin liquid state, which may have applications in quantum computing. Although being extensively investigated by neutron scattering technique

Kiarash Banihashem, MohammadTaghi Hajiaghayi, Mahdi JafariRaviz, Danny Mittal

The standard oracle model for matroid algorithms assumes that each independence query can be answered in constant time, regardless of the size of the queried set. While this abstraction has underpinned much of the theoretical progress in matroid optimization, it masks the true computational effort required by these algorithms. In particular, for natural and

Jugal Gajjar, Kamalasankari Subramaniakuppusamy

When a language model answers a table question, users have no way to verify which cells informed which reasoning steps. We introduce RSAT, a method that trains small language models (SLMs, 1-8B) to produce step-by-step reasoning with cell-level citations grounded in table evidence. Phase 1 (SFT) teaches a structured JSON output format from verified reasoning

Aurélien Bück-Kaeffer, Sneheel Sarangi, Maximilian Puelma Touzel, Reihaneh Rabbany

Studies attempting to simulate human behavior with $\textit{Silicon Societies}$ grow in numbers while LLM-only social networks have started appearing outside of controlled settings. However, the design space of these networks remains under-studied, which contributes to a gap in validating model realism. To enable future works to make more informed design dec

Tomasz J. Kozubowski, Andrey Sarantsev, James A. Spiker

We consider a generalization of the variance-gamma (generalized asymmetric Laplace) distribution, defined as a normal mean - variance mixture with a gamma mixing distribution. While this model is typically studied in the univariate setting, we assume that the gamma mixing variable is observed alongside the primary variable, resulting in a bivariate framework

Roman Klypa, Oleksandr Cherednichenko

Supervised Fine-Tuning (SFT) is essential for aligning Large Language Models (LLMs) with user intent, yet it is believed to suppress generative diversity. Although this reduction is frequently referenced, formal empirical testing of the phenomenon remains limited. The expressiveness of LLMs by itself was addressed by multiple prior methods. Their varying per

Jeffrey Shallit

In 2013 Cooper and Dutle invented a dueling scenario where Alice and Bob shoot at each other until one is hit. Each shot is successful with some fixed probability $p$, $0 < p < 1$. The shooting order is given by a greedy algorithm, where at each step a shot is assigned to the player whose current probability of success is smaller. Cooper and Dutle observed t

Renjun Hu, Hyun-Soo Ahn

Many machine learning systems make constrained decisions by optimizing factorized objectives, but the context-specific objective is often treated as fixed. We study contextual decision-weight learning: from logged decisions and proxy outputs, learn an optimizer-facing weight vector w(x) over interpretable decision factors z(x,d), rather than a direct policy

Ignasi Sau, Nicole Schirrmacher, Sebastian Siebertz, Giannos Stamoulis

Algorithmic meta-theorems explain the tractability of large classes of computational problems by linking logical expressibility with structural graph properties. While extensions of first-order logic such as FO+dp admit efficient model checking on graph classes excluding a fixed topological minor, comparable results for richer fragments of CMSO were previous

Ademola Isaac Adebimpe, Sajjad Foroughi, Branko Bijeljic, Martin J. Blunt

We present a time-dependent pore-network model that couples transient mass transfer in the aqueous phase, capillary pressure heterogeneity, and realistic pore-throat geometries to capture the dynamic evolution of gas clusters during Ostwald ripening in porous media. The model is applied to Bentheimer sandstone to study Ostwald ripening after imbibition to re

Murilo Coelho, Denivan Campos, Mariana Maia Bezerra, Matheus Paixao

Context: Leadership has been extensively studied in management and agile software development; however, prior research predominantly focuses on formal roles and predefined leadership models, offering limited insight into how leadership is experienced and demonstrated by software practitioners in everyday practice. Objective: Our goal is to identify and categ

Kostiantyn Drach, Leon Staresinic, Sebastian van Strien

An interval translation map (ITM) is a piece-wise translation $T \colon I \to I$ defined on a finite partition $I_1, \ldots, I_r$ of an interval $I$ into $r \ge 2$ subintervals. In contrast to classical interval exchange transformations (IETs), we do not require that the images of these subintervals are disjoint; in particular, ITMs are not assumed to be bij

Alberto Bressan, Laura Caravenna, Wen Shen

Solutions to hyperbolic conservation laws can be approximated in many different ways: by vanishing viscosity, relaxations, discrete or semi-discrete numerical schemes, approximation with a nonlocal flux, etc$\ldots$ For some of these methods, general ${\bf L}^1$ convergence results are available. Aim of this paper is to understand the local behavior of these

Majid Bahraminasr, Anand Yethiraj

Recently, we reported the electrorheoimaging (ERI) technique (Bahraminasr et al, 2026), and found that frequency-dependent electric field of an oil-in-oil emulsion yields two distinct regimes: a high-frequency dipolar, electrorheological (ER) regime and a low-frequency electrohydrodynamic (EHD) regime. In this work, we identify a phenomenological model to fi

Ali Sadeghi Jahromi, Jason Jaskolka

Iran conducted two nationwide Internet shutdowns in January and March 2026, the latter ongoing at the time of writing and the longest documented Iranian disruption. Using a three-plane methodology combining passive Censys scan data, active TCP reachability probing from five vantage points, and BGP analysis across 33 RIPE RIS snapshots from 2019 to 2026, we s

Kostiantyn Drach, Leon Staresinic, Sebastian van Strien

An interval translation map (ITM) is a map $T \colon I \to I$ defined as a piecewise translation on a finite partition of an interval $I$ into $r \ge 2$ subintervals. Unlike classical interval exchange transformations (IETs), the images of these subintervals are allowed to overlap, making ITMs a natural generalisation of IETs. An ITM $T$ is said to be \texti

Karim Alghoul, Faisal Mohd, Fedwa Laamarti, Hussein Al Osman

With the growing integration of human-computer interaction into everyday life, advances in machine learning have enabled systems to better perceive and respond to users' emotional states. Most existing affect recognition datasets focus on static environments, limiting their applicability to immersive multimedia contexts such as Virtual Reality (VR). In this

Ying Yuan, Cristiano Alex Rado, Giovanni Apruzzese, Mauro Conti

Phishing webpages are continuously polluting the Web. Plenty of countermeasures have been proposed and the most advanced techniques leverage machine-learning methods that infer whether a webpage is benign or not by inspecting its visual representation. Yet, despite the demonstrated effectiveness of such detection methods, this class of defenses is, by design

Raviteja Bommireddy, Varshith Bandaru, Lohith Pakala, Pradeep Kumar B

Multivariate time series anomaly detection in ICS has attracted growing attention due to the increasing threat of cyber-physical attacks on critical infrastructure. State-of-the-art methods model inter-sensor relationships from raw time-domain amplitude values, using graph neural networks, Transformers. However, these methods discard the phase spectrum produ