November 2025 arXiv papers — page 165

Chenpeng Su, Wenhua Wu, Chensheng Peng, Tianchen Deng

Urban scene reconstruction is critical for autonomous driving, enabling structured 3D representations for data synthesis and closed-loop testing. Supervised approaches rely on costly human annotations and lack scalability, while current self-supervised methods often confuse static and dynamic elements and fail to distinguish individual dynamic objects, limit

Shi-Chao Fang, Xin-Yi Liao

We employ a large-scale, unbiased constrained-path quantum Monte Carlo method to systematically simulate the effective two-orbital Hubbard model for twisted bilayer graphene in order to gain deeper insight into the relationship between correlated states and the superconducting pairing mechanism in twisted bilayer graphene, as well as the influence of the twi

Rui Huang, Jordan Austin-Frank Wilson, Allen Sun, Artemis Harlow

Organic capping ligands can selectively bind to crystal facets to modulate growth kinetics and are important in chemical synthesis of inorganic nanocrystals. Using the capping ligands for shape-controlled growth of colloidal crystals is challenging due to the size mismatch of molecules and nanoparticle building blocks. In existing synthetic pathways, colloid

Changfeng Gui, Shanfa Lai, Yong Liu, Juncheng Wei

This paper establishes the conditional orbital stability of fully localized solitary waves for the three-dimensional capillary-gravity water wave problem in finite depth under strong surface tension. The waves, constructed via a non-variational Lyapunov-Schmidt reduction in [26], are not energy minimizers and thus require a direct stability analysis. We adap

Yuchen Cao, Jiongmin Yong

This paper is concerned with stochastic impulse control problems in which the running cost changes depending on the impulse control. Because of such a dependence, it brings several difficulties when the usual dynamic programming principle is to be used. The corresponding Hamilton-Jacobi-Bellman (HJB) equation (a quasi-variational inequality) is derived, whic

Toby Sanders, Hayden Carlton, Preethi Korangath, Olivia C. Sehl

Magnetic particle imaging (MPI) is a tracer-based imaging modality that detects superparamagnetic iron oxide nanoparticles in vivo, with applications in cancer cell tracking, lymph node mapping, and cell therapy monitoring. We introduce a new 3D image reconstruction framework for MPI data acquired using multi-angle field-free line (FFL) scans, demonstrating

Chloe Li, Mary Phuong, Daniel Tan

As AI systems become more capable of complex agentic tasks, they also become more capable of pursuing undesirable objectives and causing harm. Previous work has attempted to catch these unsafe instances by interrogating models directly about their objectives and behaviors. However, the main weakness of trusting interrogations is that models can lie. We propo

Hojin Chu, Shinya Fujita, Boram Park, Homoon Ryu

In 2012, Mader conjectured that for any tree $T$ of order $m$, every $k$-connected graph $G$ with minimum degree at least $\lfloor \frac{3k}{2}\rfloor+m-1$ contains a subtree $T'\cong T$ such that $G-V(T')$ remains $k$-connected. In 2022, Luo, Tian, and Wu considered an analogous problem for bipartite graphs and conjectured that for any tree $T$ with biparti

Antonina Kosikova, Apostolos Psaros, Andrew Smyth

System identification under unknown external excitation is an inherently ill-posed problem, typically requiring additional knowledge or simplifying assumptions to enable reliable state and parameter estimation. The difficulty of the problem is further amplified in structural systems subjected to natural hazards such as earthquakes or windstorms, where respon

Sumin Lim

Universal quantum computers require fault-tolerant logical qudits, as qudits naturally align with the simulation of multi-level physical systems. Here, we present a general framework and working examples for encoding fault-tolerant logical qudits in finite-dimensional spin systems. We construct distance-$3$, distance-$5$ codewords, and general $2t+1$-distanc

Chuheng Zhang, Rushuai Yang, Xiaoyu Chen, Kaixin Wang

Vision-language-action (VLA) models hold the promise to attain generalizable embodied control. To achieve this, a pervasive paradigm is to leverage the rich vision-semantic priors of large vision-language models (VLMs). However, the fundamental question persists: How do VLAs effectively inherit the prior knowledge from VLMs? To address this critical question

Moriya Dechtiar, Daniel Martin Katz, Mari Sundaresan, Sylvain Jaume

Contracts are complex documents featuring detailed formal structures, explicit and implicit dependencies and rich semantic content. Given these document properties, contract drafting and manual examination of contracts have proven to be both arduous and susceptible to errors. This work aims to simplify and automate the task of contract review and analysis us

Bjørn Kjos-Hanssen

The hydrophobic-polar (HP) model represents proteins as binary strings embedded in lattices, with fold quality measured by an energy score. We prove that the optimal fold energy is not monotonic under concatenation for several standard lattices, including the 2D and 3D rectangular, hexagonal, and triangular lattices. In other words, concatenating two polymer

Martijn Caspers, Jesse Reimann

Let $f \in C^n(\mathbb{R})$ be such that $\Vert f^{(n)} \Vert_\infty < \infty$. Let $f^{[n]} \in C(\mathbb{R}^{n+1})$ be the $n$th order divided difference. A special case of our main result states that for $1 < p < \infty$ we have \[\Vert T_{f^{[n]}}: S_{np} \times \ldots \times S_{np} \rightarrow S_{p} \Vert \lesssim p^\ast p^n \Vert f^{(n)} \Vert_\infty,

George Avalos, Yuhao Mu

In this paper we construct a novel technique for eliminating and recovering the pressure for a fluid-structure interaction model. This pressure elimination methodology is valid for general bounded Lipschitz domains. The specific fluid-structure interaction (FSI) that we consider is a well-known model of Stokes flow coupled to a system of linear elasticity, w

Ruyin Wan, George Em Karniadakis, Panos Stinis

Spiking neural networks (SNNs) offer biologically inspired computation but remain underexplored for continuous regression tasks in scientific machine learning. In this work, we introduce and systematically evaluate Quadratic Integrate-and-Fire (QIF) neurons as an alternative to the conventional Leaky Integrate-and-Fire (LIF) model in both directly trained SN

Jun Hao Hue, Martin-Isbjörn Trappe, Piotr T. Grochowski, Jonathan Lau

We design an atomtronic filter device that spatially separates the components of a two-component Fermi gas with repulsive contact interactions in a two-dimensional geometry. With the aid of density--potential functional theory (DPFT), which can accurately simulate Fermi gases in realistic settings, we propose and characterize a barbell-shaped trapping potent

Jiajun Jiang, Xiao Hu, Wancheng Liu, Wei Jiang

Accurate measurement of circular centers is a fun-damental geometric sensing problem in instrumentation and measurement tasks involving cameras, LiDARs, and other spa-tial sensors. In circular-target-based LiDAR-camera extrinsic calibration, a 3D circular center measured from LiDAR and its corresponding 2D projected center measured in the image serve as cros

Kaidong Wang, Jiale Li, Shao-Bo Lin, Yao Wang

The non-rival nature of data creates a dilemma for firms: sharing data unlocks value but risks eroding competitive advantage. Existing data synthesis methods often exacerbate this problem by creating data with symmetric utility, allowing any party to extract its value. This paper introduces the Encapsulation-Forging (EnFo) framework, a novel approach to gene

Asela Hevapathige, Asiri Wijesinghe, Ahad N. Zehmakan

Graph Neural Networks (GNNs) have achieved significant success in addressing node classification tasks. However, the effectiveness of traditional GNNs degrades on heterophilic graphs, where connected nodes often belong to different labels or properties. While recent work has introduced mechanisms to improve GNN performance under heterophily, certain key limi

Seshu Kumar Damarla, Xiuli Zhu

Lost circulation remains a major and costly challenge in drilling operations, often resulting in wellbore instability, stuck pipe, and extended non-productive time. Accurate prediction of fluid loss is therefore essential for improving drilling safety and efficiency. This study presents a probabilistic machine learning framework based on Gaussian Process Reg

S Sakshi, Vaibhavi Lokegaonkar, Neil Zhang, Ramani Duraiswami

Spatial perception is central to auditory intelligence, enabling accurate understanding of real-world acoustic scenes and advancing human-level perception of the world around us. While recent large audio-language models (LALMs) show strong reasoning over complex audios, most operate on monaural inputs and lack the ability to capture spatial cues such as dire

Suchita Pati, Shaizeen Aga, Mahzabeen Islam, Ryan Quach

Offloading communication to existing direct memory access (DMA) engines, available on most state-of-the-art commercial GPUs, has emerged as an interesting and low-cost solution to efficiently overlap computation and communication in machine learning (ML). That said, so far, the reach of DMA offloads has been limited to bandwidth-bound scenarios only (10s of

Aswath Muthuselvam, Jeevak Raj S, Mohanaprasad K

Road conditions play an important role in our everyday commute. With the proliferating number of vehicles on the road each year, it has become necessary to access the road conditions very frequently, this would ensure that the traffic also flows smoothly. Even the smallest crack in the road could be easily be chipped into a large pothole due to changing surf

Victor Ivo, Zimo Sun

In this note, we compute the phase of the one-loop Euclidean path integral around charged Nariai solutions in 4 dimensions, including both metric and gauge field fluctuations. These solutions have a $S^{2} \times S^{2}$ geometry, and a magnetic flux in one of the spheres. For charges smaller than a critical value, the phase matches the result for the uncharg

Yuwen Li

This work develops polynomial-degree-robust (p-robust) equilibrated a posteriori error estimates for $H(\rm curl)$, $H(\rm div)$ and $H(\rm divdiv)$ problems, based on $H^1$ auxiliary space decomposition. The proposed framework employs auxiliary space preconditioning and regular decompositions to decompose the finite element residual into $H^{-1}$ residuals

Kasra Jalaldoust, Erfan Zabeh

Understanding the relationship between brain activity and behavior is a central goal of neuroscience. Despite significant advances, a fundamental dichotomy persists: neural activity manifests as both discrete spikes of individual neurons and collective waves of populations. Both neural codes correlate with behavior, yet correlation alone cannot determine whe

Zi-Niu Wu

Rhetorical modes are useful in both academic and non-academic writing, and can be subjects to be studied within linguistic research and computational modeling. Establishing a conceptual bridge among these domains could enable each to benefit from the others. This paper proposes duality-based mode operations (split-unite, forward-backward, expansion-reduction

Hamed Sajadinia, Zhuo Feng

This paper introduces HyperEF 2.0, a scalable framework for spectral coarsening and clustering of large-scale hypergraphs through hyperedge effective resistances, aiming to decompose hypergraphs into multiple node clusters with a small number of inter-cluster hyperedges. Building on the recent HyperEF framework, our approach offers three primary contribution

Siddharth Agarwal, Maria A. Rodriguez, Rajkumar Buyya

FaaS offers significant advantages with its infrastructure abstraction, on-demand execution, and attractive no idle resource pricing for modern cloud applications. Despite these benefits, challenges such as startup latencies, static configurations, sub-optimal resource allocation and scheduling still exist due to coupled resource offering and workload-agnost

Mohammad Shirzadi, Ali Safarpoor Dehkordi, Ahad N. Zehmakan

Message passing is the core operation in graph neural networks, where each node updates its embeddings by aggregating information from its neighbors. However, in deep architectures, this process often leads to diminished expressiveness. A popular solution is to use residual connections, where the input from the current (or initial) layer is added to aggregat

Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou

In this work, we study offline convex optimization with smooth objectives, where the classical Nesterov's Accelerated Gradient (NAG) method achieves the optimal accelerated convergence. Extensive research has aimed to understand NAG from various perspectives, and a recent line of work approaches this from the viewpoint of online learning and online-to-batch

Z. Bazzi, D. Colombo, F. Bigiel, A. K. Leroy

High-resolution JWST images of nearby spiral galaxies reveal polycyclic aromatic hydrocarbon (PAH) emission structures that trace molecular gas, including CO-dark regions. We identify ISM cloud structures in PHANGS-JWST 7.7 $\mu$m PAH maps for 66 galaxies, smoothed to 30 pc and at native resolution, extracting 108,466 and 146,040 clouds, respectively. Molecu

Yanmin Mu, Dehua Wang

In this paper, we prove the global existence of solutions to the relativistic Vlasov-Poisson system for general initial data in convex bounded domains of two space dimensions, assuming the specular reflection boundary conditions for the distribution density. The boundary conditions for the electric potential are considered in two cases: Neumann boundary cond

Mykyta Bilodid, Maryna Ishchenko, Peter Berczik

We investigated Gaia-Enceladus/Sausage globular cluster samples and studied their orbital and dynamical evolution over cosmological timescales in external time-variable potential. We estimated the limits of distribution of the escaped stars from the globular clusters' orbital evolution in energy angular momentum space. To reconstruct the orbital evolution of

Hui Sun, Long Lv, Pingping Zhang, Tongdan Tang

Multi-Modal Image Fusion (MMIF) aims to integrate complementary image information from different modalities to produce informative images. Previous deep learning-based MMIF methods generally adopt Convolutional Neural Networks (CNNs) or Transformers for feature extraction. However, these methods deliver unsatisfactory performances due to the limited receptiv

Zhi Rui Tam, Yun-Nung Chen

As large language models transition from text-based interfaces to audio interactions in clinical settings, they might introduce new vulnerabilities through paralinguistic cues in audio. We evaluated these models on 170 clinical cases, each synthesized into speech from 36 distinct voice profiles spanning variations in age, gender, and emotion. Our findings re

Ittetsu Uchiyama, Chihiro Tsutake, Keita Takahashi, Toshiaki Fujii

We introduce a novel phase-shifting digital holography (PSDH) method leveraging a hybrid event-based vision sensor (EVS). The key idea of our method is the phase shift during a single exposure. The hybrid EVS records a hologram blurred by the phase shift, together with the events corresponding to blur variations. We present analytical and optimization-based

Hua Wang

In this paper, the author establishes some interpolation results between Lorentz, Morrey and BMO spaces. Let $1<p<\infty$ and $p\leq r\leq\infty$. It is proved that the space $L^{p,r}(\mathbb R^n)\cap\mathrm{BMO}(\mathbb R^n)$ is continuously embedded into $L^q(\mathbb R^n)$ for all $q$ with $p<q<\infty$, where $L^{p,r}(\mathbb R^n)$ denotes the classical Lo

Seungwoo Shin, Federico Cao, Robert A. Pelcovits, Thomas R. Powers

Formation and rupture of vesicles is a fundamental process underlying diverse phenomena in biology, materials science, and biomedical applications. Vesicles form when the area of a growing disk-like membrane exceeds a critical value at which the edge and bending energies balance each other. Observing such topological transitions in lipid bilayers is a challe

Lewis Bowen, Michael Chapman

A group is surjunctive if every injective cellular automaton on it is also surjective. Gottschalk famously conjectured that all groups are surjunctive. This remains a central open problem in symbolic dynamics and descriptive set theory. Gromov and Weiss termed the notion of sofic groups, and proved that all such groups are surjunctive, providing the largest

Aaryesh Deshpande

The convergence of statistical learning and molecular physics is transforming our approach to modeling biomolecular systems. Physics-informed machine learning (PIML) offers a systematic framework that integrates data-driven inference with physical constraints, resulting in models that are accurate, mechanistic, generalizable, and able to extrapolate beyond o

Matheus Duzi, Paul Szeptycki, Walter Tholen

Relying on recent generalizations of the Fra\"iss\'e theory to a broader category-theoretic context, we study the class of abstract finite games played between two players and show the existence of an infinitetly countable game which is ultrahomogeneous and universal with respect to said class. Certain peculiarities of our game categories which clash with th

Siddharth Ramakrishnan Cherukara

In this paper, we obtain Atkin--Lehner decompositions for spaces of modular forms on definite quaternion algebras. Similar to Casselman's approach our methods are representation theoretic. Using Jacquet--Langlands correspondence we also obtain isomorphisms between spaces of quaternionic modular forms and corresponding spaces of Hilbert modular forms.

Ying Zhang, Yihao Wang, Yuanshuo Zhang, Eric Larson

Traditional statistical optimization-based state estimation (DSSE) algorithms rely on detailed grid parameters and mathematical assumptions of all possible uncertainties. Furthermore, random data missing due to communication failures, congestion, and cyberattacks, makes these methods easily infeasible. Inspired by recent advances in digital twins (DTs), this

Jacob Si, Mike Qu, Michelle Lee, Marek Rei

Incorporating external knowledge bases in traditional retrieval-augmented generation (RAG) relies on parsing the document, followed by querying a language model with the parsed information via in-context learning. While effective for text-based documents, question answering on tabular documents often fails to generate plausible responses. Standard parsing te

Fengxu Yang, Weitong Chen, Jack D. Evans

Large language models (LLMs) are rapidly transforming materials science. This review examines recent LLM applications across the materials discovery pipeline, focusing on three key areas: mining scientific literature , predictive modelling, and multi-agent experimental systems. We highlight how LLMs extract valuable information such as synthesis conditions f

Samiran Sardar, Nasif Morshed, Shezan Ahmed

Although Digital Twin is actively deployed in manufacturing, its human-centric counterpart - Human Digital Twin (HDT) is understudied, especially in job-shop production with high task variability and manual labor. HDT applications like ergonomic posture monitoring, fatigue prediction and health-based task assignment offer benefits to industries in emerging e

Fei Yang, Dong E. Liu

Conventional wisdom holds that static disorder is indispensable to the integer quantum Hall effect, underpinning both quantized plateaus and the plateau-plateau transition. We show that pure dephasing, without elastic disorder, is sufficient to generate the same $θ$ driven criticality. Starting from a Keldysh formulation, we derive an open system nonlinear $

Ufuk Aydemir

The lack of clear new-physics signals at the LHC searches motivates models that can guide current and future collider searches. The spectral action principle within the noncommutative geometry (NCG) framework yields such models with distinctive phenomenology. This formalism derives the actions of the Standard Model, General Relativity, and beyond from the un

Luis Walther, Josef Willsher, Johannes Knolle

The Berezinskii-Kosterlitz-Thouless (BKT) transition is an archetypal example of a topological phase transition, which is driven by the proliferation of vortices. In this Letter, we analyze the persistence of the BKT transition in the XY model under the influence of long-range algebraically decaying interactions of the form $\sim 1/{r^{2+σ}}$. The model host

Oleksandr Masyutka, Mikhail Moklyachuk, Maria Sidei

The problem of the mean-square optimal linear estimation of linear functionals which depend on the unknown values of a multidimensional continuous time stationary stochastic process is considered. Estimates are based on observations of the process with an additive stationary stochastic noise process at points which do not belong to some finite intervals of a

Oleksandr Masyutka, Mikhail Moklyachuk, Maria Sidei

This paper focuses on the problem of the mean square optimal estimation of linear functionals which depend on the unknown values of a multidimensional stationary stochastic sequence. Estimates are based on observations of the sequence with an additive stationary noise sequence. The aim of the paper is to develop methods of finding the optimal estimates of th

Liudas Giraitis, George Kapetanios, Yufei Li, Alexia Ventouri

This paper introduces and analyzes a framework that accommodates general heterogeneity in regression modeling. It demonstrates that regression models with fixed or time-varying parameters can be estimated using the OLS and time-varying OLS methods, respectively, across a broad class of regressors and noise processes not covered by existing theory. The propos

C. Ding, M. L. Palm, K. Kohli, T. Taniguchi

Magnetotransport underlines many important phenomena in condensed matter physics, such as the Hall effect and magnetoresistance (MR) effect. Thus far, most magnetotransport studies are based on bulk resistance measurements without direct access to microscopic details of the spatial transport pattern. Here, we report nanoscale imaging of magnetotransport usin

Vitor Bueno, Ali Azarbahram, Marcello Farina, Lorenzo Fagiano

This paper presents a Koopman-based model predictive control (MPC) framework for safe UAV navigation in dynamic environments using real-time LiDAR data. By leveraging the Koopman operator to linearly approximate the dynamics of surrounding objets, we enable efficient and accurate prediction of the position of moving obstacles. Embedding this into an MPC form

Alex Greilich, Nataliia E. Kopteva, Vladimir L. Korenev, Philipp A. Haude

Synchronization resulting in unified collective behavior of the individual elements of a system that are weakly coupled to each other has long fascinated scientists. Examples range from the periodic oscillation of coupled pendulum clocks to the rhythmic behavior in biological systems. Here we demonstrate this effect in a solid-state platform: spatially remot

Thomas Chuna, Nicholas Barnfield, Paul Hamann, Sebastian Schwalbe

Quantum Monte Carlo (QMC) methods are uniquely capable of providing exact simulations of quantum many-body systems. Unfortunately, the applications of a QMC simulation are limited because extracting dynamic properties requires solving the analytic continuation (AC) problem. Across the many fields that use QMC methods, there is no universally accepted analyti

Javier Jiménez-Garrido, Ignacio Miguel-Cantero, Javier Sanz, Gerhard Schindl

The Stieltjes moment problem is studied in a new framework within the general Gelfand-Shilov spaces defined via weight sequences. The novelty consists of allowing for a naturally larger target space for the moment mapping, which sends a function to its sequence of Stieltjes moments. The motivation comes from a recent version of the Borel-Ritt theorem, concer

Oluwaleke Yusuf, Morten Breivik, Adil Rasheed

This study develops a cybernetically inspired mixed-methods framework that bridges the gap between policy formation and implementation through feedback-driven analysis of mobility transitions. Using a major campus consolidation in Trondheim, Norway as a case study, we examine how this framework supports sustainable mobility through integrated analysis of mob

Emad Abukhousa, Syed Sohail Feroz Syed Afroz, Fahad Alsaeed, Abdulaziz Qwbaiban

This paper presents a high-fidelity evaluation framework for machine learning (ML)-based classification of cyber-attacks and physical faults using electromagnetic transient simulations with digital substation emulation at 4.8 kHz. Twelve ML models, including ensemble algorithms and a multi-layer perceptron (MLP), were trained on labeled time-domain measureme

Karl Pederson, Sam Keller, Daniel Kindem, Hayden Hommes

Thermal management in small satellites is constrained by limited radiative area and strict mass budgets, necessitating the development of radiator structures that are simultaneously lightweight, thermally conductive, and mechanically robust. Here, we present a topology-optimization and design-space analysis framework for multifunctional lightweight radiators

Renato Castro, Rodrigo Paredes, Douglas Kahn

In the fast-moving consumer goods (FMCG) industry, deciding where to place physical assets, such as commercial beverage coolers, can directly impact revenue growth and execution efficiency. Although churn prediction and demand forecasting have been widely studied in B2B contexts, the use of machine learning to guide asset allocation remains relatively unexpl

Madison Caballero, Behrang Mahjani

The discovery of genetic risk factors has transformed human genetics, yet the pace of new gene identification has slowed despite the exponential expansion of sequencing and biobank resources. Current approaches are optimized for the extremes of the allele frequency spectrum: rare, high-penetrance variants identified through burden testing, and common, low-ef

Likun Xie

Using Katz&#39;s equidistribution framework, we show that for any squarefree polynomial $f \in \mathbb{F}_q[t]$ of degree $n \ge 2$, every residue class modulo $f$ can be represented as a product of two monic irreducible polynomials of degree at most $n$, provided $q$ is sufficiently large in terms of $n$. This gives the function-field analogue of a conjectu

Rostyslav O. Serha, Carsten Dubs, Christo Guguschev, Bernd Aichner

Quantum magnonics leverages the quantum properties of magnons to advance nanoscale quantum information technologies. Ferrimagnetic yttrium iron garnet (YIG), known for exceptionally long magnon lifetimes, is a cornerstone material typically grown as thin films on gadolinium gallium garnet (GGG) for lattice matching. However, paramagnetic GGG introduces detri

Vijay Pal Singh, Luigi Amico, Ludwig Mathey

We study the dynamics of a driven atomic Josephson junction that we propose as a parametric amplifier. By periodically modulating the position of the barrier, we induce a small current across the junction, serving as our input signal. The pump field is implemented by modulating the barrier height at twice the Josephson plasma frequency. The resulting dynamic

Johannes Kainz, Nikitabahen N. Makwana, Bipin Kumar, S. Ravichandran

Considering turbulence is crucial to understanding clouds. However, covering all scales involved in the turbulent mixing of clouds with their environment is computationally challenging, urging the development of simpler models to represent some of the processes involved. By using full direct numerical simulations as a reference, this study compares several s

Raisa Fabiha, Patrick J. Taylor, Supriyo Bandyopadhyay

Antennas are the quintessential means to communicate information wirelessly over long distances via electromagnetic waves. Traditional antennas have two shortcomings that have prevented miniaturization: (1) their radiation efficiencies plummet and (2) they radiate isotropically when miniaturized to small fractions of the radiated wavelength. Here, we report

Charles L. Epstein, Rafe Mazzeo

This paper continues the analysis of the scattering problem for a network of open wave-guides started in [arXiv:2302.04353, arXiv:2310.05816]. In this part we present explicit, physically motivated radiation conditions that ensure uniqueness of the solution to the scattering problem. These conditions stem from a 2000 paper of A. Vasy on 3-body Schrodinger op

Charles L. Epstein

The paper continues the analysis, started in [1] (Part I,arXiv:2302.04353), of the model open wave-guide problem defined by 2 semi-infinite, rectangular wave-guides meeting along a common perpendicular line. In Part I we reduce the solution of the physical problem to a transmission problem rephrased as a system of integral equations on the common perpendicul

Charles L. Epstein

We introduce a layer potential representation for the solution of the transmission problem defined by two dielectric channels, or open wave-guides, meeting along the straight-line interface, $\{x_1=0\}.$ The main observation is that the outgoing fundamental solution for the operator $Δ+k_1^2+q(x_2),$ acting on functions defined in ${\mathbb R}^2,$ is easily

Ian Gleason, Dong Gyu Lim, Yujie Xu

We compute the connected components of arbitrary parahoric level affine Deligne-Lusztig varieties and local Shimura varieties, thus resolving a folklore conjecture in full generality (even for non-quasisplit groups). We achieve this by relating them to the connected components of infinite level moduli spaces of p-adic shtukas, where we use v-sheaf-theoretic

Christoph Grunau, Rasmus Kyng, Goran Zuzic

We combine several recent advancements to solve $(1+\varepsilon)$-transshipment and $(1+\varepsilon)$-maximum flow with a parallel algorithm with $\tilde{O}(1/\varepsilon)$ depth and $\tilde{O}(m/\varepsilon)$ work. We achieve this by developing and deploying suitable parallel linear cost approximators in conjunction with an accelerated continuous optimizati

Huan-Cheng Liao, Shunyao Zhang, Yumin Su, Arvind Govinday

Wearable photoacoustic imaging devices hold great promise for continuous health monitoring and point-of-care diagnostics. However, the large data volume generated by high-density transducer arrays presents a major challenge for realizing compact and power-efficient wearable systems. This paper presents a photoacoustic imaging receiver (RX) that embeds compre

Kaustubh Singh, Shivam Kumar, Shashikant Pawar, Sandeep Manjanna

In this paper, we present an underactuated biomimetic underwater robot that is suitable for ecosystem monitoring in both marine and freshwater environments. We present an updated mechanical design for a fish-like robot and propose minimal actuation behaviors learned using reinforcement learning techniques. We present our preliminary mechanical design of the

Kaoru Ohno, Yutaka Okabe

In a recent paper [JHEP 11 (2020) 118], S. Giombi and H. Khanchandani studied the 1/N expansion of the O(N) model in semi-infinite space within the framework of conformal field theory in anti-de Sitter space. They presented a series expansion for the O(1/N) correction to the boundary anomalous dimension in the case of the ordinary transition. Although they w

Mohammad Javad Najafirad, Shirantha Welikala, Lei Wu, Panos J. Antsaklis

This paper introduces a dissipativity-based framework for the joint design of distributed controllers and communication topologies in AC microgrids (MGs), providing robust performance guarantees for voltage regulation, frequency synchronization, and proportional power sharing across distributed generators (DGs). The closed-loop AC MG is represented as a netw

Jun Wang, Yevgeniy Vorobeychik, Yiannis Kantaros

Large Language Models (LLMs) have recently emerged as planners for language-instructed agents, generating sequences of actions to accomplish natural language tasks. However, their reliability remains a challenge, especially in long-horizon tasks, since they often produce overconfident yet wrong outputs. Conformal Prediction (CP) has been leveraged to address

Daniel Agassy, Dani Dorfman, Haim Kaplan

A \emph{tree cut-sparsifier} $T$ of quality $\alpha$ of a graph $G$ is a single tree that preserves the capacities of all cuts in the graph up to a factor of $\alpha$. A \emph{tree flow-sparsifier} $T$ of quality $\alpha$ guarantees that every demand that can be routed in $T$ can also be routed in $G$ with congestion at most $\alpha$. We present a near-linea

Biswajit Kumar Sahoo, Pedro Machado, Isibor Kennedy Ihianle, Andreas Oikonomou

Secure data hiding remains a fundamental challenge in digital communication, requiring a careful balance between computational efficiency and perceptual transparency. The balance between security and performance is increasingly fragile with the emergence of generative AI systems capable of autonomously generating and optimising sophisticated cryptanalysis an

Marcelo Maciel Amaral

We introduce an infinite, scale-aligned hierarchy of one-dimensional, frustration-free Hamiltonians by forbidding the minimal forbidden factors of the Fibonacci word up to length $F_K$, the $K$-th Fibonacci number. The ground-state languages have exponential growth constants $\lambda_K$ that decrease monotonically, starting from the value associated with the

Reimbay Reimbayev

Strongly regular graphs are highly symmetrical and can be described fully with just a few parameters, yet the existence of many of them is still under the question. In this paper, we continue the study of the famuly of strongly regular graphs with parameters $\lambda =1$ and $\mu =2$ and establish all of their possible Hamiltonian subgraphs of order seven. B

Haiyan Zhao, Zirui He, Yiming Tang, Fan Yang

Large language models (LLMs) have achieved remarkable progress across diverse tasks, yet their internal mechanisms remain largely opaque. In this work, we investigate a fundamental question: to what extent can the original input text be recovered from a single last-token representation in an LLM? To this end, we propose Rep2Text, a novel framework for decodi

Marvin Fritz, Endre Süli, Barbara Wohlmuth

In this work, we study a class of nonlocal-in-time kinetic models of incompressible dilute polymeric fluids. The system couples a macroscopic balance of linear momentum equation with a mezoscopic subdiffusive Fokker-Planck equation governing the evolution of the probability density function of polymer configurations. The model incorporates nonlocal features

Reimbay Reimbayev

An $srg(19,6,1,2)$ is the graph with the smallest parameter set in the family of strongly regular graphs with parameters $\lambda=1$ and $\mu=2$ for which the respective graph doesn't exist. The proof of that fact is based on algebraic arguments, particularly, on the Integrality Test, the very usefull tool for studying strongly regular graphs. To our best kn

João Mattos, Debolina Halder Lina, Arlei Silva

Link prediction is a fundamental task in graph machine learning with applications, ranging from social recommendation to knowledge graph completion. Fairness in this setting is critical, as biased predictions can exacerbate societal inequalities. Prior work adopts a dyadic definition of fairness, enforcing fairness through demographic parity between intra-gr

Miguel Pino, Francisco Reyes

We present the spectrum of stationary black hole solutions associated with the first non-trivial member of the Dym family of boundary conditions. The analysis combines the holonomy conditions provided by the Chern-Simons formulation of AdS$_3$ gravity with the periodic solutions of the stationary Dym equation. The resulting spacetimes are characterized by tw

Robert Oeckl

We provide a framework and explicit construction for the regularized measurement of a large class of spacetime-localized observables in bosonic quantum field theory. The measurements fully satisfy relativistic causality and causal transparency, i.e., avoid unphysical superluminal signaling. We show explicitly how the measurement of time-extended observables

Arnab A Purkayastha, Jay Tharwani, Shobhit Aggarwal

The growing complexity of computational workloads has amplified the need for efficient and specialized hardware accelerators. Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) have emerged as prominent solutions, each excelling in specific domains. Although there is substantial research comparing FPGAs and GPUs, most of the work foc

Michał Szyfelbein

Consider the following generalization of the classic binary search problem: A searcher is required to find a hidden target vertex $x$ in a graph $G$. To do so, they iteratively perform queries to an oracle, each about a chosen vertex $v$. After each such call, the oracle responds whether the target was found and if not, the searcher receives as a reply the c

Sara Khosravi, Burak Demirel, Linghui Zhou, Javier Rasines

Adopting artificial intelligence (AI) in radio access networks (RANs) presents several challenges, including limited availability of link-level measurements (e.g., CQI reports), stringent real-time processing constraints (e.g., sub-1 ms per TTI), and network heterogeneity (different spectrum bands, cell types, and vendor equipment). A critical yet often over

Saannidhya Rawat

We exploit quasi-random variation around the multi-threshold criteria used to classify Census Towns (CTs) and focus on settlements near the thresholds that are likely to obtain statutory recognition. Using a local fuzzy regression discontinuity design and a multi-threshold criteria, we show that meeting the CT eligibility in 2001 raises the probability of st

Otavio Higino Moura de Alencar, James Mu, Marcus Müller, Debashish Mukherji

Understanding the thermal conductivity of polymers under high-pressure conditions is essential for a range of applications, from aerospace and deep-sea engineering to common lubricants. However, the complex relationship between pressure, $P$, the thermal transport coefficient, $\kappa$, and polymer architecture poses substantial challenges to both experiment

Walaa M. Moursi, Andrew Naguib, Viktor Pavlovic, Stephen A. Vavasis

Recent works by Bot-Fadili-Nguyen (arXiv:2510.22715) and by Jang-Ryu (arXiv:2510.23513) resolve long-standing iterate convergence questions for accelerated (proximal) gradient methods. In particular, Bot-Fadili-Nguyen prove weak convergence of discrete accelerated gradient descent (AGD) iterates and, crucially, convergence of the accelerated proximal gradien

Rui Ai, David Simchi-Levi, Haifeng Xu

With the rapid advancement of generative AI (GenAI), mechanism design adapted to its unique characteristics poses new theoretical and practical challenges. Unlike traditional goods, content from one domain can enhance the training and performance of GenAI models in other domains. For example, OpenAI's video generation model Sora (Liu et al., 2024b) relies he

Akshay Revankar, Charan Renganathan, Sartaj Wariah

The data centers of today are running diverse workloads sharing many common lower level functions called tax components. Any optimization to any tax component will lead to performance improvements across the data center fleet. Typically, performance enhancements in tax components are achieved by offloading them to accelerators, however, it is not practical t

Alexander Pakpour Tabrizi, Artur Lozovoi, Sean Karg, Tecla Bottinelli Mondandon

Deploying nitrogen vacancy (NV) centers in diamond as nanoscale quantum sensors for condensed matter and materials physics requires placing the NV centers close to the sensing target. One solution is to fabricate diamond nanostructures and integrate them with materials and devices. However, diamond etching and ion milling can introduce subsurface damage and

Pelin Keşrit, Chelliah Sriskandarajah, Jon M. Stauffer

Increasing the efficiency and effectiveness of the healthcare system is a challenge faced worldwide. Many outpatient clinics have implemented two-stage service systems, with both a physician and physician assistant, to enhance capacity and reduce costs. Some patients only visit a physician assistant while some patients visit both providers depending on their

José A. Díaz-García, Francisco J. Caro-Lopra

A generalisation of the Charnes-Cooper chance-constrained approach is proposed in the setting of the family of elliptically contoured distributions. The new relaxed stochastic linear programming is notably invariant under the entire class of probability distributions.