November 2025 arXiv papers — page 189

Yuwen Tao, Kanglei Zhou, Xin Tan, Yuan Xie

Referring 3D Gaussian Splatting Segmentation (R3DGS) aims to ground free-form language queries in 3D Gaussian fields. However, existing methods rely on single-view pseudo supervision, leading to viewpoint drift and inconsistent predictions across views. We propose CaRF (Camera-aware Referring Field), a camera-aware cross-view alignment framework for view-con

Dina Grace Banguilan, Yuto Iye, Kazuhiko Ogawa, Eiji Kajikawa

We report watt-level femtosecond pulses in the 1.75 $\mu$m region using a thulium-doped core, terbium-doped cladding fluoride (Tm:Tb:ZBLAN) fiber laser system. The seed pulse is generated through stimulated Raman scattering in a silica fiber pumped by an erbium-doped fiber laser. The soliton is subsequently amplified through a multi-stage Tm:Tb:ZBLAN amplifi

V. G. Gurzadyan, N. N. Fimin, V. M. Chechetkin

We study the appearance and specific properties of the structures in the local Universe by means of the Vlasov kinetic technique. We consider the role of the cosmological constant in local structure formation via the theorem on the general function that satisfies the identity of the gravity of the sphere and of the point mass. Then, the Hubble tension is nat

Pendo B. Nyanda, Gowoon Kim, Youngduk Kim, Kyungmin Seo

We report on R&D study to improve the photon detection efficiency of water Cherenkov detectors by doping ultra-pure water with 4-methylumbelliferone (4-MU), a wavelength shifting additive. Cherenkov light yields from cosmic-ray muons were measured for various 4-MU concentrations and compared with those from pure water. At a concentration of 1 ppm, the detect

Wenshuo Qin, Leyla Isik

Humans effortlessly recognize social interactions from visual input, yet the underlying computations remain unknown, and social interaction recognition challenges even the most advanced deep neural networks (DNNs). Here, we hypothesized that humans rely on 3D visuospatial pose information to make social judgments, and that this information is largely absent

Ahmed A. Metwally, Heyjun Park, Yue Wu, Tracey McLaughlin

The classification of diabetes and prediabetes by static glucose thresholds obscures the pathophysiological dysglycemia heterogeneity, primarily driven by insulin resistance (IR), beta-cell dysfunction, and incretin deficiency. This review demonstrates that continuous glucose monitoring and wearable technologies enable a paradigm shift towards non-invasive,

Zhuowen Yuan, Tao Liu, Yang Yang, Yang Wang

Recent LLM-based agents have demonstrated strong capabilities in automated ML engineering. However, they heavily rely on repeated full training runs to evaluate candidate solutions, resulting in significant computational overhead, limited scalability to large search spaces, and slow iteration cycles. To address these challenges, we introduce ArchPilot, a mul

Hanfu Zhang, Erwu Liu

Low earth orbit (LEO) satellite-assisted integrated sensing and communications (ISAC) systems have been extensively studied to achieve ubiquitous connectivity. However, the severe signal attenuation and limited transmit power at LEO satellites can degrade ISAC performance. To address this issue, this paper investigated movable antenna (MA)-assisted LEO ISAC

Michael Menezes, Barbara Su, Xinze Feng, Yehya Farhat

We introduce TwIST, a distributed training framework for efficient large language model (LLM) sparsification. TwIST trains multiple subnetworks in parallel, periodically aggregates their parameters, and resamples new subnetworks during training. This process identifies high-quality subnetworks ("golden tickets") without requiring post-training procedures suc

Nicholas Godfrey, Ted Sichelman

Quantum entanglement is a phenomenon in which two physical systems are correlated in such a way that they appear to instantaneously affect one another, regardless of the distance between them. As commonly understood, Bell's Theorem famously demonstrates that any causal explanation of entanglement must discard either locality (the principle that nothing, incl

Yuxiao Wang, Di Wu, Feng Liu, Zhimin Qiu

This paper proposes a composable fine-tuning method that integrates graph structural priors with modular adapters to address the high computational cost and structural instability faced by large-scale pre-trained models in multi-task adaptation. The method introduces a relation matrix to model dependencies among tasks, explicitly encoding correlations betwee

Bram Bulté, Ayla Rigouts Terryn

Large Language Models (LLMs) are rapidly being adopted by users across the globe, who interact with them in a diverse range of languages. At the same time, there are well-documented imbalances in the training data and optimisation objectives of this technology, raising doubts as to whether LLMs can represent the cultural diversity of their broad user base. I

George E. Andrews, Rahul Kumar, Ae Ja Yee

Euler's theorem asserts that $A(n)=B(n)$ where $A(n)$ is the number of partitions of $n$ into distinct parts and $B(n)$ is the number of partitions of $n$ into odd parts. In this paper, it is proved that for $n>0$, \begin{align*} A(n)=B(n)=C(n+1)=\frac{1}{2}D(n+1), \end{align*} where $C(n)$ is the number of partitions of $n$ with largest part even and parts

Ramsey L. Karim, Marc W. Pound, Alexander G. G. M. Tielens, Jelle S. Kaastra

We characterize the physical conditions and energy budget of the M16 H II region using SOFIA FEEDBACK observations of the [C II] 158 $\mu$m line. The O stars in the $\sim 10^{4}~{\rm M}_{\odot}$ NGC 6611 cluster powering this H II region have blown at least 2 cavities into the giant molecular cloud: the large M16 cavity and the small N19 bubble. We detect th

Michael Warnock, David A. Hague, Vesna F. Mitrovic

In this manuscript, we introduce an exact expression for the response of a semi-classical two-level quantum system subject to arbitrary periodic driving. Determining the transition probabilities of a two-level system driven by an arbitrary periodic waveform necessitates numerical calculations through methods such as Floquet theory, requiring the truncation o

Xu Zou

Since its emergence, SARS-CoV-2 has demonstrated a rapid and unpredictable evolutionary trajectory, characterized by the continual emergence of immune-evasive variants. This poses persistent challenges to public health and vaccine development. While large-scale generative pre-trained transformers (GPTs) have revolutionized the modeling of sequential data, th

V. Ryzhii, M. Ryzhii, M. S. Shur, T. Otsuji

We analyze the thermal and electrical characteristics of the metasurface consisting of the coplanar interdigital array of the graphene microribbons (GMRs) connected by nanobridges (NBs). These nanobridges could be implemented using graphene nanoribbons (GNRs), single-wall semiconducting carbon nanotubes (CNTs), or black-arsenic-phosphorus (b-AsP) nanostructu

Mu-Chun Chen, Shaheed Perez, Michael Ratz

Theories of flavor operate at various scales. Recently it has been pointed out that in the context of modular flavor symmetries certain combinations of observables are highly constrained, or even uniquely fixed, by modular invariance and holomorphicity. We find that even in the absence of supersymmetry these combinations are surprisingly immune against quant

Mingliang Hou, Yinuo Wang, Teng Guo, Zitao Liu

Cognitive diagnosis models (CDMs) are pivotal for creating fine-grained learner profiles in modern intelligent education platforms. However, these models are trained on sensitive student data, raising significant privacy concerns. While membership inference attacks (MIA) have been studied in various domains, their application to CDMs remains a critical resea

Semih Cayci

An important question in deep learning is how higher-order optimization methods affect generalization. In this work, we analyze a stochastic Gauss-Newton (SGN) method with Levenberg-Marquardt damping and mini-batch sampling for training overparameterized deep neural networks with smooth activations in a regression setting. Our theoretical contributions are t

Victor Mattos, João Henrique Schmidt, Amit Bhaya, Alan Oliveira de Sá

Cyberattacks targeting critical infrastructures, such as water treatment facilities, represent significant threats to public health, safety, and the environment. This paper introduces a systematic approach for modeling and assessing covert man-in-the-middle (MitM) attacks that leverage system identification techniques to inform the attack design. We focus on

Sam Bahrami, Dylan Campbell

Modern scene reconstruction methods are able to accurately recover 3D surfaces that are visible in one or more images. However, this leads to incomplete reconstructions, missing all occluded surfaces. While much progress has been made on reconstructing entire objects given partial observations using generative models, the structural elements of a scene, like

Hangyu Teng

Co-simulation is a critical approach for the design and analysis of complex cyber-physical systems. It will enhance development efficiency and reduce costs. This paper presents a co-simulation framework integrating ROS 2 and MATLAB/Simulink for quadrotor unmanned aerial vehicle (UAV) control system design and verification. First, a six-degree-of-freedom nonl

Ioannis Anagnostides, Maria-Florina Balcan, Kiriaki Fragkia, Tuomas Sandholm

The complexity of computing equilibrium refinements has been at the forefront of algorithmic game theory research, but it has remained open in the seminal class of potential games; we close this fundamental gap in this paper. We first show that computing a pure(-strategy) perfect or proper equilibrium is $\mathsf{PLS}$-complete in concise potential games in

Wuxia Chen, Sean Moushegian, Vahid Tarokh, Taposh Banerjee

This paper introduces an approach to multi-stream quickest change detection and fault isolation for unnormalized and score-based statistical models. Traditional optimal algorithms in the quickest change detection literature require explicit pre-change and post-change distributions to calculate the likelihood ratio of the observations, which can be computatio

Mingliang Hou, Yinuo Wang, Teng Guo, Zitao Liu

The need to remove specific student data from cognitive diagnosis (CD) models has become a pressing requirement, driven by users' growing assertion of their "right to be forgotten". However, existing CD models are largely designed without privacy considerations and lack effective data unlearning mechanisms. Directly applying general purpose unlearning algori

Kihiro T. Yamada, Akira Izumi, Tetsuya Ikebuchi, Sumiyuki Okabe

Light polarization is one of the most fundamental features, equivalent to energy and coherence. Magnetism changes light polarization, and vice versa. The irradiation of intense circularly polarized femtosecond pules to magnetic materials can alter the magnetic orders and elementary excitations, particularly in the visible to infrared spectral regions. Furthe

Yinrui Liu, Laura Munteanu, Stephen Dolan

We present a quantitative demonstration that, without additional theoretical and experimental efforts, realistic variations in final-state-interaction (FSI) modeling may alter reconstructed neutrino-energy spectra at next-generation long-baseline experiments by amounts comparable to, or larger than, variations induced by oscillation-parameter shifts at their

Jarah Evslin, Tomasz Romańczukiewicz, Katarzyna Slawińska, Andrzej Wereszczynski

Just as linearized perturbations of time-independent configurations can be decomposed into normal modes, those of periodic systems can be decomposed into Floquet modes, which each evolve by a fixed phase over one period. We show that in the case of a (1+1)-dimensional relativistic field theory with a single scalar of mass $m$, all breathers, quasi-breathers

Qian Li, Xin Lyu

We prove multi-pass streaming lower bounds for uniformity testing over a domain of size $2m$. The tester receives a stream of $n$ i.i.d. samples and must distinguish (i) the uniform distribution on $[2m]$ from (ii) a Paninski-style planted distribution in which, for each pair $(2i-1,2i)$, the probabilities are biased left or right by $\epsilon/2m$. We show t

Kia Karbasi, Kevin Hong, Mohammad Amin Samadi, Gregory Pottie

Automatic question generation (AQG) for mathematics education remains an elusive goal for Intelligent Tutoring Systems and educators. While pre-trained transformer-based language models have significantly advanced natural language generation, they often struggle to precisely control problem complexity and cognitive demands. In this paper, we introduce a coll

Yangyang Cheng, Zhenyu Li, Wanting Sun, Guanghui Wang

An equitable $k$-coloring of a graph is a proper $k$-coloring where the sizes of any two different color classes differ by at most one. In 1973, Meyer conjectured that every connected graph $G$ has an equitable $k$-coloring for some $k\leq \Delta(G)$, unless $G$ is a complete graph or an odd cycle. Chen, Lih, and Wu strengthened this in 1994 by conjecturing

Lingyun Ding, Terry Wang, Marcus Roper

Over 125 years ago, Henry Selby Hele-Shaw realized that the depth-averaged flow in thin gap geometries can be closely approximated by two-dimensional (2D) potential flow, in a surprising marriage between the theories of viscous-dominated and inviscid flows. Hele-Shaw approximation allows visualization of potential flows over 2D airfoils and also undergirds i

Wuxia Chen, Taposh Banerjee, Vahid Tarokh

We address the problem of quickest change detection in Markov processes with unknown transition kernels. The key idea is to learn the conditional score $\nabla_{\mathbf{y}} \log p(\mathbf{y}|\mathbf{x})$ directly from sample pairs $( \mathbf{x},\mathbf{y})$, where both $\mathbf{x}$ and $\mathbf{y}$ are high-dimensional data generated by the same transition k

Aukosh Jagannath, Taj Jones-McCormick, Varnan Sarangian

We develop a high-dimensional scaling limit for Stochastic Gradient Descent with Polyak Momentum (SGD-M) and adaptive step-sizes. This provides a framework to rigourously compare online SGD with some of its popular variants. We show that the scaling limits of SGD-M coincide with those of online SGD after an appropriate time rescaling and a specific choice of

Nagananda K G, Jong Sung Kim

A unified framework is presented to study the two-sample Behrens--Fisher problem -- testing equality of means when two normal populations have unequal, unknown variances -- and a compact expression is derived for the null distribution of the classical test statistic. Our new approach involves a Mellin--Barnes factorization that decouples the square root of a

Zhejia Cai, Puhua Jiang, Shiwei Mao, Hongkun Cao

Reconstructing real-world objects from multi-view images is essential for applications in 3D editing, AR/VR, and digital content creation. Existing methods typically prioritize either geometric accuracy (Multi-View Stereo) or photorealistic rendering (Novel View Synthesis), often decoupling geometry and appearance optimization, which hinders downstream editi

B P Pandey, Mark Wardle

Transport coefficients are calculated for a partially ionized plasma consisting of approximately 90% hydrogen and 10\% helium, representative of a model solar atmosphere with an assumed magnetic field profile. The ion Hall parameter, defined as the ratio of ion cyclotron to ion collision frequency, is determined by considering dominant resonance charge excha

Kevin Hong, Kia Karbasi, Gregory Pottie

A longstanding goal in computational educational research is to develop explainable knowledge tracing (KT) models. Deep Knowledge Tracing (DKT), which leverages a Recurrent Neural Network (RNN) to predict student knowledge and performance on exercises, has been proposed as a major advancement over traditional KT methods. Several studies suggest that its perf

Soumyajit Maity, Pranjal Kamboj, Sneha Maity, Rajat Singh

This paper proposes a MedGemma-based framework for automatic abnormality detection in musculoskeletal radiographs. Departing from conventional autoencoder and neural network pipelines, the proposed method leverages the MedGemma foundation model, incorporating a SigLIP-derived vision encoder pretrained on diverse medical imaging modalities. Preprocessed X-ray

Dmytro Vitel, Anshuman Chhabra

Identifying how training samples influence/impact Large Language Model (LLM) decision-making is essential for effectively interpreting model decisions and auditing large-scale datasets. Current training sample influence estimation methods (also known as influence functions) undertake this goal by utilizing information flow through the model via its first-ord

Marcelo P. Fiore, Ohad Kammar, Georg Moser, Sam Staton

We adapt Fiore, Plotkin, and Turi's treatment of abstract syntax with binding, substitution, and holes to account for languages with second-class sorts. These situations include programming calculi such as the Call-by-Value lambda-calculus (CBV) and Levy's Call-by-Push-Value (CBPV). Prohibiting second-class sorts from appearing in variable contexts changes t

Fu-Chun Yang, Jason Eshraghian

In multi-agent settings, such as debate, reflection, or tool-calling, large language models (LLMs) pass messages as plain tokens, discarding most latent semantics. This constrains information transfer and adds unnecessary computational overhead. We form a latent bridge via vector translations, which use learned mappings that enable direct semantic exchange b

Tong Zhang, Vikram Sharma Mailthody, Fei Sun, Linsen Ma

In 1987, Jim Gray and Gianfranco Putzolu introduced the five-minute rule, a simple, storage-memory-economics-based heuristic for deciding when data should live in DRAM rather than on storage. Subsequent revisits to the rule largely retained that economics-only view, leaving host costs, feasibility limits, and workload behavior out of scope. This paper revisi

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Temporal action localization requires both precise boundary detection and computational efficiency. Current methods apply uniform computation across all temporal positions, wasting resources on easy boundaries while struggling with ambiguous ones. We address this through two complementary innovations: Boundary Distance Regression (BDR), which replaces classi

Fabio Diniz Rossi

Edge Computing enables low-latency processing for real-time applications but introduces challenges in power management due to the distributed nature of edge devices and their limited energy resources. This paper proposes a stochastic modeling approach using Markov Chains to analyze power state transitions in Edge Computing. By deriving steady-state probabili

Jifeng Chu, Kang Lyu, Chuan-Fu Yang

In this paper, we consider the discrete periodic Schr\"odinger operators $\Delta+V$ on $\Z^d$, where $V$ is $\Gamma$-periodic with $\Gamma=q_1 \mathbb{Z}\oplus q_2\mathbb{Z}\oplus\cdots\oplus q_d\mathbb{Z}$ and positive integers $q_j$, $j=1,2,\cdots,d,$ are pairwise coprime. We introduce the notions of generalized partial Fermi isospectrality and weak separa

Raghav Sharma, Manan Mehta, Sai Tiger Raina

Reinforcement Learning from Human Feedback (RLHF) is the standard for aligning Large Language Models (LLMs), yet recent progress has moved beyond canonical text-based methods. This survey synthesizes the new frontier of alignment research by addressing critical gaps in multi-modal alignment, cultural fairness, and low-latency optimization. To systematically

Sanggeon Yun, Hyunwoo Oh, Ryozo Masukawa, Pietro Mercati

Hyperdimensional computing (HDC) suits memory, energy, and reliability-constrained systems, yet the standard "one prototype per class" design requires $O(CD)$ memory (with $C$ classes and dimensionality $D$). Prior compaction reduces $D$ (feature axis), improving storage/compute but weakening robustness. We introduce LogHD, a logarithmic class-axis reduction

Kohei Kurahara, Takuya Akahori, Takumi Ohmura, Shintaro Yoshiura

A head-tail galaxy is thought to be a radio galaxy with bent active galactic nuclei (AGN) jets interacting with the intracluster medium (ICM). Study of head-tail galaxies provides us with fruitful insights into the mechanisms of shock waves and turbulence, as well as magnetic-field amplification and cosmic-ray acceleration. A recent MeerKAT observation revea

Valentin Barandun, Nina Glaser, Markus Reiher

Theoretical predictions of photochemical processes are essential for interpreting and understanding spectral features. Reliable quantum dynamics calculations of vibronic systems require precise modeling of anharmonic effects in the potential energy surfaces and off-diagonal nonadiabatic coupling terms. In this work, we present the n-mode quantization of all

Vahid Salari, Yingwen Zhang, Sepideh Ahmadi, Dilip Paneru

Quantum imaging is emerging as a transformative approach for biomedical applications, applying nonclassical properties of light, such as entanglement, squeezing, and quantum correlations, to overcome fundamental limits of conventional techniques. These methods promise superior spatial resolution, enhanced signal-to-noise ratios, improved phase sensitivity, a

Athma Narayanan, Mahesh Subedar, Omesh Tickoo

We present an agentic flow consisting of multiple agents that combine specialized LLMs and hardware simulation tools to collaboratively complete the complex task of Register Transfer Level (RTL) generation without human intervention. A key feature of the proposed flow is the progressive error feedback system of agents (PEFA), a self-correcting mechanism that

Joshua Renner, Matthew A. Wright, Kristofer Bouchard, Bruce E. Cohen

Electron microscopy (EM) is a foundational tool for directly assessing the structure of materials. Recent advances in direct electron detectors have improved signal-to noise ratios via single-electron counting. However, accurately counting electrons at high fluence remains challenging. We developed a new method of electron counting for direct electron detect

Will G. Hopkins

This article presents a pragmatic framework for making formal, utility-based decisions from statistical inferences. The method calculates an expected utility score for an intervention by combining Bayesian posterior probabilities of different effect magnitudes with points representing their practical value. A key innovation is a unified, non-arbitrary points

Iman Adibnazari, Harsh Sharma, Myungsun Park, Jacobo Cervera-Torralba

Soft robots have shown immense promise in settings where they can leverage dynamic control of their entire bodies. However, effective dynamic shape control requires a controller that accounts for the robot's high-dimensional dynamics--a challenge exacerbated by a lack of general-purpose tools for modeling soft robots amenably for control. In this work, we co

C. E. Schaefer, A. C. Sontag, N. M. Ferraro, J. D. Weberski

Solenoid-free tokamak startup techniques are essential for spherical tokamaks and offer a pathway to cost reduction and design simplification in fusion energy systems. Local helicity injection (LHI) is one such approach, employing compact edge current sources to drive open field line current that initiates and sustains tokamak plasmas. The recently commissio

NVIDIA, :, Amala Sanjay Deshmukh, Kateryna Chumachenko

We introduce Nemotron Nano V2 VL, the latest model of the Nemotron vision-language series designed for strong real-world document understanding, long video comprehension, and reasoning tasks. Nemotron Nano V2 VL delivers significant improvements over our previous model, Llama-3.1-Nemotron-Nano-VL-8B, across all vision and text domains through major enhanceme

Ljudmila Kamenova, Steven Lu

By restricting to (a linear subspace of) an affine chart in projective space, a complex stably rational or unirational manifold of dimension $m$ is meromorphically dominable by $\mathbb C^m$, i.e., admits a meromorphic dominating map from $\mathbb C^m$. So are varieties that are birational to abelian varieties and Kummer K3 surfaces. G. Buzzard and the secon

Arthur Chen, Victor Zhong

We introduce and formalize the Synthetic Dataset Quality Estimation (SynQuE) problem: ranking synthetic datasets by their expected real-world task performance using only limited unannotated real data. This addresses a critical and open challenge where data is scarce due to collection costs or privacy constraints. We establish the first comprehensive benchmar

Yize Dong, V. Ashley Villar, Anya Nugent, Griffin Hosseinzadeh

In this paper, we first present observations of SN~2024acyl, a normal Type Ibn supernova with a large projected offset ($\sim$35~kpc) from its host galaxy. The low star-formation rate measured at the explosion site raises the possibility that the progenitor of SN~2024acyl may not have been a massive star. We then examine, more broadly, the spectral diversity

Nikta Akbarpour, Mahdieh Sadat Benis, Fatemeh Hendijani Fard, Ali Ouni

Automated Program Repair (APR) has advanced rapidly with Large Language Models (LLMs), but most existing methods remain computationally expensive, and focused on a small set of languages. Ruby, despite its widespread use in web development and the persistent challenges faced by its developers, has received little attention in APR research. In this paper, we

Ekin Uğurel, Cynthia Chen, Brian H. Y. Lee, Filipe Rodrigues

Inferring sociodemographic attributes from mobility data could help transportation planners better leverage passively collected datasets, but this task remains difficult due to weak and inconsistent relationships between mobility patterns and sociodemographic traits, as well as limited generalization across contexts. We address these challenges from three an

Benjamin Hawks, Jason Weitz, Dmitri Demler, Karla Tame-Narvaez

As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not pre

Sebastián Passanante, Dafne Goijman, M. R. Neyra Astudillo, Carlos D. Anello

Materials used in nuclear reactors are constantly exposed to the effects of neutron irradiation, which leads to changes in their mechanical properties. In particular, the steels employed in reactor pressure vessels experience a reduction in the ductile-to brittle transition temperature. Given that the pressure vessel is a non-redundant component, understandi

Shiyu Li, Dongha Kim, Shanhui Fan, Cheng Guo

Controlling multiple wave properties simultaneously poses a key challenge in coherent control of wave transport. We present a theory for joint coherent control of transmission, reflection, and absorption in linear systems. We prove that the numerical range provides the mathematical structure governing achievable responses, and reveal non-abelian effects due

Roberto Carrasco, Federico Escudero, Zhen Zhan, Eva Cortes-del Rio

We report the first observation of controlled, strain-induced square moire patterns in stacked graphene. By selectively displacing native wrinkles, we drive a reversible transition from the usual trigonal to square moire order. Scanning tunneling microscopy reveals elliptically shaped AA domains, while spectroscopy shows strong electronic correlation in the

Sören Arlt, Mario Krenn, Xuemei Gu

Interactions between quantum systems enable quantum gates, the building blocks of quantum information processing. In photonics, direct photon-photon interactions are too weak to be practically useful, so effective interactions are engineered with linear optics and measurement. A central challenge is to realize such interactions non-locally, i.e., between pho

Sourav Karmakar, Sutirtha Paul, Adrian Del Maestro, Barak Hirshberg

We propose an improved Path Integral Monte Carlo (PIMC) algorithm called Harmonic PIMC (H-PIMC) and its generalization, Mixed PIMC (M-PIMC). PIMC is a powerful tool for studying quantum condensed phases. However, it often suffers from a low acceptance ratio for solids and dense confined liquids. We develop two sampling schemes especially suited for such prob

F. Rabec, G. Brochier, S. Wattellier, G. Chauveau

We experimentally investigate the superfluid properties of a two-dimensional, weakly interacting Bose-Einstein condensate in the zero-temperature regime, when it is subjected to a triangular optical lattice potential. We implement an original method, which involves solving the hydrodynamic continuity equation to extract the superfluid fraction tensor from th

Tecla Gabbrielli, Jacopo Pelini, Chenhong Zhang, Francesco Cappelli

Versatile, ultracompact, easy-to-handle, high-sensitivity sensors are compelling tools for in situ pivotal applications, such as medical diagnostics, security and safety assessments, and environmental control. In this work, we combine photoacoustic spectroscopy and feedback interferometry, proposing a novel trace-gas sensor equipped with a self-mixing readou

Ahmad Reza Haj Saeedi Sadegh, Jody Trout

We extend the deformation to the normal cone and tangent groupoid constructions from finite-dimensional manifolds to infinite-dimensional Banach and Fredholm manifolds. Next, we generalize the concept of Fredholm filtrations to get a more flexible and functorial theory. In particular, we show that if $M$ is a Banach (or Fredholm) manifold with generalized fi

Pedro Pereira, José Gouveia, João Vitorino, Eva Maia

Magecart skimming attacks have emerged as a significant threat to client-side security and user trust in online payment systems. This paper addresses the challenge of achieving robust and explainable detection of Magecart attacks through a comparative study of various Machine Learning (ML) models with a real-world dataset. Tree-based, linear, and kernel-base

Patrik Valábek, Michaela Horváthová, Martin Klaučo

This paper presents a deep Koopman-based Economic Model Predictive Control (EMPC) for efficient operation of a laboratory-scale pasteurization unit (PU). The method uses Koopman operator theory to transform the complex, nonlinear system dynamics into a linear representation, enabling the application of convex optimization while representing the complex PU ac

Amir Molzam Sharifloo, Maedeh Heydari, Parsa Kazerooni, Daniel Maninger

Large Language Models (LLMs) have achieved remarkable success in code generation, and the race to improve their performance has become a central focus of AI research. Benchmarks and leaderboards are increasingly popular, offering quantitative rankings of LLMs. However, they provide limited insight into the tasks that LLMs consistently fail to solve - informa

Filip Vuković, Anna Niggas, Levin Mihlan, Zhen Yao

Carbon nanomembranes (CNMs) are nanometer-thin disordered carbon materials that are suitable for a range of applications, from energy generation and storage, through to water filtration. The structure-property relationships of these nanomembranes are challenging to study using traditional experimental characterization techniques, primarily due to the radiati

Alessandra Aimi, Giulia Di Credico, Heiko Gimperlein, Chiara Guardasoni

This article initiates the study of space-time adaptive mesh refinements for time-dependent boundary element formulations of wave equations. Based on error indicators of residual type, we formulate an adaptive boundary element procedure for acoustic soft-scattering problems with local tensor-product refinements of the space-time mesh. We discuss the algorith

J. Z. Kamiński, K. Krajewska

Ionization by a sequence of extreme ultraviolet pulses is investigated based on the rigorous numerical solution of the time-dependent Schrödinger equation, when the driving laser field is treated exactly. This goes beyond the typically used first-order nondipole approximation and reveals the effects of radiation pressure to its full extent. Specifically, we

H. Matsudaira, S. Kitagawa, K. Ishida, Y. Tokumoto

Physical properties in the normal and superconducting (SC) state are investigated with $^{125}$Te-nuclear magnetic resonance (NMR) measurements in a quasicrystal $\mathrm{(Ta_{0.95}Cu_{0.05})_{1.6}Te}$, which was a recently discovered superconductor with the SC transition temperature $T_{\mathrm{c}}$ = 0.94 K. The nuclear spin-lattice relaxation rate $1/T_1$

Zhen Li

It is widely believed that Sgr A$^*$, located at the center of our Galaxy, is a supermassive black hole. Recent observations of its shadow and long-term monitoring of the S2 star have provided compelling evidence supporting this hypothesis. These observational advancements also offer valuable opportunities to explore the physical properties of the black hole

Sabina Drăgoi, Alberto Baiardi, Daniel J. Egger

Combinatorial optimization problems have wide-ranging applications in industry and academia. Quantum computers may help solve them by sampling from carefully prepared Ansatz quantum circuits. However, current quantum computers are limited by their qubit count, connectivity, and noise. This is particularly restrictive when considering optimization problems be

Dmitriy Yavorskiy, Jan Suffczyński, Rafał Kowerdziej, Olga Strzeżysz

Strong coupling of light to a collective spin excitation in antiferromagnets gives rise to hybrid modes called magnon-polaritons. They are highly promising for data manipulation and transfer at terahertz rates, much faster than in the case of ferromagnetic magnon-polaritons, which operate at GHz frequencies. Yet, control of terahertz magnon-polaritons by the

Ivan V. Dudinets, Stanislav S. Straupe, Aleksey K. Fedorov, Oleg V. Lychkovskiy

Rydberg atom arrays are a front-running platform for quantum processors. A major challenge threatening the scalability of this platform is the limited qubit connectivity due to the finite range of interatomic interactions. We explore an approach to realize dynamical all-to-all connectivity with the use of moving "messenger" atomic qubits that couple

Zichao Dong, Jun Gao, Hong Liu

For graphs $H_1$ and $H_2$, if we glue them by identifying a given pair of vertices $u \in V(H_1)$ and $v \in V(H_2)$, what is the extremal number of the resulting graph $H_1^u \odot H_2^v$? In this paper, we study this problem and show that interestingly it is equivalent to an old question of Erdős and Simonovits on the Zarankiewicz problem. When $H_1, H_2$

Jiawei Yu, Guihao Jia, Qian Li, Zhen Zhan

In twisted bilayer graphene (TBG) devices, local strains frequently coexist and intertwine with the twist-angle-dependent moiré superlattice, significantly influencing the electronic properties of TBG, yet their combined effects remain incompletely understood. Here, using low-temperature scanning tunneling microscopy, we study a TBG device exhibiting both a

Kaustav Das, Ivan Guo, Grégoire Loeper

The price of a financial derivative can be expressed as an iterated conditional expectation, where the inner term conditions on the future of an auxiliary process. We show that this inner conditional expectation solves an SPDE (a 'conditional Feynman-Kac formula'). The problem requires conditioning on a backward filtration generated by the noise of t

Xianhua Yu, Dong Li, Bowen Gu, Liuqing Yang

Intelligent reflecting surfaces (IRSs) have become a vital technology for improving the spectrum and energy efficiency of forthcoming wireless networks. Nevertheless, practical implementation is obstructed by the excessive overhead associated with the frequent transmission of phase shift information (PSI) over bandwidth-constrained control lines. Current dee

Yusuke Koshio, Masayuki Nakahata, Hiroyuki Sekiya, Mark R. Vagins

Super-Kamiokande [SK] was upgraded through the addition of gadolinium sulfate to its ultrapure water, initiating the SK-Gd program. This development enables efficient neutron tagging via the large capture cross section of gadolinium, greatly improving the identification of inverse beta decay events, the primary channel for detecting the diffuse supernova neu

Itai Maimon

Topological quantum error-correcting codes (QECC) encode a variety of topological invariants in their code space. A classic structure that has not been encoded directly is that of obstruction classes of a fiber bundle, such as the Chern or Euler class. Here, we construct and analyze extensions of toric codes. We then analyze the topological structure of thei

Alexandra Dupuy, Donghui Jeong, Sungwook E. Hong, Ho Seong Hwang

We present a refined deep-learning-based method to reconstruct the three-dimensional dark matter density, gravitational potential, and peculiar velocity fields in the Zone of Avoidance (ZOA), a region near the galactic plane with limited observational data. Using a convolutional neural network (V-Net) trained on A-SIM simulation data, our approach reconstruc

Henry C. Hammer, Caleb Whittier, Nathan A. Helvy, Christopher Rouleau

Quantum photonic networks require two distinct functionalities: bright single-photon sources and long-lived quantum memories. III-V semiconductor quantum dots excel as deterministic and coherent photon emitters, while rare-earth ions such as erbium (Er$^{3+}$) in crystalline oxides offer exceptional spin and optical coherence at telecom wavelengths. Combinin

Ellen Simpson, Ryan Ermovick, Mona Sloane

AI tools are proliferating in human resources management (HRM) and recruiting, helping to mediate access to the labor market. As these systems spread, profession-specific transparency needs emerging from black-boxed systems in HRM move into focus. Prior work often frames transparency technically or abstractly, but we contend AI transparency is a social proje

Stefano M. Iacus, Devika Jain, Andrea Nasuto, Giuseppe Porro

Quantifying human flourishing, a multidimensional construct including happiness, health, purpose, virtue, relationships, and financial stability, is critical for understanding societal well-being beyond economic indicators. Existing measures often lack fine spatial and temporal resolution. Here we introduce the Human Flourishing Geographic Index (HFGI), deri

Hok-Yin Chu

Let $A$ be the (rescaled) adjacency matrix of the Erd\H{o}s-R\'enyi graphs $\cal G(N,p)$. For $N^{-1+\tau} \leqslant p\leqslant N^{-\tau}$, we study the fluctuation of $f(A)_{ii}$ on the global and mesoscopic spectral scales. We show that the distribution of $f(A)_{ii}$ is asymptotically the sum of two independent Gaussian random variables on different scale

Domício Pereira Neto, João Correia, Penousal Machado

Deep diffusion models have revolutionized image generation by producing high-quality outputs. However, achieving specific objectives with these models often requires costly adaptations such as fine-tuning, which can be resource-intensive and time-consuming. An alternative approach is inference-time control, which involves optimizing the prompt embeddings to

Nand Kumar Yadav, Rodrigue Rizk, William CW Chen, KC Santosh

Unknown anomaly detection in medical imaging remains a fundamental challenge due to the scarcity of labeled anomalies and the high cost of expert supervision. We introduce an unsupervised, oracle-free framework that incrementally expands a trusted set of normal samples without any anomaly labels. Starting from a small, verified seed of normal images, our met

Sanggeon Yun, Hyunwoo Oh, Ryozo Masukawa, Mohsen Imani

Decomposition is a proven way to shrink deep networks without changing input-output dimensionality or interface semantics. We bring this idea to hyperdimensional computing (HDC), where footprint cuts usually shrink the feature axis and erode concentration and robustness. Prior HDC decompositions decode via fixed atomic hypervectors, which are ill-suited for

Nils Heyer, Christian Glaser, Thorsten Glüsenkamp, Martin Ravn

The detection of ultra-high-energy (UHE) neutrinos in the EeV range is the goal of current and future in-ice radio arrays at the South Pole and in Greenland. Here, we present a deep neural network that can reconstruct the main neutrino properties of interest from the raw waveforms recorded by the radio antennas: the neutrino direction, the energy of the part

Guillermo Federico Umbricht, Diana Rubio, Domingo Alberto Tarzia

This work investigates the two-dimensional thermal behavior of a bilayer medium subject to both internal and external heat sources. The model incorporates diffusion, advection, and temperature-dependent volumetric heat generation or absorption in each layer, as well as general convective conditions on the external boundaries. The influence of interfacial the

Jessi Cisewski-Kehe, Brittany Terese Fasy, Alexander McCleary, Eli Quist

The weighted Euler characteristic transform (WECT) and Euler characteristic function (ECF) have proven to be useful tools in a variety of applications. However, current methods for computing these functions are either not optimized for GPU computation or do not scale to higher-dimensional settings. In this work, we present a tensor-based framework for comput