November 2025 arXiv papers — page 17

Xiang Cheng, Weibo Wen, Haotian Zhang, Boxun Liu

The evolution toward the sixth-generation (6G) and beyond mobile communication systems is marked by a fundamental shift from merely connecting devices to enabling pervasive and embodied intelligence. While recent advances in artificial intelligence (AI)-native wireless communication designs have achieved remarkable progress, the prevailing paradigm remains l

Anne Broadbent, Joshua Nevin

Cloud-based quantum computing, coupled with the rapid progress in quantum algorithms, brings to the forefront the question of verifiability in delegated quantum computations. In the current landscape of noisy quantum devices, this question must be addressed alongside noise tolerance. In this work, we revisit the circuit-based framework for verifiable quantum

Dosung Lee, Sangwon Jung, Boyoung Kim, Minyoung Kim

Existing Multimodal Knowledge-Based Visual Question Answering (MKB-VQA) benchmarks suffer from "visual shortcuts", as the query image typically matches the primary subject entity of the target document. We demonstrate that models can exploit these shortcuts, achieving comparable results using visual cues alone. To address this, we introduce Relational Entity

Panayiotis Panayiotou, Audrey Poinsot, Alessandro Leite, Nicolas Chesneau

Causal machine learning (Causal ML) aims to answer "what if" questions using machine learning algorithms, making it a promising tool for high-stakes decision-making. Yet, empirical evaluation practices in Causal ML remain limited. Existing benchmarks often rely on a handful of hand-crafted or semi-synthetic datasets, leading to brittle, non-generaliz

Hong Zheng, Shimin Su, Caiqi Liu, Jingzhi Lou

The genetic evolution of SARS-CoV-2 has caused recurring epidemic waves, understanding its global dispersal patterns is critical for effective surveillance. We developed the Site-based mutation dynamics - Equal Power Sampling (S-EPS) framework, a phylogenetic-free, bias-correcting framework for profiling viral source-sink dynamics. Applying S-EPS to 6.6 mill

Yan Liu, Qing-Ao Xiang, Xin-Yuan Yang, Ji-Bing Yuan

Precise control of a single photon transport in broadband, multi-mode waveguides is a fundamental challenge for scalable quantum networks. We propose a theoretical scheme for on-demand control of single-photon scattering using a driven $\Lambda$-type emitter coupled to a rectangular waveguide. By employing the Lippmann-Schwinger formalism, we derive the exac

Ruike Lyu, Anna Li, Jianxiao Wang, Hongxi Luo

In many countries, declining demand in energy-intensive industries (EIIs) such as cement, steel, and aluminum is leading to industrial overcapacity. Although industrial overcapacity is traditionally envisioned as problematic and resource-wasteful, it could unlock EIIs' flexibility in electricity use. Here, using China's aluminum smelting industry as a case s

Pierre Bonami, Sanjeeb Dash, Anton Derkach, Andrea Lodi

We consider integer programming problems with bounded general-integer variables belonging to the general class of network flow problems. For those, we computationally investigate the effect on mixed-integer linear programming (MIP) solvers of the different ways of producing extended formulations that replace a bounded general integer variable by a linear com

Ruipeng Shen

In this article we discuss the long-time dynamics of the radial solutions to the focusing energy-critical wave equation in 5-dimensional space. We give some details about the asymptotic behaviour, topological structure and time evolution of the non-radiative solutions to this equation. As an application we prove a quantitative version of soliton resolution t

Angus Lewis, Antonio Parrella, John Maclean, Andrew J. Black

We consider the problem of inference for the states and parameters of a continuous-time multitype branching process from partially observed time series data. Exact inference for this class of models, typically using sequential Monte Carlo, can be computationally challenging when the populations that are being modelled grow exponentially or the time series is

Rohan Bopardikar, Jin Wang, Jia Zou

Entity matching is a fundamental task in data cleaning and data integration. With the rapid adoption of large language models (LLMs), recent studies have explored zero-shot and few-shot prompting to improve entity matching accuracy. However, most existing approaches rely on single-step prompting and offer limited investigation into structured reasoning strat

Hiroshi Nakajima, Satoshi Hiroi, Hirofumi Tsukasaki, Yonghong Bing

Pb-based perovskites with multiple cations are fascinating materials showing various phenomena such as high piezoelectric, electromechanical, and relaxor properties. While chemical disordering accompanied by polar nanoregions and nanosized domains is commonly believed to cause the relaxor nature, little is known about ferroelectric microstructures of chemica

Zhe-Qi Yang, Si-Qi Lin, Zhi-Rong Zhong

Ultrahigh nonreciprocal transmission has been achieved in a cavity-magnon system, which consists of two whispering gallery modes (WGMs) and a single magnon mode within a magnetic insulator yttrium iron garnet sphere. The nonreciprocal frequency shift induced by the Sagnac effect enables unidirectional transmission of an input field, while suppressing propaga

Yijun Lu, Zhihao Lin, Zhen Tian

This paper presents a novel trajectory planning pipeline for complex driving scenarios like autonomous lane changing, by integrating risk-aware planning with guaranteed collision avoidance into a unified optimization framework. We first construct a dynamic risk fields (DRF) that captures both the static and dynamic collision risks from surrounding vehicles.

Arman Behrad, Mitchell Ostrow, Mohammad Taha Fakharian, Ila Fiete

Understanding how nonlinear dynamical systems (e.g., artificial neural networks and neural circuits) process information requires comparing their underlying dynamics at scale, across diverse architectures and large neural recordings. While many similarity metrics exist, current approaches fall short for large-scale comparisons. Geometric methods are computat

Taku Ohwada

An operationally well-defined delayed-choice quantum-eraser experiment is proposed, realizing a genuine delayed choice within presently available quantum-optical technology. A multimode quantum memory supplies a controlled and verifiable delay, ensuring that the choice operation is applied strictly after the observation event. Electronic single-photon interf

Tianle Chen, Chaitanya Chakka, Arjun Reddy Akula, Xavier Thomas

Despite remarkable advancements in Multimodal Large Language Models (MLLMs), a fundamental question remains: are MLLMs robust to contradicting modalities? To rigorously study this, we introduce MMA-Bench comprising videos and tasks that probe a model's reliance on specific modalities. Using black-box and white-box interpretability techniques, we provide a cr

Filippo Cenacchi, Longbing Cao, Deborah Richards

AI based mental health diagnosis is often judged by benchmark accuracy, yet in practice its value depends on how psychologists respond whether they accept, adjust, or reject AI suggestions. Mental health makes this especially challenging: decisions are continuous and shaped by cues in tone, pauses, word choice, and nonverbal behaviors of patients. Current re

Filippo Cenacchi, Deborah Richards, Longbing Cao

Testing humanoid robots with users is slow, causes wear, and limits iteration and diversity. Yet screening agents must master conversational timing, prosody, backchannels, and what to attend to in faces and speech for Depression and PTSD. Most simulators omit policy learning with nonverbal dynamics; many controllers chase task accuracy while underweighting t

Tainara Borges, Tiklung Chan, Mingfeng Chen, Diankun Liu

By combining the planebrush argument of Katz and Zahl \cite{katz21} with the decoupling-incidence method of Wang and Wu \cite{WangWu2024}, we derive new bounds for the Fourier restriction problem and the Bochner--Riesz problem, extending the range to $p > 2 + \frac{200}{251}$ in $\mathbb{R}^4$. Moreover, leveraging the two-ends Furstenberg estimate in the pl

Dipankar Srirag, Xiaolin Cen, Rahat Masood, Aditya Joshi

Technology-Facilitated Abuse (TFA) encompasses a broad and rapidly evolving set of behaviours in which digital systems are used to harass, monitor, threaten, or control individuals. Although prior research has documented many forms of TFA, there is no consolidated framework for understanding how abuse types, prevention measures, detection mechanisms, and sup

Hamid Ismail, Marwan Bikdash

Machine- and deep-learning approaches for biological sequences depend critically on transforming raw DNA, RNA, and protein FASTA files into informative numerical representations. However, this process is often fragmented across multiple libraries and preprocessing steps, which creates a barrier for researchers without extensive computational expertise. To ad

Anudeex Shetty

Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language understanding and generation. Based on these LLMs, businesses have started to provide Embeddings-as-a-Service (EaaS), offering feature extraction capabilities (in the form of text embeddings) that benefit downstream natural language processing tasks. However, prior re

Yongji Wang, Tristan Léger, Ching-Yao Lai, Tristan Buckmaster

Recent work introduced a robust computational framework combining embedded mathematical structures, advanced optimization, and neural network architecture, leading to the discovery of multiple unstable self-similar solutions for key fluid dynamics equations, including the Incompressible Porous Media (IPM) and 2D Boussinesq systems. While this framework confi

Vivek Kumar, Pushpraj Singh Rajawat, Eirini Ntoutsi

Recent advancements in large language models (LLMs) have shown their potential across both general and domain-specific tasks. However, there is a growing concern regarding their lack of sensitivity, factual incorrectness in responses, inconsistent expressions of empathy, bias, hallucinations, and overall inability to capture the depth and complexity of human

Vahid R. Ramezani, Benjamin Englard

Tilted (entropic) risk, obtained by applying a log-exponential transform to a base loss, is a well established tool in statistics and machine learning for emphasizing rare or high loss events while retaining a tractable optimization problem. In this work, our aim is to interpret its structure for Flow Matching (FM). FM learns a velocity field that transports

Michihiko Fujii, Takuya Sakasai

Let $n$ be an integer greater than $1$. We consider a group presented as $G(p_1,p_2,\dots,p_n)=\langle x_1,x_2,\dots, x_n \mid x_1^{p_1} =x_2^{p_2}=\cdots =x_n^{p_n} \rangle$, with integers $p_1,p_2,\dots,p_n$ satisfying $2 \leq p_1 \leq p_2 \leq \cdots \leq p_n$. This group is an amalgamated free product of infinite cyclic groups and is geometrically realiz

Juan S. Jerez- Rodríguez, Eric S. Escobar-Aguilar, Tonatiuh Matos

This work explores the possibility of applying stochastic quantum mechanics to curved spacetimes, with an emphasis on the Schwarzschild black hole. After reviewing the fundamental concepts of this approach, the quantum stochastic equations are extended to curved spacetime using a fully covariant treatment. Subsequently, the Klein-Gordon equation is solved fo

Miodrag M. Lovric

The Jeffreys-Lindley paradox stands as the most profound divergence between frequentist and Bayesian approaches to hypothesis testing. Yet despite more than six decades of discussion, this paradox remains frequently misunderstood--even in the pages of leading statistical journals. In a 1993 paper published in Statistica Sinica, Robert characterized the Jeffr

Yu-Cheng Chou, Xingrui Wang, Yitong Li, Jiahao Wang

World engines aim to synthesize long, 3D-consistent videos that support interactive exploration of a scene under user-controlled camera motion. However, existing systems struggle under aggressive 6-DoF trajectories and complex outdoor layouts: they lose long-range geometric coherence, deviate from the target path, or collapse into overly conservative motion.

Yimu Mao, Christopher Tropp

We present a simple variational framework for planar elastica that enables distributed energies, such as gravitational loading or magnetic body torques, to be incorporated in a modular and unified manner. The formulation is based on expressing all load induced contributions directly at the level of the energy functional, which avoids the force balance constr

Ashwin Gopal, Massimiliano Esposito, Jan Meibohm

We analyze a thermodynamically consistent model of CMOS-based ring oscillators near the onset of coherent voltage oscillations. For driving voltages close to the critical value, we derive the normal form of the Hopf bifurcation that underlies the oscillation transition in the thermodynamic limit. Using this normal form, we determine the phase and amplitude d

Mohamed Abdallah Salem, Nourhan Zein Diab

Accurate material recognition is critical for safe and effective laser cutting, as misidentification can lead to poor cut quality, machine damage, or the release of hazardous fumes. Laser speckle sensing has recently emerged as a low-cost and non-destructive modality for material classification; however, prior work has either relied on computationally expens

Niklas L. Schulz, Danilo Nikolić, Matthias Eschrig

We revisit the problem of the second harmonic generation in the current-phase relation across ferromagnetic bilayers placed between BCS superconductors. In particular, we consider a strongly spin-polarized metallic ferromagnet coupled to two superconducting leads via thin spin-active (left) and non-spin-active (right) insulating layers. The system is examine

Stephon Alexander, Gregory Gabadadze, Leah Jenks, Nicolás Yunes

We show how a mass term for gravitational axions (''gravi-axions'') with a Chern-Simons coupling to gravity can naturally arise due to non-perturbative contributions from Euclidean wormholes, breaking the continuous shift symmetry of the standard theory. The induced mass can be generated in a cosmologically relevant range to be the dark matte

Jon Butterworth, Hridoy Debnath, Pavel Fileviez Perez, Peng Wang

We investigate the collider signatures of the minimal framework for quark-lepton unification at a scale not far from the electroweak symmetry breaking scale. This theory predicts a rich spectrum of new fields, including one vector leptoquark, two scalar leptoquarks, a color-octet scalar, and an additional Higgs doublet. Neutrino masses are generated via the

Ruo-Syuan Mei, Sixian Jia, Guangze Li, Soo Yeon Lee

Machine learning, particularly deep learning, is transforming industrial quality inspection. Yet, training robust machine learning models typically requires large volumes of high-quality labeled data, which are expensive, time-consuming, and labor-intensive to obtain in manufacturing. Moreover, defective samples are intrinsically rare, leading to severe clas

Becca Spejcher, David V. Martin, Jake Pandina, Andy Zhang

A question that continues to perplex astronomers is the formation of tight stellar binaries. There is too much angular momentum in a collapsing and fragmenting protostellar cloud to form a stellar binary in situ with a separation less than an AU, yet thousands of these short-period binaries have been discovered. One indication of a binary's formation is

Daniele Gerosa, Lauri Anttila, Thomas Eriksson

In modern communication systems, the fidelity of analog-to-digital converters (ADCs) is limited by sampling clock jitter, i.e., small random timing deviations that undermine ideal sampling. Traditional scalar models often treat jitter as independent Gaussian noise, which makes it essentially untrackable, whereas real ADCs also exhibit temporally correlated (

Srijan Chattopadhyay, Siddhaarth Sarkar, Arun Kumar Kuchibhotla

Quantile-based distribution families are an important subclass of parametric families, capable of exhibiting a wide range of behaviors using very few parameters. These parametric models present significant challenges for classical methods, since the CDF and density do not have a closed-form expression. Furthermore, approximate maximum likelihood estimation a

Irina Heinz, Jeroen Danon, Guido Burkard

Spin qubits have proven to be a feasible candidate for quantum computation, and some realizations of spin qubits already benefit from advanced device manufacturing in the semiconductor industry. Compared to superconducting platforms, spin qubits can operate at higher temperatures from tens of millikelvin up to a few kelvin. However, recent experiments show a

Zhihan Cao, Fumihito Nishino, Hiroaki Yamada, Nguyen Ha Thanh

We introduce JBE-QA, a Japanese Bar Exam Question-Answering dataset to evaluate large language models' legal knowledge. Derived from the multiple-choice (tanto-shiki) section of the Japanese bar exam (2015-2024), JBE-QA provides the first comprehensive benchmark for Japanese legal-domain evaluation of LLMs. It covers the Civil Code, the Penal Code, and t

Yuya Ishihara, Atsushi Keyaki, Hiroaki Yamada, Ryutaro Ohara

This study discusses the essential components that a Retrieval-Augmented Generation (RAG)-based LLM system should possess in order to support Japanese medical litigation procedures complying with legal norms. In litigation, expert commissioners, such as physicians, architects, accountants, and engineers, provide specialized knowledge to help judges clarify p

Shoichiro Yasui, Tomohiro Inaba, Hidetaka Nishi, Reina Kaji

Entangled photon-pair sources are indispensable building blocks of quantum information processing technologies. Among the available approaches, on-chip microresonators are particularly promising owing to their resonant enhancement, CMOS-compatible fabrication, and wafer-scale integration capabilities. In this study, we optimized the structure of silicon micr

Xuejun Xu, Tomohiro Inaba, Takuma Aihara, Atsushi Ishizawa

On-chip erbium-doped optical amplifiers and lasers are essential for realizing fully integrated active silicon photonic circuits, but their performance has been limited by the low gain of amorphous host materials and the difficulty of direct integration on silicon. Here, we demonstrate optical amplification and lasing from erbium-doped single-crystal gadolin

Tim Weaving, Angus Mingare, Alexis Ralli, Peter V. Coveney

A recent direction in quantum computing for molecular electronic structure sees the use of quantum devices as configuration sampling machines integrated within high-performance computing (HPC) platforms. This appeals to the strengths of both the quantum and classical hardware; where state-sampling is classically hard, the quantum computer can provide computa

Dilip Kumar

In this study, we explore the combined effects of quantum gravity induced by non-commutativity and scale-dependent gravitational coupling on the thermal properties of the thin accretion disks around a Schwarzschild black hole. We consider a $κ$-deformed Renormalization Group Improved (RGI) Schwarzschild black hole, where the classical Schwarzschild black hol

Dongwei Chen, Emily J. King, Clayton Shonkwiler

This paper studies properties of dual probabilistic frames -- in particular in relation to redundancy -- and introduces both approximately dual probabilistic frames and pseudo-dual probabilistic frames. We show that the canonical dual probabilistic frame is the only dual frame of pushforward type of a probabilistic frame with zero redundancy. Furthermore, we

Ahmed A. Barakat, Avishek Chowdhury, Anh Tuan Le, Eva M. Weig

The dynamic Stark effect and the Autler-Townes splitting (ATS) are hallmarks of driven two-level systems. We establish a direct correspondence between these quantum phenomena and the parametric normal mode splitting in coupled classical oscillators. This gives rise to a second-subharmonic ATS under a two-tone parametric drive. We find excellent agreement bet

Stevan Gajović, J. Steffen Müller

We present a new quadratic Chabauty method to compute the integral points on certain even degree hyperelliptic curves. Our approach relies on a nontrivial degree zero divisor supported at the two points at infinity to restrict the $p$-adic height to a linear function; we can then express this restriction in terms of holomorphic Coleman integrals under the st

Antoine Salomon

Biological neurons exhibit remarkable intelligence: they maintain internal states, communicate selectively with other neurons, and self-organize into complex graphs rather than rigid hierarchical layers. What if artificial intelligence could emerge from similarly intelligent computational units? We introduce Intelligent Neural Networks (INN), a paradigm shif

Kai Wang, Siyi Chen, Weicong Pang, Chenchen Zhang

Land-cover underpins ecosystem services, hydrologic regulation, disaster-risk reduction, and evidence-based land planning; timely, accurate land-cover maps are therefore critical for environmental stewardship. Remote sensing-based land-cover classification offers a scalable route to such maps but is hindered by scarce and imbalanced annotations and by geomet

Dongjae Lee, Dimos V. Dimarogonas, H. Jin Kim

This work presents an event-triggered switching control framework for a class of nonlinear underactuated multi-channel systems with input constraints. These systems are inspired by cooperative manipulation tasks involving underactuation, where multiple underactuated agents collaboratively push or pull an object to a target pose. Unlike existing approaches fo

Yiwei Xu, Saloni Dash, Sungha Kang, Wang Liao

This study examined how AI-generated summaries, which have become visually prominent in online search results, affect how users think about different issues. In a preregistered randomized controlled experiment, participants (N = 2,004) viewed mock search result pages varying in the presence (vs. absence), placement (top vs. middle), and stance (benefit-frame

Yan Fan, Ernest X. W. Xia

In 2019, Andrews investigated integer partitions in which all parts of a given parity are smaller than those of the opposite parity and introduced eight partition functions based on the parity of the smaller parts and parts of a given parity appearing at most once or an unlimited number of times. Recently, Bringmann, Craig and Nazaroglu studied the asymptoti

Nguyen Hong Duc, Vu Trung Hieu

This paper addresses the problem of deciding the lower-boundedness of an arbitrary real polynomial p in n variables.

Shengzhu Yi, Chao Zhou, Zening Hong, Xinming Chu

We investigate the evolution of topological defects in polar fluids driven by discrete inversion symmetry-breaking acrossthe nematic-to-ferroelectric nematic phase transition. Using photopatterned surface alignment to prescribe well-defined initial defect configurations in the nematic phase, we track their metamorphosis upon entering the ferroelectric phase.

Yiming Chen, Junlin Han, Tianyi Bai, Shengbang Tong

While Multimodal Large Language Models (MLLMs) are adept at answering what is in an image-identifying objects and describing scenes-they often lack the ability to understand how an image feels to a human observer. This gap is most evident when considering subjective cognitive properties, such as what makes an image memorable, funny, aesthetically pleasing, o

Osvaldo L. Santos-Pereira

This work discusses the concept of roulette, the generated curves that occur when one curve rolls without slipping along another, tracing the path of a fixed point. The coin paradox and Aristotle's wheel paradox are used as pedagogical motivations to discuss the parametric equations of epicycloids and hypocycloids, providing a geometrical intuition for the m

Wilfrid Gangbo, David Jekel, Kyeongsik Nam, Aaron Z. Palmer

Building on the free-probability stochastic control framework introduced in arXiv:2502.17329, we connect optimal control problems for $n \times n$ random matrix ensembles with their infinite-dimensional, free-probability analogues. Under natural convexity hypotheses, we prove that the non-commutative value function captures the large-$n$ limit of the corresp

Jialin He, Nicholas Popescu, Chunjiang Zhu

We initiate the study on fault-tolerant spanners in hypergraphs and develop fast algorithms for their constructions. A fault-tolerant (FT) spanner preserves approximate distances under network failures, often used in applications like network design and distributed systems. While classic (fault-free) spanners are believed to be easily extended to hypergraphs

D. Ralston, F. M. Tangerman, J. J. P. Veerman, H. Wu

We study the distribution of a sequence of points in the circle generated by rotations by a fixed irrational number $\rho$ with initial condition $x_0$, that is: $\{x_0+i\rho\}_{i=1}^n$. The \emph{discrepancy} as defined by Pisot and Van Der Corput \cite{VdCP}, quantifies how evenly distributed such a sequence is. Consider the ergodic or Birkhoff sum of mean

Yasuyuki Kato, Takeshi Hayashida, Koei Matsumoto, Tsuyoshi Kimura

Beyond conventional electric and magnetic monopoles, electric and magnetic toroidal monopoles, which are rank-0 multipoles distinguished by opposite parities under spatial inversion and time reversal, can exist in nature. The recent observation of electric-field-induced directional dichroism in antiferromagnetic olivine Co$_2$SiO$_4$ has provided the first c

Ellen Baake, Michael Baake, Jeremy Sumner

The embeddability of reversible Markov matrices into time-homogeneous Markov semigroups is revisited, with some focus on simplifications and extensions. In particular, we do not demand irreducibility and consider weakly reversible matrices as well as reversible matrices with negative eigenvalues.

Wen-Bin Chang, Xun Chen, Defu Hou

In this work, we investigate holographic complexity growth in a flavor-dependent Einstein-Maxwell-Dilaton (EMD) model, where the parameters are determined through machine learning algorithms fitted to lattice QCD equation of state (EoS) and baryon number susceptibility data. Within the Complexity=Action (CA) conjecture, we introduce a probe string into the b

Stewart Collis, Florence Kinyua, Vikram Kumar, Howard Lakougna

We report technical learnings from five AI-based agricultural advisory MVPs deployed in Kenya and Bihar, India, under the AIEP Initiative. A 800-farmer study found high user satisfaction (NPS ~60). All solutions implement a modular two-part architecture: (i) an interface component (IVR /WhatsApp / app) with ASR-MT-TTS for multilingual voice access; and (ii)

Pedro G. S. Fernandes, Jingqian Gou, Lavinia Heisenberg, Nadine Nussbaumer

Infinite towers of higher-order corrections to General Relativity have been proposed as a mechanism to resolve singularities in early-universe cosmology and black holes, in a variety of settings. In this work, we consider an infinite tower of higher-order Proca corrections inspired by dimensional regularizations of Lovelock invariants. We find that the Big B

Sayan Banerjee, Harley D. Scammell, Mathias S. Scheurer

There are multiple possible origins of transport anisotropies in metals and superconductors. For instance, rotational symmetry can be spontaneously broken in the normal state as a result of electronic nematic order inducing anisotropies in an otherwise $s$-wave superconducting phase. Another possibility is that the dominant source of rotational symmetry brea

Santiago Castañeda-Montoya, Alexander Torres-Gomez

This paper investigates the geometric and algebraic interplay between F-manifolds and a newly defined class of structures termed F$_\text{man}$-algebras. We specialize our study to the category of F-Lie groups, characterized by a Lie group whose associated commutative and associative product of vector fields is left-invariant. We construct a canonical connec

Fitria Wulandari Ramlan, Colm O'Riordan, Gabriel Kronberger, James McDermott

Many machine learning models perform well when making predictions within the training data range, but often struggle when required to extrapolate beyond it. Symbolic regression (SR) using genetic programming (GP) can generate flexible models but is prone to unreliable behaviour in extrapolation. This paper investigates whether adding synthetic data can help

Nick Rossenbach, Robin Schmitt, Tina Raissi, Simon Berger

The recently published Loquacious dataset aims to be a replacement for established English automatic speech recognition (ASR) datasets such as LibriSpeech or TED-Lium. The main goal of the Loquacious dataset is to provide properly defined training and test partitions across many acoustic and language domains, with an open license suitable for both academia a

Izabela Lipinska, Hugh Brosnahan

This paper argues that contemporary large language models (LLMs) can contribute to psychotic involvement by creating interactions that resemble the relational dynamics of folie a deux. Drawing on Bateson's double bind theory, clinical literature on shared psychotic disorder, and McGilchrist's hemisphere theory, we show how the combination of high linguistic

Bhavya Sai Nukapotula, Rishabh Tripathi, Seth Pregler, Dileep Kalathil

Channel state information (CSI) is essential for adaptive beamforming and maintaining robust links in wireless communication systems. However, acquiring CSI incurs significant overhead, consuming up to 25% of spectrum resources in 5G networks due to frequent pilot transmissions at millisecond-scale intervals. Recent approaches aim to reduce this burden by re

Xin Chen, Zhen-Qing Chen, Takashi Kumagai, Jian Wang

We establish quantitative homogenization results for time-dependent random conductance models with stable-like long range jumps on $\Z^d$, where the transition probability from $x$ to $y$ is given by $w_{t, x,y}|x-y|^{-d-\alpha}$ with $\alpha\in (0,2)$. In particular, time-dependent random coefficients $\{w_{t,x,y}: t\in \R_+, (x,y)\in E\}$ are uniformly bou

Kanchon Gharami, Shafika Showkat Moni

The rapid proliferation of unmanned aerial vehicles (UAVs) and their applications in diverse domains, such as surveillance, disaster management, agriculture, and defense, have revolutionized modern technology. While the potential benefits of swarm-based UAV networks are growing significantly, they are vulnerable to various security attacks that can jeopardiz

Liang Li, Jun Zhu, Shanqin Chen, Yong-Tao Zhang

In this paper, we extend the previous work on absolutely convergent fixed-point fast sweeping WENO methods by Li et al. (J. Comput. Phys. 443: 110516, 2021) and design a fifth-order hybrid fast sweeping scheme for solving steady state problems of hyperbolic conservation laws. Unlike many other fast sweeping methods, the explicit property of fixed-point fast

Alif Al Hasan, Subarna Saha, Mia Mohammad Imran

Novice programmers experience emotional difficulties in informal online learning environments, where confusion and frustration can hinder motivation and learning outcomes. This study investigates novice programmers' emotional experiences in informal settings, identifies the causes of emotional struggle, and explores design opportunities for affect-aware supp

Junfei Zhan, Haoxun Shen, Zheng Lin, Tengjiao He

Large Language Models (LLMs) demonstrate impressive capabilities in natural language understanding and generation, but incur high communication overhead and privacy risks in cloud deployments, while facing compute and memory constraints when confined to edge devices. Cloud-edge inference has emerged as a promising paradigm for improving privacy in LLM servic

Eunsu Kim, Junyeong Park, Na Min An, Junseong Kim

In a globalized world, cultural elements from diverse origins frequently appear together within a single visual scene. We refer to these as culture mixing scenarios, yet how Large Vision-Language Models (LVLMs) perceive them remains underexplored. We investigate culture mixing as a critical challenge for LVLMs and examine how current models behave when cultu

Yhlas Sovbetov, Muhittin Kaplan

The empirical literature provides mixed results on the relationship between inflation and unemployment, therefore, there is no consensus on validity and stability of the Phillips Curve. It also seems to be closely related with country-specific factors and the examination time periods. Considering the importance of this trade-off for policy-makers, this study

Yhlas Sovbetov, Muhittin Kaplan

Although empirical literature regarding the Phillips curve is sizeable enough, there is still no wide consensus on its validity and stability. The literature shows that the Phillips relationship is fragile and varies across countries and time periods; a statistical relationship that appears strong during one decade (country) may be weak the next (other). Thi

Pedro Catuogno, Alexandre do Nascimento Oliveira-Sousa, Paulo Ruffino

We introduce a notion of minimal uniform attractor for nonautonomous random dynamical systems, which depends jointly on time and on a random parameter. Several examples are provided to illustrate the concept and to compare it with existing notions of uniform attractors in the literature. We further apply the abstract theory to nonautonomous random differenti

Kunal Mehta, Matthew De Furio, Daniella Bardalez Gagliuffi, Trent J. Dupuy

We characterize the sensitivity of a double point-spread function (PSF) fitting algorithm -- employing empirical, position-dependent PSF models -- for detecting companions using the infrared channel of the Wide Field Camera 3 (WFC3/IR) on the Hubble Space Telescope (HST). The observed separation distribution of known brown dwarf (BD) binaries is potentially

Yhlas Sovbetov

This paper examines factors that influence prices of most common five cryptocurrencies such as Bitcoin, Ethereum, Dash, Litecoin, and Monero over 2010-2018 using weekly data. The study employs ARDL technique and documents several findings. First, cryptomarket-related factors such as market beta, trading volume, and volatility appear to be significant determi

Shyam Agarwal, Mahasweta Chakraborti

The past decade has seen a massive rise in the popularity of AI systems, mainly owing to the developments in Gen AI, which has revolutionized numerous industries and applications. However, this progress comes at a considerable cost to the environment as training and deploying these models consume significant computational resources and energy and are respons

Amir Rasouli, Montgomery Alban, Sajjad Pakdamansavoji, Zhiyuan Li

In this work, we propose an evaluation protocol for examining the performance of robotic manipulation policies in cluttered scenes. Contrary to prior works, we approach evaluation from a psychophysical perspective, therefore we use a unified measure of clutter that accounts for environmental factors as well as the distractors quantity, characteristics, and a

Shijie Yan, Douglas Dwyer, David R. Kaeli, Qianqian Fang

Significance: Monte Carlo (MC) methods are the gold-standard for modeling light-tissue interactions due to their accuracy. Mesh-based MC (MMC) offers enhanced precision for complex tissue structures using tetrahedral mesh models. Despite significant speedups achieved on graphics processing units (GPUs), MMC performance remains hindered by the computational c

Francesco Navarra, Ayesha Asloob Qureshi

Polyomino ideals, defined as the ideals generated by the inner $2$-minors of a polyomino, are a class of binomial ideals whose algebraic properties are closely related to the combinatorial structure of the underlying polyomino. We provide a unified account of recent advances on two central themes: the characterization of prime polyomino ideals and the emergi

Sajjad Pakdamansavoji, Mozhgan Pourkeshavarz, Adam Sigal, Zhiyuan Li

Learning robust visuomotor policies for robotic manipulation remains a challenge in real-world settings, where visual distractors can significantly degrade performance and safety. In this work, we propose an effective and scalable framework, Naturalistic Inpainting for Context Enhancement (NICE). Our method minimizes out-of-distribution (OOD) gap in imitatio

Helmut Farbmacher, Rebecca Groh

This study examines long-term mortality effects of combat exposure using the Vietnam War draft lottery as a quasi-experiment. We validate the lottery by analyzing combat fatalities, revealing that 1951-1952 cohorts had notably fewer lottery-induced deployments than 1950, limiting detectable long-term mortality impacts at the cohort level. Using deceased-only

J. Khatua, Kwang-Yong Choi

Condensed matter physics continues to seek new frustrated quantum materials that not only deepen our understanding of fundamental physical phenomena but also hold promise for transformative technologies. In this review article, we highlight the unique features of chiral spin topology and review the topological phenomena recently identified in trillium lattic

Mahdieh Behjat Khatooni, Mohsen Soryani

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that progressively impairs memory, decision-making, and overall cognitive function. As AD is irreversible, early prediction is critical for timely intervention and management. Mild Cognitive Impairment (MCI), a transitional stage between cognitively normal (CN) aging and AD, plays a significa

Rui Heng Yang, Xuan Zhao, Leo Maxime Brunswic, Montgomery Alban

In robotics, diffusion models can capture multi-modal trajectories from demonstrations, making them a transformative approach in imitation learning. However, achieving optimal performance following this regiment requires a large-scale dataset, which is costly to obtain, especially for challenging tasks, such as collision avoidance. In those tasks, generaliza

Wolfgang Bentz, Ian C. Cloët

In the first part of this paper, we use the framework of the Fermi liquid theory to derive model-independent relations between the slope parameters of the symmetry energy and of the incompressibility in nuclear matter to three-particle interaction parameters. Based on these relations, we present simple estimates and compare with the empirical information. In

Yejin Cho, John Heidemann

How do commercial VPNs interact with IPv6? We show two "rough edges" in how commercial VPNs handle IPv6. First, we show that many IPv4-only VPNs leak IPv6 traffic to the ISP. Individual use VPNs in part to conceal their local IP addresses, so such leaks reduce user privacy. While prior work has studied VPNs in testbeds, we use a new dataset of 129k VPN-using

Robert Okuła, Piotr Mironowicz

Quantum technologies offer significant advancements in information processing and communication, notably in the domain of random number generation (RNG). The use of Bell inequalities enables users to certify the randomness of outputs produced by untrusted quantum RNG devices. We present a method for quantitatively analyzing Bell expressions used to certify r

Jagmohan Singh, Venkat Raman

This study investigates the effect of duct aspect ratio ($AR$), defined as the ratio of major to minor axis in an elliptical duct, on shock train dynamics for a freestream Mach number of 2.1. The aspect ratio $AR$ is varied from 1.0 to 3.0 while maintaining a constant cross-sectional area and identical upstream conditions, thereby ensuring the same inlet mas

Moshe A. Milevsky, Thomas S. Salisbury, Robyn Allen

We investigate the extent to which groups with elevated mortality rates ex ante might opt out of guaranteed national pensions in favour of demographically aligned plans, which we label equitable longevity risk sharing (ELRiS) pools, even if this involves accepting some idiosyncratic risk. Technically, this paper develops a stochastic model of retirement inco

Kanchon Gharami, Quazi Sarwar Muhtaseem, Deepti Gupta, Lavanya Elluri

The development of robust transliteration techniques to enhance the effectiveness of transforming Romanized scripts into native scripts is crucial for Natural Language Processing tasks, including sentiment analysis, speech recognition, information retrieval, and intelligent personal assistants. Despite significant advancements, state-of-the-art multilingual

Toqeer Ali Syed, Sohail Khan, Salman Jan, Gohar Ali

The challenge is growing towards extreme and short-duration rainfall events like a cloudburst that are peculiar to the traditional forecasting systems, in which the predictions and the response are taken as two distinct processes. The paper outlines an agentic artificial intelligence system to study atmospheric water-cycle intelligence, which combines sensin