November 2025 arXiv papers — page 79

Xuelian Wen, Qiuqi Li, Juan Zhang

This work develops a non-intrusive, data-driven surrogate modeling framework based on Operator Inference (OpInf) for rapidly solving parameter-dependent matrix equations in many-query settings. Motivated by the requirements of the OpInf methodology, we reformulate the matrix equations into a structured representation that explicitly shows the parameter depen

Ziyan Zhu, Qingquan Jiang, Yu Liu, Puxun Wu

In this work, we constrain the phenomenological interacting dark energy (IDE) model using \emph{Fermi} gamma-ray burst (GRB) dataset and the latest baryon acoustic oscillation (BAO) data from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2). Through a joint Bayesian analysis, we perform a cosmological comparative assessment of the $\Lambd

Timilehin T. Ayanlade, Anirudha Powadi, Talukder Z. Jubery, Baskar Ganapathysubramanian

Recent advances in plant phenotyping have driven widespread adoption of multi sensor platforms for collecting crop canopy reflectance data. This includes the collection of heterogeneous data across multiple platforms, with Unmanned Aerial Vehicles (UAV) seeing significant usage due to their high performance in crop monitoring, forecasting, and prediction tas

Zijian Wu, Mingfeng Jiang, Zidian Lin, Ying Song

3D Gaussian Splatting (3DGS) has recently emerged as an efficient, high-fidelity representation for real-time scene reconstruction and rendering. However, extending 3DGS to sparse-view settings remains challenging because of supervision scarcity and overfitting caused by limited viewpoint coverage. In this paper, we present CuriGS, a curriculum-guided framew

Shurui Cao, Wenyue Hua, William Yang Wang, Hong Shen

The rapid advancement of Large Language Models (LLMs) has generated considerable speculation regarding their transformative potential for labor markets. However, existing approaches to measuring AI exposure in the workforce predominantly rely on concurrent market conditions, offering limited predictive capacity for anticipating future disruptions. This paper

Yang Wu, Yifan Zhang, Zhenxing Liang, Jian Cheng

Two-stage Stochastic Programming (2SP) is a standard framework for modeling decision-making problems under uncertainty. While numerous methods exist, solving such problems with many scenarios remains challenging. Selecting representative scenarios is a practical method for accelerating solutions. However, current approaches typically rely on clustering or Mo

Mohamed Abdallah Salem, Hamdy Ahmed Ashur, Ahmed Elshinnawy

Laser cutting is a widely adopted technology in material processing across various industries, but it generates a significant amount of dust, smoke, and aerosols during operation, posing a risk to both the environment and workers' health. Speckle sensing has emerged as a promising method to monitor the cutting process and identify material types in real-time

Amey Bhangale, Arghya Chakraborty, Prahladh Harsha

Online bipartite matching is a classical problem in online algorithms and we know that both the deterministic fractional and randomized integral online matchings achieve the same competitive ratio of $1-\frac{1}{e}$. In this work, we study classes of graphs where the online degree is restricted to $2$. As expected, one can achieve a competitive ratio of bett

Xiao He, Zhijun Tu, Kun Cheng, Mingrui Zhu

The demonstrated success of sparsely-gated Mixture-of-Experts (MoE) architectures, exemplified by models such as DeepSeek and Grok, has motivated researchers to investigate their adaptation to diverse domains. In real-world image super-resolution (Real-ISR), existing approaches mainly rely on fine-tuning pre-trained diffusion models through Low-Rank Adaptati

Yige Li, Zhe Li, Wei Zhao, Nay Myat Min

Backdoor attacks pose a serious threat to the secure deployment of large language models (LLMs), enabling adversaries to implant hidden behaviors triggered by specific inputs. However, existing methods often rely on manually crafted triggers and static data pipelines, which are rigid, labor-intensive, and inadequate for systematically evaluating modern defen

Dongkyu Lim, Eunmi Chae

Diatomic molecules are promising systems for quantum science applications due to their complex energy structures and strong dipole-dipole interactions. Achieving ultracold temperatures is essential for these applications, but the complexity of molecular energy levels requires precise optimization of experimental parameters for laser slowing and magneto-optic

Dingkun Zhou, Patrick P. K. Chan, Hengxu Wu, Shikang Zheng

Deep neural networks used for human detection are highly vulnerable to adversarial manipulation, creating safety and privacy risks in real surveillance environments. Wearable attacks offer a realistic threat model, yet existing approaches usually optimize textures frame by frame and therefore fail to maintain concealment across long video sequences with moti

Mengyun Xu, Jie Fang, Eui-Jin Kim, Tony Z. Qiu

Reconstructing complete traffic flow time-space diagrams from vehicle trajectories offer a comprehensive view on traffic dynamics at arterial intersections. However, obtaining full trajectories across networks is costly, and accurately inferring lane-changing (LC) and car-following behaviors in multi-lane environments remains challenging. This study proposes

Shreshth Rajan

LLMs generate buggy code: 29.6% of SWE-bench solved patches fail, 62% of BaxBench solutions have vulnerabilities, and existing tools only catch 65% of bugs with 35% false positives. We built CodeX-Verify, a multi-agent system that uses four specialized agents to detect different types of bugs. We prove mathematically that combining agents with different dete

Chaitanya Kumar Kolli

Artificial intelligence deployed in risk-sensitive domains such as healthcare, finance, and security must not only achieve predictive accuracy but also ensure transparency, ethical alignment, and compliance with regulatory expectations. Hybrid neuro symbolic models combine the pattern-recognition strengths of neural networks with the interpretability and log

Zijun Gao, Mutian He, Shijia Sun, Hanqun Cao

Reliable evaluation of protein structure predictions remains challenging, as metrics like pLDDT capture energetic stability but often miss subtle errors such as atomic clashes or conformational traps reflecting topological frustration within the protein folding energy landscape. We present CODE (Chain of Diffusion Embeddings), a self evaluating metric empiri

Emanuel C. Silva, Emily S. M. Salum, Gabriel M. Arantes, Matheus P. Pereira

Introduction: The application of Artificial Intelligence in games has evolved significantly, allowing for dynamic content generation. However, its use as a core gameplay co-creation tool remains underexplored. Objective: This paper proposes SpellForger, a game where players create custom spells by writing natural language prompts, aiming to provide a unique

Zi-Qi Wang, Zi-Yuan Wang, Yi-Pu Wang, J. Q. You

We demonstrate single-mode lasing of magnon polaritons in a cavity magnonic system enabled by dissipative coupling between two passive modes, microwave cavity mode and magnon mode in a ferrimagnetic spin ensemble. The cavity mode is partially compensated through a feedback circuit, which reduces its linewidth but retains its dissipative nature. By tuning the

Juncheng Dong, Yiling Liu, Ahmed Aloui, Vahid Tarokh

Large language models (LLMs) have recently demonstrated impressive capabilities across a range of reasoning and generation tasks. However, research studies have shown that LLMs lack the ability to identify causal relationships, a fundamental cornerstone of human intelligence. We first conduct an exploratory investigation of LLMs' behavior when asked to perfo

Nimeshika Udayangani, Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie

Detecting out-of-distribution (OOD) data is essential for safe deployment of deep neural networks (DNNs). This problem becomes particularly challenging in the presence of long-tailed in-distribution (ID) datasets, often leading to high false positive rates (FPR) and low tail-class ID classification accuracy. In this paper, we demonstrate that exploiting inte

Shuo Wang, Mengfan Teng, Yun Cheng, Lothar Thiele

High-resolution mapping of fine particulate matter (PM2.5) is a cornerstone of sustainable urbanism but remains critically hindered by the spatial sparsity of ground monitoring networks. While traditional data-driven methods attempt to bridge this gap using satellite Aerosol Optical Depth (AOD), they often suffer from severe, non-random data missingness (e.g

Haotong Wang, Jun Du, Chunxiao Jiang, Jintao Wang

The rapid expansion of latency-sensitive applications has sparked renewed interest in deploying edge computing capabilities aboard satellite constellations, aiming to achieve truly global and seamless service coverage. On one hand, it is essential to allocate the limited onboard computational and communication resources efficiently to serve geographically di

Philip Dalladay-Simpson, Eric Edmund, Huixin Hu, Mario Santoro

Low-frequency Raman peaks, below 250 cm-1, are observed in hydrogen between 2-174 GPa and 13-300 K. The origin of these features is attributed to reorientational transitions (DeltaJ = 0; Q0-branch), which shift from the Rayleigh line as anisotropic intermolecular interactions lift the mJ degeneracy. This family of excitations closely follows the behavior of

Joseph Jaeger

An important proof technique in the random oracle model involves reprogramming it on hard to predict inputs and arguing that an attacker cannot detect that this occurred. In the quantum setting, a particularly challenging version of this considers adaptive reprogramming wherein the points to be reprogrammed (or the output values they should be programmed to)

Masashi Wakaiki

This paper develops a data-driven framework for stabilization of discrete-time infinite-dimensional systems. We investigate informativity for stabilization, defined as the existence of a feedback gain that stabilizes all systems compatible with the available input-state data. Assuming that infinite-length data are Bessel sequences, we first establish a suffi

Wang Zhong-bao, Zhang Zhong-cheng

In this paper, we address variational inequalities (VI) with a finite sum structure by proposing a novel single-loop variance-reduced algorithm that incorporates the Bregman distance. Under the monotone setting, we establish the almost sure convergence of the proposed algorithm and prove that it achieves the optimal complexity of $\mathcal{O}\left(\frac{\sqr

Yiling Liu, Juncheng Dong, Chen Fu, Wei Shi

Estimating counterfactual outcomes from time-series observations is crucial for effective decision-making, e.g. when to administer a life-saving treatment, yet remains significantly challenging because (i) the counterfactual trajectory is never observed and (ii) confounders evolve with time and distort estimation at every step. To address these challenges, w

Qingao Dong, Mengfei Wang, Hengzhi Zhang, Zhichao Li

Large language model (LLM) agents have recently shown strong performance on repository-level issue resolution, but existing systems are almost exclusively designed for Python and rely heavily on lexical retrieval and shallow code navigation. These approaches transfer poorly to C++ projects, where overloaded identifiers, nested namespaces, template instantiat

KeFan Li, Mengfei Wang, Hengzhi Zhang, Zhichao Li

Large language models have advanced software engineering automation, yet resolving real-world software issues remains difficult because it requires repository-level reasoning, accurate diagnostics, and strong verification signals. Existing agent-based and pipeline-based methods often rely on insufficient tests, which can lead to patches that satisfy verifica

David A. Neufeld, Kedron Silsbee, Alexei V. Ivlev, Shmuel Bialy

We present James Webb Space Telescope (JWST) NIRSpec observations of the starless dark cloud Barnard 68 that reveal the spatially-resolved signature of cosmic-ray excited molecular hydrogen (CRXH$_2$) emissions for the first time. Following up on our initial detection of CRXH$_2$ emissions from B68 (Bialy et al. 2025), we now exploit JWST's sensitivity and s

Kuan He, Meng-Han Li, Shi-Da Fan, Zi-Bin Lin

Skyrmion family members, such as skyrmions, bimerons, and skyrmioniums, have been recently observed in quantum, solid-state, water, and magnetic systems. However, it remains challenging and crucial to identify a single platform for observing their coexistence and evolution. Here, we describe a bilayer twisted moire elastic system as a controllable platform f

Insang Yoo, Dong Hwan Min, Thomas Fromenteze, Okan Yurduseven

We present the design of rectangular waveguide-excited metasurfaces for near-field shaping using a coupled dipole framework. Waveguide-fed metasurfaces are array-like radiating systems typically constructed from one or more waveguides loaded with a series of subwavelength metamaterial apertures that function as radiators. The use of subwavelength radiating e

Weijie Xiong, Jingran Lin, Zhiling Xiao, Qiang Li

Joint admission control and power minimization are critical challenges in intelligent reflecting surface (IRS)-assisted networks. Traditional methods often rely on \( l_1 \)-norm approximations and alternating optimization (AO) techniques, which suffer from high computational complexity and lack robust convergence guarantees. To address these limitations, we

Andrey O. Leonov, Kaito Nakamura

We introduce and systematically investigate a new class of topological magnetic textures, skyrmionium meta-matter, composed of skyrmioniums (Skm, $Q=0$) and skyrmions (Sk, $Q=-1$) arranged in periodic lattices mimicking the richness of atomic materials. Pure skyrmionium lattices are unstable against elongation distortions and relax into the spiral phase, but

Gaurav Rudravaram, Lianrui Zuo, Adam M. Saunders, Michael E. Kim

Structural connectomes are detailed graphs that map how different brain regions are physically connected, offering critical insight into aging, cognition, and neurodegenerative diseases. However, these connectomes are high-dimensional and densely interconnected, which makes them difficult to interpret and analyze at scale. While low-dimensional spaces like P

Strahinja Janjusevic, Anna Baron Garcia, Sohrob Kazerounian

Generative AI is reshaping offensive cybersecurity by enabling autonomous red team agents that can plan, execute, and adapt during penetration tests. However, existing approaches face trade-offs between generality and specialization, and practical deployments reveal challenges such as hallucinations, context limitations, and ethical concerns. In this work, w

Noah Bissell, Ethan Paley, Joshua Harrison, Juliano Calil

Sensorium Arc (AI reflects on climate) is a real-time multimodal interactive AI agent system that personifies the ocean as a poetic speaker and guides users through immersive explorations of complex marine data. Built on a modular multi-agent system and retrieval-augmented large language model (LLM) framework, Sensorium enables natural spoken conversations w

Amin Bigdeli, Radin Hamidi Rad, Mert Incesu, Negar Arabzadeh

We present QueryGym, a lightweight, extensible Python toolkit that supports large language model (LLM)-based query reformulation. This is an important tool development since recent work on llm-based query reformulation has shown notable increase in retrieval effectiveness. However, while different authors have sporadically shared the implementation of their

Zili Tang, Ying Zhang, Meng Guo

Many robots are not equipped with a manipulator and many objects are not suitable for prehensile manipulation (such as large boxes and cylinders). In these cases, pushing is a simple yet effective non-prehensile skill for robots to interact with and further change the environment. Existing work often assumes a set of predefined pushing modes and fixed-shape

Deepthi Potluri, Aby Mammen Mathew, Jeffrey B DeWitt, Alexander L. Rasgon

Access to the right evidence does not guarantee that large language models (LLMs) will reason with it correctly. This gap between retrieval and reasoning is especially concerning in clinical settings, where outputs must align with structured protocols. We study this gap using Written Exposure Therapy (WET) guidelines as a testbed. In evaluating model respons

Yangyang Zhang, Zhenwei Li, Xuefei Chen

Helium white dwarfs (WDs) with masses less than 0.3 $\rm M_{\sun}$ are known as extremely low-mass WDs (ELM WDs), which cannot be produced by single stellar evolution in theory. Generally, these stars are believed to form through binary interactions. Recently, two ELM WDs in unusually wide orbits were reported, i.e., KIC 8145411 and HE 0430-2457. Their orbit

Shahin Zanbaghi, Ryan Rostampour, Farhan Abid, Salim Al Jarmakani

Large Language Models (LLMs) can be backdoored to exhibit malicious behavior under specific deployment conditions while appearing safe during training a phenomenon known as "sleeper agents." Recent work by Hubinger et al. demonstrated that these backdoors persist through safety training, yet no practical detection methods exist. We present a novel dual-metho

Teresa Dalle Nogare, Ben D. Fulcher

The dynamics of time-reversible systems are statistically indistinguishable when observed forward or backward in time. A rich literature of statistical methods to distinguish irreversible dynamics from the reversible dynamics of linear, Gaussian systems can provide insights into underlying mechanisms and aid modeling and statistical quantification of time-se

Osama Zafar, Rosemarie Santa González, Alfonso Morales, Erman Ayday

Digital agriculture is transforming the way we grow food by utilizing technology to make farming more efficient, sustainable, and productive. This modern approach to agriculture generates a wealth of valuable data that could help address global food challenges, but farmers are hesitant to share it due to privacy concerns. This limits the extent to which rese

Paul Webster, Samuel C. Smith, Lawrence Z. Cohen

Quantum low-density parity check (QLDPC) codes can significantly reduce the overhead of quantum computing, provided the methods for performing logical operations do not require substantial space and time resources. A popular method for performing logical operations is by measuring logical Pauli operators. We present a simple, explicit construction for fixed

Alexander Velberg, Adam Stanier, Xiaocan Li, Fan Guo

The interplay between kinetic and macroscopic scales during magnetic reconnection is investigated using particle-in-cell simulations of magnetic island coalescence in the strongly-magnetized, relativistic pair plasma regime. For large system sizes, secondary current sheet formation and downstream turbulence driven by the reconnection outflows dominate the gl

Phu Khanh Huynh, Francky Catthoor, Anup Das

Large-scale neuromorphic architectures consist of computing tiles that communicate spikes using a shared interconnect. The communication patterns in these systems are inherently sparse, asynchronous, and localized, as neural activity is characterized by temporal sparsity with occasional bursts of high traffic. These characteristics require optimized intercon

Dawei Li, Zijian Gu, Peng Wang, Chuhan Song

Multimodal large language models (MLLMs) have shown strong potential for medical image reasoning, yet fairness across demographic groups remains a major concern. Existing debiasing methods often rely on large labeled datasets or fine-tuning, which are impractical for foundation-scale models. We explore In-Context Learning (ICL) as a lightweight, tuning-free

Gabriel Marghoti, Thiago L. Prado, Miguel A. F. Sanjuán, Sergio R. Lopes

In neurosciences, the brain processes information via the firing patterns of connected neurons operating across a spectrum of frequencies. To better understand the effects of these frequencies in the neuron dynamics, we have simulated a neuronal network of Izhikevich neurons to examine the interaction between frequency allocation and intermittent phase synch

Xinyu Nan, Lingtao Mao, Huangyu Dai, Zexin Zheng

Achieving visual semantic understanding requires a unified framework that simultaneously handles object detection, category prediction, and attribute recognition. However, current advanced approaches rely on global similarity and struggle to capture fine-grained category distinctions and category-specific attribute diversity, especially in large-scale e-comm

Siqiao Mu, Diego Klabjan

Machine unlearning algorithms aim to remove the impact of selected training data from a model without the computational expenses of retraining from scratch. Two such algorithms are ``Descent-to-Delete" (D2D) and ``Rewind-to-Delete" (R2D), full-batch gradient descent algorithms that are easy to implement and satisfy provable unlearning guarantees. In particul

Chukwunonso Henry Nwokoye, Blessing Oluchi, Sharna Waldron, Peace Ezzeh

The lack of epidemiological data in wireless sensor networks (WSNs) is a fundamental difficulty in constructing robust models to forecast and mitigate threats such as viruses and worms. Many studies have examined different epidemic models for WSNs, focusing on how malware infections spread given the network's specific properties, including energy limits and

Jingcheng Dai, Atharva Vidwans, Eric H. Wan, Alexander X. Miller

Recent advancements in quantum algorithms have reached a state where we can consider how to capitalize on quantum and classical computational resources to accelerate molecular resonance state identification. Here we identify molecular resonances with a method that combines quantum computing with classical high-throughput computing (HTC). This algorithm, whic

P. F. Chen

Magnetic field is the key physical quantity in solar physics as it controls all kinds of solar activity, ranging from nanoflares to big flares and coronal mass ejections (CMEs). However, so far only the magnetic field on the solar surface can be more or less precisely measured, and the most important coronal magnetic field remains undetectable accurately. Wi

Molla Basir Ahamed, Sabir Ahammed

In this paper, we first establish the Bohr's theorem for Ces\'aro operator defined for $f\in \mathcal{SRB}(\mathbb{B})$ of slice regular functions in the open unit ball $\mathbb{B}$ of the largest alternative division algebras of octonions $\mathbb{O}$, such that $|f(x)| \leq 1$ for all $x \in \mathbb{B}$. Next, we establish Bohr type inequalities for Bernar

Xiao-Gang He

The JUNO collaboration has released its first measurement of reactor neutrino oscillations results, obtaining $\sin^2\theta_{12} = 0.3092\pm 0.0087$, an improvement in precision by a factor of 1.6 over previous combined results. We confront the minimally modified tri-bimaximal mixing pattern with the new data. Before the measurement of a non-zero $\theta_{13

Daniel Mas Montserrat, Ray Verma, Míriam Barrabés, Francisco M. de la Vega

Large-scale genomic workflows used in precision medicine can process datasets spanning tens to hundreds of gigabytes per sample, leading to high memory spikes, intensive disk I/O, and task failures due to out-of-memory errors. Simple static resource allocation methods struggle to handle the variability in per-chromosome RAM demands, resulting in poor resourc

Sarik Ghazarian, Abhinav Gullapalli, Swair Shah, Anurag Beniwal

In real-world task-oriented dialogue (TOD) settings, agents are required to strictly adhere to complex instructions while conducting multi-turn conversations with customers. These instructions are typically presented in natural language format and include general guidelines and step-by-step procedures with complex constraints. Existing TOD benchmarks often o

Yuki Kataoka, Ryuhei So, Masahiro Banno, Yasushi Tsujimoto

Evaluating adherence to PRISMA 2020 guideline remains a burden in the peer review process. To address the lack of shareable benchmarks, we constructed a copyright-aware benchmark of 108 Creative Commons-licensed systematic reviews and evaluated ten large language models (LLMs) across five input formats. In a development cohort, supplying structured PRISMA 20

Zhe Li, Yehan Qiu, Yujie Chen, Xiang Zhou

Clinical antimicrobial therapy requires the dynamic integration of pathogen profiles,host factors, pharmacological properties of antimicrobials,and the severity of infection. This complexity imposes fundamental limitations on the applicability of Large Language Models (LLMs) in high-stakes clinical decision-making including knowledge gaps, data privacy conce

Guofu Xu, Feihao Pan, Jiyang Ou, Wenjun Ma

Here, we investigate the spin waves in room-temperature van der Waals ferromagnet $(\rm Fe_{0.78}Co_{0.22})_{5}GeTe_{2}$ by utilizing Brillouin light scattering technique. The spin wave dispersion in flakes of different thicknesses shows the key role of dipolar interaction in the spin waves of vdW ferromagnets, and the non-reciprocity of spin wave in thick f

Donny Dwiputra, Mir Faizal, Francesco Marino, Freddy P. Zen

We study the universality of work statistics performed during a quench in gapless quantum systems. We show that the cumulants of work scale separately in the fast and slow quench regimes, following a power law analogous to the universal scaling in the Kibble-Zurek mechanism for topological defect formation in phase transition. As an example, we analyze the n

Junhao Wei, Yanzhao Gu, Ran Zhang, Yanxiao Li

Wireless Sensor Networks (WSNs) are essential for monitoring and communication in complex environments, where coverage optimization directly affects performance and energy efficiency. However, traditional algorithms such as the Whale Optimization Algorithm (WOA) often suffer from limited exploration and premature convergence. To overcome these issues, this p

Su Yeon Chang, M. Cerezo

Quantum machine learning (QML) is a computational paradigm that seeks to apply quantum-mechanical resources to solve learning problems. As such, the goal of this framework is to leverage quantum processors to tackle optimization, supervised, unsupervised and reinforcement learning, and generative modeling-among other tasks-more efficiently than classical mod

Muyao Yuan, Yuanhong Zhang, Weizhan Zhang, Lan Ma

Recently, the strong generalization ability of CLIP has facilitated open-vocabulary semantic segmentation, which labels pixels using arbitrary text. However, existing methods that fine-tune CLIP for segmentation on limited seen categories often lead to overfitting and degrade the pretrained vision-language alignment. To stabilize modality alignment during fi

Ben Priestley

Loop quantum gravity (LQG) attempts to unify general relativity with quantum physics to offer a complete description of the universe by quantising spacetime geometry, but the numerical calculations we encounter are extraordinarily difficult. Progress has been made in the covariant formulation of LQG, but the tools do not carry over to the canonical formulati

Luning Sun, José L. Safanelli, Jonathan Sanderman, Katerina Georgiou

We propose a self-supervised machine learning (SSML) framework for multi-fidelity learning and extended predictive soil spectroscopy based on latent space embeddings. A self-supervised representation was pretrained with the large MIR spectral library and the Variational Autoencoder algorithm to obtain a compressed latent space for generating spectral embeddi

Zhongjun Jiang, Soyaib Sohag, You Zhou

The detection and discrimination of molecular chirality are essential for advancing pharmaceutical and biological applications. While nanophotonic platforms offer a route to enhance chiral light-matter interactions, existing device concepts for chiral sensing remain heuristic, resulting in limited chiral enhancement and control over chiral hotspot placement

Mohammad R. Salmanpour, Amir Hossein Pouria, Sirwan Barichin, Yasaman Salehi

Radiomics enables the extraction of quantitative biomarkers from medical images for precision modeling, but reproducibility and scalability remain limited due to heterogeneous software implementations and incomplete adherence to standards. Existing tools also lack unified support for deep learning based radiomics. To address these limitations, we introduce P

Zichuan Wang

Zhixiang Wu has shown the existence of "change of weights" operation on $(φ,Γ)$-modules in families (arxiv:2405.16637). We interpret it in the trianguline case as pullbacks with a discussion on related stacks. Finally, we prove that it intertwines well with translation functors via a 1-1 correspondence defined by Yiwen Ding (arXiv:2407.21237) in the

Kedar Karhadkar, Jack Klys, Daniel Ting, Artem Vorozhtsov

Experimentation platforms in industry must often deal with customer trust issues. Platforms must prove the validity of their claims as well as catch issues that arise. As a central quantity estimated by experimentation platforms, the validity of confidence intervals is of particular concern. To ensure confidence intervals are reliable, we must understand and

Gabriel M. Arantes, Richard F. Pinto, Bruno L. Dalmazo, Eduardo N. Borges

Binary options trading is often marketed as a field where predictive models can generate consistent profits. However, the inherent randomness and stochastic nature of binary options make price movements highly unpredictable, posing significant challenges for any forecasting approach. This study demonstrates that machine learning algorithms struggle to outper

Haonan Liu, Varun D. Vaidya, Monica Gutierrez Galan, Alexander K. Ratcliffe

We propose high-fidelity single-qubit spin-dependent kicks (SDKs) for trapped ions using nanosecond Raman pulses via amplitude modulation of a continuous-wave laser with a tunable beat frequency. We develop a general method for maintaining SDK performance in the presence of micromotion by identifying optimal choices of the RF phase and frequency that suppres

Zhenyu Bi, Gaurav Srivastava, Yang Li, Meng Lu

While small language models (SLMs) have shown promise on various reasoning tasks, their ability to judge the correctness of answers remains unclear compared to large language models (LLMs). Prior work on LLM-as-a-judge frameworks typically relies on comparing candidate answers against ground-truth labels or other candidate answers using predefined metrics li

Nasit S Sony, Xianzhong Ding

Byzantine agreement protocols in asynchronous networks have received renewed interest because they do not rely on network behavior to achieve termination. Conventional asynchronous Byzantine agreement protocols require every party to broadcast its requests (e.g., transactions), and at the end of the protocol, parties agree on one party's request. If parties

Aliyah Smith, Monroe Kennedy

Robot sound, encompassing both consequential operational noise and intentionally designed auditory cues, plays an important role in human-robot interaction (HRI). Developing a deeper understanding of how robot sounds influence human experience, and how technologies such as augmented reality (AR) modulate these effects, can enable the design of more socially

Mohammad Ghomi

We show that total generalized mean curvatures of hypersurfaces with positive reach in Riemannian manifolds, and convex bodies in Cartan-Hadamard spaces, are continuous with respect to Hausdorff distance.

Daniel McNulty

Measurement incompatibility--the impossibility of jointly measuring certain quantum observables--is a fundamental resource for quantum information processing. We develop a graph-theoretic framework for quantifying this resource for large families of binary measurements, including Pauli observables on multi-qubit systems and $k$-body Majorana observables on $

Arif Er, Meng-Chwan Tan

We explain why on certain five-manifolds, topological 5d $\mathcal{N} = 2$ gauge theory of Haydys-Witten twist with gauge group $G$, is dual to that of Geyer-M\"{u}lsch twist with gauge group $^LG$, where $G$ is a real, compact Lie group with Langlands dual $^LG$. In turn, via their 2d and 3d gauged A/B-twisted Landau-Ginzburg model interpretations, we can s

Chengyu Li, Saleh Faghfoorian, Ivan Ruchkin

Formal guarantees for cyber-physical systems (CPS) rely on diverse assumptions. If satisfied, these assumptions enable the transfer of abstract guarantees into real-world assurances about the deployed CPS. Although assumptions are central to assured CPS, there is little systematic knowledge about what assumptions are made, what guarantees they support, and w

Michael V. DeBole, Rathinakumar Appuswamy, Neil McGlohon, Brian Taba

A vertically integrated, end-to-end, research prototype system combines 288 NorthPole neural inference accelerator cards, offline training algorithms, a high-performance runtime stack, and a containerized inference pipeline to deliver a scalable and efficient cloud inference service. The system delivers 115 peta-ops at 4-bit integer precision and 3.7 PB/s of

Marius Leonhardt, Martin Lüdtke

We prove finiteness and give an explicit upper bound on the number of $S$-integral points on affine curves satisfying a certain rank-genus inequality. We achieve this by developing an analogue of the Chabauty method, embedding the curve into its generalised Jacobian and bounding the Abel-Jacobi image of the $S$-integral points using arithmetic intersection t

Raphael Ruschel, Hardikkumar Prajapati, Awsafur Rahman, B. S. Manjunath

State-of-the-art Video Scene Graph Generation (VSGG) systems provide structured visual understanding but operate as closed, feed-forward pipelines with no ability to incorporate human guidance. In contrast, promptable segmentation models such as SAM2 enable precise user interaction but lack semantic or relational reasoning. We introduce Click2Graph, the firs

Jeongju Jee, Jeffrey G. Andrews

Tightly integrated low earth orbit (LEO) satellite communications and terrestrial integrated sensing and communication (ISAC) are expected to be key novel aspects of the 6G era. Spectrum sharing between satellite and terrestrial cellular networks may, however, cause severe interference. This paper introduces a cooperation framework for integrated coexistence

Milos Vukadinovic, Hirotaka Ieki, Yuki Sahashi, David Ouyang

Although the heart has complex three-dimensional (3D) anatomy, conventional medical imaging with cardiac ultrasound relies on a series of 2D videos showing individual cardiac structures. 3D echocardiography is a developing modality that now offers adequate image quality for clinical use, with potential to streamline acquisition and improve assessment of off-

Jason Liu

We introduce cyclic diagram monoids, a generalisation of classical diagram monoids that adds elements of arbitrary period by including internal components, with a view towards cryptography. We classify their simple representations and compute their dimensions in terms of the underlying diagram algebra. These go towards showing that cyclic diagram monoids pos

S. Sharma, M. Pula, Sajilesh K. P., J. Gautreau

Binary Bi-Pd/Pt systems have attracted a lot of interest because of their topologically non-trivial nature along with superconductivity. We report the structural and superconducting properties of high-quality single-crystalline BiPt using a comprehensive range of experimental techniques, including X-ray diffraction, electron microscopy, muon spin rotation/re

Yayan Qiu, Sean Hanna

Taking into account the regional characteristics of intrinsic and extrinsic properties of space is an essential issue in architectural design and urban renewal, which is often achieved step by step using image and graph-based GANs. However, each model nesting and data conversion may cause information loss, and it is necessary to streamline the tools to facil

Zihan Li, Yiqing Wang, Sina Farsiu, Paul Kinahan

Recent advances in image-text pretraining have significantly enhanced visual understanding by aligning visual and textual representations. Contrastive Language-Image Pretraining (CLIP) has played a pivotal role in multimodal learning. However, its focus on single-label, single-granularity alignment limits its effectiveness in complex domains such as medical

Camilla Andreozzi, Pietro Colombo, Philipp Otto

We propose a robust multi-fidelity Gaussian process for integrating sparse, high-quality reference monitors with dense but noisy citizen-science sensors. The approach replaces the Gaussian log-likelihood in the high-fidelity channel with a global Huber loss applied to precision-weighted residuals, yielding bounded influence on all parameters, including the c

David Bonet, Marçal Comajoan Cara, Alvaro Calafell, Daniel Mas Montserrat

Tabular data underpins decisions across science, industry, and public services. Despite rapid progress, advances in deep learning have not fully carried over to the tabular domain, where gradient-boosted decision trees (GBDTs) remain a default choice in practice. We present iLTM, an integrated Large Tabular Model that unifies tree-derived embeddings, dimensi

Gerardo Aquino, Mauro Bologna

We study the diffusion of a charged particle in a magnetic field subject to stochastic dichotomous fluctuations. The associated induced electric field gives rise to non-trivial dynamical regimes. In particular, when the mean magnetic field vanishes, the particle remains confined within a finite radius, regardless of the fluctuation statistics. For a non-zero

Jianting Zhang, Sen Yang, Alberto Sonnino, Sebastián Loza

Directed Acyclic Graph (DAG)-based Byzantine Fault-Tolerant (BFT) protocols have emerged as promising solutions for high-throughput blockchains. By decoupling data dissemination from transaction ordering and constructing a well-connected DAG in the mempool, these protocols enable zero-message ordering and implicit view changes. However, we identify a fundame

Sohaib Errabii, Olivier Sentieys, Marcello Traiola

Kolmogorov-Arnold Networks (KANs) have garnered significant attention for their promise of improved parameter efficiency and explainability compared to traditional Deep Neural Networks (DNNs). KANs' key innovation lies in the use of learnable non-linear activation functions, which are parametrized as splines. Splines are expressed as a linear combination

Alex S. C. Maia, John B. Hall, Hugo F. M. Milan, Izabelle A. M. A. Teixeira

Advances in technology are transforming sustainable cattle farming practices, with electronic feeding systems generating big longitudinal datasets on individual animal feed intake, offering the possibility for autonomous precision livestock systems. However, the literature still lacks a methodology that fully leverages these longitudinal big data to accurate

C. Kaufmann Ribeiro, J. C. Mutch, Q. Jiang, J. P. Ayres-Sims

The quantum limit, where magnetic fields confine carriers to the lowest Landau level, is predicted to host exotic quantum phases arising from strengthened electronic correlations, reduced dimensionality, and increased degeneracy. We report a novel quantization regime realized in the ultra-quantum limit of the narrow-gap Dirac insulator ZrTe5, marked by anoma

Jeremías Ruta, Nicolás Grandi, Tobías Canavesi

We develop an effective model to describe the dynamics of a system of particle moving in circular configurations around a central mass, by considering the continuum limit of the angular distribution, to obtain the stable configurations for different initial parameters. We compute the resulting fractal dimension and compare it with that of a statistical Canto

Andrea Fiorilli, Andrés N. Ruiz, Ariel G. Sanchez, Matteo Esposito

We present a new prescription for the halo mass function (HMF) built upon the Evolution Mapping framework. This approach provides a physical motivation to parametrise the non-universality of the HMF in terms of the recent history of structure formation and the local shape of the linear matter power spectrum. Our model was calibrated against measurements from

Takuya Igaue, Catia Correia-Caeiro, Akito Yoshida, Takako Miyabe-Nishiwaki

Generating animal faces using generative AI techniques is challenging because the available training images are limited both in quantity and variation, particularly for facial expressions across individuals. In this study, we focus on macaque monkeys, widely studied in systems neuroscience and evolutionary research, and propose a method to generate their fac

Zhexi Fu, Joseph Y. J. Chow

We formulate a mixed integer linear program (MILP) for a platoon-based vehicle-to-vehicle charging (PV2VC) technology designed for modular vehicles (MVs) and solve it with a genetic algorithm (GA). A set of numerical experiments with five scenarios are tested and the computational performance between the commercial software applied to the MILP model and the