October 2025 arXiv papers — page 111

Do Eon Cha, Imin Chen

We recall and define various kinds of supersingular $\ell$-isogeny graphs and precise graph isomorphism with a corresponding quaternion $\ell$-ideal graph. In particular, we introduce the notion of double-orientations on supersingular elliptic curves and study the structure of double-oriented supersingular $\ell$-isogeny graphs.

Rihui Lan, Jorge Reyes

In this paper, we incorporate the EMAC formulation into the Ladyzhenskaya model (LM), a large eddy simulation (LES) of incompressible flows. The EMAC formulation, which conserves energy, linear momentum, and angular momentum even with weak enforcement of incompressibility, has been shown to provide tangible benefits over the popular skew-symmetric for direct

Jacob Haqq-Misra, Mykhaylo Danylov

We suggest that the large-scale deployment of wind turbines on an M-dwarf planet could produce observable technosignatures. Motivated by observations of hypersonic wind velocities on WASP-127 b, we note that the atmospheres of such planets could serve as vast reservoirs of energy for an extraterrestrial civilization. A large-scale deployment of wind turbines

Nicola Terzaghi, Guillermo Franco Abellán, Fabian Zimmer, Shin'ichiro Ando

The anisotropies of the Cosmic Neutrino Background (C$\nu$B) offer an ideal tool to test non-standard neutrino interactions, since they directly trace the perturbations in the neutrino distribution function. Here, we study how invisible neutrino decays impact the C$\nu$B anisotropies, in a framework where neutrinos decay non-relativistically to dark radiatio

Devvrit Khatri, Pranamya Kulkarni, Nilesh Gupta, Yerram Varun

Large Language Models (LLMs) have been shown to be able to learn different tasks without explicit finetuning when given many input-output examples / demonstrations through In-Context Learning (ICL). Increasing the number of examples, called ``shots'', improves downstream task performance but incurs higher memory and computational costs. In this work, we stud

Camille Touron, Gabriel V. Cardoso, Julyan Arbel, Pedro L. C. Rodrigues

Simulation-based inference (SBI) has become a widely used framework in applied sciences for estimating the parameters of stochastic models that best explain experimental observations. A central question in this setting is how to effectively combine multiple observations in order to improve parameter inference and obtain sharper posterior distributions. Recen

Chen-Wei Wang, Peng Zhang, Shao-Lin Xiong, Yue Huang

With the growing number of gamma-ray monitors in operation, several research teams have adopted a strategy of joint operation and scientific duty to improve efficiency. A successful example is the GECAM-HXMT-SVOM (GHS) constellation collaboration, which sets a precedent for other gamma-ray monitor constellations. However, joint duty also presents challenges

Binglan Han, Anuradha Mathrani, Teo Susnjak

This study quantifies how prompting strategies interact with large language models (LLMs) to automate the screening stage of systematic literature reviews (SLRs). We evaluate six LLMs (GPT-4o, GPT-4o-mini, DeepSeek-Chat-V3, Gemini-2.5-Flash, Claude-3.5-Haiku, Llama-4-Maverick) under five prompt types (zero-shot, few-shot, chain-of-thought (CoT), CoT-few-shot

L. J. Janse van Rensburg

Academic citation integrity faces persistent challenges, with research indicating 20% of citations contain errors and manual verification requiring months of expert time. This paper presents a novel AI-powered methodology for systematic, comprehensive reference auditing using agentic AI with tool-use capabilities. We develop a zero-assumption verification pr

K. Korshynska, E. V. Gorbar, Y. M. Bidasyuk, A. I. Yakimenko

Dark-matter-dominated dwarf galaxies provide an excellent laboratory for testing dark matter models at small scale and, in particular, the ultralight dark matter (ULDM) class of models. Within the framework of self-interacting bosonic dark matter, we use the observed velocity-dispersion profiles of seven dwarf spheroidal galaxies to constrain the parameters

S. Raman, P. Varghese, L. Reyes, M. Guran

The Self-Excited Loop (SEL) architecture, used in some continuous-wave (CW) superconducting linacs, relies on a positive feedback mechanism that requires carefully defined operating limits to ensure stable operation. These limits are typically derived from amplifier calibration, which characterizes the relationship between forward power and DAC drive. Howeve

Christian Pfeifer, José Javier Relancio

The unification of all physical fields into one mathematical object and the derivation of all physical field equations from that object in one framework is a long-lasting endeavor in fundamental physics. We suggest a new approach to achieve this goal by encoding physical fields into the geometry of the 1-particle phase space on spacetime (the cotangent bundl

Vaibhava Srivastava, Jason R. Rohr, Rana D. Parshad

The Paradox of Enrichment (PoE) predicts that increasing resources, such as nutrient inputs like fertilizers or food availability, should destabilize ecological systems, such as crop-pest dynamics, leading to population cycles that can increase the risk of crop failure during environmental shocks. Yet, since the Green Revolution, fertilizer use has surged wi

Luis F. Abanto-Leon, Setareh Maghsudi

This work addresses the radio resource management (RRM) design in downlink full-duplex integrated sensing and communications (ISAC) systems, jointly optimizing timeslot allocation and beam selection under imperfect self-interference cancellation. Timeslot allocation governs the distribution of discrete channel uses between sensing and communication tasks, wh

Eoin Dolan, Jone Mencos, Williams Savero Torres, Maxen Cosset-Chéneau

The spin Hall effect in ferromagnets is of great interest in the field of spintronics, and while the effect has been quantified in many materials, the dependence of the spin Hall angle on the relative orientation of spin polarization and the magnetization is less well studied. Of equal importance for the purpose of spin-charge interconversion in ferromagnets

Benedikt Alkin, Richard Kurle, Louis Serrano, Dennis Just

The recently proposed Anchored-Branched Universal Physics Transformers (AB-UPT) shows strong capabilities to replicate automotive computational fluid dynamics simulations requiring orders of magnitudes less compute than traditional numerical solvers. In this technical report, we add two new datasets to the body of empirically evaluated use-cases of AB-UPT, c

Anna Gusakova, Anna Muranova

Let $T$ be the triangle in the plane with vertices $(0, 0)$, $(0,1)$ and $(0, 1)$. The convex hull $T_n$ of points $(0, 1)$, $(1, 0)$ and $n$ independent random points uniformly distributed in $T$ is the random convex chain. In this paper we study the moments of the volume of random polytope $T_n$ and derive exact formulas for $k$-th moments for any integer

Dibyendu Mondal, Debaarjun Mukherjee, Rahul Maitra

In the field of quantum chemistry, the variational quantum eigensolver (VQE) has emerged as a highly promising approach to determine molecular energies and properties within the noisy intermediate-scale quantum (NISQ) era. The central challenges of this approach lie in the design of an expressive ansatz capable of representing the exact ground state wavefunc

Bonnie Rushing, Shouhuai Xu

As disinformation-driven cognitive attacks become increasingly sophisticated, the ability to quantify their impact is essential for advancing cybersecurity defense strategies. This paper presents a novel framework for measuring the engagement effectiveness of cognitive attacks by introducing a weighted interaction metric that accounts for both the type and v

Shauli Ravfogel, Gilad Yehudai, Tal Linzen, Joan Bruna

Recent probing studies reveal that large language models exhibit linear subspaces that separate true from false statements, yet the mechanism behind their emergence is unclear. We introduce a transparent, one-layer transformer toy model that reproduces such truth subspaces end-to-end and exposes one concrete route by which they can arise. We study one simple

Zahra Arjmandi, Gunho Sohn

This paper presents a novel fusion technique for LiDAR Simultaneous Localization and Mapping (SLAM), aimed at improving localization and 3D mapping using LiDAR sensor. Our approach centers on the Inferred Attention Fusion (INAF) module, which integrates AI with geometric odometry. Utilizing the KITTI dataset's LiDAR data, INAF dynamically adjusts attention w

Tommaso Bonato, Sepehr Abdous, Abdul Kabbani, Ahmad Ghalayini

Cloud computing and AI workloads are driving unprecedented demand for efficient communication within and across datacenters. However, the coexistence of intra- and inter-datacenter traffic within datacenters plus the disparity between the RTTs of intra- and inter-datacenter networks complicates congestion management and traffic routing. Particularly, faster

Tuhina Ghorui, Prabir Rudra, Farook Rahaman

In this work, the classical Godel solution from general relativity is extended into the framework of modified gravity theories based on non-metricity $Q$ and the trace of the energy-momentum tensor $T$ in the context of $f(Q,T)$ gravity. The main feature of the Godel solution is the existence of closed time-like curves, which allow for causality violation an

Bonnie Rushing, Mac-Rufus Umeokolo, Shouhuai Xu

Cyber cognitive attacks leverage disruptive innovations (DIs) to exploit psychological biases and manipulate decision-making processes. Emerging technologies, such as AI-driven disinformation and synthetic media, have accelerated the scale and sophistication of these threats. Prior studies primarily categorize current cognitive attack tactics, lacking predic

Guangzhao He, Yuxi Xiao, Zhen Xu, Xiaowei Zhou

Registering an object shape to a sequence of point clouds undergoing non-rigid deformation is a long-standing challenge. The key difficulties stem from two factors: (i) the presence of local minima due to the non-convexity of registration objectives, especially under noisy or partial inputs, which hinders accurate and robust deformation estimation, and (ii)

Ritwik Pal, Sampurna Pal

Let $F$ be a Hecke-Maass cusp form for $\mathrm{SL}_3(\mathbb{Z})$ and $A(m,n)$ be its normalized Fourier coefficients. Let $V$ be a smooth function, compactly supported on $[1,2]$ and satisfying $V(y)^{j} \ll_j y^{-j}$ for any $j \in \mathbb{N} \cup \{0\}$. In this article we prove a power-saving upper bound for the `average' shifted convolution sum \begin{

Jinwoo Kim, Minjae Seo, Eduard Marin, Seungsoo Lee

Distributed SDN (Software-Defined Networking) controllers have rapidly become an integral element of Wide Area Networks (WAN), particularly within SD-WAN, providing scalability and fault-tolerance for expansive network infrastructures. However, the architecture of these controllers introduces new potential attack surfaces that have thus far received inadequa

Laszlo Gacsi, Adam K. Kiss, Tamas G. Molnar

This paper presents a safety-critical control framework to maintain bounded lateral motions for vehicles braking on asymmetric surfaces. We synthesize a brake controller that assists drivers and guarantees safety against excessive lateral motions (i.e., prevents the vehicle from spinning out) while minimizing the stopping distance. We address this safety-cri

Anton Raskovalov

This paper presents machine learning method for tuning of cavity duplexer with a large amount of adjustment screws. After testing we declined conventional reinforcement learning approach and reformulated our task in the supervised learning setup. The suggested neural network architecture includes 1d ResNet-like backbone and processing of some additional info

Rachna Raj, Diego Elias Costa

Open-source software (OSS) is a pillar of modern software development. Its success depends on the dedication of maintainers who work constantly to keep their libraries stable, adapt to changing needs, and support a growing community. Yet, they receive little to no continuous feedback on how the projects that rely on their libraries actually use their APIs. W

Jie-ping Zheng, Jorge Dukelsky, Rafael A. Molina, Antonio M. García-García

The out of equilibrium dynamics of the Sachdev-Ye-Kitaev model (SYK), comprising $N$ Majoranas with random all-to-all four-body interactions, minimally coupled to a Markovian bath modeled by the Lindblad formalism, displays intriguing nontrivial features. In particular, the decay rate towards the steady state is a non-monotonic function of the bath coupling

Apekshya Ghimire, Chandralekha Singh

Engaging in meaningful collaborations with peers, both inside and outside the classroom, can greatly enhance students' understanding of physics and other STEM disciplines. We analyzed the characteristics of women and men who typically worked alone versus those who collaborated with peers in a calculus-based introductory physics course comparing pre pandemic

William Hoy, Nurcin Celik

Large language models (LLMs) increasingly require mechanisms for continual adaptation without full retraining. However, sequential updates can lead to catastrophic forgetting, where new edits degrade previously acquired knowledge. This work presents STABLE, a gated continual self editing framework that constrains forgetting during sequential updates using pa

Jiyong Chen, Ni Du, Leyi Li

The codegree of an irreducible character $\chi$ of a finite group $G$ is defined as $|G:\ker\chi|/\chi(1)$. The codegree graph $\Gamma(G)$ of a finite group $G$ is the graph whose vertices are the prime divisors of $|G|$, where two distinct primes $p$ and $q$ are adjacent if and only if $pq$ divides the codegree of some irreducible character of $G$. In this

Chirag Pabbaraju, Gregory Valiant, Rishi Verma

This paper revisits the classical problem of determining the bias of a weighted coin, where the bias is known to be either $p = 1/2 + \varepsilon$ or $p = 1/2 - \varepsilon$, while minimizing the expected number of coin tosses and the error probability. The optimal strategy for this problem is given by Wald's Sequential Probability Ratio Test (SPRT), which c

Manuel Pavon Valderrama

I reexamine the perturbative renormalizability of chiral two-pion exchange in two-nucleon scattering for coupled triplets when one-pion exchange has been fully iterated at leading order. Improving over previous works, it is shown that only two counterterms are required to obtain cutoff independent results, which is one less than in naive dimensional analysis

Jiong Mei, Shengshan Qin, Jiangping Hu

We unveil a mechanism that enables a robust supercurrent diode effect in Josephson junctions based on multiband superconductors. We predict that interband pairing can significantly amplifies this effect, even under weak spin-orbit coupling while intraband pairing alone would render it negligible. To illustrate this, we examine monolayer FeSe/STO, a system wh

Victor Franken

In this dissertation, we review results on quantum information constraints in gravity that are relevant to cosmological models and demonstrate how this approach sheds light on cosmological holography. Using Jackiw-Teitelboim gravity as a toy model, we establish the validity of the quantum Bousso bound and prove the restricted quantum focusing conjecture (rQF

Xinyue Xu, Jieqiang Sun, Jing, Dai

We present DexCanvas, a large-scale hybrid real-synthetic human manipulation dataset containing 7,000 hours of dexterous hand-object interactions seeded from 70 hours of real human demonstrations, organized across 21 fundamental manipulation types based on the Cutkosky taxonomy. Each entry combines synchronized multi-view RGB-D, high-precision mocap with MAN

Apekshya Ghimire, Jaya Shivangani Kashyap, Emily Edwards, Diana Franklin

This study investigates the experiences of pre-high and high school teachers in implementing QuanTime and other quantum-related activities aiming to promote quantum literacy and introduce foundational quantum concepts to K-12 students. The ultimate goal is to help prepare a diverse future workforce in quantum information science and technology (QIST). Teache

Qihao Peng, Qu Luo, Zheng Chu, Neng Ye

In this paper, we proposed a hybrid simultaneous wireless information and power transfer (SWIPT)-enabled multiple-input multiple-output (MIMO) architecture, where the base station (BS) uses a conventional RF transmitter for downlink transmission and a Rydberg atomic quantum receiver (RAQR) for receiving uplink signal from Internet of Things (IoT) devices. To

Haowei Zhu, Tianxiang Pan, Rui Qin, Jun-Hai Yong

The scale and quality of datasets are crucial for training robust perception models. However, obtaining large-scale annotated data is both costly and time-consuming. Generative models have emerged as a powerful tool for data augmentation by synthesizing samples that adhere to desired distributions. However, current generative approaches often rely on complex

Philip DiGiacomo, Haoyang Wang, Jinrui Fang, Yan Leng

As AI chatbots gain adoption in clinical medicine, developing effective frameworks for complex, emerging diseases presents significant challenges. We developed and evaluated six Retrieval-Augmented Generation (RAG) corpus configurations for Long COVID (LC) clinical question answering, ranging from expert-curated sources to large-scale literature databases. O

P. Shahsavari, J. Dettmer, M. J. Unsworth, A. Schaeffer

We present a probabilistic multiphysics inversion based on Bayesian inference with trans-dimensional models. We jointly consider magnetotelluric, receiver function, and Rayleigh-wave dispersion data to infer one-dimensional lithospheric structure in the vicinity of Athabasca, Canada. The location is on the North American Craton with a cover of sediments from

Ilia Pavlov

As image generative models continue to increase not only in their fidelity but also in their ubiquity the development of tools that leverage direct interaction with their internal mechanisms in an interpretable way has received little attention In this work we introduce a system that allows users to develop a better understanding of the model through interac

Zied Ammari, Michele Correggi, Marco Falconi, Raphaël Gautier

Entropy and free energy are central concepts in both statistical physics and information theory, with quantum and classical facets. In mathematics these concepts appear quite often in different contexts (dynamical systems, probability theory, von Neumann algebras, etc.). In this work, we study the von Neumann and Wehrl entropies from the point of view of sem

Francesco Mazza, Jorge Miguel-Ramiro, Jessica Illiano, Alexander Pirker

The Quantum Internet is still in its infancy, yet identifying scalable and resilient quantum network resource states is an essential task for realizing it. We explore the use of graph states with flexible, non-trivial qubit-to-node assignments. This flexibility enables adaptable engineering of the entanglement topology of an arbitrary quantum network. In par

Gai Zhang, Xinfeng Zhang, Lv Tang, Hongyu An

Light field images capture multi-view scene information and play a crucial role in 3D scene reconstruction. However, their high-dimensional nature results in enormous data volumes, posing a significant challenge for efficient compression in practical storage and transmission scenarios. Although neural representation-based methods have shown promise in light

Imogen Forbes, Patrick Yard, Martin Bielak, Molly A. Thomas

Hybrid encodings, where multiple degrees of freedom are used to encode quantum information, can increase the size of the Hilbert space with minimal increase to hardware requirements. We show a reprogrammable integrated photonic device, with multimodal components designed to allow for control over the transverse electric modes. We use this device to generate

Qihao Peng, Tierui Gong, Zihang Song, Qu Luo

This paper investigates the performance advantages of Rydberg atomic quantum (RAQ)-based multiple-input multiple-output (MIMO) satellites for enhancing direct ground-to-space uplink access.We analytically evaluate the impact of Rydberg atoms on channel estimation by deriving closed-form expressions for the mean-square error (MSE) and normalized mean-square e

Richard M. Bailey

So-called `wicked problems', those involving complex multi-dimensional settings, non-verifiable outcomes, heterogeneous impacts and a lack of single objectively correct answers, have plagued humans throughout history. Modern examples include decisions over justice frameworks, solving environmental pollution, planning for pandemic resilience and food security

Gaoxiang Huang, Songning Lai, Yutao Yue

Although deep neural networks achieve strong predictive performance, their internal reasoning often remains difficult to inspect and control. Concept Bottleneck Models (CBMs) address this opacity by factoring predictions through human-understandable concepts, thereby enabling concept-level inspection and intervention. However, CBMs remain vulnerable to conce

Allen Daniel Sunny

Large-scale AI models such as GPT-4 have accelerated the deployment of artificial intelligence across critical domains including law, healthcare, and finance, raising urgent questions about trust and transparency. This study investigates the relationship between explainability and user trust in AI systems through a quantitative experimental design. Using an

Orr Paradise, David F. Gruber, Adam Tauman Kalai

If you had an AI Whale-to-English translator, how could you validate whether or not it is working? Does one need to interact with the animals or rely on grounded observations such as temperature? We provide theoretical and proof-of-concept experimental evidence suggesting that interaction and even observations may not be necessary for sufficiently complex la

Rathi Adarshi Rammohan, Moritz Meier, Dennis Küster, Tanja Schultz

Recent advancements in machine learning and adaptive cognitive systems are driving a growing demand for large and richly annotated multimodal data. A prominent example of this trend are fusion models, which increasingly incorporate multiple biosignals in addition to traditional audiovisual channels. This paper introduces the EASELAN annotation framework to i

Meng-Yuan Li, Peng Ye

We introduce the subdimensional entanglement entropy (SEE), defined on subdimensional entanglement subsystems (SESs) embedded in the bulk, as an entanglement-based probe of how geometry and topology jointly shape universal properties of quantum matter. By varying the dimension, geometry, and topology of the SES, we show that the subleading term of SEE exhibi

Gianvito Chiarella, Tobias Frank, Leart Zuka, Pau Farrera

Communication in quantum networks suffers notoriously from photon loss. Resulting errors can be mitigated with a suitable measurement herald at the receiving node. However, waiting for a herald and communicating the measurement result back to the sender in a repeat-until-success strategy makes the protocol slow and prone to errors from false heralds such as

Victor Delapalme, Leticia F. Cugliandolo, Grégory Schehr, Marco Tarzia

In recent years the Rosenzweig--Porter (RP) ensemble, obtained by adding a diagonal matrix with independent and identically distributed elements to a Gaussian random matrix, has been widely used as a minimal model for the emergence of fractal eigenstates in complex many-body systems. A key open question concerns the robustness of its phase diagram when the a

Qihao Peng, Jiuyu Liu, Qu Luo, Yi Ma

In this paper, we propose a novel and low-complexity atomic multiple-input multiple-output (MIMO) receiver architecture assisted by a reconfigurable intelligent surface (RIS). By introducing RIS and utilizing pulse amplitude modulation (PAM), the phase of the transmitted signal is effectively aligned with that of the local oscillator (LO), thereby mitigating

Denis Rychkovskiy

We present QSilk, a lightweight, always-on stabilization layer for latent diffusion that improves high-frequency fidelity while suppressing rare activation spikes. QSilk combines (i) a per-sample micro clamp that gently limits extreme values without washing out texture, and (ii) Adaptive Quantile Clip (AQClip), which adapts the allowed value corridor per reg

Shin-Ming Huang

Geometry and topology are fundamental to modern condensed matter physics, but their precise connection in quantum systems remains incompletely understood. Here, we develop an analytical scheme for calculating the curvature of the quantum metric of Bloch bands. Using a gauge-invariant formulation based on eigenprojectors, we construct the full Riemannian geom

Natalia Hormazábal, Carlos Martínez-Ranero

Let $K$ be a quadratic imaginary extension of $\mathbb{Q}$, let $S$ be a finite nonempty set of non archimedean places, and let $\mathcal{O}_{K,S}$ denote the ring of $S$-integers of $K$. We show that there is no algorithm which solves the following problem. Given an arbitrary system of linear equations over the integers together with divisibility conditions

Pieris Panagi, Savvas Karatsiolis, Kyriacos Mosphilis, Nicholas Hadjisavvas

Poultry farming faces increasing pressure to meet productivity targets while ensuring animal welfare and environmental compliance. Yet many small and medium-sized farms lack affordable, integrated tools for continuous monitoring and decision-making, relying instead on manual, reactive inspections. This paper presents Poultry Farm Intelligence (PoultryFI) - a

Sabbir M Saleh, Nazim Madhavji, John Steinbacher

Security is becoming a pivotal point in cloud platforms. Several divisions, such as business organisations, health care, government, etc., have experienced cyber-attacks on their infrastructures. This research focuses on security issues within Continuous Integration and Deployment (CI/CD) pipelines in a cloud platform as a reaction to recent cyber breaches.

Jort de Jong, Mike Holenderski

Semantic segmentation is the task of classifying each pixel in an image. Training a segmentation model achieves best results using annotated images, where each pixel is annotated with the corresponding class. When obtaining fine annotations is difficult or expensive, it may be possible to acquire coarse annotations, e.g. by roughly annotating pixels in an im

Atsushi Koshiba, Charalampos Mainas, Pramod Bhatotia

The adoption of FPGAs in cloud-native environments is facing impediments due to FPGA limitations and CPU-oriented design of orchestrators, as they lack virtualization, isolation, and preemption support for FPGAs. Consequently, cloud providers offer no orchestration services for FPGAs, leading to low scalability, flexibility, and resiliency. This paper presen

Mohammed Younes Gueddari, Walid Hachem

The generalized Lotka-Volterra stochastic differential equation with a symmetric food interaction matrix is frequently used to model the dynamics of the abundances of the species living within an ecosystem when these interactions are mutualistic or competitive. In the relevant cases of interest, the Markov process described by this equation has an unique inv

Ignacio Repiso, Salvador Pineda, Juan Miguel Morales

The AC Optimal Power Flow (AC-OPF) problem is a non-convex, NP-hard optimization task essential for secure and economic power system operation. While interior-point methods are widely used due to their computational efficiency, spatial branching techniques offer global optimality guarantees at significantly higher computational cost. In this work, we propose

Yitong Sun, Yao Huang, Ruochen Zhang, Huanran Chen

Despite the impressive generative capabilities of text-to-image (T2I) diffusion models, they remain vulnerable to generating inappropriate content, especially when confronted with implicit sexual prompts. Unlike explicit harmful prompts, these subtle cues, often disguised as seemingly benign terms, can unexpectedly trigger sexual content due to underlying mo

Trung-Anh Dang, Vincent Nguyen, Ngoc-Son Vu, Christel Vrain

While most continual learning methods focus on mitigating forgetting and improving accuracy, they often overlook the critical aspect of network calibration, despite its importance. Neural collapse, a phenomenon where last-layer features collapse to their class means, has demonstrated advantages in continual learning by reducing feature-classifier misalignmen

Haoran Wang, Bo Zhao, Jinghui Wang, Hanzhang Wang

In this paper, we study the content-aware layout generation problem, which aims to automatically generate layouts that are harmonious with a given background image. Existing methods usually deal with this task with a single-step reasoning framework. The lack of a feedback-based self-correction mechanism leads to their failure rates significantly increasing w

Minlin Zeng, Zhipeng Zhou, Yang Qiu, Martin J. McKeown

Parkinson's disease assessment has garnered growing interest in recent years, particularly with the advent of sensor data and machine learning techniques. Among these, multimodal approaches have demonstrated strong performance by effectively integrating complementary information from various data sources. However, two major limitations hinder their practical

Ziyang Liu, Pengjunfei Chu, Shuming Dong, Chen Zhang

In recent years, Multimodal Sentiment Analysis (MSA) has become a research hotspot that aims to utilize multimodal data for human sentiment understanding. Previous MSA studies have mainly focused on performing interaction and fusion on complete multimodal data, ignoring the problem of missing modalities in real-world applications due to occlusion, personal p

Gao Yang, Yuhang Liu, Siyu Miao, Xinyue Liang

Ideal or real - that is the question.In this work, we explore whether principles from game theory can be effectively applied to the evaluation of large language models (LLMs). This inquiry is motivated by the growing inadequacy of conventional evaluation practices, which often rely on fixed-format tasks with reference answers and struggle to capture the nuan

Niccolò Zanichelli, Maximilian Schons, Isaak Freeman, Philip Shiu

The State of Brain Emulation Report 2025 provides a comprehensive reassessment of the field's progress since Sandberg and Bostrom's 2008 Whole Brain Emulation roadmap. The report is organized around three core capabilities required for brain emulation: recording brain function (Neural Dynamics), mapping brain structure (Connectomics), and emulation and embod

F. Nisa Bostanci, Haocong Luo, Ataberk Olgun, Maria Makeenkova

A MICRO 2024 best paper runner-up publication (the Mess paper) with all three artifact badges awarded (including ``Reproducible'') proposes a new benchmark to evaluate real and simulated memory system performance. The publication contends that Ramulator 2.0 and DAMOV (ZSim+Ramulator) (along with other existing memory system simulators) ``poorly resemble the

Frauke M. Bleher, Margarita Bustos Gonzalez

Suppose $k$ is an algebraically closed field of characteristic two, let $A_4$ be an alternating group on four letters, and let $H$ be the unique Sylow two-subgroup of $A_4$. Let $X$ be a smooth projective irreducible curve over $k$ with a faithful $A_4$-action such that the quotient curve $X/H$ is a projective line and the $H$-cover $X\to X/H$ is totally ram

Qingyan Bai, Qiuyu Wang, Hao Ouyang, Yue Yu

Instruction-based video editing promises to democratize content creation, yet its progress is severely hampered by the scarcity of large-scale, high-quality training data. We introduce Ditto, a holistic framework designed to tackle this fundamental challenge. At its heart, Ditto features a novel data generation pipeline that fuses the creative diversity of a

Ondřej Faltus, Martin Doškář, Jan Havelka, Pavel Rychnovský

Honeycomb microstructures with circular voids are well known to undergo pattern transformations under macroscopic strain loading. Depending on the biaxiality of the applied strain, they deform into three different patterns. Here we demonstrate that the same three patterns can be triggered also by pneumatic actuation of the voids, with resulting patterns depe

Geon Roh, Jip Kim

Dynamic line rating (DLR) enables greater utilization of existing transmission lines by leveraging real-time weather data. However, the elevated temperature operation (ETO) of conductors under DLR, particularly in the presence of uncertainty, is often overlooked, despite its long-term impact on conductor health. This paper addresses ETO under DLR and wind po

Lorenzo Satta Chiris, Ayush Mishra

As autonomous agentic AI systems see increasing adoption across organisations, persistent challenges in alignment, governance, and risk management threaten to impede deployment at scale. We present AURA (Agent aUtonomy Risk Assessment), a unified framework designed to detect, quantify, and mitigate risks arising from agentic AI. Building on recent research a

Nora Elisa Chisari

The alignments of galaxies across the large-scale structure of the Universe are known to be a source of contamination for gravitational lensing, but they can also probe cosmology and the physics of galaxy evolution in many ways. In this review, I cover developments in our understanding of intrinsic alignments over the past 25 years on: (1) different approach

Shirin Movaghgharnezhad, Ehsan Ansari, Clayton A Baker, Ahmed A Bashatah

Ultrasound technology is crucial in diagnostic imaging, making it widely used in medical applications. However, traditional ultrasound transducers face limitations in flexibility and ease of fabrication, leading to the exploration of thin-film and flexible piezoelectric materials. Here, we present a novel approach that combines laser graphitization with 3D p

Aly El Hakie, Yiren Lu, Yu Yin, Michael Jenkins

Opaque objects reconstructed by 3DGS often exhibit a falsely transparent surface, leading to inconsistent background and internal patterns under camera motion in interactive viewing. This issue stems from the ill-posed optimization in 3DGS. During training, background and foreground Gaussians are blended via alpha-compositing and optimized solely against the

V. E. Kuzmichev, V. V. Kuzmichev

A homogeneous and isotropic quantum cosmological system (universe) initially filled with a uniform scalar field that has a potential in the power law representation is considered. Depending on the epoch, this scalar field yields barotropic matter in the form of stiff matter, perfect gas, radiation, dust, cosmic strings, domain walls, de Sitter vacuum, or pha

Stephen J. Wright

Algorithms for continuous optimization problems have a rich history of design and innovation over the past several decades, in which mathematical analysis of their convergence and complexity properties plays a central role. Besides their theoretical properties, optimization algorithms are interesting also for their practical usefulness as computational tools

Emiliano Barone, Sergio Iguri, Nicolas Kovensky, Julian H. Toro

We study spacetime four-point functions of chiral primary operators for superstrings propagating in AdS$_3\times S^3\times T^4$ from the worldsheet perspective, allowing for external states with arbitrary spectral flow charges. We work at small boundary cross-ratio and consider both extremal (supersymmetry-protected) and non-extremal configurations. The stri

Maximo Eduardo Rulli, Simone Petruzzi, Edoardo Michielon, Fabrizio Silvestri

Masked Diffusion Language Models (DLMs) have recently emerged as a promising alternative to traditional Autoregressive Models (ARMs). DLMs employ transformer encoders with bidirectional attention, enabling parallel token generation while maintaining competitive performance. Although their efficiency and effectiveness have been extensively studied, the intern

Xun Wei, Pukai Zhou, Zeyu Wang

The 2022 World Mental Health Report calls for global mental health care reform, amid rising prevalence of issues like anxiety and depression that affect nearly one billion people worldwide. Traditional in-person therapy fails to meet this demand, and the situation is worsened by stigma. While general-purpose large language models (LLMs) offer efficiency for

Ri-Hua Zheng, Jia-Hao Lü, Fan Wu, Yan Xia

The decoherence of superpositions of classically distinguishable states (cat states) is crucial for understanding quantum-to-classical transitions and quantum measurements. So far, decoherence processes of mesoscopic cat states have been demonstrated in several experiments. However, the issue of how the unitary system-reservoir dynamics can lead to irreversi

Chao Wang, Yixin Song, Jinhui Ye, Chuan Qin

Recently, large language models (LLMs) have been explored for integration with collaborative filtering (CF)-based recommendation systems, which are crucial for personalizing user experiences. However, a key challenge is that LLMs struggle to interpret the latent, non-semantic embeddings produced by CF approaches, limiting recommendation effectiveness and fur

Mahyar Alinejad, Alvaro Velasquez, Yue Wang, George Atia

Reinforcement Learning (RL) in environments with complex, history-dependent reward structures poses significant challenges for traditional methods. In this work, we introduce a novel approach that leverages automaton-based feedback to guide the learning process, replacing explicit reward functions with preferences derived from a deterministic finite automato

Sai Yashwant, Anurag Dubey, Praneeth Paikray, Gantala Thulsiram

This paper presents methods for extracting structured information from invoice documents and proposes a set of evaluation metrics (EM) to assess the accuracy of the extracted data against annotated ground truth. The approach involves pre-processing scanned or digital invoices, applying Docling and LlamaCloud Services to identify and extract key fields such a

Alan C. Maioli, Evaldo M. F. Curado, Jean-Pierre Gazeau, Tomoi Koide

We present two complementary approaches to the GKSL equation for an open qubit. The first, based on linearity, yields solutions illustrated by mixed states trajectories in the Bloch ball, including non-random asymptotic fixed points, and exceptional points. The second, exploiting the SU(2) symmetry, leads to a nonlinear dynamical system that separates angula

Tingyu Lin, Armin Dadras, Florian Kleber, Robert Sablatnig

Camera movement classification (CMC) models trained on contemporary, high-quality footage often degrade when applied to archival film, where noise, missing frames, and low contrast obscure motion cues. We bridge this gap by assembling a unified benchmark that consolidates two modern corpora into four canonical classes and restructures the HISTORIAN collectio

Johan Kolvik, Paul Burger, David Hambraeus, Trond H. Haug

Interaction between light and high-frequency sound is a key area in integrated photonics, quantum and nonlinear optics, and quantum science. However, typical suspended optomechanical structures suffer from poor thermal anchoring, making them susceptible to thermal noise arising from optical absorption. Here, we demonstrate a chip-scale, release-free silicon

Valentina D'Orazi, Giuliano Iorio, Borbála Cseh, Chris Sneden

We report the serendipitous discovery of two RR Lyrae stars exhibiting significant s-process element enrichment, a rare class previously represented solely by TY Gruis. Our goal is to characterise these objects chemically and dynamically, exploring their origins and evolutionary histories. Using high-resolution spectroscopy from HERMES@AAT and UVES@VLT, we d

Da Li, Zecheng Fang, Qiang Yan, Wei Huang

Retrieval-Augmented Generation has made significant progress in the field of natural language processing. By combining the advantages of information retrieval and large language models, RAG can generate relevant and contextually appropriate responses based on items retrieved from reliable sources. This technology has demonstrated outstanding performance acro

Divesh Aggarwal, Dexter Kwan

Worst-case to average-case reductions are a cornerstone of complexity theory, providing a bridge between worst-case hardness and average-case computational difficulty. While recent works have demonstrated such reductions for fundamental problems using deep tools from ad- ditive combinatorics, these approaches often suffer from substantial complexity and subo

Edwin Hamel-De le Court, Gaspard Ohlmann, Francesco Belardinelli

Safety is a major concern in reinforcement learning (RL): we aim at developing RL systems that not only perform optimally, but are also safe to deploy by providing formal guarantees about their safety. To this end, we introduce Probabilistic Shielding via Risk Augmentation (ProSh), a model-free algorithm for safe reinforcement learning under cost constraints