November 2025 arXiv papers — page 48

Raimondo Pala

This paper proposes an identification inspired from the SVAR-IV literature that uses external instruments to identify PVARs, and discusses associated issues of identification, estimation, and inference. I introduce a form of local average treatment effect - the $\mu$-LATE - which arises when a continuous instrument targets a binary treatment. Under standard

Bibhuti Kumar Jha, Lisa A. Upton, Greg Kopp, Odele Coddington

The historical reconstruction of the Sun's surface magnetic field remains a persistent challenge, limiting our ability to investigate the long-term global properties of the Sun, including the evolution of the large-scale magnetic field, solar cycle prediction, reconstruction of total solar irradiance (TSI), and secular solar variability. In this study, we em

Lucas A. Bonomo, Julio A. Cordioli, Edward J. Brambley, Angelo Paduano

We investigate the influence of realistic sheared grazing flow on acoustic propagation in three-dimensional rectangular ducts. We show that conclusions reached in the literature about the effects of sheared grazing flow on acoustic propagation in lined ducts are dependent on the flow profiles used in those studies, and that significantly different conclusion

Mohammed Almekhlafi, Antoine Lesage-Landry, Gunes Karabulut Kurt

Arctic regions, such as northern Canada, face significant challenges in achieving consistent connectivity and low-latency computing services due to the sparse coverage of Low Earth Orbit (LEO) satellites. To enhance service reliability in remote areas, this paper proposes a hybrid satellite architecture for task offloading that combines Medium Earth Orbit (M

Suraj Prasad, Anubha Pant

Vision-language models like CLIP have demonstrated remarkable zero-shot capabilities, yet their adaptation to federated learning scenarios presents significant challenges, particularly regarding generalization to unseen classes. The original FedTPG paper \cite{Qiu2024} addresses this limitation by introducing a text driven prompt generation network that dyna

Tianyang Duan, Zongyuan Zhang, Zheng Lin, Songxiao Guo

Multi-agent reinforcement learning (MARL) has been increasingly adopted in many real-world applications. While MARL enables decentralized deployment on resource-constrained edge devices, it suffers from severe non-stationarity due to the synchronous updates of agent policies. This non stationarity results in unstable training and poor policy con vergence, es

Saniah Kayenat Chowdhury, Rusab Sarmun, Muhammad E. H. Chowdhury, Sohaib Bassam Zoghoul

Accurate tumor staging in lung cancer is crucial for prognosis and treatment planning and is governed by explicit anatomical criteria under fixed guidelines. However, most existing deep learning approaches treat this spatially structured clinical decision as an uninterpretable image classification problem. Tumor stage depends on predetermined quantitative cr

Hunyuan Vision Team, Pengyuan Lyu, Xingyu Wan, Gengluo Li

This paper presents HunyuanOCR, a commercial-grade, open-source, and lightweight (1B parameters) Vision-Language Model (VLM) dedicated to OCR tasks. The architecture comprises a Native Vision Transformer (ViT) and a lightweight LLM connected via an MLP adapter. HunyuanOCR demonstrates superior performance, outperforming commercial APIs, traditional pipelines

Alan Jia Bao Du, Tarek S. Abdelrahman

We present HeLEx, a framework for determining the functional layout of heterogeneous spatially-configured elastic Coarse-Grained Reconfigurable Arrays (CGRAs). Given a collection of input data flow graphs (DFGs) and a target CGRA, the framework starts with a full layout in which every processing element (PE) supports every operation in the DFGs. It then empl

Zehong Ma, Longhui Wei, Shuai Wang, Shiliang Zhang

Pixel diffusion aims to generate images directly in pixel space in an end-to-end fashion. This approach avoids the limitations of VAE in the two-stage latent diffusion, offering higher model capacity. Existing pixel diffusion models suffer from slow training and inference, as they usually model both high-frequency signals and low-frequency semantics within a

Carlos Ruiz-Gonzalez, Sören Arlt, Sebastian Lehner, Arturs Berzins

Physics simulators are essential in science and engineering, enabling the analysis, control, and design of complex systems. In experimental sciences, they are increasingly used to automate experimental design, often via combinatorial search and optimization. However, as the setups grow more complex, the computational cost of traditional, CPU-based simulators

J. Rössler, G. Wolschin

The nonlinear boson diffusion equation is taken as a basis to account for the fast thermalization of gluons in the initial stages of relativistic heavy-ion collisions. For constant drift and diffusion coefficients with schematic initial conditions, this equation has previously been solved exactly. In order to achieve a more realistic time evolution towards t

Clara Lilje, Rob Fender, James H. Matthews

Jets from stellar-mass and supermassive black holes provide the unique opportunity to study similar processes in two very different mass regimes. Historically, the apparent speeds of black hole x-ray binary (BHXRBs) jets have been observed to be lower than jet speeds from active galactic nuclei (AGN) and specifically blazars. In this work, we show that selec

Allan Berele

In [A. Berele, Computing super matrix invariants, {\it Advances in Applied Math. \bf48} (2012), 273--289.] we defined integrals that approximated the Poincar\'e series of the invariants and concomitants of the general linear Lie supergroup or superalgebra. Budzik suggested in [K. Budzik, Supergroup Invariants and the Brane/Negative Brane Expansion, (preprint

Weijie Xiong, Jingran Lin, Kai Zhong, Liu Yang

Movable antennas (MAs) represent a novel approach that enables flexible adjustments to antenna positions, effectively altering the channel environment and thereby enhancing the performance of wireless communication systems. However, conventional MA implementations often adopt fully digital beamforming (FDB), which requires a dedicated RF chain for each anten

Paul Dütting, Tomer Ezra, Michal Feldman, Thomas Kesselheim

Multi-agent contract design has largely evaluated contracts through the lens of pure Nash equilibria (PNE). This focus, however, is not without loss: In general, the principal can strictly gain by recommending a complex, possibly correlated, distribution over actions, while preserving incentive compatibility. In this work, we extend the analysis of multi-age

Elefterios Soultanis

We use Almgren's framework of multi-valued maps to construct a multi-valued inverse $F:f(\Omega)\to \mathcal A_d(\mathbb R^n)$ of a quasiregular map $f:\Omega\to \mathbb R^n$ of finite degree $d$. We then develop a pull-back theory of differential forms on $\mathcal A_d(\mathbb R^n)$ by Sobolev maps, and use it to show that the multi-valued inverse is a quas

Franklin Cardenoso, Wouter Caarls

The challenge of designing effective reward functions in reinforcement learning (RL) represents a significant bottleneck, often requiring extensive human expertise and being time-consuming. Previous work and recent advancements in large language models (LLMs) have demonstrated their potential for automating the generation of reward functions. However, existi

Antonio L. Maroto, Prado Martín-Moruno, Miguel Orbaneja-Pérez

We analyze the impact of breaking diffeomorphism invariance in the inflaton sector. In particular, we consider inflaton models which are invariant under the subgroup of transverse diffeomorphisms and address the possibility of implementing a slow-roll phase. We obtain the corresponding expressions for relevant quantities such as the slow-roll parameters and

Leon J. Goertz

For a commutative Frobenius algebra $A$, we construct a $(2,3,3+\varepsilon)$-dimensional TQFT $\mathsf{AFK}_A$ that assigns to a 3-manifold a skein module of embedded $A$-decorated surfaces. These surface skein modules have been first defined by Asaeda--Frohman and Kaiser using skein relations that generalize the combinatorics of Bar-Natan's dotted cobordis

Abdurahman Ali Mohammed, Catherine Fonder, Ying Wei, Wallapak Tavanapong

Accurate cell counting is essential in various biomedical research and clinical applications, including cancer diagnosis, stem cell research, and immunology. Manual counting is labor-intensive and error-prone, motivating automation through deep learning techniques. However, training reliable deep learning models requires large amounts of high-quality annotat

Nikita Neveditsin, Pawan Lingras, Vijay Mago

Clustering short text embeddings is a foundational task in natural language processing, yet remains challenging due to the need to specify the number of clusters in advance. We introduce a scalable spectral method that estimates the number of clusters directly from the structure of the Laplacian eigenspectrum, constructed using cosine similarities and guided

Ruizhe Zhang, Jooyoung Kong, Dylan S. Small, William Bekerman

Adverse childhood experiences (ACEs) are categories of childhood abuse, neglect, and household dysfunction. Screening by a single additive ACE score (e.g., a $\ge 4$ cutoff) has poor individual-level discrimination. We instead identify replicable combinations of ACEs that elevate adult depression risk. Our data turnover framework enables a single research te

Jialiang Li, Weitong Chen, Mingyu Guo

Neural models have shown promise in solving NP-hard graph combinatorial optimization (CO) problems. Once trained, they offer fast inference and reasonably high-quality solutions for in-distribution testing instances, but they generally fall short in terms of absolute solution quality compared to classical search-based algorithms that are admittedly slower bu

Nour Jedidi, Jimmy Lin

Recent approaches that leverage large language models (LLMs) for pseudo-relevance feedback (PRF) have generally not utilized well-established feedback models like Rocchio and RM3 when expanding queries for sparse retrievers like BM25. Instead, they often opt for a simple string concatenation of the query and LLM-generated expansion content. But is this optim

Donglin Wu, Yinuo Han, Peredur M. Williams, Takashi Onaka

Binaries that host a carbon-rich Wolf-Rayet (WC) star and an OB-type companion can be copious dust producers. Yet the properties of dust, particularly the grain size distribution, in these systems remain uncertain. We present Band 6 observations of WR 112 by the Atacama Large Millimeter/submillimeter Array telescope (ALMA), which are the first millimeter obs

Komal Komal, Frances Cleary, Ram Prasadh Narayanan, John Wells

The wearable EEG device sector is advancing rapidly, enabling fast and reliable detection of brain activity for investigating brain function and pathology. However, many current EEG systems remain challenging for users with neurological conditions due to bulky wiring, lengthy skin preparation, gel-induced discomfort, risk of irritation, and high cost, all of

Hongyi Wang, Xiuli Zheng, Weimin Liu, Zitian Tang

The discovery of high-performance photosensitizers has long been hindered by the time-consuming and resource-intensive nature of traditional trial-and-error approaches. Here, we present \textbf{A}I-\textbf{A}ccelerated \textbf{P}hoto\textbf{S}ensitizer \textbf{I}nnovation (AAPSI), a closed-loop workflow that integrates expert knowledge, scaffold-based molecu

Pablo Lechon-Alonso, Andrew Dennehy, Ruizheng Bai, Nicolas Sanchez

Evolutionary game theory studies populations that change in response to an underlying game. Often, the functional form relating outcome to player attributes or strategy is complex, preventing mathematical progress. In this work, we axiomatically derive a latent space representation for pairwise, symmetric, zero-sum games by seeking a coordinate space in whic

Hari Chandana Kuchibhotla, K S Ananth, Vineeth N Balasubramanian

Despite significant progress in continual learning ranging from architectural novelty to clever strategies for mitigating catastrophic forgetting most existing methods rest on a strong but unrealistic assumption the availability of labeled data throughout the learning process. In real-world scenarios, however, data often arrives sequentially and without anno

Qihan Huang, Haofei Zhang, Rong Wei, Yi Wang

RL (reinforcement learning) methods (e.g., GRPO) for MLLM (Multimodal LLM) perception ability has attracted wide research interest owing to its remarkable generalization ability. Nevertheless, existing reinforcement learning methods still face the problem of low data quality, where data samples cannot elicit diverse responses from MLLMs, thus restricting the

Jerrold Franklin

The notion of an infinite electromagnetic self energy of point charges (presumably electrons) is accepted by many electromagnetic textbooks. See, for instance,\cite{jdj,dg,rf}. However, each of these sources acknowledge that they don't understand that result. In this paper, we show that electrons must be point particles with no electromagnetic self energy.

Maral Ebrahimzadeh, Gilberto Bernardes, Sebastian Stober

State-of-the-art symbolic music generation models have recently achieved remarkable output quality, yet explicit control over compositional features, such as tonal tension, remains challenging. We propose a novel approach that integrates a computational tonal tension model, based on tonal interval vector analysis, into a Transformer framework. Our method emp

Thomas Hughes, Michael J. Wilensky, Philip Bull

Statistically-significant differences in the value of the Hubble parameter are found depending on the measurement method that is used, a result known as the Hubble tension. A variety of ways of comparing, grouping, and excluding measurements have been used to try to explain this, either in terms of physical effects or systematic errors. We present a systemat

Anjie Le, Can Peng, Yuyuan Liu, J. Alison Noble

In computer vision, machine unlearning aims to remove the influence of specific visual concepts or training images without retraining from scratch. Studies show that existing approaches often modify the classifier while leaving internal representations intact, resulting in incomplete forgetting. In this work, we extend the notion of unlearning to the represe

Michele Giovanni Bianchi, Michele Re Fiorentin, Francesca Risplendi, Candido Fabrizio Pirri

Simulating electrified metal/water interfaces with explicit solvent under constant potential is essential for understanding electrochemical processes, yet remains prohibitively expensive with ab initio methods. We present TRECI, a data-efficient workflow for constructing machine learning force-fields (ML-FFs) that achieve ab initio-level accuracy in electron

Daxx W. Delucchi

We analyze linear Einstein--Maxwell perturbations of the superextremal Reissner--Nordstr\"om geometry in its static Kerr--Schild rest frame, viewing it as the nonlinear self-field of a single static point charge. In optical radial coordinates, and using the Kodama--Ishibashi gauge-invariant formalism, each radiative multipole is encoded by a single scalar ma

M. A. Arroyo-Ureña, E. A. Herrera-Chacón, Iran Melendez-Hernández, S. Rosado-Navarro

We explore the discovery potential for lepton flavor-violating Higgs boson decays, $h \to \ell_i \ell_j$ ($\ell_i \neq \ell_j$; $\ell = e, \mu, \tau$), in proton-proton collisions. The analysis is performed within a Froggatt-Nielsen framework, which offers a well-motivated theoretical mechanism for such processes. By performing a systematic scan over the phe

Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano

In this paper, we propose a suboptimal and reduced-order Model Predictive Control (MPC) architecture for discrete-time feedback-interconnected systems. The numerical MPC solver: (i) acts suboptimally, performing only a finite number of optimization iterations at each sampling instant, and (ii) relies only on a reduced-order model that neglects part of the sy

Changjun Cheng, Daniel Persaud, Kangming Li, Michael J. Moorehead

High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast compositional spaces. Computational approaches, including Density Functional Theory (DFT) and calculation of phase diagrams (CALPHAD), facilitate screening of phase formability as a functi

Sumit Agarwal, H. Luke Shaefer, Samiul Jubaed, William Schneider

Infants are most vulnerable to child maltreatment, which may be due in part to economic instability during the perinatal period. In 2024, Rx Kids was launched in Flint, Michigan, achieving near 100% aggregate take up and providing every expectant mother with unconditional cash transfers during pregnancy and infancy. Synthetic difference-in-differences was us

Axel Constant, Mahault Albarracin, Karl J. Friston

This paper presents a computational account of how legal norms can influence the behavior of artificial intelligence (AI) agents, grounded in the active inference framework (AIF) that is informed by principles of economic legal analysis (ELA). The ensuing model aims to capture the complexity of human decision-making under legal constraints, offering a candid

Wentao Ye, Jiaqi Hu, Haobo Wang, Xinpeng Ti

Language model inversion (LMI), i.e., recovering hidden prompts from outputs, emerges as a concrete threat to user privacy and system security. We recast LMI as reusing the LLM's own latent space and propose the Invariant Latent Space Hypothesis (ILSH): (1) diverse outputs from the same source prompt should preserve consistent semantics (source invariance),

Shaltiel Shmidman, Asher Fredman, Oleg Sudakov, Meriem Bendris

Test-time scaling, which leverages additional computation during inference to improve model accuracy, has enabled a new class of Large Language Models (LLMs) that are able to reason through complex problems by understanding the goal, turning this goal into a plan, working through intermediate steps, and checking their own work before answering . Frontier lar

Arianna Favale, Adrià Gómez-Valent, Marina Migliaccio

Model-independent approaches have gained increasing attention as powerful tools to investigate persistent tensions between cosmological observations and the predictions of $\Lambda$CDM. Notably, recent DESY5 Type Ia Supernovae (SNIa) and DESI Baryon Acoustic Oscillation (BAO) data challenge the validity of the cosmological constant, and they remain in tensio

Yao Chen, Jeff Yan

The science of science is an emerging field that studies the practice of science itself. We present the first study of the cybersecurity discipline from a science of science perspective. We examine the evolution of two comparable interdisciplinary communities in cybersecurity: the Symposium on Usable Privacy and Security (SOUPS) and Financial Cryptography an

Dominik Luszczynski

A common method of attacking deep learning models is through adversarial attacks, which occur when an attacker specifically modifies the input of a model to produce an incorrect result. Adversarial attacks have been deeply investigated in the image domain; however, there is less research in the time-series domain and very little for forecasting financial dat

Christian Velten, Adam Bayliss, Jiayi Huang, Wolfgang A. Tomé

Temporally modulated pulsed radiotherapy (TMPRT) delivers conventional fraction doses of radiation using temporally separated pulses of low doses (<30 cGy) yielding fraction-effective dose rates of around 6.7 cGy/min with the goal to exploit tumor radiation hypersensitivity, which was observed in both, preclinical models and in human clinical trials. To faci

Rohan Saha, Farzane Aminmansour, Alona Fyshe

Language modeling has shown us that transformers can discover latent structure from context, but the dynamics of how they acquire different components of that structure remain poorly understood, leading to assertions that models just remix training data. In this work, we use the Alchemy benchmark in a controlled setting (Wang et al.,2021) to investigate late

Liwei Yuan, Hideaki Ishii

This paper examines resilient dynamic leader-follower consensus within multi-agent systems, where agents share first-order or second-order dynamics. The aim is to develop distributed protocols enabling nonfaulty/normal followers to accurately track a dynamic/time-varying reference value of the leader while they may receive misinformation from adversarial nei

Olivia Macmillan-Scott, Roksana Goworek, Eda B. Özyiğit

Query expansion is the reformulation of a user query by adding semantically related information, and is an essential component of monolingual and cross-lingual information retrieval used to ensure that relevant documents are not missed. Recently, multilingual large language models (mLLMs) have shifted query expansion from semantic augmentation with synonyms

Roksana Goworek, Olivia Macmillan-Scott, Eda B. Özyiğit

Cross-lingual information retrieval (CLIR) enables access to multilingual knowledge but remains challenging due to disparities in resources, scripts, and weak cross-lingual semantic alignment in embedding models. Existing pipelines often rely on translation and monolingual retrieval heuristics, which add computational overhead and noise, degrading performanc

Bryce Dixon, Calvin M. Hooper, Ian R. Hooper, Simon A. R. Horsley

Motivated by recent experiments, the theoretical study of wave propagation in time varying materials is of current interest. Although significant in nearly all such experiments, material dispersion is commonly neglected in theoretical studies. Yet, as we show here, understanding the precise microscopic model for the material dispersion is crucial for predict

Weijie Xiong, Zhenglan Zhao, Jingran Lin, Zhiling Xiao

In this paper, we employ a hardware-efficient hybrid beamforming (HB) architecture to achieve balanced performance in an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system. We consider a scenario where a multi-antenna, dual-function base station (BS) performs secure beamforming for a multi-antenna legitimate rece

Lingwei Dang, Zonghan Li, Juntong Li, Hongwen Zhang

Hand-Object Interaction (HOI) generation plays a critical role in advancing applications across animation and robotics. Current video-based methods are predominantly single-view, which impedes comprehensive 3D geometry perception and often results in geometric distortions or unrealistic motion patterns. While 3D HOI approaches can generate dynamically plausi

Aleksandra Lelek

The Transverse Momentum Dependent (TMD) Parton Branching (PB) method incorporates elements of TMD physics into a Monte Carlo (MC) framework to produce high-energy QCD predictions for collider processes. It derives TMDs from the PB evolution equation - solvable with MC techniques - fits them to data, and then enables their use in MC event generators for QCD p

Md. Tanzim Ferdous, Naeem Ahsan Chowdhury, Prithwiraj Bhattacharjee

This study developed a new Bangla abstractive summarization dataset to generate concise summaries of Bangla articles from diverse sources. Most existing studies in this field have concentrated on news articles, where journalists usually follow a fixed writing style. While such approaches are effective in limited contexts, they often fail to adapt to the vari

Weiliang Tang, Jialin Gao, Jia-Hui Pan, Gang Wang

Vision-Language Model (VLM) is an important component to enable robust robot manipulation. Yet, using it to translate human instructions into an action-resolvable intermediate representation often needs a tradeoff between VLM-comprehensibility and generalizability. Inspired by context-free grammar, we design the Semantic Assembly representation named SEAM, b

Bisi Bernard Ogunwale, Yossi Zaguri, Volker Perdelwitz, Marcel V"olschow

Stellar variability and transient events provide critical insights into astrophysics, accelerated by missions like CoRoT, Kepler, and K2. NASA's Transiting Exoplanet Survey Satellite (TESS) adds a unique combination of long baseline and all-sky coverage, though extracting light curves from full-frame images (FFIs) is challenging due to scattered light and bl

Christopher Baker, Stephen Hinton, Akashdeep Nijjar, Riccardo Poli

Human-AI teams can be vulnerable to catastrophic failure when feedback from the AI is incorrect, especially under high cognitive workload. Traditional team aggregation methods, such as voting, are susceptible to these AI errors, which can actively bias the behaviour of each individual and inflate the likelihood of an erroneous group decision. We hypothesised

Shao-Hang Shi, Xiao-Qi Sun, Zi-Xiang Li

Characterizing topological phases for strongly interacting fermions in the mixed-state regime remains a major challenge. Here we introduce a general and numerically efficient framework to diagnose mixed-state topological phases in strongly interacting systems via the disorder parameter (DP) of the U(1) charge operator. Specifically, from the finite-size scal

Mohammadhadi Mesmarian, Mohammad Mahdi Kharidar, Hossein Nejat Pishkenari

Flow measurement in partially filled pipes presents greater complexity compared to fully filled systems, primarily due to the complex velocity distribution within the cross-section, which is a key source of measurement inaccuracy. To address this challenge, an ultrasonic flow meter was designed and developed, capable of simultaneously measuring both flow vel

Sunder Ram Krishnan, Junaid Farooq, Kumar Vijay Mishra, Xingchen Liu

While stochastic geometry provides a powerful framework for the analysis of cellular networks, standard Monte Carlo simulations often suffer from slow convergence due to the stochasticity of the infinite far-field. This work introduces the \textit{Rao-Blackwellized Hybrid Estimator} (RBHE), which enhances simulation efficiency by analytically marginalizing t

Lucia De Luca, Antonia Diana, Marcello Ponsiglione

We prove the existence and the 1/2-H\"older continuity in time of flat flows for periodic Lipschitz subgraphs, whose evolution is governed by the gradient flow of generalized nonlocal perimeters. Moreover, we show that the flat flow satisfies the semigroup property and, as a consequence, the generalized perimeter decreases along the evolution. Finally, we pr

Markus Ebke, Torben Krüger

The empirical spectral distribution of Hermitian $K \times K$-block random matrices converges to a deterministic density on the real line with a potential atom at the origin as the dimension of the blocks tends to infinity. In this model the variances of the entries depends on the block and the limiting density is determined by these variances. In the absenc

Michail Fasoulakis, Leonidas Bakopoulos, Charilaos Akasiadis, Georgios Chalkiadakis

One common assumption in game theory is that any player optimizes a utility function that takes into account only its own payoff. However, it has long been observed that in real life players may adopt an altruistic or even spiteful behaviour. As such, there are numerous attempts in the economics literature that strive to explain the fact that players are not

Zixuan Wang, Haoran Sun, Jiaming Lu, Wenxuan Wang

Infrared small target detection remains challenging due to limited feature representation and severe background interference, resulting in sub-optimal performance. While recent CLIP-inspired methods attempt to leverage textual guidance for detection, they are hindered by inaccurate text descriptions and reliance on manual annotations. To overcome these limit

Sara Geremia, Domenico De Stefano, Michael Fop

Uncovering structural patterns in collaboration networks is key for understanding how knowledge flows and innovation emerges. These networks often exhibit a rich interplay of meso-scale structures, such as communities, core-periphery organization, and influential hubs, which shape the complexity of scientific collaboration. The coexistence of such structures

Jiayi Zhang, Yiran Peng, Fanqi Kong, Cheng Yang

Humans naturally adapt to diverse environments by learning underlying rules across worlds with different dynamics, observations, and reward structures. In contrast, existing agents typically demonstrate improvements via self-evolving within a single domain, implicitly assuming a fixed environment distribution. Cross-environment learning has remained largely

Wing Hong Leung, Matthew P. Young

We provide a power-saving bound for certain smoothed shifted convolution sums for Fourier coefficients of Siegel cusp forms. This result is the first nontrivial estimate for a shifted convolution sum with two cusp forms on a group of higher rank than $\GL_2$. Our approach is based on a novel automorphic reinterpretation of the delta method of Duke, Friedland

Johannes Meier, Florian Günther, Riccardo Marin, Oussema Dhaouadi

Monocular 3D detection relies on just a single camera and is therefore easy to deploy. Yet, achieving reliable 3D understanding from monocular images requires substantial annotation, and 3D labels are especially costly. To maximize performance under constrained labeling budgets, it is essential to prioritize annotating samples expected to deliver the largest

Pouria Bazyarrezaei, Mohammad Abdollahi Azgomi

Identifying influential nodes in complex networks is a fundamental challenge for understanding how information, influence, and contagion propagate through interconnected systems. Conventional centrality measures, particularly gravity-based models, often depend on pairwise interaction forces and a fixed radius of influence, which oversimplify the heterogeneou

James R. M. Black, Moritz S. Hanke, Aaron Maiwald, Tina Hernandez-Boussard

Novel deep learning architectures are increasingly being applied to biological data, including genetic sequences. These models, referred to as genomic language models (gLMs), have demonstrated impressive predictive and generative capabilities, raising concerns that such models may also enable misuse, for instance via the generation of genomes for human-infec

Ryan B. Sills, Alejandro Hinojos, Trevor J. Murray, Shane H. Cooley

Coherent crystalline interfaces form when a pair of joined crystals share lattice sites. Such interfaces are ubiquitous in materials, minerals, and compounds, with examples including grain boundaries in polycrystals and phase boundaries in multi-phase systems. Existing methodologies such as the topological model provide a framework for understanding the natu

Yakov Berchenko-Kogan, Lily DiPaulo

We develop finite element spaces of symmetric tensor products of two-forms with polynomial coefficients. In three dimensions, these give higher order finite element spaces of matrix fields with normal-normal continuity, which have applications to the TDNNS method for elasticity, for example. In general dimension, these spaces can be used to represent the Rie

Fedor Bakharev, Sergey Matveenko

The spectral properties of the restricted fractional Laplacian with Dirichlet boundary conditions in a smoothly bent waveguide is investigated. The existence of eigenvalues below the threshold of the continuous spectrum is proved, generalizing classical results known for the local Laplace operator. Our approach utilizes the Caffarelli--Silvestre extension, a

Phurtivilai Patt, Leyang Huang, Yinqiang Zhang, Yang Lei

This paper addresses the limitations of existing 3D Gaussian Splatting (3DGS) methods, particularly their reliance on adaptive density control, which can lead to floating artifacts and inefficient resource usage. We propose a novel densify beforehand approach that enhances the initialization of 3D scenes by combining sparse LiDAR data with monocular depth es

Huan Jia, Yinhuo Zhang

Let $k$ be a field. In this paper, we introduce the notions of $\textit{reduction order}$ and $\textit{reduction-factorization}$ on words, and use them to show that any right or left Noetherian pointed Hopf algebra over $k$ is affine. This result offers a partial affirmative answer to the classical affineness question for Noetherian Hopf algebras posed by Wu

Ilnar Zinnatullin, Alexander Vasiliev

Quantum hashing is a widely used technique in quantum computation that allows us to design space-efficient algorithms and protocols. Recently, Vasiliev has shown that the phase form of shallow quantum hashing can be implemented by a circuit of depth 2. In this paper, we establish a connection between shallow quantum hashing and single-qubit quantum hashing f

Oliver Knitter, Jonathan Mei, Masako Yamada, Martin Roetteler

TorchQuantumDistributed (tqd) is a PyTorch-based [Paszke et al., 2019] library for accelerator-agnostic differentiable quantum state vector simulation at scale. This enables studying the behavior of learnable parameterized near-term and fault- tolerant quantum circuits with high qubit counts.

Eduardo J. Aguilar, Valmir C. Barbosa, Raul Donangelo, Welles A. M. Morgado

We study the tiling of a two-dimensional region of the plane by $K$-cell one-dimensional tiles, or $K$-mers. Unlike previous studies, which typically allowed for one single value of $K$ or sometimes a small assortment of fixed values, here a tiling may concomitantly employ $K$-mers comprising any number $K$ of cells, provided a maximality constraint is satis

Mamoon Aamir, Mariyam Sattar, Naveed Ur Rehman Junejo, Aqsa Zafar Abbasi

This paper presents the geometry design and analysis of a novel triple scissors links deployable antenna mechanism (TSDAM) to deal with the problems of large aperture and high precision space antennas for deep space communication and Earth observation. This mechanism has only one degree of freedom (DoF) and thus makes for efficient and reliable deployment wi

Ali Fatemiabhari, Horatiu Nastase, Dibakar Roychowdhury

We propose and calculate a holographic Krylov complexity in ${\cal N}=4$ SYM via the proper momentum for motion in $AdS_5$ sliced by $AdS_3$. The motion in an $AdS_3$ subgroup corresponds to the Krylov complexity of the $Sl(2)$ subsector. The general motion corresponds to the Krylov complexity of the ${\cal N}=4$ SYM.

Mohammad Javaheri

We prove that Anderson's conjecture on symmetric sequencings and Bailey's conjecture on 2-sequencings hold for sufficiently large groups. In addition, we discuss extensions of partial harmonious sequences and partial R-sequencings. Several further results on double sequencings are presented, both in the context of abelian groups and for sufficiently large no

Eichi Uehara

This document proposes a Unified Robust Framework that re-engineers the estimation of the Average Treatment Effect on the Overlap (ATO). It synthesizes gamma-Divergence for outlier robustness, Graduated Non-Convexity (GNC) for global optimization, and a "Gatekeeper" mechanism to address the impossibility of higher-order orthogonality in Gaussian regimes.

Ndaka. A, Avila-Acosta. F, Mbula-Ndaka. H, Amera. C

This chapter seeks to frame the elemental and invisible problems of AI and big data in the African context by examining digital sites and infrastructure through the lens of power and interests. It will present reflections on how these sites are using AI recommendation algorithms to recreate new digital societies in the region, how they have the potential to

Ruoxi Li

We study the notion of $1$-twisted semi-homogeneous vector bundles on $\mathbb{G}_m$-gerbes over abelian varieties, and classify point objects in the twisted derived categories of abelian varieties. As an application, we classify the twisted Fourier-Mukai partners of abelian varieties.

M. V. Milovanović

The fractional quantum Hall effect (FQHE) at filling 5/2, which is usually understood as a $p$-wave paired state of underlying quasiparticles - composite fermions, transforms into a nematic phase under pressure \cite{csathy0, csathy}. A pair density wave (PDW) may be a precursor, underlying state for this behaviour, and such state(s) were proposed that maint

Ajaz Mir, Rauoof Wani, Sanat Tiwari, Abhijit Sen

We investigate the Two-Stream Instability in a strongly coupled plasma using classical molecular dynamics simulations with long-range Coulomb interactions between particles. The nonlinear evolution of the instability is identified by the emergence of a Bernstein-Greene-Kruskal (BGK) mode. Our simulations capture key microscopic effects, such as inter-particl

Jinyuan Wu, Zachary H. Withers, Thomas K. Allison, Diana Y. Qiu

Recent advances in time- and angle-resolved photoemission spectroscopy (tr-ARPES) allow for the probing of multiparticle excited-states in reciprocal space. While neutral two-particle excitations (excitons) have been observed in tr-ARPES, signatures of trions -- three-quasiparticle bound states -- have only been probed via optical spectroscopy. Here, we deve

Victor Rambaud, Salvador Mascarenhas, Yair Lakretz

A cognitive map is an internal model which encodes the abstract relationships among entities in the world, giving humans and animals the flexibility to adapt to new situations, with a strong out-of-distribution (OOD) generalization that current AI systems still do not possess. To bridge this gap, we introduce $\textit{MapFormers}$, new Transformer-based arch

Qianying Liu, Xiao Liang, Zhiqiang Zhang, Zhongfei Qing

We present ReMatch, a framework that leverages the generative strength of MLLMs for multimodal retrieval. Previous approaches treated an MLLM as a simple encoder, ignoring its generative nature, and under-utilising its compositional reasoning and world knowledge. We instead train the embedding MLLM end-to-end with a chat-style generative matching stage. The

G. L. Klimchitskaya, V. M. Mostepanenko

We derive constraints on the parameters of the Yukawa-type interaction and on the coupling constant of axionlike particles to nucleons from the results of recent diffraction experiment on measuring the Casimir-Polder interaction between Ar atoms and a silicon nitride nanograting. It is shown that within the interaction range from 1 to 2 nm the obtained const

Ellie L. Zhang, Duoduo Liao, Callie C. Liao

Generation of dynamic, scalable multi-species bird soundscapes remains a significant challenge in computer music and algorithmic sound design. Birdsongs involve rapid frequency-modulated chirps, complex amplitude envelopes, distinctive acoustic patterns, overlapping calls, and dynamic inter-bird interactions, all of which require precise temporal and spatial

Mingyang Chen, Jiawei Du, Bo Huang, Yi Wang

Existing core-set selection methods predominantly rely on heuristic scoring signals such as training dynamics or model uncertainty, lacking explicit modeling of data likelihood. This omission may hinder the constructed subset from capturing subtle yet critical distributional structures that underpin effective model training. In this work, we propose a novel,

Kunal Dumbre, Lei Jiao, Ole-Christoffer Granmo

The PC algorithm is a widely used method in causal inference for learning the structure of Bayesian networks. Despite its popularity, the PC algorithm suffers from significant time complexity, particularly as the size of the dataset increases, which limits its applicability in large-scale real-world problems. In this study, we propose a novel approach that u

Felix Birkel

We present Tiny-TSM, a time series foundation model characterized by small scale, economical training, and state-of-the-art performance. It comprises 23M total parameters, trained on a single A100 GPU in less than a week using a new synthetic data generation and data augmentation pipeline (SynthTS). Without any neural architecture search, hyperparameter tuni

Junwei Yu

We study the existence and multiplicity of positive normalized solutions with prescribed $L^{2}$-norm for the Sobolev critical Schr\"odinger equation $-\Delta U + V(x) U = \lambda U + |U|^{2^*-2} U$ in $\mathbb{R}^N$, $\int_{\mathbb{R}^N} U^2\,dx = \rho^2$, where $N \ge 3$, $V\ge 0$ is a trapping potential, $\lambda \in \mathbb{R}$ and $2^*=\frac{2N}{N-2}$.

Narinder Kumar Wadhawan

Purpose of writing this paper is to solve a transcendental function containing a product of a variable and its double exponential by a unique method of approximation. If the value of the said product is given, then its inverse function is approximated by use of linear expression in place of natural logarithm of a positive real quantity and, that transforms t

Minseo Kim, Chenfeng Xu, Coleman Hooper, Harman Singh

Diffusion Language Models (DLMs) offer a promising parallel generation paradigm but suffer from slow inference due to numerous refinement steps and the inability to use standard KV caching. We introduce CDLM (Consistency Diffusion Language Models), a training-based acceleration method that simultaneously tackles both bottlenecks. CDLM integrates consistency