October 2025 arXiv papers — page 120

Fikresilase Wondmeneh Abebayew

Large Language Models (LLMs) are increasingly deployed as autonomous agents, yet their practical utility is fundamentally constrained by a limited context window and state desynchronization resulting from the LLMs' stateless nature and inefficient context management. These limitations lead to unreliable output, unpredictable behavior, and inefficient resourc

Rikiya Takehi, Benjamin Clavié, Sean Lee, Aamir Shakir

In this work, we introduce mxbai-edge-colbert-v0 models, at two different parameter counts: 17M and 32M. As part of our research, we conduct numerous experiments to improve retrieval and late-interaction models, which we intend to distill into smaller models as proof-of-concepts. Our ultimate aim is to support retrieval at all scales, from large-scale retrie

V. Alfradique, R. da Mata, J. C. Rodríguez-Ramírez, C. R. Bom

GRB 230307A is one of the brightest long-duration gamma-ray bursts (GRBs) ever detected, yet its progenitor remains uncertain due to the variety of plausible astrophysical scenarios. In this work, we investigate four possible progenitors for GRB 230307A: a binary neutron star (BNS), a neutron star--white dwarf (NS--WD) system, a neutron star--black hole (NS-

Dhruva Karkada, Joseph Turnbull, Yuxi Liu, James B. Simon

We study kernel regression with common rotation-invariant kernels on real datasets including CIFAR-5m, SVHN, and ImageNet. We give a theoretical framework that predicts learning curves (test risk vs. sample size) from only two measurements: the empirical data covariance matrix and an empirical polynomial decomposition of the target function $f_*$. The key ne

Nirmal D. Wickramasinghe, John Dooley, Dirk Pesch, Indrakshi Dey

Massive number of simultaneous Internet of Things (IoT) uplinks strain gateways with interference and energy limits, yet devices often lack neighbors' Channel State Information (CSI) and cannot sustain centralized Mobile Edge Computing (MEC) or heavy Machine Learning (ML) coordination. Classical Bayesian solvers help with uncertainty but become intractable a

Dominic Agius, Rouven Essig, Daniele Gaggero, Sergio Palomares-Ruiz

The 21-cm signal is a powerful probe of the early Universe's thermal history and could provide a unique avenue for constraining exotic physics. Previous studies have forecasted stringent constraints on energy injections from exotic sources that heat, excite, and ionize the background gas and thereby modify the 21-cm signal. In this work, we quantify the subs

Roni Goldshmidt, Hamish Scott, Lorenzo Niccolini, Shizhan Zhu

Existing collision prediction methods often fail to distinguish between ego-vehicle threats and random accidents not involving the ego vehicle, leading to excessive false alerts in real-world deployment. We present BADAS, a family of collision prediction models trained on Nexar's real-world dashcam collision dataset -- the first benchmark designed explicitly

Jing-Ze Ma, Stephen Justham, Ruediger Pakmor, Andrea Chiavassa

Recent observations have revealed a surprisingly large fraction of hydrogen-rich supernovae (SNe) interacting with dense confined circumstellar material (CSM), whose origin is heavily debated. Exploiting our recent implementation of a sophisticated radiation transport scheme in the moving-mesh code AREPO, we perform full-sphere 3D radiation hydrodynamic simu

Guangyi Han, Wei Zhai, Yuhang Yang, Yang Cao

Hand-object interaction (HOI) is fundamental for humans to express intent. Existing HOI generation research is predominantly confined to fixed grasping patterns, where control is tied to physical priors such as force closure or generic intent instructions, even when expressed through elaborate language. Such an overly general conditioning imposes a strong in

Rui Luo, Peng Hu, Haikun Qi

Density Compensation Function (DCF) is widely used in non-Cartesian MRI reconstruction, either for direct Non-Uniform Fast Fourier Transform (NUFFT) reconstruction or for iterative undersampled reconstruction. Current state-of-the-art methods involve time-consuming tens of iterations, which is one of the main hurdles for widespread application of the highly

Din Amir, Bar Hoter, Moran Koren

This study examines strategic behavior in crowdfunding using a large-scale online experiment. Building on the model of Arieli et. al 2023, we test predictions about risk aversion (i.e., opting out despite seeing a positive private signal) and mutual insurance (i.e., opting in despite seeing a negative private signal) in a static, single-shot crowdfunding gam

Erwei Wang, Samuel Bayliss, Andra Bisca, Zachary Blair

General-purpose compilers abstract away parallelism, locality, and synchronization, limiting their effectiveness on modern spatial architectures. As modern computing architectures increasingly rely on fine-grained control over data movement, execution order, and compute placement for performance, compiler infrastructure must provide explicit mechanisms for o

Jeremy P Parker, David Goluskin

Two approaches are presented for computing upper bounds on Lyapunov exponents and their sums, and on the Lyapunov dimension, among all trajectories of a dynamical system governed by ordinary differential equations. The first approach expresses a sum of Lyapunov exponents as a time average in an augmented dynamical system and then applies methods for bounding

Guorong Gao, Jianfeng Hou, Shuping Huang, Hezhi Wang

The classical Zarankiewicz problem, which concerns the maximum number of edges in a bipartite graph without a forbidden complete bipartite subgraph, motivates a direct analogue for hypergraphs. Let $K_{s_1,\ldots, s_r}$ be the complete $r$-partite $r$-graph such that the $i$-th part has $s_i$ vertices. We say an $r$-partite $r$-graph $H=H(V_1,\ldots,V_r)$ co

Shubham Ghadigaonkar, Sachchidanand Das, Abhiram Soori

Altermagnets (AMs) - magnetic materials that have spin-split bandstructure with zero net spin polarization can be classified as weak or strong depending upon the strength of altermagnetic term in the Hamiltonian. We theoretically investigate electron transport in junctions between the two AMs in strong and weak altermagnetic phases. The charge and spin condu

Ivan Iorsh, Mikhail Titov

We introduce a minimal interface-scattering mechanism that produces a sizable anisotropic magnetoresistance (AMR) in metal/ferromagnet bilayers (e.g., Pt/YIG) without invoking bulk spin or orbital Hall currents. In a $\delta$-layer model with interfacial exchange and Rashba spin-orbit coupling, charge transfer at a high-quality interface creates a spin-selec

Hatef Otroshi Shahreza, Sébastien Marcel

Multimodal large language models (MLLMs) have achieved remarkable performance across diverse vision-and-language tasks. However, their potential in face recognition remains underexplored. In particular, the performance of open-source MLLMs needs to be evaluated and compared with existing face recognition models on standard benchmarks with similar protocol. I

Emmy Liu, Graham Neubig, Chenyan Xiong

Midtraining, the practice of mixing specialized data with more general pretraining data in an intermediate training phase, has become widespread in language model development, yet there is little understanding of what makes it effective. We propose that midtraining functions as distributional bridging by providing better initialization for posttraining. We c

Aobo Lyu, Andrew Clark, Netanel Raviv

Partial Information Decomposition (PID) was proposed by Williams and Beer in 2010 as a tool for analyzing fine-grained interactions between multiple random variables, and has since found numerous applications ranging from neuroscience to privacy. However, a unified theoretical framework remains elusive due to key conceptual and technical challenges. We ident

Le Cong, David Smerkous, Xiaotong Wang, Di Yin

Modern science advances fastest when thought meets action. LabOS represents the first AI co-scientist that unites computational reasoning with physical experimentation through multimodal perception, self-evolving agents, and Extended-Reality(XR)-enabled human-AI collaboration. By connecting multi-model AI agents, smart glasses, and robots, LabOS allows AI to

Daniel Tan

Given any vertex operator algebra $ V $ with an automorphism $ g $, we derive a Jacobi identity for an intertwining operator $ \mathcal{Y} $ of type $ \left( \begin{smallmatrix} W_3\\ W_1 \, W_2 \end{smallmatrix}\right) $ when $ W_1 $ is an untwisted $ V $-module, and $ W_2 $ and $ W_3 $ are $ g $-twisted $ V $-modules. We say such an intertwining operator i

Michael E. Brown, Samantha K. Trumbo, M. Ryleigh Davis, Swaroop Chandra

The deuterium to hydrogen ratio in water ice in a planetary body carries important information on the history of water processing and delivery in the protostellar nebula. For a giant planet satellite, the D/H ratio is also affected by the processes and temperatures of the circumplanetary or circumstellar environment in which the satellites formed. Here we pr

Yifeng Qin, Jing Chen, Zhi Hao Jiang, Zhining Chen

Near-field blockage changes the beam-design objective in millimeter-wave links: maximizing the unblocked on-axis gain does not necessarily maximize blocked-link performance. This paper studies when phase-only, aperture-constrained non-diffracting (ND) beams provide a blocked-link advantage over equal-aperture, equal-power conventional reference beams. We dev

Gabriele Barlacchi, Margherita Lalli, Emanuele Ferragina, Fosca Giannotti

Recommender systems continuously interact with users, creating feedback loops that shape both individual behavior and collective market dynamics. This paper introduces a simulation framework to model these loops in online retail environments, where recommenders are periodically retrained on evolving user-item interactions. Using the Amazon e-Commerce dataset

Ayman Zahr, Emna Ben Yacoub, Balázs Matuz, Gianluigi Liva

Rate-adaptive MacKay-Neal (MN) codes based on protographs are analyzed. The code construction employs an outer distribution matcher (DM) to adapt the rate of the scheme. The DM is coupled with an inner protograph-based low-density parity-check (LDPC) code. The performance achievable by the resulting code structure, that is nonlinear, is studied by means of a

Harsha Kotla, Arun Kumar Rajasekaran, Hannah Rana

Early detection of melanoma has grown to be essential because it significantly improves survival rates, but automated analysis of skin lesions still remains challenging. ABCDE, which stands for Asymmetry, Border irregularity, Color variation, Diameter, and Evolving, is a well-known classification method for skin lesions, but most deep learning mechanisms tre

Guinan Su, Yanwu Yang, Li Shen, Lu Yin

Mixture-of-Experts (MoE) models achieve efficient scaling through sparse expert activation, but often suffer from suboptimal routing decisions due to distribution shifts in deployment. While existing test-time adaptation methods could potentially address these issues, they primarily focus on dense models and require access to external data, limiting their pr

Hrishikesh Danawe, Serife Tol

Electro-momentum coupling in piezoelectric metamaterials with broken inversion symmetry enables asymmetric elastic wave transport by linking macroscopic electric fields to momentum, an effect analogous to Willis coupling in elastic media. A one-dimensional layered piezoelectric metamaterial integrated with shunt circuits, consisting of a resistor, inductor,

Jakob Bichler, Andreu Matoses Gimenez, Javier Alonso-Mora

We present Sadcher, a real-time task assignment framework for heterogeneous multi-robot teams that incorporates dynamic coalition formation and task precedence constraints. Sadcher is trained through Imitation Learning and combines graph attention and transformers to predict assignment rewards between robots and tasks. Based on the predicted rewards, a relax

Anna Neuweiler, Henrique Gieg, Henrik Rose, Hauke Koehn

The rich phenomenology of binary neutron star mergers offers a unique opportunity to test general relativity, investigate matter at supranuclear densities, and learn more about the origin of heavy elements. As multi-messenger sources, they emit both gravitational waves and electromagnetic radiation across several frequency bands. The interpretation of these

Marcello Sorge, Nicola Cigarini, Riccardo Lorigiola, Giulia Michieletto

Source seeking is an important topic in robotic research, especially considering sound-based sensors since they allow the agents to locate a target even in critical conditions where it is not possible to establish a direct line of sight. In this work, we design a multi- agent switching mode control strategy for acoustic-based target localization. Two scenari

Gavin Kerrigan, Christian A. Naesseth, Tom Rainforth

We introduce a novel geometric framework for optimal experimental design (OED). Traditional OED approaches, such as those based on mutual information, rely explicitly on probability densities, leading to restrictive invariance properties. To address these limitations, we propose the mutual transport dependence (MTD), a measure of statistical dependence groun

Meiqi Wu, Jiashu Zhu, Xiaokun Feng, Chubin Chen

Video generation models have achieved remarkable progress, particularly excelling in realistic scenarios; however, their performance degrades notably in imaginative scenarios. These prompts often involve rarely co-occurring concepts with long-distance semantic relationships, falling outside training distributions. Existing methods typically apply test-time s

Zhuo-Yang Song

The generate-filter-refine (iterative paradigm) based on large language models (LLMs) has achieved progress in reasoning, programming, and program discovery in AI+Science. However, the effectiveness of search depends on where to search, namely, how to encode the domain prior into an operationally structured hypothesis space. To this end, this paper proposes

Amel Abdelraheem, Alessandro Favero, Gerome Bovet, Pascal Frossard

Foundation models have revolutionized computer vision by enabling broad generalization across diverse tasks. Yet, they remain highly susceptible to adversarial perturbations and targeted backdoor attacks. Mitigating such vulnerabilities remains an open challenge, especially given that the large-scale nature of the models prohibits retraining to ensure safety

Odelia Melamed, Gilad Yehudai, Gal Vardi

Machine Unlearning aims to remove specific data from trained models, addressing growing privacy and ethical concerns. We provide a theoretical analysis of a simple and widely used method - gradient ascent - used to reverse the influence of a specific data point without retraining from scratch. Leveraging the implicit bias of gradient descent towards solution

Ayman Zahr, Gianluigi Liva

We analyze by density evolution the asymptotic performance of rate-adaptive MacKay-Neal (MN) code ensembles, where the inner code is a protograph spatially coupled (SC) low-density parity-check code. By resorting to a suitably-defined parallel channel model, we compute belief propagation decoding thresholds, showing that SC MN code ensembles can perform with

Ben Elder, Evelyn Duesterwald, Vinod Muthusamy

A typical approach developers follow to influence an LLM's behavior in an application is through careful manipulation of the prompt, such as by adding or modifying instructions. However, merely adding more instructions provides little assurance that they will actually be followed. We introduce Instruction Boosting as a post-generation method to increase the

Edgar Alcalá-Arroyo, Alonso Castillo-Ramirez

We study the most elementary family of cellular automata defined over an arbitrary group universe $G$ and an alphabet $A$: the lazy cellular automata, which act as the identity on configurations in $A^G$, except when they read a unique active transition $p \in A^S$, in which case they write a fixed symbol $a \in A$. As expected, the dynamical behavior of laz

Arpan Chandra Mazumder, Giorgos Kapetanakis, Dhiren Kumar Basnet

In this article, we study the existence and distribution of elements in finite field extensions with prescribed traces in several intermediate extensions that are also either normal or primitive normal. In the former case, we fully characterize the conditions under which such elements exist and provide an explicit enumeration of these elements. In the latter

Zhongnan Dong, Bin Ma, Haoran Zhang, Jinji Li

Infrared time-domain surveys remain significantly underdeveloped compared with their optical counterparts. We have developed the Antarctic Infrared Binocular Telescope (AIRBT) to study the dynamic infrared sky at Dome A, Antarctica, taking advantage of the superb infrared observational conditions at this site. AIRBT consists of two identical 15 cm f/3 optica

Ziqing Zhu

To address growing cybersecurity challenges in modern power dispatch systems, this paper proposes a multi-layer modeling and optimization framework for SCADA systems enhanced with quantum key distribution (QKD). While most existing applications of QKD in the power sector focus on building secure point-to-point communication tunnels, they rarely consider the

Jan Corazza, Ivan Gavran, Daniel Neider

Reward machines are an established tool for dealing with reinforcement learning problems in which rewards are sparse and depend on complex sequences of actions. However, existing algorithms for learning reward machines assume an overly idealized setting where rewards have to be free of noise. To overcome this practical limitation, we introduce a novel type o

Larry Doolittle, Shreeharshini Murthy, Matei Guran, Lennon Reyes

In 1978, Delayen showed how Self-Excited Loops (SEL) can be used to great advantage for controlling narrow-band SRF cavities. Its key capability is establishing closed-loop amplitude control early in the setup process, stabilizing Lorentz forces to allow cavity tuning and phase loop setup in a stable environment. As people around the world implement this bas

Jack Kendrick

The clustering and visualisation of high-dimensional data is a ubiquitous task in modern data science. Popular techniques include nonlinear dimensionality reduction methods like t-SNE or UMAP. These methods face the `scale-problem' of clustering: when dealing with the MNIST dataset, do we want to distinguish different digits or do we want to distinguish diff

Daniel Russell, Dakota Hamilton, Mads R. Almassalkhi, Hamid R. Ossareh

Integration of distributed energy resources has created a need for autonomous, dynamic voltage regulation. Decentralized Volt-VAr Control (VVC) of grid-connected inverters presents a unique opportunity for voltage management but, if designed poorly, can lead to unstable behavior when in feedback with the grid. We model the grid-VVC closed-loop dynamics with

Susmita Sarkar, Nayan Sarkar, Abdelmalek Bouzenada, Farook Rahaman

In this article, we investigate asymptotically flat non-exotic traversable wormhole geometries within the King and Dekel-Zhao dark matter halos in the framework of $f(R, L_m)$ gravity. Two functional forms of the theory are considered: Model-I: $f(R, L_m)=(R/2) + L_m^{\alpha}$ and Model-II: $f(R, L_m)=(R/2) + (1 + \lambda R)L_m$. For both models, wormhole so

Maria Lamprini A. Bartsioka, Anastasios Giannopoulos, Sotirios Spantideas

The emerging paradigm of 6G multiple Radio Access Technology (multi-RAT) networks, where cellular and Wireless Fidelity (WiFi) transmitters coexist, requires mobility decisions that remain reliable under fast channel dynamics, interference, and heterogeneous coverage. Handover in multi-RAT deployments is still highly reactive and event-triggered, relying on

Qi Chen, Xinze Zhou, Chen Liu, Hao Chen

AI for tumor segmentation is limited by the lack of large, voxel-wise annotated datasets, which are hard to create and require medical experts. In our proprietary JHH dataset of 3,000 annotated pancreatic tumor scans, we found that AI performance stopped improving after 1,500 scans. With synthetic data, we reached the same performance using only 500 real sca

Kun Lei, Huanyu Li, Dongjie Yu, Zhenyu Wei

Real-world robotic manipulation in homes and factories demands reliability, efficiency, and robustness that approach or surpass those of skilled human operators. We present RL-100, a real-world reinforcement learning framework built on diffusion visuomotor policies. RL-100 unifies imitation and reinforcement learning under a single clipped PPO surrogate obje

Fernando Hidalgo-Pineda, Max Gronke, Philipp Grete

Galactic outflows are a key agent of galaxy evolution, yet their observed multiphase nature remains difficult to reconcile with theoretical models, which often fail to explain how cold gas survives interactions with hot, fast winds. We present high-resolution 3D hydrodynamic simulations of hot outflows interacting with a multiphase interstellar medium (ISM),

Yufei Zhu, Shih-Min Yang, Andrey Rudenko, Tomasz P. Kucner

Safe and efficient robot operation in complex human environments can benefit from good models of site-specific motion patterns. Maps of Dynamics (MoDs) provide such models by encoding statistical motion patterns in a map, but existing representations use discrete spatial sampling and typically require costly offline construction. We propose a continuous spat

Eran Malach, Omid Saremi, Sinead Williamson, Arwen Bradley

State Space Models (SSMs) have become the leading alternative to Transformers for sequence modeling. Their primary advantage is efficiency in long-context and long-form generation, enabled by fixed-size memory and linear scaling of computational complexity. We begin this work by showing a simple theoretical result stating that SSMs cannot accurately solve an

Gabriel Poesia, Georgia Gabriela Sampaio

Classical models for supervised machine learning, such as decision trees, are efficient and interpretable predictors, but their quality is highly dependent on the particular choice of input features. Although neural networks can learn useful representations directly from raw data (e.g., images or text), this comes at the expense of interpretability and the n

Ziqi Dai, Xin Zhang, Mingxin Li, Yanzhao Zhang

In information retrieval, training reranking models mainly focuses on two types of objectives: metric learning (e.g. contrastive loss to increase the predicted scores on relevant query-document pairs) and classification (binary label prediction of relevance vs. irrelevance). For BERT-style encoders, various studies have shown that contrastive learning (CL) c

Luca Morreale, Alberto Gil C. P. Ramos, Malcolm Chadwick, Mehid Noroozi

State-of-the-art (SOTA) generative models have demonstrated impressive capabilities in image synthesis or text generation, often with a large capacity model. However, these large models cannot be deployed on smartphones due to the limited availability of on-board memory and computations. Quantization methods lower the precision of the model parameters, allow

Amaze Lusompa

Model selection criteria are one of the most important tools in statistics. Proofs showing a model selection criterion is asymptotically optimal are tailored to the type of model (linear regression, quantile regression, penalized regression, etc.), the estimation method (linear smoothers, maximum likelihood, generalized method of moments, etc.), the type of

Peter Holy, Jonathan Schilhan

We provide, for any regular uncountable cardinal $\kappa$, a new argument for Pincus' result on the consistency of $\mathrm{ZF}$ with the higher dependent choice principle $\mathrm{DC}_{<\kappa}$ and the ordering principle in the presence of a failure of the axiom of choice. We also generalise his methods and obtain these consistency results in a larger clas

Emily M. Silich, Jack Sayers, Philip F. Hopkins, Charles Romero

We explore the possibility that inverse-Compton (IC) scattering of cosmic microwave background photons by $\sim$GeV cosmic rays (CRs) injected by the central active galactic nucleus (AGN) in cool core (CC) clusters produces a non-negligible continuum-like X-ray signal that is easily misinterpreted as intracluster medium (ICM) thermal bremsstrahlung continuum

Christine Sowa Lepird, Lynnette Hui Xian Ng, Kathleen M. Carley

Pink slime journalism is a practice where news outlets publish low-quality or inflammatory partisan articles, claiming to be local news networks. This paper examines the spread of pink slime sites on Facebook using public posts from Pages and Groups. We evaluate the trends of sharing pink slime sites on Facebook and patterns regarding the advertisements purc

Christopher Lamb, Robert M. Konik, Hubert Saleur, Ananda Roy

Topological symmetries, invertible and otherwise, play a fundamental role in the investigation of quantum field theories. Despite their ubiquitous importance across a multitude of disciplines ranging from string theory to condensed matter physics, controlled realizations of models exhibiting these symmetries in physical systems are rare. Quantum simulators b

Hayden Henson, Ronald B. Morgan

Polynomial preconditioning is an important tool in solving large linear systems and eigenvalue problems. A polynomial from GMRES can be used to precondition restarted GMRES and restarted Arnoldi. Here we give methods for indefinite matrices that make polynomial preconditioning more generally applicable. The new techniques include balancing the polynomial so

Oliver Gough

We study finite-time blow-up for the one-dimensional nonlinear wave equation with a quadratic time-derivative nonlinearity, \[ u_{tt}-u_{xx}=(u_t)^2,\qquad (x,t)\in\mathbb R\times[0,T). \] Building on the work of Ghoul, Liu, and Masmoudi \cite{ghoul2025blow} on the spatial-derivative analogue, we establish the non-existence of smooth, exact self-similar blow

Zhiyuan Zhao, Haoxin Liu, B. Aditya Prakash

Time-series forecasting finds broad applications in real-world scenarios. Due to the dynamic nature of time series data, it is important for time-series forecasting models to handle potential distribution shifts over time. In this paper, we initially identify two types of distribution shifts in time series: concept drift and temporal shift. We acknowledge th

Qifei Wei, Shengshi Pang

Quantum metrology leverages quantum resources such as entanglement and squeezing to enhance parameter estimation precision beyond classical limits. While optimal quantum control strategies can assist to reach or even surpass the Heisenberg limit, their practical implementation often requires the knowledge of the parameters to be estimated, necessitating adap

Shikuang Deng, Jiayuan Zhang, Yuhang Wu, Ting Chen

Hebbian learning is a biological principle that intuitively describes how neurons adapt their connections through repeated stimuli. However, when applied to machine learning, it suffers serious issues due to the unconstrained updates of the connections and the lack of accounting for feedback mediation. Such shortcomings limit its effective scaling to complex

S. H. J. Wallström, P. Scicluna, S. Srinivasan, J. G. A. Wouterloot

Low- to intermediate-mass ($\sim$0.8$-$8 M$_\odot$) evolved stars contribute significantly to the chemical enrichment of the interstellar medium in the local Universe, making accurate mass-return estimates in their final stages crucial. The Nearby Evolved Stars Survey (NESS) is a large multi-telescope project targeting a volume-limited sample of $\sim$850 st

Dominik Jehle, Lennart Purucker, Frank Hutter

Translating natural language queries into SQL queries (NL2SQL or Text-to-SQL) has recently been empowered by large language models (LLMs). Using LLMs to perform NL2SQL methods on a large collection of SQL databases necessitates processing large quantities of meta-information about the databases, which in turn results in lengthy prompts with many tokens and h

Bowen Li, Junting Chen, Nikolaos Pappas

Complete awareness of the wireless environment, crucial for future intelligent networks, requires sensing all transmitted signals, not just the strongest. A fundamental barrier is estimating the target signal when it is buried under strong co-channel interference from other transmitters, a failure of which renders the signal unusable. This work proposes a ma

Mengtao Lv, Ruiqi Zhu, Xinyu Wang, Yun Li

Large language models (LLMs) have demonstrated remarkable capabilities in various kinds of tasks, while the billion or even trillion parameters bring storage and efficiency bottlenecks for inference. Quantization, particularly floating-point quantization, is known to be capable of speeding up LLM inference by reducing memory footprint and data movement durin

Jun Gao

We prove that for any $k \ge 3$, every $k$-uniform hypergraph on $n$ vertices contains at most $n - \omega(1)$ different sizes of cliques (maximal complete subgraphs). In particular, the 3-uniform case answers a question of Erd\H{o}s.

Pedro R. A. S. Bassi, Xinze Zhou, Wenxuan Li, Szymon Płotka

Early tumor detection save lives. Each year, more than 300 million computed tomography (CT) scans are performed worldwide, offering a vast opportunity for effective cancer screening. However, detecting small or early-stage tumors on these CT scans remains challenging, even for experts. Artificial intelligence (AI) models can assist by highlighting suspicious

Sanjaya Herath, Armin Gerami, Kevin Wagner, Ramani Duraiswami

The Minimum Variance Distortionless Response (MVDR) beamforming technique is widely applied in array systems to mitigate interference. However, applying MVDR to large arrays is computationally challenging; its computational complexity scales cubically with the number of antenna elements. In this paper, we introduce a scalable MVDR beamforming method tailored

Seb T. Millward, Vedad Kunovac

Eclipsing brown dwarfs are important calibrators of sub-stellar evolution models used to infer the characteristics of directly imaged brown dwarfs and giant exoplanets. Only two double brown dwarf eclipsing binary systems are known, among them 2MASS J15104786-2818174 (2M1510 AB), published in 2020 with a poorly constrained orbital period. Here we analyse TES

Usama Sajjad, Abdul Rehman Akbar, Ziyu Su, Deborah Knight

Colorectal cancer (CRC) remains the third most prevalent malignancy globally, with approximately 154,000 new cases and 54,000 projected deaths anticipated for 2025. The recent advancement of foundation models in computational pathology has been largely propelled by task agnostic methodologies that can overlook organ-specific crucial morphological patterns th

Nikita Deniskin, Federico Poloni

We study the accuracy of a class of methods to compute the Inverse Laplace Transform, the so-called \emph{Abate--Whitt methods} [Abate, Whitt 2006], which are based on a linear combination of evaluations of $\widehat{f}$ in a few points. We provide error bounds which relate the accuracy of a method to the rational approximation of the exponential function. W

Petra Berenbrink, Tom Friedetzky, Peter Kling, Lars Nagel

We consider an infinite balls-into-bins process with deletions where in each discrete step $t$ a coin is tossed as to whether, with probability $\beta(t) \in (0,1)$, a new ball is allocated using the Greedy[2] strategy (which places the ball in the lower loaded of two bins sampled uniformly at random) or, with remaining probability $1-\beta(t)$, a ball is de

Teng Man, Yimin Lu, Zhongrong Wang, Herbert Huppert

The grain size distribution (GSD) plays an important role in the mechanical properties of amorphous disordered systems and complex granular materials. Varying GSD causes segregation issues and alters critical behaviors. This work used the discrete element method (DEM) to investigate the rheological and critical behaviors of sheared granular flows with variou

Sam P. Fisher

We introduce the $\Sigma^*$-invariant of a group of finite type, which is defined to be the subset of non-zero characters $\chi \in \mathrm H^1(G;\mathbb R)$ with vanishing associated top-dimensional Novikov cohomology. We prove an analogue of Sikorav's Theorem for this invariant, namely that $\mathrm{cd}(\ker \chi) = \mathrm{cd}(G) - 1$ if and only if $\pm

Halvin Yang, Yizhe Zhao, Kai-Kit Wong, Hsiao-Hwa Chen

Fluid antennas, including those based on liquid, mechanical, and pixel-based technologies, are poised to significantly enhance next-generation wireless systems by adaptively optimizing their radiation characteristics. Many theoretical analyses assumed near-instant reconfiguration, perfect channel knowledge, static or slowly varying propagation environments,

Apekshya Ghimire, Chandralekha Singh

We focus on reflections and suggestions of five college quantum educators from four different institutions (two from same institution) regarding what can be done to diversify the second quantum revolution. They are leading QIST researchers, and very passionate about improving quantum education. The educators were asked about their thoughts on whether the int

Mingjie Jian, Xiaoting Fu, Valentina D'Orazi, Angela Bragaglia

We present phosphorus abundance measurements for a total of 102 giant stars, including 82 stars in 24 open clusters and 20 Cepheids, based on high-resolution near-infrared spectra obtained with GIANO-B. Evolution of phosphorus abundance, despite its astrophysical and biological significance, remains poorly understood due to a scarcity of observational data.

Luis González-Gudiño, Mariona Jaramillo-Civill, Pau Closas, Tales Imbiriba

We propose an active jammer localization framework that combines Bayesian optimization with acquisition-aware path planning. Unlike passive crowdsourced methods, our approach adaptively guides a mobile agent to collect high-utility Received Signal Strength measurements while accounting for urban obstacles and mobility constraints. For this, we modified the A

Omar Mustafa, Abdullah Guvendi

We present an exact analytical study of Klein-Gordon (KG) scalar bosons and antibosons confined in the Bonnor-Melvin (BM) spacetime under a uniform magnetic field, incorporating rainbow gravity (RG) corrections with a positive cosmological constant. The cosmological constant partitions spacetime into an infinite sequence of confinement domains bounded by imp

Manjie Xu, Cheng Chen, Xin Jia, Jingyi Zhou

User interests on content platforms are inherently diverse, manifesting through complex behavioral patterns across heterogeneous scenarios such as search, feed browsing, and content discovery. Traditional recommendation systems typically prioritize business metric optimization within isolated specific scenarios, neglecting cross-scenario behavioral signals a

Lorenzo Zino, Alessandro Casu, Alessandro Rizzo

We propose an epidemic model for the spread of vector-borne diseases. The model, which is built extending the classical susceptible-infected-susceptible model, accounts for two populations -- humans and vectors -- and for cross-contagion between the two species, whereby humans become infected upon interaction with carrier vectors, and vectors become carriers

Jiří Černý, Ramon Locher

We continue the study of the level-set percolation of the discrete Gaussian free field (GFF) on regular trees in the critical regime, initiated in arXiv:2302.02753. First, we derive a sharp asymptotic estimate for the probability that the connected component of the critical level set containing the root of the tree reaches generation $n$. In particular, we s

M. El Maghri, Y. Elboulqe

In a recent work, we presented the reduced Jacobian method (RJM) as an extension of Wolfe's reduced gradient method to multicriteria (multiobjective) optimization problems dealing with linear constraints. This approach reveals that using a reduction technique of the Jacobian matrix of the objective avoids scalarization. In the present work, we intend to gene

Zeng Zhang, Wenjie Yin, Xiaoqi Li

Identification of anomalous events within system logs constitutes a pivotal element within the frame- work of cybersecurity defense strategies. However, this process faces numerous challenges, including the management of substantial data volumes, the distribution of anomalies, and the precision of con- ventional methods. To address this issue, the present pa

Yu. B. Kudasov

In crystalline systems with a superstructure, the electron dispersion can form a nontrivial covering of the Brillouin zone. It is proved that the number of sheets in this covering and its monodromy are topological invariants under ambient isotopy. As a concrete manifestation of this nontrivial topology, we analyze three-sublattice models for 120$^\circ$-orde

Aderik Verraest, Stavrow Bahnam, Robin Ferede, Guido de Croon

Autonomous drone racing (ADR) systems have recently achieved champion-level performance, yet remain highly specific to drone racing. While end-to-end vision-based methods promise broader applicability, no system to date simultaneously achieves full sim-to-real transfer, onboard execution, and champion-level performance. In this work, we present SkyDreamer, t

Wasyhun A. Gemechu, Guohao Fu, Mario Zitelli

We experimentally and numerically investigate Raman-driven power depletion in the fundamental mode of few-mode fibers (FMFs) excited by visible ultrashort pulses. Using a tunable femtosecond laser and SMF-28 fibers operated below the single-mode cutoff wavelength, we explore nonlinear mode dynamics through precise coupling, holographic mode decomposition, an

Ming Cai, Penggang Gao, Hisayuki Hara

This paper addresses the problem of estimating causal directed acyclic graphs in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM). Existing methods assume mutually independent latent confounders or cannot properly handle models with causal relationships among observed variables. We propose a novel algorithm that identifies causal DAGs in

A. Lola Danhaive, Sandro Tacchella, Andrew J. Bunker, Emma Curtis-Lake

JWST/NIRCam slitless spectroscopy enables dynamical mass measurements for typical star-forming galaxies only a billion years after the Big Bang. We model the H$\alpha$ morpho-kinematics of 163 galaxies at redshift $z\approx4$-6 from FRESCO and CONGRESS (with JADES imaging), using the $\texttt{geko}$ code, and infer rotational velocities and dispersions withi

Maor Reuben, Ido Mendel, Or Feldman, Moshe Kravchik

Supply chain attacks significantly threaten software security with malicious code injections within legitimate projects. Such attacks are very rare but may have a devastating impact. Detecting spurious code injections using automated tools is further complicated as it often requires deciphering the intention of both the inserted code and its context. In this

Sebastian Haslebacher, Jonas Lill

We present a simple new algorithm for finding a Tarski fixed point of a monotone function $F : [N]^3 \rightarrow [N]^3$. Our algorithm runs in $O(\log^2 N)$ time and makes $O(\log^2 N)$ queries to $F$, matching the $\Omega(\log^2 N)$ query lower bound due to Etessami et al. as well as the existing state-of-the-art algorithm due to Fearnley et al.

Zihan Xi, Chen Wu, Wenjun Guo

This paper explores the metric of Piero Nicolini's noncommutative black hole spacetime, calculates its effective potential, and presents the corresponding potential curve. By analyzing this curve, we identify various orbit types for test particles and photons in this spacetime. Using the dynamical equations for particles and photons near the black hole, we p

Viktor Y. D. Sumida, Raissa Estrela, Adriana Valio

M dwarfs have long been prime targets in the search for habitable exoplanets, owing to their abundance in the galaxy and the relative ease of detecting Earth-sized worlds within their narrower habitable zones. Yet, these low-mass stars can emit high-energy radiation that may gradually erode planetary atmospheres, raising concerns about long-term habitability

Sam Taziaux, Megan C. Johnson, Onic I. Shuvo, Dominik J. Bomans

Dwarf galaxies, due to their shallow gravitational potentials, provide critical environments for studying feedback mechanisms from star formation and its impacts on dwarf galaxy evolution. In particular, radio continuum (RC) observations offer valuable insights into cosmic ray dynamics, which play a significant role in shaping these processes. This study inv

Sudip Malick, Michał J. Winiarski, Joanna Bławat, Hanna Świątek

We report a systematic investigation of the physical properties and Fermi-surface topology of layered single-crystal \ce{SrCu4As2} using electrical transport, magnetotransport, and quantum-oscillation experiments plus band-structure calculations. The temperature-dependent electrical resistivity reveals a hysteretic phase transition at $T_P$ = 59 K, most like