March 2025 arXiv papers — page 219

Katya M. Papais, Daniil Lisus, Cedric Le Gentil, David J. Yoon

Most autonomous vehicles rely on accurate and efficient localization, which is achieved by comparing live sensor data to a preexisting map, to navigate their environment. Balancing the accuracy of localization with computational efficiency remains a significant challenge, as high-accuracy methods often come with higher computational costs. In this paper, we

Qianwei Wang, Yifan Xu, Vineet Kamat, Carol Menassa

Object search is a fundamental task for robots deployed in indoor building environments, yet challenges arise due to observation instability, especially for open-vocabulary models. While foundation models (LLMs/VLMs) enable reasoning about object locations even without direct visibility, the ability to recover from failures and replan remains crucial. The Mu

Kenneth G. Carpenter, Tabetha Boyajian, Derek Buzasi, Jim Clark

This report presents the findings of a NIAC Phase I feasibility study for the Artemis-enabled Stellar Imager (AeSI), a proposed high-resolution, UV/Optical interferometer designed for deployment on the lunar surface. Its primary science goal is to image the surfaces and interiors of stars with unprecedented detail, revealing new details about their magnetic

Xiangrui Liu, Yuanyuan Zhang, Qianyu Shang, Yingzhou Lu

Foundation models, first introduced in 2021, refer to large-scale pretrained models (e.g., large language models (LLMs) and vision-language models (VLMs)) that learn from extensive unlabeled datasets through unsupervised methods, enabling them to excel in diverse downstream tasks. These models, like GPT, can be adapted to various applications such as questio

Xiangchi Yuan, Chunhui Zhang, Zheyuan Liu, Dachuan Shi

As scaled language models (LMs) approach human-level reasoning capabilities, self-improvement emerges as a solution to synthesizing high-quality data corpus. While previous research has identified model collapse as a risk in self-improvement, where model outputs become increasingly deterministic, we discover a more fundamental challenge: the superficial self

Xiangyu Chang, Yingcong Li, Muti Kara, Samet Oymak

The in-context learning capabilities of modern language models have motivated a deeper mathematical understanding of sequence models. A line of recent work has shown that linear attention models can emulate projected gradient descent iterations to implicitly learn the task vector from the data provided in the context window. In this work, we consider a novel

Boyong He, Yuxiang Ji, Qianwen Ye, Zhuoyue Tan

Domain generalization (DG) for object detection aims to enhance detectors' performance in unseen scenarios. This task remains challenging due to complex variations in real-world applications. Recently, diffusion models have demonstrated remarkable capabilities in diverse scene generation, which inspires us to explore their potential for improving DG tasks. I

Rajko Nenadov

The Cayley sum graph $\Gamma_S$ of a set $S \subseteq \mathbb{Z}_n$ is defined on the vertex set $\mathbb{Z}_n$, with an edge between distinct $x, y \in \mathbb{Z}_n$ if $x + y \in S$. Campos, Dahia, and Marciano have recently shown that if $S$ is formed by taking each element in $\mathbb{Z}_n$ independently with probability $p$, for $p > (\log n)^{-1/80}$,

Eduardo Davalos, Yike Zhang, Namrata Srivastava, Jorge Alberto Salas

Reading assessments are essential for enhancing students' comprehension, yet many EdTech applications focus mainly on outcome-based metrics, providing limited insights into student behavior and cognition. This study investigates the use of multimodal data sources -- including eye-tracking data, learning outcomes, assessment content, and teaching standards --

Masanori Iye, Takashi Ito

In 1962, Yoshihide Kozai reported his findings on the secular dynamics of asteroids moving in orbits with high inclination and eccentricity. In contrast to the classic understanding of the stability of planetary motion in the solar system, Kozai showed that asteroids can significantly change their orbital shape over a long timescale in an oscillatory manner

Naveen Srinivasan, Nathan Naveen, Neil Naveen

Inaccuracies in conventional dependency-tracking methods frequently undermine the security and integrity of modern software supply chains. This paper introduces a kernel-level framework leveraging extended Berkeley Packet Filter (eBPF) to capture software build dependencies transparently in real time. Our approach provides tamper-evident, intrinsic identifie

Hadleigh Frost, Martijn Hidding, Deepak Kamlesh, Carlos Rodriguez

Multiple polylogarithms are equipped with rich algebraic structures including the motivic coaction and the single-valued map which both found fruitful applications in high-energy physics. In recent work arXiv:2312.00697, the current authors presented a conjectural reformulation of the motivic coaction and the single-valued map via zeta generators, certain op

Hesam Bakhtiary Yekta, Andrew N. Norris

Total acoustic transmission between air and water was shown in our recent paper to be attainable with a solid interface comprising two parallel thin elastic plates connected by rigid ribs, although the transmissivity is a narrow-band effect. We demonstrate here that broadband transmission can be obtained by introducing a third, central plate. A theoretical a

Ahmed Khalil, Yoonjae Lee, Efstathios Bakolas

This work is concerned with the finite-horizon optimal covariance steering of networked systems governed by discrete-time stochastic linear dynamics. In contrast with existing work that has only considered systems with dynamically decoupled agents, we consider a dynamically coupled system composed of interconnected subsystems subject to local communication c

Emam Hossain, Muhammad Hasan Ferdous, Jianwu Wang, Aneesh Subramanian

Traditional machine learning and deep learning techniques rely on correlation-based learning, often failing to distinguish spurious associations from true causal relationships, which limits robustness, interpretability, and generalizability. To address these challenges, we propose a causality-driven deep learning framework that integrates Multivariate Grange

Ayush Gaggar, Todd D. Murphey

Current methods based on Neural Radiance Fields fail in the low data limit, particularly when training on incomplete scene data. Prior works augment training data only in next-best-view applications, which lead to hallucinations and model collapse with sparse data. In contrast, we propose adding a set of views during training by rejection sampling from a pos

Chia-Yi Su, Aakash Bansal, Vijayanta Jain, Sepideh Ghanavati

A "privacy behavior" in software is an action where the software uses personal information for a service or a feature, such as a website using location to provide content relevant to a user. Programmers are required by regulations or application stores to provide privacy notices and labels describing these privacy behaviors. Although many tools and research

Cameron Heather, Teeraparb Chantavat, Siri Chongchitnan, Poemwai Chainakun

Given recent X-ray observations of high-redshift active galactic nuclei (AGNs), we consider whether the extreme luminosities of these AGNs are consistent with current semi-analytical models. In particular, we apply extreme-value statistics (EVS) to obtain predictions of extreme X-ray luminosities of AGNs in the redshift range $3\lesssim z\lesssim 6$. We appl

Jacob Morrison, Clara Na, Jared Fernandez, Tim Dettmers

As the performance of artificial intelligence systems has dramatically increased, so too has the environmental impact of creating these systems. While many model developers release estimates of the power consumption and carbon emissions from the final training runs for their latest models, there is comparatively little transparency into the impact of model d

Jugal Garg, Parnian Shahkar

We study fair division of indivisible goods under the maximin share (MMS) fairness criterion in settings where agents are grouped into a small number of types, with agents within each type having identical valuations. For the special case of a single type, an exact MMS allocation is always guaranteed to exist. However, for two or more distinct agent types, e

Pooja Kulkarni, Ruta Mehta, Parnian Shahkar

We investigate the problem of fairly allocating $m$ indivisible items among $n$ sequentially arriving agents with additive valuations, under the sought-after fairness notion of maximin share (MMS). We first observe a strong impossibility: without appropriate knowledge about the valuation functions of the incoming agents, no online algorithm can ensure any no

Limeng Deng, Yiping Shu, Lei Wang, Guoliang Li

We report the discovery of an intriguing, low-mass galaxy-scale strong-lens system in the SMACS J0723.3-7327 galaxy cluster. By modeling James Webb Space Telescope imaging and Very Large Telescope Multi-Unit Spectroscopic Explorer spectroscopic data, we find that the lens is cluster member galaxy at $z=0.397$ with an Einstein radius of $0^{\prime \prime}.424

Leonardo Câmara, Alexandre Fernandes

In this paper we address the problem of classifying complex (non-homogeneous) quasihomogeneous polynomials in two variables under bi-Lipschitz equivalence. We prove that pairs of such polynomials are (right) bi-Lipschitz equivalent as function-germs at $0\in\mathbb{C}^{2}$ iff they are analytically equivalent.

Marius Lehmann, Min-Kai Lin

The Convective Overstability (COS) is a hydrodynamic instability occurring in protoplanetary disk (PPD) regions with an adverse radial entropy gradient. It is a potential driver of turbulence and may influence planetesimal formation. In this second paper of our series, we study the effects of the COS on dust dynamics in radially global PPD simulations, focus

Nathan Tibbetts, Sifat Ibtisum, Satish Puri

The emergence of new, off-path smart network cards (SmartNICs), known generally as Data Processing Units (DPU), has opened a wide range of research opportunities. Of particular interest is the use of these and related devices in tandem with their host's CPU, creating a heterogeneous computing system with new properties and strengths to be explored, capable o

D. A. Carvajal, P. A. González, Marco Olivares, Eleftherios Papantonopoulos

We study the motion of particles in the background of a scalar-tensor theory of gravity in which the scalar field is kinetically coupled to the Einstein tensor and we present the null geodesic structure for asymptotically flat, AdS, and dS Horndeski black holes, studying the effect of the cosmological constant on the orbits. Also, we consider three classical

Fabio Celli, Aleksandar Kartelj, Miljan Đorđević, Derwin Suhartono

Personality Computing is a field at the intersection of Personality Psychology and Computer Science. Started in 2005, research in the field utilizes computational methods to understand and predict human personality traits. The expansion of the field has been very rapid and, by analyzing digital footprints (text, images, social media, etc.), it helped to deve

Leonardo G. Barbosa, Victor Hugo M. Ramos, Luis Cesar N. dos Santos, Celso C. Barros

We investigate the properties of a charged rotating black string immersed in a Kiselev anisotropic fluid in anti-de Sitter (AdS) spacetime. The Einstein-Maxwell equations with an anisotropic stress-energy tensor and cosmological constant are analyzed and solved exactly. In this work, we calculate the Kretschmann scalar, obtaining a consistent result that agr

Junsol Kim, James Evans, Aaron Schein

Large language models (LLMs) have demonstrated the ability to generate text that realistically reflects a range of different subjective human perspectives. This paper studies how LLMs are seemingly able to reflect more liberal versus more conservative viewpoints among other political perspectives in American politics. We show that LLMs possess linear represe

Paul-Hermann Balduf

In dimensional regularization with $D=D_0-2\epsilon$, the minimal subtraction (MS) scheme is characterized by counterterms that only consist of singular terms in $\epsilon$. We develop a general method to compute the infinite sums of massless ladder or rainbow Feynman integrals in MS at $D_0$. Our method is based on relating the MS-solution to a kinematic so

Jonathan Jacobi, Gal Niv

Understanding and interpreting the internal representations of large language models (LLMs) remains an open challenge. Patchscopes introduced a method for probing internal activations by patching them into new prompts, prompting models to self-explain their hidden representations. We introduce Superscopes, a technique that systematically amplifies superposed

Ziyan Wang, Zhicheng Zhang, Fei Fang, Yali Du

Designing effective reward functions in multi-agent reinforcement learning (MARL) is a significant challenge, often leading to suboptimal or misaligned behaviors in complex, coordinated environments. We introduce Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality ($\text{M}^3\text{HF}$), a novel framework that integrates mult

Shanting Wang, Panagiotis Typaldos, Andreas A. Malikopoulos

In this paper, we present Corridor-Agent (CorrA), a framework that integrates large language models (LLMs) with model predictive control (MPC) to address the challenges of dynamic obstacle avoidance in autonomous vehicles. Our approach leverages LLM reasoning ability to generate appropriate parameters for sigmoid-based boundary functions that define safe cor

Matthias Burkhardt, Tobias Schmähling, Pascal Stegmann, Michael Layh

Aligning a lens system relative to an imager is a critical challenge in camera manufacturing. While optimal alignment can be mathematically computed under ideal conditions, real-world deviations caused by manufacturing tolerances often render this approach impractical. Measuring these tolerances can be costly or even infeasible, and neglecting them may resul

Adam Rauh, In Song Kim, Kosuke Imai

Analyzing time-series cross-sectional (also known as longitudinal or panel) data is an important process across a number of fields, including the social sciences, economics, finance, and medicine. PanelMatch is an R package that implements a set of tools enabling researchers to apply matching methods for causal inference with time-series cross-sectional data

Yuvaraj Elangovan, Mandar Saraf, B. Satyanarayana, S. S. Upadhya

The INO-ICAL experiment consist of 28,800 RPCs each equipped with a Front-End FPGA-based Data Acquisition (RPC-DAQ) module for acquiring detector signals. Due to the large number of RPC-DAQs are required, an automated test system is essential. RPC-DAQ Test-Jig is an FPGA module designed to generate standard test inputs to the RPC-DAQ supporting complete func

Jenny Shen, Dane Isenberg, Kristin A. Linn, Rebecca A. Hubbard

Although increasingly used for research, electronic health records (EHR) often lack gold-standard assessment of key data elements. Linking EHRs to other data sources with higher-quality measurements can improve statistical inference, but such analyses must account for selection bias if the linked data source arises from a non-probability sample. We propose a

Yantao Li, Wang-Kong Tse

We report the discovery of a series of non-equilibrium magic angles at which isolated topological flat quasienergy bands form in AA-stacked twisted multilayer graphene under circularly polarized light. These non-equilibrium magic angles can be traced back to specific static twist angles where the bandwidth reaches a minimum \textit{without} the formation of

Davide Donadio, Margaret L. Berrens, Wanyu Zhao, Shunda Chen

The crystallisation of carbon from the melt under extreme conditions is highly relevant to earth and planetary science, materials manufacturing, and nuclear fusion research. The thermodynamic conditions near the graphite-diamond-liquid (GDL) triple point are especially of interest for geological and technological applications, but high-pressure flash heating

Will Epperson, Gagan Bansal, Victor Dibia, Adam Fourney

Fully autonomous teams of LLM-powered AI agents are emerging that collaborate to perform complex tasks for users. What challenges do developers face when trying to build and debug these AI agent teams? In formative interviews with five AI agent developers, we identify core challenges: difficulty reviewing long agent conversations to localize errors, lack of

Alexander Doudkin, Pat Pataranutaporn, Pattie Maes

Pro-environmental behavior (PEB) is vital to combat climate change, yet turning awareness into intention and action remains elusive. We explore large language models (LLMs) as tools to promote PEB, comparing their impact across 3,200 participants: real humans (n=1,200), simulated humans based on actual participant data (n=1,200), and fully synthetic personas

Eleftheria Sarafidou, Oliver Gressel, Barbara Ercolano

Context. Transition disks (TDs) are a type of protoplanetary disk characterized by a central dust and gas cavity. The processes behind how these cavities are formed and maintained, along with their observed high accretion rates of $10^{-8} -10^{-7} \, M_{\odot} \, \mathrm{yr}^{-1}$, continue to be subjects of active research. Aims. This work aims to investig

Nidhi Rastogi, Shirid Pant, Devang Dhanuka, Amulya Saxena

Explainable AI (XAI) holds significant promise for enhancing the transparency and trustworthiness of AI-driven threat detection in Security Operations Centers (SOCs). However, identifying the appropriate level and format of explanation, particularly in environments that demand rapid decision-making under high-stakes conditions, remains a complex and underexp

Vincent Van Dongen

This paper presents a tileset of 3 squares with local constraints on their borders and corners that enforce non-periodic tiling. We start with a description of the tileset and we demonstrate that it can tile the entire plane non-periodically creating interesting patterns. Rules are also proposed to generate the tiling. They make use of 9 supertiles with bord

Adnen Abdessaied, Anna Rohrbach, Marcus Rohrbach, Andreas Bulling

We present V$^2$Dial - a novel expert-based model specifically geared towards simultaneously handling image and video input data for multimodal conversational tasks. Current multimodal models primarily focus on simpler tasks (e.g., VQA, VideoQA, video-text retrieval) and often neglect the more challenging conversational counterparts, such as video and visual

James Schneeloch

These comments contain two primary improvements to the aforementioned article: Where previously mentioned in passing in section 3.4 of our article, we now provide, using the same basic steps as in Bennink's paper, [Phys. Rev. A 81, 053805 (2010)] (with some expanded discussion), a full derivation for the absolute generation rate of photon pairs from Spontane

Tianchi Li, Marc Bernacki

The front-capturing Level-Set (LS) method is widely employed in academia and industry to model grain boundary (GB) migration during the microstructure evolution of polycrystalline materials under thermo-mechanical treatments. During capillarity-driven grain growth, the conventional mean curvature flow equation, $\vec{v} = - \mu \gamma \kappa \vec{n}$, is use

Yuvaraj Elangovan, Mandar Saraf, B. Satyanarayana, S. S. Upadhya

The INO ICAL (India-based Neutrino Observatory Iron Calorimeter) experiment is an upcoming mega-science project currently in the developmental stages. This initiative employs over 28,800 Resistive Plate Chambers (RPC) based charged particle detectors used for tracking muon events. Each of these detectors incorporates an FPGA-based Digital Front End known as

David J. Setton, Jenny E. Greene, Justin S. Spilker, Christina C. Williams

Luminous broad H$\alpha$ emission and red rest-optical SEDs are the hallmark of compact Little Red Dots (LRDs), implying highly attenuated dusty starbursts and/or obscured active galactic nuclei. However, the lack of observed FIR emission has proved difficult to reconcile with the implied attenuated luminosity in these models. Here, we utilize deep new ALMA

Dana Rubin, Allan dos Santos Costa, Manvitha Ponnapati, Joseph Jacobson

Ribonucleic acid (RNA) plays fundamental roles in biological systems, from carrying genetic information to performing enzymatic function. Understanding and designing RNA can enable novel therapeutic application and biotechnological innovation. To enhance RNA design, in this paper we introduce RiboGen, the first deep learning model to simultaneously generate

P. Myles Eugenio

Learning in the brain is local and unsupervised (Hebbian). We derive the foundations of an effective human language model inspired by these microscopic constraints. It has two parts: (1) a hierarchy of neurons which learns to tokenize words from text (whichiswhatyoudowhenyoureadthis); and (2) additional neurons which bind the learned symanticless patterns of

Julian Rosenberger, Lukas Wolfrum, Sven Weinzierl, Mathias Kraus

The rapidly evolving labor market, driven by technological advancements and economic shifts, presents significant challenges for traditional job matching and consultation services. In response, we introduce an advanced support tool for career counselors and job seekers based on CareerBERT, a novel approach that leverages the power of unstructured textual dat

N. Dimakis

Motivated by the parametrization invariance of cosmological Lagrangians and their equivalence to systems describing the motion of particles in curved backgrounds, we identify the phase space analogue of the notion of proper time. We define the corresponding quantum operator, which results in being canonically conjugate to that of the vanishing Hamiltonian. I

Michael R. Powers

Three mathematical constants bear the name of the venerable Leonhard Euler: Euler's number, $e=2.718281\ldots$; the Euler-Mascheroni constant, $\gamma=0.577216\ldots$; and the Euler-Gompertz constant, $\delta=0.596347\ldots$. In the present work, we consider two joint appearances of these constants, one in a well-known equation of Hardy (interpretable in con

Zaifu Zhan, Shuang Zhou, Huixue Zhou, Zirui Liu

Foundation models, including language models, e.g., GPT, and vision models, e.g., CLIP, have significantly advanced numerous biomedical tasks. Despite these advancements, the high inference latency and the "overthinking" issues in model inference impair the efficiency and effectiveness of foundation models, thus limiting their application in real-time clinic

Yeqin Liu

There are no Noetherian or Artinian bounded t-structures on geometric phantom or quasi-phantom categories.

Anna Biggs

We study the energy probability density function of an evaporating near-extremal charged black hole. At sufficiently low energies, such black holes experience large quantum metric fluctuations in the $AdS_{2}$ throat which are governed by a Schwarzian action. These fluctuations modify Hawking evaporation rates, and therefore also affect how the black hole st

Marco Giberna, Muhammad Shaheer, Miguel Fernandez-Cortizas, Jose Andres Millan-Romera

Robots operating in dynamic environments face significant challenges due to the presence of moving agents and displaced objects. Traditional SLAM systems typically assume a static world or treat dynamic as outliers, discarding their information to preserve map consistency. As a result, they cannot exploit dynamic entities as persistent landmarks, do not mode

Daniel Hallmann, Kerstin Jacob, Gerald Lüttgen, Ute Schmid

User stories are widely applied for conveying requirements within agile software development teams. Multiple user story quality guidelines exist, but authors like Product Owners in industry projects frequently fail to write high-quality user stories. This situation is exacerbated by the lack of tools for assessing user story quality. In this paper, we propos

Xirui Shi, Jun Jin

We consider the problem of using diffusion models to generate fast, smooth, and temporally consistent robot motions. Although diffusion models have demonstrated superior performance in robot learning due to their task scalability and multi-modal flexibility, they suffer from two fundamental limitations: (1) they often produce non-smooth, jerky motions due to

Yumeng Song, Yu Gu, Tianyi Li, Yushuai Li

As large volumes of trajectory data accumulate, simplifying trajectories to reduce storage and querying costs is increasingly studied. Existing proposals face three main problems. First, they require numerous iterations to decide which GPS points to delete. Second, they focus only on the relationships between neighboring points (local information) while negl

Jun Yin, Marian Verhelst

Robust sound source localization for environments with noise and reverberation are increasingly exploiting deep neural networks fed with various acoustic features. Yet, state-of-the-art research mainly focuses on optimizing algorithmic accuracy, resulting in huge models preventing edge-device deployment. The edge, however, urges for real-time low-footprint a

J. I. Katz, M. Nowak

Modern X-ray and gamma-ray observatories time-tag detected photons. The distribution of intervals between successive photons may reveal variations of the flux on time scales too short for direct flux measurement of the mean count rate, provided a sufficient number of photons have been detected cumulatively. We demonstrate this with synthetic data and apply t

Nikolaos Roidos, Elmar Schrohe

We show that, on a manifold with conical singularities, the asymptotics of the solutions to the porous medium equation near the conical points are determined by the spectrum of the Laplacian on the cross-section of the cone. The key to this result is a precise description of the maximal domain of the cone Laplacian.

Aditya Gangrade, Venkatesh Saligrama

We study safe linear bandits (SLBs), where an agent selects actions from a convex set to maximize an unknown linear objective subject to unknown linear constraints in each round. Existing methods for SLBs provide strong regret guarantees, but require solving expensive optimization problems (e.g., second-order cones, NP hard programs). To address this, we pro

Maximilian Rettinger, Leander Hacker, Philipp Wolters, Gerhard Rigoll

Conventional robot programming methods are complex and time-consuming for users. In recent years, alternative approaches such as mixed reality have been explored to address these challenges and optimize robot programming. While the findings of the mixed reality robot programming methods are convincing, most existing methods rely on gesture interaction for ro

Jiachen Guo, Xiaoyu Xie, Chanwook Park, Hantao Zhang

Deep learning has been extensively employed as a powerful function approximator for modeling physics-based problems described by partial differential equations (PDEs). Despite their popularity, standard deep learning models often demand prohibitively large computational resources and yield limited accuracy when scaling to large-scale, high-dimensional physic

Erfan Mehdipour Abadi, Hamid Varmazyari, Masoud H. Nazari

This paper introduces a distributed contingency detection algorithm for detecting unobservable contingencies in power distribution systems using stochastic hybrid system (SHS) models. We aim to tackle the challenge of limited measurement capabilities in distribution networks that restrict the ability to detect contingencies promptly. We incorporate the dynam

Xiner Li, Masatoshi Uehara, Xingyu Su, Gabriele Scalia

Diffusion models have shown promising generative capabilities across diverse domains, yet aligning their outputs with desired reward functions remains a challenge, particularly in cases where reward functions are non-differentiable. Some gradient-free guidance methods have been developed, but they often struggle to achieve optimal inference-time alignment. I

Angana Borah, Rada Mihalcea, Verónica Pérez-Rosas

Existing challenges in misinformation exposure and susceptibility vary across demographic groups, as some populations are more vulnerable to misinformation than others. Large language models (LLMs) introduce new dimensions to these challenges through their ability to generate persuasive content at scale and reinforcing existing biases. This study investigate

Lucas Happ, Pascal Naidon

Three-body resonances are ubiquitous in quantum few-body physics and are characterized by a finite lifetime before decaying into continuum states of their composing subsystems. In this work we present a theoretical study on the possibility to stabilize three-body resonances to so-called bound states in a continuum: resonances with vanishing width that do not

Ruth Crasto, Esther Rolf

Geographic distribution shift arises when the distribution of locations on Earth in a training dataset is different from what is seen at inference time. Using standard empirical risk minimization (ERM) in this setting can lead to uneven generalization across different spatially-determined groups of interest such as continents or biomes. The most common appro

Steen Ryom-Hansen

We consider the bt-algebra ${ \mathcal E}_n(q)$ of knot theory, defined over an arbitrary field $ \Bbbk$. We find a KLR-like presentation for $ {\mathcal E}_n(q) $ showing that it is a $ \mathbb Z$-graded algebra if $ q \in \Bbbk^{\times} \setminus \{1 \} $ admits a square root in $ \Bbbk $. We introduce the ordered bt-algebra $ {\mathcal E}^{\rm{ord}}_n(q)$

Yash Gupta

Federated Learning often relies on sharing full or partial model weights, which can burden network bandwidth and raise privacy risks. We present a loss-based alternative using distributed mutual learning. Instead of transmitting weights, clients periodically share their loss predictions on a public test set. Each client then refines its model by combining it

Zhusi Zhong, Yuli Wang, Lulu Bi, Zhuoqi Ma

Medical imaging plays a pivotal role in modern healthcare, with computed tomography pulmonary angiography (CTPA) being a critical tool for diagnosing pulmonary embolism and other thoracic conditions. However, the complexity of interpreting CTPA scans and generating accurate radiology reports remains a significant challenge. This paper introduces Abn-BLIP (Ab

Devin Pohl, Aaron Young, Kazi Asifuzzaman, Narasinga Miniskar

Advances in novel hardware devices and architectures allow Spiking Neural Network evaluation using ultra-low power, mixed-signal, memristor crossbar arrays. As individual network sizes quickly scale beyond the dimensional capabilities of single crossbars, networks must be mapped onto multiple crossbars. Crossbar sizes within modern Memristor Crossbar Archite

Aniruddha Maiti, Samuel Adewumi, Temesgen Alemayehu Tikure, Zichun Wang

This study examines how large language models categorize sentences from scientific papers using prompt engineering. We use two advanced web-based models, GPT-4o (by OpenAI) and DeepSeek R1, to classify sentences into predefined relationship categories. DeepSeek R1 has been tested on benchmark datasets in its technical report. However, its performance in scie

Richard T. A. Samuel, Charles Chimedza, Caston Sigauke

This study utilised the dynamics of five time-varying models to estimate six essential features of financial return volatility that are relevant for robust risk management. These features include pronounced persistence, mean reversion, leverage effect or volatility asymmetry, conditional skewness, conditional fat-tailedness, and the long memory behaviour of

Yitao Bai, Sihan Zeng, Justin Romberg, Thinh T. Doan

We study policy evaluation problems in multi-task reinforcement learning (RL) under a low-rank representation setting. In this setting, we are given $N$ learning tasks where the corresponding value function of these tasks lie in an $r$-dimensional subspace, with $r<N$. One can apply the classic temporal-difference (TD) learning method for solving these probl

Ovidiu Savin, Chilin Zhang

We establish Liouville theorems for global minimizers $u$ of the Allen-Cahn energy $$\int |\nabla u|^2 + W(u) \, dx,$$ which have subquadratic growth at infinity. In particular we extend the results of \cite{S1,S3} concerning the De Giorgi's conjecture to the setting of unbounded solutions. Part of the analysis relies on the regularity of minimizers for a Di

Augustus Brown, Francesco Galvagno, Alba Grassi, Cristoforo Iossa

We study the large-charge sector of $\mathcal{N}=4$ super Yang-Mills theory (SYM) with $SU(N)$ gauge group by constructing a special class of half-BPS heavy operators, termed "canonical operators". Such operators exhibit remarkable simplicity in the large-charge 't Hooft limit, where the dimension of the operators $\Delta \to \infty$ with $\Delta\, g_{\text{

Benjamin Calvin, Michael Fitzgerald, Sam Ragland

There are scientific and technological needs to improve the co-phasing of the primary mirrors of segmented telescopes. We have developed a methodology for using the wavefront sensor of an adaptive optics (AO) system to disentangle the phase of a Controllable Segmented Primary mirror (CSP) from the residual phase aberrations to be corrected by the rest of the

Benjamin Calvin, Michael Fitzgerald

The maintenance of primary mirror segment co-phasing is a critical aspect to the operation of segmented telescopes. However, speckle-based measurements of the phasing of the Keck primary have estimated semi-static surface aberrations of approximately 65 nm rms, which were not sensed by the current phasing control system. We propose directly sensing and contr

Iacopo P. Longo, Rafael Obaya, Ana M. Sanz

Starting from a classical Budyko-Sellers-Ghil energy balance model for the average surface temperature of the Earth, a nonautonomous version is designed by allowing the solar irradiance and the cloud cover coefficients to vary with time in a fast timescale, and to exhibit chaos in a precise sense. The dynamics of this model is described in terms of three exi

Daniella C. Bardalez Gagliuffi, Jacqueline K. Faherty, Genaro Suarez, Sherelyn Alejandro Merchan

We present the first brown dwarf spectral binary characterized with JWST: WISE J014656.66+423410.0, the coldest blended-light brown dwarf binary straddling the T/Y transition. We obtained a moderate resolution (R$\sim$2700) G395H spectrum of this unresolved binary with JWST/NIRSpec and we fit it to late-T and Y dwarf spectra from JWST/NIRSpec, and model spec

B. Kapanadze, A. Gurchumelia, M. Aller

This paper presents the gamma-ray spectral and timing results from the long-term regular observations of Mrk 421 with the Large Area Telescope (LAT) onboard Fermi during 2008 August - 2023 August. We discerned six periods the relatively stronger 0.3-300 GeV activity compared to other time intervals. The baseline brightness level varied on timescales from sev

Qingkai Kong, Caifeng Zou, Youngsoo Choi, Eric M. Matzel

The recent development of Neural Operator (NeurOp) learning for solutions to the elastic wave equation shows promising results and provides the basis for fast large-scale simulations for different seismological applications. In this paper, we use the Fourier Neural Operator (FNO) model to directly solve the 3D Helmholtz wave equation for fast seismic ground

Kaixuan Zhu, Xinrui Luo, Yuanlei Wang, Ze Wang

The drive to miniaturize optical frequency combs for practical deployment has spotlighted microresonator solitons as a promising chip-scale candidate. However, these soliton microcombs could be very power-hungry when their span increases, especially with fine comb spacings. As a result, realizing an octave-spanning comb at microwave repetition rates for dire

Geonwoong Moon, Youngil Ko, Minwoo Yi, Eunji Jun

Atmosphere-breathing electric propulsion (ABEP) systems are emerging for orbit maintenance in very-low-Earth orbit (VLEO) by capturing atmospheric propellant \textit{in situ} using an intake device. A previous study proposed the cryocondensation-regeneration active intake device (CRAID) to significantly enhance intake performance. This study investigates the

Sergei Merkulov

For any integer $d\in \mathbb{Z}$ we introduce a complex $\mathsf{ORGC}_{d}^{(g,m)}$ spanned by genus $g$ ribbon quivers with $m$ marked boundaries and prove that its cohomology computes (up to a degree shift) the compactly supported cohomology of the moduli space $\mathcal{M}_{g,m}$ of genus $g$ algebraic curves with $m$ marked points. We show that the tota

Saleh Darzi, Attila A. Yavuz

The rapid advancements in wireless technology have significantly increased the demand for communication resources, leading to the development of Spectrum Access Systems (SAS). However, network regulations require disclosing sensitive user information, such as location coordinates and transmission details, raising critical privacy concerns. Moreover, as a dat

Li Li

China's Great Firewall (GFW) exemplifies one of the most extensive and technologically sophisticated internet censorship frameworks worldwide. Serving as a cornerstone of state-directed digital governance, it integrates a multitude of methods - ranging from DNS manipulation and IP blocking to keyword filtering and active surveillance - to control online info

Reza Fotohi, Fereidoon Shams Aliee, Bahar Farahani

The ever growing Internet of Things (IoT) connections drive a new type of organization, the Intelligent Enterprise. In intelligent enterprises, machine learning based models are adopted to extract insights from data. Due to the efficiency and privacy challenges of these traditional models, a new federated learning (FL) paradigm has emerged. In FL, multiple e

Angana Borah, Marwa Houalla, Rada Mihalcea

Social biases and belief-driven behaviors can significantly impact Large Language Models (LLMs) decisions on several tasks. As LLMs are increasingly used in multi-agent systems for societal simulations, their ability to model fundamental group psychological characteristics remains critical yet under-explored. In this study, we present a multi-agent framework

Sergen Özdemir, John Eduard Martínez-Fernández, Rodolfo Smiljanic

Numerous stellar surveys have been or will provide photometric, astrometric, and spectroscopic data for a large number of stars in the Milky Way and neighbouring galaxies. Modern data processing tools and analysis methods are needed to deal with these data sets and obtain accurate and precise results. In this context, we are developing a new spectroscopic an

Rebecca G. Martin, Stephen H. Lubow, David Vallet, Madeline Overton

Be stars are rapidly rotating, with angular frequency around $0.7-0.8$ of their Keplerian break up frequency, as a result of significant accretion during the earlier stellar evolution of a companion star. Material from the equator of the Be star is ejected and forms a decretion disc, although the mechanism for the disc formation has remained elusive. We find

Adalbert Fono, Manjot Singh, Ernesto Araya, Philipp C. Petersen

Deep learning's success comes with growing energy demands, raising concerns about the long-term sustainability of the field. Spiking neural networks, inspired by biological neurons, offer a promising alternative with potential computational and energy-efficiency gains. This article examines the computational properties of spiking networks through the lens of

Parv Kapoor, Abigail Hammer, Ashish Kapoor, Karen Leung

We propose an approach to formally specifying the behavioral properties of systems that rely on a perception model for interactions with the physical world. The key idea is to introduce embeddings -- mathematical representations of a real-world concept -- as a first-class construct in a specification language, where properties are expressed in terms of dista

Tung L Nguyen, Toby Dylan Hocking

Regression models are essential for a wide range of real-world applications. However, in practice, target values are not always precisely known; instead, they may be represented as intervals of acceptable values. This challenge has led to the development of Interval Regression models. In this study, we provide a comprehensive review of existing Interval Regr

David Kirkpatrick, Paul Liu

We study the problem of determining coordinated motions, of minimum total length, for two arbitrary convex centrally-symmetric (CCS) robots in an otherwise obstacle-free plane. Using the total path length traced by the two robot centres as a measure of distance, we give an exact characterization of a (not necessarily unique) shortest collision-avoiding motio