February 2025 arXiv papers — page 23

Douglas Gough

Observations of stars other than the Sun are sensitive to oscillations of only low degree. Many are high-order acoustic modes. Acoustic frequencies of main-sequence stars, for example, satisfy a well-known pattern, which some astronomers have adopted even for red-giant stars. That is not wise, because the internal structures of these stars can be quite diffe

Jiawei Huang, Bingcong Li, Christoph Dann, Niao He

Sample efficiency is critical for online Reinforcement Learning from Human Feedback (RLHF). While existing works investigate sample-efficient online exploration strategies, the potential of utilizing misspecified yet relevant reward models to accelerate learning remains underexplored. This paper studies how to transfer knowledge from those imperfect reward m

Vladimir Vovk

Conformal predictors provide set or functional predictions that are valid under the assumption of randomness, i.e., under the assumption of independent and identically distributed data. The question asked in this paper is whether there are predictors that are valid in the same sense under the assumption of randomness and that are more efficient than conforma

Solomon Asghar, Ran Ni, Giorgio Volpe

Biophysical processes within living systems rely on encounters and interactions between molecules in complex environments such as cells. They are often described by anomalous diffusion transport. Recent advances in single-molecule microscopy and particle-tracking techniques have yielded an abundance of data in the form of videos and trajectories that contain

Li Ju, Xingyi Yang, Qi Li, Xinchao Wang

Graph neural networks (GNNs) are conventionally trained on a per-domain, per-task basis. It creates a significant barrier in transferring the acquired knowledge to different, heterogeneous data setups. This paper introduces GraphBridge, a novel framework to enable knowledge transfer across disparate tasks and domains in GNNs, circumventing the need for modif

Dustin Gaskins

This work studies simply connected, noncompact $G/H$ in which $G$ is semi-simple, $H$ is connected, and $G/H$ has two irreducible summands. Here, we classify all such spaces and we provide solutions to the so-called Prescribed Ricci Curvature problem for all such spaces.

Minjie Zhu, Yichen Zhu, Jinming Li, Zhongyi Zhou

Imitation learning has proven to be highly effective in teaching robots dexterous manipulation skills. However, it typically relies on large amounts of human demonstration data, which limits its scalability and applicability in dynamic, real-world environments. One key challenge in this context is object generalization, where a robot trained to perform a tas

Michael Y. Hu, Jackson Petty, Chuan Shi, William Merrill

Pretraining language models on formal language can improve their acquisition of natural language. Which features of the formal language impart an inductive bias that leads to effective transfer? Drawing on insights from linguistics and complexity theory, we hypothesize that effective transfer occurs when two conditions are met: the formal language should cap

René Oswald, Victor Vogt, Stephan Schiller

Ultralight dark matter (UDM) may manifest itself through oscillating fundamental constants of normal matter. These can be experimentally searched for by implementing two dissimilar oscillators producing a beat between their frequencies and analyzing the beat-frequency time series for the presence of any temporal oscillations. Typically, the time series of su

Qihang Peng, Henry Zheng, Gao Huang

Embodied intelligence requires agents to interact with 3D environments in real time based on language instructions. A foundational task in this domain is ego-centric 3D visual grounding. However, the point clouds rendered from RGB-D images retain a large amount of redundant background data and inherent noise, both of which can interfere with the manifold str

Celina Janet Bartlett

This thesis centers around the concept of Subset Search Problems (SSP), a type of computational problem introduced by Gr\"une and Wulf to analyze the complexity of more intricate optimization problems. These problems are given an input set, a so-called universe, and their solution lies within their own universe, e.g. the shortest path between two point is a

Emil Albrychiewicz, Andrés Franco Valiente, Vi Hong

We continue our investigation of tropical branes by exploring the tropicalization of topological sigma models with boundaries. We show that the tropical limit naturally decomposes conventional A-branes into two distinct classes: tropical Lagrangian branes and tropical coisotropic branes. By carefully analyzing the modified boundary conditions emerging from t

Andrea Boccia, Fabio Iocco

We investigate whether the ultra high energy neutrino inferred by the recent KM3NeT observation could have originated from an evaporating black hole. Given the characteristics of black hole (BH) evaporation mechanism, any object capable of producing particles in the energy range of the detected event (around 100-800 PeV) must have a mass below 10^7 g. No kno

Carmelo Evoli

We analyze the electron cosmic-ray spectrum from AMS-02, focusing on the spectral hardening around 42 GeV. Our findings confirm that this feature is intrinsic to the primary electron component rather than a byproduct of contamination from primary positron sources. Even under conservative assumptions, its significance remains at about $7\sigma$, strongly indi

Hussah Alghanem, Alastair Buckley

Great Britain aims to meet growing electricity demand and achieve a fully decarbonised grid by 2035, targeting 70 GW of solar photovoltaic (PV) capacity. However, grid constraints and connection delays hinder solar integration. To address these integration challenges, various connection reform processes and policies are being developed [1]. This study suppor

Haoxin Cai, Shenghai Yuan, Xinyi Li, Junfeng Guo

This work introduces BEV-LIO(LC), a novel LiDAR-Inertial Odometry (LIO) framework that combines Bird's Eye View (BEV) image representations of LiDAR data with geometry-based point cloud registration and incorporates loop closure (LC) through BEV image features. By normalizing point density, we project LiDAR point clouds into BEV images, thereby enabling effi

R. Sammani, E. H Saidi, R. Ahl Laamara

This paper aims to construct exceptional Banados-Teitelboim-Zanelli (BTZ) black holes carrying E$_{6}$ charges as solutions to the 3D higher spin Anti-de Sitter (AdS) gravity with E$_{6}$ boundary conditions. Guided by Tits-Satake graphs of real forms of the e$_{6} $ Lie algebra, we build three remarkable E$_{6}$-higher spin black hole models: the linear-exc

Luxu Liang, Yuhang Jia, Feng Zhou

While gradient-based discrete samplers are effective in sampling from complex distributions, they are susceptible to getting trapped in local minima, particularly in high-dimensional, multimodal discrete distributions, owing to the discontinuities inherent in these landscapes. To circumvent this issue, we combine parallel tempering, also known as replica exc

Jorge F. Soriano, Shimon Wohlberg, Luis A. Anchordoqui

The proposal for a sudden sign-switching cosmological constant $\Lambda$ in the local universe, emulating a phase transition from anti-de Sitter (AdS) to de Sitter (dS) space, has markedly revamped the fit to observational data and lays out a propitious framework for ameliorating major cosmological tensions, such as the $H_0$ and $S_8$ tensions. This proposa

Steve Lawford

I investigate how incumbents in the U.S. airline industry respond to threatened and actual route entry by Southwest Airlines. I use a two-way fixed effects and event study approach, and the latest available data from 1999-2022, to identify a firm's price and quantity response. I find evidence that incumbents cut fares preemptively (post-entry) by 6-8% (16-18

Tharindu Samarakoon, Kalana Abeywardena, Chamira U. S. Edussooriya

A four-dimensional light field (LF) captures both textural and geometrical information of a scene in contrast to a two-dimensional image that captures only the textural information of a scene. Post-capture refocusing is an exciting application of LFs enabled by the geometric information captured. Previously proposed LF refocusing methods are mostly limited t

Fabio Elnecave Xavier, Matis Viozelange, Guillaume Burger, Marine Pétriaux

For leg exoskeletons to operate effectively in real-world environments, they must be able to perceive and understand the terrain around them. However, unlike other legged robots, exoskeletons face specific constraints on where depth sensors can be mounted due to the presence of a human user. These constraints lead to a limited Field Of View (FOV) and greater

N. H. Kwong, Jan Wingenbach, Laura Ares, Jan Sperling

Exceptional points (EPs) are non-Hermitian degeneracies where eigenvalues and eigenvectors coalesce, giving rise to unusual physical effects across scientific disciplines. The concept of EPs has recently been extended to nonlinear physical systems. We theoretically demonstrate a universal topology in the nonlinear parameter space for a large class of physica

Stefano Pantaleone, Marta Corno, Albert Rimola, Nadia Balucani

Among the biogenic macroelements, phosphorus is the one bringing the most fascinating and unsolved mysteries for what concern its prebiotic history. It possibly landed on Earth as a metal phosphide (Schreibersite, (Fe,Ni)3P), throughout the Heavy Meteor Bombardment during the Archean Era. Its subsequent corrosion by water led to P-oxygenated compounds, which

K Shuvo Bakar, Sourish Das, Sudeep Shukla, Anirban Chakraborti

Arctic sea-ice loss is a defining feature of climate change and offers insight into its impact on mid-latitude air quality. Here, we investigate how variability in Arctic sea-ice extent (ASI) affects ground-level ozone ($O_3$) across eastern US states through physically and chemically mediated atmospheric pathways. Using observations and causal-inference met

Yiqi Chen, Xiping Dong, Zhe Zhou, Zhao Wang

The Compute Express Link (CXL) technology facilitates the extension of CPU memory through byte-addressable SerDes links and cascaded switches, creating complex heterogeneous memory systems where CPU access to various endpoints differs in latency and bandwidth. Effective tiered memory management is essential for optimizing system performance in such systems.

Stein Meereboer

The theory of quantum symmetric pairs is applied to $q$-special functions. Previous work shows the existence of a family $χ$-spherical functions indexed by the integers for each Hermitian quantum symmetric pair. A distinguished family of such functions, invariant under the Weyl group of the restricted roots, is shown to be a family of Macdonald-Koornwinder p

Sean O'Hagan, Veronika Ročková

The advent of Generative Artificial Intelligence (GAI) has heralded an inflection point that changed how society thinks about knowledge acquisition. While GAI cannot be fully trusted for decision-making, it may still provide valuable information that can be integrated into a decision pipeline. Rather than seeing the lack of certitude and inherent randomness

Jiazheng Li, Yuxiang Zhou, Junru Lu, Gladys Tyen

Although preference optimization methods have improved reasoning performance in Large Language Models (LLMs), they often lack transparency regarding why one reasoning outcome is preferred over another. This limitation is especially critical in Automated Student Answer Scoring (ASAS), where explainability is essential to justify assessment outcomes. Verbal re

Ye Xing, Zhi-Peng Xing, Yu-Ji Shi

This thesis considers the decays of fully light four quark exotic as the main subject. In the context, we study the possible decays from general symmetric analysis: $uu\bar d\bar s$, $dd\bar s\bar u$, $ds\bar u \bar u$, and $su\bar d \bar d$. Using the light quark flavor symmetry, we discuss the decay modes of decuplet and 27 multiplet with $J^P=1^+$. Furthe

Peter Reimann, Christian Eidecker-Dunkel

We consider the common spin-1/2 XX-model in one dimension with open boundary conditions and a large but finite number of spins. The system is in thermal equilibrium at times t<0, and is subject to a weak local perturbation (quantum quench) at t=0. Focusing mainly on single-spin perturbations and observables, we show that the system re-thermalizes for suffici

Yunyang Li, Zaishuo Xia, Lin Huang, Xinran Wei

Density Functional Theory (DFT) is a pivotal method within quantum chemistry and materials science, with its core involving the construction and solution of the Kohn-Sham Hamiltonian. Despite its importance, the application of DFT is frequently limited by the substantial computational resources required to construct the Kohn-Sham Hamiltonian. In response to

Daniel Elander, Antón F. Faedo, Maurizio Piai, Ronnie Rodgers

We study a class of UV-complete, strongly coupled, confining three-dimensional field theories, that exhibit a novel stabilisation mechanism for the mass of the lightest scalar composite state, relying on the existence of a critical point. The theories admit a holographic dual description in terms of regular backgrounds in eleven-dimensional supergravity. The

Jacopo G. Chen

We give an explicit construction of a family of closed arithmetic hyperbolic 5-manifolds, tessellated by $117 964 800 = 512 \cdot 16 \cdot 14400$ copies of a Coxeter simplicial prism. We proceed to study various properties of these manifolds, such as the volume and the first Betti number. We also describe a related family of 5-manifolds with a larger volume,

Kyoung-Sun Lee, Jongchul Chae, Hannah Kwak, Kyuhyoun Cho

Elemental abundances in the solar corona differ from those in the photosphere, with low first ionization potential (FIP) elements being enhanced, a phenomenon known as the FIP effect. This enhancement is attributed to ponderomotive forces linked to magnetohydrodynamic (MHD) waves, particularly incompressible transverse waves. Our study investigates the relat

Ivan Panin, Anastasia Stavrova

We prove that for any simply connected isotropic reductive group G over a Dedekind domain D, any Zariski-locally trivial principal G-bundle over D is trivial. The corresponding result for quasi-split groups was proved in 1967 by G. Harder.

Iman Abdoli, Abhinav Sharma, Hartmut Löwen

A Brownian gyrator is a system in which a particle experiences thermal noise from two distinct heat baths. This nonequilibrium setup inherently generates a nonzero torque, leading to gyrating motion around a potential energy minimum. As a minimal model for a heat engine, the Brownian gyrator provides valuable insights into energy conversion and nonequilibriu

Jiaxi Gu, Xinjuan Chen, Kwanghyuk Park, Jae-Hun Jung

The central-upwind weighted essentially non-oscillatory (WENO) scheme introduces the downwind substencil to reconstruct the numerical flux, where the smoothness indicator for the downwind substencil is of critical importance in maintaining high order in smooth regions and preserving the essentially nonoscillatory behavior in shock capturing. In this study, w

Silpa Babu, Sajan Goud Lingala, Namrata Vaswani

In this work, we develop novel MRI reconstruction approaches that are accurate, fast and low-latency for a large number of dynamic MRI applications, sampling schemes and sampling rates; without any problem-specific parameter tuning. We refer to this property of a single algorithm, without parameter tuning, being accurate and fast for many settings as general

Lida Zhao, Shihan Dou, Yutao Hu, Yueming Wu

Code cloning, a widespread practice in software development, involves replicating code fragments to save time but often at the expense of software maintainability and quality. In this paper, we address the specific challenge of detecting "essence clones", a complex subtype of Type-3 clones characterized by sharing critical logic despite different peripheral

Yuhao Jiang, Serge El Asmar, Ziqiao Wang, Serhat Demirtas

Robotic manipulators often face challenges in handling objects of different sizes and materials, limiting their effectiveness in practical applications. This issue is particularly pronounced when manipulating meter-scale objects or those with varying stiffness, as traditional gripping techniques and strategies frequently prove inadequate. In this letter, we

Henrik Abgaryan, Tristan Cazenave, Ararat Harutyunyan

Large Language Models (LLMs) have shown remarkable capabilities across various domains, but their potential for solving combinatorial optimization problems remains largely unexplored. In this paper, we investigate the applicability of LLMs to the Job Shop Scheduling Problem (JSSP), a classic challenge in combinatorial optimization that requires efficient job

Nikita Shvetsov, Thomas K. Kilvaer, Masoud Tafavvoghi, Anders Sildnes

Developing clinically useful cell-level analysis tools in digital pathology remains challenging due to limitations in dataset granularity, inconsistent annotations, high computational demands, and difficulties integrating new technologies into workflows. To address these issues, we propose a solution that enhances data quality, model performance, and usabili

Rafiya Javed, Cassandra Parent, Jackie Kay, David Yanni

Hedging and non-affirmation are behaviors exhibited by large language models (LLMs) that limit the clear endorsement of specific statements. While these behaviors are desirable in subjective contexts, they are undesirable in the context of human rights - which apply unambiguously to all groups. We present a systematic framework to measure these behaviors in

Gina DAngelo, Guannan Chen, Di Ran

The initiation of dose optimization has driven a paradigm shift in oncology clinical trials to determine the optimal biological dose (OBD). Early-phase trials with randomized doses can facilitate additional investigation of the identified OBD in targeted populations by incorporating safety, efficacy, and biomarker data. To support dose comparison in such set

Luca Colagrande, Luca Benini

To keep up with the growing computational requirements of machine learning workloads, many-core accelerators integrate an ever-increasing number of processing elements, putting the efficiency of memory and interconnect subsystems to the test. In this work, we present the design of a multicast-capable AXI crossbar, with the goal of enhancing data movement eff

Anthony M. Smaldone, Yu Shee, Gregory W. Kyro, Marwa H. Farag

The success of the self-attention mechanism in classical machine learning models has inspired the development of quantum analogs aimed at reducing computational overhead. Self-attention integrates learnable query and key matrices to calculate attention scores between all pairs of tokens in a sequence. These scores are then multiplied by a learnable value mat

Yevhen Havrylenko

We consider an optimal investment-consumption problem for a utility-maximizing investor who has access to assets with different liquidity and whose consumption rate as well as terminal wealth are subject to lower-bound constraints. Assuming utility functions that satisfy standard conditions, we develop a methodology for deriving the optimal strategies in sem

Yufan Zhang, Tobias Binninger, Jun Huang, Michael Eikerling

Nanoscopic heterogeneities in composition and structure are quintessential for the properties of electrocatalyst materials. Here, we present a semiclassical model to study the electrochemical properties of supported electrocatalyst nanoparticles (NP). The model captures the correlated electronic and ionic equilibration across NP, support, and electrolyte. It

Francesca Capuano, Ellen Boschert, Barbara Kaup

The study explores whether Large Language Models (LLMs) exhibit negation-induced forgetting (NIF), a cognitive phenomenon observed in humans where negating incorrect attributes of an object or event leads to diminished recall of this object or event compared to affirming correct attributes (Mayo et al., 2014; Zang et al., 2023). We adapted Zang et al. (2023)

Yi Feng, Xiao Wang, Tian Xie

We consider nonconvex optimization problem over simplex, and more generally, a product of simplices. We provide an algorithm, Langevin Multiplicative Weights Update (LMWU) for solving global optimization problems by adding a noise scaling with the non-Euclidean geometry in the simplex. Non-convex optimization has been extensively studied by machine learning

Zhouyu Jiang, Mengshu Sun, Zhiqiang Zhang, Lei Liang

Retrieval-Augmented Generation (RAG) effectively reduces hallucinations in Large Language Models (LLMs) but can still produce inconsistent or unsupported content. Although LLM-as-a-Judge is widely used for RAG hallucination detection due to its implementation simplicity, it faces two main challenges: the absence of comprehensive evaluation benchmarks and the

Arezo Shakeri, Mina Farmanbar, Krisztian Balog

Dementia is a progressive cognitive syndrome with Alzheimer's disease (AD) as the leading cause. Conversation-based AD detection offers a cost-effective alternative to clinical methods, as language dysfunction is an early biomarker of AD. However, most prior research has framed AD detection as a binary classification problem, limiting the ability to identify

Zhanpeng He, Yifeng Cao, Matei Ciocarlie

Human-in-the-loop (HitL) robot deployment has gained significant attention in both academia and industry as a semi-autonomous paradigm that enables human operators to intervene and adjust robot behaviors at deployment time, improving success rates. However, continuous human monitoring and intervention can be highly labor-intensive and impractical when deploy

Nakyeong Yang, Minsung Kim, Seunghyun Yoon, Joongbo Shin

Various studies have attempted to remove sensitive or private knowledge from a language model to prevent its unauthorized exposure. However, prior studies have overlooked the complex and interconnected nature of knowledge, where related knowledge must be carefully examined. Specifically, they have failed to evaluate whether an unlearning method faithfully er

Zsolt Vizi, Evans Kiptoo Korir, Norbert Bogya, Csaba Rosztóczy

Understanding the role of different age groups in disease transmission is crucial for designing effective intervention strategies. A key parameter in age-structured epidemic models is the contact matrix, which defines the interaction structure between age groups. However, accurately estimating contact matrices is challenging, as different age groups respond

Luca Becchetti, Andrea Clementi, Luciano Gualà, Luca Pepè Sciarria

In this work, we propose, analyze and empirically validate a lazy-update approach to maintain accurate approximations of the $2$-hop neighborhoods of dynamic graphs resulting from sequences of edge insertions. We first show that under random input sequences, our algorithm exhibits an optimal trade-off between accuracy and insertion cost: it only performs $O(

Xiankang He, Dongyan Guo, Hongji Li, Ruibo Li

Recent advances in zero-shot monocular depth estimation(MDE) have significantly improved generalization by unifying depth distributions through normalized depth representations and by leveraging large-scale unlabeled data via pseudo-label distillation. However, existing methods that rely on global depth normalization treat all depth values equally, which can

Christa Cuchiero, Janka Möller

We study a new class of McKean-Vlasov stochastic differential equations (SDEs), possibly with common noise, applying the theory of time-inhomogeneous polynomial processes. The drift and volatility coefficients of these SDEs depend on the state variables themselves as well as their conditional moments in a way that mimics the standard polynomial structure. Ou

Thanh-Phong Le, Trung Le Chi Phan, Nghia Hieu Nguyen, Kiet Van Nguyen

Document Visual Question Answering (Document VQA) challenges multimodal systems to holistically handle textual, layout, and visual modalities to provide appropriate answers. Document VQA has gained popularity in recent years due to the increasing amount of documents and the high demand for digitization. Nonetheless, most of document VQA datasets are develope

Merlin A. Nau, Luca A. Nutricati, Bruno Camino, Paul A. Warburton

We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. Feature selection is often formulated as a k of n selection problem, where the complexity grows binomially with increasing k and n. As problem sizes grow, classical approach

Zekang Weng, Jinjin Shi, Jinwei Wang, Zeming Han

Image anomaly detection plays a vital role in applications such as industrial quality inspection and medical imaging, where it directly contributes to improving product quality and system reliability. However, existing methods often struggle with complex and diverse anomaly patterns. In particular, the separation between generation and discrimination tasks l

Linshan Jia

Feature extraction is crucial in intelligent fault diagnosis of rotating machinery. It is easier for convolutional neural networks(CNNs) to visually recognize and learn fault features by converting the complicated one-dimensional (1D) vibrational signals into two-dimensional (2D) images with simple textures. However, the existing representation methods for e

Sunben Chiu, Pingzhi Yuan, Hongjian Li

If an irreducible fraction $\frac mn>0$ can be decomposed into the sum of several irreducible proper fractions with different denominators, and the positive number smaller than $\frac mn$ in fractional ideal $\frac 1n\mathbb Z$ can not be obtained by replacing some numerator with smaller non-negative integers, then the decomposition is said to be faithful. F

Martí Berenguer, Javier Mas, Masataka Matsumoto, Keiju Murata

We study the effects of helical magnetic fields on chiral symmetry breaking within the AdS/QCD framework using the D3/D7-brane model. By analyzing the brane embeddings, we obtain three types of massless solutions, corresponding to three phases with different behavior in the dual field theory. From the study of quark condensates, free energy, and electric cur

Péter Csikvári

The Merino-Welsh conjecture states that for a graph $G$ without loops and bridges the Tutte polynomial $T_G(x,y)$ satisfies the inequality $$\max(T_G(2,0),T_G(0,2))\geqslant T_G(1,1).$$ Later Jackson proved that for any matroid $M$ without loops and coloops we have $$T_M(3,0)T_M(0,3)\geqslant T_M(1,1)^2.$$ The value $3$ in this statement was improved to $2.9

Thomas Apostolidis, Umut Gürsoy, Edwan Préau

Recent Bayesian analyses of heavy ion collision data have established a non-trivial temperature dependence of the shear and bulk viscosity per entropy. Motivated by this, we consider higher derivative corrections to realistic, bottom-up holographic models of quark-gluon plasma based on five-dimensional Einstein-dilaton theories and determine the dilaton pote

Bernardin Tamo Amougou, Marcelo Pereyra, Barbara Pascal

Image restoration problems are often ill-posed, leading to significant uncertainty in reconstructed images. Accurately quantifying this uncertainty is essential for the reliable interpretation of reconstructed images. However, image restoration methods often lack uncertainty quantification capabilities. Conformal prediction offers a rigorous framework to aug

Alberto Comoretto, Tanaya Mandke, Johannes T. B. Overvelde

Soft machines display shape adaptation to external circumstances due to their intrinsic compliance. To achieve increasingly more responsive behaviors upon interactions without relying on centralized computation, embodying memory directly in the machines' structure is crucial. Here, we harness the bistability of elastic shells to alter the fluidic properties

Anastasios P. Pagiaslis

This literature review interrogates the intersections between artificial intelligence, poetry, and art, offering a comprehensive exploration of both historical evolution and current debates in digital creative practices. It traces the development of computer-generated poetry from early template-based systems to generative models, critically assessing evaluat

Timo Sommer, Agnes Zinth, Aditya, Menno Poot

Recently, it was shown that strongly driven micromechanical resonators show mode shapes that strongly differ from the eigenmodes. This raises the question of the origin of this nonlinear behavior. We measure the spatial dependence of the nonlinearities of high-stress micromechanical membranes. The mechanical nonlinearity is determined from the frequency resp

Lauren Klein, Meredith Martin, André Brock, Maria Antoniak

The effects of generative AI are experienced by a broad range of constituencies, but the disciplinary inputs to its development have been surprisingly narrow. Here we present a set of provocations from humanities researchers -- currently underrepresented in AI development -- intended to inform its future applications and enrich ongoing conversations about it

Jun-Rong Liu, Jian-Min Wang, Fermi-LAT Collaboration

Relativistic jets around supermassive black holes (SMBHs) are well-known powerful $\gamma$-ray emitters. In absence of the jets in radio-quiet active galactic nuclei (AGNs), how the SMBHs work in $\gamma$-ray bands is still unknown despite of great observational efforts made in the last 3 decades. Considering the previous efforts, we carefully select an AGN

Dragoljub J. Kečkić, Zlatko Lazović

Let $G$ be a locally compact group, $\mu$ its Haar measure, $\hat G$ its Pontryagin dual and $\nu$ the dual measure. For any $A_\theta\in L^1(G;\mathcal C_p)\cap L^2(G;\mathcal C_p)$, ($\mathcal C_p$ is Schatten ideal), and $1<p\le2$ we prove $$\int_{\hat G}\left\|\int_GA_\theta\overline{\xi(\theta)}\,\mathrm d\mu(\theta)\right\|_p^q\,\mathrm d\nu(\xi)\le \l

Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch

Large language models (LLMs) are increasingly deployed in everyday applications, demanding robust general reasoning capabilities and diverse reasoning skillset. However, current LLM reasoning benchmarks predominantly focus on mathematical and coding abilities, leaving a gap in evaluating broader reasoning proficiencies. One particular exception is the BIG-Be

Yinzhou Tang, Jinghua Piao, Huandong Wang, Shaw Rajib

Cascading failures (CF) entail component breakdowns spreading through infrastructure networks, causing system-wide collapse. Predicting CFs is of great importance for infrastructure stability and urban function. Despite extensive research on CFs in single networks such as electricity and road networks, interdependencies among diverse infrastructures remain o

Marcos V. de S. Silva, T. M. Crispim, G. Alencar, R. R. Landim

In this work, following our recent findings in [1], we extend our analysis to explore the generalization of spherically symmetric and static black-bounce solutions, known from General Relativity, within the framework of the $f(R)$ theory in the metric formalism. We develop a general approach to determine the sources for any model where $f(R) = R + H(R)$, pro

Amit Kumar Mandal, Jong-Hak Woo, Shu Wang

We present a continuum lag analysis for a sample of 37 relatively high-luminosity active galactic nuclei (AGNs) from the Seoul National University AGN Monitoring Project (SAMP), utilizing the light curve data in $B$ and $V$ bands from SAMP and in $g,r,i$ bands from the Zwicky Transient Facility. We find that the inter-band lags ($\tau$) increase with wavelen

Alberto Foresti, Giulio Franzese, Pietro Michiardi

Information-theoretic quantities play a crucial role in understanding non-linear relationships between random variables and are widely used across scientific disciplines. However, estimating these quantities remains an open problem, particularly in the case of high-dimensional discrete distributions. Current approaches typically rely on embedding discrete da

Quan Quan, Dun Dai

6D pose estimation is a central problem in robot vision. Compared with pose estimation based on point correspondences or its robust versions, correspondence-free methods are often more flexible. However, existing correspondence-free methods often rely on feature representation alignment or end-to-end regression. For such a purpose, a new correspondence-free

Arun J Manattu, Aparna Lakshmanan S

Given an edge labeling $f$ of a graph $G$, a vertex $v$ is called an $AR$-vertex, if $v$ has distinct edge weight sums for each distinct subset of edges incident on $v$. An injective edge labeling $f$ of a graph $G$ is called an $AR$-labeling of $G$, if $f:E(G) \rightarrow \mathbb{N}$ is such that every vertex in $G$ is an $AR$-vertex under $f$. The minimum

Jiatao Jiang, Zhen Cui, Chunyan Xu, Jian Yang

In recent years, deep learning has achieved remarkable success in the field of image restoration. However, most convolutional neural network-based methods typically focus on a single scale, neglecting the incorporation of multi-scale information. In image restoration tasks, local features of an image are often insufficient, necessitating the integration of g

Melanie Schaller, Mathis Kruse, Antonio Ortega, Marius Lindauer

Addressing sensor drift is essential in industrial measurement systems, where precise data output is necessary for maintaining accuracy and reliability in monitoring processes, as it progressively degrades the performance of machine learning models over time. Our findings indicate that the standard cross-validation method used in existing model training over

D. K. He, Z. Song

The non-analyticity induced by exceptional points (EPs) has manifestations not only in non-Hermitian but also in Hermitian systems. In this work, we focus on a minimal Hermitian bosonic Kitaev model to reveal the dynamical demonstration of EPs in a Hermitian system. It is shown that the EPs separate the parameter space into four regions, in which the systems

Langming Liu, Shilei Liu, Yujin Yuan, Yizhen Zhang

Large language models (LLMs) achieve remarkable success in natural language processing (NLP). In practical scenarios like recommendations, as users increasingly seek personalized experiences, it becomes crucial to incorporate user interaction history into the context of LLMs to enhance personalization. However, from a practical utility perspective, user inte

Anton Backhaus, Thorsten Luettel, Mirko Maehlisch

An increasing number of datasets sharing similar domains for semantic segmentation have been published over the past few years. But despite the growing amount of overall data, it is still difficult to train bigger and better models due to inconsistency in taxonomy and/or labeling policies of different datasets. To this end, we propose a knowledge distillatio

Amirhossein Azarbahram, Onel L. A. Lopez, Bruno Clerckx, Marco Di Renzo

Radio frequency (RF) wireless power transfer (WPT) is a promising technology to seamlessly charge low-power devices, but its low end-to-end power transfer efficiency remains a critical challenge. To address the latter, low-cost transmit/radiating architectures, e.g., based on reconfigurable intelligent surfaces (RISs), have shown great potential. Beyond diag

Daniel Rose, Chia-Chien Hung, Marco Lepri, Israa Alqassem

Differential Diagnosis (DDx) is a fundamental yet complex aspect of clinical decision-making, in which physicians iteratively refine a ranked list of possible diseases based on symptoms, antecedents, and medical knowledge. While recent advances in large language models (LLMs) have shown promise in supporting DDx, existing approaches face key limitations, inc

Axel Runnholm, Matthew J. Hayes, Vihang Mehta, Matthew A. Malkan

Lyman $\alpha$ (Ly$\alpha$) emission is one of few observable features of galaxies that can trace the neutral hydrogen content in the Universe during the Epoch of Reionization (EoR). To accomplish this we need an efficient way to survey for Ly$\alpha$ emitters (LAEs) at redshifts beyond 7, requiring unbiased emission-line observations that are both sufficien

Gregory W. Kyro, Tianyin Qiu, Victor S. Batista

Deep learning has transformed protein design, enabling accurate structure prediction, sequence optimization, and de novo protein generation. Advances in single-chain protein structure prediction via AlphaFold2, RoseTTAFold, ESMFold, and others have achieved near-experimental accuracy, inspiring successive work extended to biomolecular complexes via AlphaFold

Alejandro Díaz-Caro, Gilles Dowek

We extend Natural Deduction for intuitionistic logic with a third introduction rule for the disjunction, $\vee$-i3, with a conclusion $\Gamma\vdash A\vee B$, but both premises $\Gamma\vdash A$ and $\Gamma\vdash B$. This rule is admissible in Natural Deduction. This extension is interesting in several respects. First, it permits to solve a well-known problem

Albin Zeqiri, Julian Britten, Clara Schramm, Pascal Jansen

Urban gardening is widely recognized for its numerous health and environmental benefits. However, the lack of suitable garden spaces, demanding daily schedules and limited gardening expertise present major roadblocks for citizens looking to engage in urban gardening. While prior research has explored smart home solutions to support urban gardeners, these app

Emanuele Mengoli, Luzius Moll, Virgilio Strozzi, El-Mahdi El-Mhamdi

In distributed learning, sign-based compression algorithms such as signSGD with majority vote provide a lightweight alternative to SGD with an additional advantage: fault tolerance (almost) for free. However, for signSGD with majority vote, this fault tolerance has been shown to cover only the case of weaker adversaries, i.e., ones that are not omniscient or

Petri Mäkinen, Pauli Mustalahti, Tuomo Kivelä, Jouni Mattila

Recent advances in visual 6D pose estimation of objects using deep neural networks have enabled novel ways of vision-based control for heavy-duty robotic applications. In this study, we present a pipeline for the precise tool positioning of heavy-duty, long-reach (HDLR) manipulators using advanced machine vision. A camera is utilized in the so-called eye-in-

Ze-Rui Liang, Han-Xue Chen, Feng-Kun Guo, Zhi-Hui Guo

We calculate the nucleon mass in a manifestly relativistic baryon chiral perturbation theory up to the leading two-loop order. Through dimensional counting analysis, we perform the chiral expansion and verify the validity of the extended-on-mass-shell scheme at the two-loop level. As a result, we obtain the complete chiral representation of the nucleon mass

Mohammad Moulaeifard, Peter H. Charlton, Nils Strodthoff

Photoplethysmography (PPG)-based blood pressure (BP) estimation represents a promising alternative to cuff-based BP measurements. Recently, an increasing number of deep learning models have been proposed to infer BP from the raw PPG waveform. However, these models have been predominantly evaluated on in-distribution test sets, which immediately raises the qu

Kaiwen Yan, Hongcheng Guo, Xuanqing Shi, Shaosheng Cao

With the rapid advancement of Large Language Models (LLMs), the demand for robust instruction-following capabilities in code generation tasks has grown significantly. Code generation not only facilitates faster prototyping and automated testing, but also augments developer efficiency through improved maintainability and reusability of code. In this paper, we

Maxime Culot

We characterize projective objects in the category of internal crossed modules within any semi-abelian category. When this category forms a variety of algebras, the internal crossed modules again constitute a semi-abelian variety, ensuring the existence of free objects, and thus of enough projectives. We show that such a variety is not necessarily Schreier,

Vijay Kumar Sutrakar, Anjana PK, Rohit Bisariya, Soumya KK

In this paper, a regression-based machine learning model is used for the design of cavity backed slotted antenna. This type of antenna is commonly used in military and aviation communication systems. Initial reflection coefficient data of cavity backed slotted antenna is generated using electromagnetic solver. These reflection coefficient data is then used a

Henry Peng Zou, Zhengyao Gu, Yue Zhou, Yankai Chen

Test-time computing approaches, which leverage additional computational resources during inference, have been proven effective in enhancing large language model performance. This work introduces a novel, linearly scaling approach, TestNUC, that improves test-time predictions by leveraging the local consistency of neighboring unlabeled data-it classifies an i