March 2025 arXiv papers — page 128

Ruixi Luo, Taikun Zhu, Kai Jin

Path partition problems on trees have found various applications. In this paper, we present an $O(n \log n)$ time algorithm for solving the following variant of path partition problem: given a rooted tree of $n$ nodes $1, \ldots, n$, where vertex $i$ is associated with a weight $w_i$ and a cost $s_i$, partition the tree into several disjoint chains $C_1,\ldo

Freya Husstedt, Motoi Kimata, Sajal Naduvile Thadathil, Beat Valentin Schwarze

Details of the electronic band structure in unconventional superconductors are key to the understanding of their fundamental ground state. The potential spin-triplet superconductor UTe$_2$, with $T_\mathrm{c}\approx 2.1\,$K, has attracted attention recently. Its main Fermi surface consists of weakly corrugated, two-dimensional Fermi-surface cylinders that ru

Matías Ezequiel Hernández Rodríguez

This article presents Underdamped Particle Swarm Optimization (UEPS), a novel metaheuristic inspired by both the Particle Swarm Optimization (PSO) algorithm and the dynamic behavior of an underdamped system. The underdamped motion acts as an intermediate solution between undamped systems, which oscillate indefinitely, and overdamped systems, which stabilize

Manas Vishal, Scott E. Field, Sigal Gottlieb, Jennifer Ryan

The long-time evolution of extreme mass-ratio inspiral systems requires minimal phase and dispersion errors to accurately compute far-field waveforms, while high accuracy is essential near the smaller black hole (modeled as a Dirac delta distribution) for self-force computations. Spectrally accurate methods, such as nodal discontinuous Galerkin (DG) methods,

Joshua Daniels-Holgate, Or Hershkovits

Suppose $(M^i_t)_{t\in [0,T)}$, $i=1,2$, are two mean curvature flows in $\mathbb{R}^{n+1}$ encountering a multiplicity one compact singularity at time $T$, in such a manner that for every $k$, the Hausdorff distance between the two flows, $d_H$, satisfies $d_{H}(M^1_t,M^2_t)/(T-t)^k \rightarrow 0$. We demonstrate that $M^1_t=M^2_t$ for every $t$. This gener

I. Kraus, Ph. -A. Bourdin, J. Zender, M. Bergmann

Context. The solar corona maintains temperatures of a million Kelvin or more. The plasma heating mechanisms responsible for these extreme temperatures are still unclear. Large regions of magnetic activity in the photosphere cause extreme ultraviolet (EUV) emission in the corona. Even smaller regions with bipolar and multipolar magnetic fields can generate co

Yaroslav Marchukov, Luis Montano

In the present paper we develop a distributed method to reconnect a multi-robot team after connectivity failures, caused by unpredictable environment changes, i.e. appearance of new obstacles. After the changes, the team is divided into different groups of robots. The groups have a limited communication range and only a partial information in their field of

Hao Cheng, Erjia Xiao, Yichi Wang, Lingfeng Zhang

Current Cross-Modality Generation Models (GMs) demonstrate remarkable capabilities in various generative tasks. Given the ubiquity and information richness of vision modality inputs in real-world scenarios, Cross-Vision tasks, encompassing Vision-Language Perception (VLP) and Image-to-Image (I2I), have attracted significant attention. Large Vision Language M

Fernando Cornet-Gomez, Víctor Miralles, Marcos Miralles López, María Moreno Llácer

In this paper we present updated constraints on the top-quark sector of the Standard Model Effective Field Theory using data available from Tevatron, LEP and the LHC. Bounds are obtained for the Wilson coefficients from a global fit including the relevant two-fermion operators, four-quark operators and two-quark two-lepton operators. We compare the current b

Diego Gosmar, Deborah A. Dahl, Dario Gosmar

Prompt injection constitutes a significant challenge for generative AI systems by inducing unintended outputs. We introduce a multi-agent NLP framework specifically designed to address prompt injection vulnerabilities through layered detection and enforcement mechanisms. The framework orchestrates specialized agents for generating responses, sanitizing outpu

Simon Köhnes, Jiongning Che, Barbara Dietz, Thomas Guhr

At lower energies, the resonances in scattering experiments are often isolated. The crucial parameter is the ratio of average resonance width and average mean level spacing. Towards larger energies, this parameter grows, because the resonances overlap. Eventually the cross-section becomes a random function and the scattering matrix elements follow a universa

Zirui Yuan, Siqi Lai, Hao Liu

Traffic Signal Control (TSC) plays a critical role in urban traffic management by optimizing traffic flow and mitigating congestion. While Large Language Models (LLMs) have recently emerged as promising tools for TSC due to their exceptional problem-solving and generalization capabilities, existing approaches fail to address the essential need for inter-agen

Elena Ballante, Pietro Muliere, Silvia Figini

This paper proposes a new class of predictive models for survival analysis called Generalized Bayesian Ensemble Survival Tree (GBEST). It is well known that survival analysis poses many different challenges, in particular when applied to small data or censorship mechanism. Our contribution is the proposal of an ensemble approach that uses Bayesian bootstrap

G. Lugones, A. G. Grunfeld

We derive scaling laws that connect certain macroscopic observables of strange quark stars with key microscopic properties of self-bound quark matter, such as the energy per baryon at zero pressure and the strength of repulsive interactions. We also identify universal relations linking global properties of strange quark stars - specifically, their moment of

Ziqin Zhou, Yifan Yang, Yuqing Yang, Tianyu He

Text-to-video generation poses significant challenges due to the inherent complexity of video data, which spans both temporal and spatial dimensions. It introduces additional redundancy, abrupt variations, and a domain gap between language and vision tokens while generation. Addressing these challenges requires an effective video tokenizer that can efficient

Vladimir Dergachev, Maria Alessandra Papa

We present the full release of the atlas of continuous gravitational waves, covering frequencies from 20 Hz to 1700 Hz and spindowns from -5e-10 to 5e-10 Hz/s. Compared to the early atlas release, we have extended the frequency range and have performed follow-up on the outliers. Conducting continuous wave searches is computationally intensive and time-consum

Siva Manohar Reddy Kesu, Neelam Sinha, Hariharan Ramasangu, Thomas Gregor Issac

Retinal optical coherence tomography (OCT) images are the biomarkers for neurodegenerative diseases, which are rising in prevalence. Early detection of Alzheimer's disease using retinal OCT is a primary challenging task. This work utilizes advanced deep learning techniques to classify retinal OCT images of subjects with Alzheimer's disease (AD) and healthy c

Jonas Belouadi, Eddy Ilg, Margret Keuper, Hideki Tanaka

Automatically synthesizing figures from text captions is a compelling capability. However, achieving high geometric precision and editability requires representing figures as graphics programs in languages like TikZ, and aligned training data (i.e., graphics programs with captions) remains scarce. Meanwhile, large amounts of unaligned graphics programs and c

Thuy M. Pham, Linda Senigagliesi, Marco Baldi, Rafael F. Schaefer

In this paper, we investigate the utilization of the angle of arrival (AoA) as a feature for robust physical layer authentication (PLA). While most of the existing approaches to PLA focus on common features of the physical layer of communication channels, such as channel frequency response, channel impulse response or received signal strength, the use of AoA

Juha Leppäkangas, Pascal Stadler, Dmitry Golubev, Rolando Reiner

Modeling composite systems of spins or electrons coupled to bosonic modes is of significant interest for many fields of applied quantum physics and chemistry. A quantum simulation can allow for the solution of quantum problems beyond classical numerical methods. However, implementing this on existing noisy quantum computers can be challenging due to the mapp

Piotr Hajłasz, Jacob Mirra, Armin Schikorra

We develop analysis of H\"older continuous mappings with applications to geometry and topology of the Heisenberg groups. We cover the theory of distributional Jacobians of H\"older continuous mappings and pullbacks of differential forms under H\"older continuous mappings. That includes versions of the change of variables formula and the Stokes theorem for H\

Tian-Hua Yang, Chen Fang

We derive the asymptotic forms of the Green's function at the open edges of general non-Hermitian band systems in all dimensions in the long-time limit, using a modified saddle-point approximation and the analytic continuation of the momentum. The edge dynamics is determined by the "dominant saddle point", a complex momentum, which, contrary to previous conj

Yaroslav Marchukov, Luis Montano

In this paper we develop a method for planning and coordinating a multi-agent team deployment to periodically gather information on demand. A static operation center (OC) periodically requests information from changing goal locations. The objective is to gather data in the goals and to deliver it to the OC, balancing the refreshing time and the total number

Vladimir Dergachev, Maria Alessandra Papa

We report on the first loosely coherent search for binary systems. We searched 0.06 rad disk in the Orion spur, covering gravitational wave frequencies from 100 to 700 Hz and frequency derivatives between -1e-11 to 1e-11 Hz/s. A follow-up was performed, which found no outliers. An atlas of results from the first stage of the search is made publicly available

Purnima P. Balakrishnan, Hemian Yi, Zi-Jie Yan, Wei Yuan

The search for chiral topological superconductivity in magnetic topological insulator (TI)-FeTe heterostructures is a key frontier in condensed matter physics, with potential applications in topological quantum computing. The combination of ferromagnetism, superconductivity, and topologically nontrivial surface states brings together the key elements require

Rémy Poudevigne, Paul Thévenin

We are interested in the structure of multitype Bienaym\'e-Galton-Watson (BGW) trees conditioned on integer linear combinations of the numbers of vertices of given types. We show that, under regularity assumptions on the offspring distributions, it is always possible to find a critical BGW tree having the same conditional distribution. This allows us to prov

Rui Asaoka, Yasunari Suzuki, Yuuki Tokunaga

Exploring an efficient and scalable architecture of fault-tolerant quantum computing (FTQC) is vital for demonstrating useful quantum computing. Here, we propose and evaluate a scalable and practical architecture with a cavity-quantum-electrodynamics (CQED) network. Our architecture takes advantage of the stability of neutral atoms and the flexibility of a C

Daniel Cunha Oliveira, Dylan Sandfelder, André Fujita, Xiaowen Dong

This paper extends the tactical asset allocation literature by incorporating regime modeling using techniques from machine learning. We propose a novel model that classifies current regimes, forecasts the distribution of future regimes, and integrates these forecasts with the historical performance of individual assets to optimize portfolio allocations. Util

Slávek Zbirovský, Václav Nežerka

Building Information Modeling (BIM) is an essential component in the sustainable reconstruction and revitalization of ageing structures. However, model creation usually relies on laborious manual transformation of the unstructured point cloud data provided by laser scans or photogrammetry. This paper presents Cloud2BIM, an open-source software tool designed

Adam Cicherski, Norbert Dojer

Pangenomes serve as a framework for joint analysis of genomes of related organisms. Several pangenome models were proposed, offering different functionalities, applications provided by available tools, their efficiency etc. Among them, two graph-based models are particularly widely used: variation graphs and de Bruijn graphs. In the current paper we propose

Jonas Erhardt, Mattia Iannetti, Fernando Dominguez, Ewelina M. Hankiewicz

Spin-momentum-locked edge states of quantum spin Hall insulators (QSHIs) provide a compelling platform for spintronic applications, owing to their intrinsic protection against backscattering from non-magnetic disorder. This protection emerges from time-reversal symmetry, which pairs Kramers partners of helical edge modes with opposite spin and momentum, ther

Shaofeng Liang, Runwei Guan, Wangwang Lian, Daizong Liu

As a significant application of multi-source information fusion in intelligent transportation perception systems, Referring Multi-Object Tracking (RMOT) involves localizing and tracking specific objects in video sequences based on language references. However, existing RMOT approaches often treat language descriptions as holistic embeddings and struggle to e

Zixu Cheng, Jian Hu, Ziquan Liu, Chenyang Si

Human processes video reasoning in a sequential spatio-temporal reasoning logic, we first identify the relevant frames ("when") and then analyse the spatial relationships ("where") between key objects, and finally leverage these relationships to draw inferences ("what"). However, can Video Large Language Models (Video-LLMs) also "reason through a sequential

Marco Schumann, Tobias Stollenwerk, Alessandro Ciani

Quantum circuit cutting refers to a series of techniques that allow one to partition a quantum computation on a large quantum computer into several quantum computations on smaller devices. This usually comes at the price of a sampling overhead, that is quantified by the $1$-norm of the associated decomposition. The applicability of these techniques relies on

Carsten Carstensen, Norbert Heuer

The classical continuous mixed formulation of linear elasticity with pointwise symmetric stresses allows for a conforming finite element discretization with piecewise polynomials of degree at least three. Symmetric stress approximations of lower polynomial order are only possible when their div-conformity is weakened to the continuity of normal-normal compon

Shardul Mukim, Meric E. Kucukbas, Stephen R. Power, Mauro S. Ferreira

It is difficult to completely eliminate disorder during the fabrication of graphene-based nanodevices. From a simulation perspective, it is straightforward to determine the electronic transport properties of disordered devices if complete information about the disorder and the Hamiltonian describing it is available. However, to do the reverse and determine i

Huixin Dong, Yijie Wu, Feiyu Li, Wei Kuang

Bluetooth Low Energy (BLE) backscatter is a promising candidate for battery-free Internet of Things (IoT) applications. Unlike existing commodity-level BLE backscatter systems that only enable one-shot communication through BLE advertising packets, we propose PassiveBLE, a backscatter system that can establish authentic and fully compatible BLE connections o

Michał Czakon, Terry Generet, Alexander Mitov, Rene Poncelet

In this work we calculate for the first time the next-to-next-to leading order (NNLO) QCD corrections to identified hadron production at hadron colliders. The inclusion of the NNLO correction has an important impact on all observables considered in this work. Higher order corrections reduce scale uncertainty and in almost all cases are moderate. Overall, goo

Yifeng Zhang, Yilin Liu, Ping Gong, Peizhuo Li

Adaptive traffic signal control (ATSC) is crucial in reducing congestion, maximizing throughput, and improving mobility in rapidly growing urban areas. Recent advancements in parameter-sharing multi-agent reinforcement learning (MARL) have greatly enhanced the scalable and adaptive optimization of complex, dynamic flows in large-scale homogeneous networks. H

Wako Aoki, Timothy C. Beers, Satoshi Honda, Tadafumi Matsuno

Abundances of five elements, Na, Mg, Al, Si, and Sr, are investigated for 44 very metal-poor stars (-4.0 < [Fe/H] < -1.5) in the Galactic halo system based on an Local Thermodinamic Equilibrium (LTE) analysis of high-resolution near-infrared spectra obtained with the Infrared Doppler instrument (IRD) on the Subaru Telescope. Mg and Si abundances are determin

Chengen Wang, Murat Kantarcioglu

DeepSeek-V3 and DeepSeek-R1 are leading open-source Large Language Models (LLMs) for general-purpose tasks and reasoning, achieving performance comparable to state-of-the-art closed-source models from companies like OpenAI and Anthropic -- while requiring only a fraction of their training costs. Understanding the key innovative techniques behind DeepSeek's s

Norihiro Tanahashi, Seiji Terashima, Shiki Yoshikawa

In this paper, we extend the study of wave packets from the AdS$_3$/CFT$_2$ correspondence to AdS$_4$/CFT$_3$ and examine properties of their energy density. We find that, while the energy still localizes on the light cone, its spatial distribution exhibits momentum dependence and is no longer localized in higher dimensions. This result is significant as it

Kevin-Martin Aigner, Sebastian Denzler, Frauke Liers, Sebastian Pokutta

Stochastic and (distributionally) robust optimization problems often become computationally challenging as the number of scenarios or data points increases. Scenario reduction is therefore a key technique for improving tractability. We introduce a general scenario reduction method for distributionally robust optimization (DRO), which includes stochastic and

Agi Villanyi, Yariv Yanay, Ari Mizel

A recent promising arena for quantum advantage is simulating exponentially large classical systems. Here, we show how this advantage can be used to calculate the dynamics of open classical systems experiencing dissipation, including the effects of non-Markovian baths. This is a particularly interesting class of systems since dissipation plays a key role in c

Piotr Bialas, Piotr Korcyl, Tomasz Stebel, Dawid Zapolski

We present the \texttt{NeuMC} software package, based on \pytorch, aimed at facilitating the research on neural samplers in lattice field theories. Neural samplers based on normalizing flows are becoming increasingly popular in the context of Monte-Carlo simulations as they can effectively approximate target probability distributions, possibly alleviating so

Seyed Mohammad Hadi Hosseini, Amir Mohammad Izadi, Ali Abdollahi, Armin Saghafian

Although recent text-to-image generative models have achieved impressive performance, they still often struggle with capturing the compositional complexities of prompts including attribute binding, and spatial relationships between different entities. This misalignment is not revealed by common evaluation metrics such as CLIPScore. Recent works have proposed

Mehmet Akhoroz, Caglar Yildirim

Conversational AI interfaces powered by large language models (LLMs) are increasingly used as coding assistants. However, questions remain about how programmers interact with LLM-based conversational agents, the challenges they encounter, and the factors influencing adoption. This study investigates programmers' usage patterns, perceptions, and interaction s

C. F. Groß, S. Romiti, L. Funcke, K. Jansen

At finite lattice spacing, Lagrangian and Hamiltonian predictions differ due to discretization effects. In the Hamiltonian limit, i.e. at vanishing temporal lattice spacing $a_t$, the path integral approach in the Lagrangian formalism reproduces the results of the Hamiltonian theory. In this work, we numerically calculate the Hamiltonian limit of a U$(1)$ ga

Zubair Iftikhar, Jonas Müller, Yuri Mukharsky, Philippe Joyez

We present an experimental test of Kubo formula performed on a nonlinear quantum conductor, a Superconductor-Insulator-Superconductor tunnel junction, driven far from equilibrium by a DC voltage bias. We implement the proposal of Lesovik and Loosen [1] and demonstrate experimentally that it is possible to extract both the emission and absorption noise of the

S. A. Lemziakov, B. Karimi, S. Nakamura, D. S. Lvov

The importance and non-trivial properties of superconductor normal metal interfaces was discovered by Alexander Fyodorovich Andreev more than 60 years ago. Only much later these hybrids have found wide interest in applications such as thermometry and refrigeration, electrical metrology, and quantum circuit engineering. Here we discuss the central properties

William Fishell, Andoni Rodriguez, Mark Santolucito

We propose the problem of multi-agent path planning for a generalization of the classic Cops and Robbers game via reactive synthesis. Specifically, through the application of LTLt and Coordination Synthesis, we aim to check whether various Cops and Robbers games are realizable (a strategy exists for the cops which guarantees they catch the robbers). Addition

Yifang Zhang, Arash Ajoudani, Nikos G Tsagarakis

In this work, we introduce the principle, design and mechatronics of Exo-Muscle, a novel assistive device for the knee joint. Different from the existing systems based on rigid exoskeleton structures or soft-tendon driven approaches, the proposed device leverages a new semi-rigid principle that explores the benefits of both rigid and soft systems. The use of

Ning Song, Jinze Hu, Shengjin Ji, Qing Cui

For a fixed graph $H$, a graph $G$ is called $H$-saturated if $G$ does not contain $H$ as a (not necessarily induced) subgraph, but $G+e$ contains a copy of $H$ for any $e\in E(\overline{G})$. The saturation number of $H$, denoted by ${\rm sat}(n,H)$, is the minimum number of edges in an $n$-vertex $H$-saturated graph. A wheel $W_n$ is a graph obtained from

Avi Kenny, Emily C. Voldal, Fan Xia, Kwun Chuen Gary Chan

Stepped wedge cluster randomized trials (SW-CRTs) have historically been analyzed using immediate treatment (IT) models, which assume the effect of the treatment is immediate after treatment initiation and subsequently remains constant over time. However, recent research has shown that this assumption can lead to severely misleading results if treatment effe

L. G. Vacher, V. T. H. Phan, L. Bonal, M. Iskakova

C-type asteroids, such as asteroid (162173) Ryugu, may have played a key role in delivering light elements to early Earth. Nitrogen (N)-bearing molecules have been chemically identified in some Ryugu grains, and based on the faint 3.06 um absorption band observed by the hyperspectral microscope MicrOmega, NH-bearing compounds appear to be globally distribute

Enrico Grimaldi, Claudio Battiloro, Paolo Di Lorenzo

The aim of this paper is to introduce a novel dictionary learning algorithm for sparse representation of signals defined over combinatorial topological spaces, specifically, regular cell complexes. Leveraging Hodge theory, we embed topology into the dictionary structure via concatenated sub-dictionaries, each as a polynomial of Hodge Laplacians, yielding loc

Jens Engel, Andrea Castellani, Patricia Wollstadt, Felix Lanfermann

We present a large real-world dataset obtained from monitoring a smart company facility over the course of six years, from 2018 to 2023. The dataset includes energy consumption data from various facility areas and components, energy production data from a photovoltaic system and a combined heat and power plant, operational data from heating and cooling syste

Ning Jiang, Liu Liu, Huimin Yu

In this paper, we investigate a class of tumor growth models governed by porous medium-type equations with uncertainties arisen from the growth function, initial condition, tumor support radius or other parameters in the model. We develop a stochastic asymptotic preservation (s-AP) scheme in the generalized polynomial chaos-stochastic Galerkin (gPC-SG) frame

Jose-Luis Holgado-Alvarez, Aryaman Reddi, Carlo D'Eramo

Reinforcement Learning (RL) has proven largely effective in obtaining stable locomotion gaits for legged robots. However, designing control algorithms which can robustly navigate unseen environments with obstacles remains an ongoing problem within quadruped locomotion. To tackle this, it is convenient to solve navigation tasks by means of a hierarchical appr

Azhar Ali Khaked, Nobuyuki Oishi, Daniel Roggen, Paula Lago

Human Activity Recognition (HAR) using wearable inertial measurement unit (IMU) sensors can revolutionize healthcare by enabling continual health monitoring, disease prediction, and routine recognition. Despite the high accuracy of Deep Learning (DL) HAR models, their robustness to real-world variabilities remains untested, as they have primarily been traine

Hang Shao, Lei Luo, Jianjun Qian, Mengkai Yan

Physiological activities can be manifested by the sensitive changes in facial imaging. While they are barely observable to our eyes, computer vision manners can, and the derived remote photoplethysmography (rPPG) has shown considerable promise. However, existing studies mainly rely on spatial skin recognition and temporal rhythmic interactions, so they focus

Aryan Eftekhari, Michel Juillard, Normann Rion, Simon Scheidegger

For over three decades, Dynare has been a cornerstone of dynamic stochastic modeling in economics, relying primarily on perturbation-based local solution methods. However, these techniques often falter in high-dimensional, non-linear models that demand more comprehensive approaches. This paper demonstrates that global solutions of economic models with substa

Kyle B. Treleaven

Inspired by a common technique for shuffling a deck of cards on a table without riffling, we formalize the pile shuffle and investigate its capabilities as a sorting device. Our study is novel in that we consider pile shuffle in three variations: (1) using queue-like piles, (2) using stack-like piles, and (3) using a heterogeneous mixture of those two pile t

Mingjia Shi, Ruihan Lin, Xuxi Chen, Yuhao Zhou

Learning to Optimize (L2O) enhances optimization efficiency with integrated neural networks. L2O paradigms achieve great outcomes, e.g., refitting optimizer, generating unseen solutions iteratively or directly. However, conventional L2O methods require intricate design and rely on specific optimization processes, limiting scalability and generalization. Our

Runze Xiao, Yongdong Wang, Yusuke Tsunoda, Koichi Osuka

Navigating unknown three-dimensional (3D) rugged environments is challenging for multi-robot systems. Traditional discrete systems struggle with rough terrain due to limited individual mobility, while modular systems--where rigid, controllable constraints link robot units--improve traversal but suffer from high control complexity and reduced flexibility. To

Alessandro Fogli, Bo Zhao, Peter Pietzuch, Jana Giceva

The growing disparity between CPU core counts and available memory bandwidth has intensified memory contention in servers. This particularly affects highly parallelizable applications, which must achieve efficient cache utilization to maintain performance as CPU core counts grow. Optimizing cache utilization, however, is complex for recent chiplet-based CPUs

Qingmei Wang, Yi Bi, Hongfei Liang, Jiayan Yang

Solar microflares are ubiquitous in the solar corona, yet their driving mechanisms remain a subject of ongoing debate. Using high-resolution coronal observations from the Solar Orbiter's Extreme Ultraviolet Imager (EUI), we identified about a dozen distinct moving plasma structures (hereafter, `` tiny ejections'') originating from the centers of three homolo

Carlos J. Costa

In this work, a thorough mathematical framework for incorporating Large Language Models (LLMs) into gamified systems is presented with an emphasis on improving task dynamics, user engagement, and reward systems. Personalized feedback, adaptive learning, and dynamic content creation are all made possible by integrating LLMs and are crucial for improving user

Minami Nakane, Masami Ouchi, Kimihiko Nakajima, Yoshiaki Ono

We derive Fe-abundance ratios of 7 galaxies at $z=9-12$ with $-22<M_{\mathrm{UV}}<-19$ whose JWST/NIRSpec spectra achieve very high signal-to-noise ratios, $\mathrm{SNR}=60-320$, at the rest-frame UV wavelength. We fit stellar population synthesis model spectra to these JWST spectra, masking out nebular emission lines, and obtain Fe-abundance ratios of $\mat

Linghao Huang, Dongheng Qian, Jing Wang

Inducing superconducting correlations in quantum anomalous Hall (QAH) states offers a promising route to realize topological superconductivity with chiral Majorana edge modes. However, the definitive identification of these modes is challenging. Here we propose detecting superconducting chiral edge modes via the probability distribution of the resistance, or

Hanjin Kim, Jiseong Park, Seojin Kim, Jueun Choi

Graph pooling, which compresses a whole graph into a smaller coarsened graph, is an essential component of graph representation learning. To efficiently compress a given graph, graph pooling methods often drop their nodes with attention-based scoring with the task loss. However, this often results in simply removing nodes with lower degrees without considera

Léo Meynent, Michael Bang Petersen, Sune Lehmann, Benjamin F. Maier

Understanding the factors that impact how a communicable disease like COVID-19 spreads is of central importance to mitigate future outbreaks. Traditionally, epidemic surveillance and forecasting analyses have focused on epidemiological data but recent advancements have demonstrated that monitoring behavioural changes may be equally important. Prior studies h

Xudong Pan, Jiarun Dai, Yihe Fan, Minyuan Luo

Self-replication with no human intervention is broadly recognized as one of the principal red lines associated with frontier AI systems. While leading corporations such as OpenAI and Google DeepMind have assessed GPT-o3-mini and Gemini on replication-related tasks and concluded that these systems pose a minimal risk regarding self-replication, our research p

Noah Kaufmann, Maria Quadeer, David Elkouss

Quantum network protocols depend on the availability of shared entanglement. Given that entanglement generation and distribution are affected by noise, characterization of the shared entangled states is essential to bound the errors of the protocols. This work analyzes the estimation of Bell diagonal states within quantum networks, where operations are limit

Changwen Li, Christoph Treude, Ofir Turel

This paper investigates the factors influencing programmers' adoption of AI-generated JavaScript code recommendations within the context of lightweight, function-level programming tasks. It extends prior research by (1) utilizing objective (as opposed to the typically self-reported) measurements for programmers' adoption of AI-generated code and (2) examinin

Aryaman Reddi

We present heuristically optimal strategies expressed by deep learning agents playing a simple avoidance game. We analyse the learning and behaviour of two agents within a symmetrical grid world that must cross paths to reach a target destination without crashing into each other or straying off of the grid world in the wrong direction. The agent policy is de

Jonas Müller, Quentin Coppée, Saskia Hekker

Strong magnetic fields in the core of red-giant branch stars are expected to suppress the amplitudes of the multipole modes. This occurs when the strength of the internal magnetic field approaches the critical field strength, at which the magnetic forces become comparable to the buoyancy. We performed Hamiltonian ray tracing simulations of magneto-gravity wa

Vincenzo De Maio, Ivona Brandic, Ewa Deelman, Jürgen Cito

Quantum computing exhibits the unique capability to natively and efficiently encode various natural phenomena, promising theoretical speedups of several orders of magnitude. However, not all computational tasks can be efficiently executed on quantum machines, giving rise to hybrid systems, where some portions of an application run on classical machines, whil

Jie Zhang, Swarna Chetty, Qiao Wang, Chenrui Sun

As the Metaverse envisions deeply immersive and pervasive connectivity in 6G networks, Integrated Access and Backhaul (IAB) emerges as a critical enabler to meet the demanding requirements of massive and immersive communications. IAB networks offer a scalable solution for expanding broadband coverage in urban environments. However, optimizing IAB node deploy

M. Kachelriess, E. Lammert

The high-mass X-ray binary Cygnus X-3 has been suggested for a long time to be a source of high-energy photons and neutrinos. In view of the increased sensitivity of current experiments, we examine the acceleration and interactions of high-energy cosmic rays (CRs) in this binary system, assuming that the compact object is a black hole. Using a test-particle

E. -A. Kolonia, C. J. A. P. Martins, Konstantinos N. Gourgouliatos

It is well known that alternative theories to the Standard Model allow -- and sometimes require -- fundamental constants, such as the fine-structure constant, $\alpha$, to vary in spacetime. We demonstrate that one way to investigate these variations is through the Mass-Radius relation of compact astrophysical objects, which is inherently affected by $\alpha

Yang Gu, Zhehao Qiu, Shui Lin, Yong Han

The characterization of the electronic structure and chemical states of gases, solids, and liquids can be effectively performed using ambient pressure X-ray photoelectron spectroscopy (AP-XPS). However, the acquisition of electronic and chemical information under plasma conditions poses significant challenges. In this study, we have developed an advanced exp

Zhu Cao

In this paper, we establish a connection between integral quadratic forms and exact covering systems (ECS) and present a structural framework for a class of product identities involving Ramanujan's theta functions. This approach yields infinitely many such identities. As applications, we provide a unified interpretation for twenty-two of Ramanujan's forty id

Balaji Rama, Kai Mei, Yongfeng Zhang

Autonomous LLM-based agents have emerged as a powerful paradigm for complex task execution, yet the field lacks standardized tools for development, deployment, distribution and discovery of agents. We present Cerebrum, an Agent SDK for AIOS that addresses this gap through three key components: (1) a comprehensive SDK featuring a modular four-layer architectu

Wenbo Wang, Guangyan Jia

We investigate a class of quadratic backward stochastic differential equations (BSDEs) with generators singular in $ y $. First, we establish the existence of solutions and a comparison theorem, thereby extending results in the literature. Additionally, we analyze the stability property and the Feynman-Kac formula, and prove the uniqueness of viscosity solut

Dong Liu, Sike Zeng, Ji-Hai Liao, Yu-Jun Zhao

The engineering of magnetic order and electronic states in two-dimensional (2D) materials is pivotal for advanced spintronic technologies. Despite their potential, the scarcity of intrinsic 2D ferromagnets remains a critical challenge. Here, we employ density functional theory with Hubbard-U corrections to systematically investigate adsorbate-driven magnetic

Gonzalo Contreras-Aso, Regino Criado, Miguel Romance

We present a comprehensive analysis of algebraic methods for controlling the stationary distribution of PageRank-like random walkers. Building upon existing literature, we compile and extend results regarding both structural control (through network modifications) and parametric control (through measure parameters) of these centralities. We characterize the

Jia Zhang, Chen-Xi Zhang, Yao Liu, Yi-Xuan Jin

Recent advancements in instruction tuning for large language models (LLMs) suggest that a small, high-quality dataset can significantly equip LLMs with instruction-following capabilities, outperforming large datasets often burdened by quality and redundancy issues. However, the challenge lies in automatically identifying valuable subsets from large datasets

Yueyi Wang, Kristjan Haule

We develop a variational perturbation expansion around dynamical mean-field theory (DMFT) that systematically incorporates nonlocal correlations beyond the local correlations treated by DMFT. We apply this approach to investigate how the DMFT critical temperature is suppressed from its mean-field value and how the critical behavior near the finite-temperatur

Sanghyun Jo, Seo Jin Lee, Seungwoo Lee, Seohyung Hong

Cell instance segmentation (CIS) is crucial for identifying individual cell morphologies in histopathological images, providing valuable insights for biological and medical research. While unsupervised CIS (UCIS) models aim to reduce the heavy reliance on labor-intensive image annotations, they fail to accurately capture cell boundaries, causing missed detec

Robert Lasarzik

We consider different measure-valued solvability concepts from the literature and show that they could be simplified by using the energy-variational structure of the underlying system of partial differential equations. In the considered examples, we prove that a certain class of improved measure-valued solutions can be equivalently expressed as an energy-var

Mohammad Zarshenas, Takuya Kuwahara, Bartosz Szczefanowicz, Andreas Klemenz

Friction force microscopy (FFM) with silicon tips on epitaxial graphene supported by SiC(0001) previously revealed a sharp increase in friction at a threshold normal force, linked to the intermittent rehybridization of graphene and the formation of single-layer diamond above 12 GPa. In this study, the FFM behavior of a diamond tip is compared with that of th

Marianne Defresne, Jayanta Mandi, Tias Guns

Real-life combinatorial optimization problems often involve several conflicting objectives, such as price, product quality and sustainability. A computationally-efficient way to tackle multiple objectives is to aggregate them into a single-objective function, such as a linear combination. However, defining the weights of the linear combination upfront is har

Dmytro Rak, Dusan Lorenc, Ayan A. Zhumekenov, Osman M. Bakr

Lead-halide perovskites exhibit remarkable efficiency in photovoltaics, driven by exceptionally long carrier diffusion lengths and recombination times. Paradoxically, this performance persists even in defect-rich, solution-grown samples. Here, we use a suite of optical and charge transport measurements to reveal that key optoelectronic properties of perovski

Andrés Chavarrías, David Rodriguez-Cianca, Pablo Lanillos

Spasticity is a common movement disorder symptom in individuals with cerebral palsy, hereditary spastic paraplegia, spinal cord injury and stroke, being one of the most disabling features in the progression of these diseases. Despite the potential benefit of using wearable robots to treat spasticity, their use is not currently recommended to subjects with a

Wolfgang Hogger, Alexander Riedel, Debadrito Roy, Angelika Knothe

High-harmonic generation in solids by intense laser pulses provides a fascinating platform for studying material properties and ultra-fast electron dynamics, where its coherent character is a central aspect. Using the semiconductor Bloch equations, we uncover a mechanism suppressing the high harmonic spectrum arising from the coherent superposition of intra-

Mingze Wu, Yan Pan, Junhui Li, Heng Wang

Continuous-variable quantum key distribution holds the potential to generate high secret key rates, making it a prime candidate for high-rate metropolitan quantum network applications. However, despite these promising opportunities, the realization of high-rate continuous-variable quantum key distribution systems with composable security remains an elusive g

F. B. Baalbergen, I. E. Zadeh, M. P. van Exter, M. J. A. de Dood

We demonstrate the use of a flexible and highly accurate Markov chain Monte Carlo Quantum Detector Tomography method as a minimization algorithm to best describe the response of an efficient $120 nm$ wide NbTiN superconducting nanobridge single photon detector. Separation of the internal quantum efficiency and external quantum efficiency is possible due to t

Haifeng Zhang, Changjing Zhuge, Jinzhi Lei

The interactions between tumor cells and the immune system play a crucial role in cancer evolution. In this study, we explore how these interactions influence cancer progression by modeling the relationships among naive T cells, effector T cells, and chronic myeloid leukemia cells. We examine the existence of equilibria, the asymptotic stability of the posit

Theodora-Mara Pîslar, Sara Magliacane, Atticus Geiger

Mechanistic interpretability aims to reverse engineer neural networks by uncovering which high-level algorithms they implement. Causal abstraction provides a precise notion of when a network implements an algorithm, i.e., a causal model of the network contains low-level features that realize the high-level variables in a causal model of the algorithm. A typi