March 2025 arXiv papers — page 91

Yining Lu, Noah Ziems, Hy Dang, Meng Jiang

Current research on the \textit{Decompose-Then-Verify} paradigm for evaluating the factuality of long-form text typically treats decomposition and verification in isolation, overlooking their interactions and potential misalignment. We find that existing decomposition policies, typically hand-crafted demonstrations, do not align well with downstream verifier

Elena Agliari, Alberto Fachechi, Domenico Luongo

We consider Hopfield networks, where neurons interact pair-wise by Hebbian couplings built over $i$. a set of definite patterns (ground truths), $ii$. a sample of labeled examples (supervised setting), $iii$. a sample of unlabeled examples (unsupervised setting). We focus on the case where ground-truths are Rademacher vectors and examples are noisy versions

Abhi Kamboj, Minh N. Do

Multimodal alignment constructs a joint latent vector space where modalities representing the same concept map to neighboring latent vectors. We formulate this as an inverse problem and show that, under certain conditions, paired data from each modality can map to equivalent latent vectors, which we refer to as perfect alignment. When perfect alignment canno

I-Fan Lin, Faegheh Hasibi, Suzan Verberne

In this paper, we propose Selection and Pooling with Large Language Models (SPILL), an intuitive and domain-adaptive method for intent clustering without fine-tuning. Existing embeddings-based clustering methods rely on a few labeled examples or unsupervised fine-tuning to optimize results for each new dataset, which makes them less generalizable to multiple

Shinsuke Takasao, Takashi Hosokawa, Kengo Tomida, Kazunari Iwasaki

In the early stages of star formation, boundary layer accretion, where protostars accrete material from disks extending down to their surfaces, plays a crucial role. Understanding how a magneto-rotational-instability (MRI)-active disk connects to a protostar's surface remains a significant challenge. To investigate the mechanisms of mass and angular momentum

Adrian S. Abazi, Roland Jaha, Connor A. Graham-Scott, Wolfram H. P. Pernice

Superconducting nanowire single photon detectors (SNSPDs) emerged in the last decade as a disruptive technology that features performance characteristics, such as high sensitivity, dynamic range and temporal accuracy, which are ideally suited for light detection and ranging (lidar) applications. Here, we report a time-of-flight (TOF) lidar system based on wa

D. L. Canedo, P. Moniz, G. Oliveira-Neto

In this work, we apply fractional calculus to study quantum cosmology. Specifically, our Wheeler-DeWitt equation includes a FRW geometry, a radiation fluid, a positive cosmological constant, and an ad hoc potential; we employ the Riesz fractional derivative, which brings a parameter $\alpha$, where $1 < \alpha \leq 2$, appearing explicitly in the mentioned e

Helene Götz, Jan Nagel

In this paper, we show limit theorems for the weighted spectral measure of the Laguerre ensemble under a nonstandard scaling, when the parameter grows faster than the matrix size. For this parameter scaling, the limit behavior is similar to the case of the Gaussian ensemble. We show a large deviation principle, moderate deviations and a CLT for the spectral

Julien Grand-Clément, Nicolas Vieille

This paper investigates properties of Blackwell $\epsilon$-optimal strategies in zero-sum stochastic games when the adversary is restricted to stationary strategies, motivated by applications to robust Markov decision processes. For a class of absorbing games, we show that Markovian Blackwell $\epsilon$-optimal strategies may fail to exist, yet we prove the

Itzhak Goldman

We use the observed vertical velocity field, of various young tracers of the gas kinematics, obtained by Li and Chen (2022), Konietzka et al. (2024) and Zhu et al. (2024), in order to test for the existence of turbulence. We do so by computing the power spectrum and the structure function of the vertical velocity field. The latter suggest the existence of co

Andrea Maroncelli, Jean-Marie Stéphan

We investigate a one-dimensional free fermion model with nearest and next-nearest neighbor hopping, evolving in imaginary time from a product state with N consecutive fermions, and conditioned to go back to the same state after a given time. Such types of models are quantum reformulations of well-studied two-dimensional classical lattice models, which are kn

Ze Tao, Fujun Liu, Jinhua Li, Guibo Chen

Accurately predicting nonlinear transient thermal fields in two-dimensional domains is a significant challenge in various engineering fields, where conventional analytical and numerical methods struggle to balance physical fidelity with computational efficiency when dealing with strong material nonlinearities and evolving multiphysics boundary conditions. To

Dmitry Marchenko, Maryam Sajedi, Maxim Krivenkov, Saleem Ayaz Khan

Halide perovskites are a promising class of materials for optoelectronic and photovoltaic applications, exhibiting high power conversion efficiency due to strong light absorption and long carrier diffusion lengths. While various aspects of their crystal and electronic structure have been studied, we identify a fundamental property previously overlooked that

Ritabrata Chakraborty, Rajatsubhra Chakraborty, Ali Khaleghi Rahimian, Thomas MacDougall

The proliferation of synthetic images generated by advanced AI models poses significant challenges in identifying and understanding manipulated visual content. Current fake image detection methods predominantly rely on binary classification models that focus on accuracy while often neglecting interpretability, leaving users without clear insights into why an

Tabitha Merrithew, Jessica Sorrells

This project mathematically models the self-assembly of DNA nanostructures in the shape of select Archimedean graphs using the flexible tile model. Under three different sets of restrictions called scenarios, we employ principles of linear algebra and graph theory to determine the minimum number of different DNA branched molecules and bond types needed to co

Yuqi Zhu, Ge Li, Xue Jiang, Jia Li

Chain-of-Thought (CoT) reasoning has been demonstrated as an effective technique for improving the problem-solving capabilities of large language models (LLMs) in the context of code generation. However, existing CoT methods often exhibit a tendency toward "overthinking", where the LLM consistently applies reasoning strategies without adequately considering

Martin Ritzert, Polina Turishcheva, Laura Hansel, Paul Wollenhaupt

Hierarchical clustering is an effective, interpretable method for analyzing structure in data. It reveals insights at multiple scales without requiring a predefined number of clusters and captures nested patterns and subtle relationships, which are often missed by flat clustering approaches. However, existing hierarchical clustering methods struggle with hig

Arjun Sharma, Peter A. Bosler, Rama Govindarajan, Donald L. Koch

A generalized reciprocal theorem is used to relate the force and torque induced on a particle in an inertia-less fluid with small variation in viscosity to integrals involving Stokes flow fields and the spatial dependence of viscosity. These resistivity expressions are analytically evaluated using spheroidal harmonics and then used to obtain the mobility of

Fernando Koch, Aladin Djuhera, Alecio Binotto

Large Foundation Models (LFMs), including multi-modal and generative models, promise to unlock new capabilities for next-generation Edge AI applications. However, performing inference with LFMs in resource-constrained and heterogeneous edge environments, such as Multi-access Edge Computing (MEC), presents significant challenges for workload orchestration due

Abdourahman Khaireh-Walieh, Alexandre Arnoult, Sébastien Plissard, Peter R. Wiecha

Reflection High-Energy Electron Diffraction (RHEED) is a powerful tool to probe the surface reconstruction during MBE growth. However, raw RHEED patterns are difficult to interpret, especially when the wafer is rotating. A more accessible representation of the information is therefore the so-called Azimuthal RHEED (ARHEED), an angularly resolved plot of the

Junyi Ao, Dekun Chen, Xiaohai Tian, Wenjie Feng

Large Language Models (LLMs) have recently shown remarkable ability to process not only text but also multimodal inputs such as speech and audio. However, most existing models primarily focus on analyzing input signals using text instructions, overlooking scenarios in which speech instructions and audio are mixed and serve as inputs to the model. To address

Hao Tan, Zichang Tan, Jun Li, Ajian Liu

Identifying multiple novel classes in an image, known as open-vocabulary multi-label recognition, is a challenging task in computer vision. Recent studies explore the transfer of powerful vision-language models such as CLIP. However, these approaches face two critical challenges: (1) The local semantics of CLIP are disrupted due to its global pre-training ob

Soham Roy, Abhishek Mishra, Shirish Karande, Murari Mandal

Modern text-to-image generative models can inadvertently reproduce copyrighted content memorized in their training data, raising serious concerns about potential copyright infringement. We introduce Guardians of Generation, a model agnostic inference time framework for dynamic copyright shielding in AI image generation. Our approach requires no retraining or

Jonah J. Glunt, Jacob A. Siefert, Andrew F. Thompson, Justin Ruths

Functional decomposition is a powerful tool for systems analysis because it can reduce a function of arbitrary input dimensions to the sum and superposition of functions of a single variable, thereby mitigating (or potentially avoiding) the exponential scaling often associated with analyses over high-dimensional spaces. This paper presents automated methods

F. R. Marleau, R. Habas, D. Carollo, C. Tortora

The Euclid Q1 fields were selected for calibration purposes in cosmology and are therefore relatively devoid of nearby galaxies. However, this is precisely what makes them interesting fields in which to search for dwarf galaxies in local density environments. We take advantage of the unprecedented depth, spatial resolution, and field of view of the Euclid Qu

C. Duffy, E. Cappellaro, M. T. Botticella, I. M. Hook

We report on serendipitous Euclid observations of previously known transients, using the Euclid Q1 data release. By cross-matching with the Transient Name Server (TNS) we identify 164 transients that coincide with the data release. Although the Euclid Q1 release only includes single-epoch data, we are able to make Euclid photometric measurements at the locat

Euclid Collaboration, C. Laigle, C. Gouin, F. Sarron

Galaxy morphologies and shape orientations are expected to correlate with their large-scale environment, since they grow by accreting matter from the cosmic web and are subject to interactions with other galaxies. Cosmic filaments are extracted in projection from the Euclid Quick Data Release 1 (covering 63.1 $\mathrm{deg}^2$) at $0.5<z<0.9$ in tomographic s

Euclid Collaboration, N. Mai, S. Mei, C. Cleland

Euclid will detect tens of thousands of clusters and protoclusters at $z$>1.3. With a total coverage of 63.1deg$^2$, the Euclid Quick Data Release 1 (Q1) is large enough to detect tens of clusters and hundreds of protoclusters at these early epochs. The Q1 photometric redshift catalogue enables us to detect clusters out to $z$ < 1.5; however, infrared imagin

Euclid Collaboration, P. Bergamini, M. Meneghetti, A. Acebron

We present the first catalogue of strong lensing galaxy clusters identified in the Euclid Quick Release 1 observations (covering $63.1\,\mathrm{deg^2}$). This catalogue is the result of the visual inspection of 1260 cluster fields. Each galaxy cluster was ranked with a probability, $\mathcal{P}_{\mathrm{lens}}$, based on the number and plausibility of the id

Euclid Collaboration, V. Busillo, C. Tortora, R. B. Metcalf

The Euclid mission aims to survey around 14000 deg^{2} of extragalactic sky, providing around 10^{5} gravitational lens images. Modelling of gravitational lenses is fundamental to estimate the total mass of the lens galaxy, along with its dark matter content. Traditional modelling of gravitational lenses is computationally intensive and requires manual input

Euclid Collaboration, P. Holloway, A. Verma, M. Walmsley

The Euclid Wide Survey (EWS) is expected to identify of order $100\,000$ galaxy-galaxy strong lenses across $14\,000$deg$^2$. The Euclid Quick Data Release (Q1) of $63.1$deg$^2$ Euclid images provides an excellent opportunity to test our lens-finding ability, and to verify the anticipated lens frequency in the EWS. Following the Q1 data release, eight machin

Euclid Collaboration, T. Li, T. E. Collett, M. Walmsley

Strong gravitational lensing systems with multiple source planes are powerful tools for probing the density profiles and dark matter substructure of the galaxies. The ratio of Einstein radii is related to the dark energy equation of state through the cosmological scaling factor $\beta$. However, galaxy-scale double-source-plane lenses (DSPLs) are extremely r

Euclid Collaboration, N. E. P. Lines, T. E. Collett, M. Walmsley

Strong gravitational lensing has the potential to provide a powerful probe of astrophysics and cosmology, but fewer than 1000 strong lenses have been confirmed so far. With a 0.16'' resolution covering a third of the sky, the Euclid telescope will revolutionise the identification of strong lenses, with 170 000 lenses forecasted to be discovered amongst the 1

Euclid Collaboration, K. Rojas, T. E. Collett, J. A. Acevedo Barroso

We present a search for strong gravitational lenses in Euclid imaging with high stellar velocity dispersion ($\sigma_\nu > 180$ km/s) reported by SDSS and DESI. We performed expert visual inspection and classification of $11\,660$ \Euclid images. We discovered 38 grade A and 40 grade B candidate lenses, consistent with an expected sample of $\sim$32. Palomar

Euclid Collaboration, M. Walmsley, P. Holloway, N. E. P. Lines

We present a catalogue of 497 galaxy-galaxy strong lenses in the Euclid Quick Release 1 data (63 deg$^2$). In the initial 0.45\% of Euclid's surveys, we double the total number of known lens candidates with space-based imaging. Our catalogue includes 250 grade A candidates, the vast majority of which (243) were previously unpublished. Euclid's resolution rev

Euclid Collaboration, L. Bisigello, G. Rodighiero, S. Fotopoulou

Recent James Webb Space Telescope (JWST) observations have revealed a population of sources with a compact morphology and a characteristic `v-shaped' continuum, namely blue at rest-frame $\lambda<4000$A and red at longer wavelengths. The nature of these sources, called `little red dots' (LRDs), is still debated, as it is unclear if they host active galactic

Euclid Collaboration, G. Girardi, G. Rodighiero, L. Bisigello

Our understanding of cosmic star-formation at $z>3$ used to largely rely on rest-frame UV observations. However, these observations overlook dusty and massive sources, resulting in an incomplete census of early star-forming galaxies. Recently, infrared data from Spitzer and the James Webb Space Telescope (JWST) have revealed a hidden population at $z\sim$3-6

Euclid Collaboration, G. Stevens, S. Fotopoulou, M. N. Bremer

Light emission from galaxies exhibit diverse brightness profiles, influenced by factors such as galaxy type, structural features and interactions with other galaxies. Elliptical galaxies feature more uniform light distributions, while spiral and irregular galaxies have complex, varied light profiles due to their structural heterogeneity and star-forming acti

Euclid Collaboration, T. Matamoro Zatarain, S. Fotopoulou, F. Ricci

We present a catalogue of candidate active galactic nuclei (AGN) in the $Euclid$ Quick Release (Q1) fields. For each $Euclid$ source we collect multi-wavelength photometry and spectroscopy information from Galaxy Evolution Explorer (GALEX), $Gaia$, Dark Energy Survey (DES), Wise-field Infrared Survey Explorer (WISE), $Spitzer$, Dark Energy Survey (DESI), and

Euclid Collaboration, F. Tarsitano, S. Fotopoulou, M. Banerji

Red quasars constitute an important but elusive phase in the evolution of supermassive black holes, where dust obscuration can significantly alter their observed properties. They have broad emission lines, like other quasars, but their optical continuum emission is significantly reddened, which is why they were traditionally identified based on near- and mid

Euclid Collaboration, B. Margalef-Bentabol, L. Wang, A. La Marca

Active galactic nuclei (AGN) play a key role in galaxy evolution but are challenging to identify due to their varied observational signatures. Furthermore, understanding their impact requires quantifying their strength relative to their host galaxies. We developed a deep learning (DL) model for identifying AGN in imaging data by deriving the contribution of

Euclid Collaboration, A. La Marca, L. Wang, B. Margalef-Bentabol

Galaxy major mergers are a key pathway to trigger AGN. We present the first detection of major mergers in the Euclid Deep Fields and analyse their connection with AGN. We constructed a stellar-mass-complete ($M_*>10^{9.8}\,M_{\odot}$) sample of galaxies from the first quick data release (Q1), in the redshift range z=0.5-2. We selected AGN using X-ray data, o

Euclid Collaboration, W. Roster, M. Salvato, J. Buchner

To better understand the role of active galactic nuclei (AGN) in galaxy evolution, it is crucial to achieve a complete and pure AGN census. X-ray surveys are key to this, but identifying their counterparts (CTPs) at other wavelengths remains challenging due to their larger positional uncertainties and limited availability of deeper, uniform ancillary data. E

Euclid Collaboration, P. Corcho-Caballero, Y. Ascasibar, G. Verdoes Kleijn

Investigating what drives the quenching of star formation in galaxies is key to understanding their evolution. The Euclid mission will provide rich data from optical to infrared wavelengths for millions of galaxies, and enable precise measurements of their star formation histories. Using the first Euclid Quick Data Release (Q1), we developed a probabilistic

Euclid Collaboration, A. Enia, L. Pozzetti, M. Bolzonella

The star-forming main sequence (SFMS) is a tight relation observed between stellar masses and star formation rates (SFR) in a population of galaxies. This relation is observed at different redshifts, in various morphological, and environmental domains, and is key to understanding the underlying relations between a galaxy budget of cold gas and its stellar co

Euclid Collaboration, M. Siudek, M. Huertas-Company, M. Smith

Modern astronomical surveys, such as the Euclid mission, produce high-dimensional, multi-modal data sets that include imaging and spectroscopic information for millions of galaxies. These data serve as an ideal benchmark for large, pre-trained multi-modal models, which can leverage vast amounts of unlabelled data. In this work, we present the first explorati

Euclid Collaboration, M. Huertas-Company, M. Walmsley, M. Siudek

Stellar bars are key structures in disc galaxies, driving angular momentum redistribution and influencing processes such as bulge growth and star formation. Quantifying the bar fraction as a function of redshift and stellar mass is therefore important for constraining the physical processes that drive disc formation and evolution across the history of the Un

Euclid Collaboration, M. Walmsley, M. Huertas-Company, L. Quilley

We present a detailed visual morphology catalogue for Euclid's Quick Release 1 (Q1). Our catalogue includes galaxy features such as bars, spiral arms, and ongoing mergers, for the 378000 bright ($I_E < 20.5$) or extended (area $\geq 700\,$pixels) galaxies in Q1. The catalogue was created by finetuning the Zoobot galaxy foundation models on annotations from a

Euclid Collaboration, L. Quilley, I. Damjanov, V. de Lapparent

We present the results of the single-component S\'ersic profile fitting for the magnitude-limited sample of \IE$<23$ galaxies within the 63.1 deg$^2$ area of the Euclid Quick Data Release (Q1). The associated morphological catalogue includes two sets of structural parameters fitted using \texttt{SourceXtractor++}: one for VIS \IE images and one for a combina

Euclid Collaboration, V. Le Brun, M. Bethermin, M. Moresco

The SPE processing function (PF) of the \Euclid pipeline is dedicated to the automatic analysis of one-dimensional spectra to determine redshifts, line fluxes, and spectral classifications. The first \Euclid Quick Data Release (Q1) delivers these measurements for all $H_\mathrm{E}<22.5$ objects identified in the photometric survey. In this paper, we present

Euclid Collaboration, Y. Copin, M. Fumana, C. Mancini

The Euclid space mission aims to investigate the nature of dark energy and dark matter by mapping the large-scale structure of the Universe. A key component of Euclid's observational strategy is slitless spectroscopy, conducted using the Near Infrared Spectrometer and Photometer (NISP). This technique enables the acquisition of large-scale spectroscopic data

Euclid Collaboration, M. Tucci, S. Paltani, W. G. Hartley

The ESA Euclid mission will measure the photometric redshifts of billions of galaxies in order to provide an accurate 3D view of the Universe at optical and near-infrared wavelengths. Photometric redshifts are determined by the PHZ processing function on the basis of the multi-wavelength photometry of Euclid and ground-based observations. In this paper, we d

Euclid Collaboration, E. Romelli, M. Kümmel, H. Dole

The Euclid satellite is an ESA mission that was launched in July 2023. \Euclid is working in its regular observing mode with the target of observing an area of $14\,000~\text{deg}^2$ with two instruments, the Visible Camera (VIS) and the Near IR Spectrometer and Photometer (NISP) down to $I_{\rm E} = 24.5~\text{mag}$ ($10\, \sigma$) in the Euclid Wide Survey

Euclid Collaboration, G. Polenta, M. Frailis, A. Alavi

This paper describes the near-infrared processing function (NIR PF) that processes near-infrared images from the Near-Infrared Spectrometer and Photometer (NISP) instrument onboard the Euclid satellite. NIR PF consists of three main components: (i) a common pre-processing stage for both photometric (NIR) and spectroscopic (SIR) data to remove instrumental ef

Euclid Collaboration, H. J. McCracken, K. Benson, C. Dolding

This paper describes the VIS Processing Function (VIS PF) of the Euclid ground segment pipeline, which processes and calibrates raw data from the VIS camera. We present the algorithms used in each processing element, along with a description of the on-orbit performance of VIS PF, based on Performance Verification (PV) and Q1 data. We demonstrate that the pri

Euclid Collaboration, H. Aussel, I. Tereno, M. Schirmer

The first Euclid Quick Data Release, Q1, comprises 63.1 sq deg of the Euclid Deep Fields (EDFs) to nominal wide-survey depth. It encompasses visible and near-infrared space-based imaging and spectroscopic data, ground-based photometry in the u, g, r, i and z bands, as well as corresponding masks. Overall, Q1 contains about 30 million objects in three areas n

Jia Li, Hao Zhu, Huanyu Liu, Xianjie Shi

Large Language Models (LLMs) have shown promising results in repository-level code completion, which completes code based on the in-file and cross-file context of a repository. The cross-file context typically contains different types of information (e.g., relevant APIs and similar code) and is lengthy. In this paper, we found that LLMs struggle to fully uti

Weixiao Gao, Liangliang Nan, Hugo Ledoux

Semantic segmentation in urban scene analysis has mainly focused on images or point clouds, while textured meshes - offering richer spatial representation - remain underexplored. This paper introduces SUM Parts, the first large-scale dataset for urban textured meshes with part-level semantic labels, covering about 2.5 km2 with 21 classes. The dataset was cre

Zorik Gekhman, Eyal Ben David, Hadas Orgad, Eran Ofek

This work presents a framework for assessing whether large language models (LLMs) encode more factual knowledge in their parameters than what they express in their outputs. While a few studies hint at this possibility, none has clearly defined or demonstrated this phenomenon. We first propose a formal definition of knowledge, quantifying it for a given quest

Urszula D. Wdowik, Vaibhav Varade, Jana Vejpravova, Dominik Legut

Magneto-Raman spectroscopy is applied to study spin-phonon coupling in the layered two-dimensional (2D) van der Waals antiferromagnet CrSBr. We report on the effects of temperature and external magnetic field on Raman-active phonons of $A_g$ symmetry in bulk and one-to-six-layer forms of CrSBr that are reflected by the Raman spectral patterns measured at dif

Samie Mostafavi, Gourav Prateek Sharma, Ahmad Traboulsi, James Gross

With the emergence of new application areas such as cyber-physical systems and human-in-the-loop applications ensuring a specific level of end-to-end network latency with high reliability (e.g., 99.9%) is becoming increasingly critical. To align wireless links with these reliability requirements, it is essential to analyze and control network latency in term

Wei-Tian Li, Po-Wen Yang

For a graph on $m$ edges, a bijective function between the edge set of the graph and $\{1,2,\ldots,m\}$ is an antimagic labeling provided that when adding the labels of the edges incident to the same vertex, the sums are pairwise distinct. Hartsfield and Ringel conjectured that every connected graph has antimagic labeling. On the other hand, it is known that

Aoting Zhang, Dongbao Yang, Chang Liu, Xiaopeng Hong

Incremental object detection (IOD) aims to cultivate an object detector that can continuously localize and recognize novel classes while preserving its performance on previous classes. Existing methods achieve certain success by improving knowledge distillation and exemplar replay for transformer-based detection frameworks, but the intrinsic forgetting mecha

Ari Blondal, Hamed Hatami, Pooya Hatami, Chavdar Lalov

We prove that the list replicability number of $d$-dimensional $\gamma$-margin half-spaces satisfies \[ \frac{d}{2}+1 \le \mathrm{LR}(H^d_\gamma) \le d, \] which grows with dimension. This resolves several open problems: $\bullet$ Every disambiguation of infinite-dimensional large-margin half-spaces to a total concept class has unbounded Littlestone dimensio

Juan P. Aguilera, Guillaume Massas

Following a suggestion of Birkhoff and Von Neumann [Ann. Math. 37 (1936), 23-32], we pursue a joint study of quantum logic and intuitionistic logic. We exhibit a linear-time translation which for each quantum logic $Q$ and each superintuitionistic logic $I$ yields an axiomatization of $Q\cap I$ from axiomatizations of $Q$ and $I$. The translation is centered

Pritam Ganguly, Abhishek Ghosh

In this article, we establish dimension-free Fefferman-Stein inequalities for the Hardy-Littlewood maximal function associated with averages over Kor\'anyi balls in the Heisenberg group. We also generalize the result to more general UMD lattices. As a key stepping stone, we establish the $L^p$- boundedness of the vector-valued Nevo-Thangavelu spherical maxim

Felix Wiebe, Niccolò Turcato, Alberto Dalla Libera, Jean Seong Bjorn Choe

In the field of robotics many different approaches ranging from classical planning over optimal control to reinforcement learning (RL) are developed and borrowed from other fields to achieve reliable control in diverse tasks. In order to get a clear understanding of their individual strengths and weaknesses and their applicability in real world robotic scena

Junnan Zhu, Min Xiao, Yining Wang, Feifei Zhai

LLMs have achieved remarkable fluency and coherence in text generation, yet their widespread adoption has raised concerns about content reliability and accountability. In high-stakes domains, it is crucial to understand where and how the content is created. To address this, we introduce the Text pROVEnance (TROVE) challenge, designed to trace each sentence o

Justin Le Louëdec, Maike Bauer, Tanja Amerstorfer, Jackie A. Davies

Observing and forecasting coronal mass ejections (CME) in real-time is crucial due to the strong geomagnetic storms they can generate that can have a potentially damaging effect, for example, on satellites and electrical devices. With its near-real-time availability, STEREO/HI beacon data is the perfect candidate for early forecasting of CMEs. However, previ

Zhenlin Qin, Leizhen Wang, Yancheng Ling, Francisco Camara Pereira

Individual mobility prediction plays a key role in urban transport, enabling personalized service recommendations and effective travel management. It is widely modeled by data-driven methods such as machine learning, deep learning, as well as classical econometric methods to capture key features of mobility patterns. However, such methods are hindered in pro

Anna Masiero, Alexander Venus, Erik Leitinger

Multipath-based simultaneous localization and mapping (MP-SLAM) is a promising approach in wireless networks to jointly obtain position information of transmitters/receivers and information of the propagation environment. MP-SLAM models specular reflections at flat surfaces as virtual anchors (VAs), which are mirror images of base stations. Particle-based me

Yuanchao Yue, Hui Yuan, Zhengxin Li, Shuai Li

The primary requirement for cross-modal data fusion is the precise alignment of data from different sensors. However, the calibration between LiDAR point clouds and camera images is typically time-consuming and needs external calibration board or specific environmental features. Cross-modal registration effectively solves this problem by aligning the data di

Yuanchao Yue, Hui Yuan, Qinglong Miao, Xiaolong Mao

Cross-modal data registration has long been a critical task in computer vision, with extensive applications in autonomous driving and robotics. Accurate and robust registration methods are essential for aligning data from different modalities, forming the foundation for multimodal sensor data fusion and enhancing perception systems' accuracy and reliability.

Mootez Saad, José Antonio Hernández López, Boqi Chen, Neil Ernst

Large Language Models have significantly advanced the field of code generation, demonstrating the ability to produce functionally correct code snippets. However, advancements in generative AI for code overlook foundational Software Engineering (SE) principles such as modularity, and single responsibility, and concepts such as cohesion and coupling which are

Duxiao Wang, Disheng Xu, Qi Zhou

We demonstrate the existence of an open dense subset within the class of real analytic one-frequency quasi-periodic $\mathrm{\Sp}(4,\mathbb{R})$-cocycles, characterized by either the distinctness of all their Lyapunov exponents or the non-zero nature of all their accelerations, which partially answers an open problem raised by A. Avila.

Carlos M. Mora

We develop a system of non-linear stochastic evolution equations that describes the continuous measurements of quantum systems with mixed initial state. We address quantum systems with unbounded Hamiltonians and unbounded interaction operators. Using arguments of the theory of quantum measurements we derive a system of stochastic interacting wave functions (

Rohit Juneja, Amita Das, Trishul Dhalia, Animesh Sharma

The absorption of laser energy by plasma is of paramount importance for various applications. Collisional and resonant processes are often invoked for this purpose. However, in some contexts (e.g. in vacuum and the JxB heating), the energy transfer occurs even when plasma is collisionless, and there is no resonant process involved. The energy absorption in t

Christopher G. Albert, Georg S. Grassler, Sergei V. Kasilov, Markus Markl

Symplectic integrators with long-term preservation of integrals of motion are introduced for the guiding-center model of plasma particles in toroidal magnetic fields of general topology. An efficient transformation to canonical coordinates from cylindrical and flux-like coordinates is discussed and applied using one component of the magnetic vector potential

Haoye Wang, Zhipeng Gao, Tingting Bi, John Grundy

Software development is a collaborative process that involves various interactions among individuals and teams. TODO comments in source code play a critical role in managing and coordinating diverse tasks during this process. However, this study finds that a large proportion of open-source project TODO comments are left unresolved or take a long time to be r

Samuele Mosso, Karl Lapo, Ivana Stiperski

Turbulence anisotropy was recently integrated into Monin-Obukhov Similarity Theory (MOST), extending its applicability to complex terrain and diverse surface conditions. Implementing this generalized MOST in numerical models, however, requires understanding the key drivers of turbulence anisotropy across various terrain conditions. This study therefore emplo

Xiang Li, Heqian Qiu, Lanxiao Wang, Hanwen Zhang

With the rapid development of artificial intelligence technologies and wearable devices, egocentric vision understanding has emerged as a new and challenging research direction, gradually attracting widespread attention from both academia and industry. Egocentric vision captures visual and multimodal data through cameras or sensors worn on the human body, of

Paul Balmer, Martin Gallauer

The spectrum of a tensor-triangulated category carries a compact Hausdorff topology, called the constructible topology, also known as the patch topology. We prove that patch-dense subsets detect tt-ideals and we prove that any infinite family of tt-functors that detects nilpotence provides such a patch-dense subset. We review several applications and example

Yiming Cui, Shiyu Fang, Peng Hang, Jian Sun

Autonomous driving has entered the testing phase, but due to the limited decision-making capabilities of individual vehicle algorithms, safety and efficiency issues have become more apparent in complex scenarios. With the advancement of connected communication technologies, autonomous vehicles equipped with connectivity can leverage vehicle-to-vehicle (V2V)

William Schoenberg, Davidson Girard, Saras Chung, Ellen O'Neill

Introduction: As system dynamics (SD) embraces automation, AI offers efficiency but risks bias from missing data and flawed models. Models that omit multiple perspectives and data threaten model quality, whether created by humans or with the assistance of AI. To reduce uncertainty about how well AI can build SD models, we introduce two metrics for evaluation

Chenxin Yu, Zihong Lu, Jie Mei, Boyu Zhou

Various studies on perception-aware planning have been proposed to enhance the state estimation accuracy of quadrotors in visually degraded environments. However, many existing methods heavily rely on prior environmental knowledge and face significant limitations in previously unknown environments with sparse localization features, which greatly limits their

David Wan, Justin Chih-Yao Chen, Elias Stengel-Eskin, Mohit Bansal

Multi-agent collaboration among models has shown promise in reasoning tasks but is underexplored in long-form generation tasks like summarization and question-answering. We extend multi-agent multi-model reasoning to generation, specifically to improving faithfulness through refinement, i.e., revising model-generated outputs to remove factual inconsistencies

Shaohui Yang, Toshiyuki Ohtsuka, Colin N. Jones

In almost all algorithms for Model Predictive Control (MPC), the most time-consuming step is to solve some form of Linear Quadratic (LQ) Optimal Control Problem (OCP) repeatedly. The commonly recognized best option for this is a Riccati recursion based solver, which has a time complexity of $\mathcal{O}(N(n_x^3 + n_x^2 n_u + n_x n_u^2 + n_u^3))$. In this pap

Jianhang Xiang, Zhipeng Gao, Lingfeng Bao, Xing Hu

Recently, smart contracts have played a vital role in automatic financial and business transactions. To help end users without programming background to better understand the logic of smart contracts, previous studies have proposed models for automatically translating smart contract source code into their corresponding code summaries. However, in practice, o

Shaohui Yang, Toshiyuki Ohtsuka, Brian Plancher, Colin N. Jones

The efficient solution of moderately large-scale linear systems arising from the KKT conditions in optimal control problems (OCPs) is a critical challenge in robotics. With the stagnation of Moore's law, there is growing interest in leveraging GPU-accelerated iterative methods, and corresponding parallel preconditioners, to overcome these computational chall

Roberto Araya

Learning to reason and carefully explain arguments is central to students' cognitive, mathematical, and computational thinking development. This is particularly challenging in problems under uncertainty and in Bayesian reasoning. With the new generation of large language models (LLMs) capable of reasoning using Chain-of-Thought (CoT), there is an excellent o

Alessio Micheli, Alejandro Moreo, Marco Podda, Fabrizio Sebastiani

Network quantification (NQ) is the problem of estimating the proportions of nodes belonging to each class in subsets of unlabelled graph nodes. When prior probability shift is at play, this task cannot be effectively addressed by first classifying the nodes and then counting the class predictions. In addition, unlike non-relational quantification, NQ demands

Xinyue Wang, Yue Sun, Wei Wei, Qiang Hou

In strange metals, a strong and anomalous scattering effect exists and increases linearly with temperature. In FeSe, we observed that the temperature dependence of resistivity exhibits non-Fermi liquid behavior in two regions below and above a critical pressure, $p_\text{c}$$\sim$2 GPa. As pressure increases, a transition from quadratic to nonsaturating magn

Ruowen Zhao, Junliang Ye, Zhengyi Wang, Guangce Liu

Triangle meshes play a crucial role in 3D applications for efficient manipulation and rendering. While auto-regressive methods generate structured meshes by predicting discrete vertex tokens, they are often constrained by limited face counts and mesh incompleteness. To address these challenges, we propose DeepMesh, a framework that optimizes mesh generation

Hengrui Kang, Siwei Wen, Zichen Wen, Junyan Ye

The rapid advancements in generative technology have emerged as a double-edged sword. While offering powerful tools that enhance convenience, they also pose significant social concerns. As defenders, current synthetic image detection methods often lack artifact-level textual interpretability and are overly focused on image manipulation detection, and current

Mirmukhsin Makhmudov, Evgeny Verbitskiy

We consider the problem of equivalence of Gibbs states and equilibrium states for continuous potentials on full shift spaces $E^{\mathbb{Z}}$. Sinai, Bowen, Ruelle and others established equivalence under various assumptions on the potential $\phi$. At the same time, it is known that every ergodic measure is an equilibrium state for some continuous potential

Eunsun Kim, Ian P. Roberts, Taekyun Lee, Jeffrey G. Andrews

We study spectrum sharing between two dense low-earth orbit (LEO) satellite constellations, an incumbent primary system and a secondary system that must respect interference protection constraints on the primary system. In particular, we propose a secondary satellite selection framework and algorithm that maximizes capacity while guaranteeing that the time-a

Fardad Rahkheir, Soroush Akhlaghi

This paper attempts to jointly optimize the hybrid precoding (HP) and intelligent reflecting surfaces (IRS) beamforming matrices in a multi-IRS-aided mmWave communication network, utilizing the Alamouti scheme at the base station (BS). Considering the overall signal-to-noise ratio (SNR) as the objective function, the underlying problem is cast as an optimiza

Lei Shi, Xi Fang, Naiyu Wang, Junxing Zhang

Automatic medical image segmentation plays a crucial role in computer aided diagnosis. However, fully supervised learning approaches often require extensive and labor-intensive annotation efforts. To address this challenge, weakly supervised learning methods, particularly those using extreme points as supervisory signals, have the potential to offer an effec

Bowen Tan, Ying Cui

Fast and accurate device activity detection is the critical challenge in grant-free access for supporting massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC) in 5G and beyond. The state-of-the-art methods have unsatisfactory error rates or computation times. To address these outstanding issues, we propose new maxi

Michele Benzi, Milo Viviani

In this paper we use some basic facts from the theory of (matrix) Lie groups and algebras to show that many of the classical matrix splittings used to construct stationary iterative methods and preconditioniers for Krylov subspace methods can be interpreted as linearizations of matrix factorizations. Moreover, we show that new matrix splittings are obtained