March 2025 arXiv papers — page 156

Soumya Das

This thesis investigates the entanglement of distinguishable and indistinguishable particles, introducing a new error model for Hardy's test, experimentally verified using superconducting qubits. We address challenges in implementing quantum protocols based on this test and propose potential solutions and present two performance measures for qubits in superc

Zhanzhao Li, Christopher A. Gorski, Aaron Thompson, Jeffrey R. Shallenberger

Deleterious aggregate reactions induced by iron sulfide minerals, especially pyrrhotite and pyrite, have devastated concrete structures across many global regions. While these minerals have been extensively studied under acidic conditions, their behavior in alkaline environments, such as concrete, remains poorly understood. This study investigates the kineti

Apan Dastider, Hao Fang, Mingjie Lin

Cross-embodiment robotic manipulation synthesis for complicated tasks is challenging, partially due to the scarcity of paired cross-embodiment datasets and the impediment of designing intricate controllers. Inspired by robotic learning via guided human expert demonstration, we here propose a novel cross-embodiment robotic manipulation algorithm via CycleVAE

Evangelos Afxonidis, Alessio Caddeo, Carlos Hoyos, Daniele Musso

Fractonic matter with dipole symmetry can be coupled to a two-index symmetric tensor gauge field. In this work, we show that this symmetric tensor field, along with other related generalized Maxwell theories, can be consistently coupled to curved backgrounds in a covariant and gauge-invariant way by reformulating dipole symmetry using conventional vector gau

Michele Viscardi, Marcello Dalmonte, Alioscia Hamma, Emanuele Tirrito

Understanding the interplay between nonstabilizerness and entanglement is crucial for uncovering the fundamental origins of quantum complexity. Recent studies have proposed entanglement spectral quantities, such as antiflatness of the entanglement spectrum and entanglement capacity, as effective complexity measures, establishing direct connections to stabili

Xianfeng Wu, Yajing Bai, Haoze Zheng, Harold Haodong Chen

Recent advances in text-to-image generation have primarily relied on extensive datasets and parameter-heavy architectures. These requirements severely limit accessibility for researchers and practitioners who lack substantial computational resources. In this paper, we introduce \model, an efficient training paradigm for image generation models that uses know

D. Tripathi

{In 2020, Carney et.al. proved the quaternionic version of the Enestr\"{o}m-Kakeya Theorem, which states that a polynomial $p(q)=\sum_{\nu=0}^n q^\nu a_\nu$ with non-negative and monotonically increasing coefficients $(0<a_0\le a_1\le \cdots \le a_n)$ has all of its zeros within the unit ball $|q|\le 1$. Numerous generalizations of Enestr\"{o}m-Kakeya Theore

Sabyasachi Maulik, Arpita Mitra, Debangshu Mukherjee, Augniva Ray

We calculate the logarithmic temperature corrections to the thermodynamic entropy of four-dimensional near-extremal Reissner-Nordstr\"{o}m de Sitter (dS) black hole by computing a one-loop contribution within the path integral framework in the near-horizon limit. Due to the presence of three horizons, the extremal limit of a charged dS black hole is fundamen

Gianluca Inverso, Mario Trigiante

We review recent progress in constructing maximal, classical supergravity models and their applications.

Laura Cossu, Salvatore Tringali

Given a monoid $H$ (written multiplicatively), the family $\mathcal{P}_{\mathrm{fin},1}(H)$ of all non-empty finite subsets of $H$ containing the identity element $1_H$ is itself a monoid, called the reduced finitary power monoid of $H$, under the operation of setwise multiplication induced by $H$. We investigate the arithmetic of $\mathcal P_{\mathrm{fin},1

Lilia Mehidi

In the first part of the paper, we study conformal groups that act properly discontinuously and cocompactly on simply connected, non-flat homogeneous plane waves. We show that proper cocompact similarity actions that are not isometric can occur, in contrast to the behavior of Riemannian and Lorentzian affine similarity actions. In the second part, we conside

Ipek Efe, Alexander Vogel, William S. Huxter, Elzbieta Gradauskaite

Nanoscale electrostatic control of oxide interfaces enables physical phenomena and exotic functionalities beyond the realm of the bulk material. In technologically-relevant ferroelectric thin films, the interface-mediated polarization control is usually exerted by engineering the depolarizing field. Here, in contrast, we introduce polarizing surfaces and lat

Yingqi Tang, Zhuoran Xu, Zhaotie Meng, Erkang Cheng

Although end-to-end autonomous driving (E2E-AD) technologies have made significant progress in recent years, there remains an unsatisfactory performance on closed-loop evaluation. The potential of leveraging planning in query design and interaction has not yet been fully explored. In this paper, we introduce a multi-granularity planning query representation

Linnea Heitmeier, Thomas Voigtmann

We investigate the interface of a glass-forming fluid showing non-Newtonian rheology. By applying shear flow in the interface, we detect that the surface tension depends on the shear rate. Importantly, the standard way of determining surface tension from the pressure drop across the interface gives rise to an effective surface tension in the non-Newtonian fl

Piotr Białas, Vaibhav Chahar, Piotr Korcyl, Tomasz Stebel

Autoregressive Neural Networks (ANN) have been recently proposed as a mechanism to improve the efficiency of Monte Carlo algorithms for several spin systems. The idea relies on the fact that the total probability of a configuration can be factorized into conditional probabilities of each spin, which in turn can be approximated by a neural network. Once train

Mehdi Hosseini Chagahi, Md. Jalil Piran, Niloufar Delfan, Behzad Moshiri

Intracranial hemorrhage (ICH) is a critical medical emergency caused by the rupture of cerebral blood vessels, leading to internal bleeding within the skull. Accurate and timely classification of hemorrhage subtypes is essential for effective clinical decision-making. To address this challenge, we propose an advanced pyramid vision transformer (PVT)-based mo

Sven Krausse, Emre Neftci, Friedrich T. Sommer, Alpha Renner

The entorhinal-hippocampal formation is the mammalian brain's navigation system, encoding both physical and abstract spaces via grid cells. This system is well-studied in neuroscience, and its efficiency and versatility make it attractive for applications in robotics and machine learning. While continuous attractor networks (CANs) successfully model entorhin

Sokratis Vavilis, Harris Niavis, Konstantinos Loupos

With the rapid growth of hyperconnected devices and decentralized data architectures, safeguarding Internet of Things (IoT) transactions is becoming increasingly challenging. Blockchain presents a promising solution, yet its effectiveness depends on the underlying consensus algorithm. Conventional mechanisms, such as Proof of Work and Proof of Stake, are oft

Seungjun Ahn, Weijia Fu, Maaike van Gerwen, Lei Liu

Survival analysis plays a crucial role in understanding time-to-event (survival) outcomes such as disease progression. Despite recent advancements in causal mediation frameworks for survival analysis, existing methods are typically based on Cox regression and primarily focus on a single exposure or individual omics layers, often overlooking multi-omics inter

Subin Kim, Seoung Wug Oh, Jui-Hsien Wang, Joon-Young Lee

While recent advancements in text-to-video diffusion models enable high-quality short video generation from a single prompt, generating real-world long videos in a single pass remains challenging due to limited data and high computational costs. To address this, several works propose tuning-free approaches, i.e., extending existing models for long video gene

Dongping Li, Tielong Cai, Tianci Tang, Wenhao Chai

Developing autonomous home robots controlled by natural language has long been a pursuit of humanity. While advancements in large language models (LLMs) and embodied intelligence make this goal closer, several challenges persist: the lack of a unified benchmark for more complex robot tasks, limited evaluation methods and metrics, data incompatibility between

Matthias Möller, Arvid Norlander, Pedro Zuidberg Dos Martires, Luc De Raedt

Neurosymbolic (NeSy) AI studies the integration of neural networks (NNs) and symbolic reasoning based on logic. Usually, NeSy techniques focus on learning the neural, probabilistic and/or fuzzy parameters of NeSy models. Learning the symbolic or logical structure of such models has, so far, received less attention. We introduce neurosymbolic decision trees (

Rüveyda Yilmaz, Zhu Chen, Yuli Wu, Johannes Stegmaier

Cell microscopy data are abundant; however, corresponding segmentation annotations remain scarce. Moreover, variations in cell types, imaging devices, and staining techniques introduce significant domain gaps between datasets. As a result, even large, pretrained segmentation models trained on diverse datasets (source datasets) struggle to generalize to unsee

Vassily Gorbounov, Christian Korff, Leonardo C. Mihalcea

In an earlier paper, two of the authors defined a $5$-vertex Yang-Baxter algebra (a Hopf algebra) which acts on the sum of the equivariant quantum K-rings of Grassmannians $\mathrm{Gr}(k;n)$, where $k$ varies from $0$ to $n$. We construct geometrically defined operators on quantum K-rings describing this action. In particular, the $R$-matrix defining the Yan

Dušan Malić, Christian Fruhwirth-Reisinger, Samuel Schulter, Horst Possegger

While surface normals are widely used to analyse 3D scene geometry, surface normal estimation from LiDAR point clouds remains severely underexplored. This is caused by the lack of large-scale annotated datasets on the one hand, and lack of methods that can robustly handle the sparse and often noisy LiDAR data in a reasonable time on the other hand. We addres

Oscar Adamuz-Hinojosa, Lanfranco Zanzi, Vincenzo Sciancalepore, Xavier Costa-Pérez

The Open Radio Access Network (O-RAN)-compliant solutions often lack crucial details for implementing effective control loops at various time scales. To overcome this, we introduce MAREA, an O-RAN-compliant mathematical framework designed for the allocation of radio resources to multiple ultra-Reliable Low Latency Communication (uRLLC) services. In the near-

Gabriel R. Palma, Rocío Alaiz, Alexandre S. Araújo, Marcoandre Savaris

Identifying \textit{Anastrepha} species from the \textit{pseudoparallela} group is problematic due to morphological similarities among species and a broad geographic variation. This group comprises $31$ species of fruit flies and pests affecting passion fruit crops. Identifying these species utilises the morphological characteristics of the specimens' wings

Manuela Fritz

Evidence on the heat-mental health nexus remains mixed. I show that this can be partly explained by previous studies focusing solely on temperature while neglecting temperature-humidity interactions. Using a measure that considers both indicators (wet bulb temperature), I assess the causal link between extreme heat and mental health, and its heterogeneity ac

Yuhang Yao, Syed A. Jafar

An open question posed by Fawzi and Ferme [Transactions on Information Theory 2024], asks whether non-signaling (NS) assistance can increase the capacity of a broadcast channel (BC). We answer this question in the affirmative, by showing that for a certain K-receiver BC setting, called Coordinated Multipoint (CoMP) that arises naturally in wireless networks,

Feiran Wang, Jiachen Tao, Junyi Wu, Haoxuan Wang

X-ray imaging is indispensable in medical diagnostics, yet its use is tightly regulated due to potential health risks. To mitigate radiation exposure, recent research focuses on generating novel views from sparse inputs and reconstructing Computed Tomography (CT) volumes, borrowing representations from the 3D reconstruction area. However, these representatio

Anne Boutet de Monvel, Kiran Kumar A. S., Mostafa Sabri

In this expository note, we study several families of periodic graphs which satisfy a sufficient condition for the ergodicity of the associated continuous-time quantum walk. For these graphs, we compute the limiting distribution of the walk explicitly. We uncover interesting behavior where in some families, the walk is ergodic in both horizontal and sectiona

Ziqiao Meng, Qichao Wang, Zhiyang Dou, Zixing Song

Autoregressive point cloud generation has long lagged behind diffusion-based approaches in quality. The performance gap stems from the fact that autoregressive models impose an artificial ordering on inherently unordered point sets, forcing shape generation to proceed as a sequence of local predictions. This sequential bias emphasizes short-range continuity

Yixin Lin, Jan Humplik, Sandy H. Huang, Leonard Hasenclever

In robot learning, it is common to either ignore the environment semantics, focusing on tasks like whole-body control which only require reasoning about robot-environment contacts, or conversely to ignore contact dynamics, focusing on grounding high-level movement in vision and language. In this work, we show that advances in generative modeling, photorealis

Miles H. Currie, Victoria S. Meadows

The upcoming extremely large telescopes will provide the first opportunity to search for signs of habitability and life on non-transiting terrestrial exoplanets using high-contrast, high-resolution instrumentation. However, the suite of atmospheric gases in terrestrial exoplanet environments that are accessible to ground-based reflected light observations ha

Enzo Sinacola, Arnault Pachot, Thierry Petit

Large Language Models (LLMs) offer a promising alternative to traditional survey methods, potentially enhancing efficiency and reducing costs. In this study, we use LLMs to create virtual populations that answer survey questions, enabling us to predict outcomes comparable to human responses. We evaluate several LLMs-including GPT-4o, GPT-3.5, Claude 3.5-Sonn

Seyed Morteza Hosseini, Alberto Zaffaroni

We propose an entropy function for AdS$_4$ BPS black holes in M-theory with general magnetic charges, resolving in particular a long-standing puzzle about baryonic charges. The entropy function is constructed from a gravitational block defined solely in terms of topological data of the internal manifold. We show that the entropy of twisted black holes can al

Hua Liu, Xinyang Zhang

In this paper we study the essential spectra of the Toeplitz operator on the Hardy space $H^1$. We give a counterexample to show that the Toeplitz operator with symbol is not Fredholm, which gives a counterexample to the conjecture by J.A. Virtanen J A in 2006.

Paul Calle, Averi Bates, Justin C. Reynolds, Yunlong Liu

Background and Objectives: The variability and biases in the real-world performance benchmarking of deep learning models for medical imaging compromise their trustworthiness for real-world deployment. The common approach of holding out a single fixed test set fails to quantify the variance in the estimation of test performance metrics. This study introduces

Xin Xu, Wei Xu, Ningyu Zhang, Julian McAuley

Previous studies have established that language models manifest stereotyped biases. Existing debiasing strategies, such as retraining a model with counterfactual data, representation projection, and prompting often fail to efficiently eliminate bias or directly alter the models' biased internal representations. To address these issues, we propose BiasEdit, a

Nicolas Hoferer, Kilian Sprenkamp, Dorian Christoph Quelle, Daniel Gordon Jones

In today's media landscape, propaganda distribution has a significant impact on society. It sows confusion, undermines democratic processes, and leads to increasingly difficult decision-making for news readers. We investigate the lasting effect on critical thinking and propaganda awareness on them when using a propaganda detection and contextualization tool.

Xue-Feng Pan, Peng-Bo Li

Coherent nonlinear tripartite interactions are critical for advancing quantum simulation and information processing in hybrid quantum systems, yet they remain experimentally challenging and still evade comprehensive exploration. Here, we predict a nonlinear tripartite coupling mechanism in a hybrid setup comprising a single trapped electron and a nearby micr

Xiaoyue Cao, Ran Li, Nan Li, Yun Chen

Galaxy-galaxy strong lensing provides a powerful probe of galaxy formation, evolution, and the properties of dark matter and dark energy. However, conventional lens-modeling approaches are computationally expensive and require fine-tuning to avoid local optima, rendering them impractical for the hundreds of thousands of lenses expected from surveys such as E

Shehreen Azad, Vibhav Vineet, Yogesh Singh Rawat

Despite advancements in multimodal large language models (MLLMs), current approaches struggle in medium-to-long video understanding due to frame and context length limitations. As a result, these models often depend on frame sampling, which risks missing key information over time and lacks task-specific relevance. To address these challenges, we introduce Hi

Michal Jelínek, Milan Frank, Václav Kubeček, Ondřej Novák

Comparative study of nonlinear crystals for picosecond difference frequency generation in mid-IR is presented. Nonlinear crystals of AgGaS$_2$, BaGa$_4$Se$_7$, LiGaSe$_2$, and LiGaS$_2$ were studied. Samples of AgGaS$_2$, BaGa$_4$Se$_7$, LiGaSe$_2$, and LiGaS$_2$ were tested in thee sets having lengths of 2, 4, or 8 mm. In order to investigate the dependence

Joseph E. Hand, Malena Rice, Konstantin Gerbig

Most Sun-like and higher-mass stars reside in systems that include one or more gravitationally bound stellar companions. These systems offer an important probe of planet formation in the most common stellar systems, while also providing key insights into how gravitational perturbations and irradiation differences from a companion star alter the outcomes of p

Rune M. Jacobsen, Samuel Rhys Cox, Carla F. Griggio, Niels van Berkel

Surveys are a widespread method for collecting data at scale, but their rigid structure often limits the depth of qualitative insights obtained. While interviews naturally yield richer responses, they are challenging to conduct across diverse locations and large participant pools. To partially bridge this gap, we investigate the potential of using LLM-based

Jiangping Wen, Jinyu Wen, Meie Fang

Bag-based Multiple Instance Learning (MIL) approaches have emerged as the mainstream methodology for Whole Slide Image (WSI) classification. However, most existing methods adopt a segmented training strategy, which first extracts features using a pre-trained feature extractor and then aggregates these features through MIL. This segmented training approach le

Justus Karlsson, Yonghao Xu, Amanda Berg, Leif Haglund

Multiple studies have performed next-day fire prediction using satellite imagery. Two main satellites are used to detect wildfires: MODIS and VIIRS. Both satellites provide fire mask products, called MOD14 and VNP14, respectively. Studies have used one or the other, but there has been no comparison between them to determine which might be more suitable for n

Keyue Jiang, Bohan Tang, Xiaowen Dong, Laura Toni

Inferring the graph structure from observed data is a key task in graph machine learning to capture the intrinsic relationship between data entities. While significant advancements have been made in learning the structure of homogeneous graphs, many real-world graphs exhibit heterogeneous patterns where nodes and edges have multiple types. This paper fills t

Sanyou Wu, Dan Yang, Yan Xu, Long Feng

Jointly modeling and forecasting economic and financial variables across a large set of countries has long been a significant challenge. Two primary approaches have been utilized to address this issue: the vector autoregressive model with exogenous variables (VARX) and the matrix autoregression (MAR). The VARX model captures domestic dependencies, but treats

Hui Huang, Hicham Kouhkouh, Lukang Sun

We analyze the Consensus-Based Optimization (CBO) algorithm with a consensus point rescaled by a small fixed parameter $\kappa \in (0,1)$. Under minimal assumptions on the objective function and the initial data, we establish its unconditional convergence to the global minimizer. Our results hold in the asymptotic regime where both the time--horizon $t \to \

Oskar Słowik, Oliver Reardon-Smith, Adam Sawicki

The concepts of $\epsilon$-nets and unitary ($\delta$-approximate) $t$-designs are important and ubiquitous across quantum computation and information. Both notions are closely related and the quantitative relations between $t$, $\delta$ and $\epsilon$ find applications in areas such as (non-constructive) inverse-free Solovay-Kitaev like theorems and random

Xichen Tan, Yunfan Ye, Yuanjing Luo, Qian Wan

Multi-modal Large Language Models (MLLMs) capable of video understanding are advancing rapidly. To effectively assess their video comprehension capabilities, long video understanding benchmarks, such as Video-MME and MLVU, are proposed. However, these benchmarks directly use uniform frame sampling for testing, which results in significant information loss an

Mingkang Zhu, Xi Chen, Zhongdao Wang, Bei Yu

Recent diffusion model customization has shown impressive results in incorporating subject or style concepts with a handful of images. However, the modular composition of multiple concepts into a customized model, aimed to efficiently merge decentralized-trained concepts without influencing their identities, remains unresolved. Modular customization is essen

Siddhant Dutta, Nouhaila Innan, Khadijeh Najafi, Sadok Ben Yahia

Recent advancements in Single-Image Super-Resolution (SISR) using deep learning have significantly improved image restoration quality. However, the high computational cost of processing high-resolution images due to the large number of parameters in classical models, along with the scalability challenges of quantum algorithms for image processing, remains a

Andrea Meo, Chengcen Sha, Emily Darwin, Riccardo Tomasello

Magnetic tunnel junctions (MTJs) are key enablers of spintronic technologies used in a variety of applications including information storage, microwave generation and detection, as well as unconventional computing. Here, we present experimental and theoretical studies of quantized spin wave eigenmodes in perpendicular MTJs focusing on a coupled magnetization

Hao Chen, Dayuan Tan

Convolutional Dictionary Learning (CDL) has emerged as a powerful approach for signal representation by learning translation-invariant features through convolution operations. While existing CDL methods are predominantly designed and used for fully supervised settings, many real-world classification tasks often rely on weakly labeled data, where only bag-lev

Yagmur Kati, Ralf Toenjes, Benjamin Lindner

We investigate the role of inertia in the asynchronous state of a disordered Kuramoto model. We extend an iterative simulation scheme to the case of the Kuramoto model with inertia in order to determine the self-consistent fluctuation statistics, specifically, the power spectra of network noise and single oscillators. Comparison with network simulations demo

Gabriele Domaine, Moritz H. Hirschmann, Kirill Parshukov, Mihir Date

It has recently been proposed that all achiral non-centrosymmetric crystals host so-called Kramers nodal lines, which are doubly degenerate band crossings connecting time-reversal invariant momenta in the Brillouin zone that arise due to spin-orbit coupling. When Kramers nodal lines intersect the Fermi level, they form exotic three-dimensional Fermi surfaces

Yichen Zhang, Yuxiang Gao, Aki Pulkkinen, Xingyao Guo

Kramers degeneracy is one fundamental embodiment of the quantum mechanical nature of particles with half-integer spin under time reversal symmetry. Under the chiral and noncentrosymmetric achiral crystalline symmetries, Kramers degeneracy emerges respectively as topological quasiparticles of Weyl fermions and Kramers nodal lines (KNLs), anchoring the Berry p

Minjun Zhu, Yixuan Weng, Linyi Yang, Yue Zhang

Large Language Models (LLMs) are increasingly utilized in scientific research assessment, particularly in automated paper review. However, existing LLM-based review systems face significant challenges, including limited domain expertise, hallucinated reasoning, and a lack of structured evaluation. To address these limitations, we introduce DeepReview, a mult

Tao Jing, Yao Li, Jingzhou Ye, Jie Wang

In recent years, major privacy laws like the GDPR have brought about positive changes. However, challenges remain in enforcing the laws, particularly due to under-resourced regulators facing a large number of potential privacy-violating software applications (apps) and the high costs of investigating them. Since 2019, China has launched a series of privacy e

Christian Käding, Mario Pitschmann

Density matrices are powerful mathematical tools for the description of closed and open quantum systems. Recently, methods for the direct computation of density matrix elements in scalar quantum field theory were developed based on thermo field dynamics (TFD) and the Schwinger-Keldysh formalism. In this article, we provide a more detailed discussion of these

Willem van der Maden, Vera van der Burg, Brett A. Halperin, Petra Jääskeläinen

This essay examines how Generative AI (GenAI) is rapidly transforming design practices and how discourse often falls into over-simplified narratives that impede meaningful research and practical progress. We identify and deconstruct five prevalent "semantic stopsigns" -- reductive framings about GenAI in design that halt deeper inquiry and limit productive e

Runhan Huang, Shaoting Zhu, Yilun Du, Hang Zhao

We present MoE-Loco, a Mixture of Experts (MoE) framework for multitask locomotion for legged robots. Our method enables a single policy to handle diverse terrains, including bars, pits, stairs, slopes, and baffles, while supporting quadrupedal and bipedal gaits. Using MoE, we mitigate the gradient conflicts that typically arise in multitask reinforcement le

C J Arizmendi, W L Garcia, M A Quintero

This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called smart pig in Oil and Gas pipelines . The model uses a signal noise reduction phase by means of preprocessing algorithms and attributeselection techniques. The noise reduction techniques were selected after a li

Maricarmen A. Winkler, Felipe A. Asenjo

We present a one-fluid pair plasma magnetohydrodynamical model for asymmetric relativistic magnetic reconnection that incorporates the thermal-inertial effects of the plasma. We find the general scaling relation for the reconnection rate in a Sweet-Parker-type configuration. However, we show that under a specific highly asymmetric scenario, this magnetic rec

Neil K. R. Sehgal, Hita Kambhamettu, Sai Preethi Matam, Lyle Ungar

Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. Through a mixed-methods study involving a survey of 278 adolescents and follow-up interviews with 12 participants, we explore how adolescents perceive mental health challenges and interact with

Emanuele Vivoli, Artemis Llabrés, Mohamed Ali Souibgui, Marco Bertini

Large multimodal models (LMMs) have made impressive strides in image captioning, VQA, and video comprehension, yet they still struggle with the intricate temporal and spatial cues found in comics. To address this gap, we introduce ComicsPAP, a large-scale benchmark designed for comic strip understanding. Comprising over 100k samples and organized into 5 subt

Tajron Jurić, A. Naveena Kumara, Filip Požar

We present a self-contained and consistent formulation of noncommutative (NC) gauge theory of gravity, focusing on spherically symmetric black hole geometries. Our construction starts from the gauge-theoretic viewpoint of Poincar\'{e} (or de Sitter) gravity and introduces noncommutativity through the Moyal star product and the Seiberg-Witten map, retaining N

Maxime Garnier, Dominik Leichtle, Luka Music, Harold Ollivier

A client can delegate a quantum computation to a powerful remote server while ensuring the privacy and the integrity of its computation via Secure Delegated Quantum Computation (SDQC). Thanks to recent results making them noise-robust and resource-efficient, proofs-of-concept implementations of generic SDQC protocols have already been demonstrated. Yet, the

Chen Xu, Tony Khuong Nguyen, Emma Dixon, Christopher Rodriguez

Recent years have witnessed impressive robotic manipulation systems driven by advances in imitation learning and generative modeling, such as diffusion- and flow-based approaches. As robot policy performance increases, so does the complexity and time horizon of achievable tasks, inducing unexpected and diverse failure modes that are difficult to predict a pr

Maryam Asadi Ahmadabadi, S. Mohammad Razavizadeh, Vahid Jamali

This paper studies Integrated Sensing, Communication, and Powering (ISCAP) as a novel framework designed to enhance Internet of Things (IoT) applications within sixth-generation wireless networks. In these applications, in addition to IoT devices requiring an energy supply and receiving information or control data to perform their tasks, the base station ser

Carlos Arizmendi, Alfredo Vellido, Enrique Romero

The diagnosis of brain tumours is an extremely sensitive and complex clinical task that must rely upon information gathered through non-invasive techniques. One such technique is magnetic resonance, in the modalities of imaging or spectroscopy. The latter provides plenty of metabolic information about the tumour tissue, but its high dimensionality makes reso

Jorge R. Colon-Berrios, Jeffrey A. Nanzer

We present an approach for improving spatial frequency sampling in active incoherent millimeter-wave (AIM) imaging systems using frequency diversity. AIM imaging relies on active transmission of spatio-temporally incoherent signals to illuminate a scene, from which interferometric Fourier-domain imaging can be implemented using a sparse receiving antenna arr

Jannis O. Luebsen, Annika Eichler

This paper addresses the integration of additional information sources into a Bayesian optimization framework while ensuring that safety constraints are satisfied. The interdependencies between these information sources are modeled using an unknown correlation matrix. We explore how uniform error bounds must be adjusted to maintain constraint satisfaction th

Zhiguo Ding, H. Vincent Poor

This letter is to investigate the impact of line-of-sight (LoS) blockage on pinching-antenna systems. Analytical results are developed for both single-user and multi-user cases to reveal that the presence of LoS blockage is beneficial for increasing the performance gain of pinching antennas over conventional antennas. This letter also reveals that LoS blocka

Damaris Meier, Noa Vikman, Stefan Wenger

In their seminal 1981 article, Sacks-Uhlenbeck famously proved the existence of non-trivial harmonic 2-spheres in every closed Riemannian manifold with non-zero second homotopy group. Their arguments heavily rely on PDE techniques. The purpose of the present paper is to develop a conceptually simple metric approach to the existence of harmonic spheres. This

Maria C. Borges, Sebastian Werner

When faults occur in microservice applications -- as they inevitably do -- developers depend on observability data to quickly identify and diagnose the issue. To collect such data, microservices need to be instrumented and the respective infrastructure configured. This task is often underestimated and error-prone, typically relying on many ad-hoc decisions.

Wanyong Feng, Peter Tran, Stephen Sireci, Andrew Lan

The difficulty of multiple-choice questions (MCQs) is a crucial factor for educational assessments. Predicting MCQ difficulty is challenging since it requires understanding both the complexity of reaching the correct option and the plausibility of distractors, i.e., incorrect options. In this paper, we propose a novel, two-stage method to predict the difficu

Craig Messner, Tom Lippincott

We present an ngram model-based logit scaling technique that effectively transfers extreme subword stylistic variation to large language models at inference time. We demonstrate its efficacy by tracking the perplexity of generated text with respect to the ngram interpolated and original versions of an evaluation model. Minimizing the former measure while the

Xian Gao, Zongyun Zhang, Ting Liu, Yuzhuo Fu

With the rapid advancement of artificial intelligence technology, AI students are confronted with a significant "information-to-innovation" gap: they must navigate through the rapidly expanding body of literature, trace the development of a specific research field, and synthesize various techniques into feasible innovative concepts. An additional critical st

Peng Hao, Chaofan Zhang, Dingzhe Li, Xiaoge Cao

Significant progress has been made in vision-language models. However, language-conditioned robotic manipulation for contact-rich tasks remains underexplored, particularly in terms of tactile sensing. To address this gap, we introduce the Tactile-Language-Action (TLA) model, which effectively processes sequential tactile feedback via cross-modal language gro

Yiran Sun, Osama Mawlawi

Positron Emission Tomography (PET) is a functional imaging modality that enables the visualization of biochemical and physiological processes across various tissues. Recently, deep learning (DL)-based methods have demonstrated significant progress in directly mapping sinograms to PET images. However, regression-based DL models often yield overly smoothed rec

I. Grinberg, A. Levin, E. D. Rimon

Manipulation of deformable linear objects (DLOs) in constrained environments is a challenging task. This paper describes a two-layered approach for placing DLOs on a flat surface using a single robot hand. The high-level layer is a novel DLO surface placement method based on Euler's elastica solutions. During this process one DLO endpoint is manipulated by t

Ilya L. Shapiro

This is a contribution to the memorial edition devoted to Professor Vladislav Gavrilovich Bagrov, who was my official adviser from the beginning of undergraduate period to the end of Ph.D. The text includes a mentioning of two my publications in Izvestia VUZov Fisica (Russian Physics Journal), where Vladislav Gavrilovich served as an Editor. The rest of this

Cassandra Overney, Cassandra Moe, Alvin Chang, Nabeel Gillani

School districts across the United States (US) play a pivotal role in shaping access to quality education through their student assignment policies -- most prominently, school attendance boundaries. Community engagement processes for changing such policies, however, are often opaque, cumbersome, and highly polarizing -- hampering equitable access to quality

Sher Badshah, Moamen Moustafa, Hassan Sajjad

Evaluating free-form Question Answering (QA) remains a challenge due to its diverse and open-ended nature. Traditional automatic metrics fail to capture semantic equivalence or accommodate the variability of open-ended responses. Leveraging Large Language Models (LLMs) as evaluators offers a promising alternative due to their strong language understanding an

Matteo Cancellieri, Alaa El-Ebshihy, Tobias Fink, Petra Galuščáková

This paper presents the third edition of the LongEval Lab, part of the CLEF 2025 conference, which continues to explore the challenges of temporal persistence in Information Retrieval (IR). The lab features two tasks designed to provide researchers with test data that reflect the evolving nature of user queries and document relevance over time. By evaluating

Soham Deshmukh, Satvik Dixit, Rita Singh, Bhiksha Raj

Multimodal Audio-Language Models (ALMs) can understand and reason over both audio and text. Typically, reasoning performance correlates with model size, with the best results achieved by models exceeding 8 billion parameters. However, no prior work has explored enabling small audio-language models to perform reasoning tasks, despite the potential application

Zhaohui Liang, Yonglin Chen, Naser Al Madi, Can Liu

Transcripts displayed on dictation interfaces can be hard to read due to recognition errors and disfluencies. LLM-based text auto-correction could help, but changing the text during production could lead to distraction and unintended phrasing. To understand how to balance readability, attention, and accuracy, we conducted an eye-tracking experiment with 20 p

Vyoma Muralidhara

The Cosmic Microwave Background (CMB) is a fundamental observational tool in modern cosmology. The linear polarization of the CMB provides a crucial observational tool for exploring new physics, including the inflationary paradigm and parity-violating phenomena. The spectral distortion of the CMB can be used as a probe of the intracluster medium (ICM) of gal

Andres M Bran, Theo A Neukomm, Daniel P Armstrong, Zlatko Jončev

While automated chemical tools excel at specific tasks, they have struggled to capture the strategic thinking that characterizes expert chemical reasoning. Here we demonstrate that large language models (LLMs) can serve as powerful tools enabling chemical analysis. When integrated with traditional search algorithms, they enable a new approach to computer-aid

Francesco Lin, Bruno Martelli

The Davis hyperbolic four-manifold $\mathcal{D}$ is not almost-complex, so that its Seiberg-Witten invariants corresponding to zero-dimensional moduli spaces are vanishing by definition. In this paper, we show that all the Seiberg-Witten invariants involving higher-dimensional moduli spaces also vanish. Our proof involves the adjunction inequalities correspo

Denis M. F. Illesca, Andrés E. Piatti, Matías Chiarpotti, Roberto Butrón

We report on the astrophysical properties of a sample of star clusters in the Small Magellanic Cloud (SMC). They have been selected with the aim of looking for the connection between their ages, heliocentric distances and metallicities with the existence of tidally perturbed/induced outermost SMC regions. We derived the star cluster fundamental parameters fr

Mingshi Li, Dusan Grujicic, Ben Somers, Stien Heremans

Remote sensing imagery from systems such as Sentinel provides full coverage of the Earth's surface at around 10-meter resolution. The remote sensing community has transitioned to extensive use of deep learning models due to their high performance on benchmarks such as the UCMerced and ISPRS Vaihingen datasets. Convolutional models such as UNet and ResNet var

Siddhant Arora, Yifan Peng, Jiatong Shi, Jinchuan Tian

Advancements in audio foundation models (FMs) have fueled interest in end-to-end (E2E) spoken dialogue systems, but different web interfaces for each system makes it challenging to compare and contrast them effectively. Motivated by this, we introduce an open-source, user-friendly toolkit designed to build unified web interfaces for various cascaded and E2E

Julian Aron Prenner, Romain Robbes

The performance of a machine learning system is not only determined by the model but also, to a substantial degree, by the data it is trained on. With the increasing use of machine learning, issues related to data quality have become a concern also in automated program repair research. In this position paper, we report some of the data-related issues we have

Kai-Fu Yang, Yong-Jie Li

Visual attention plays a critical role when our visual system executes active visual tasks by interacting with the physical scene. However, how to encode the visual object relationship in the psychological world of our brain deserves to be explored. In the field of computer vision, predicting visual fixations or scanpaths is a usual way to explore the visual

Marco Carbone, Adele Veschetti

We present a choreographic framework for modelling and analysing concurrent probabilistic systems based on the PRISM model-checker. This is achieved through the development of a choreography language, which is a specification language that allows to describe the desired interactions within a concurrent system from a global viewpoint. Using choreographies giv