November 2024 arXiv papers — page 181

Edward Bormashenko, Nir Shvalb

Ramsey theory enables re-shaping of the basic ideas of quantum mechanics. Quantum observables represented by linear Hermitian operators are seen as the vertices of a graph. Relations of commutation define the coloring of edges linking the vertices: if the operators commute, they are connected with a red link; if they do not commute, they are connected with a

William J. Crilly

Experiments conducted since 2018, using three geographically spaced synchronized radio telescopes, and a radio interferometer, indicate the presence of anomalous narrow bandwidth pulse pairs, conjectured to be sourced from a celestial direction near the star Rigel. Many explanatory hypotheses are possible. In the current work, a measurement method is propose

F. Carralot, A. Carones, N. Krachmalnicoff, T. Ghigna

Future cosmic microwave background (CMB) experiments are primarily targeting a detection of the primordial $B$-mode polarisation. The faintness of this signal requires exquisite control of systematic effects which may bias the measurements. In this work, we derive requirements on the relative calibration accuracy of the overall polarisation gain ($\Delta g_\

Nathan Smith, Christopher Ridgers, Kate Lancaster, Chris Arran

As the repetition rates of ultra-high intensity lasers increase, simulations used for the prediction of experimental results may need to be augmented with machine learning to keep up. In this paper, the usage of gaussian process regression in producing surrogate models of laser-plasma interactions from particle-in-cell simulations is investigated. Such a mod

Aapo Laukkarinen

Convex body domination is a technique, where operators acting on vector-valued functions are estimated via certain convex body averages of the input functions. This domination lets one deduce various matrix weighted bounds for these operators and their commutators. In this paper, we extend the sparse domination results for rough singular integrals due to Con

Christoph Hochrainer, Anastasia Isychev, Valentin Wüstholz, Maria Christakis

Zero-knowledge (ZK) protocols have recently found numerous practical applications, such as in authentication, online-voting, and blockchain systems. These protocols are powered by highly complex pipelines that process deterministic programs, called circuits, written in one of many domain-specific programming languages, e.g., Circom, Noir, and others. Logic b

Rhimon A. Assis Souza, Asnakew Bewketu Belete, Bruno L. Canto Martins, Lívia M. C. de Azevedo

Variations in scaling behavior in the flux and emissions of gravitational lensed quasars can provide valuable information about the dynamics within the sources and their cosmological evolution with time. Here, we study the multifractal behavior of the light curves of 14 lensed quasars with multiple images in the $r$ band, with redshift ranging from 0.657 to

Ze Chen, Kohei Kawabata, Anish Kulkarni, Shinsei Ryu

The interplay between non-Hermiticity and disorder gives rise to unique universality classes of Anderson transitions. Here, we develop a field-theoretical description of non-Hermitian disordered systems based on fermionic replica nonlinear sigma models. We classify the target manifolds of the nonlinear sigma models across all the 38-fold symmetry classes of

Lucas Lacasa, Abel Pardo, Pablo Arbelo, Miguel Sánchez

Methods of Machine and Deep Learning are gradually being integrated into industrial operations, albeit at different speeds for different types of industries. The aerospace and aeronautical industries have recently developed a roadmap for concepts of design assurance and integration of neural network-related technologies in the aeronautical sector. This paper

Bhupendra Solanki, Ashwin Nair, Mainak Singha, Souradeep Mukhopadhyay

Generalized Category Discovery (GCD) aims to cluster unlabeled images into known and novel categories using labeled images from known classes. To address the challenge of transferring features from known to unknown classes while mitigating model bias, we introduce GraphVL, a novel approach for vision-language modeling in GCD, leveraging CLIP. Our method inte

Edmund Karasiewicz, Emile Okada, Runze Wang

We compute the stable wave front set of theta representations for certain tame Brylinski-Deligne covers of a connected reductive $p$-adic group. The computation involves two main inputs. First we use a theorem of Okada, adapted to covering groups, to reduce the computation of the wave front set to computing the Kawanaka wave front set of certain representati

Nadeem Dar, Athanassios Manikas

This paper is concerned with the investigation of the bistatic MIMO radar for estimating various multitarget parameters of interest in the presence of clutter and noise. The parameters of interest include Direction of Departure (DOD), Direction of Arrival (DOA), range and velocity and a novel algorithm is proposed for estimating these target parameters based

Jingyu Lu, Ke Ye

The classical Cayley transform is a birational map between a quadratic matrix group and its Lie algebra, which was first discovered by Cayley in 1846. Because of its essential role in both pure and applied mathematics, the classical Cayley transform has been generalized from various perspectives. This paper is concerned with a representation theoretic genera

Sagar Ramchandani, Simon Trebst, Ciarán Hickey

Frustrated magnets can elude the paradigm of conventional symmetry breaking and instead exhibit signatures of emergent symmetries at low temperatures. Such symmetries arise from "accidental" degeneracies within the ground state manifold and have been explored in a number of disparate models, in both two and three dimensions. Here we report the systematic con

Olivier Debarre

We discuss a conjecture made by Alexander Polishchuk and David Kazhdan at the 2022 ICM about a variety naturally attached to any stable vector bundle of rank 2 and degree $2g- 1$ on a smooth projective complex curve of genus $g$.

Andrea Schioppa, Emiel Hoogeboom, Jonathan Heek

The rapid advancement of text-to-image Diffusion Models has led to their widespread public accessibility. However these models, trained on large internet datasets, can sometimes generate undesirable outputs. To mitigate this, approximate Machine Unlearning algorithms have been proposed to modify model weights to reduce the generation of specific types of ima

Kevin Doherty, Emma Kallina, Kayley Moylan, María Paula Silva

We find ourselves on the ever-shifting cusp of an AI revolution -- with potentially metamorphic implications for the future practice of healthcare. For many, such innovations cannot come quickly enough; as healthcare systems worldwide struggle to keep up with the ever-changing needs of our populations. And yet, the potential of AI tools and systems to shape

Weibo Gao, Qi Liu, Linan Yue, Fangzhou Yao

Learners sharing similar implicit cognitive states often display comparable observable problem-solving performances. Leveraging collaborative connections among such similar learners proves valuable in comprehending human learning. Motivated by the success of collaborative modeling in various domains, such as recommender systems, we aim to investigate how col

Tanay Agrawal, Abid Ali, Antitza Dantcheva, Francois Bremond

Deep learning models, in particular \textit{image} models, have recently gained generalisability and robustness. %are becoming more general and robust by the day. In this work, we propose to exploit such advances in the realm of \textit{video} classification. Video foundation models suffer from the requirement of extensive pretraining and a large training ti

Daolang Huang, Yujia Guo, Luigi Acerbi, Samuel Kaski

Many critical decisions, such as personalized medical diagnoses and product pricing, are made based on insights gained from designing, observing, and analyzing a series of experiments. This highlights the crucial role of experimental design, which goes beyond merely collecting information on system parameters as in traditional Bayesian experimental design (B

Xingtai Lv, Ning Ding, Kaiyan Zhang, Ermo Hua

Improving the effectiveness and efficiency of large language models (LLMs) simultaneously is a critical yet challenging research goal. In this paper, we find that low-rank pre-training, normally considered as efficient methods that will compromise performance, can be scalably effective when reduced parameters are precisely targeted. Specifically, applying th

Alvaro Calvo, Jesus Capitan

We present a framework for Multi-Robot Task Allocation (MRTA) in heterogeneous teams performing long-endurance missions in dynamic scenarios. Given the limited battery of robots, especially for aerial vehicles, we allow for robot recharges and the possibility of fragmenting and/or relaying certain tasks. We also address tasks that must be performed by a coal

Masoud Elyasi, Azadeh Vosoughi

We consider a multi-cell massive multiple-input-multiple-output (MIMO) system with correlated Rayleigh fading channels, where pilot reuse is permitted within each cell (to reduce pilot overhead), and each base station (BS) utilizes multi-cell minimum mean square (M-MMSE) precoders and combining. We derive a large-scale approximation of the uplink signal-to-i

Aleksandra Deptuch, Anna Paliga, Anna Drzewicz, Marcin Piwowarczyk

The new equimolar mixture comprises liquid crystalline compounds MHPOBC and partially fluorinated 3F2HPhF6. The phase sequence of the mixture is determined by differential scanning calorimetry, polarizing optical microscopy, X-ray diffraction, and broadband dielectric spectroscopy. The enantiotropic smectic A*, C*, and CA* phases are observed for the mixture

Aofeng Su, Aowen Wang, Chao Ye, Chen Zhou

The emergence of models like GPTs, Claude, LLaMA, and Qwen has reshaped AI applications, presenting vast new opportunities across industries. Yet, the integration of tabular data remains notably underdeveloped, despite its foundational role in numerous real-world domains. This gap is critical for three main reasons. First, database or data warehouse data int

Gionni Marchetti

A data-driven approach based on unsupervised machine learning is proposed to infer the intrinsic dimension $m^{\ast}$ of the high-dimensional trajectories of the Fermi-Pasta-Ulam-Tsingou (FPUT) model. Principal component analysis (PCA) is applied to trajectory data consisting of $n_s = 4,000,000$ datapoints, of the FPUT $\beta$ model with $N = 32$ coupled os

Yan Li, Weiwei Guo, Xue Yang, Ning Liao

In recent years, aerial object detection has been increasingly pivotal in various earth observation applications. However, current algorithms are limited to detecting a set of pre-defined object categories, demanding sufficient annotated training samples, and fail to detect novel object categories. In this paper, we put forth a novel formulation of the aeria

Marijn Siemonsa, Martijn Veen, Irina Malysheva, Johannes Algera

The need of atmospheric information with a higher spatial and temporal resolution drives the development of small satellites and satellite constellations to complement satellite flagship missions. Since optical systems are a main contributor to the satellite size, these are the prime candidate for their miniaturization. We present here a novel optical system

Daomin Cao, Boquan Fan, Rui Li, Guolin Qin

Helical Kelvin waves were conjectured to exist for the 3D Euler equations in Lucas and Dritschel \cite{LucDri} (as well as in \cite{Chu}) by studying dispersion relation for infinitesimal linear perturbations of a circular helically symmetric vortex patch. This paper aims to rigorously establish the existence of these $m$-fold symmetric helical Kelvin waves,

Sooraj Sanjay, Jalaja M. A, Navakanta Bhat, Pavan Nukala

Ferroelectric field-effect transistors integrated on 2D semiconducting platforms are extremely relevant for low power electronics. Here, we propose and demonstrate a novel phase-change ferroelectric field effect transistor (PCFE-FET) for steep switching applications. Our gate stack is engineered as a ferroelectric Lanthanum doped hafnium oxide (LHO) proximit

Timofei Miryashkin, Ivan Novoselov, Alexey Yanilkin

Diffusion plays a key role in microstructure evolution at multicomponent alloys: diffusion controls the kinetics of phase transformations and alloy homogenization. This study aims at developing computationally efficient approaches to estimate the solute diffusion coefficients in two-component systems. We consider silicon as the solute example because it is h

Avirup Ghosh, Deep Ghosh

The spectrum of inflationary tensor perturbations is one of the very few available probes of the post-inflationary reheating epoch, and it is strongly influenced by the Universe's equation of state during this period. In the current era of precision cosmology, an accurate estimation of this primordial tensor perturbation spectrum is crucial. Unlike the conve

Felix Morsbach, Jan Reubold, Thorsten Strufe

Research on the effects of essential hyperparameters of DP-SGD lacks consensus, verification, and replication. Contradictory and anecdotal statements on their influence make matters worse. While DP-SGD is the standard optimization algorithm for privacy-preserving machine learning, its adoption is still commonly challenged by low performance compared to non-p

C. B. Jacobs, M. E. Messinger, A. N. Trenk

We study a discrete-time model for the spread of information in a graph, motivated by the idea that people believe a story when they learn of it from two different origins. Similar to the burning number, in this problem, information spreads in rounds and a new source can appear in each round. For a graph $G$, we are interested in $b_2(G)$, the minimum number

Lina Jaurigue, Kathy Lüdge

We show that passively mode-locked lasers subject to feedback from a single external cavity can exhibit large timing fluctuations on short time scales despite having a relatively small long-term timing jitter, meaning that the commonly used von Linde and K\'{e}f\'{e}lian techniques of experimentally estimating the timing jitter can lead to large errors in th

Marijn Siemons, Ralf Kohlhaas

As global climate change severely impacts our world, there is an increasing demand to monitor trace gases with a high spatial resolution and accuracy. At the same time, these instruments need to be compact in order have constellations for short revisit times. Here we present a new spectrometer instrument concept for trace gas detection, where photonic crysta

Ruidong Fan, Xiao Ouyang, Hong Tao, Yuhua Qian

As a prominent challenge in addressing real-world issues within a dynamic environment, label shift, which refers to the learning setting where the source (training) and target (testing) label distributions do not match, has recently received increasing attention. Existing label shift methods solely use unlabeled target samples to estimate the target label di

Lucas R. de Lima, Daniel Valesin

This study delves into first-passage percolation on random geometric graphs in the supercritical regime, where the graphs exhibit a unique infinite connected component. We investigate properties such as geodesic paths, moderate deviations, and fluctuations, aiming to establish a quantitative shape theorem. Furthermore, we examine fluctuations in geodesic pat

Avner Kantor, Sheizaf Rafaeli

Users in the data age have access to more data than ever before, but little is known how they interact with it. Using transparency and multimedia, data journalism (DJ) lets users explore and interpret data on their own. This study examines how DJ affects online comments as a case study of user interactions with data. The corpus comprises 6,400 stories and th

Tomas Basile, Karel Proesmans

In this paper, we study the thermodynamic cost associated with erasing a static random access memory. By combining the stochastic thermodynamics framework of electronic circuits with machine learning-based optimization techniques, we show that it is possible to erase an electronic random access memory at arbitrarily fast speed and finite heat dissipation. Th

C. B. Oliveira, O. L. Dors, I. A. Zinchenko, M. V. Cardaci

In this paper, we present a semi-empirical calibration between the oxygen abundance and the $N2$ emission-line ratio for Low Ionization Nuclear Emission Regions (LINERs). This relation was derived by comparing the optical spectroscopic data of 118 nuclear spaxels classified as LINERs using three different BPT diagrams from the Mapping Nearby Galaxies survey

Meysam Bagheri, Sudeshna Roy, Thorsten Poeschel

We present the implementation of two advanced capillary bridge approximations within the Discrete Element Method (DEM) framework of the open-source code MercuryDPM. While MercuryDPM already includes a simplified version of the Willett approximation, our work involves implementing both the classical Willett approximation and the recently published Bagheri app

Lei Chen, Chen Gao, Xiaoyi Du, Hengliang Luo

Large Language Models (LLMs) have recently garnered significant attention in various domains, including recommendation systems. Recent research leverages the capabilities of LLMs to improve the performance and user modeling aspects of recommender systems. These studies primarily focus on utilizing LLMs to interpret textual data in recommendation tasks. Howev

ATLAS Collaboration

A search for the production of three Higgs bosons ($HHH$) in the $b\bar{b}b\bar{b}b\bar{b}$ final state is presented. The search uses $126~\text{fb}^{-1}$ of proton-proton collision data at $\sqrt{s}=13$ TeV collected with the ATLAS detector at the Large Hadron Collider. The analysis targets both non-resonant and resonant production of $HHH$. The resonant in

Cong Li

We derived the BK equation for quark-quark scattering, extending the dipole-hadron scattering framework. This derivation reveals that the quark-quark scattering amplitude grows with increasing quark rapidity. Since the momentum dot product is Lorentz invariant, the coupling constant plays a crucial role in accounting for it.

Xuanqian Wang, Jing Li, Ivor W. Tsang, Yew-Soon Ong

Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require prior knowledge of demographics. In this study, we explore the potential for achieving fairness without compromising its utility when no prior demographics are provided to the train

Yongxin Zhu, Bocheng Li, Yifei Xin, Zhihua Xia

Vector Quantization (VQ) is essential for discretizing continuous representations in unsupervised learning but suffers from representation collapse, causing low codebook utilization and limiting scalability. Existing solutions often rely on complex optimizations or reduce latent dimensionality, which compromises model capacity and fails to fully solve the pr

Sofiane Azzouz, Pierre-André Vuissoz, Yves Laprie

Acoustic articulatory inversion is a major processing challenge, with a wide range of applications from speech synthesis to feedback systems for language learning and rehabilitation. In recent years, deep learning methods have been applied to the inversion of less than a dozen geometrical positions corresponding to sensors glued to easily accessible articula

Francisco de Arriba-Pérez, Silvia García-Méndez, Javier Otero-Mosquera, Francisco J. González-Castaño

Cognitive and neurological impairments are very common, but only a small proportion of affected individuals are diagnosed and treated, partly because of the high costs associated with frequent screening. Detecting pre-illness stages and analyzing the progression of neurological disorders through effective and efficient intelligent systems can be beneficial f

Gaspard Quenard, Damier Pellier, Humbert Fiorino

This paper presents SibylSat, a novel SAT-based method designed to efficiently solve totally-ordered HTN problems (TOHTN). In contrast to prevailing SAT-based HTN planners that employ a breadth-first search strategy, SibylSat adopts a greedy search approach, enabling it to identify promising decompositions for expansion. The selection process is facilitated

M. Mastropietro, Y. Kim, H. H. Hsieh, J. Agarwal

We study the activity evolution of the main-belt comet 324P/La Sagra over time and the properties of its emitted dust. We performed aperture photometry on images taken by a wide range of telescopes at optical and thermal infrared wavelengths between 2010 and 2021. We derived the combined scattering cross section of the nucleus and dust (when present) as a fu

Viki Peeva, Marvin Porsil, Wil M. P. van der Aalst

Process mining is a technology that helps understand, analyze, and improve processes. It has been present for around two decades, and although initially tailored for business processes, the spectrum of analyzed processes nowadays is evermore growing. To support more complex and diverse processes, subdisciplines such as object-centric process mining and behav

Christian Paroissin

Based on the Arps equation, we propose two stochastic models for curve decline useful in oil engineering context. Theoretical properties and simulations of these models are provided. The first passage time distribution of these stochastic models to a constant level is then studied. In conclusion, we discuss about statistical inference of the parameters from

Huifang Dong, Lina Jaurigue, Kathy Lüdge

Reservoir computing with optical devices offers an energy-efficient approach for time-series forecasting. Quantum dot lasers with feedback are modelled in this paper to explore the extent to which increased complexity in the charge carrier dynamics within the nanostructured semiconductor can enhance the prediction performance. By tuning the scattering intera

Prabir Rudra

In this work, we study the gravitational collapse procedure in generalized Vaidya spacetime with Bose-Einstein condensate dark matter density profile. We use the generalized Vaidya metric to simulate the spacetime of a big star and subsequently obtain the field equations. Then we proceed to determine the star system's mass parameter by solving the field equa

Chunrong Feng, Wen Huang, Chunlin Liu, Huaizhong Zhao

Under the notion of ergodicity of upper probability in the sense of Feng and Zhao (2021) that any invariant set either has capacity $0$ or its complement has capacity 0, we introduce the definition of finite ergodic components (FEC). We prove an invariant upper probability has FEC if and only if it is in the regime that any invariant set has either capacity

Anastasia Mantziou, Kerstin Hotte, Mihai Cucuringu, Gesine Reinert

Real-time economic information is essential for policy-making but difficult to obtain. We introduce a granular nowcasting method for macro- and industry-level GDP using a network approach and data on real-time monthly inter-industry payments in the UK. To this purpose we devise a model which we call an extended generalised network autoregressive (GNAR-ex) mo

Qingchao Zhang, Wei Ouyang, Jiale Han, Qi Cai

The lightweight Multi-state Constraint Kalman Filter (MSCKF) has been well-known for its high efficiency, in which the delayed update has been usually adopted since its proposal. This work investigates the immediate update strategy of MSCKF based on timely reconstructed 3D feature points and measurement constraints. The differences between the delayed update

Goran Hristovski, Gjorgji Gockov, Viktor Stojkoski

Recent studies highlight economic complexity's role in mitigating fiscal crises, often measured via an economy's trade structure. Trade, however, is just one facet of an economy's structure and omits critical innovative activities like research. Here, we investigate how a multidimensional approach to economic complexity-including both trade and research stru

Robin Trombetta, Olivier Rouvière, Carole Lartizien

Fully supervised deep models have shown promising performance for many medical segmentation tasks. Still, the deployment of these tools in clinics is limited by the very timeconsuming collection of manually expert-annotated data. Moreover, most of the state-ofthe-art models have been trained and validated on moderately homogeneous datasets. It is known that

Yu Pan, Yuguang Yang, Jixun Yao, Lei Ma

Despite recent advances in zero-shot voice conversion (VC), achieving speaker similarity and naturalness comparable to ground-truth recordings remains a significant challenge. In this letter, we propose CTEFM-VC, a zero-shot VC framework that integrates content-aware timbre ensemble modeling with conditional flow matching. Specifically, CTEFM-VC decouples ut

Rami Albasha, Julien Thiesson, Solène Buvat, J. -M Lopez

Studies conducted on the experimental site of Lavalette (IRSTEA Montpellier) have shown variability in the observed agricultural yield, either attributable to spatial or temporal heterogeneities in water and nitrogen supply or to gradients of soil properties. The latter is addressed by performing a multi-depthg geophysical prospection that delivers maps of a

Christine Michel, Laëtitia Pierrot

This article examines how models of teacher digital maturity can be combined to produce a unified version that can be used to design diagnostic tools and methods. 11 models applicable to the field of compulsory education were identified through a literature review. The models and how their constituent dimensions contribute to the determination of maturity le

Valery V. Ryzhikov

For every natural $n>1$, there is an operator $T$ of dynamical origin such that its tensor power $T^{\otimes n}$ has singular spectrum, and $T^{\otimes (n+1)}$ has absolutely continuous one. For a set $D$ of positive measure there are mixing zero entropy automorphisms $S,T$ such that $S^nD\cap T^nD=\varnothing$ for all $n>0$. The following answers, in partic

Edwige Cyffers, Muni Sreenivas Pydi, Jamal Atif, Olivier Cappé

Performative learning addresses the increasingly pervasive situations in which algorithmic decisions may induce changes in the data distribution as a consequence of their public deployment. We propose a novel view in which these performative effects are modelled as push-forward measures. This general framework encompasses existing models and enables novel pe

Akash Gupta, Valentin D. Ivanov, Thomas Preibisch, Dante Minniti

Aims. Our goal is twofold. First, to detect new clusters we apply the newest methods for the detection of clustering with the best available wide-field sky surveys in the mid-infrared because they are the least affected by extinction. Second, we address the question of cluster detection's completeness, for now limiting it to the most massive star clusters. M

Philippe Boisnard

The acceleration of the use of generative artificial intelligences (AI), since 2015 and the turning point operated by Deepdream, tends to obscure a real analysis of what could be defined as artificial imagination. AIs are either reduced to simple instruments or thought of according to a form of techno-theologism. Our research tends to suspend any form of jud

Ali Youssef, Kristina Vodorezova, Yannick Aarts, Wisdom E. K. Agbeti

IUMENTA (Latin for livestock) is an innovative software framework designed to construct and simulate digital twins of animals. By leveraging the powerful capability of the Open Digital Twin Platform (ODTP) alongside advanced software sensors, IUMENTA offers researchers a user-friendly tool to seamlessly develop adaptive digital replicas of animal-based proce

Surjit S. Wadhwa, Natalia R. Landin, Bojan Arbutina, Nicholas F. H. Tothill

With the ever-increasing number of light curve solutions of contact binary systems increasing number of potential bright red nova progenitors are being reported. There remains, however, only one confirmed event. In the present study we undertake a comprehensive review of orbital stability of contact binary systems considering the effects of the stellar inter

Longbiao Cheng, Ashutosh Pandey, Buye Xu, Tobi Delbruck

Deep learning-based speech enhancement (SE) methods often face significant computational challenges when needing to meet low-latency requirements because of the increased number of frames to be processed. This paper introduces the SlowFast framework which aims to reduce computation costs specifically when low-latency enhancement is needed. The framework cons

Rui Song, Yingji Li, Lida Shi, Fausto Giunchiglia

Shortcut learning refers to the phenomenon where models employ simple, non-robust decision rules in practical tasks, which hinders their generalization and robustness. With the rapid development of large language models (LLMs) in recent years, an increasing number of studies have shown the impact of shortcut learning on LLMs. This paper provides a novel pers

Wilhelm Zwerger

We present a thermodynamic description of ultracold gases with dipolar interactions which properly accounts for the long-range nature and broken rotation invariance of the interactions. It involves an additional thermodynamic field conjugate to the linear extension of the gas along the direction of the dipoles. The associated uniaxial pressure shows up as a

Kuranage Roche Rayan Ranasinghe, Kengo Ando, Giuseppe Thadeu Freitas de Abreu

We propose a novel flexible and scalable framework to design integrated communication and computing (ICC) -- a.k.a. over-the-air computing (AirComp)-- receivers. To elaborate, while related literature so far has generally focused either on theoretical aspects of ICC or on the design of beamforming (BF) algorithms for AirComp, we propose a framework to design

Paul Malisani, Adrien Spagnol, Vivien Smis-Michel

This paper addresses a class of robust stochastic optimal control problems. Its main contribution lies in the introduction of a general optimization model with variance penalization and an associated solution algorithm that improves out-of-sample robustness while preserving numerical complexity. The proposed variance-penalized model is inspired by a well-est

Gyula Polónyi, Zoltán Tibai, György Tóth, Gergő Krizsán

Organic crystals offer promising potential for THz generation, but face limitations in wavelength tunability and damage threshold. By applying tilted pulse front pumping to organic crystals an additional degree of freedom is introduced into the pumping conditions enabling a wider range of pumping wavelengths without compromising phase matching. Additionally,

Mickaël Martin-Nevot, Lotfi Lakhal

Multi-criteria decision analysis in databases has been actively studied, especially through the Skyline operator. Yet, few approaches offer a relevant comparison of Pareto optimal, or Skyline, points for high cardinality result sets. We propose to improve the dp-idp method, inspired by tf-idf, a recent approach computing a score for each Skyline point, by in

Koki Okajima, Yoshiyuki Kabashima

We analyze the quantum Hopfield model in which an extensive number of patterns are embedded in the presence of a uniform transverse field. This analysis employs the replica method under the replica symmetric ansatz on the Suzuki-Trotter representation of the model, while keeping the number of Trotter slices $M$ finite. The statistical properties of the quant

Tim Kokkeler, Alexander Golubov, F. Sebastian Bergeret, Yukio Tanaka

Noise and current measurements are key tools for studying mesoscopic systems, revealing insights beyond conductance alone. For instance, noise measurements show that transport carriers in conventional superconductors have charge 2e. The noise power also depends on junction type, distinguishing different transport processes. Existing theories focus primarily

James S. Cummins, Natalia G. Berloff

This paper presents the Complex Vector Gain-Based Annealer (CoVeGA), an analog computing platform designed to overcome energy barriers in XY Hamiltonians through a higher-dimensional representation. Traditional gain-based solvers utilizing optical or photonic hardware typically represent each XY spin with a single complex field. These solvers often struggle

Jai G Singla, Gautam Jaiswal

In this study, 0.5m high resolution satellite datasets over Indian urban region was used to demonstrate the applicability of deep learning models over Ahmedabad, India. Here, YOLOv7 instance segmentation model was trained on well curated trees canopy dataset (6500 images) in order to carry out the change detection. During training, evaluation metrics such as

Rujing Xiong, Ke Yin, Jialong Lu, Kai Wan

Despite extensive research on reconfigurable intelligent surfaces (RISs) in recent years, existing beamforming methods still face significant challenges in achieving flexible and robust beam synthesis, which is an essential capability for a wide range of communication scenarios. This paper introduces a Max-min criterion with nonlinear constraints, leveraging

Xiangdi Huang, Zhouping Xin, Wei Yan

In this manuscript, we aim to establish global existence of weak solutions with higher regularity to the compressible Navier-Stokes equations under no-slip boundary conditions. Though Lions\cite{L1} and Feireisl\cite{F1} have established global weak solutions with finite energy under Dirichelet boundary conditions by making use of so called effective viscous

Biao Wu, Yanda Li, Zhiwei Zhang, Yunchao Wei

Mobile agents are essential for automating tasks in complex and dynamic mobile environments. As foundation models evolve, the demands for agents that can adapt in real-time and process multimodal data have grown. This survey provides a comprehensive review of mobile agent technologies, focusing on recent advancements that enhance real-time adaptability and m

Paul Donner, Clemens Blümel

The concept of epistemic breadth of the work of a researcher refers to the scope of their knowledge claims, as reflected in published research reports. Studies of epistemic breadth have been hampered by the lack of a validated measure of the concept. Here we introduce a knowledge space approach to the measurement of epistemic breadth and propose to use the s

Stella Civelli, Marco Secondini

We propose a low-complexity sign-dependent metric for sequence selection and study the nonlinear shaping gain achievable for a given computational cost, establishing a benchmark for future research. Small gains are obtained with feasible complexity. Higher gains are achievable in principle, but with high complexity or a more sophisticated metric.

Zhuoning Guo, Ruiqian Han, Hao Liu

Federated Graph Learning (FGL) aims to collaboratively and privately optimize graph models on divergent data for different tasks. A critical challenge in FGL is to enable effective yet efficient federated optimization against multifaceted graph heterogeneity to enhance mutual performance. However, existing FGL works primarily address graph data heterogeneity

Souvik Manna, Subhashis Roy, Tapas Baug

We present the results of deep radio observations of 7 nearby large galaxies observed using the upgraded Giant Metrewave Radio Telescope (uGMRT) 0.3-0.5 GHz receivers with an angular resolution of $\sim$10 arcsec. The achieved sensitivities of these observations range from $\approx$15 to 50 $\mu$Jy/beam which is $\approx$3-4 factor lower than the previous ob

Satoki Ishikawa, Rio Yokota, Ryo Karakida

Local learning, which trains a network through layer-wise local targets and losses, has been studied as an alternative to backpropagation (BP) in neural computation. However, its algorithms often become more complex or require additional hyperparameters because of the locality, making it challenging to identify desirable settings in which the algorithm progr

Manuele Leonelli

Biathlon is a unique winter sport that combines precision rifle marksmanship with the endurance demands of cross-country skiing. We develop a Bayesian hierarchical model to predict and understand shooting performance using data from the 2021/22 Women's World Cup season. The model captures athlete-specific, position-specific, race-type, and stage-dependent ef

Eugen Klein, Rostyslav Lesyuk, Christian Klinke

Control over the lateral dimensions of colloidal nanostructures is a complex task which requires a deep understanding of the formation mechanism and reactivity in the corresponding systems. As a result, it provides a well-founded insight to the physical and chemical properties of these materials. In this work, we demonstrate the preparation of quasi-2D methy

Jianguo Huang, Haohao Wu, Tao Zhou

This paper aims to devise an adaptive neural network basis method for numerically solving a second-order semilinear partial differential equation (PDE) with low-regular solutions in two/three dimensions. The method is obtained by combining basis functions from a class of shallow neural networks and the resulting multi-scale analogues, a residual strategy in

Hoonick Lee, Mogan Gim, Donghyeon Park, Donghee Choi

The advent of Large Language Models (LLMs) have shown promise in various creative domains, including culinary arts. However, many LLMs still struggle to deliver the desired level of culinary creativity, especially when tasked with adapting recipes to meet specific cultural requirements. This study focuses on cuisine transfer-applying elements of one cuisine

Marwa Mannaï, Hisham Sati, Tim Byrnes, Chandrashekar Radhakrishnan

A comprehensive investigation of the entanglement characteristics is carried out on tripartite spin-1/2 systems, examining prototypical tripartite states, the thermal Heisenberg model, and the transverse field Ising model. The entanglement is computed using the R\'enyi relative entropy. In the traditional R\'enyi relative entropy, the generalization paramete

Dominic Breit, Arnab Roy

We consider the interaction of a compressible fluid with a flexible plate in two space dimensions. The fluid is described by the Navier--Stokes equations in a domain that is changing in accordance with the motion of the structure. The displacement of the latter evolves according to a beam equation. Both are coupled through kinematic boundary conditions and t

Syed Abdul Basit Shah, Sushant Ghimire, Rostyslav Lesyuk, Maria Vittoria Diamanti

The surface ligands in colloidal metal halide perovskites influence not only their intrinsic optoelectronic properties but also their interaction with other materials and molecules. We explore donor-acceptor interactions of CsPbBr3 perovskite nanocrystals with TiO2 nanoparticles and nanotubes by replacing long-chain oleylamine ligands with short-chain butyla

Ruizhong Qiu, Zhe Xu, Wenxuan Bao, Hanghang Tong

Since the success of GPT, large language models (LLMs) have been revolutionizing machine learning and have initiated the so-called LLM prompting paradigm. In the era of LLMs, people train a single general-purpose LLM and provide the LLM with different prompts to perform different tasks. However, such empirical success largely lacks theoretical understanding.

Yuandi Li, Zhe Xiang, Fei Yu, Zhangshuang Guan

The exponential growth in wireless data traffic, driven by the proliferation of mobile devices and smart applications, poses significant challenges for modern communication systems. Ensuring the secure and reliable transmission of multimodal semantic information is increasingly critical, particularly for tasks like Audio-Visual Event (AVE) localization. This

Jorge Otaegi, Iñigo Rodríguez-Vidal

The Basque Country's Habitability Decree of 2022 redefines natural lighting requirements in residential spaces, increasing the Wall-to-Floor Ratio (WFR) compared to previous municipal and sectoral standards, based on the depth of the space considered. This regulatory adjustment seeks to optimize the quality of natural light, a key factor for well-being. Howe

A. V. Toporensky, O. B. Zaslavskii

We consider near-horizon collisions between two particles moving freely in the Schwarzschild metric in the region outside the horizon. One of them emerges from a white hole. We scrutiny when such a process can lead to the indefinitely large growth of the energy in the center of mass frame in the point of collision. We also trace how the kinematics of collisi

Chengpeng Wang, Li Chen, Lili Wang, Zhaofan Li

Facial expression recognition faces challenges where labeled significant features in datasets are mixed with unlabeled redundant ones. In this paper, we introduce Cross Similarity Attention (CSA) to mine richer intrinsic information from image pairs, overcoming a limitation when the Scaled Dot-Product Attention of ViT is directly applied to calculate the sim