November 2024 arXiv papers — page 64

Stefan Schestakov, Simon Gottschalk

Trajectory representation learning is a fundamental task for applications in fields including smart city, and urban planning, as it facilitates the utilization of trajectory data (e.g., vehicle movements) for various downstream applications, such as trajectory similarity computation or travel time estimation. This is achieved by learning low-dimensional repr

Matías Pizarro, Mike Laszkiewicz, Dorothea Kolossa, Asja Fischer

As speech generation technologies advance, so do risks of impersonation, misinformation, and spoofing. We present a lightweight, training-free approach for detecting synthetic speech and attributing it to its source model. Our method addresses three tasks: (1) single-model attribution in an open-world setting, (2) multi-model attribution in a closed-world se

Aldo Gangemi, Andrea Giovanni Nuzzolese

Semantic Knowledge Graphs (SKG) face challenges with scalability, flexibility, contextual understanding, and handling unstructured or ambiguous information. However, they offer formal and structured knowledge enabling highly interpretable and reliable results by means of reasoning and querying. Large Language Models (LLMs) overcome those limitations making t

Yurii A. Aleshchenko, Andrey V. Muratov, Elena S. Zhukova, Lenar S. Kadyrov

The uncertainty of the nature of the normal state and superconducting condensate of unconventional superconductors continues to stimulate considerable speculation about the mechanism of superconductivity in these materials. Of particular interest are the type of symmetry of the order parameter and the basic electronic characteristics of the superconducting a

Oskar Gustafsson, Mattias Villani, Robert Kohn

Inference for locally stationary processes is often based on some local Whittle-type approximation of the likelihood function defined in the frequency domain. The main reasons for using such a likelihood approximation is that i) it has substantially lower computational cost and better scalability to long time series compared to the time domain likelihood, pa

Rebekah Rousi, Niko Makitalo, Hooman Samani, Kai-Kristian Kemell

The emergence of generative artificial intelligence (GAI) and large language models (LLMs) such ChatGPT has enabled the realization of long-harbored desires in software and robotic development. The technology however, has brought with it novel ethical challenges. These challenges are compounded by the application of LLMs in other machine learning systems, su

Alan Winfield, Nicola Webb, Appolinaire Etoundi, Romain Derval

This paper describes the enactment of a simulated (mock) accident involving an upper-body exoskeleton and its investigation. The accident scenario is enacted by role-playing volunteers, one of whom is wearing the exoskeleton. Following the mock accident, investigators - also volunteers - interview both the subject of the accident and relevant witnesses. The

Salil Gokhale, Harshul Sagar, Rohit Vaish, Vignesh Viswanathan

We study the problem of maximizing Nash social welfare, which is the geometric mean of agents' utilities, in two well-known models. The first model involves one-sided preferences, where a set of indivisible items is allocated among a group of agents (commonly studied in fair division). The second model deals with two-sided preferences, where a set of workers

Ali Ganbarov, Jicheng Yuan, Anh Le-Tuan, Manfred Hauswirth

In this paper, we present an experimental comparison of various graph-based approximate nearest neighbor (ANN) search algorithms deployed on edge devices for real-time nearest neighbor search applications, such as smart city infrastructure and autonomous vehicles. To the best of our knowledge, this specific comparative analysis has not been previously conduc

Conor Crilly, Oliver Johnson, Alexander Lewis, Jonathan Rougier

Computer models (simulators) are vital tools for investigating physical processes. Despite their utility, the prohibitive run-time of simulators hinders their direct application for uncertainty quantification. Gaussian process emulators (GPEs) have been used extensively to circumvent the cost of the simulator and are known to perform well on simulators with

Valentin Wilhelm, Matthias Krüger, Matthias Fuchs, Florian Vogel

The probability current is a vital quantity in the Fokker-Planck description of stochastic processes. It characterizes non-equilibrium stationary states and appears in linear response calculations. We recover and review the probability current in the Onsager-Machlup functional approach to Markov processes by deriving a self-contained expression in general no

Nora Schneider, Lars Lorch, Niki Kilbertus, Bernhard Schölkopf

We consider the problem of predicting perturbation effects via causal models. In many applications, it is a priori unknown which mechanisms of a system are modified by an external perturbation, even though the features of the perturbation are available. For example, in genomics, some properties of a drug may be known, but not their causal effects on the regu

Xin Liu, Hao Wang, Shibei Xue, Dezong Zhao

In computer vision, estimating the six-degree-of-freedom pose from an RGB image is a fundamental task. However, this task becomes highly challenging in multi-object scenes. Currently, the best methods typically employ an indirect strategy, which identifies 2D and 3D correspondences, and then solves with the Perspective-n-Points method. Yet, this approach can

Yuntao Shou, Peiqiang Yan, Xingjian Yuan, Xiangyong Cao

In recent years, histopathological whole slide image (WSI)- based survival analysis has attracted much attention in medical image analysis. In practice, WSIs usually come from different hospitals or laboratories, which can be seen as different domains, and thus may have significant differences in imaging equipment, processing procedures, and sample sources.

Yi-Ting Sun, Hsin-Chieh Lee, Yun-Chen Yu, Ting-Feng Wu

The evolution of vehicle-to-everything (V2X) communication brings significant challenges, such as data integrity and vulnerabilities stemming from centralized management. This paper presents an innovative integration of decentralized blockchain technology with V2X communication through a multi-layered architecture that combines the Simulation of Urban Mobili

Georgii Bychkov, Darina Dvinskikh, Anastasia Antsiferova, Alexander Gasnikov

We present a novel gradient-free algorithm to solve a convex stochastic optimization problem, such as those encountered in medicine, physics, and machine learning (e.g., adversarial multi-armed bandit problem), where the objective function can only be computed through numerical simulation, either as the result of a real experiment or as feedback given by the

Satoshi Morita, Naoki Kawashima

We propose a multi-impurity method for the bond-weighted tensor renormalization group (BWTRG) to compute the higher-order moment of physical quantities in a two-dimensional system. The replacement of the bond weight with an impurity matrix in a bond-weighted triad tensor network represents a physical quantity such as the magnetization and the energy. We demo

Jian Liang, JunSheng Cheng

Fires can cause severe damage to heritage buildings, making timely fire detection essential. Traditional dense cabling and drilling can harm these structures, so reducing the number of cameras to minimize such impact is challenging. Additionally, avoiding false alarms due to noise sensitivity and preserving the expertise of managers in fire-prone areas is cr

Aleksander Kozłowski, Daniel Ponikowski, Piotr Żukiewicz, Paweł Twardowski

We propose an End-to-end Convolutional Activation Anomaly Analysis (E2E-CA$^3$), which is a significant extension of A$^3$ anomaly detection approach proposed by Sperl, Schulze and B\"ottinger, both in terms of architecture and scope of application. In contrast to the original idea, we utilize a convolutional autoencoder as a target network, which allows for

Joong-Ku Lee, Young Soo Park

This paper delves into various robotic manipulation control methods designed for dynamic contact tooling operations on a robotic repair platform. The explored control strategies include hybrid position-force control, admittance control, bilateral telerobotic control, virtual fixture, and shared control. Each approach is elucidated and assessed in terms of it

Hrishikesh Pingulkar, Cédric Ayela, Jean-Baptiste Salmon

Electrokinetic energy harvesting from evaporation-driven flows in porous materials has recently been the subject of numerous studies, particularly with the development of nanomaterials with high conversion efficiencies. The configuration in which the energy conversion element is located upstream of the element which passively drives the evaporative flow has

Sudeep R. Bapat, Aditya Maheshwari

Modelling wildfire events has been studied in the literature using the Poisson process, which essentially assumes the independence of wildfire events. In this paper, we use the fractional Poisson process to model the wildfire occurrences in California between June 2019 - April 2023 and predict the wildfire events that explains the underlying memory between t

Joong-Ku Lee, Young Soo Park

This paper introduces a dual-arm telerobotic platform designed to efficiently and safely execute hot cell operations for nuclear waste disposition at EM sites. The proposed system consists of a remote robot arm platform and a teleoperator station, both integrated with a software architecture to control the entire system. The dual-arm configuration of the rem

Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Luigi Foscari

We consider a sequential decision-making setting where, at every round $t$, a market maker posts a bid price $B_t$ and an ask price $A_t$ to an incoming trader (the taker) with a private valuation for one unit of some asset. If the trader's valuation is lower than the bid price, or higher than the ask price, then a trade (sell or buy) occurs. If a trade happ

Hirak Kansara, Siamak F. Khosroshahi, Leo Guo, Miguel A. Bessa

In the pursuit of designing safer and more efficient energy-absorbing structures, engineers must tackle the challenge of improving crush performance while balancing multiple conflicting objectives, such as maximising energy absorption and minimising peak impact forces. Accurately simulating real-world conditions necessitates the use of complex material model

Zhentong Wang, Oskar J. Haidn, Xiangyu Hu

The standard smoothed particle hydrodynamics (SPH) method suffers from tensile instability, resulting in particle clumping and void regions under negative pressure conditions. In this study, we extend the transport-velocity formulation of Adami et al. (2013) \cite{adami2013transport} in the weakly-compressible SPH (WCSPH) framework to address this long-stand

Marija R. Jankovic, Mark C. Wyatt, Torsten Löhne

Context. Dusty debris discs around main sequence stars are observed to vary widely in terms of their vertical thickness. Their vertical structure may be affected by damping in inelastic collisions. Although kinetic models have often been used to study the collisional evolution of debris discs, these models have not yet been used to study the evolution of the

Guangsheng Ou, Mingwei Liu, Yuxuan Chen, Yanlin Wang

Recent advancements in large language models (LLMs) have demonstrated impressive capabilities in code translation, typically evaluated using benchmarks like CodeTransOcean and RepoTransBench. However, dependency-free benchmarks fail to capture real-world complexities by focusing primarily on simple function-level translations and overlooking repository-level

Marcello Morini, Eugenio Moro, Ilario Filippini, Danilo De Donno

The fronthaul connection is a key component of Centralized RAN (C-RAN) architectures, consistently required to handle high capacity demands. However, this critical feature is at risk when the transport link relies on wireless technology. Fortunately, solutions exist to enhance the reliability of wireless links. In this paper, we recall the theoretical fronth

Vidya Sudevan, Fakhreddine Zayer, Taimur Hassan, Sajid Javed

This paper delves into the potential of DU-VIO, a dehazing-aided hybrid multi-rate multi-modal Visual-Inertial Odometry (VIO) estimation framework, designed to thrive in the challenging realm of extreme underwater environments. The cutting-edge DU-VIO framework is incorporating a GAN-based pre-processing module and a hybrid CNN-LSTM module for precise pose e

Laurent-Stéphane Didier, Jean-Marc Robert

Residue Number Systems (RNS) are parallel number systems that allow the computation on large numbers. They are used in high performance digital signal processing devices and cryptographic applications. However, the rigidity of instruction set architectures of the market-dominant microprocessors limits the use of such number systems in software applications.

Muneer Al-ZuBi, Mohamed-Slim Alouini

In this era of advanced communication technologies, many remote rural and hard-to-reach areas still lack Internet access due to technological, geographical, and economic challenges. The TV white space (TVWS) technology has proven to be effective and feasible in connecting these areas to Internet service in many parts of the world. The TVWS-based systems oper

Zehua Pei, Hui-Ling Zhen, Xianzhi Yu, Sinno Jialin Pan

Structured pruning of Generative Pre-trained Transformers (GPTs) offers a promising path to efficiency but often suffers from irreversible performance degradation due to the discarding of transformer blocks. In this paper, we introduce FuseGPT, a compression paradigm that reframes structured pruning as iterative knowledge grafting rather than simple removal.

S. Lambie, D. Kats, D. Usyvat, A. Alavi

In light of the recent discrepancies reported between fixed node diffusion Monte Carlo and local natural orbital coupled cluster with single, double and perturbative triples (CCSD(T)) methodologies for non-covalent interactions in large molecular systems [Al-Hamdani et al., Nat. Comm., 2021, 12, 3927], the applicability of CCSD(T) is assessed using a model f

Ryota Fujii, Makoto Sakaguchi

We examine the higher-spin gauge models which are free on-shell and whose cubic vertex is BRST-exact. We show that these are free off-shell as well as on-shell. The key equation for this relates the derivative of the total deformed action $S(g)$ with respect to the deformation parameter $g$ to an $S(g)$-exact term.

Hansung Kim, Edward L. Zhu, Chang Seok Lim, Francesco Borrelli

We introduce an Implicit Game-Theoretic MPC (IGT-MPC), a decentralized algorithm for two-agent motion planning that uses a learned value function that predicts the game-theoretic interaction outcomes as the terminal cost-to-go function in a model predictive control (MPC) framework, guiding agents to implicitly account for interactions with other agents and m

Jordan Vice, Naveed Akhtar, Mubarak Shah, Richard Hartley

Training multimodal generative models on large, uncurated datasets can result in users being exposed to harmful, unsafe and controversial or culturally-inappropriate outputs. While model editing has been proposed to remove or filter undesirable concepts in embedding and latent spaces, it can inadvertently damage learned manifolds, distorting concepts in clos

Jordan Vice, Naveed Akhtar, Leonid Sigal, Richard Hartley

The rapid proliferation of multimodal generative models has sparked critical discussions on their reliability, fairness and potential for misuse. While text-to-image models excel at producing high-fidelity, user-guided content, they often exhibit unpredictable behaviors and vulnerabilities that can be exploited to manipulate class or concept representations.

Igor Honoré

We obtain new controls for the Leray solutions $u$ of the incompressible Navier-Stokes equation in $\mathbb{R}^3$. Specifically, we estimate $u$, $\nabla u$, and $\nabla^2 u$ in suitable Lebesgue spaces $L^{\tilde r}_TL^r$, $r <+ \infty$ with some constraints on $\tilde r>0$. Our method is based on a Duhamel formula around a perturbed heat equation, allowing

Yijun Zhai, Pengzhan Zhou, Yuepeng He, Fang Qu

The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great potential for utilizing explosively growing autonomous driving data. However, considering the complicated traffic environments and driving scenarios, deploying federated learning for a

Cristina Luna, Augusto Gómez Eguíluz, Jorge Barrientos-Díez, Almudena Moreno

This document compiles results obtained from the test campaign of the European Moon Rover System (EMRS) project. The test campaign, conducted at the Planetary Exploration Lab of DLR in Wessling, aimed to understand the scope of the EMRS breadboard design, its strengths, and the benefits of the modular design. The discussion of test results is based on rover

Andrzej Herdegen

Asymptotic properties of classical field electrodynamics are considered. Special attention is paid to the long-range structure of the electromagnetic field. It is shown that conserved Poincare quantities may be expressed in terms of the asymptotic fields. Long-range variables are shown to be responsible for an angular momentum contribution which mixes Coulom

Mohammad Kafini

The system under studying is for a piezoelectric beams system with magnetic effects, frictional dampings and source terms. We use the concavity method to study the competition of the dampings and the sources that leads to a blow-up result for solutions with negative initial energy.

Suhwan Cho, Minhyeok Lee, Jungho Lee, Sangyoun Lee

In many video processing tasks, leveraging large-scale image datasets is a common strategy, as image data is more abundant and facilitates comprehensive knowledge transfer. A typical approach for simulating video from static images involves applying spatial transformations, such as affine transformations and spline warping, to create sequences that mimic tem

Clément Dombry, Ahmed Zaoui

Distributional regression aims at estimating the conditional distribution of a targetvariable given explanatory co-variates. It is a crucial tool for forecasting whena precise uncertainty quantification is required. A popular methodology consistsin fitting a parametric model via empirical risk minimization where the risk ismeasured by the Continuous Rank Pro

Da-Chun Qiang, Zhiqiang You, Sheng Yang, Zong-Hong Zhu

Fast radio bursts (FRBs) are high-energy, short-duration phenomena in radio astronomy. Identifying their host galaxies can provide insights into their mysterious origins. In this paper, we introduce a novel approach to identifying potential host galaxies in three-dimensional space. We use FRB 20190425A and GW190425 as an example to illustrate our method. Rec

Felix Schloms, Øystein Gullbrekken, Signe Kjelstrup

A nonequilibrium thermodynamic model is presented for the nonisothermal lithium-ion battery cell. Coupling coefficients, all significant for transport of heat, mass, charge and chemical reaction, were used to model profiles of temperature, concentration and electric potential for each layer of the cell. Electrode surfaces were modelled with excess properties

Hugo Magaldi

We know from Ram{\'i}rez and Rider that the hard edge of the spectrum of the Beta-Laguerre ensemble converges, in the high-dimensional limit, to the bottom of the spectrum of the stochastic Bessel operator. Using stochastic analysis techniques, we show that, in the high temperatures limit, the rescaled eigenvalues point process of the stochastic Bessel opera

Andrea Giudici, Weicheng Huang, Qiong Wang, Yuzhe Wang

A symmetrically-buckled arch whose boundaries are clamped at an angle has two stable equilibria: an inverted and a natural state. When the distance between the clamps is increased (i.e. the confinement is decreased) the system snaps from the inverted to the natural state. Depending on the rate at which the confinement is decreased ('unloading'), the symmetry

Yu Bai, Boxuan Xie, Ruifan Zhu, Zheng Chang

Backscatter communication (BC) becomes a promising energy-efficient solution for future wireless sensor networks (WSNs). Unmanned aerial vehicles (UAVs) enable flexible data collection from remote backscatter devices (BDs), yet conventional UAVs rely on omni-directional fixed-position antennas (FPAs), limiting channel gain and prolonging data collection time

Weiheng Lu, Jian Li, An Yu, Ming-Ching Chang

Multimodal Large Language Models (MLLMs) are widely used for visual perception, understanding, and reasoning. However, long video processing and precise moment retrieval remain challenging due to LLMs' limited context size and coarse frame extraction. We propose the Large Language-and-Vision Assistant for Moment Retrieval (LLaVA-MR), which enables accurate m

Clément Cancès, Daniel Matthes, Ismael Medina, Bernhard Schmitzer

We study a system of drift-diffusion PDEs for a potentially infinite number of incompressible phases, subject to a joint pointwise volume constraint. Our analysis is based on the interpretation as a collection of coupled Wasserstein gradient flows or, equivalently, as a gradient flow in the space of couplings under a `fibered' Wasserstein distance. We prove

Sen-Lin Pang, Zi-Gao Dai

The afterglow of gamma-ray bursts (GRBs) has been extensively discussed in the context of shocks generated during an interaction of relativistic outflows with their ambient medium. This process leads to the formation of both a forward and a reverse shock. While the emission from the forward shock, observed off-axis, has been well-studied as a potential elect

Daniel Schaub, Mark Spivakovsky

The Casas--Alvero conjecture predicts that every univariate polynomial over a field of characteristic zero having a common factor with each of its derivatives $H_i(f)$ is a power of a linear polynomial. One approach to proving the conjecture is to first prove it for polynomials of some small degree $n$, compile a list of bad primes for that degree (namely, t

James J. Hebda, Mikhail G. Katz

We study the maximum ratio of the Euclidean norm to the comass norm of p-covectors in Euclidean n-space and improve the known upper bound found in the standard references by Whitney and Federer. We go on to prove stable systolic inequalities when the fundamental cohomology class of the manifold is a cup product of forms of lower degree.

Yuki Sato, Kiyoshi Kanazawa

Universal power laws have been scrutinised in physics and beyond, and a long-standing debate exists in econophysics regarding the strict universality of the nonlinear price impact, commonly referred to as the square-root law (SRL). The SRL posits that the average price impact $I$ follows a power law with respect to transaction volume $Q$, such that $I(Q) \pr

Arnaud Guillin, Leo Hahn, Manon Michel

Run-and-Tumble Particles (RTPs) are a key model of active matter. They are characterized by alternating phases of linear travel and random direction reshuffling. By this dynamic behavior, they break time reversibility and energy conservation at the microscopic level. It leads to complex out-of-equilibrium phenomena such as collective motion, pattern formatio

Kenichi Yoshida

[Background] Nuclear pairing is a well-established many-body correlation, particularly among like particles in a spin-singlet state. However, the strength of spin-triplet proton-neutron (pn) pairing in nuclei has remained a long-standing and unresolved issue. [Purpose] The relative strength of spin-triplet pn pairing compared to spin-singlet one is investiga

Vidya Sudevan, Fakhreddine Zayer, Sajid Javed, Hamad Karki

This paper introduces the concept of employing neuromorphic methodologies for task-oriented underwater robotics applications. In contrast to the increasing computational demands of conventional deep learning algorithms, neuromorphic technology, leveraging spiking neural network architectures, promises sophisticated artificial intelligence with significantly

Jinhong He, Shivakumara Palaiahnakote, Aoxiang Ning, Minglong Xue

Due to the singularity of real-world paired datasets and the complexity of low-light environments, this leads to supervised methods lacking a degree of scene generalisation. Meanwhile, limited by poor lighting and content guidance, existing zero-shot methods cannot handle unknown severe degradation well. To address this problem, we will propose a new zero-sh

Amin Ghodousian, Mohammad Sedigh Chopannavaz

As a starting point, this paper develops the system of bipolar fuzzy relational equations (FRE) to the most general case, where bipolar FREs are defined by an arbitrary continuous t-norm. Due to the fact that fuzzy relational equations are special cases of bipolar FREs, the proposed system can also be viewed as a generalization of traditional FREs, in which

Christopher C. Lovell, Tjitske Starkenburg, Matthew Ho, Daniel Anglés-Alcázar

We perform the first direct cosmological and astrophysical parameter inference from the combination of galaxy luminosity functions and colours using a simulation based inference approach. Using the Synthesizer code we simulate the dust attenuated ultraviolet-near infrared stellar emission from galaxies in thousands of cosmological hydrodynamic simulations fr

Stéphane Seuret

In this article, we investigate the bivariate multifractal analysis of pairs of Borel probability measures. We prove that, contrarily to what happens in the univariate case, the natural extension of the Legendre spectrum does not yield an upper bound for the bivariate multifractal spectrum. For this we build a pair of measures for which the two spectra have

Luca Barbaglia, Sergio Consoli, Sebastiano Manzan, Luca Tiozzo Pezzoli

We propose an Economic Lexicon (EL) specifically designed for textual applications in economics. We construct the dictionary with two important characteristics: 1) to have a wide coverage of terms used in documents discussing economic concepts, and 2) to provide a human-annotated sentiment score in the range [-1,1]. We illustrate the use of the EL in the con

Maria Strazzullo, Francesco Ballarin, Traian Iliescu, Tomás Chacón Rebollo

The evolve-filter (EF) model is a filter-based numerical stabilization for under-resolved convection-dominated flows. EF is a simple, modular, and effective strategy for both full-order models (FOMs) and reduced-order models (ROMs). It is well-known, however, that when the filter radius is too large, EF can be overdiffusive and yield inaccurate results. To a

Carlos D. Gonzalez-Gomez, Jose Garcia-Guirado, Romain Quidant, Felix Carrique

We demonstrate that a set of microfabricated electrodes can be coupled to a commercial optical tweezers device, implementing a hybrid electro-optical trap with multiple functionalities to manipulate micro/nanoparticles in suspension. Our design allows us to simultaneously trap tens of particles in a single potential well generated in the low electric field r

Daun Chung, Kwangyeul Choi, Woojun Lee, Chiyoon Kim

Silicon-based ion trap chips can benefit from existing advanced fabrication technologies, such as multi-metal layer techniques for two-dimensional architectures and silicon photonics for the integration of on-chip optical components. However, the scalability of these technologies may be compromised by semiconductor charging, where photogenerated charge carri

G. Gatti, N. Tancogne-Dejean, H. Hübener, U. De Giovannini

Chirality in tellurium derives from a Peierls distortion driven by strong electron-phonon coupling, making this material a unique candidate for observing a light-induced topological phase transition. By using time- and angle-resolved photoelectron spectroscopy (trARPES), we reveal that upon near-infrared photoexcitation the Peierls gap is modulated by displa

Usman Syed, Federico Cunico, Uzair Khan, Eros Radicchi

In this position paper, we propose an approach for sustainable data collection in the field of optimal mix design for marble sludge reuse. Marble sludge, a calcium-rich residual from stone-cutting processes, can be repurposed by mixing it with various ingredients. However, determining the optimal mix design is challenging due to the variability in sludge com

Nipuni Ginige, Arthur Sousa de Sena, Nurul Huda Mahmood, Nandana Rajatheva

Novel reconfigurable intelligent surface (RIS) architectures, known as beyond diagonal RISs (BD-RISs), have been proposed to enhance reflection efficiency and expand RIS capabilities. However, their passive nature, non-diagonal reflection matrix, and the large number of coupled reflecting elements complicate the channel state information (CSI) estimation pro

Chao Zhao, Chunli Jiang, Lifan Luo, Shuai Yuan

Robotic manipulation has made significant advancements, with systems demonstrating high precision and repeatability. However, this remarkable precision often fails to translate into efficient manipulation of thin deformable objects. Current robotic systems lack imprecise dexterity, the ability to perform dexterous manipulation through robust and adaptive beh

Lucas Correia, Jan-Christoph Goos, Thomas Bäck, Anna V. Kononova

Benchmarking anomaly detection approaches for multivariate time series is a challenging task due to a lack of high-quality datasets. Current publicly available datasets are too small, not diverse and feature trivial anomalies, which hinders measurable progress in this research area. We propose a solution: a diverse, extensive, and non-trivial dataset generat

Kiev Gama, Grischa Liebel, Miguel Goulão, Aline Lacerda

The concept of neurodiversity, encompassing conditions such as Autism Spectrum Disorder (ASD), Attention-Deficit/Hyperactivity Disorder (ADHD), dyslexia, and dyspraxia, challenges traditional views of these neurodevelopmental variations as disorders and instead frames them as natural cognitive differences that contribute to unique ways of thinking and proble

Ziqi Wang, Chang Che, Qi Wang, Yangyang Li

Visual instruction tuning (VIT) enables multimodal large language models (MLLMs) to effectively handle a wide range of vision tasks by framing them as language-based instructions. Building on this, continual visual instruction tuning (CVIT) extends the capability of MLLMs to incrementally learn new tasks, accommodating evolving functionalities. While prior w

Xoel Sixto, Álvaro Navarrete, Margarida Pereira, Guillermo Currás-Lorenzo

Most security proofs of quantum key distribution (QKD) assume that there is no unwanted information leakage about the state preparation process. However, this assumption is impossible to guarantee in practice, as QKD systems can leak information to the channel due to device imperfections or the active action of an eavesdropper. Here, we solve this pressing i

Taiki Nishijima, Ei Shigematsu, Ryo Ohshima, Keigo Matsushita

Spin-to-charge conversion in monolayer and bilayer germanium(Ge) nanosheets was demonstrated via the circular photogalvanic effect (CPGE). The CPGE current generated in a spin-splitting state of the Ge nanosheet reached a maximum value when the thickness of the Ge nanosheet corresponded to bilayer germanene, indicating that the top layer of the bilayer Ge na

Stefan Graser, Stephan Böhm

Augmented Reality (AR) is an emerging technology that ranks among the top innovations in interactive media. With the emergence of new technologies, the question about the factors influencing user acceptance arises. Many research models on the user acceptance of technologies were developed and extended to answer this question in the last decades. This researc

Stein Stroobants, Christophe de Wagter, Guido C. H. E. De Croon

The real-world application of small drones is mostly hampered by energy limitations. Neuromorphic computing promises extremely energy-efficient AI for autonomous flight but is still challenging to train and deploy on real robots. To reap the maximal benefits from neuromorphic computing, it is necessary to perform all autonomy functions end-to-end on a single

Abdollah Masoud Darya, Saeed Abdallah

This letter proposes decision-directed semi-blind channel estimation for massive multiple-input multiple-output low-Earth-orbit satellite communications. Two semi-blind estimators are proposed. The first utilizes detected data symbols in addition to pilot symbols. The second, a modified semi-blind estimator, is specially designed to mitigate the channel-agin

Lai Zhou, Jinping Lin, Chengfang Ge, Yuanbin Fan

Owing to its repeater-like rate-loss scaling, twin-field quantum key distribution (TF-QKD) has repeatedly exhibited in laboratory its superiority for secure communication over record fiber lengths. Field trials pose a new set of challenges however, which must be addressed before the technology's roll-out into real-world. Here, we verify in field the viabilit

Ing-Sheng Bernard-Tiong, Yoshihisa Tsurumine, Ryosuke Sota, Kazuki Shibata

Cooperative grasping and transportation require effective coordination to complete the task. This study focuses on the approach leveraging force-sensing feedback, where robots use sensors to detect forces applied by others on an object to achieve coordination. Unlike explicit communication, it avoids delays and interruptions; however, force-sensing is highly

Aslan S. Dizaji

It has been shown that social institutions impact human motivations to produce different behaviours, such as amount of working or specialisation in labor. With advancement in artificial intelligence (AI), specifically large language models (LLMs), now it is possible to perform in-silico simulations to test various hypotheses around this topic. Here, I simula

Guanzhou Lan, Yuqi Yang, Zhigang Wang, Dong Wang

Night-to-Day translation (Night2Day) aims to achieve day-like vision for nighttime scenes. However, processing night images with complex degradations remains a significant challenge under unpaired conditions. Previous methods that uniformly mitigate these degradations have proven inadequate in simultaneously restoring daytime domain information and preservin

Yalan Lin, Yingwei Ma, Rongyu Cao, Binhua Li

Reproducing buggy code is the first and crucially important step in issue resolving, as it aids in identifying the underlying problems and validating that generated patches resolve the problem. While numerous approaches have been proposed for this task, they primarily address common, widespread errors and struggle to adapt to unique, evolving errors specific

Hongting Hou, Long Zhang

Motivated by the recent observation of real-space edge modes with ultracold atoms [Braun et al., Nat. Phys. 20, 1306 (2024)], we investigate the preparation and detection of anomalous counterpropagating edge states -- a defining feature of the anomalous Floquet valley-Hall (AFVH) phase -- in a two-dimensional periodically driven optical Raman lattice. Modeli

Xin-Feng Zhang, Yuan-Fen Liu, Huan-Bo Luo, Bin Liu

We investigate phase transitions of the ground state (GS) of spin-1 Bose-Einstein condensates under the combined action of the spin-orbit coupling (SOC) and gradient magnetic field. Introducing appropariate raising and lowering operators, we exactly solve the linear system. Analyzing the obtained energy spectrum, we conclude that simultaneous variation of th

Kittisak Chumpong, Khamron Mekchay, Fukiat Nualsri, Phiraphat Sutthimat

This paper introduces an analytical formula for the fractional-order conditional moments of nonlinear drift constant elasticity of variance (NLD-CEV) processes under regime switching, governed by continuous-time finite-state irreducible Markov chains. By employing a hybrid system approach, we derive exact closed-form expressions for these moments across arbi

Dmitry A. Zezyulin, Georgy L. Alfimov

We investigate the formation of steady states in one-dimensional Bose-Einstein condensates of repulsively interacting ultracold atoms loaded into a quasiperiodic potential created by two incommensurate periodic lattices. We study the transformations between linear and nonlinear modes and describe the general patterns that govern the birth of nonlinear modes

Hotae Lee, Francesco Borrelli

We propose a novel Stochastic Model Predictive Control (MPC) for uncertain linear systems subject to probabilistic constraints. The proposed approach leverages offline learning to extract key features of affine disturbance feedback policies, significantly reducing the computational burden of online optimization. Specifically, we employ offline data-driven sa

Yancheng Liang, Daphne Chen, Abhishek Gupta, Simon S. Du

Training agents that can coordinate zero-shot with humans is a key mission in multi-agent reinforcement learning (MARL). Current algorithms focus on training simulated human partner policies which are then used to train a Cooperator agent. The simulated human is produced either through behavior cloning over a dataset of human cooperation behavior, or by usin

Chao Lei, Yanchuan Chang, Nir Lipovetzky, Krista A. Ehinger

The strong performance of large language models (LLMs) raises extensive discussion on their application to code generation. Recent research suggests continuous program refinements through visible tests to improve code generation accuracy in LLMs. However, these methods suffer from LLMs' inefficiency and limited reasoning capacity. In this work, we propose an

Sonia Deswal, Deepu Kumar, Dibyata Rout, Surjeet Singh

Trilayer nickelates are a rich class of materials exhibiting diverse correlated phenomena, including superconductivity, density wave transitions, non-Fermi liquid behavior along with an unusual metal-to-metal transition around T* ~ 150 K. Understanding the electronic correlations, lattice and charge dynamics are crucial to unreveal the origin of superconduct

Hailong Yang, Mingxian Gu, Renhuo Zhao, Fuping Hu

Extracting implicit knowledge and logical reasoning abilities from large language models (LLMs) has consistently been a significant challenge. The advancement of multi-agent systems has further en-hanced the capabilities of LLMs. Inspired by the structure of multi-polar neurons (MNs), we propose the XAgents framework, an in-terpretable multi-agent cooperativ

Mario Molina-Molina, Lorenzo J. Tardon, Ana M. Barbancho, Isabel Barbancho

This manuscript describes and implementation of scripts of code aimed at reducing the influence of artifacts, specifically focused on ocular artifacts, in the measurement and processing of electroencephalogram (EEG) signals. This process is of importance because it benefits the analysis and study of long trial samples when the appearance of ocular artifacts

Mingjun Feng, Lan Wu, Guobin Liu

Spin masers with optical detection and artificial feedback are widely used in fundamental and practical applications. However, a full picture of the maser dynamics is still absent. By solving the feedback driven Bloch equations, we simulated the dynamics of an ideal spin maser in a broad parameter space. Rich nonlinear dynamics including high order harmonics

Jan Marius Stürmer, Marius Graumann, Tobias Koch

Digitizing engineering diagrams like Piping and Instrumentation Diagrams (P&IDs) plays a vital role in maintainability and operational efficiency of process and hydraulic systems. Previous methods typically decompose the task into separate steps such as symbol detection and line detection, which can limit their ability to capture the structure in these diagr

Anton Galajinsky

The Ruijsenaars-Schneider models are integrable dynamical realizations of the Poincare group in 1+1 dimensions, which reduce to the Calogero and Sutherland systems in the nonrelativistic limit. In this work, a possibility to construct a one-parameter deformation of the Ruijsenaars-Schneider models by uplifting the Poincare algebra in 1+1 dimensions to the an

Xueying Jiang, Lewei Lu, Ling Shao, Shijian Lu

The recent development in multimodal learning has greatly advanced the research in 3D scene understanding in various real-world tasks such as embodied AI. However, most existing studies are facing two common challenges: 1) they are short of reasoning ability for interaction and interpretation of human intentions and 2) they focus on scenarios with single-cat

Stein Andreas Bethuelsen, Florian Völlering

Consider the dynamic environment governed by a Poissonian field of independent particles evolving as simple random walks on $\mathbb{Z}^d$. The random walk on random walks model refers to a particular stochastic process on $\mathbb{Z}^d$ whose evolution at time $t$ depends on the number of such particles at its location. We derive classical limit theorems fo

Xiaojun Jia, Yihao Huang, Yang Liu, Peng Yan Tan

This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks. With the increasing integration of LLMs in critical sectors such as h