March 2024 arXiv papers — page 59

M. M. Majczak, K. Krajewska, J. Z. Kamiński, A. Bechler

Dynamical Sauter-Schwinger mechanism of electron-positron pair creation by a time-dependent electric field pulses is considered using the $S$-matrix approach and reduction formulas. They lead to the development of framework based on the solutions of the Dirac equation with the Feynman- or anti-Feynman boundary conditions. Their asymptotic properties are link

Jean-Luc Lehners, Jerome Quintin

Theoretical considerations motivate us to consider vacuum energy to be able to decay and to assume that the spatial geometry of the universe is closed. Combining both aspects leads to the possibility that the universe, or certain regions thereof, can collapse and subsequently undergo a curvature bounce. This may have occurred in the very early universe, in a

Zhaobo Zhou, Min Li, Thomas Frauenheim, Junjie He

Exploring ultrafast magnetization control in two-dimensional (2D) magnets through optically driven coherent phonons has been well-established. Yet, the microscopic interplay between spin dynamics and lattice degrees of freedom remains less explored. Employing real-time time-dependent density functional theory (rt-TDDFT) coupled with Ehrenfest dynamics, we sy

Nick Heppert, Max Argus, Tim Welschehold, Thomas Brox

Teaching robots new skills quickly and conveniently is crucial for the broader adoption of robotic systems. In this work, we address the problem of one-shot imitation from a single human demonstration, given by an RGB-D video recording. We propose a two-stage process. In the first stage we extract the demonstration trajectory offline. This entails segmenting

Masoume Reyhani, Mahdi Najafi, Javad T. Firouzjaee, Eleonora Di Valentino

This research investigates the impact of the nature of Dark Energy (DE) on structure formation, focusing on the matter power spectrum and the Integrated Sachs-Wolfe effect (ISW). By analyzing the matter power spectrum at redshifts $z = 0$ and $z = 5$, as well as the ISW effect on the scale of $\ell = 10-100$, the study provides valuable insights into the inf

Jan Dreier, Nikolas Mählmann, Szymon Toruńczyk

A conjecture in algorithmic model theory predicts that the model-checking problem for first-order logic is fixed-parameter tractable on a hereditary graph class if and only if the class is monadically dependent. Originating in model theory, this notion is defined in terms of logic, and encompasses nowhere dense classes, monadically stable classes, and classe

Enzo Brox, Michael Lechner

This article shows how coworker performance affects individual performance evaluation in a teamwork setting at the workplace. We use high-quality data on football matches to measure an important component of individual performance, shooting performance, isolated from collaborative effects. Employing causal machine learning methods, we address the assortative

Vinicius M. Netto, Otavio Peres, Caio Cacholas

Entropy is arguably one of the most powerful concepts to understand the world, from the behavior of molecules to the expansion of the universe, from how life emerges to how hybrid complex systems like cities come into being and continue existing. Yet, despite its widespread application, it is also one of the most misunderstood concepts across the sciences. T

H. A. Scheppink, S. Ahmadi, P. Desain, M. Tangermann

Auditory attention decoding (AAD) aims to extract from brain activity the attended speaker amidst candidate speakers, offering promising applications for neuro-steered hearing devices and brain-computer interfacing. This pilot study makes a first step towards AAD using the noise-tagging stimulus protocol, which evokes reliable code-modulated evoked potential

S. Narayanan, S. Ahmadi, P. Desain, J. Thielen

A limitation of brain-computer interface (BCI) spellers is that they require the user to be able to move the eyes to fixate on targets. This poses an issue for users who cannot voluntarily control their eye movements, for instance, people living with late-stage amyotrophic lateral sclerosis (ALS). This pilot study makes the first step towards a gaze-independ

Andrea Aveni, Ludovico Crippa, Giulio Principi

We study a family of distance functions on rankings that allow for asymmetric treatments of alternatives and consider the distinct relevance of the top and bottom positions for ordered lists. We provide a full axiomatic characterization of our distance. In doing so, we retrieve new characterizations of existing axioms and show how to effectively weaken them

Håkon Harnes, Donn Morrison

WebAssembly has gained significant traction as a high-performance, secure, and portable compilation target for the Web and beyond. However, its growing adoption has also introduced new security challenges. One such threat is cryptojacking, where websites mine cryptocurrencies on visitors' devices without their knowledge or consent, often through the use of W

Alessio Cucciari, Dionisia Naddeo, Simone Di Cataldo, Lilia Boeri

We present the first $ab$-$initio$ study of superconductivity in NbTi, the workhorse for many applications. Despite its apparent simplicity, NbTi turns out to be a major challenge for computational superconductivity. In fact, anharmonic effects are crucial to obtain dynamically stable phonons for the ordered bcc phase, unstable at the harmonic level, and bey

Joe Oakley, Hakan Ferhatosmanoglu

Serverless computing offers attractive scalability, elasticity and cost-effectiveness. However, constraints on memory, CPU and function runtime have hindered its adoption for data-intensive applications and machine learning (ML) workloads. Traditional 'server-ful' platforms enable distributed computation via fast networks and well-established inter-process c

Sofia Casarin, Cynthia I. Ugwu, Sergio Escalera, Oswald Lanz

The landscape of deep learning research is moving towards innovative strategies to harness the true potential of data. Traditionally, emphasis has been on scaling model architectures, resulting in large and complex neural networks, which can be difficult to train with limited computational resources. However, independently of the model size, data quality (i.

Profirie Bardetski, Mihai A. Macovei

We investigate the resonance fluorescence features of a small ensemble of closely packed and moderately laser pumped two-level emitters at resonance. The mean distance between any two-level radiators is smaller than the corresponding emission wavelength, such that the dipole-dipole interactions are not negligible. We have found that under the secular approxi

Yimeng Fan, Wei Zhang, Changsong Liu, Mingyang Li

Event cameras, characterized by high temporal resolution, high dynamic range, low power consumption, and high pixel bandwidth, offer unique capabilities for object detection in specialized contexts. Despite these advantages, the inherent sparsity and asynchrony of event data pose challenges to existing object detection algorithms. Spiking Neural Networks (SN

Vijay Kumar, Kolin Paul

Continuous advancements in medical technology have led to the creation of affordable mobile imaging devices suitable for telemedicine and remote monitoring. However, the rapid examination of large populations poses challenges, including the risk of fraudulent practices by healthcare professionals and social workers exchanging unverified images via mobile app

Di Wu, Jingyu Liu, Xuechao Wang, Jian Liu

Cross-chain transactions today remain slow, costly, and fragmented. Existing custodial exchanges expose users to counterparty and centralization risks, while non-custodial liquidity bridges suffer from capital inefficiency and slow settlement; critically, neither approach guarantees users a unilateral path to recover assets if the infrastructure fails. We in

J. Thielen, J. Sosulski, M. Tangermann

This study explores two zero-training methods aimed at enhancing the usability of brain-computer interfaces (BCIs) by eliminating the need for a calibration session. We introduce a novel method rooted in the event-related potential (ERP) domain, unsupervised mean maximization (UMM), to the fast code-modulated visual evoked potential (c-VEP) stimulus protocol

Zixu Li, Yu Qiu, Yu Zhou

We show that the Koszul functor of a homologically smooth graded gentle algebra can be realized as the half rotation in a geometric model. As a byproduct, we prove an intersection-dim formula involving the Koszul functor.

Thi-Thu-Tam Nguyen, Adnane Cabani, Iyadh Cabani, Koen De Turck

The growth of e-commerce has resulted in a surge in parcel deliveries, increasing transportation costs and pollution issues. Alternatives to home delivery have emerged, such as the delivery to so-called parcel pick-up points (PUPs), which eliminates delivery failure due to customers not being at home. Nevertheless, parcels reaching overloaded PUPs may need t

Dejan Milutinovic, Alexander Von Moll, Satyanarayana G. Manyam, David W. Casbeer

In classical works on a planar differential pursuit-evasion game with a faster pursuer, the intercept point resulting from the equilibrium strategies lies on the Apollonius circle. This property was exploited for the construction of the equilibrium strategies for two faster pursuers against one evader. Extensions for planar multiple-pursuer single-evader sce

Du Wang, Ke-Rang Song, Wen-Ling Wang, Fei Huang

Inspired by the resonance $T_{cc}^+(3875)$ recently observed by the LHCb Collaboration, we systematically explore the $S$- and $P$-wave $cc\bar{q}\bar{q}'$ $(\bar{q},\bar{q}' = \bar{u}, \bar{d}, \bar{s})$ systems in a chiral SU(3) quark model. The Hamiltonian contains the kinetic energy, the one-gluon-exchange (OGE) potential, the confinement potential, and

Harshit Verma, Fabio Costa

We consider the question: Is it possible to measure two temperatures simultaneously using a single thermometer? Under common circumstances, where the thermometer can interact with only one bath at a time and the interaction leads to complete thermalization, this is clearly impossible because the final state of the thermometer would be independent of the temp

Tim van Dam, Frank van der Heijden, Philippe de Bekker, Berend Nieuwschepen

Language model-based code completion models have quickly grown in use, helping thousands of developers write code in many different programming languages. However, research on code completion models typically focuses on imperative languages such as Python and JavaScript, which results in a lack of representation for functional programming languages. Conseque

Simon Donaldson, Fabian Lehmann

We develop the deformation theory of Calabi-Yau threefolds, by which we mean 3-dimensional complex manifolds with a nowhere-vanishing holomorphic 3-form, on manifolds with boundary. The boundary data is a closed, real 3-form on the 5-dimensional boundary. In the case of strongly pseudoconvex boundary, we obtain an analogue of Hitchin's local Torelli Theorem

Shaowei Fu, Yifan Duan, Yao Li, Chengzhen Meng

The integration of complementary characteristics from camera and radar data has emerged as an effective approach in 3D object detection. However, such fusion-based methods remain unexplored for place recognition, an equally important task for autonomous systems. Given that place recognition relies on the similarity between a query scene and the corresponding

Gijs Bellaard, Sei Sakata, Bart M. N. Smets, Remco Duits

PDE-based Group Convolutional Neural Networks (PDE-G-CNNs) use solvers of evolution PDEs as substitutes for the conventional components in G-CNNs. PDE-G-CNNs can offer several benefits simultaneously: fewer parameters, inherent equivariance, better accuracy, and data efficiency. In this article we focus on Euclidean equivariant PDE-G-CNNs where the feature m

Alexandre Valentin Jamet, Georgios Vavouliotis, Daniel A. Jiménez, Lluc Alvarez

To alleviate the performance and energy overheads of contemporary applications with large data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism that effectively combines predicting whether an access will be off-chip with adaptive prefetch filtering at the first-level data cache (L1D). TLP is composed of two connected microa

Jonathan Pirnay, Dominik G. Grimm

Current methods for end-to-end constructive neural combinatorial optimization usually train a policy using behavior cloning from expert solutions or policy gradient methods from reinforcement learning. While behavior cloning is straightforward, it requires expensive expert solutions, and policy gradient methods are often computationally demanding and complex

Kazufumi Tanji, Hiroki Takahashi, Wojciech Roga, Masahiro Takeoka

The qubit scalability imposes a paramount challenge in the field of quantum computing. Photonic interconnects between distinct quantum computing modules provide a solution to deal with this issue. The fundamental part of this approach is entanglement distribution via travelling photons emitted by matter qubits. However, randomness of the spontaneous emission

Alie Abbasi Yazdi, Gaetano DAvino

The sedimentation dynamics of a prolate spheroidal particle in an unbounded elastoviscoplastic (EVP) fluid is studied by direct finite element simulations under inertialess flow conditions. The Saramito-Giesekus constitutive equation is employed to model the suspending liquid. The Arbitrary Lagrangian-Eulerian formulation is used to handle the particle motio

Hiroyasu Koizumi, Haruki Nakayama, Hayato Taya

A theory for cuprate superconductivity predicts the existence of nano-sized loop currents called, `` spin-vortex-induced loop currents (SVILCs)''. We calculate magnetic fields produced by them for a model of Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ (Bi-2212) thin films composed of one surface and two bulk CuO$_2$ bilayers. In this model, bulk CuO$_2$ layers host s

Siddharth Agrawal

In the pursuit of advancing the state-of-the-art (SOTA) in road safety, traffic monitoring, surveillance, and logistics automation, we introduce the Global License Plate Dataset (GLPD). The dataset consists of over 5 million images, including diverse samples captured from 74 countries with meticulous annotations, including license plate characters, license p

Shirin Vafaei, Ryohei Fukuma, Takufumi Yanagisawa, Huixiang Yang

The development of algorithms to accurately decode neural information has long been a research focus in the field of neuroscience. Brain decoding typically involves training machine learning models to map neural data onto a preestablished vector representation of stimulus features. These vectors are usually derived from image- and/or text-based feature space

Alec McClean, Sivaraman Balakrishnan, Edward H. Kennedy, Larry Wasserman

Doubly robust estimators with cross-fitting have gained popularity in causal inference due to their favorable structure-agnostic error guarantees. However, when additional structure, such as H\"{o}lder smoothness, is available then more accurate "double cross-fit doubly robust" (DCDR) estimators can be constructed by splitting the training data and undersmoo

Remo Garattini, Mir Faizal

In this paper, we have for the first time considered the consequences of thermal fluctuations to the Casimir effect on a traversable wormhole. This was done by using finite temperature generalization of the Casimir effect as a source of a hot traversable wormhole. Thus, we have considered a more physical scenario, where the effects of thermal fluctuations ar

Tuo Feng, Wenguan Wang, Fan Ma, Yi Yang

Autonomous systems need to process large-scale, sparse, and irregular point clouds with limited compute resources. Consequently, it is essential to develop LiDAR perception methods that are both efficient and effective. Although naively enlarging 3D kernel size can enhance performance, it will also lead to a cubically-increasing overhead. Therefore, it is cr

Han He, Bei You, Ning Jiang, Xinwu Cao

A sample of 17 FR I radio galaxies constructed from the 3CR catalog, which is characterized by edge-darkened radio structures, is studied. The optical core luminosities derived from Hubble Space Telescope observation are used to estimate the Eddington ratios which are found to be below $10^{-3.4}$ for this sample. This is supported by the Baldwin-Phillips-Te

Emily Sullivan, Atoosa Kasirzadeh

We argue that the trend toward providing users with feasible and actionable explanations of AI decisions, known as recourse explanations, comes with ethical downsides. Specifically, we argue that recourse explanations face several conceptual pitfalls and can lead to problematic explanation hacking, which undermines their ethical status. As an alternative, we

Sarah Barendswaard, Tong Duy Son

Advancements in autonomous vehicle (AV) technologies necessitate precise estimation of perceived risk to enhance user comfort, acceptance and trust. This paper introduces a novel AV-Occupant Risk (AVOR) model designed for perceived risk estimation during AV cut-in scenarios. An empirical study is conducted with 18 participants with realistic cut-in scenarios

Rohan Kumar Gupta, Rohit Sinha

Self-supervised learning (SSL) has been investigated to generate task-agnostic representations across various domains. However, such investigation has not been conducted for detecting multiple mental disorders. The rationale behind the existence of a task-agnostic representation lies in the overlapping symptoms among multiple mental disorders. Consequently,

Majid Abdulsatar, Hussain Ahmad, Diksha Goel, Faheem Ullah

The widespread adoption of microservice architectures has given rise to a new set of software security challenges. These challenges stem from the unique features inherent in microservices. It is important to systematically assess and address software security challenges such as software security risk assessment. However, existing approaches prove inefficient

Yongjie Wang, Qingzhou Bu, Dongshuo Li, Shuai Yang

Near-infrared phosphor-converted light-emitting diodes (NIR pc-LEDs) are compact light sources of great interest for NIR spectroscopy applications. Beyond typical Cr3+-activated NIR-emitting phosphors, there exists a strong demand for Cr3+-free alternatives with high efficiency and broadband emission to rich the landscape of NIR luminescent materials and ext

Levon Aslanyan, Hasmik Sahakyan

Target class classification is a mixed classification and transition model whose integrated goal is to assign objects to a certain, so called target or normal class. The classification process is iterative, and in each step an object in a certain class undergoes an action attached to that class, initiating the transition of the object to one of the classes.

Miguel Ortega, Buse Yalçın

In this study, we deal with non-degenerate translators of the mean curvature flow in the well-known hyperbolic Einstein's static universe. We classify translators foliated by horospheres and rotationally invariant ones, both space-like and time-like. For space-like translators, we show a uniqueness theorem as well as a result to extend an isometry of the bou

Juan Vidal Alegría, Johan Thunberg, Ove Edfors

We envision a future in which multi-antenna technology effectively exploits the spatial domain as a set of non-interfering orthogonal resources, allowing for flexible resource allocation and efficient modulation/demodulation. We may refer to this paradigm as orthogonal space-division multiplexing (OSDM). On the other hand, reconfigurable intelligent surface

Ankan Mukherjee, Yeshma Ibrahim, Michal Hajdušek, Sai Vinjanampathy

We demonstrate the inadequacy of mean-field theory by exploring the effects of initial state correlations on the dynamics of continuous time crystals, necessitating higher-order cumulant expansions. We exemplify this using cat states for which the mean field fails to predict a phase transition but the second order cumulant expansion theory captures it. Motiv

Shaowei Wei, Zhengwei Wu, Xin Li, Qintong Wu

Sequential recommendation methods play a pivotal role in modern recommendation systems. A key challenge lies in accurately modeling user preferences in the face of data sparsity. To tackle this challenge, recent methods leverage contrastive learning (CL) to derive self-supervision signals by maximizing the mutual information of two augmented views of the ori

Yundong Sun, Dongjie Zhu, Yansong Wang, Zhaoshuo Tian

Graph Neural Networks (GNNs) have emerged as the most powerful weapon for various graph tasks due to the message-passing mechanism's great local information aggregation ability. However, over-smoothing has always hindered GNNs from going deeper and capturing multi-hop neighbors. Unlike GNNs, Transformers can model global information and multi-hop interaction

Nick James, Max Menzies

This paper develops new mathematical techniques to identify temporal shifts among a collection of US equities partitioned into a new and more detailed set of market sectors. Although conceptually related, our three analyses reveal distinct insights about financial markets, with meaningful implications for investment managers. First, we explore a variety of m

Alberto Cialdea

The completeness on the boundary (in the sense of Picone) of certain systems related to the III and IV BVPs for the elasticity system is proved. The completeness is obtained in both $L^p$ ($1\leq 1<\infty$) and uniform norms.

Florian Langer, Jihong Ju, Georgi Dikov, Gerhard Reitmayr

Digitising the 3D world into a clean, CAD model-based representation has important applications for augmented reality and robotics. Current state-of-the-art methods are computationally intensive as they individually encode each detected object and optimise CAD alignments in a second stage. In this work, we propose FastCAD, a real-time method that simultaneou

Sergei P. Roshchupkin, Sergey B. Makarov

The article presents a theoretical study of Oleinik resonances in the process of scattering a gamma quantum by an ultrarelativistic electron in the field of a strong electromagnetic wave. It is shown that under resonant conditions, the scattering channels of the reaction effectively split into two first-order processes according to a fine structure constant

Yiming Li

We present the logarithm laws for the partial sum of the itinerary function over non-periodic BCZ orbits, utilizing the even and odd Diophantine exponent defined by Athreya-Margulis. We also give a detailed description of the BCZ map and its excursions.

Jonas Schießl, Ruchuan Ou, Timm Faulwasser, Michael H. Baumann

This paper presents first results for near optimality in expectation of the closed-loop solutions for stochastic economic MPC. The approach relies on a recently developed turnpike property for stochastic optimal control problems at an optimal stationary process, combined with techniques for analyzing time-varying economic MPC schemes. We obtain near optimali

Ruslan K. Muharlyamov, Tatiana N. Pankratyeva, Shehabaldeen O. A. Bashir

We study the evolution of Bianchi-I space-times within the framework of the Horndeski theory with $G_5=\text{const}/X$. The space-times are filled a global unidirectional electromagnetic field interacting with a scalar field. We consider the minimal interaction and the non-minimal interaction by the law $f^2(\phi)F_{\mu\nu}F^{\mu\nu}$. The Horndeski theory a

Chaoyun Zhang, Zicheng Ma, Yuhao Wu, Shilin He

Verbatim feedback constitutes a valuable repository of user experiences, opinions, and requirements essential for software development. Effectively and efficiently extracting valuable insights from such data poses a challenging task. This paper introduces Allhands , an innovative analytic framework designed for large-scale feedback analysis through a natural

Yukuan Jia, Jiawen Zhang, Shimeng Lu, Baokang Fan

Environmental perception in Automated Valet Parking (AVP) has been a challenging task due to severe occlusions in parking garages. Although Collaborative Perception (CP) can be applied to broaden the field of view of connected vehicles, the limited bandwidth of vehicular communications restricts its application. In this work, we propose a BEV feature-based C

Yongqi Shi, Thomas Ruster, Melvyn Ho, Sylvain Karlen

We report on broad instantaneous bandwidth microwave spectrum analysis with hot $^{87}\mathrm{Rb}$ atoms in a microfabricated vapor cell in a large magnetic field gradient. The sensor is a MEMS atomic vapor cell filled with isotopically pure $^{87}\mathrm{Rb}$ and $\mathrm{N}_2$ buffer gas to localize the motion of the atoms. The microwave signals of interes

Bozhen Zhou, Shu Chen

We study the spread complexity in two-mode Bose-Einstein condensations and unveil that the long-time average of the spread complexity $\overline{C}_{K}$ can probe the dynamical transition from self-trapping to Josephson oscillation. When the parameter $\omega$ increases over a critical value $\omega_{c}$, we reveal that the spread complexity exhibits a sharp

L. C. Ugwuoke, M. S. Tame

We propose a generalized formula for calculating the dipole polarizability of spherical multilayer nanoshells (MNSs) within the long-wavelength approximation (LWA). Given a MNS with a finite number of concentric layers, radii, and dielectric properties, embedded in a dielectric medium, in the presence of a uniform electric field, we show that its frequency-d

Erik Schlachhoff, Nils Dengler, Leif Van Holland, Patrick Stotko

In 1997, the very first tour guide robot RHINO was deployed in a museum in Germany. With the ability to navigate autonomously through the environment, the robot gave tours to over 2,000 visitors. Today, RHINO itself has become an exhibit and is no longer operational. In this paper, we present RHINO-VR, an interactive museum exhibit using virtual reality (VR)

Víctor Toscano-Durán, Javier Perera-Lago, Eduardo Paluzo-Hidalgo, Rocío Gonzalez-Diaz

In recent years, Deep Learning has gained popularity for its ability to solve complex classification tasks, increasingly delivering better results thanks to the development of more accurate models, the availability of huge volumes of data and the improved computational capabilities of modern computers. However, these improvements in performance also bring ef

Matteo Ciniselli, Alberto Martin-Lopez, Gabriele Bavota

Code completion is a key feature of Integrated Development Environments (IDEs), aimed at predicting the next tokens a developer is likely to write, helping them write code faster and with less effort. Modern code completion approaches are often powered by deep learning (DL) models. However, the swift evolution of programming languages poses a critical challe

Shuai Yang, Yongjie Wang, Guotao Xiang, Sha Jiang

The synthesis, structural, and luminescence properties have been carried out for Cr3+-activated Al2(WO4)3 (AWO) and Sc2(WO4)3 (SWO) phosphors for application in pc-NIR LED. Upon blue excitation, these compounds are capable of exhibiting broadband NIR emission stems primarily from 4T2-->4A2 transition in the range of 670-1200 nm (maxima ~808 nm, FWHM ~140 nm)

Arieh Iserles, Karolina Kropielnicka

This paper considers computational methods that split a vector field into three components in the case when both the vector field and the split components might be unbounded. We first employ classical Taylor expansion which, after some algebra, results in an expression for a second-order splitting which, strictly speaking, makes sense only for bounded operat

CMS Collaboration

The operation and performance of the Compact Muon Solenoid (CMS) electromagnetic calorimeter (ECAL) are presented, based on data collected in pp collisions at $\sqrt{s}$ = 13 TeV at the CERN LHC, in the years from 2015 to 2018 (LHC Run 2), corresponding to an integrated luminosity of 151 fb$^{-1}$. The CMS ECAL is a scintillating lead-tungstate crystal calor

Bohan Wang, Huishuai Zhang, Qi Meng, Ruoyu Sun

This paper aims to clearly distinguish between Stochastic Gradient Descent with Momentum (SGDM) and Adam in terms of their convergence rates. We demonstrate that Adam achieves a faster convergence compared to SGDM under the condition of non-uniformly bounded smoothness. Our findings reveal that: (1) in deterministic environments, Adam can attain the known lo

Guangyu Zhu, Xidong Mu, Li Guo, Ao Huang

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted simultaneous wireless information and power transfer (SWIPT) system is proposed. More particularly, an STAR-RIS is deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) an

Callum Vidler, Michael Halwes, Kirill Kolesnik, Philipp Segeritz

Additive manufacturing is an expanding multidisciplinary field encompassing applications including medical devices, aerospace components, microfabrication strategies, and artificial organs. Among additive manufacturing approaches, light-based printing technologies, including two-photon polymerization, projection micro stereolithography, and volumetric printi

Md Abdul Kadir, Hasan Md Tusfiqur Alam, Pascale Maul, Hans-Jürgen Profitlich

Image annotation is one of the most essential tasks for guaranteeing proper treatment for patients and tracking progress over the course of therapy in the field of medical imaging and disease diagnosis. However, manually annotating a lot of 2D and 3D imaging data can be extremely tedious. Deep Learning (DL) based segmentation algorithms have completely trans

Michele Focchi, Andrea Del Prete, Daniele Fontanelli, Marco Frego

Mountain slopes are perfect examples of harsh environments in which humans are required to perform difficult and dangerous operations such as removing unstable boulders, dangerous vegetation or deploying safety nets. A good replacement for human intervention can be offered by climbing robots. The different solutions existing in the literature are not up to t

Meenu Upadhyay, Markus Meuwly

The dynamics for molecule formation, relaxation, diffusion, and desorption on amorphous solid water is studied in a quantitative fashion. We aim at characterizing, at a quantitative level, the formation probability, stabilization, energy relaxation and diffusion dynamics of CO$_2$ and NO$_2$ on cold amorphous solid water following atom+diatom recombination r

Kanghong Shi, Ian R. Petersen

We introduce a hybrid control system called a hybrid integrator-gain system (HIGS) based integral resonant controller (IRC) to stabilize negative imaginary (NI) systems. A HIGS-based IRC has a similar structure to an IRC, with the integrator replaced by a HIGS. We show that a HIGS-based IRC is an NI system. Also, for a SISO NI system with a minimal realizati

Yuanbang Liang, Bhavesh Garg, Paul L Rosin, Yipeng Qin

In this paper, we propose Image Downscaling Assessment by Rate-Distortion (IDA-RD), a novel measure to quantitatively evaluate image downscaling algorithms. In contrast to image-based methods that measure the quality of downscaled images, ours is process-based that draws ideas from rate-distortion theory to measure the distortion incurred during downscaling.

Peter Danchev, Esther García, Miguel Gómez Lozano

Let $\mathbb{F}$ be a field. We show that given any $n$th degree monic polynomial $q(x)\in \mathbb{F}[x]$ and any matrix $A\in\mathbb{M}_n(\mathbb{F})$ whose trace coincides with the trace of $q(x)$ and consisting in its main diagonal of $k$ 0-blocks of order one, with $k<n-k$, and an invertible non-derogatory block of order $n-k$, we can construct a square-

Peng Xu, Haoran Wang, Chuang Wang, Xu Liu

As AI Agents based on Large Language Models (LLMs) have shown potential in practical applications across various fields, how to quickly deploy an AI agent and how to conveniently expand the application scenario of AI agents has become a challenge. Previous studies mainly focused on implementing all the reasoning capabilities of AI agents within a single LLM,

Kaska Porayska-Pomsta, Wayne Holmes, Selena Nemorin

The transition of Artificial Intelligence (AI) from a lab-based science to live human contexts brings into sharp focus many historic, socio-cultural biases, inequalities, and moral dilemmas. Many questions that have been raised regarding the broader ethics of AI are also relevant for AI in Education (AIED). AIED raises further specific challenges related to

Wietse Marijn Boon, Omar Duran, Jan Martin Nordbotten

We consider the equilibrium equations for a linearized Cosserat material and provide two perspectives concerning well-posedness. First, the system can be viewed as the Hodge Laplace problem on a differential complex. On the other hand, we show how the Cosserat materials can be analyzed by inheriting results from linearized elasticity. Both perspectives give

Paul B. Rimmer, Oliver Shorttle

Giant impacts can generate transient hydrogen-rich atmospheres, reducing atmospheric carbon. The reduced carbon will form hazes that rain out onto the surface and can become incorporated into the crust. Once heated, a large fraction of the carbon would be converted into graphite. The result is that local regions of the Hadean crust were plausibly saturated w

Michael J. Williams, Hayley J. Macpherson, David L. Wiltshire, Chris Stevens

We apply and extend standard tools for void statistics to cosmological simulations that solve Einstein's equations with numerical relativity (NR). We obtain a simulated void catalogue without Newtonian approximations, using a new watershed void finder which operates on fluid-based NR simulations produced with the Einstein Toolkit. We compare and contrast mea

Xiaokun Zhai, Ying Gao, Xuekai Ma, Chunzi Xing

Spin orbit coupling gives rise to intriguing physical phenomena in bosonic condensates, such as formation of stripe phases and domains with vortex arrays. However, how the non-Hermiticity affects the spatial distribution of spin orbit coupled topological defects such as vortex pair is still challenging to study. In the present work, we realize a non-equilibr

Jun Cheng, Dong Liang, Shan Tan

Image denoising is a fundamental task in computer vision. While prevailing deep learning-based supervised and self-supervised methods have excelled in eliminating in-distribution noise, their susceptibility to out-of-distribution (OOD) noise remains a significant challenge. The recent emergence of contrastive language-image pre-training (CLIP) model has show

Houcem Ben Salem, Alberto Tarable, Alessandro Nordio, Behrooz Makki

We consider a constellation of low-earth-orbit (LEO) satellites connected to a handheld device on the ground. Due to the very large orbital speed, an effective handover strategy becomes of paramount importance. In particular, we study the benefits of soft handover in the uplink from the physical-layer point of view. We give a realistic model for both the gro

Ao Huang, Li Guo, Xidong Mu, Chao Dong

A novel coexisting passive reconfigurable intelligent surface (RIS) and active decode-and-forward (DF) relay assisted non-orthogonal multiple access (NOMA) transmission framework is proposed. In particular, two communication protocols are conceived, namely Hybrid NOMA (H-NOMA) and Full NOMA (F-NOMA). Based on the proposed two protocols, both the sum rate max

René Thierry Djoumessi, Pietro Lenarda, Alessio Gizzi, Simone Giusti

The present study introduces an advanced multi-physics and multi-scale modeling approach to investigate in silico colon motility. We introduce a generalized electromechanical framework, integrating cellular electrophysiology and smooth muscle contractility, thus advancing a first-of-its-kind computational model of laser tissue soldering after incision resect

Elena Yan, Luis G. Nardin, Jomi F. Hübner, Olivier Boissier

In increasingly autonomous and highly distributed multi-agent systems, centralized coordination becomes impractical and raises the need for governance and enforcement mechanisms from an agent-centric perspective. In our conceptual view, sanctioning norm enforcement is part of this agent-centric approach and they aim at promoting norm compliance while preserv

Jiaming Li, Xiangru Lin, Wei Zhang, Xiao Tan

Current semi-supervised object detection (SSOD) algorithms typically assume class balanced datasets (PASCAL VOC etc.) or slightly class imbalanced datasets (MS-COCO, etc). This assumption can be easily violated since real world datasets can be extremely class imbalanced in nature, thus making the performance of semi-supervised object detectors far from satis

Ioannis Dalianis, Chris Kouvaris

If an early matter phase of the Universe existed after inflation with the proper power spectrum, enhanced density perturbations can decouple from the Hubble flow, turn around and collapse. In contrast to what happens in a radiation dominated Universe where pressure nullifies deviations from sphericity in these perturbations, in a matter dominated Universe, t

Marcel Schmidt, Ian Zimmermann

We study non-linear Schr\"odinger operators on graphs. We construct minimal nonnegative solutions to corresponding semi-linear elliptic equations and use them to introduce the notion of stochastic completeness at infinity in a non-linear setting. We provide characterizations for this property in terms of a semi-linear Liouville theorem. It is employed to est

Kailing Wang, Chen Yang, Yuehao Wang, Sikuang Li

Precise camera tracking, high-fidelity 3D tissue reconstruction, and real-time online visualization are critical for intrabody medical imaging devices such as endoscopes and capsule robots. However, existing SLAM (Simultaneous Localization and Mapping) methods often struggle to achieve both complete high-quality surgical field reconstruction and efficient co

Olaya Pérez-Mon, Alejandro Moreo, Juan José del Coz, Pablo González

Quantification, also known as class prevalence estimation, is the supervised learning task in which a model is trained to predict the prevalence of each class in a given bag of examples. This paper investigates the application of deep neural networks to tasks of quantification in scenarios where it is possible to apply a symmetric supervised approach that el

Fattoum Harrathi

The purpose of this paper is to introduce the notion of pre-Malcev superalgebras as the algebraic structure behind the super $\mathcal{O}$-operators on Malcev superalgebras. Moreover, the relations among Malcev superalgebras, pre-Malcev superalgebras and pre-alternative superalgebras are established. Then, we study the operator forms of the classical Yang-Ba

Sacha-Élie Ayoun, Xavier Denis, Petar Maksimović, Philippa Gardner

We propose a hybrid approach to end-to-end Rust verification where the proof effort is split into powerful automated verification of safe Rust and targeted semi-automated verification of unsafe Rust. To this end, we present Gillian-Rust, a proof-of-concept semi-automated verification tool built on top of the Gillian platform that can reason about type safety

Vladyslav Zalevskyi, Kristoffer Hougaard Madsen

Bipolar disorder (BD) and schizophrenia (SZ) are severe mental disorders with profound societal impact. Identifying risk markers early is crucial for understanding disease progression and enabling preventive measures. The Danish High Risk and Resilience Study (VIA) focuses on understanding early disease processes, particularly in children with familial high

Ao Huang, Xidong Mu, Li Guo

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted downlink (DL) active and uplink (UL) backscatter communication (BackCom) framework is proposed. More particularly, a full-duplex (FD) base station (BS) communicates with the DL users via the STAR-RIS's transmission link, while exciting and receiving the inform

Lei Zhang, Xiaowei Fu, Fuxiang Huang, Yi Yang

Person re-identification (ReID) has made great strides thanks to the data-driven deep learning techniques. However, the existing benchmark datasets lack diversity, and models trained on these data cannot generalize well to dynamic wild scenarios. To meet the goal of improving the explicit generalization of ReID models, we develop a new Open-World, Diverse, C

B. Margalef-Bentabol, L. Wang, A. La Marca, C. Blanco-Prieto

Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. We aim to benchmark the relative performance of machine learning (ML) merger detection methods. We explore six leading ML methods using three main datasets. The first one (the training data) consists of mock observations fro