April 2023 arXiv papers — page 91

M. G. Vasin, V. G. Lebedev, K. Y. Shklyaev, S. G. Menshikova

Ascertainment of the nature of the slow relaxation processes observed after melting in glass-forming eutectic melts is the subject of this work. We claim that the diffusion processes nonlinearity in heterogeneous melt with inclusions of refractory stoichiometry is the origin of this phenomenon. The cause for this nonlinearity is the thermodynamic instability

Valentin Garino, Ivan Nourdin

Let $K$ be a convex body in $\mathbb{R}^d$. Let $X_K$ be a $d$-dimensional random vector distributed according to the Hadwiger-Wills density $\mu_K$ associated with $K$, defined as $\mu_K(x)=ce^{-\pi {\rm dist}^2(x,K)}$, $x\in \mathbb{R}^d$. Finally, let the information content $H_K$ be defined as $H_K={\rm dist}^2(X_K,K)$. The goal of this paper is to study

Emanuel Carneiro, Cristian González-Riquelme, Lucas Oliveira, Andrea Olivo

In this paper we address the problem of estimating the operator norm of the embeddings between multidimensional weighted Paley-Wiener spaces. These can be equivalently thought as Fourier uncertainty principles for bandlimited functions. By means of radial symmetrization mechanisms, we show that such problems can all be shifted to dimension one. We provide pr

Garima Singh, Baidyanath Kundu, Harshitha Menon, Alexander Penev

As we reach the limit of Moore's Law, researchers are exploring different paradigms to achieve unprecedented performance. Approximate Computing (AC), which relies on the ability of applications to tolerate some error in the results to trade-off accuracy for performance, has shown significant promise. Despite the success of AC in domains such as Machine Learn

Kai Zhao, Kun Yuan, Ming Sun, Xing Wen

Video quality assessment (VQA) aims to simulate the human perception of video quality, which is influenced by factors ranging from low-level color and texture details to high-level semantic content. To effectively model these complicated quality-related factors, in this paper, we decompose video into three levels (\ie, patch level, frame level, and clip leve

Euijun Song

Sustained spiral waves, also known as rotors, are pivotal mechanisms in persistent atrial fibrillation (AF). Stochasticity is inevitable in nonlinear biological systems such as the heart; however, it is unclear how noise affects the instability of spiral waves in human AF. This study presents a stochastic two-dimensional mathematical model of human AF and ex

Nam V. Tran, M. C. Righi

Diamond displays outstanding chemical, physical, and tribological properties, making it attractive for numerous applications ranging from biomedicine to tribology. However, the reaction of the materials with molecules present in the air, such as oxygen, hydrogen, and water, could significantly change the electronic and tribological properties of the films. I

Andrea Montessori, Marco Lauricella, Adriano Tiribocchi, Mihir Durve

We present thread-safe, highly-optimized lattice Boltzmann implementations, specifically aimed at exploiting the high memory bandwidth of GPU-based architectures. At variance with standard approaches to LB coding, the proposed strategy, based on the reconstruction of the post-collision distribution via Hermite projection, enforces data locality and avoids th

Z. Guguchia, D. J. Gawryluk, Soohyeon Shin, Z. Hao

Charge ordered kagome lattices have been demonstrated to be intriguing platforms for studying the intertwining of topology, correlation, and magnetism. The recently discovered charge ordered kagome material ScV_6Sn_6 does not feature a magnetic groundstate or excitations, thus it is often regarded as a conventional paramagnet. Here, using advanced muon-spin

L. Guerra, P. Salvatore, D. Sinha

We calculate mod-p cohomology of extended powers, and their group completions which are free infinite loop spaces. We consider the cohomology of all extended powers of a space together and identify a Hopf ring structure with divided powers within which cup product structure is more readily computable than on its own. We build on our previous calculations of

Christian Kanzow, Fabius Krämer, Patrick Mehlitz, Gerd Wachsmuth

In this paper, we denoise a given noisy image by minimizing a smoothness promoting function over a set of local similarity measures which compare the mean of the given image and some candidate image on a large collection of subboxes. The associated convex optimization problem possesses a huge number of constraints which are induced by extended real-valued fu

Andrei-Timotei Ardelean, Tim Weyrich

We propose a novel method for Zero-Shot Anomaly Localization on textures. The task refers to identifying abnormal regions in an otherwise homogeneous image. To obtain a high-fidelity localization, we leverage a bijective mapping derived from the 1-dimensional Wasserstein Distance. As opposed to using holistic distances between distributions, the proposed app

Huan Jia, Yinhuo Zhang

In this paper we use the Lyndon-Shirshov basis to study the shuffle type polynomials. We give a free noncommutative binomial (or multinomial) theorem in terms of the Lyndon-Shirshov basis. Another noncommutative binomial theorem given by the shuffle type polynomials with respect to an adjoint derivation is established. As a result, the Bell differential poly

Yong Hu, Junzhang Ma, Yinxiang Li, Dariusz Jakub Gawryluk

Charge density wave (CDW) orders in vanadium-based kagome metals have recently received tremendous attention due to their unique properties and intricate interplay with exotic correlated phenomena, topological and symmetry-breaking states. However, the origin of the CDW order remains a topic of debate. The discovery of ScV$_{6}$Sn$_{6}$, a vanadium-based bil

Astha Verma, A V Subramanyam, Siddhesh Bangar, Naman Lal

Certified defense methods against adversarial perturbations have been recently investigated in the black-box setting with a zeroth-order (ZO) perspective. However, these methods suffer from high model variance with low performance on high-dimensional datasets due to the ineffective design of the denoiser and are limited in their utilization of ZO techniques.

Hao Jiao, Robert Brandenberger, Alexandre Refregier

Cosmic strings, if they exist, source nonlinear and non-Gaussian perturbations all the way back to the time of equal matter and radiation (and earlier). Here, we compute the mass function of halos seeded by a scaling distribution of cosmic string loops, and we compare the results with the predictions of the standard Gaussian $\Lambda$CDM model. Assuming a si

O. Olendski

Motion along semi-infinite straight line in a potential that is a combination of positive quadratic and inverse quadratic functions of the position is considered with the emphasis on the analysis of its quantum-information properties. Classical measure of symmetry of the potential is proposed and its dependence on the particle energy and the factor $\mathfra

Sahar Soltanieh, Javad Hashemi, Ali Etemad

This paper presents a systematic investigation into the effectiveness of Self-Supervised Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection. We begin by conducting a novel analysis of the data distributions on three popular ECG-based arrhythmia datasets: PTB-XL, Chapman, and Ribeiro. To the best of our knowledge, our study is the first t

Xiongfang Liu, Xuechun Wang, Jinyu He, Yixue Fu

RE-Ba-Cu-O (RE denotes rare earth elements) single-grain superconductors have garnered considerable attention owning to their ability to trap strong magnetic field and self-stability for maglev. Here, we employed a modified melt-growth method by adding liquid source (LS) to provide a liquid rich environment during crystal growth. It further enables a signifi

Tonghua Liu, Ziqiang Liu, Jiamin Wang, Shengnan Gong

As one of the possible extensions of Einstein's General Theory of Relativity, it has been recently suggested that the presence of spacetime torsion could solve problems of the very early and the late-time universe undergoing accelerating phases. In this paper, we use the latest observations of high-redshift data, coming from multiple measurements of quasars

Antonio Noto, Roberto Passante, Lucia Rizzuto, Salvatore Spagnolo

We investigate the dynamical Casimir-Polder force between an atom and a conducting wall during the time evolution of the system from a partially dressed state. This state is obtained by a sudden change of the atomic position with respect to the plate. To evaluate the time-dependent atom-plate Casimir-Polder force we solve the Heisenberg equations for the fie

V. N. Temlyakov

We prove some results on the rate of convergence of greedy algorithms, which provide expansions. We consider both the case of Hilbert spaces and the more general case of Banach spaces. The new ingredient of the paper is that we bound the error of approximation by the product of both norms -- the norm of $f$ and the $A_1$-norm of $f$. Typically, only the $A_1

Jie Ren, Chenya Zhao, Lanshan He, Congcong Wu

Self-doping can not only suppress the photogenerated charge recombination of semiconducting quantum dots by self-introducing trapping states within the bandgap, but also provide high-density catalytic active sites as the consequence of abundant non-saturated bonds associated with the defects. Here, we successfully prepared semiconducting copper selenide (CuS

Thomas M. Surowiec, Shawn W. Walker

We present an analysis and numerical study of an optimal control problem for the Landau-de Gennes (LdG) model of nematic liquid crystals (LCs), which is a crucial component in modern technology. They exhibit long range orientational order in their nematic phase, which is represented by a tensor-valued (spatial) order parameter $Q = Q(x)$. Equilibrium LC stat

Apurba Biswas, R. Rajesh, Arnab Pal

The Mpemba effect is a fingerprint of the anomalous relaxation phenomenon wherein an initially hotter system equilibrates faster than an initially colder system when both are quenched to the same low temperature. Experiments on a single colloidal particle trapped in a carefully shaped double well potential have demonstrated this effect recently [Nature 584,

Denys Rozumnyi, Jiri Matas, Marc Pollefeys, Vittorio Ferrari

Most model-free visual object tracking methods formulate the tracking task as object location estimation given by a 2D segmentation or a bounding box in each video frame. We argue that this representation is limited and instead propose to guide and improve 2D tracking with an explicit object representation, namely the textured 3D shape and 6DoF pose in each

Sahar Zandi

This research project seeks to investigate the incorporation of augmented reality (AR) and virtual reality (VR) technology with human-computer interaction (HCI) in order to revitalize the Silk Road - specifically in Kermanshah, Iran - and its effect on cultural tourism. Kermanshah has underexplored the rich historical significance of the Silk Road, despite t

Maarten Solleveld

Let G be a quasi-split reductive group over a non-archimedean local field. We establish a local Langlands correspondence for all irreducible smooth complex G-representations in the principal series. The parametrization map is injective, and its image is an explicitly described set of enhanced L-parameters. Our correspondence is determined by the choice of a

Iacopo P. Longo, Carmen Núñez, Rafael Obaya

The global dynamics of a nonautonomous Carath\'eodory scalar ordinary differential equation $x'=f(t,x)$, given by a function $f$ which is concave in $x$, is determined by the existence or absence of an attractor-repeller pair of hyperbolic solutions. This property, here extended to a very general setting, is the key point to classify the dynamics of an equat

Siddhi Balu Ambhore, Kamalesh Saha, Indranath Sengupta

The invariant $\mathrm{v}$-number was introduced very recently in the study of Reed-Muller-type codes. Jaramillo and Villarreal (J Combin. Theory Ser. A 177:105310, 2021) initiated the study of the $\mathrm{v}$-number of edge ideals. Inspired by their work, we take the initiation to study the $\mathrm{v}$-number of binomial edge ideals in this paper. We disc

Ziming Wang, Dicong Liang, Junjie Zhao, Chang Liu

In future gravitational-wave (GW) detections, a large number of overlapping GW signals will appear in the data stream of detectors. When extracting information from one signal, the presence of other signals can cause large parameter estimation biases. Using the Fisher matrix (FM), we develop a bias analysis procedure to investigate how each parameter of othe

Njegos Jankovic, Javier Roldan-Perez, Milan Prodanovic, Luis Rouco

Low-frequency oscillations are an inherent phenomena in transmission networks and renewable energy plants should be configured to damp them. Commonly, a centralised controller is used in PV plants to coordinate PV generators via communication channels. However, the communication systems of PV plants introduce delays of a stochastic nature that degrade the pe

Demetris Trihinas, Lauritz Thamsen, Jossekin Beilharz, Moysis Symeonides

Energy consumption and carbon emissions are expected to be crucial factors for Internet of Things (IoT) applications. Both the scale and the geo-distribution keep increasing, while Artificial Intelligence (AI) further penetrates the "edge" in order to satisfy the need for highly-responsive and intelligent services. To date, several edge/fog emulators are cat

Patric Feldmeier, Gordon Fraser

Although automated test generation is common in many programming domains, games still challenge test generators due to their heavy randomisation and hard-to-reach program states. Neuroevolution combined with search-based software testing principles has been shown to be a promising approach for testing games, but the co-evolutionary search for optimal network

Nijat Mehdiyev, Maxim Majlatow, Peter Fettke

This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-making processes within the domain of operations research. The proposed framework adeptly addresses common limitations of existing solutions, such as the neglect of data-driven estimat

Qiongjie Cui, Huaijiang Sun, Jianfeng Lu, Bin Li

Predicting high-fidelity future human poses, from a historically observed sequence, is decisive for intelligent robots to interact with humans. Deep end-to-end learning approaches, which typically train a generic pre-trained model on external datasets and then directly apply it to all test samples, emerge as the dominant solution to solve this issue. Despite

Fabio Palumbo, Samela Draga, Gabriele Magon, Giovanni Gabelli

The Cichorium genus offers a unique opportunity to study the sporophytic self incompatibility (SSI) system, being composed of species characterized by highly efficient SI (C. intybus) and complete self compatibility (C. endivia). The chicory genome was used to map 7 previously identified SSI locus-associated markers. The region containing the S locus was res

Julian Giehler, Pierre Grenson, Reynald Bur

Porous bleed systems are a common technique to control shock-boundary layer interactions and/or supersonic boundary layers. However, the influence of various design parameters is still unknown. Even though bleed models are required to minimize the costs of the design process, they often do not include parameter effects. In the present study, the effect of th

Riccardo Corvi, Davide Cozzolino, Giovanni Poggi, Koki Nagano

Detecting fake images is becoming a major goal of computer vision. This need is becoming more and more pressing with the continuous improvement of synthesis methods based on Generative Adversarial Networks (GAN), and even more with the appearance of powerful methods based on Diffusion Models (DM). Towards this end, it is important to gain insight into which

L. Sunil Chandran, Rishikesh Gajjala

The most efficient automated way to construct a large class of quantum photonic experiments is via abstract representation of graphs with certain properties. While new directions were explored using Artificial intelligence and SAT solvers to find such graphs, it becomes computationally infeasible to do so as the size of the graph increases. So, we take an an

Oscar Barragán, Edward Gillen, Suzanne Aigrain, Annabella Meech

Detecting planetary signatures in radial velocity time-series of young stars is challenging due to their inherently strong stellar activity. However, it is possible to learn information about the properties of the stellar signal by using activity indicators measured from the same stellar spectra used to extract radial velocities. In this manuscript, we prese

Simone E. D'Aurelio, Mauro Valeri, Emanuele Polino, Valeria Cimini

Quantum parameter estimation offers solid conceptual grounds for the design of sensors enjoying quantum advantage. This is realised not only by means of hardware supporting and exploiting quantum properties, but data analysis has its impact and relevance, too. In this respect, Bayesian methods have emerged as an effective and elegant solution, with the perk

Anupam Ghosh

This paper aims to review the measure synchronization, a weak form of synchronization observed in coupled Hamiltonian systems, briefly. This synchronization is characterized by a Hamiltonian system that displays either quasiperiodic or chaotic dynamics. Each system, in the presence of either linear or nonlinear coupling, shares a phase space domain with an i

E. Bueno-Benito, B. Tura, M. Dimiccoli

In this paper, we propose a novel fully unsupervised framework that learns action representations suitable for the action segmentation task from the single input video itself, without requiring any training data. Our method is a deep metric learning approach rooted in a shallow network with a triplet loss operating on similarity distributions and a novel tri

Bin Han, Yao Zhu, Anke Schmeink, Hans D. Schotten

We propose a new security framework for physical layer security (PLS) in the finite blocklength (FBL) regime that incorporates deception technology, allowing for active countermeasures against potential eavesdroppers. Using a symmetric block cipher and power-domain non-orthogonal multiplexing (NOM), our approach is able to achieve high secured reliability wh

Martin Thißen, Elke Hergenröther

More information leads to better decisions and predictions, right? Confirming this hypothesis, several studies concluded that the simultaneous use of optical and thermal images leads to better predictions in crowd counting. However, the way multimodal models extract enriched features from both modalities is not yet fully understood. Since the use of multimod

Carlos Segundo Muñoz-Valencia, José Antonio Quesada, Domingo Orozco, Xavier Barber

Background: Bayesian Networks (BNs) are probabilistic graphical models that leverage Bayes' theorem to portray dependencies and cause-and-effect relationships between variables. These networks have gained prominence in the field of health sciences, particularly in diagnostic processes, by allowing the integration of medical knowledge into models and addressi

Ton Smeele, Sung-Shik Jongmans

Implementing distributed systems is hard; choreographic programming aims to make it easier. In this paper, we present the design of a new choreographic programming language that supports isolated transactions among overlapping sets of processes. The first idea is to track for every variable which processes are permitted to use it. The second idea is to use m

Ross Horne, Luca Padovani

We study the notion of subtyping for session types in a logical setting, where session types are propositions of multiplicative/additive linear logic extended with least and greatest fixed points. The resulting subtyping relation admits a simple characterization that can be roughly spelled out as the following lapalissade: every session type is larger than t

Matteo Cimini

Rule formats can quickly establish meta-theoretic properties of process algebras. It is then desirable to identify domain-specific languages (DSLs) that can easily express rule formats. In prior work, we have developed Lang-n-Change, a DSL that includes convenient features for browsing language definitions and retrieving information from them. In this paper,

Bernardo Almeida, Andreia Mordido, Vasco T. Vasconcelos

We present a kind inference algorithm for the FREEST programming language. The input to the algorithm is FREEST source code with (possibly part of) kind annotations replaced by kind variables. The algorithm infers concrete kinds for all kind variables. We ran the algorithm on the FREEST test suite by first replacing kind annotation on all type variables by f

Dominic Orchard, Mihail Munteanu, Paulo Torrens

Brand and Zafiropulo's notion of Communicating Finite-State Machines (CFSMs) provides a succinct and powerful model of message-passing concurrency, based around channels. However, a major variant of message-passing concurrency is not readily captured by CFSMs: the actor model. In this work, we define a variant of CFSMs, called Communicating Actor Automata, t

Timur Aslyamov, Francesco Avanzini, Étienne Fodor, Massimiliano Esposito

We develop a general classification of the nature of the instabilities yielding spatial organization in open nonideal reaction-diffusion systems, based on linear stability analysis. This encompasses dynamics where chemical species diffuse, interact with each other, and undergo chemical reactions driven out of equilibrium by external chemostats. We find analy

Ziwei Wang, Jiwen Lu, Han Xiao, Shengyu Liu

In this paper, we propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Conventional non-differen-tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models, and existing differentiable methods optimize an extremely large supernet to

Sonalika Srivastava, Subrata Sarkar

A quantitative analysis illustrating salient features of a Laminar Separation Bubble (LSB), its transition forming coherent structures, and associated heat transfer has been performed on a flat plate for varying free stream turbulence (fst) between 1.2% to 10.3%. A well-resolved Large Eddy Simulation (LES) developed in-house is used for the purpose. Flow sep

Angelos Nalmpantis, Apostolos Panagiotopoulos, John Gkountouras, Konstantinos Papakostas

The lack of interpretability of the Vision Transformer may hinder its use in critical real-world applications despite its effectiveness. To overcome this issue, we propose a post-hoc interpretability method called VISION DIFFMASK, which uses the activations of the model's hidden layers to predict the relevant parts of the input that contribute to its final p

Diogo E. P. Pinto, Petr Sulc, Francesco Sciortino, John Russo

The control over the self-assembly of complex structures is a long-standing challenge of material science, especially at the colloidal scale, as the desired assembly pathway is often kinetically derailed by the formation of amorphous aggregates. Here we investigate in detail the problem of the self-assembly of the three Archimedean shells with five contact p

Bernard Pire, Qin-Tao Song

We carry out the calculation of kinematical higher-twist corrections to the cross section of $\gamma^* \to M_1 M_2 \gamma$ up to twist 4, where $M_i$ is a scalar or pseudoscalar neutral meson. The three independant helicity amplitudes are presented in terms of the twist-2 generalized distribution amplitudes (GDAs), which are important non-perturbative quanti

Jun Yang, Yijian Chen, Yijun Cui, Qingqing Wu

Reconfigurable intelligent surface (RIS) is a new technique that is able to manipulate the wireless environment smartly and has been exploited for assisting the wireless communications, especially at high frequency band. However, it suffers from hardware impairments (HWIs) in practical designs, which inevitably degrades its performance and thus limits its fu

Jian-Wei Guo, Jian-Long Wang, Shang-kun Jiang, Li Li

It is important to measure in-plane conductivity of Nafion membrane for fuel cell, but this target is generally inhibited by measuring system with heterogeneous interfaces and immature electrochemical measurements. This paper simply used water media to establish stable measuring system with metal electrode and Nafion membrane, representing system as equivale

Nathanael Skrepek

In this work we investigate the Sobolev space $\mathrm{H}^{1}(\partial\Omega)$ on a strong Lipschitz boundary $\partial\Omega$, i.e., $\Omega$ is a strong Lipschitz domain. In most of the literature this space is defined via charts and Sobolev spaces on flat domains. We show that there is a different approach via differential operators on $\Omega$ and a weak

Wenhao Wang, Yifan Sun, Wei Li, Yi Yang

This paper explores a hierarchical prompting mechanism for the hierarchical image classification (HIC) task. Different from prior HIC methods, our hierarchical prompting is the first to explicitly inject ancestor-class information as a tokenized hint that benefits the descendant-class discrimination. We think it well imitates human visual recognition, i.e.,

Kevin Ewig, Xiangwen Lin, Tucker Stewart, Katherine Stern

Sepsis is a life-threatening organ malfunction caused by the host's inability to fight infection, which can lead to death without proper and immediate treatment. Therefore, early diagnosis and medical treatment of sepsis in critically ill populations at high risk for sepsis and sepsis-associated mortality are vital to providing the patient with rapid therapy

Nicola Melchioni, Giacomo Trupiano, Giorgio Tofani, Riccardo Bertini

Controlling the charge density in low-dimensional materials with an electrostatic potential is a powerful tool to explore and influence their electronic and optical properties. Conventional solid gates impose strict geometrical constraints to the devices and often absorb electromagnetic radiation in the infrared (IR) region. A powerful alternative is ionic l

Milou Temmink, Alice S. Booth, Nienke van der Marel, Ewine F. van Dishoeck

The atmospheric composition of planets is determined by the chemistry of the disks in which they form. Studying the gas-phase molecular composition of disks thus allows us to infer what the atmospheric composition of forming planets might be. Recent observations of the IRS 48 disk have shown that (asymmetric) dust traps can directly impact the observable che

Diandian Gu, Xintong Xie, Gang Huang, Xin Jin

Training deep neural networks (DNNs) is a major workload in datacenters today, resulting in a tremendously fast growth of energy consumption. It is important to reduce the energy consumption while completing the DL training jobs early in data centers. In this paper, we propose PowerFlow, a GPU clusters scheduler that reduces the average Job Completion Time (

Gustavo A. Fernández-Alcober, Matteo Pintonello

Let $w=w(x_1,\ldots,x_r)$ be a lower central word or a derived word. We show that the word $w(u_1,\ldots,u_r)$ is concise whenever $u_1,\ldots,u_r$ are non-commutator words in disjoint sets of variables, thus proving a generalized version of a conjecture of Azevedo and Shumyatsky. This applies in particular to words of the form $w(x_1^{n_1},\ldots,x_r^{n_r})

Mario Sperl, Luca Saluzzi, Lars Grüne, Dante Kalise

An efficient approach for the construction of separable approximations of optimal value functions from interconnected optimal control problems is presented. The approach is based on assuming decaying sensitivities between subsystems, enabling a curse-of-dimensionality free approximation, for instance by deep neural networks.

Arun Palla, Sriprabha Ramanarayanan, Keerthi Ram, Mohanasankar Sivaprakasam

Magnetic Resonance (MR) images suffer from various types of artifacts due to motion, spatial resolution, and under-sampling. Conventional deep learning methods deal with removing a specific type of artifact, leading to separately trained models for each artifact type that lack the shared knowledge generalizable across artifacts. Moreover, training a model fo

Yang Chen, Liangxuan Guo, Shan Yu

The capacity to generate meaningful symbols and effectively employ them for advanced cognitive processes, such as communication, reasoning, and planning, constitutes a fundamental and distinctive aspect of human intelligence. Existing deep neural networks still notably lag human capabilities in terms of generating symbols for higher cognitive functions. Here

Sheel Shah, Kaishva Shah, Karthik S. Gurumoorthy, Ajit Rajwade

It is well known that a band-limited signal can be reconstructed from its uniformly spaced samples if the sampling rate is sufficiently high. More recently, it has been proved that one can reconstruct a 1D band-limited signal even if the exact sample locations are unknown, but given a uniform distribution of the sample locations and their ordering in 1D. In

Salvatore Citraro, Simon De Deyne, Massimo Stella, Giulio Rossetti

Semantic networks provide a useful tool to understand how related concepts are retrieved from memory. However, most current network approaches use pairwise links to represent memory recall patterns. Pairwise connections neglect higher-order associations, i.e. relationships between more than two concepts at a time. These higher-order interactions might covari

C. Medina, A. Ø. Lægdsmand, L. Ben Ltaief, Z. Hoque

We study the dynamics of avalanche ionization of pure helium nanodroplets activated by a weak extreme-ultraviolet (XUV) pulse and driven by an intense near-infrared (NIR) pulse. In addition to a transient enhancement of ignition of a nanoplasma at short delay times $\sim200$~fs, long-term activation of the nanodroplets lasting up to a few nanoseconds is obse

Matteo Toso, Matteo Taiana, Stuart James, Alessio Del Bue

Efficient visual localization is crucial to many applications, such as large-scale deployment of autonomous agents and augmented reality. Traditional visual localization, while achieving remarkable accuracy, relies on extensive 3D models of the scene or large collections of geolocalized images, which are often inefficient to store and to scale to novel envir

Bingxin Zhou, Outongyi Lv, Kai Yi, Xinye Xiong

Directed evolution as a widely-used engineering strategy faces obstacles in finding desired mutants from the massive size of candidate modifications. While deep learning methods learn protein contexts to establish feasible searching space, many existing models are computationally demanding and fail to predict how specific mutational tests will affect a prote

Quentin Le Lidec, Wilson Jallet, Louis Montaut, Ivan Laptev

Physics simulation is ubiquitous in robotics. Whether in model-based approaches (e.g., trajectory optimization), or model-free algorithms (e.g., reinforcement learning), physics simulators are a central component of modern control pipelines in robotics. Over the past decades, several robotic simulators have been developed, each with dedicated contact modelin

Laia Tarrés, Gerard I. Gállego, Amanda Duarte, Jordi Torres

The advances in automatic sign language translation (SLT) to spoken languages have been mostly benchmarked with datasets of limited size and restricted domains. Our work advances the state of the art by providing the first baseline results on How2Sign, a large and broad dataset. We train a Transformer over I3D video features, using the reduced BLEU as a refe

Yiming Ma, Victor Sanchez, Soodeh Nikan, Devesh Upadhyay

Driver Monitoring Systems (DMSs) are crucial for safe hand-over actions in Level-2+ self-driving vehicles. State-of-the-art DMSs leverage multiple sensors mounted at different locations to monitor the driver and the vehicle's interior scene and employ decision-level fusion to integrate these heterogenous data. However, this fusion method may not fully utiliz

Lianna Zhao, Andrew Cullen, Sebastian Müller, Olivia Saa

This paper discusses congestion control and inconsistency problems in DAG-based distributed ledgers and proposes an additional filter to mitigate these issues. Unlike traditional blockchains, DAG-based DLTs use a directed acyclic graph structure to organize transactions, allowing higher scalability and efficiency. However, this also introduces challenges in

Stanislav Babak, Chiara Caprini, Daniel G. Figueroa, Nikolaos Karnesis

We use the latest constraints on the population of stellar origin binary black holes (SOBBH) from LIGO/Virgo/KAGRA (LVK) observations, to estimate the stochastic gravitational wave background (SGWB) they generate in the frequency band of LISA. We account for the faint and distant binaries, which contribute the most to the SGWB, by extending the merger rate a

Anne Henning, Lukas Schulte, Steffen Herbold, Oksana Kulyk

Data protection regulations such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the US affect how software may handle the personal data of its users. Prior literature focused on how data protection regulations are discussed for software in operation, or how this topic is discussed in v

Shivani Bathla, Vinita Vasudevan

Exact computation of the partition function is known to be intractable, necessitating approximate inference techniques. Existing methods for approximate inference are slow to converge for many benchmarks. The control of accuracy-complexity trade-off is also non-trivial in many of these methods. We propose a novel incremental build-infer-approximate (IBIA) fr

Tayyaba Naz, G. Mustafa, M. Farasat Shamir

The current study examines the geometry of static wormholes with anisotropic matter distribution in context of modified $f(\mathcal{G})$ gravity. We consider the well known Noether and conformal symmetries, which help in investigating wormholes in $f(\mathcal{G})$ gravity. For this purpose, we develop symmetry generators associated with conserved quantities

Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang

Evaluating the general abilities of foundation models to tackle human-level tasks is a vital aspect of their development and application in the pursuit of Artificial General Intelligence (AGI). Traditional benchmarks, which rely on artificial datasets, may not accurately represent human-level capabilities. In this paper, we introduce AGIEval, a novel benchma

Yuhang Nie, Jun-Hui Zheng, Tao Yang

Optical lattice plays an important role on stability and dynamics of quantum droplets. In this letter, we investigate the Bogoliubov excitation spectrum of quantum droplets in optical lattice in the thermodynamic limit. We classify the collective excitations as synchronous modes, Bloch phononic modes, and site-density imbalanced modes. For synchronous modes,

Chuqi Cao, Kleber Carrapatoso

This work deals with the non-cutoff Boltzmann equation for all type of potentials, in both the torus $\mathbf{T}^3$ and in the whole space $\mathbf{R}^3$, under the incompressible Navier-Stokes scaling. We first establish the well-posedness and decay of global mild solutions to this rescaled Boltzmann equation in a perturbative framework, that is for solutio

Sławomir Kusiński

In this paper we provide solutions of the Harrington problem (along with a few generalizations) proposed in a book Analytic Sets. The original problem asks if for arbitrary sequence of continuous functions from \( \R^\omega \) to a fixed compact interval we can find a subsequence point-wise convergent on some product of perfect subsets of \( \R \). We reduce

Liang Zhang, Cheng Long

Road network is a critical infrastructure powering many applications including transportation, mobility and logistics in real life. To leverage the input of a road network across these different applications, it is necessary to learn the representations of the roads in the form of vectors, which is named \emph{road network representation learning} (RNRL). Wh

Justin Kin Jun Hew, Emanuele Martelli, Mahdi Davoodianidalik, Rod W. Boswell

We perform a combined numerical and experimental study to investigate the transonic shock-wave/turbulent-boundary-layer interactions (STBLI) in a shock-induced separated subscale planar nozzle with fully-expanded Mach number,$M_j = 1.05$ and jet Reynolds number $Re \sim 10^5$. The nozzle configuration is tested via time-resolved schlieren visualisation. Whil

Shun Lei, Yixuan Zhou, Liyang Chen, Zhiyong Wu

Recent advances in text-to-speech have significantly improved the expressiveness of synthesized speech. However, it is still challenging to generate speech with contextually appropriate and coherent speaking style for multi-sentence text in audiobooks. In this paper, we propose a context-aware coherent speaking style prediction method for audiobook speech sy

Jian Zhu, Zhangmin Huang, Xiaohu Ruan, Yu Cui

Learning the hash representation of multi-view heterogeneous data is an important task in multimedia retrieval. However, existing methods fail to effectively fuse the multi-view features and utilize the metric information provided by the dissimilar samples, leading to limited retrieval precision. Current methods utilize weighted sum or concatenation to fuse

Luigi Zanovello

Microswimmers are microscopic active agents capable of harvesting energy from the surrounding environment and converting it into self-propulsion and directed motion. This peculiar feature characterizes them as out-of-equilibrium systems that break microscopic reversibility. The problem of finding a specific target in a complex environment is essential for th

Karlie A. Noon, Mark R. Krumholz, Enrico M. Di Teodoro, Naomi M. McClure-Griffiths

Hundreds of high-velocity atomic gas clouds exist above and below the Galactic Centre, with some containing a molecular component. However, the origin of these clouds in the Milky Way's wind is unclear. This paper presents new high-resolution MeerKAT observations of three atomic gas clouds and studies the relationship between the atomic and molecular phases

David Jones, Todd Hillwig, Nicole Reindl

Common envelope events have been associated with the formation of a planetary nebulae since its proposition more than forty five years ago. However, until recently there have been doubts as to whether a common envelope while the donor is ascending the red giant branch, rather than the subsequent asymptotic red giant branch, would result in a planetary nebula

Chenyang Zhao, Antoni B. Chan

We propose the gradient-weighted Object Detector Activation Maps (ODAM), a visualized explanation technique for interpreting the predictions of object detectors. Utilizing the gradients of detector targets flowing into the intermediate feature maps, ODAM produces heat maps that show the influence of regions on the detector's decision for each predicted attri

Alexandra Blenkinsop, Lysandros Sofocleous, Francesco Di Lauro, Evangelia Georgia Kostaki

In stopping the spread of infectious diseases, pathogen genomic data can be used to reconstruct transmission events and characterize population-level sources of infection. Most approaches for identifying transmission pairs do not account for the time passing since divergence of pathogen variants in individuals, which is problematic in viruses with high withi

H. Kamada, M. Kohno, K. Miyagawa

Faddeev calculations of hypertriton ($_\Lambda^3$H) separation energy are performed, incorporating $2\pi$-exchange $\Lambda $NN three-baryon force. Repulsive contributions of the three-baryon force in the order of 20 keV are found, depending on the NN interactions employed. The effect is not negligible compared with the small separation-energy of $_\Lambda^3

Lorenzo Berlincioni, Luca Cultrera, Chiara Albisani, Lisa Cresti

Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Recognition. NEFER is composed of paired R

Santiago Jaraba, Juan García-Bellido, Sachiko Kuroyanagi, Sarah Ferraiuolo

Astrometric surveys can be used to constrain the stochastic gravitational wave background (SGWB) at very low frequencies. We use proper motion data provided by Gaia DR3 to fit a generic dipole+quadrupole field. We analyse several quasar-based datasets and discuss their purity and idoneity to set constraints on gravitational waves. For the cleanest dataset, w

Marco Forgione, Dario Piga

Effective quantification of uncertainty is an essential and still missing step towards a greater adoption of deep-learning approaches in different applications, including mission-critical ones. In particular, investigations on the predictive uncertainty of deep-learning models describing non-linear dynamical systems are very limited to date. This paper is ai