April 2024 arXiv papers — page 36

V. V. Nautiyal, R. S. Watson, K. V. Kheruntsyan

We undertake a theoretical study of a finite-time quantum Otto engine cycle driven by inter-particle interactions in a weakly interacting one-dimensional Bose gas in the quasicondensate regime. Utilizing a $c$-field approach, we simulate the entire Otto cycle, i.e. the two work strokes and the two equilibration strokes. More specifically, the interaction-ind

Yixin Liu, Yuchuan Liu, Zulei Xu, Aobo Yu

The interplay between substrates and superconducting thin films has attracted increasing attention. Here, we report an in-depth investigation on superconducting properties of the epitaxial TiN thin films grown on three different substrates by dc reactive magnetron sputtering. The TiN films grown on (0001) sapphire exhibit (111) crystal orientation, while tha

Bram De Cooman, Johan Suykens

Model-free reinforcement learning methods lack an inherent mechanism to impose behavioural constraints on the trained policies. Although certain extensions exist, they remain limited to specific types of constraints, such as value constraints with additional reward signals or visitation density constraints. In this work we unify these existing techniques and

Cecilia Aubrun, Rudy Morel, Michael Benzaquen, Jean-Philippe Bouchaud

Cascades of events and extreme occurrences have garnered significant attention across diverse domains such as financial markets, seismology, and social physics. Such events can stem either from the internal dynamics inherent to the system (endogenous), or from external shocks (exogenous). The possibility of separating these two classes of events has critical

Tim Van Wesemael, Luis E. C. Rocha, Jan M. Baetens

The course of an epidemic is not only shaped by infection transmission over face-to-face contacts, but also by preventive behaviour caused by risk perception and social interactions. This study explores the dynamics of coupled awareness and biological infection spread within a two-layer multiplex network framework. One layer embodies face-to-face contacts, w

Valentina Rein, Florian Letellier, Maciej Jankowski, Marc de Voogd

Liquid metal catalysts (LMCats), primarily molten copper, have demonstrated their efficiency in the chemical vapour deposition (CVD) approach for synthesising high-quality, large-area graphene. However, their high melting temperatures limit broader applications. Reducing the temperature of graphene production on LMCats would lead to a more efficient and cost

Stefan Neumann, Yinhao Dong, Pan Peng

Online social networks are ubiquitous parts of modern societies and the discussions that take place in these networks impact people's opinions on diverse topics, such as politics or vaccination. One of the most popular models to formally describe this opinion formation process is the Friedkin--Johnsen (FJ) model, which allows to define measures, such as the

Agustin Zaballos, Adria Mallorqui, Joan Navarro

Device redundancy is one of the most well-known mechanisms in distributed systems to increase the overall system fault tolerance and, consequently, trustworthiness. Existing algorithms in this regard aim to exchange a significant number of messages among nodes to identify and agree which communication links or nodes are faulty. This approach greatly degrades

Abdulrezzak Zekiye, Ouns Bouachir, Öznur Özkasap, Moayad Aloqaily

Carbon footprint reduction can be achieved through various methods, including the adoption of renewable energy sources. The installation of such sources, like photovoltaic panels, while environmentally beneficial, is cost-prohibitive for many. Those lacking photovoltaic solutions typically resort to purchasing energy from utility grids that often rely on fos

Isabelle Lorge, Dan W. Joyce, Andrey Kormilitzin

Mental health in children and adolescents has been steadily deteriorating over the past few years. The recent advent of Large Language Models (LLMs) offers much hope for cost and time efficient scaling of monitoring and intervention, yet despite specifically prevalent issues such as school bullying and eating disorders, previous studies on have not investiga

Mattia Magnabosco, Tommaso Rossi

We review the construction of the tangent space to a sub-Finsler manifold in the measured Gromov-Hausdorff sense. Under suitable assumptions on the measure, the metric measure tangent is described by the nilpotent approximation, equipped with a scalar multiple of the Lebesgue measure. We apply this result in the study of the Lott-Sturm-Villani curvature-dime

Valentin Brunn, Christian Rab, Alexandre Marcowith, Christophe Sauty

T Tauri stars are known to be magnetically active stars subject to strong flares observed in X-rays. These flares are likely due to intense magnetic reconnection events during which a part of the stored magnetic energy is converted into kinetic energy of supra-thermal particles. Since T Tauri stars are surrounded by an accretion disc, these particles may inf

Di Yang, Cheng Zhang, Zejun Zhou

Inspired by a recent work of Dubrovin [7], for each simple Lie algebra $\mathfrak{g}$, we introduce an infinite family of pairwise commuting ODEs and define their $\tau$-functions. We show that these $\tau$-functions can be identified with the $\tau$-functions for the Drinfeld--Sokolov hierarchy of $\mathfrak{g}$-type. Explicit examples for $\mathfrak{g}=A_1

Wenchuan Mu, Kwan Hui Lim

In deep learning applications, robustness measures the ability of neural models that handle slight changes in input data, which could lead to potential safety hazards, especially in safety-critical applications. Pre-deployment assessment of model robustness is essential, but existing methods often suffer from either high costs or imprecise results. To enhanc

Mingcheng Li, Dingkang Yang, Xiao Zhao, Shuaibing Wang

Multimodal sentiment analysis (MSA) aims to understand human sentiment through multimodal data. Most MSA efforts are based on the assumption of modality completeness. However, in real-world applications, some practical factors cause uncertain modality missingness, which drastically degrades the model's performance. To this end, we propose a Correlation-decou

Massimo Michelutti, Gabriele Masina, Giuseppe Spallitta, Roberto Sebastiani

Decision diagrams (DDs) are powerful tools to represent effectively propositional formulas, which are largely used in many domains, in particular in formal verification and in knowledge compilation. Some forms of DDs (e.g., OBDDs, SDDs) are canonical, that is, (under given conditions on the atom list) they univocally represent equivalence classes of formulas

Tobias Schmale, Hendrik Weimer

Simulations of lattice gauge theories on noisy quantum hardware inherently suffer from violations of the gauge symmetry due to coherent and incoherent errors of the underlying physical system that implements the simulation. These gauge violations cause the simulations to become unphysical requiring the result of the simulation to be discarded. We investigate

Giovanna Lani, Nicola Marzari

We investigate analytically the performance of many-body energy functionals, derived respectively by Klein and Luttinger and Ward, at different levels of diagrammatic approximations, ranging from second Born, to GW, to the so-called T-matrix, for the calculation of total energies and potential energy surfaces. We benchmark our theoretical results on the exte

Lihua Jing, Rui Wang, Wenqi Ren, Xin Dong

Adversarial patch attacks present a significant threat to real-world object detectors due to their practical feasibility. Existing defense methods, which rely on attack data or prior knowledge, struggle to effectively address a wide range of adversarial patches. In this paper, we show two inherent characteristics of adversarial patches, semantic independence

Dona A. Franci, Tarik A. Rashid

This paper presents an enhanced version of the Learner Performance-based Behavior (LPB), a novel metaheuristic algorithm inspired by the process of accepting high-school students into various departments at the university. The performance of the LPB is not according to the required level. This paper aims to improve the performance of a single objective LPB b

Zongyao He, Zhi Jin

The recent work Local Implicit Image Function (LIIF) and subsequent Implicit Neural Representation (INR) based works have achieved remarkable success in Arbitrary-Scale Super-Resolution (ASSR) by using MLP to decode Low-Resolution (LR) features. However, these continuous image representations typically implement decoding in High-Resolution (HR) High-Dimensio

Cédric Pilatte

In 1994, Shor introduced his famous quantum algorithm to factor integers and compute discrete logarithms in polynomial time. In 2023, Regev proposed a multi-dimensional version of Shor's algorithm that requires far fewer quantum gates. His algorithm relies on a number-theoretic conjecture on the elements in $(\mathbb{Z}/N\mathbb{Z})^{\times}$ that can be

Beier Liu, Haiyun Zhu

In this study, we utilize the Kalman-Filter analysis to assess market efficiency in major stock markets. The Kalman-Filter operates in two stages, assuming that the data contains a consistent trendline representing the true market value prior to being affected by noise. Unlike traditional methods, it can forecast stock price movements effectively. Our findin

Matti Lassas, Jinpeng Lu, Takao Yamaguchi

We consider the inverse problem of determining the metric-measure structure of collapsing manifolds from local measurements of spectral data. In the part I of the paper, we proved the uniqueness of the inverse problem and a continuity result for the stability in the closure of Riemannian manifolds with bounded diameter and sectional curvature in the measured

Lianet De la Cruz Toranzo, Ricardo Abreu Blaya, Swanhild Bernstein

In this paper we find a decomposition of higher order Lipschitz functions into the traces of a polymonogenic function and solve a related Riemann-Hilbert problem. Our approach lies in using a cliffordian Cauchy-type operator, which behaves as an involution operator on higher order Lipschitz spaces. The result obtained is a multidimensional sharpened version

Yevhen Yazvinskyi, Jasmin Bogatinovski, Jorge Cardoso, Odej Kao

Distributed systems in general and cloud systems in particular, are susceptible to failures that can lead to substantial economic and data losses, security breaches, and even potential threats to human safety. Software ageing is an example of one such vulnerability. It emerges due to routine re-usage of computational systems units which induce fatigue within

Xinshi Zang, Wenhao Lin, Shiju Lin, Jinwei Liu

In the face of escalating complexity and size of contemporary FPGAs and circuits, routing emerges as a pivotal and time-intensive phase in FPGA compilation flows. In response to this challenge, we present an open-source parallel routing methodology designed to expedite routing procedures for commercial FPGAs. Our approach introduces a novel recursive partiti

G. Angloher, M. R. Bharadwaj, M. Cababie, I. Dafinei

COSINUS is a dark matter (DM) direct search experiment that uses sodium iodide (NaI) crystals as cryogenic calorimeters. Thanks to the low nuclear recoil energy threshold and event-by-event discrimination capability, COSINUS will address the long-standing DM claim made by the DAMA/LIBRA collaboration. The experiment is currently under construction at the Lab

Jorge Martinez Romeral, Luis E. F. Foa Torres, Stephan Roche

In the context of the measurement problem, we propose to model the interaction between a quantum particle and an "apparatus" through a non-Hermitian Hamiltonian term. We simulate the time evolution of a normalized quantum state split into two spin components (via a Stern-Gerlach experiment) and that undergoes a wave-function collapse driven by a non-Hermitia

Weizhen Li, Rui Carvalho

Identifying partial differential equations (PDEs) from data is crucial for understanding the governing mechanisms of natural phenomena, yet it remains a challenging task. We present an extension to the ARGOS framework, ARGOS-RAL, which leverages sparse regression with the recurrent adaptive lasso to identify PDEs from limited prior knowledge automatically. O

Lionel Eyraud-Dubois, Guillaume Iooss, Julien Langou, Fabrice Rastello

When designing an algorithm, one cares about arithmetic/computational complexity, but data movement (I/O) complexity plays an increasingly important role that highly impacts performance and energy consumption. For a given algorithm and a given I/O model, scheduling strategies such as loop tiling can reduce the required I/O down to a limit, called the I/O com

Zineddine Bettouche, Anas Safi, Andreas Fischer

Managing the semantic quality of the categorization in large textual datasets, such as Wikipedia, presents significant challenges in terms of complexity and cost. In this paper, we propose leveraging transformer models to distill semantic information from texts in the Wikipedia dataset and its associated categories into a latent space. We then explore differ

Mainak Mondal, Pawni Manchanda, Soumadeep Saha, Abhishek Jangid

Trapped materials at the interfaces of two-dimensional heterostructures (HS) lead to reduced coupling between the layers, resulting in degraded optoelectronic performance and device variability. Further, nanobubbles can form at the interface during transfer or after annealing. The question of what is inside a nanobubble, i.e. the trapped material, remains un

J. M. García-Regaña, I. Calvo, E. Sánchez, H. Thienpondt

CIEMAT-QI4 is a quasi-isodynamic stellarator configuration that simultaneously features very good fast-ion confinement in a broad range of $\beta$ values, low neoclassical transport and bootstrap current, and ideal magnetohydrodynamic stability up to $\beta=5\%$. In this paper it is shown that CIEMAT-QI4 also exhibits reduced turbulent transport. This is dem

Lvchang Li, Jiaqing Ding, Haichou Li

In this paper, we mainly study the necessary and sufficient conditions for the boundedness and compactness of Toeplitz operators on weighted Bergman spaces over a tubular domains by using the Carlson measures on tubular domains. We also give some related results about Carlson measures.

Jan W. Cholewa, Anibal Rodriguez-Bernal

We discuss exponential decay in $L^p(R^N)$, $1\leq p \leq \infty$, of solutions of a fractional Schr\"odinger parabolic equation with a locally uniformly integrable potential. The exponential type of the semigroup of solutions is considered and its dependence in $1\leq p \leq \infty$ is addressed. We characterise a large class of potentials for which solutio

Xing-Hui Feng, Jun Peng

We study the axial gravitational perturbations of slowly-rotating compact objects which are assumed to be supported by anisotropic fluids. We find that the gravitational perturbations decouple from the matter perturbations for axial sectors. We obtain a master wave equation whose potential is fully determined by the metric functions. This equation makes the

Eleonora Cappuccio, Daniele Fadda, Rosa Lanzilotti, Salvatore Rinzivillo

Artificial Intelligence algorithms have now become pervasive in multiple high-stakes domains. However, their internal logic can be obscure to humans. Explainable Artificial Intelligence aims to design tools and techniques to illustrate the predictions of the so-called black-box algorithms. The Human-Computer Interaction community has long stressed the need f

Jean-Marie Malherbe

We present in this paper a compilation of solar atlases from $\lambda$ 3000 {\AA} to $\lambda$ 8800 {\AA} with spectral lines identified by the Moore table and with the corresponding equivalent Lande factors g*. We used two spectra at disk centre ($\mu$ = 1.0), from Delbouille and Kurucz, and two spectra at the limb from Stenflo and Gandorfer, respectively a

Ben Williams, Bart van Merriënboer, Vincent Dumoulin, Jenny Hamer

Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and compute costs limit the field's efficacy. Generalizable pretrained networks can overcome these costs, but high-quality pretraining requires vast annotated libraries, limiting its current applicability primarily to bir

Alvaro Domínguez, Mihail N. Popescu

We re-examine the self-phoresis of a particle that releases(removes) pairs of ions into(from) the electrolyte solution. We show analytically that in the linear regime the mathematical description of this system maps onto that of the correlation-induced (self-)chemophoresis (CICP). This connection provides a unifying perspective of the two phenomena, within w

Kirone Mallick, Hiroki Moriya, Tomohiro Sasamoto

We give a short overview of recent developments in exact solutions for macroscopic fluctuation theory by using connections to classical integrable systems. A calculation of the cumulant generating function for a tagged particle is also given, agreeing with a previous result obtained from a microscopic analysis.

Xiangsheng Xu

We study the existence of a strong solution to the initial value problem for the Nernst-Planck-Navier-Stokes (NPNS) system in $\mathbb{R}^N, N\geq 3$. The system describes the electrodiffusion of ions in a viscous Newtonian fluid. A strong solution is obtained in any dimension of space without constraints on the number of species or the size of the given dat

Ayumu Saito, Prachi Kudeshia, Jiju Poovvancheri

Recent advancements in self-supervised learning in the point cloud domain have demonstrated significant potential. However, these methods often suffer from drawbacks, including lengthy pre-training time, the necessity of reconstruction in the input space, or the necessity of additional modalities. In order to address these issues, we introduce Point-JEPA, a

Shanuja Sasi, Onur Günlü

In this paper, we consider two critical aspects of security in the distributed computing (DC) model: secure data shuffling and secure coded computing. It is imperative that any external entity overhearing the transmissions does not gain any information about the intermediate values (IVs) exchanged during the shuffling phase of the DC model. Our approach ensu

Guillaume Theyssier

We introduce an extension of classical cellular automata (CA) to arbitrary labeled graphs, and show that FO logic on CA orbits is equivalent to MSO logic. We deduce various results from that equivalence, including a characterization of finitely generated groups on which FO model checking for CA orbits is undecidable, and undecidability of satisfiability of a

Xu Chao, Guan Zhiqiang, Xu Hongxing

The non-Hermitian skin effect (NHSE) is a phenomenon where the bulk states tend to the boundary within a non-Hermitian Hamiltonian system, with broad applications across various fields. A comprehensive understanding of anomalies in skin modes associated with NHSEs is essential for practical applications. Recently, some innovative works reported the suppressi

Andrea Ceccarelli, Leonardo Montecchi

Object detection in autonomous driving consists in perceiving and locating instances of objects in multi-dimensional data, such as images or lidar scans. Very recently, multiple works are proposing to evaluate object detectors by measuring their ability to detect the objects that are most likely to interfere with the driving task. Detectors are then ranked a

Vincent Hackstein, Paul Fauth-Mayer, Matthias Rothermel, Norbert Haala

While originally developed for novel view synthesis, Neural Radiance Fields (NeRFs) have recently emerged as an alternative to multi-view stereo (MVS). Triggered by a manifold of research activities, promising results have been gained especially for texture-less, transparent, and reflecting surfaces, while such scenarios remain challenging for traditional MV

Metha Prathaban, Will Handley

Nested sampling parameter estimation differs from evidence estimation, in that it incurs an additional source of uncertainty. This uncertainty affects estimates of parameter means and credible intervals in gravitational wave analyses and beyond, and yet, it is typically not accounted for in standard uncertainty estimation methods. In this paper, we present t

Elena Morotti, Davide Evangelista, Andrea Sebastiani, Elena Loli Piccolomini

This paper focuses on the development of a space-variant regularization model for solving an under-determined linear inverse problem. The case study is a medical image reconstruction from few-view tomographic noisy data. The primary objective of the proposed optimization model is to achieve a good balance between denoising and the preservation of fine detail

Daichi Matsuzuki

We study algebraic independence problem for the Taylor coefficients of the Anderson-Thakur series arisen as deformation series of positive characteristic multiple zeta values (abbreviated as MZV's). These Taylor coefficients are simply specialization of hyperderivatives of the Anderson-Thakur series. We consider the prolongation of t-motives associated with

Susanne Dandl, Marc Becker, Bernd Bischl, Giuseppe Casalicchio

This work introduces a novel R package for concise, informative summaries of machine learning models. We take inspiration from the summary function for (generalized) linear models in R, but extend it in several directions: First, our summary function is model-agnostic and provides a unified summary output also for non-parametric machine learning models; Seco

Luca Martinazzi, Tristan Rivière

Given a connection $A$ on a $SU(2)$-bundle $P$ over $\mathbb{R}^4$ with finite Yang-Mills energy $YM(A)$ and nonzero curvature $F_A(0)$ at the origin, and given $\rho>0$ small enough, we construct a new connection $\hat A$ on a bundle $\hat P$ of different Chern class ($|c_2(A)-c_2(\hat A)|=8\pi^2$), in such a way that $\hat A$ is gauge equivalent to $A$ in

Y. Liu, H. Sun, D. Xu, D. S. Svinkin

Long gamma-ray bursts (GRBs) are believed to originate from core collapse of massive stars. High-redshift GRBs can probe the star formation and reionization history of the early universe, but their detection remains rare. Here we report the detection of a GRB triggered in the 0.5--4 keV band by the Wide-field X-ray Telescope (WXT) on board the Einstein Probe

Bernd Köhler, Yuui Amano, Frank Schubert, Kazuyuki Nakahata

The common believe about strict measurement reciprocity between scanning laser detection and scanning laser excitation is disproved by a simple experiment. Nevertheless, a deeper study based on the reciprocity relation reveals correct reciprocal measurement set-ups for both the probe-excitation / laser-detection and the laser-excitation / probe-detection cas

Hongyu Yan, Yadong Mu

Image-guided object assembly represents a burgeoning research topic in computer vision. This paper introduces a novel task: translating multi-view images of a structural 3D model (for example, one constructed with building blocks drawn from a 3D-object library) into a detailed sequence of assembly instructions executable by a robotic arm. Fed with multi-view

Zhibo Zhang, Ximing Yang, Weizhong Zhang, Cheng Jin

This paper presents a robust fine-tuning method designed for pre-trained 3D point cloud models, to enhance feature robustness in downstream fine-tuned models. We highlight the limitations of current fine-tuning methods and the challenges of learning robust models. The proposed method, named Weight-Space Ensembles for Fine-Tuning then Linear Probing (WiSE-FT-

Moritz Sturm, Lorenzo Cerrone, Fred A. Hamprecht

Cell tracking remains a pivotal yet challenging task in biomedical research. The full potential of deep learning for this purpose is often untapped due to the limited availability of comprehensive and varied training data sets. In this paper, we present SynCellFactory, a generative cell video augmentation. At the heart of SynCellFactory lies the ControlNet a

Nikita Shishmarov, Serge Skryabin

We consider Hecke symmetries on a 3-dimensional vector space with the associated R-symmetric algebra isomorphic to the polynomial algebra $k[x_1,x_2,x_3]$ twisted by an automorphism. The main result states that any such a Hecke symmetry is itself a twist of a Hecke symmetry with the associated R-symmetric algebra isomorphic to $k[x_1,x_2,x_3]$. This allows u

A. S. L. Ribeiro, R. Schott, C. Reichl, W. Dietsche

We observed magnetocapacitance oscillations in InAs/GaSb quantum wells separated by a $20$\,nm AlSb middle barrier. By realizing independent ohmic contacts for electrons in InAs and holes in the GaSb layer, we found an out-of-plane oscillatory response in capacitance representing the density of states of this system. We were able to tune the charge carrier d

Changho Lee, Janghoon Han, Seonghyeon Ye, Stanley Jungkyu Choi

Instruction tuning has been proven effective in enhancing zero-shot generalization across various tasks and in improving the performance of specific tasks. For task-specific improvements, strategically selecting and training on related tasks that provide meaningful supervision is crucial, as this approach enhances efficiency and prevents performance degradat

David Winderl, Nicola Franco, Jeanette Miriam Lorenz

With the rapid advancement of Quantum Machine Learning (QML), the critical need to enhance security measures against adversarial attacks and protect QML models becomes increasingly evident. In this work, we outline the connection between quantum noise channels and differential privacy (DP), by constructing a family of noise channels which are inherently $\ep

Yu Wang, Sanping Zhou, Kun Xia, Le Wang

Semi-supervised action recognition aims to improve spatio-temporal reasoning ability with a few labeled data in conjunction with a large amount of unlabeled data. Albeit recent advancements, existing powerful methods are still prone to making ambiguous predictions under scarce labeled data, embodied as the limitation of distinguishing different actions with

Daoguan Ning, Andreas Dreizler

The paper presents an analytical theory quantitatively describing the heterogeneous combustion of nonvolatile (metal) particles in the diffusion-limited regime. It is assumed that the particle is suspended in an unconfined, isobaric, quiescent gaseous mixture and the chemisorption of the oxygen takes place evenly on the particle surface. The exact solution o

Kyle J. Arnold, Scott Bustabad, Qin Qichen, Zhao Zhang

We review our progress in developing a frequency reference with singly ionized lutetium and give estimates of the levels of inaccuracy we expect to achieve in the near future with both the $^1S_0\leftrightarrow{}^3D_1$ and $^1S_0\leftrightarrow{}^3D_2$ transitions. Based on established experimental results, we show that inaccuracies at the low $10^{-19}$ lev

Md Nayem Uddin, Enfa Rose George, Eduardo Blanco, Steven Corman

This paper presents a question-answering approach to extract document-level event-argument structures. We automatically ask and answer questions for each argument type an event may have. Questions are generated using manually defined templates and generative transformers. Template-based questions are generated using predefined role-specific wh-words and even

Hefu Ye, Changyun Wen, Yongduan Song

In this paper, we address the problem of prescribed-time leader-following consensus of heterogeneous multi-agent systems (MASs) in the presence of unknown sensor sensitivity. Under a connected undirected topology, we propose a time-varying dual observer/controller design framework that makes use of regular local and inaccurate feedback to achieve consensus t

Wenchuan Mu, Kwan Hui Lim

In today's data and information-rich world, summarization techniques are essential in harnessing vast text to extract key information and enhance decision-making and efficiency. In particular, topic-focused summarization is important due to its ability to tailor content to specific aspects of an extended text. However, this usually requires extensive labelle

Samson Worku, Pratik Mullick

The escalating process of urbanization has raised concerns about incidents arising from overcrowding, necessitating a deep understanding of large human crowd behavior and the development of effective crowd management strategies. This study employs computational methods to analyze real-world crowd behaviors, emphasizing self-organizing patterns. Notably, the

Aimi Okabayashi, Nicolas Audebert, Simon Donike, Charlotte Pelletier

Satellite imaging generally presents a trade-off between the frequency of acquisitions and the spatial resolution of the images. Super-resolution is often advanced as a way to get the best of both worlds. In this work, we investigate multi-image super-resolution of satellite image time series, i.e. how multiple images of the same area acquired at different d

Yu Chen, Wei Wang

In this paper, the event-triggered resilient filtering problem is investigated for a class of two-dimensional systems with asynchronous-delay under binary encoding-decoding schemes with probabilistic bit flips. To reduce unnecessary communications and computations in complex network systems, alleviate network energy consumption, and optimize the use of netwo

Xingchen Song, Di Wu, Binbin Zhang, Dinghao Zhou

Scale has opened new frontiers in natural language processing, but at a high cost. In response, by learning to only activate a subset of parameters in training and inference, Mixture-of-Experts (MoE) have been proposed as an energy efficient path to even larger and more capable language models and this shift towards a new generation of foundation models is g

João Barbosa, Mário Florido, Vítor Santos Costa

Here we define a new unification algorithm for terms interpreted in semantic domains denoted by a subclass of regular types here called deterministic regular types. This reflects our intention not to handle the semantic universe as a homogeneous collection of values, but instead, to partition it in a way that is similar to data types in programming languages

Florian Plötzky, Niklas Kiehne, Wolf-Tilo Balke

Our world is shaped by events of various complexity. This includes both small-scale local events like local farmer markets and large complex events like political and military conflicts. The latter are typically not observed directly but through the lenses of intermediaries like newspapers or social media. In other words, we do not witness the unfolding of s

Remi Cocou Avohou, Joseph Ben Geloun, Reiko Toriumi

$U(N)^{\otimes r} \otimes O(N)^{\otimes q}$ invariants are constructed by contractions of complex tensors of order $r+q$, also denoted $(r,q)$. These tensors transform under $r$ fundamental representations of the unitary group $U(N)$ and $q$ fundamental representations of the orthogonal group $O(N)$. Therefore, $U(N)^{\otimes r} \otimes O(N)^{\otimes q}$ inv

Priyanka, A. Chauhan, M. S. Mehta, M. Bhuyan

The ground state properties of Z = 125 and 126 nuclei are investigated, taking the isotopic series from the proton to neutron drip-lines. This analysis is conducted using the relativistic mean-field approach with NL3 and the Relativistic-Hartree-Bogoliubov model with DD-ME2 parameterization. The bulk properties under examination include the binding energy, t

Sangwoo Park, Arman Shafieloo, Satadru Bag, Mikhail Denissenya

Internal kinematics of galaxies, traced through the stellar rotation curve or two dimensional velocity map, carry important information on galactic structure and dark matter. With upcoming surveys, the velocity map may play a key role in the development of kinematic lensing as an astrophysical probe. We improve techniques for extracting velocity information

Imroi El-Habib, Hassan Maatouk, Alex Lemarchand, Anne Roynette

Nosocomial infections (NIs) are prevalent in intensive care units due to antibiotic overuse. Metal oxide nanoparticles (NPs), like ZnO, offer potential solutions, yet understanding how NPs size impacts their antibacterial efficacy are lacking. This study focuses on the effect of nanoparticle size on kinetics of bacterial strains growth. NPs were synthesized

Padmanaba Srinivasan, William Knottenbelt

Offline reinforcement learning (RL) algorithms are applied to learn performant, well-generalizing policies when provided with a static dataset of interactions. Many recent approaches to offline RL have seen substantial success, but with one key caveat: they demand substantial per-dataset hyperparameter tuning to achieve reported performance, which requires p

Ryoya Nara, Yu-Chieh Lin, Yuji Nozawa, Youyang Ng

Many image retrieval studies use metric learning to train an image encoder. However, metric learning cannot handle differences in users' preferences, and requires data to train an image encoder. To overcome these limitations, we revisit relevance feedback, a classic technique for interactive retrieval systems, and propose an interactive CLIP-based image retr

Tiago Gonçalves, Dagoberto Pulido-Arias, Julian Willett, Katharina V. Hoebel

The interactions between tumor cells and the tumor microenvironment (TME) dictate therapeutic efficacy of radiation and many systemic therapies in breast cancer. However, to date, there is not a widely available method to reproducibly measure tumor and immune phenotypes for each patient's tumor. Given this unmet clinical need, we applied multiple instance le

André Nicolov, Murthy S. Gudipati, Paul M. Bellan

Grains of ice are formed spontaneously when water vapor is injected into a weakly-ionized laboratory plasma in which the background gas has been cooled to cryogenic temperatures comparable to those of deep space. These ice grains are levitated indefinitely within the plasma so that their time evolution can be observed under free-floating conditions. Using mi

Aron Karatayev, Anna Ogorodova, Pakizar Shamoi

In the realm of software development, testing is crucial for ensuring software quality and adherence to requirements. However, it can be time-consuming and resource-intensive, especially when dealing with large and complex software systems. Test case prioritization (TCP) is a vital strategy to enhance testing efficiency by identifying the most critical test

Anastasios Papazafeiropoulos, Pandelis Kourtessis, Symeon Chatzinotas

Integrated sensing and communication (ISAC) is expected to play a prominent role among emerging technologies in future wireless communications. In particular, a communication radar coexistence system is degraded significantly by mutual interference. In this work, given the advantages of promising reconfigurable intelligent surface (RIS), we propose a simulta

Entong He, Yuxiang Yang, Chenshu Wu

Localization is critical to numerous applications. The performance of classical localization protocols is limited by the specific form of distance information and suffer from considerable ranging errors. This paper foresees a new opportunity by utilizing the exceptional property of entangled quantum states to measure a linear combination of target-anchor dis

Lazar Cvetković, François Costa, Mihajlo Djokic, Michal Friedman

While Function as a Service (FaaS) platforms can initialize function sandboxes on worker nodes in 10-100s of milliseconds, the latency to schedule functions in real FaaS clusters can be orders of magnitude higher. The current approach of building FaaS cluster managers on top of legacy orchestration systems (e.g., Kubernetes) leads to high scheduling delays w

Tomohiro Nishiyama, Yoshihiko Hasegawa

Non-Hermitian Hamiltonians play a crucial role in describing open quantum systems and nonequilibrium dynamics. In this paper, we derive trade-off relations for systems governed by non-Hermitian Hamiltonians, focusing on the Margolus-Levitin-type and Mandelstam-Tamm-type bounds, which are originally derived as quantum speed limits in isolated quantum dynamics

Yuan Li, Subham Sahoo, Sergio Vazquez, Yichao Zhang

This paper introduces a novel approach in designing prediction horizons on a generalized predictive control for a DC/DC boost converter. This method involves constructing a closed-loop system model and assessing the impact of different prediction horizons on system stability. In contrast to conventional design approaches that often rely on empirical predicti

Cornelis J. G. Mommers, Marc Vanderhaeghen

Recent years have seen much activity in searches for dark-sector messenger particles in the 10-100 MeV mass range, especially in view of a potential new light boson conjectured by the ATOMKI collaboration, X17. Under the assumption that the messenger particle has definite parity and either zero or unit spin, quite stringent bounds already exist on its coupli

M. Zmeškal, L. Thulliez, P. Tamagno, E. Dumonteil

Whether for shielding applications or for criticality safety studies, solving the neutron transport equation with good accuracy requires to take into account the resonant structure of cross sections in part of the Unresolved Resonance Region (URR). In this energy range even if the resonances can no longer be resolved experimentally, neglecting them can lead

Samuel Tovey, Christoph Lohrmann, Tobias Merkt, David Zimmer

This work introduces SwarmRL, a Python package designed to study intelligent active particles. SwarmRL provides an easy-to-use interface for developing models to control microscopic colloids using classical control and deep reinforcement learning approaches. These models may be deployed in simulations or real-world environments under a common framework. We e

Xindi Zhang, Zhihan Chen, Shaowei Cai

SAT solvers are indispensable in formal verification for hardware and software with many important applications. CDCL is the most widely used framework for modern SAT solvers, and restart is an essential technique of CDCL. When restarting, CDCL solvers cancel the current variable assignment while maintaining the branching order, variable phases, and learnt c

Zhimeng Zheng, Tao Huang, Gongsheng Li, Zuyi Wang

Recently, the performance of monocular depth estimation (MDE) has been significantly boosted with the integration of transformer models. However, the transformer models are usually computationally-expensive, and their effectiveness in light-weight models are limited compared to convolutions. This limitation hinders their deployment on resource-limited device

Jiawei Chen, Dingkang Yang, Yue Jiang, Mingcheng Li

In the realm of Medical Visual Language Models (Med-VLMs), the quest for universal efficient fine-tuning mechanisms remains paramount, especially given researchers in interdisciplinary fields are often extremely short of training resources, yet largely unexplored. Given the unique challenges in the medical domain, such as limited data scope and significant d

Bruno Premoselli, Frédéric Robert

We investigate in this work families $(u_\epsilon)_{\epsilon >0}$ of sign-changing blowing-up solutions of asymptotically critical stationary nonlinear Schr\"odinger equations of the following type: $$\Delta_g u_\epsilon + h_\epsilon u_\epsilon = |u_{\epsilon}|^{p_\epsilon-2} u_\epsilon $$ in a closed manifold $(M,g)$, where $h_\epsilon$ converges to $h$ in

Seung Hun Lee, Yuting Qian, Bohm-Jung Yang

The Euler class characterizes the topology of two real bands isolated from other bands in two-dimensions. Despite various intriguing topological properties predicted up to now, the candidate real materials hosting electronic Euler bands are extremely rare. Here, we show that in a quantum spin Hall insulator with two-fold rotation $C_{2z}$ about the $z$-axis,

Vikraman Arvind, Pushkar S Joglekar

We study the noncommutative rank problem, ncRANK, of computing the rank of matrices with linear entries in $n$ noncommuting variables and the problem of noncommutative Rational Identity Testing, RIT, which is to decide if a given rational formula in $n$ noncommuting variables is zero on its domain of definition. Motivated by the question whether these proble

Takuma Yoshioka, Taro Sekiyama, Atsushi Igarashi

Many effect systems for algebraic effect handlers are designed to guarantee that all invoked effects are handled adequately. However, respective researchers have developed their own effect systems that differ in how to represent the collections of effects that may happen. This situation results in blurring what is required for the representation and manipula

Zuocheng Wen, Lingzhong Guo

Deep convolutional neural networks (DCNNs) are a class of artificial neural networks, primarily for computer vision tasks such as segmentation and classification. Many nonlinear operations, such as activation functions and pooling strategies, are used in DCNNs to enhance their ability to process different signals with different tasks. Conceptional convolutio