April 2024 arXiv papers — page 138

Haocheng Yuan, Ajian Liu, Junze Zheng, Jun Wan

Face Anti-Spoofing (FAS) is crucial to safeguard Face Recognition (FR) Systems. In real-world scenarios, FRs are confronted with both physical and digital attacks. However, existing algorithms often address only one type of attack at a time, which poses significant limitations in real-world scenarios where FR systems face hybrid physical-digital threats. To

Jianwei Xu

Baumgratz, Cramer and Plenio established a rigorous framework (BCP framework) for quantifying the coherence of quantum states [\href{http://dx.doi.org/10.1103/PhysRevLett.113.140401}{Phys. Rev. Lett. 113, 140401 (2014)}]. In BCP framework, a quantum state is called incoherent if it is diagonal in the fixed orthonormal basis, and a coherence measure should sa

Sebastian Bordt, Harsha Nori, Vanessa Rodrigues, Besmira Nushi

While many have shown how Large Language Models (LLMs) can be applied to a diverse set of tasks, the critical issues of data contamination and memorization are often glossed over. In this work, we address this concern for tabular data. Specifically, we introduce a variety of different techniques to assess whether a language model has seen a tabular dataset d

Alexis Commereuc, Sandrine Mariot, Emmanuelle Rio, François Boulogne

The structure of liquid foams follows simple geometric rules formulated by Plateau 150 years ago. By placing such foam on a microtextured hydrophilic surface, we show that the bubble footprint exhibits a morphological transition. This transition concerns the liquid channels, also called pseudo Plateau borders, which are straight between vertices on a smooth

Xiaoyun Chang, Yi Sun

The hand plays a pivotal role in human ability to grasp and manipulate objects and controllable grasp synthesis is the key for successfully performing downstream tasks. Existing methods that use human intention or task-level language as control signals for grasping inherently face ambiguity. To address this challenge, we propose a grasp synthesis method guid

Theo Di Piazza, Enric Meinhardt-Llopis, Gabriele Facciolo, Benedicte Bascle

We propose a novel method for geolocalizing Unmanned Aerial Vehicles (UAVs) in environments lacking Global Navigation Satellite Systems (GNSS). Current state-of-the-art techniques employ an offline-trained encoder to generate a vector representation (embedding) of the UAV's current view, which is then compared with pre-computed embeddings of geo-referenced i

Bo Lin, Weiqing Ren

The committor function is a central object for quantifying the transitions between metastable states of dynamical systems. Recently, a number of computational methods based on deep neural networks have been developed for computing the high-dimensional committor function. The success of the methods relies on sampling adequate data for the transition, which st

Martin C. Arnold, Thilo Reinschlüssel

We show that the activation knot of a potentially non-stationary regressor on the adaptive Lasso solution path in autoregressions can be leveraged for selection-free inference about a unit root. The resulting test has asymptotic power against local alternatives in $1/T$ neighbourhoods, unlike post-selection inference methods based on consistent model selecti

ATLAS Collaboration

This paper describes measurements of the transverse momentum spectra of $W$ and $Z$ bosons produced in proton-proton collisions at centre-of-mass energies of $\sqrt{s}=5.02$ TeV and $\sqrt{s}=13$ TeV with the ATLAS experiment at the Large Hadron Collider. Measurements are performed in the electron and muon channels, $W \to \ell\nu$ and $Z \to \ell\ell$ ($\el

Adriano Vogel, Sören Henning, Esteban Perez-Wohlfeil, Otmar Ertl

Nowadays, several software systems rely on stream processing architectures to deliver scalable performance and handle large volumes of data in near real-time. Stream processing frameworks facilitate scalable computing by distributing the application's execution across multiple machines. Despite performance being extensively studied, the measurement of fault

Hasan Nasrallah, Abed Ellatif Samhat, Cristiano Nattero, Ali J. Ghandour

Developing countries usually lack the proper governance means to generate and regularly update a national rooftop map. Using traditional photogrammetry and surveying methods to produce a building map at the federal level is costly and time consuming. Using earth observation and deep learning methods, we can bridge this gap and propose an automated pipeline t

Zhihao Lin, Wei Ma, Tao Lin, Yaowen Zheng

Large Language Models (LLMs) have become instrumental in advancing software engineering (SE) tasks, showcasing their efficacy in code understanding and beyond. Like traditional SE tools, open-source collaboration is key in realising the excellent products. However, with AI models, the essential need is in data. The collaboration of these AI-based SE models h

Anthony Stephenson, Robert Allison, Edward Pyzer-Knapp

We show that common choices of kernel functions for a highly accurate and massively scalable nearest-neighbour based GP regression model (GPnn: \cite{GPnn}) exhibit gradual convergence to asymptotic behaviour as dataset-size $n$ increases. For isotropic kernels such as Mat\'{e}rn and squared-exponential, an upper bound on the predictive MSE can be obtained a

Samuel Beaulieu, Sylvain Larroque, Dominique Descamps, Baptiste Fabre

Strong-field ionization by counter-rotating two-color laser fields produces quantum interference between photoelectrons emitted on the leading and trailing edges of the laser field oscillations. We show that in chiral molecules, this interference is asymmetric along the light propagation direction and strongly enhances the sensitivity of the attoclock scheme

Claudia Ehrig, Benedikt Sonnleitner, Ursula Neumann, Catherine Cleophas

Pre-trained models have become pivotal in enhancing the efficiency and accuracy of time series forecasting on target data sets by leveraging transfer learning. While benchmarks validate the performance of model generalization on various target data sets, there is no structured research providing similarity and diversity measures to explain which characterist

Pedro Ribeiro, André Coelho, Rui Campos

Unmanned Aerial Vehicles (UAVs) are used for a wide range of applications. Due to characteristics such as the ability to hover and carry cargo on-board, rotary-wing UAVs have been considered suitable platforms for carrying communications nodes, including Wi-Fi Access Points and cellular Base Stations. This gave rise to the concept of Flying Networks (FNs), n

Piotr Frąckiewicz, Marek Szopa

We study the extension of classical games to the quantum domain, generated by the addition of one unitary strategy to two classical strategies of each player. The conditions that need to be met by unitary operations to ensure that the extended game is invariant with respect to the isomorphic transformations of the input game are determined. It has been shown

Sushil Gorai, Golam Mostafa Mondal

In this paper we extend the dichotomy given by Samuelsson and Wold that can be thought of as an analogue of the Wermer maximality theorem in $\mathbb{C}^2$ for certain polynomial polyhedra. We consider complex non-degenerate simply connected polynomial polyhedra of the form $\Omega:=\{z\in\mathbb{C}^2: |p_1(z)|<1, |p_2(z)|<1\}$ such that $\overline{\Omega}$

Ting Lei, Shaofeng Yin, Yang Liu

Open-vocabulary human-object interaction (HOI) detection, which is concerned with the problem of detecting novel HOIs guided by natural language, is crucial for understanding human-centric scenes. However, prior zero-shot HOI detectors often employ the same levels of feature maps to model HOIs with varying distances, leading to suboptimal performance in scen

Arkady Pikovsky, Lev A. Smirnov

We explore large populations of phase oscillators interacting via random coupling functions. Two types of coupling terms, the Kuramoto-Daido coupling and the Winfree coupling, are considered. Under the assumption of statistical independence of the phases and the couplings, we derive reduced averaged equations with effective non-random coupling terms. As a pa

Mario Román

We universally characterize the produoidal category of monoidal lenses over a monoidal category. In the same way that each category induces a cofree promonoidal category of spliced arrows, each monoidal category induces a cofree produoidal category of monoidal spliced arrows; monoidal lenses are the free normalization of the cofree produoidal category of mon

Di Guo, Qin-He Yang, Ling-Yun Dai

In this paper, the reaction of electron-positron annihilation into $\Lambda_c^+\bar{\Lambda}_c^-$ is investigated. The $\Lambda_c^+\bar{\Lambda}_c^-$ scattering amplitudes are obtained by solving the Lippmann-Schwinger equation. The contact, annihilation, and two pseudoscalar-exchange potentials are taken into account in the spirit of the chiral effective fi

Maria Saladina, Carsten Deibel

The fill factor (FF) is a critical parameter for solar cell efficiency, but its analytical description is challenging due to the interplay between recombination and charge extraction processes. An often overlooked yet significant factor contributing to FF losses, beyond recombination, is the influence of charge transport. In most state-of-the-art organic sol

Lina Zhang, Sebastian V. Pios, Mikołaj Martyka, Fuchun Ge

We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev (LZBL) algorithm. The dynamics can be performed via Python API with a wide range of quantum mechanical (QM) and machine learning (ML) methods, including ab initio QM (CASSCF and ADC(2)), semi-empirical QM met

Xudong Yu, Chenjia Bai, Hongyi Guo, Changhong Wang

Offline Reinforcement Learning (RL) faces distributional shift and unreliable value estimation, especially for out-of-distribution (OOD) actions. To address this, existing uncertainty-based methods penalize the value function with uncertainty quantification and demand numerous ensemble networks, posing computational challenges and suboptimal outcomes. In thi

Guangzhao Yang, Marek Gluza, Si Yan Koh, Calvin Pei Yu Wong

Semiconductor double quantum dot (DQD) qubits coupled via superconducting microwave resonators provide a powerful means of long-range manipulation of the qubits' spin and charge degrees of freedom. Quantum gates can be implemented by parametrically driving the qubits while their transition frequencies are detuned from the resonator frequency. Long-range two-

Andrea Zugarini, Kamyar Zeinalipour, Surya Sai Kadali, Marco Maggini

Crossword puzzles are popular linguistic games often used as tools to engage students in learning. Educational crosswords are characterized by less cryptic and more factual clues that distinguish them from traditional crossword puzzles. Despite there exist several publicly available clue-answer pair databases for traditional crosswords, educational clue-answ

Shota Saito

We investigate the four-dimensional Wess-Zumino-Witten (WZW) terms within the framework of $Sp$ quantum chromodynamics (QCD) using invertible field theory through bordism theory. We present a novel approach aimed at circumventing both perturbative and non-perturbative gauge anomalies on spacetime manifolds endowed with spin structures. We study both ungauged

Simon Rerucha, Miroslava Hola, Ondrej Cip, Josef Lazar

Our research efforts in displacement measurement interferometry focused on long-term drifts initiated an extended experimental investigation in the interferometric assemblies of our design. We aimed to analyze, characterize and tackle the long-term measurement stability, expressed as the zero-drift, with particular attention to the thermal effects. For the e

Lu Qin, Chao Hang, Guoxiang Huang, Weibin Li

We investigate the generation of optical shock waves in strongly interacting Rydberg atomic gases with a spatially homogeneous dissipative potential. The Rydberg atom interaction induces an optical nonlocal nonlinarity. We focus on local nonlinear ($R_b\ll R_0$) and nonlocal nonlinear ($R_b\sim R_0$) regimes, where $R_b$ and $R_0$ are the characteristic leng

Wanting Yang, Zehui Xiong, Tony Q. S. Quek, Xuemin Shen

In the era of 6G, featuring compelling visions of digital twins and metaverses, Extended Reality (XR) has emerged as a vital conduit connecting the digital and physical realms, garnering widespread interest. Ensuring a fully immersive wireless XR experience stands as a paramount technical necessity, demanding the liberation of XR from the confines of wired c

Yuanpeng He

Although current semi-supervised medical segmentation methods can achieve decent performance, they are still affected by the uncertainty in unlabeled data and model predictions, and there is currently a lack of effective strategies that can explore the uncertain aspects of both simultaneously. To address the aforementioned issues, we propose Evidential Proto

Chenguang Liu, Guangshuai Gao, Ziyue Huang, Zhenghui Hu

Detecting objects from aerial images poses significant challenges due to the following factors: 1) Aerial images typically have very large sizes, generally with millions or even hundreds of millions of pixels, while computational resources are limited. 2) Small object size leads to insufficient information for effective detection. 3) Non-uniform object distr

Michael Meindl, Simon Bachhuber, Thomas Seel

This work proposes Autonomous Iterative Motion Learning (AI-MOLE), a method that enables systems with unknown, nonlinear dynamics to autonomously learn to solve reference tracking tasks. The method iteratively applies an input trajectory to the unknown dynamics, trains a Gaussian process model based on the experimental data, and utilizes the model to update

Oxana Shamilyan, Ievgen Kabin, Zoya Dyka, Peter Langendoerfer

The paper presents an experimental study of resilient path planning for con-tinuum robots taking into account the multi-objective optimisation problem. To do this, we used two well-known algorithms, namely Genetic algorithm and A* algorithm, for path planning and the Analytical Hierarchy Process algorithm for paths evaluation. In our experiment Analytical Hi

Yuanpeng He, Lijian Li

Although the existing uncertainty-based semi-supervised medical segmentation methods have achieved excellent performance, they usually only consider a single uncertainty evaluation, which often fails to solve the problem related to credibility completely. Therefore, based on the framework of evidential deep learning, this paper integrates the evidential pred

Eugenio Gambari, Sebastian Meyer, Sacha Guesne, Pascal David

Topological defects are ubiquitous, they manifest in a wide variety of systems such as liquid crystals, magnets or superconductors. The recent quest for nonabelian anyons in condensed matter physics stimulates the interest for topological defects since they can be hosted in vortices in quantum magnets or topological superconductors. In addition to these vort

Bosiljka Tadic, Roderick Melnik

In functionally complex systems, higher-order connectivity is often revealed in the underlying geometry of networked units. Furthermore, such systems often show signatures of self-organized criticality, a specific type of non-equilibrium collective behaviour associated with an attractor of internal dynamics with long-range correlations and scale invariance,

Abhishek Sadhu, Aritra Sarkar, Akash Kundu

In the field of quantum computing, variational quantum algorithms (VQAs) represent a pivotal category of quantum solutions across a broad spectrum of applications. These algorithms demonstrate significant potential for realising quantum computational advantage. A fundamental aspect of VQAs involves formulating expressive and efficient quantum circuits (namel

Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan

Aligning a user query and video clips in cross-modal latent space and that with semantic concepts are two mainstream approaches for ad-hoc video search (AVS). However, the effectiveness of existing approaches is bottlenecked by the small sizes of available video-text datasets and the low quality of concept banks, which results in the failures of unseen queri

Oxana Shamilyan, Ievgen Kabin, Zoya Dyka, Oleksandr Sudakov

Many technical solutions are bio-inspired. Octopus-inspired robotic arms belong to continuum robots which are used in minimally invasive surgery or for technical system restoration in areas difficult-toaccess. Continuum robot missions are bounded with their motions, whereby the motion of the robots is controlled by humans via wireless communication. In case

Lakshmi Nair

Contrastive Language-Image Pre-training (CLIP) has been shown to improve zero-shot generalization capabilities of language and vision models. In this paper, we extend CLIP for efficient knowledge distillation, by utilizing embeddings as teachers. Typical knowledge distillation frameworks require running forward passes through a teacher model, which is often

Kolluru Venkata Kiran, Dario Vincenzi, Rahul Pandit

Energy cascades lie at the heart of the dynamics of turbulent flows. In a recent study of turbulence in fluids with odd-viscosity [de Wit \textit{et al.}, Nature \textbf{627}, 515 (2024)], the two-dimensionalization of the flow at small scales leads to the arrest of the energy cascade and selection of an intermediate scale, between the forcing and the viscou

Huafeng Quan, Yiting Li, Dashuai Liu, Yue Zhou

In the globalization trend, China's cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper focuses on the Miao batik culture in Guizhou Province, China, and explores the application of knowledge graphs, natural language processing, and deep learning

Ping Xu, Zhiyuan Ning, Meng Xiao, Guihai Feng

Single-cell RNA sequencing (scRNA-seq) is essential for unraveling cellular heterogeneity and diversity, offering invaluable insights for bioinformatics advancements. Despite its potential, traditional clustering methods in scRNA-seq data analysis often neglect the structural information embedded in gene expression profiles, crucial for understanding cellula

Alberto García Martín-Caro, Gerardo García-Moreno, Javier Olmedo, Jose M. Sánchez Velázquez

We present a detailed description of a quantum scalar field theory within a flat spacetime confined to a cavity with perfectly reflecting moving boundaries. Moreover, we establish an equivalence between this time-dependent setting and a field theory on an acoustic metric with static Dirichlet boundary conditions. We discuss the classical and quantum aspects

Huawei Sun, Hao Feng, Gianfranco Mauro, Julius Ott

Radar and camera fusion yields robustness in perception tasks by leveraging the strength of both sensors. The typical extracted radar point cloud is 2D without height information due to insufficient antennas along the elevation axis, which challenges the network performance. This work introduces a learning-based approach to infer the height of radar points a

Yusuke Nakamura, Kohsuke Shibata

We give a counterexample to the PIA (precise inversion of adjunction) conjecture for minimal log discrepancies. We also give a counterexample to the LSC conjecture for families.

Tomoki Uchimura

In this paper, we introduce notions called inverse set and inverse correspondence over inverse semigroups. These are analogies of Hilbert $C^*$-modules and \Ccorrs in the $C^*$-algebra theory. We show that inverse semigroups and inverse correspondences form a bicategory. In this bicategory, two inverse semigroups are equivalent if and only if they are Morita

Tianyu Cao, Natraj Raman, Danial Dervovic, Chenhao Tan

As large language models (LLMs) expand the power of natural language processing to handle long inputs, rigorous and systematic analyses are necessary to understand their abilities and behavior. A salient application is summarization, due to its ubiquity and controversy (e.g., researchers have declared the death of summarization). In this paper, we use financ

Yawen Feng, Mikko Parviainen, Saara Sarsa

We study a general class of parabolic equations $$ u_t-|Du|^\gamma\big(\Delta u+(p-2) \Delta_\infty^N u\big)=0, $$ which can be highly degenerate or singular. This class contains as special cases the standard parabolic $p$-Laplace equation and the normalized version that arises from stochastic game theory. Utilizing the systematic approach developed in our p

S. G. Salnikov, A. I. Milstein

It is shown that the nontrivial energy dependencies of $D\bar{D}$, $D\bar{D}^{*}$, and $D^{*}\bar{D}^{*}$ pair production cross sections in $e^{+}e^{- }$ annihilation are well described within the approach based on account for the final-state interaction of produced particles. This statement is valid for production of charged and neutral particles. Interacti

Oxana Shamilyan, Ievgen Kabin, Zoya Dyka, Peter Langendoerfer

Using sensors as a means to achieve self-awareness and artificial intelligence for decision-making, may be a way to make complex systems self-adaptive, autonomous and resilient. Investigating the combination of distributed artificial intelligence methods and bio-inspired robotics can provide results that will be helpful for implementing autonomy of such robo

Giulio Fattore, Maria Elena Valcher

In this paper we propose a data-driven approach to the design of a residual generator, based on a dead-beat unknown-input observer, for linear time-invariant discrete-time state-space models, whose state equation is affected both by disturbances and by actuator faults. We first review the modelbased conditions for the existence of such a residual generator,

Daniel Gromada

We introduce quantum association schemes. This allows to define distance regular and strongly regular quantum graphs. We bring examples thereof. In addition, we formulate the duality for translation quantum association schemes corresponding to finite quantum groups.

Diya Joseph, Juan Luis Aragón, Joan-Manuel Parcerisa, Antonio Gonzalez

Contemporary GPUs are designed to handle long-latency operations effectively; however, challenges such as core occupancy (number of warps in a core) and pipeline width can impede their latency management. This is particularly evident in Tile-Based Rendering (TBR) GPUs, where core occupancy remains low for extended durations. To address this challenge, we int

Tianyu Huang, Haoang Li, Liangzu Peng, Yinlong Liu

Estimating the rigid transformation with 6 degrees of freedom based on a putative 3D correspondence set is a crucial procedure in point cloud registration. Existing correspondence identification methods usually lead to large outlier ratios ($>$ 95 $\%$ is common), underscoring the significance of robust registration methods. Many researchers turn to paramete

Raphael Sulzer, Florent Lafarge

Plane arrangements are a useful tool for surface and volume modelling. However, their main drawback is poor scalability. We introduce two key novelties that enable the construction of plane arrangements for complex objects and entire scenes: (i) an ordering scheme for the plane insertion and (ii) the direct use of input points during arrangement construction

Shengze Dong, Zhuorui Cui, Ding Liu, Jinzhi Lei

Motivation: Single-cell RNA sequencing (scRNA-seq) is a groundbreaking technology extensively utilized in biological research, facilitating the examination of gene expression at the individual cell level within a given tissue sample. While numerous tools have been developed for scRNA-seq data analysis, the challenge persists in capturing the distinct feature

Arnab Dey, Di Yang, Antitza Dantcheva, Jean Martinet

In recent advancements in novel view synthesis, generalizable Neural Radiance Fields (NeRF) based methods applied to human subjects have shown remarkable results in generating novel views from few images. However, this generalization ability cannot capture the underlying structural features of the skeleton shared across all instances. Building upon this, we

Shi Pi

The most promising mechanism of generating PBHs is by the enhancement of power spectrum of the primordial curvature perturbation, which is usually accompanied by the the enhancement of non-Gaussianity that crucially changes the abundance of PBHs. In this review I will discuss how non-Gaussianity is generated in single field inflation as well as in the curvat

Ben Nagy

This article summarizes some mostly unsuccessful attempts to understand authorial style by examining the attention of various neural networks (LSTMs and CNNs) trained on a corpus of classical Latin verse that has been encoded to include sonic and metrical features. Carefully configured neural networks are shown to be extremely strong authorship classifiers,

Ramgopal Agrawal, Leticia F. Cugliandolo, Lara Faoro, Lev B. Ioffe

By considering the quench dynamics of two-dimensional frustrated Ising models through numerical simulations, we investigate the dynamical critical behavior on the multicritical Nishimori point (NP). We calculate several dynamical critical exponents, namely, the relaxation exponent $z_{\rm c}$, the autocorrelation exponent $\lambda_{\rm c}$, and the persisten

Zi-Qi Zhao, Zhen-Wei Li, Lin Xiao, Hong-Wei Ge

Many previous works studied the dynamical timescale mass transfer stability criteria based on the donor response with neglecting the stellar structure of the accretor. In this letter, we investigate the radial response of accretors with mass accumulation and its effect on the binary mass transfer stability. We perform a series of detailed stellar evolution s

Jonathan B. Curtis, Nikola Maksimovic, Nicholas R. Poniatowski, Amir Yacoby

The melting of quasi-long-range superconductivity in two spatial dimensions occurs through the proliferation and unbinding of vortex-antivortex pairs -- a phenomenon known as the Berezinskii-Kosterlitz-Thouless (BKT) transition. Although signatures of this transition have been observed in bulk measurements, these experiments are often complicated, ambiguous,

Mohammad Azam, Manasa Manasa, Tatiana Zajarniuk, Taras Palasyuk

A systematic investigation has been performed by synthesis and comprehensive characterization of a series of SmFe1-xCuxAsO0.8F0.2 bulks (x = 0 to 0.2). These samples are well characterized by structural, Raman spectroscopy, microstructural, transport, magnetic measurements, and supplementary calculations within density functional theory (DFT). The parent com

Bo Li, Clément Foucart, Xiaowen Zhou

We study the explosion phenomenon of nonlinear continuous-state branching processes (nonlinear CSBPs). First an explicit integral test for explosion is designed when the rate function does not increase too fast. We then exhibit three different regimes of explosion when the branching mechanism and the rate function are regularly varying respectively at $0$ an

Fatima Ezzeddine, Mirna Saad, Omran Ayoub, Davide Andreoletti

Anomaly detection (AD), also referred to as outlier detection, is a statistical process aimed at identifying observations within a dataset that significantly deviate from the expected pattern of the majority of the data. Such a process finds wide application in various fields, such as finance and healthcare. While the primary objective of AD is to yield high

Gurpreet Singh, Trishala Mitra, Søren P. Madsen, Aurélien Dantan

We theoretically investigate the design of thin subwavelength gratings possessing high-reflectivity and high-$Q$ resonances when illuminated at normal incidence by a Gaussian beam. We compare the performances of single-period and dual-period rectangular gratings using Finite Element Method-based optimization and predict one to two orders of magnitude improve

Carlo Pagani, Janik Sobieray

We consider the critical relaxation of the Ising model, the so-called model A, and study its operator product expansion. Within perturbation theory, we focus on the operator product expansions of the two-point function and the response function. At the fixed point, we normalize the coefficients and the scaling variables so that the result displays universali

Xilun Li, Yanan Ye

We show that every gradient shrinking soliton of the generalized Ricci flow on compact manifold is a Ricci soliton. And we prove that the pluriclosed soliton is gradient Kahler-Ricci soliton under a broad cohomological condition. Moreover, we construct the first example of non-trivial shrinking generalized soliton, which can serve as a singularity model of t

Indu K. Dihingia, Christian Fendt

We review some recent results of general relativistic magnetohydrodynamic (GR-MHD) simulations considering the evolution of geometrically thin disks around a central black hole. Thin disk GR-MHD simulations complement the widely used MAD (Magnetically Arrested Disk) or SANE (Standard And Normal Evolution) approaches of evolving from an initial disk torus. In

Pengfei Zhou, Fangxiang Feng, Xiaojie Wang

Image harmonization, which involves adjusting the foreground of a composite image to attain a unified visual consistency with the background, can be conceptualized as an image-to-image translation task. Diffusion models have recently promoted the rapid development of image-to-image translation tasks . However, training diffusion models from scratch is comput

Samuel Cahyawijaya, Holy Lovenia, Fajri Koto, Rifki Afina Putri

Large language models (LLMs) show remarkable human-like capability in various domains and languages. However, a notable quality gap arises in low-resource languages, e.g., Indonesian indigenous languages, rendering them ineffective and inefficient in such linguistic contexts. To bridge this quality gap, we introduce Cendol, a collection of Indonesian LLMs en

Radosław Nowak, Adam Małkowski, Daniel Cieślak, Piotr Sokół

Graph embeddings have emerged as a powerful tool for representing complex network structures in a low-dimensional space, enabling the use of efficient methods that employ the metric structure in the embedding space as a proxy for the topological structure of the data. In this paper, we explore several aspects that affect the quality of a vertex embedding of

Virginia Grande, Natalie Kiesler, Maria Andreina Francisco R

The advent of Large Language Models (LLMs) started a serious discussion among educators on how LLMs would affect, e.g., curricula, assessments, and students' competencies. Generative AI and LLMs also raised ethical questions and concerns for computing educators and professionals. This experience report presents an assignment within a course on professional c

Elisei Rykov, Yana Shishkina, Kseniia Petrushina, Kseniia Titova

In this paper, we present our novel systems developed for the SemEval-2024 hallucination detection task. Our investigation spans a range of strategies to compare model predictions with reference standards, encompassing diverse baselines, the refinement of pre-trained encoders through supervised learning, and an ensemble approaches utilizing several high-perf

Matilde Gargiani, Robin Sieber, Efe Balta, Dominic Liao-McPherson

We consider inexact policy iteration methods for large-scale infinite-horizon discounted MDPs with finite spaces, a variant of policy iteration where the policy evaluation step is implemented inexactly using an iterative solver for linear systems. In the classical dynamic programming literature, a similar principle is deployed in optimistic policy iteration,

Pin-Hung Kuo, Jinshan Pan, Shao-Yi Chien, Ming-Hsuan Yang

The Transformer architecture has achieved remarkable success in natural language processing and high-level vision tasks over the past few years. However, the inherent complexity of self-attention is quadratic to the size of the image, leading to unaffordable computational costs for high-resolution vision tasks. In this paper, we introduce Concertormer, featu

Martin Gugat, Michael Herty, Jiehong Liu, Chiara Segala

We investigate the interior turnpike phenomenon for discrete-time multi-agent optimal control problems. While for continuous systems the turnpike property has been established, we focus here on first-order discretizations of such systems. It is shown that the resulting time-discrete system inherits the turnpike property with estimates of the same type as in

Claudio Hernández-Vera, Viviana V. Guzmán, Elizabeth Artur de la Villarmois, Karin I. Öberg

H$_2$CO is a small organic molecule widely detected in protoplanetary disks. As a precursor to grain-surface formation of CH$_3$OH, H$_2$CO is considered an important precursor of O-bearing organic molecules that are locked in ices. Still, since gas-phase reactions can also form H$_2$CO, there remains an open question on the channels by which organics form i

Oscar Barragán, Suzanne Aigrain, James McCormac

Space-based photometry missions produce exquisite light curves that contain a wealth of stellar variability on a wide range of timescales. Light curves also typically contain significant instrumental systematics -- spurious, non-astrophysical trends that are common, in varying degrees, to many light curves. Empirical systematics-correction approaches using t

Nick Bezhanishvili, Vincenzo Ciancia, David Gabelaia, Mamuka Jibladze

In the context of spatial logics and spatial model checking for polyhedral models -- mathematical basis for visualisations in continuous space -- we propose a weakening of simplicial bisimilarity. We additionally propose a corresponding weak notion of $\pm$-bisimilarity on cell-poset models, a discrete representation of polyhedral models. We show that two po

Shiva P. Pudasaini

We propose a novel multi-phase thermo-mechanical rock-ice avalanche model. It considers rock, ice and fluid; includes rigorously derived ice melt rate, melting efficiency dependent fluid production rate and a general temperature equation. It explains advection-diffusion of heat including heat exchange across the avalanche, basal heat conduction, production a

Faseeh Ahmad, Matthias Mayr, Sulthan Suresh-Fazeela, Volker Krueger

In dynamic operational environments, particularly in collaborative robotics, the inevitability of failures necessitates robust and adaptable recovery strategies. Traditional automated recovery strategies, while effective for predefined scenarios, often lack the flexibility required for on-the-fly task management and adaptation to expected failures. Addressin

Sierra Bonilla, Shuai Zhang, Dimitrios Psychogyios, Danail Stoyanov

Within colorectal cancer diagnostics, conventional colonoscopy techniques face critical limitations, including a limited field of view and a lack of depth information, which can impede the detection of precancerous lesions. Current methods struggle to provide comprehensive and accurate 3D reconstructions of the colonic surface which can help minimize the mis

Francisco Herrera, Daniel Jiménez-López, Alberto Argente-Garrido, Nuria Rodríguez-Barroso

In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and handling of sensitive data raise concerns about individual privacy protection. Federated Learning (FL) emerges as a promising solution to address these challenges by enabling decent

Kingman Cheung, Fei-Tung Chung, Giovanna Cottin, Zeren Simon Wang

We study axion-like particles (ALPs) with quark-flavor-violating couplings at the LHC. Specifically, we focus on the theoretical scenario with ALP-top-up and ALP-top-charm interactions, in addition to the more common quark-flavor-diagonal couplings. The ALPs can thus originate from decays of top quarks which are pair produced in large numbers at the LHC, and

Sebastian Hirt, Andreas Höhl, Joachim Schaeffer, Johannes Pohlodek

Tuning parameters in model predictive control (MPC) presents significant challenges, particularly when there is a notable discrepancy between the controller's predictions and the actual behavior of the closed-loop plant. This mismatch may stem from factors like substantial model-plant differences, limited prediction horizons that do not cover the entire time

Mariella Dreissig, Simon Ruehle, Florian Piewak, Joschka Boedecker

Safety-critical applications such as autonomous driving require robust 3D environment perception algorithms capable of handling diverse and ambiguous surroundings. The predictive performance of classification models is heavily influenced by the dataset and the prior knowledge provided by the annotated labels. While labels guide the learning process, they oft

Naofumi Honda, Luca Prelli

In this paper, we construct the multi-microlocalization functor $\mu hom_{{\chi}}$ of homomorphisms, which is a counterpart of the functor $\mu hom$ studied by M.Kashiwara and P.Schapira. Furthermore, using the new functor, we also introduce several sheaves of multi-microlocal operators which act on multi-microlocalized objects such as a multi-microfunction.

Cristina Sainz Martinez, José P. Marques, Gabriele Bonanno, Tom Hilbert

The thalamus is a subcortical structure of central importance to brain function, which is organized in smaller nuclei with specialized roles. Despite significant functional and clinical relevance, locating and distinguishing the different thalamic nuclei in vivo, non-invasively, has proved challenging with conventional imaging techniques, such as T$_{1}$ and

Mariana Neves

Focusing on particular facts, instead of the complete text, can potentially improve searching for specific information in the scientific literature. In particular, argumentative elements allow focusing on specific parts of a publication, e.g., the background section or the claims from the authors. We evaluated some tools for the extraction of argumentation e

Yu Wu, Teng Liu, Haiyang Song, Yinghe Zhao

Through learning from past literature, artificial intelligence models have been able to predict the future applications of various stoichiometric inorganic materials in a variety of subfields of materials science. This capacity offers exciting opportunities for boosting the research and development (R&D) of new functional materials. Unfortunately, the previo

Abhiram B. Nair, Abhinand K., Anamika U., Denil Tom Jaison

Sentiment analysis has become increasingly important in healthcare, especially in the biomedical and pharmaceutical fields. The data generated by the general public on the effectiveness, side effects, and adverse drug reactions are goldmines for different agencies and medicine producers to understand the concerns and reactions of people. Despite the challeng

Junkai Yan, Yipeng Gao, Qize Yang, Xihan Wei

Text-to-3D generation, which synthesizes 3D assets according to an overall text description, has significantly progressed. However, a challenge arises when the specific appearances need customizing at designated viewpoints but referring solely to the overall description for generating 3D objects. For instance, ambiguity easily occurs when producing a T-shirt

Urko Reinosa

These lectures cover two aspects: first, irrespectively of the particular approach followed to tackle the QCD phase diagram, we introduce some tools that help discussing the confinement/deconfinement transition in the continuum; second, within one particular continuum approach, based on the Curci-Ferrari model, we illustrate the use of these various notions

Ahmed H. El-Saeed, Alaa Elshazly, Hakim Kobbi, Rafal Magdziak

We demonstrate a design for a high-performance $2 \times 2$ splitter meeting the essential requirements of broadband coupling, support for arbitrary coupling ratio, ultra low-loss, high fabrication tolerance, and a compact footprint. This is achieved based on a rigorous coupled mode theory analysis of the broadband response of the bent directional coupler (D

M. Eriksson, M. Laine

We estimate the equilibration rate of a nearly homogeneous Higgs field, displaced from its ground state during the onset of an electroweak phase transition. The computation is carried out with Hard Thermal Loop resummed perturbation theory, and a significant part of the result originates from Bose-enhanced $t$-channel $2\leftrightarrow 2$ scatterings. The ex

Kengo Matsumoto, Taro Sogabe

In this paper, we first present the homotopy groups of the automorphism groups of Cuntz--Krieger algebras in terms of the underlying matrices of the Cuntz--Krieger algebras. We also show that the homotopy groups are complete invariants of the isomorphism class of the Cuntz--Krieger algebras. As a result, the isomorphism type of Cuntz--Krieger algebras are co