March 2024 arXiv papers — page 164

Zihan Luo, Xiran Song, Hong Huang, Jianxun Lian

Improving the general capabilities of large language models (LLMs) is an active research topic. As a common data structure in many real-world domains, understanding graph data is a crucial part of advancing general intelligence. To this end, we propose a dynamic benchmark named GraphInstruct in this paper, which comprehensively includes 21 classical graph re

Junwei Su, Chuan Wu

Many computer vision and machine learning problems are modelled as learning tasks on graphs where graph neural networks GNNs have emerged as a dominant tool for learning representations of graph structured data A key feature of GNNs is their use of graph structures as input enabling them to exploit the graphs inherent topological properties known as the topo

Shangjian Yin, Peijie Huang, Jiatian Chen, Haojing Huang

Large Language Models (LLMs) have demonstrated impressive capabilities in language generation and general task performance. However, their application to spoken language understanding (SLU) remains challenging, particularly for token-level tasks, where the autoregressive nature of LLMs often leads to misalignment issues. They also struggle to capture nuanced

Stanisław Stępień, Jarosław Jankowski, Piotr Bródka, Radosław Michalski

Interaction with others influences our opinions and behaviours. Our activities within various social circles lead to different opinions expressed in various situations, groups, and ways of communication. Earlier studies on agent-based modelling of conformism within networks were based on a single-layer approach. Contrary to that, in this work, we propose a m

Quentin Peyle, Imene Ben Rejeb-Mzah, Baptiste Piofret, Antoine Benoit

We propose a methodology for modelling methane intensities of Oil and Gas upstream activities for different production profiles with diverse combinations of region of operation and production volumes associated. This methodology leverages different data sources, including satellite measurements and public estimates of methane emissions but also country-level

Yujiang Chen, Mei Xie

Recently, lung nodule detection methods based on deep learning have shown excellent performance in the medical image processing field. Considering that only a few public lung datasets are available and lung nodules are more difficult to detect in CT images than in natural images, the existing methods face many bottlenecks when detecting lung nodules, especia

Kiran Madhusudhanan, Shayan Jawed, Lars Schmidt-Thieme

Time series forecasting attempts to predict future events by analyzing past trends and patterns. Although well researched, certain critical aspects pertaining to the use of deep learning in time series forecasting remain ambiguous. Our research primarily focuses on examining the impact of specific hyperparameters related to time series, such as context lengt

F. Biagiotti, L. Mancini, J. Southworth, J. Tregloan-Reed

Our aim in this paper is to refine the orbital and physical parameters of the HATS-2 planetary system and study transit timing variations and atmospheric composition thanks to transit observations that span more than ten years and that were collected using different instruments and pass-band filters. We also investigate the orbital alignment of the system by

Jia-Hao Lü, Wen Ning, Fan Wu, Ri-Hua Zheng

Critical systems near quantum phase transitions were predicted to be useful for improvement of metrological precision, thanks to their ultra-sensitive response to a tiny variation of the control Hamiltonian. Despite the promising perspective, realization of criticality-enhanced quantum metrology is an experimentally challenging task, mainly owing to the extr

Stefanos Giaremis, Noujoud Nader, Clint Dawson, Hartmut Kaiser

Physics simulation results of natural processes usually do not fully capture the real world. This is caused for instance by limits in what physical processes are simulated and to what accuracy. In this work we propose and analyze the use of an LSTM-based deep learning network machine learning (ML) architecture for capturing and predicting the behavior of the

Stefan Glock, Jaehoon Kim, Lyuben Lichev, Oleg Pikhurko

Let $f^{(r)}(n;s,k)$ denote the maximum number of edges in an $n$-vertex $r$-uniform hypergraph containing no subgraph with $k$ edges and at most $s$ vertices. Brown, Erd\H{o}s and S\'os [New directions in the theory of graphs (Proc. Third Ann Arbor Conf., Univ. Michigan 1971), pp. 53--63, Academic Press 1973] conjectured that the limit $\lim_{n\rightarrow \

Yuliang Liu, Biao Yang, Qiang Liu, Zhang Li

We present TextMonkey, a large multimodal model (LMM) tailored for text-centric tasks. Our approach introduces enhancement across several dimensions: By adopting Shifted Window Attention with zero-initialization, we achieve cross-window connectivity at higher input resolutions and stabilize early training; We hypothesize that images may contain redundant tok

Tomoyuki Arakawa, Xuanzhong Dai, Justine Fasquel, Bohan Li

We study the representations of some simple affine vertex algebras at non-admissible level arising from rank one 4D SCFTs. In particular, we classify the irreducible highest weight modules of $L_{-2}(G_2)$ and $L_{-2}(B_3)$. It is known by the works of Adamovi\'{c} and Per\v{s}e that these vertex algebras can be conformally embedded into $L_{-2}(D_4)$. We al

Elliott Thornley

I explain the shutdown problem: the problem of designing artificial agents that (1) shut down when a shutdown button is pressed, (2) don't try to prevent or cause the pressing of the shutdown button, and (3) otherwise pursue goals competently. I prove three theorems that make the difficulty precise. These theorems show that agents satisfying some innocuous-s

Vaclav Voracek, Tomas Werner

A popular approach to the MAP inference problem in graphical models is to minimize an upper bound obtained from a dual linear programming or Lagrangian relaxation by (block-)coordinate descent. This is also known as convex/convergent message passing; examples are max-sum diffusion and sequential tree-reweighted message passing (TRW-S). Convergence properties

Matteo De Tullio, Giovanni Novi Inverardi, Michella Karam, Jonathan Houard

This study investigates the emission of cations from silica samples by single-cycle THz pulses, focusing on the influence of pulse polarity. Negative THz pulses were found to efficiently trigger the evaporation of cations from nanoneedles in amorphous silica samples compared to positive pulses. Conversely, this dependence on pulse polarity could not be found

Paul Nikolaev, David J. Prömel, Mathias Trabs

Besov spaces with dominating mixed smoothness, on the product of the real line and the torus as well as bounded domains, are studied. A characterization of these function spaces in terms of differences is provided. Applications to random fields, like Gaussian fields and the stochastic heat equation, are discussed, based on a Kolmogorov criterion for Besov re

Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao

Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and network security. Significant strides have been made in leveraging Graph Neural Networks (GNNs) to achieve remarkable success in these areas. However, in real-world scenarios, the train

Moonkwang Jeong, Xiangzhou Tan, Felix Fischer, Tian Qiu

Small-scale robots hold great potential for targeted cargo delivery in minimally-inv asive medicine. However, current robots often face challenges to locomote efficiently on slip pery biological tissue surfaces, especially when loaded with heavy cargos. Here, we report a magnetic millirobot that can walk on rough and slippery biological tissues by anchoring

Prashanta K. Mukharjee, Bin Shen, Sebastian Erdmann, Anton Jesche

We use magnetometry, calorimetry, and high-resolution capacitive dilatometry, as well as single-crystal neutron diffraction to explore temperature-field phase diagram of the anisotropic honeycomb magnet BaCo$_2$(AsO$_4)_2$. Our data reveal four distinct ordered states observed for in-plane magnetic fields. Of particular interest is the narrow region between

Hansueli Jud, Clément Tauber

We study the Dirac Hamiltonian in dimension two with a mass term and a large momentum regularization, and show that bulk-edge correspondence fails. Despite a well defined bulk topological index --the Chern number--, the number of edge modes depends on the boundary condition. The origin of this anomaly is rooted in the unbounded nature of the spectrum. It is

L. Palethorpe, A. Anna John, A. Mortier, J. Davoult

We report on the detailed characterization of the HD 77946 planetary system. HD 77946 is an F5 ($M_*$ = 1.17 M$_{\odot}$, $R_*$ = 1.31 R$_{\odot}$) star, which hosts a transiting planet recently discovered by NASA's Transiting Exoplanet Survey Satellite (TESS), classified as TOI-1778 b. Using TESS photometry, high-resolution spectroscopic data from HARPS-N,

Mehdi Sadeghi, Faramaz Rahmani

In this paper, AdS black brane solution of Einstein-Hilbert gravity with non-abelian exponential guage theory of Yang-Mills type is introduced. DC conductivity and the ratio of shear viscosity to entropy density as two important transport coefficients are calculated by using of Kubo formula in the context of AdS/CFT duality. Our results recover the Yang-Mill

Trishul Dhalia, Rohit Juneja, Amita Das

There has been significant interest lately in the study of Electromagnetic (EM) waves interacting with magnetized plasmas. The variety of resonances and the existence of several pass and stop bands in the dispersion curve for different orientations of the magnetic field offer new mechanisms of EM wave energy absorption \cite{PhysRevE.105.055209,Juneja_2023,v

Quanyong Chen, Zhaobing Fan

In this paper, we study the structures of Schur algebra and Lusztig algebra associated to partial flag varieties of affine type D. We show that there is a subalgebra of Lusztig algebra and the quantum groups arising from this subalgebras via stabilization procedures is a coideal subalgebra of quantum group of affine $\mathfrak{sl}$ type. We construct monomia

Minjin Kim, Minju Kim, Hana Kim, Beong-woo Kwak

Conversational recommender system is an emerging area that has garnered an increasing interest in the community, especially with the advancements in large language models (LLMs) that enable diverse reasoning over conversational input. Despite the progress, the field has many aspects left to explore. The currently available public datasets for conversational

Nan Zhang, Ya Yan Lu

Many photonic devices, such as photonic crystal slabs, cross gratings, and periodic metasurfaces, are biperiodic structures with two independent periodic directions, and are sandwiched between two homogeneous media. Many applications of these devices are closely related to resonance phenomena. Therefore, efficient computation of resonant modes is crucial in

Jiong-Hang Liang, Tian-Xing Hu, D. Wu, Zheng-Mao Sheng

Time-dependent density functional theory (TDDFT) is widely used for understanding and predicting properties and behaviors of matter. As one of the fundamental theorems in TDDFT, van Leeuwen's theorem [Phys. Rev. Lett. 82, 3863 (1999)] guarantees how to construct a unique potential with the same one-body density evolution. Here we extend van Leeuwen's theorem

Jeremy Z. Yan, Prashant Kumar, Wolfgang Rauch

In this study, the impact of turbulent diffusion on mixing of biochemical reaction models is explored by implementing and validating different models. An original codebase called CHAD (Coupled Hydrodynamics and Anaerobic Digestion) is extended to incorporate turbulent diffusion and validate it against results from OpenFOAM with 2D Rayleigh-Taylor Instability

Dawid Bucki

We investigate relations between the pseudo-orbit-tracing property, topological stability and openness for tree-shifts. We prove that a tree-shift is of finite type if and only if it has the pseudo-orbit-tracing property which implies that the tree-shift is topologically stable and all shift maps are open. We also present an example of a tree-shift for which

Hua-Lin Huang, Gongxiang Liu, Yuping Yang, Yu Ye

Using a variety of methods developed in the theory of finite-dimensional quasi-Hopf algebras, we classify all finite-dimensional coradically graded pointed coquasi-Hopf algebras over abelian groups. As a consequence, we partially confirm the generation conjecture of pointed finite tensor categories due to Etingof, Gelaki, Nikshych and Ostrik.

Shuaiqi Liu, Jiannong Cao, Yicong Li, Ruosong Yang

Common law courts need to refer to similar precedents' judgments to inform their current decisions. Generating high-quality summaries of court judgment documents can facilitate legal practitioners to efficiently review previous cases and assist the general public in accessing how the courts operate and how the law is applied. Previous court judgment summariz

Fabian Otto, Philipp Becker, Ngo Anh Vien, Gerhard Neumann

Existing off-policy reinforcement learning algorithms often rely on an explicit state-action-value function representation, which can be problematic in high-dimensional action spaces due to the curse of dimensionality. This reliance results in data inefficiency as maintaining a state-action-value function in such spaces is challenging. We present an efficien

Fang Wang, Zhihong Xia

We study the homotopical minimal measures for positive definite autonomous Lagrangian systems. Homotopical minimal measures are action-minimizers in their homotopy classes, while the classical minimal measures (Mather measures) are action-minimizers in homology classes. Homotopical minimal measures are much more general, they are not necessarily homological

Nico Manzonelli, Wanrong Zhang, Salil Vadhan

Recent research shows that large language models are susceptible to privacy attacks that infer aspects of the training data. However, it is unclear if simpler generative models, like topic models, share similar vulnerabilities. In this work, we propose an attack against topic models that can confidently identify members of the training data in Latent Dirichl

Lin-Jing Qi, Dong-Meng Zhang, Song Luo, Gui-Qing Zhang

In the present work, the cluster radioactivity preformation probability Pc in the scheme of NpNn for the effective number of the valence particles (holes) in trans-lead nuclei has been systematically investigated. This quantity has been explored in the simplified parametrization of NpNn as well as the multiplication NpNnI of this product with the isospin asy

Imen Azaiz, Natalie Kiesler, Sven Strickroth

Ever since Large Language Models (LLMs) and related applications have become broadly available, several studies investigated their potential for assisting educators and supporting students in higher education. LLMs such as Codex, GPT-3.5, and GPT 4 have shown promising results in the context of large programming courses, where students can benefit from feedb

Maria V. Kulikova, Gennady Yu. Kulikov

In this paper, we continue to study the derivative-free extended Kalman filtering (DF-EKF) framework for state estimation of continuous-discrete nonlinear stochastic systems. Having considered the Euler-Maruyama and It\^{o}-Taylor discretization schemes for solving stochastic differential equations, we derive the related filters' moment equations based on th

Henri Bollaert, Marko Palangetić, Chris Cornelis, Salvatore Greco

Interpretability is the next frontier in machine learning research. In the search for white box models - as opposed to black box models, like random forests or neural networks - rule induction algorithms are a logical and promising option, since the rules can easily be understood by humans. Fuzzy and rough set theory have been successfully applied to this ar

Jiuqiang Liu, Guihai Yu

Sperner theory is one of the most important branches in extremal set theory. It has many applications in the field of operation research, computer science, hypergraph theory and so on. The LYM property has become an important tool for studying Sperner property. In this paper, we provide a general relationship for LYM inequalities between Boolean lattices and

Walid El Maouaki, Alberto Marchisio, Taoufik Said, Mohamed Bennai

Recent advancements in quantum computing have led to the development of hybrid quantum neural networks (HQNNs) that employ a mixed set of quantum layers and classical layers, such as Quanvolutional Neural Networks (QuNNs). While several works have shown security threats of classical neural networks, such as adversarial attacks, their impact on QuNNs is still

Zhian Jia, Sheng Tan, Dagomir Kaszlikowski

We investigate the multifusion generalization of string-net ground states and lattice Hamiltonians, delving into its associated weak Hopf symmetry. For the multifusion string-net, the gauge symmetry manifests as a general weak Hopf algebra, leading to a reducible vacuum string label; the charge symmetry, serving as a quantum double of gauge symmetry, constit

Amanda Cercas Curry, Giuseppe Attanasio, Zeerak Talat, Dirk Hovy

Since the foundational work of William Labov on the social stratification of language (Labov, 1964), linguistics has made concentrated efforts to explore the links between sociodemographic characteristics and language production and perception. But while there is strong evidence for socio-demographic characteristics in language, they are infrequently used in

Qingyuan Cai, Xuecai Hu, Saihui Hou, Li Yao

Recently, diffusion-based methods for monocular 3D human pose estimation have achieved state-of-the-art (SOTA) performance by directly regressing the 3D joint coordinates from the 2D pose sequence. Although some methods decompose the task into bone length and bone direction prediction based on the human anatomical skeleton to explicitly incorporate more huma

Yihua Fan, Yongzhen Wang, Mingqiang Wei, Fu Lee Wang

Adverse weather conditions often impair the quality of captured images, inevitably inducing cutting-edge object detection models for advanced driver assistance systems (ADAS) and autonomous driving. In this paper, we raise an intriguing question: can the combination of image restoration and object detection enhance detection performance in adverse weather co

Ali Khoshvishkaie, Petrus Mikkola, Pierre-Alexandre Murena, Samuel Kaski

We introduce a cooperative Bayesian optimization problem for optimizing black-box functions of two variables where two agents choose together at which points to query the function but have only control over one variable each. This setting is inspired by human-AI teamwork, where an AI-assistant helps its human user solve a problem, in this simplest case, coll

Ádám Bácsi, Rok Žitko

An electron tunneling across a junction integrated into an electric circuit can generate an excitation in the photonic field (electromagnetic environment) and lose energy in the process. Such inelastic tunneling of particles is commonly described using the $P(E)$ theory. In the conventional approach to this theory, the tunneling rate and the electric current

Yu Liu, Aitor Hernandez Herranz, Roberto C. Sundin

Cloud native technologies have been observed to expand into the realm of Internet of Things (IoT) and Cyber-physical Systems, of which an important application domain is robotics. In this paper, we review the cloudification practice in the robotics domain from both literature and industrial perspectives. We propose RoboKube, an adaptive framework that is bas

Sergei Nayakshin, Fernando Cruz Saenz de Miera, Agnes Kospal, Aleksandra Calovic

In the classical grouping of large magnitude episodic variability of young accreting stars, FUORs outshine their stars by a factor of $\sim$ 100, and can last for up to centuries; EXORs are dimmer, and last months to a year. A disc Hydrogen ionisation Thermal Instability (TI) scenario was previously proposed for FUORs but required unrealistically low disc vi

Jared Miller, Chiara Meroni, Matteo Tacchi, Mauricio Velasco

This paper presents an algorithm to maximize the volume of an affine slice through a given semialgebraic set. This slice-volume task is formulated as an infinite-dimensional linear program in continuous functions, inspired by prior work in volume computation of semialgebraic sets. A convergent sequence of upper-bounds to the maximal slice volume are computed

Yutao Cui, Xiaotong Zhao, Guozhen Zhang, Shengming Cao

Point-based image editing has attracted remarkable attention since the emergence of DragGAN. Recently, DragDiffusion further pushes forward the generative quality via adapting this dragging technique to diffusion models. Despite these great success, this dragging scheme exhibits two major drawbacks, namely inaccurate point tracking and incomplete motion supe

Tairan He, Zhengyi Luo, Wenli Xiao, Chong Zhang

We present Human to Humanoid (H2O), a reinforcement learning (RL) based framework that enables real-time whole-body teleoperation of a full-sized humanoid robot with only an RGB camera. To create a large-scale retargeted motion dataset of human movements for humanoid robots, we propose a scalable "sim-to-data" process to filter and pick feasible motions usin

Anuj Abraham, Yi Zhang, Shitala Prasad

A smart city solution toward future 6G network deployment allows small and medium sized enterprises (SMEs), industry, and government entities to connect with the infrastructures and play a crucial role in enhancing emergency preparedness with advanced sensors. The objective of this work is to propose a set of coordinated technological solutions to transform

Weiyang Xu, Ruiguang Wang, Yuan Zhang, Hien Quoc Ngo

The channel hardening effect is less pronounced in the cell-free massive multiple-input multiple-output (mMIMO) system compared to its cellular counterpart, making it necessary to estimate the downlink effective channel gains to ensure decent performance. However, the downlink training inadvertently creates an opportunity for adversarial nodes to launch pilo

A. S. Wadge, K. Zberecki, B. J. Kowalski, D. Jastrzebski

Topological nodal-line semimetals represent a unique class of materials with intriguing electronic structures and rich of symmetries, hosting electronic states with nontrivial topological properties. Among these, ZrAs$_2$ stands out, characterized by its nodal lines in a momentum space, governed by nonsymmorphic symmetries. This study integrates angle-resolv

Ondřej Mokrý, Pavel Rajmic

A novel variant of the Janssen method for audio inpainting is presented and compared to other popular audio inpainting methods based on autoregressive (AR) modeling. Both conceptual differences and practical implications are discussed. The experiments demonstrate the importance of the choice of the AR model estimator, window/context length, and model order.

Zhaohua Tian, Qi Liu, Yu Tian, Ying Gu

Quantum interferences based on beam splitting are widely used for entanglement. However, the quantitative measurement of the entanglement in terms of temporal modes and wavepacket shaping facilitated by this entanglement remain unexplored. Here we analytically study the interference of two photons with different temporal shapes through a beam splitter (BS),

Halil Yigit Oksuz, Fabio Molinari, Henning Sprekeler, Joerg Raisch

Over-the-Air Computation is a beyond-5G communication strategy that has recently been shown to be useful for the decentralized training of machine learning models due to its efficiency. In this paper, we propose an Over-the-Air federated learning algorithm that aims to provide fairness and robustness through minmax optimization. By using the epigraph form of

Bingkun Lai, Jiayi He, Jiawen Kang, Gaolei Li

Generative Artificial Intelligence (GAI) shows remarkable productivity and creativity in Mobile Edge Networks, such as the metaverse and the Industrial Internet of Things. Federated learning is a promising technique for effectively training GAI models in mobile edge networks due to its data distribution. However, there is a notable issue with communication c

Mahsun Altin, Altan Cakir

This paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models. The study involves a comprehensive evaluation across three different datasets: MSL, SMAP, and SWaT. Each dataset poses unique challen

Pankaj Jain, Harishyam Kumar

We study the process of nuclear fusion at low energies in a medium using the second order time dependent perturbation theory. We consider a specific process which involves fusion of a low energy proton with a Nickel nucleus. The reaction proceeds in two steps or interactions. We refer to the amplitudes corresponding to these two interactions as the the molec

Raffaele Giuseppe Cestari, Simone Formentin

Predicting financial returns accurately poses a significant challenge due to the inherent uncertainty in financial time series data. Enhancing prediction models' performance hinges on effectively capturing both social and financial sentiment. In this study, we showcase the efficacy of leveraging sentiment information extracted from tweets using the FinBERT l

Gurupada Ghorai, Kalyan Ghosh, Abhilash Patra, Prasanjit Samal

Spin-phonon interaction plays an important role in 2D magnetic materials and motivates the development of next-generation spin- and charge-dependent microelectronic devices. Understanding the spin-phonon interaction by tuning the growth parameter of single crystal Cr$_2Te_3$, a robust quasi-2D room temperature magnetic material, is crucial for spintronic dev

Francisco J. Díaz-Fernández, Luis Manuel Máñez-Espina, Ana Díaz-Rubio, Viktar Asadchy

Spaceplates have emerged in the context of nonlocal metasurfaces, enabling the compression of optical systems by minimizing the required empty space between their components. In this work, we design and analyze spaceplates that support resonances with opposite symmetries, operating under the so-called Huygens' condition. Using the temporal coupled-mode theor

Matti Harjula, Ville Havu, Inkeri Kontro, Kimmo Kulmala

A teaching experiment was carried out in a university-level thermodynamics course using adaptive and interactive e-learning material, created in the new Moodle question type Stateful extending the original e-learning platform STACK. The system collects data about the students that is used to algorithmically classify them according to their behaviour in solvi

Junjie Hua, Masahiro Furukawa, Taro Maeda

The two-point discrimination threshold(2PDT) serves as a critical indicator in the study of tactile acuity, representing the minimal distance at which an individual can differentiate two distinct points of contact on the skin. This measurement is instrumental in exploring the neural mechanisms underlying tactile perception. On the other hand, tactile acuity

Andrei Plotnikov, Valentina Abramenko, Alexander Kutsenko

We explore properties and behavior of slow-decaying unipolar sunspot groups in the framework of the turbulent erosion model suggested by Petrovay and Moreno-Insertis (1997). The basic concept of the model is the suppression of a turbulent diffusivity inside a magnetic flux tube by strong magnetic fields. As a result, the outer turbulent plasma detaches magne

Yuri Fukaya, Maria Teresa Mercaldo, Daniel Margineda, Alessandro Crippa

Nonreciprocal supercurrent refers to the phenomenon where the maximum dissipationless current in a superconductor depends on its direction of flow. This asymmetry underlies the operation of superconducting diodes and is often associated with the presence of vortices. Here, we investigate supercurrent nonreciprocal effects in a superconducting weak-link hosti

Nikica Peric, Slaven Begovic, Vinko Lesic

Logistics and transport are core of many industrial and business processes. One of the most promising segments in the field is optimisation of vehicle routes. Scientific effort is focused primarily on algorithms developed in simplified environment and cover a fraction of real industrial application due to complex combinatorial algorithms required to be promp

Md Rayhanul Masud, Michalis Faloutsos

Are malicious repositories hiding under the educational label in GitHub? Recent studies have identified collections of GitHub repositories hosting malware source code with notable collaboration among the developers. Thus, analyzing GitHub repositories deserves inevitable attention due to its open-source nature providing easy access to malicious software code

Guru Sreevanshu Yerragolam, Christopher J. Howland, Richard J. A. M. Stevens, Roberto Verzicco

We provide scaling relations for the Nusselt number $Nu$ and the friction coefficient $C_{S}$ in sheared Rayleigh-B\'enard convection, i.e., in Rayleigh-B\'enard flow with Couette or Poiseuille type shear forcing, by extending the Grossmann & Lohse (2000,2001,2002,2004) theory to sheared thermal convection. The control parameters for these systems are the Ra

Atiyah Elsheikh

The execution and runtime performance of model-based analysis tools for realistic large-scale ABMs (Agent-Based Models) can be excessively long. This due to the computational demand exponentially proportional to the model size (e.g. Population size) and the number of model parameters. Even the runtime of a single simulation of a realistic ABM may demand huge

Debasmita Dey, Nirnay Ghosh

Routing Protocol for Low Power and Lossy Networks (RPL) is the de-facto routing standard in IoT networks. It enables nodes to collaborate and autonomously build ad-hoc networks modeled by tree-like destination-oriented direct acyclic graphs (DODAG). Despite its widespread usage in industry and healthcare domains, RPL is susceptible to insider attacks. Althou

A. Espinosa-Rodriguez, V. V. Onecha, V. M. Nouvilas, S. Viñals i Onsès

The use of contrast agents has previously been proposed as a novel method to increase the activation close to the Bragg peak, aiming to improve the quality of proton range monitoring in vivo. In a recent work, we demonstrated the feasibility of $^{127}$I for online verification, thanks to its high cross-section (200 mbarn at 10 MeV) and low energy production

José L. Risco-Martín, J. Manuel Colmenar, J. Ignacio Hidalgo, Juan Lanchares

Modern consumer devices must execute multimedia applications that exhibit high resource utilization. In order to efficiently execute these applications, the dynamic memory subsystem needs to be optimized. This complex task can be tackled in two complementary ways: optimizing the application source code or designing custom dynamic memory management mechanisms

Ibrokhimbek Akramov, Isroil A. Ikromov

In this article, we study the convolution operator $M_k$ with oscillatory kernel, which is related with solutions to the Cauchy problem for the strictly hyperbolic equations. The operator $M_k$ is associated to the characteristic hypersurface $\Sigma\subset \mathbb{R}^3$ of the equation and the smooth amplitude function, which is homogeneous of order $-k$ fo

Jing Guo Jing Guo, Xiushan Jiang, Weihai Zhang

The stochastic $H_{\infty}$ control is studied for a linear stochastic It\^o system with an unknown system model. The linear stochastic $H_{\infty}$ control issue is known to be transformable into the problem of solving a so-called generalized algebraic Riccati equation (GARE), which is a nonlinear equation that is typically difficult to solve analytically.

Joseph Askey, Matthew Oliver Hunt, Lukas Payne, Arjen van den Berg

The study of 3D magnetic nanostructures has uncovered a range of rich phenomena including the stabilization and control of topological spin textures using nanoscale curvature, dynamic effects allowing controlled spin-wave emission, and novel ground states enabled by collective 3D frustrated interactions. From a technological perspective, 3D nanostructures of

Loïc Miller, Marc-Oliver Pahl

Collaborative cybersecurity relies on organizations sharing information to boost security, but trust management is a key concern. Decentralized solutions like distributed ledgers, particularly blockchain, are crucial for eliminating single points of failure. However, the existing literature on blockchain-based collaborative cybersecurity is limited, lacking

Hayley N. Williamson, Annie Johansson, Romain Canu-Blot, Gabriella Stenberg Wieser

The Ion Composition Analyzer (ICA) on the Rosetta spacecraft observed both the solar wind and the cometary ionosphere around comet 67P/Churyumov-Gerasimenko for nearly two years. However, observations of low energy cometary ions were affected by a highly negative spacecraft potential, and the ICA ion density estimates were often much lower than plasma densit

S. S. Jensen, S. Spezzano, P. Caselli, O. Sipilä

We aim to determine the $^{14}$N/$^{15}$N and $^{12}$C/$^{13}$C ratios for HCN in six starless and prestellar cores and compare the results between the direct method using radiative transfer modeling and the indirect double isotope method assuming a fixed $^{12}$C/$^{13}$C ratio. We present IRAM 30m observations of the HCN 1-0, HCN 3-2, HC15N 1-0 and H13CN 1

Jiarui Du, Zhijian He

In statistical analysis, Monte Carlo (MC) stands as a classical numerical integration method. When encountering challenging sample problem, Markov chain Monte Carlo (MCMC) is a commonly employed method. However, the MCMC estimator is biased after a fixed number of iterations. Unbiased MCMC, an advancement achieved through coupling techniques, addresses this

Alessio Figalli, Somayeh Khademloo, Sunghan Kim, Henrik Shahgholian

Given $\Omega\subset \mathbb{R}^n$ with $n\geq 2$, $D\subset \Omega$ open, and $u:\Omega \to \mathbb{R}^m$, we study elliptic systems of the type $$ {\rm div} \big( ( A + (B- A)\chi_D)\nabla u\big) = 0 \quad \text{in $\Omega\cap B_1$,} $$ for some uniformly elliptic tensors $A$ and $B$ with H\"{o}lder continuous entries. We show that, given appropriate bound

Marta Campi, Guillaume Staerman, Gareth W. Peters, Tomoko Matsui

Functional Isolation Forest (FIF) is a recent state-of-the-art Anomaly Detection (AD) algorithm designed for functional data. It relies on a tree partition procedure where an abnormality score is computed by projecting each curve observation on a drawn dictionary through a linear inner product. Such linear inner product and the dictionary are a priori choice

A. J. Chaves, D. R. da Costa, F. M. Peeters, Nuno M. R. Peres

Magnetized ferromagnetic disks or wires support strong inhomogeneous fields in their borders. Such magnetic fields create an effective potential, due to Zeeman and diamagnetic contributions, that can localize charge carriers. For the case of two-dimensional transition metal dichalcogenides, this potential can valley-localize excitons due to the Zeeman term,

Joseph Bond, Cristina David, Minh Nguyen, Dominic Orchard

Charts, figures, and text derived from data play an important role in decision making, from data-driven policy development to day-to-day choices informed by online articles. Making sense of, or fact-checking, outputs means understanding how they relate to the underlying data. Even for domain experts with access to the source code and data sets, this poses a

Jørgen Olsen Lye, Boris Vertman

We study the renormalized analytic torsion of complete manifolds with fibred boundary metrics, also referred to as $\phi$-metrics. We establish invariance of the torsion under suitable deformations of the metric, and establish a gluing formula. As an application, we relate the analytic torsions for complete $\phi$- and incomplete wedge-metrics. As a simple c

Yair Caro, Xandru Mifsud

This paper concerns $(r,c)$-constant graphs, which are $r$-regular graphs in which the subgraph induced by the open neighbourhood of every vertex has precisely $c$ edges. The family of $(r,c)$-graphs contains vertex-transitive graphs (and in particular Cayley graphs), graphs with constant link (sometimes called locally isomorphic graphs), $(r,b)$-regular gra

Xiyan Fu, Anette Frank

Compositional Natural Language Inference has been explored to assess the true abilities of neural models to perform NLI. Yet, current evaluations assume models to have full access to all primitive inferences in advance, in contrast to humans that continuously acquire inference knowledge. In this paper, we introduce the Continual Compositional Generalization

Wenjie Wang, Yang Zhang, Xinyu Lin, Fuli Feng

The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This workshop serves as a platform for researchers to explore and exchange innovative concepts related to the integration of generative models into recommender systems. It primarily focuses

Kanglei Zhou, Liyuan Wang, Xingxing Zhang, Hubert P. H. Shum

Action Quality Assessment (AQA) evaluates diverse skills but models struggle with non-stationary data. We propose Continual AQA (CAQA) to refine models using sparse new data. Feature replay preserves memory without storing raw inputs. However, the misalignment between static old features and the dynamically changing feature manifold causes severe catastrophi

Mohammad Sadek, Mohamed Wafik, Tuğba Yesin

Let $f$ be a polynomial with integer coefficients whose degree is at least 2. We consider the problem of covering the orbit $\operatorname{Orb}_f(t)=\{t,f(t),f(f(t)),\cdots\}$, where $t$ is an integer, using arithmetic progressions each of which contains $t$. Fixing an integer $k\ge 2$, we prove that it is impossible to cover $\operatorname{Orb}_f(t)$ using

Julián López-Gómez, Paul H. Rabinowitz, Fabio Zanolin

In this paper the existence of solutions, $(\lambda,u)$, of the problem $$-\Delta u=\lambda u -a(x)|u|^{p-1}u \quad \hbox{in }\Omega, \qquad u=0 \quad \hbox{on}\;\;\partial\Omega,$$ is explored for $0 < p < 1$. When $p>1$, it is known that there is an unbounded component of such solutions bifurcating from $(\sigma_1, 0)$, where $\sigma_1$ is the smallest eig

Zhaoqun Li, Jingcheng Yu, Qiwei Ye

Deep learning has made significant progress in protein structure prediction, advancing the development of computational biology. However, despite the high accuracy achieved in predicting single-chain structures, a significant number of large homo-oligomeric assemblies exhibit internal symmetry, posing a major challenge in structure determination. The perform

Aubin JC. M. Prot, Michele Melchiorre, Tilly Schaaf, Ricardo G. Poeira

Alloying small quantities of silver into Cu(In,Ga)Se2 was shown to improve the efficiency for wide and low band gap solar cells. We study low band gap industrial Cu(In,Ga)(S,Se)2 absorbers, substituting less than 10% of the copper with silver, using absolute photoluminescence and cathodoluminescence spectroscopy. Silver improves the grain size and promotes t

Bojana Pavlica, Christian Pech, Maja Pech

The modern theory of homogeneous structures begins with the work of Roland Fra\"iss\'e. The theory developed in the last seventy years is placed in the border area between combinatorics, model theory, algebra, and analysis. We turn our attention to its combinatorial pillar, namely, the work on the classification of structures for given homogeneity types, and

Markus Gahn

We study incompressible fluid flow through a thin poroelastic layer and rigorously derive a macroscopic model when the thickness of the layer tends to zero. Within the layer we assume a periodic structure and both, the periodicity and the thickness of the layer, are of order $\varepsilon$ which is small compared to the length of the layer. The fluid flow is

Feng Jin, Wei Ren, Mingshu Tan, Mingtai Xie

The stacking degree of freedom is a crucial factor in tuning material properties and has been extensively investigated in layered materials. The kagome superconductor CsV$_3$Sb$_5$ was recently discovered to exhibit a three-dimensional CDW phase below TCDW ~94 K. Despite the thorough investigation of in-plane modulation, the out-of-plane modulation has remai

Angelo A. Casulli, Igor Simunec

In this work we introduce a memory-efficient method for computing the action of a Hermitian matrix function on a vector. Our method consists of a rational Lanczos algorithm combined with a basis compression procedure based on rational Krylov subspaces that only involve small matrices. The cost of the compression procedure is negligible with respect to the co

Ataru Tanikawa

A large number of mergers of binary black holes (BHs) have been discovered by gravitational wave observations since the first detection of gravitational waves 2015. Binary BH mergers are the loudest events in the universe, however their origin(s) have been under debate. There have been many suggestions for merging binary BHs. Isolated binary stars are one of