October 2023 arXiv papers — page 151

Sepehr Assadi, Janani Sundaresan

We prove that any semi-streaming algorithm for $(1-\epsilon)$-approximation of maximum bipartite matching requires \[ \Omega(\frac{\log{(1/\epsilon)}}{{\log{(1/\beta)}}}) \] passes, where $\beta \in (0,1)$ is the largest parameter so that an $n$-vertex graph with $n^{\beta}$ edge-disjoint induced matchings of size $\Theta(n)$ exist (such graphs are referred

Weimin Xiong, Yiwen Guo, Hao Chen

Recent development of large language models (LLMs) for code like CodeX and CodeT5+ demonstrates tremendous promise in achieving code intelligence. Their ability of synthesizing code that completes a program for performing a pre-defined task has been intensively tested and verified on benchmark datasets including HumanEval and MBPP. Yet, evaluation of these L

Pablo Costa Rico

One of the oldest problems in quantum information theory is to study if there exists a state with negative partial transpose which is undistillable. This problem has been open for almost 30 years, and still no one has been able to give a complete answer to it. This work presents a new strategy to try to solve this problem by translating the distillability co

Wenlong Chen, Yegor Klochkov, Yang Liu

We consider a binary classification problem under group fairness constraints, which can be one of Demographic Parity (DP), Equalized Opportunity (EOp), or Equalized Odds (EO). We propose an explicit characterization of Bayes optimal classifier under the fairness constraints, which turns out to be a simple modification rule of the unconstrained classifier. Na

Yao Ji, Jakob Schoenleber

We calculate the two-loop flavor-singlet axial-vector and gluon transversity coefficient functions for deeply virtual Compton scattering in QCD. We observe interesting properties regarding the transcendentality of the transversity coefficient function. Our results complete the calculation of the full next-to-next-to-leading order coefficient function in deep

Trevor McInroe, Adam Jelley, Stefano V. Albrecht, Amos Storkey

Offline pretraining with a static dataset followed by online fine-tuning (offline-to-online, or OtO) is a paradigm well matched to a real-world RL deployment process. In this scenario, we aim to find the best-performing policy within a limited budget of online interactions. Previous work in the OtO setting has focused on correcting for bias introduced by the

Francesco Bei, Paolo Piazza, Boris Vertman

Let $\overline{M}$ be a compact smoothly stratified pseudo-manifold endowed with a wedge metric $g$. Let $\overline{M}_\Gamma$ be a Galois $\Gamma$-covering. Under additional assumptions on $\overline{M}$, satisfied for example by Witt pseudo-manifolds, we show that the $L^2$-Betti numbers and the Novikov-Shubin invariants are well defined. We then establish

Yaosen Chen, Yu Yao, Zhiqiang Li, Wei Wang

Talking face generation has a wide range of potential applications in the field of virtual digital humans. However, rendering high-fidelity facial video while ensuring lip synchronization is still a challenge for existing audio-driven talking face generation approaches. To address this issue, we propose HyperLips, a two-stage framework consisting of a hypern

Moritz Imfeld, Jacopo Graldi, Marco Giordano, Thomas Hofmann

Fusion is a technique for merging multiple independently-trained neural networks in order to combine their capabilities. Past attempts have been restricted to the case of fully-connected, convolutional, and residual networks. This paper presents a systematic approach for fusing two or more transformer-based networks exploiting Optimal Transport to (soft-)ali

Gulcin Baykal, Melih Kandemir, Gozde Unal

Codebook collapse is a common problem in training deep generative models with discrete representation spaces like Vector Quantized Variational Autoencoders (VQ-VAEs). We observe that the same problem arises for the alternatively designed discrete variational autoencoders (dVAEs) whose encoder directly learns a distribution over the codebook embeddings to rep

Yuxuan Kuang, Qin Han, Danshi Li, Qiyu Dai

In this work, we present STOPNet, a framework for 6-DoF object suction detection on production lines, with a focus on but not limited to transparent objects, which is an important and challenging problem in robotic systems and modern industry. Current methods requiring depth input fail on transparent objects due to depth cameras' deficiency in sensing their

Jian-Feng Ge, Koen M. Bastiaans, Jiasen Niu, Tjerk Benschop

Bogoliubov quasiparticles play a crucial role in understanding the behavior of a superconductor, and in achieving reliable operations of superconducting quantum circuits. Diagnosis of quasiparticle poisoning at the nanoscale provides invaluable benefits in designing superconducting qubits. Here, we use scanning tunneling noise microscopy to locally quantify

Riccardo Rende, Luciano Loris Viteritti, Lorenzo Bardone, Federico Becca

Neural-network architectures have been increasingly used to represent quantum many-body wave functions. These networks require a large number of variational parameters and are challenging to optimize using traditional methods, as gradient descent. Stochastic Reconfiguration (SR) has been effective with a limited number of parameters, but becomes impractical

Shivam Sood, Ge Sun, Peizhuo Li, Guillaume Sartoretti

Optimal Control for legged robots has gone through a paradigm shift from position-based to torque-based control, owing to the latter's compliant and robust nature. In parallel to this shift, the community has also turned to Deep Reinforcement Learning (DRL) as a promising approach to directly learn locomotion policies for complex real-life tasks. However, mo

Xiaoxu Li, Huajie Chen

The multiple scattering theory (MST) is a Green's function method that has been widely used in electronic structure calculations for crystalline disordered systems. The key property of the MST method is the scattering path matrix (SPM) that characterizes the Green's function within a local solution representation. This paper studies various approximations of

Xiong-Hui Chen, Junyin Ye, Hang Zhao, Yi-Chen Li

Imitation learning (IL) enables agents to mimic expert behaviors. Most previous IL techniques focus on precisely imitating one policy through mass demonstrations. However, in many applications, what humans require is the ability to perform various tasks directly through a few demonstrations of corresponding tasks, where the agent would meet many unexpected c

Harsh Beohar, Sebastian Gurke, Barbara König, Karla Messing

We address the task of deriving fixpoint equations from modal logics characterizing behavioural equivalences and metrics (summarized under the term conformances). We rely on earlier work that obtains Hennessy-Milner theorems as corollaries to a fixpoint preservation property along Galois connections between suitable lattices. We instantiate this to the setti

Johannes Koppenwallner, Erich Schikuta

Outsourcing a relational database to the cloud offers several benefits, including scalability, availability, and cost-effectiveness. However, there are concerns about the confidentiality and security of the outsourced data. A general approach here would be to encrypt the data with a standardized encryption algorithm and then store the data only encrypted in

Julio Elias Normey-Rico, Marcelo Menezes Morato

In this article, we discuss a novel education approach to control theory in undergraduate engineering programs. In particular, we elaborate on the inclusion of an introductory course on process control during the first years of the program, to appear right after the students undergo basic calculus and physics courses. Our novel teaching proposal comprises de

Francesca Badaracco, Jan Harms, Luca Rei

Seismic Newtonian noise is predicted to limit the sensitivity of the Einstein Telescope. It can be reduced with coherent noise cancellation techniques using data from seismometers. To achieve the best results, it is important to place the seismic sensors in optimal positions. A preliminary study on this topic was conducted for the Einstein Telescope (ET): it

Martin Doležal, Jan Kolář, Janusz Morawiec

We give a necessary and sufficient condition for two circles, each with finitely many points added inside, to be betweenness isomorphic. We fully characterize the betweenness isomorphism classes in the family consisting of all circles with three collinear points inside.

Xinyi Wang, Lucas Caccia, Oleksiy Ostapenko, Xingdi Yuan

Large language models (LLMs) have recently attracted considerable interest for their ability to perform complex reasoning tasks, such as chain-of-thought (CoT) reasoning. However, most of the existing approaches to enhance this ability rely heavily on data-driven methods, while neglecting the structural aspects of the model's reasoning capacity. To encourage

David Sinclair, Willem Pye

This paper shows how LLMs (Large Language Models) may be used to estimate a summary of the emotional state associated with piece of text. The summary of emotional state is a dictionary of words used to describe emotion together with the probability of the word appearing after a prompt comprising the original text and an emotion eliciting tail. Through emotio

Chris Kapulkin, Yufeng Li

Revisiting a classic result from M. Hofmann's dissertation, we give a direct proof of Morita equivalence, in the sense of V. Isaev, between extensional type theory and intensional type theory extended by the principles of functional extensionality and of uniqueness of identity proofs.

Marcelo V. Flamarion, Efim Pelinovsky, Ekaterina Didenkulova

Soliton gas or soliton turbulence is a subject of intense studies due to its great importance to optics, hydrodynamics, electricity, chemistry, biology and plasma physics. Usually, this term is used for integrable models where solitons interact elastically. However, soliton turbulence can also be a part of non-integrable dynamics, where long-lasting solution

Giacomo Alliata

Audiovisual archives are the mnemonic archives of the 21st century, with important cultural institutions increasingly digitizing their video collections. However, these remain mostly inaccessible, due to the sheer amount of content combined with the lack of innovative forms of engagement through compelling frameworks for their exploration. The present resear

Yu-Feng Li, Shuo-yu Xia

In this work, we introduce the theoretical framework of the phonon-mediated Migdal effect for neutrino-nucleus scattering in semiconductors, considering both the Standard Model and the presence of the neutrino magnetic moment. We calculate the rate of electron excitation resulting from the Migdal effect and observe a substantial coherent enhancement compared

Lucas Möller, Dmitry Nikolaev, Sebastian Padó

Despite the success of Siamese encoder models such as sentence transformers (ST), little is known about the aspects of inputs they pay attention to. A barrier is that their predictions cannot be attributed to individual features, as they compare two inputs rather than processing a single one. This paper derives a local attribution method for Siamese encoders

Anders Björn, Jana Björn

We study the condenser capacity $\mathrm{cap}_p(E,\Omega)$ on \emph{unbounded} open sets $\Omega$ in a proper connected metric space $X$ equipped with a locally doubling measure supporting a local $p$-Poincar\'e inequality, where $1<p<\infty$. Using a new definition of capacitary potentials, we show that $\mathrm{cap}_p$ is countably subadditive and that it

Ayesha Afzal, Georg Hager, Gerhard Wellein

A parallel program together with the parallel hardware it is running on is not only a vehicle to solve numerical problems, it is also a complex system with interesting dynamical behavior: resynchronization and desynchronization of parallel processes, propagating phases of idleness, and the peculiar effects of noise and system topology are just a few examples

Cameron R. D. Bunney, Leo Parry, T. Rick Perche, Jorma Louko

It is well known that the experience of a linearly accelerated observer with acceleration $a$, interacting with a massless scalar field in its vacuum state in $3+1$ Minkowski spacetime, is identical to that of a static observer interacting with a massless scalar field in a thermal state of temperature $a/2\pi$ in $3+1$ Minkowski spacetime. We study the robus

Zhenyu Wang, Yali Li, Xi Chen, Hengshuang Zhao

Existing point cloud based 3D detectors are designed for the particular scene, either indoor or outdoor ones. Because of the substantial differences in object distribution and point density within point clouds collected from various environments, coupled with the intricate nature of 3D metrics, there is still a lack of a unified network architecture that can

Runhua Wang, Qing Ling, Zhi Tian

This paper investigates the problem of decentralized resource allocation in the presence of Byzantine attacks. Such attacks occur when an unknown number of malicious agents send random or carefully crafted messages to their neighbors, aiming to prevent the honest agents from reaching the optimal resource allocation strategy. We characterize these malicious b

Carla Nascimento Neves, Raul Queiroz Feitosa, Mabel X. Ortega Adarme, Gilson Antonio Giraldi

With its vast expanse, exceeding that of Western Europe by twice, the Amazon rainforest stands as the largest forest of the Earth, holding immense importance in global climate regulation. Yet, deforestation detection from remote sensing data in this region poses a critical challenge, often hindered by the persistent cloud cover that obscures optical satellit

Amr Abourayya, Jens Kleesiek, Kanishka Rao, Erman Ayday

In many critical applications, sensitive data is inherently distributed and cannot be centralized due to privacy concerns. A wide range of federated learning approaches have been proposed to train models locally at each client without sharing their sensitive data, typically by exchanging model parameters, or probabilistic predictions (soft labels) on a publi

Faith Johnson, Kristin Dana

In this work, we explore the use of hierarchical reinforcement learning (HRL) for the task of temporal sequence prediction. Using a combination of deep learning and HRL, we develop a stock agent to predict temporal price sequences from historical stock price data and a vehicle agent to predict steering angles from first person, dash cam images. Our results i

Kai He, Rui Mao, Qika Lin, Yucheng Ruan

The utilization of large language models (LLMs) in the Healthcare domain has generated both excitement and concern due to their ability to effectively respond to freetext queries with certain professional knowledge. This survey outlines the capabilities of the currently developed LLMs for Healthcare and explicates their development process, with the aim of p

Matt A. Roth, Mark R. Krumholz, Roland M. Crocker, Todd A. Thompson

Galaxies obey a number of empirical correlations between their radio, {\gamma}-ray, and infrared emission, but the physical origins of these correlations remain uncertain. Here we use the CONGRuENTS model for broadband non-thermal emission from star-forming galaxies, which self-consistently calculates energy-dependent transport and non-thermal emission from

Cheng Kang, Xujing Yao

How can the stability and efficiency of Artificial Neural Networks (ANNs) be ensured through a systematic analysis method? This paper seeks to address that query. While numerous factors can influence the learning process of ANNs, utilizing knowledge from control systems allows us to analyze its system function and simulate system responses. Although the comp

Simon Schrodi, Ferdinand Briegel, Max Argus, Andreas Christen

Climate change is increasing the intensity and frequency of many extreme weather events, including heatwaves, which results in increased thermal discomfort and mortality rates. While global mitigation action is undoubtedly necessary, so is climate adaptation, e.g., through climate-sensitive urban planning. Among the most promising strategies is harnessing th

Sengjie Liu, Christopher G. Healey

This paper proposes a method of abstractive summarization designed to scale to document collections instead of individual documents. Our approach applies a combination of semantic clustering, document size reduction within topic clusters, semantic chunking of a cluster's documents, GPT-based summarization and concatenation, and a combined sentiment and text

Thomas Falconer, Jalal Kazempour, Pierre Pinson

Despite recent advancements in machine learning, in practice, relevant datasets are often distributed among market competitors who are reluctant to share. To incentivize data sharing, recent works propose analytics markets, where multiple agents share features and are rewarded for improving the predictions of others. These rewards can be computed by treating

Wanyue Xu, Zhongzhi Zhang

Opinion dynamics is a central subject of computational social science, and various models have been developed to understand the evolution and formulation of opinions. Existing models mainly focus on opinion dynamics on graphs that only capture pairwise interactions between agents. In this paper, we extend the popular Friedkin-Johnsen model for opinion dynami

Gianluca Vico, Gerasimos Spanakis

Etruscan is an ancient language spoken in Italy from the 7th century BC to the 1st century AD. There are no native speakers of the language at the present day, and its resources are scarce, as there exist only around 12,000 known inscriptions. To the best of our knowledge, there are no publicly available Etruscan corpora for natural language processing. Ther

Max Franke, Sebastian Neef

Home fiber connections are largely realized by using passive optical networks, in their most common form today relying on the GPON standard. Among other things, this standard specifies how the first node inside of customers' homes, the so called ONU or ONT, has to behave, and which security features have to be supported. Currently, customers in some European

Angel Udias, Antonio Alonso-Ayuso, Ignacio Sanchez, Sonia Hernandez

In this paper, we assess the efficacy of ChatGPT (version Feb 2023), a large-scale language model, in solving probability problems typically presented in introductory computer engineering exams. Our study comprised a set of 23 probability exercises administered to students at Rey Juan Carlos University (URJC) in Madrid. The responses produced by ChatGPT were

Joe Suzuki

When the model is not known and parameter testing or interval estimation is conducted after model selection, it is necessary to consider selective inference. This paper discusses this issue in the context of sparse estimation. Firstly, we describe selective inference related to Lasso as per \cite{lee}, and then present polyhedra and truncated distributions w

Bruno de Mendonça Braga, Gilles Lancien

We introduce a new notion of embeddability between Banach spaces. By studying the classical Mazur map, we show that it is strictly weaker than the notion of coarse embeddability. We use the techniques from metric cotype introduced by M. Mendel and A. Naor to prove results about cotype preservation and complete our study of embeddability between $\ell_p$ spac

Mingfeng Liu, Jiantao Wang, Junwei Hu, Peitao Liu

Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological applications. In contrast to displacive phase transitions, the dynamics of reconstructive phase transitions are usually slow due to the large energy barrier. Nevertheless, the reconstructive phase transformation

Punsisi Rajakaruna, Surajit Ghosh, Bunyod Holmatov

This study presents a comprehensive remote sensing analysis of rainfall patterns and selected hydropower reservoir water extent in two tropical monsoon countries, Vietnam and Sri Lanka. The aim is to understand the relationship between remotely sensed rainfall data and the dynamic changes (monthly) in reservoir water extent. The analysis utilizes high-resolu

Alexander Petrov

YbOH molecule is one of the most sensitive systems for the electron electric dipole moment ($e$EDM) searches. The $e$EDM-induced energy shift is proportional to polarization ($P$) of the molecule. In Ref. [A. Petrov and A. Zakharova, Phys. Rev. A 105, L050801 (2022)] was shown that the value of l-doubling and spin-rotation splitting directly influences the m

Oscar Tuvey, Procheta Sen

The count of pending cases has shown an exponential rise across nations (e.g., with more than 10 million pending cases in India alone). The main issue lies in the fact that the number of cases submitted to the law system is far greater than the available number of legal professionals present in a country. Given this worldwide context, the utilization of AI t

Xinjuan Chen, Jiaxi Gu, Jae-Hun Jung

In this paper we analyze the weighted essentially non-oscillatory (WENO) schemes in the finite volume framework by examining the first step of the explicit third-order total variation diminishing Runge-Kutta method. The rationale for the improved performance of the finite volume WENO-M, WENO-Z and WENO-ZR schemes over WENO-JS in the first time step is that t

A. M. Arnold, P. Aleynikov, B. N. Breizman

The problem of the expansion and assimilation of a cryogenic fuel pellet injected into a hot plasma is considered. Due to the transparency of the plasmoid to ambient particles, it is found that electrons reach a `quasi-equilibrium' (QE) which is characterised by a steady-state on the fastest collisional timescale. The simplified electron kinetic equation of

Huajian Li, Longjian Li, Jiajian Liang

Over the past two decades, some scholars have noticed the correlation between quantum mechanics and finance/economy, making some novel attempts to introduce the theoretical framework of quantum mechanics into financial and economic research, subsequently a new research domain called quantum finance or quantum economy was set up. In particular, some studies h

Shu-Yu Ho, Pyungwon Ko, Dibyendu Nanda

In this paper, we construct a viable model for a GeV scale self-interacting dark matter (DM), where the DM was thermally produced in the early universe. Here, a new vector-like fermion with a dark charge under the $U(1)_{D}$ gauge symmetry serves as a secluded WIMP DM and it can dominantly annihilate into the light dark gauge boson and singlet scalar through

Hamidreza Maleki Almani, Foad Shokrollahi, Tommi Sottinen

We consider a Gaussian Volterra process with compound Poisson jumps and derive its prediction law.

Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger

Despite its flexibility to learn diverse inductive biases in machine learning programs, meta learning (i.e., learning to learn) has long been recognized to suffer from poor scalability due to its tremendous compute/memory costs, training instability, and a lack of efficient distributed training support. In this work, we focus on making scalable meta learning

Sochima Biereagu

Set Shaping Theory, an emerging area of study, delves into the transformation of data sets via bijection functions. Central to this theory is the parameter $K$, which determines the extent of transformation, essentially reshaping the data. This article introduces the pivotal role of $K$ in the Set Shaping Theory, shedding light on its implications for data c

Abdelhakim Benechehab, Giuseppe Paolo, Albert Thomas, Maurizio Filippone

In model-based reinforcement learning (MBRL), most algorithms rely on simulating trajectories from one-step dynamics models learned on data. A critical challenge of this approach is the compounding of one-step prediction errors as length of the trajectory grows. In this paper we tackle this issue by using a multi-timestep objective to train one-step models.

S. R. Mane

This note focuses on the properties of two blocks of elements of the probability mass function (pmf) of the Poisson distribution of order $k\ge2$. The first block is the elements for $n\in[1,k]$ and the second block is the elements for $n\in[k+1,2k]$. It is proved that elements in the first block form an ``absolutely monotonic sequence'' by which is meant th

Muhan Li, David Matthews, Sam Kriegman

Inspired by the necessity of morphological adaptation in animals, a growing body of work has attempted to expand robot training to encompass physical aspects of a robot's design. However, reinforcement learning methods capable of optimizing the 3D morphology of a robot have been restricted to reorienting or resizing the limbs of a predetermined and static to

The MICE Collaboration, M. Bogomilov, R. Tsenov, G. Vankova-Kirilova

Accelerated muon beams have been considered for next-generation studies of high-energy lepton-antilepton collisions and neutrino oscillations. However, high-brightness muon beams have not yet been produced. The main challenge for muon acceleration and storage stems from the large phase-space volume occupied by the beam, derived from the muon production mecha

Feiyi Chen, Zhen Qin, Yingying Zhang, Shuiguang Deng

Most of current anomaly detection models assume that the normal pattern remains same all the time. However, the normal patterns of Web services change dramatically and frequently. The model trained on old-distribution data is outdated after such changes. Retraining the whole model every time is expensive. Besides, at the beginning of normal pattern changes,

Yungi Jeong, Hangyeol Park, Taeho Kim, Kenji Watanabe

In Bernal-stacked bilayer graphene (BBG), the Landau levels give rise to an intimate connection between valley and layer degrees of freedom. Adding a moir\'e superlattice potential enriches the BBG physics with the formation of topological minibands - potentially leading to tunable exotic quantum transport. Here, we present magnetotransport measurements of a

Yilong Lv, Min Li, Yujie He, Shaopeng Li

Anchor-based detectors have been continuously developed for object detection. However, the individual anchor box makes it difficult to predict the boundary's offset accurately. Instead of taking each bounding box as a closed individual, we consider using multiple boxes together to get prediction boxes. To this end, this paper proposes the \textbf{Box Decoupl

Simone Devoto

In this contribution we present our recent computation of the NNLO QCD corrections to the production of a Higgs boson associated with a top-antitop quark pair. This process is of great importance since it allows for a direct measurement of the top-quark Yukawa coupling, and the inclusion of NNLO corrections is crucial in order to provide theory predictions w

Md Sohag Mia, Abu Bakor Hayat Arnob, Abdu Naim, Abdullah Al Bary Voban

Transformer design is the de facto standard for natural language processing tasks. The success of the transformer design in natural language processing has lately piqued the interest of researchers in the domain of computer vision. When compared to Convolutional Neural Networks (CNNs), Vision Transformers (ViTs) are becoming more popular and dominant solutio

Jia-Ning Wang, Yong-Chen Xiong, Wang-Huai Zhou, Tan Peng

Semiconductor quantum dots in close proximity to superconductors may provoke localized bound states within the superconducting energy gap known as Yu-Shiba-Rusinov (YSR) state, which is a promising candidate for constructing Majorana zero modes and topological qubits. Side-coupled double quantum dot systems are ideal platforms revealing the secondary proximi

Javier González-Carbajal, Marius Lemm, Joaquin Garcia-Suarez

This manuscript puts forward and verifies an analytical approach to design phononic crystals that feature a bandgap at the lowest possible frequencies. This new approach is verified against numerical optimization. It rests on the exact form of the trace of the cumulative transfer matrix. This matrix arises from the product of N elementary transfer matrices,

Andrzej Pietruszczak

With his research on Aristotle's syllogistic, Jan {\L}ukasiewicz (1934, 1939, 1957, 1963) initiates the branch of logic known as the calculus of names. This field deals with axiomatic systems that analyse various fragments of the logic of names, i.e., that branch of logic that studies various forms of names and functors acting on them, as well as logical rel

Aymen Mahmoudi, Meryem Bouaziz, Niels Chapuis, Geoffroy Kremer

The growth of bilayers of two-dimensional (2D) materials on conventional 3D semiconductors results in 2D/3D hybrid heterostructures, which can provide additional advantages over more established 3D semiconductors while retaining some specificities of 2D materials. Understanding and exploiting these phenomena hinge on knowing the electronic properties and the

Serena Della Corte, Richard C. Kraaij

We prove comparison principle for viscosity solutions of a Hamilton-Jacobi-Bellman equation in a strong coupling regime considering a stationary and a time-dependent version of the equation. We consider a Hamiltonian that has a representation as the supremum of a difference of two functions: an internal Hamiltonian depending on a control variable and a funct

Narendra Kumar Bodaballa, Soumyajyoti Biswas, Parongama Sen

We study the temporal evolution of avalanches in the fiber bundle model of disordered solids, when the model is gradually driven towards the critical breakdown point. We use two types of loading protocols: (i) the quasi-static loading, and (ii) loading by a discrete amount. In the quasi-static loading, where the load is increased by the minimum amount needed

Cheng-Han Chiang, Hung-yi Lee

Using large language models (LLMs) to evaluate text quality has recently gained popularity. Some prior works explore the idea of using LLMs for evaluation, while they differ in some details of the evaluation process. In this paper, we analyze LLM evaluation (Chiang and Lee, 2023) and G-Eval (Liu et al., 2023), and we discuss how those details in the evaluati

Lucien Jezequel, Pierre Delplace

We propose a theory, that we call the \textit{mode-shell correspondence}, which relates the topological zero-modes localised in phase space to a \textit{shell} invariant defined on the surface forming a shell enclosing these zero-modes. We show that the mode-shell formalism provides a general framework unifying important results of topological physics, such

Moritz Schauer, Marcel Wienöbst

In the context of inferring a Bayesian network structure (directed acyclic graph, DAG for short), we devise a non-reversible continuous time Markov chain, the ``Causal Zig-Zag sampler'', that targets a probability distribution over classes of observationally equivalent (Markov equivalent) DAGs. The classes are represented as completed partially directed acyc

Xuwei Xu, Changlin Li, Yudong Chen, Xiaojun Chang

Vision Transformers (ViTs) have demonstrated outstanding performance in computer vision tasks, yet their high computational complexity prevents their deployment in computing resource-constrained environments. Various token pruning techniques have been introduced to alleviate the high computational burden of ViTs by dynamically dropping image tokens. However,

Jonathan M. Dodd, René D. Oudmaijer, Isaac C. Radley, Miguel Vioque

Be stars make up almost 20% of the B star population, and are rapidly rotating stars surrounded by a disc; however the origin of this rotation remains unclear. Mass transfer within close binaries provides the leading hypothesis, with previous detections of stripped companions to Be stars supporting this. Here, we exploit the exquisite astrometric precision o

Veronika Witzke, Alexander I. Shapiro, Nadiia M. Kostogryz, Lucien Mauviard

Three-dimensional (3D) radiative magnetohydrodynamics (MHD) simulations are the only way to model stellar atmospheres without any ad hoc parameterisations. Several 3D radiative MHD codes have achieved good quantitative agreement with observables for our Sun. We aim to validate the most up-to-date version of the MURaM code against well established quiet Sun m

Akriti Upreti, Kartavya Kothari, Utkarsh Thukral, Vishal Verma

Dream11 takes pride in being a unique platform that enables over 190 million fantasy sports users to demonstrate their skills and connect deeper with their favorite sports. While managing such a scale, one issue we are faced with is duplicate/multiple account creation in the system. This is done by some users with the intent of abusing the platform, typicall

Johan Jeuring, Roel Groot, Hieke Keuning

Since the release of LLM-based tools such as GitHub Copilot and ChatGPT the media and popular scientific literature, but also journals such as the Communications of the ACM, have been flooded with opinions how these tools will change programming. The opinions range from ``machines will program themselves'', to ``AI does not help programmers''. Of course, the

Shuyu Jiang, Wenyi Tang, Xingshu Chen, Rui Tang

The proliferation of hate speech (HS) on social media poses a serious threat to societal security. Automatic counter narrative (CN) generation, as an active strategy for HS intervention, has garnered increasing attention in recent years. Existing methods for automatically generating CNs mainly rely on re-training or fine-tuning pre-trained language models (P

Adelinde Uhrmacher, Peter Frazier, Reiner Hähnle, Franziska Klügl

Simulation has become, in many application areas, a sine-qua-non. Most recently, COVID-19 has underlined the importance of simulation studies and limitations in current practices and methods. We identify four goals of methodological work for addressing these limitations. The first is to provide better support for capturing, representing, and evaluating the c

Carsten Carstensen, Benedikt Gräßle, Neela Nataraj

An abstract property (H) is the key to a complete a priori error analysis in the (discrete) energy norm for several nonstandard finite element methods in the recent work [Lowest-order equivalent nonstandard finite element methods for biharmonic plates, Carstensen and Nataraj, M2AN, 2022]. This paper investigates the impact of (H) to the a posteriori error an

Peng Li, Yuping Ji, Yue Hu

Estimating tissue parameter maps with high accuracy and precision from highly undersampled measurements presents one of the major challenges in MR fingerprinting (MRF). Many existing works project the recovered voxel fingerprints onto the Bloch manifold to improve reconstruction performance. However, little research focuses on exploiting the latent manifold

Fan Wang, Yi Yu

We study transfer learning for estimating piecewise-constant signals when source data, which may be relevant but disparate, are available in addition to the target data. We first investigate transfer learning estimators that respectively employ $\ell_1$- and $\ell_0$-penalties for unisource data scenarios and then generalise these estimators to accommodate m

Athul Puthusseri, D. Ramesh Kumar

The intention of this article is to introduce a generalization of Proinov-type contraction via simulation functions. We name this generalized contraction map as Proinov-type Z-contraction. This article establishes the existence and uniqueness of fixed points for these contraction mappings in quasi-metric space and also, include explanatory examples with grap

Daniel Anthes, Sushrut Thorat, Peter König, Tim C. Kietzmann

Unlike primates, training artificial neural networks on changing data distributions leads to a rapid decrease in performance on old tasks. This phenomenon is commonly referred to as catastrophic forgetting. In this paper, we investigate the representational changes that underlie this performance decrease and identify three distinct processes that together ac

Patrick Langer, Elgar Fleisch, Filipe Barata

The increasing number of edge devices with enhanced sensing capabilities, such as smartphones, wearables, and IoT devices equipped with sensors, holds the potential for innovative smart-edge applications in healthcare. These devices generate vast amounts of multimodal data, enabling the implementation of digital biomarkers which can be leveraged by machine l

Xuwei Xu, Sen Wang, Yudong Chen, Jiajun Liu

Vision Transformers (ViTs) have demonstrated remarkable performance in various computer vision tasks. However, the high computational complexity hinders ViTs' applicability on devices with limited memory and computing resources. Although certain investigations have delved into the fusion of convolutional layers with self-attention mechanisms to enhance the e

V. A. Idrisova, N. N. Tokareva, A. A. Gorodilova, I. I. Beterov

Every year the International Olympiad in Cryptography Non-Stop University CRYPTO (NSUCRYPTO) offers mathematical problems for university and school students and, moreover, for professionals in the area of cryptography and computer science. The mail goal of NSUCRYPTO is to draw attention of students and young researchers to modern cryptography and raise aware

Konstantinos Kourtzanidis

We investigate the full-cycle operation of a coaxial Packed Bed Dielectric Barrier Discharge (PB-DBD) reactor operating in pure $\mathrm{CO_{2}}$. The reactor is packed with high permittivity dielectric rods and is analyzed with a two-dimensional (2D) self-consistent plasma model. We show that the PB-DBD operation is governed by both glow and volume/surface

M. V. Semotiuk, A. V. Palagin

In this work, we investigate the phenomenon of transverse resonance and transverse standing waves that occur within the cochlea of living organisms. It is demonstrated that the predisposing factor for their occurrence is the cochlear shape, which resembles a conical acoustic tube coiled into a spiral and exhibits non-uniformities on its internal surface. Thi

Shiyi Wang, Yang Nan, Simon Walsh, Guang Yang

We propose a novel Deep Active Learning (DeepAL) model-3D Wasserstein Discriminative UNet (WD-UNet) for reducing the annotation effort of medical 3D Computed Tomography (CT) segmentation. The proposed WD-UNet learns in a semi-supervised way and accelerates learning convergence to meet or exceed the prediction metrics of supervised learning models. Our method

Ramla Abdellatif, Mabud Ali Sarkar

In this paper, we construct a class of $2$-dimensional formal groups over $\mathbb{Z}_p$ that provide a higher-dimensional analogue of the usual $1$-dimensional Lubin-Tate formal groups, then we initiate the study of the extensions generated by their $p^{n}$-torsion points. For instance, we prove that the coordinates of the $p^{\infty}$-torsion points of suc

Mojtaba Moradi-Sepahvand, Turaj Amraee

This paper presents a multi-stage expansion model for the co-planning of transmission lines, battery energy storage (ES), and wind power plants (WPP). High penetration of renewable energy sources (RES) is integrated into the proposed model concerning renewable portfolio standard (RPS) policy goals. The possibility of bundling existing transmission lines to u

Kieren Harkins, Christoph Fleckenstein, Noella D'Souza, Paul M. Schindler

Thermalization phenomena, while ubiquitous in quantum systems, have traditionally been viewed as obstacles to be mitigated. In this study, we demonstrate the ability, instead, to harness thermalization to dynamically engineer and stabilize structured quantum states in a mesoscopically large ensemble of spins. Specifically, we showcase the capacity to generat

Xinze Li, Yixin Cao, Liangming Pan, Yubo Ma

Although achieving great success, Large Language Models (LLMs) usually suffer from unreliable hallucinations. Although language attribution can be a potential solution, there are no suitable benchmarks and evaluation metrics to attribute LLMs to structured knowledge. In this paper, we define a new task of Knowledge-aware Language Model Attribution (KaLMA) th

Roya Moghaddasi Fereidani, Jiří JL Vaníček

Gaussian wavepacket dynamics has proven to be a useful semiclassical approximation for quantum simulations of high-dimensional systems with low anharmonicity. Compared to Heller's original local harmonic method, the variational Gaussian wavepacket dynamics is more accurate, but much more difficult to apply in practice because it requires evaluating the expec