April 2023 arXiv papers — page 132

Haipeng An, Chen Yang

We study the properties of the stochastic gravitational wave background (SGWB) produced by domain walls (DWs) during inflation without forming a network. We numerically simulate the DW production caused by a second-order phase transition and calculate the SGWB spectrum using a $1000\times1000\times1000$ lattice. We show that the SGWB can be observed directly

Pierre Fraigniaud, Maël Luce, Ioan Todinca

It is known that, for every $k\geq 2$, $C_{2k}$-freeness can be decided by a generic Monte-Carlo algorithm running in $n^{1-1/\Theta(k^2)}$ rounds in the CONGEST model. For $2\leq k\leq 5$, faster Monte-Carlo algorithms do exist, running in $O(n^{1-1/k})$ rounds, based on upper bounding the number of messages to be forwarded, and aborting search sub-routines

Khaled Wahba, Wolfgang Hönig

We consider transporting a heavy payload that is attached to multiple multirotors. The current state-of-the-art controllers either do not avoid inter-robot collision at all, leading to crashes when tasked with carrying payloads that are small in size compared to the cable lengths, or use computational demanding nonlinear optimization. We propose an efficient

Bao Gia Bach, Akash Kundu, Tamal Acharya, Aritra Sarkar

This research applies concepts from algorithmic probability to Boolean and quantum combinatorial logic circuits. A tutorial-style introduction to states and various notions of the complexity of states are presented. Thereafter, the probability of states in the circuit model of computation is defined. Classical and quantum gate sets are compared to select som

Martin R. Bridson

We describe a flexible construction that produces triples of finitely generated, residually finite groups $M\hookrightarrow P \hookrightarrow \Gamma$, where the maps induce isomorphisms of profinite completions $\widehat{M}\cong\widehat{P}\cong\widehat{\Gamma}$, but $M$ and $\Gamma$ have Serre's property FA while $P$ does not. In this construction, $P$ is fi

Davide Lauria, W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu

We introduce a discrete binary tree for pricing contingent claims with the underlying security prices exhibiting history dependence characteristic of that induced by market microstructure phenomena. Example dependencies considered include moving average or autoregressive behavior. Our model is market-complete, arbitrage-free, and preserves all of the paramet

Tatiana Tatarenko, Maryam Kamgarpour

We derive the rate of convergence to the strongly variationally stable Nash equilibrium in a convex game, for a zeroth-order learning algorithm. Though we do not assume strong monotonicity of the game, our rates for the one-point feedback and for the two-point feedback match the best known rates for strongly monotone games under zeroth-order information.

Doan Dai Nguyen

In biology, predicting RNA secondary structures plays a vital role in determining its physical and chemical properties. Although we have powerful energy models to predict them as well as parametric analysis to understand the models themselves, the large number of parameters involved makes exploring the parameter space and effective fine-tuning complicated at

Jian Tang, Siddhant Kumar, Laura De Lorenzis, Ehsan Hosseini

We propose Neural Cellular Automata (NCA) to simulate the microstructure development during the solidification process in metals. Based on convolutional neural networks, NCA can learn essential solidification features, such as preferred growth direction and competitive grain growth, and are up to six orders of magnitude faster than the conventional Cellular

Ricardo Coimbra Brioso, Damiano Dei, Ciro Franzese, Nicola Lambri

Radiotherapy (RT) is a key component in the treatment of various cancers, including Acute Lymphocytic Leukemia (ALL) and Acute Myelogenous Leukemia (AML). Precise delineation of organs at risk (OARs) and target areas is essential for effective treatment planning. Intensity Modulated Radiotherapy (IMRT) techniques, such as Total Marrow Irradiation (TMI) and T

Peter Macgregor, He Sun

Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient spectral graph algorithms, and its development starts in September 2022. We have so far finished the component on local graph clustering, and this technical report presents a user's guide to STAG, showcase studies, and several technical considerations behind our developm

Tim Rensmeyer, Benjamin Craig, Denis Kramer, Oliver Niggemann

Even though Bayesian neural networks offer a promising framework for modeling uncertainty, active learning and incorporating prior physical knowledge, few applications of them can be found in the context of interatomic force modeling. One of the main challenges in their application to learning interatomic forces is the lack of suitable Monte Carlo Markov cha

Debashish Mondal, Arnob Kumar Ghosh, Tanay Nag, Arijit Saha

We theoretically explore the Floquet generation of Majorana end modes~(MEMs) (both regular $0$- and anomalous $\pi$-modes) implementing a periodic sinusoidal modulation in chemical potential in an experimentally feasible setup based on a one-dimensional chain of magnetic impurity atoms having spin spiral configuration (out-of-plane N\'eel-type) fabricated on

Andria L. Smith, Simon Heuschkel, Ksenia Keplinger, Charley M. Wu

Bias exists in how we pick leaders, who we perceive as being influential, and who we interact with, not only in society, but in organizational contexts. Drawing from leadership emergence and social influence theories, we investigate potential interventions that support diverse leaders. Using agent-based simulations, we model a collective search process on a

Kim Yong Tan, Yueming Lyu, Yew Soon Ong, Ivor W. Tsang

Approximate nearest neighbour (ANN) search is an essential component of search engines, recommendation systems, etc. Many recent works focus on learning-based data-distribution-dependent hashing and achieve good retrieval performance. However, due to increasing demand for users' privacy and security, we often need to remove users' data information from Machi

Jose Sosa, David Hogg

We propose a new self-supervised method for predicting 3D human body pose from a single image. The prediction network is trained from a dataset of unlabelled images depicting people in typical poses and a set of unpaired 2D poses. By minimising the need for annotated data, the method has the potential for rapid application to pose estimation of other articul

Jacopo Romano, Benoît Mahault, Ramin Golestanian

Point-like topological defects are singular configurations that occur in a variety of in and out of equilibrium systems with two-dimensional orientational order. As they are associated with a nonzero circuitation condition, the presence of defects induces a long-range perturbation of the orientation landscape around them. The effective dynamics of defects is

David Cimasoni, Maciej Markiewicz, Wojciech Politarczyk

We investigate the limits of the multivariable signature function $\sigma_L$ of a $\mu$-component link $L$ as some variable tends to $1$ via two different approaches: a three-dimensional and a four-dimensional one. The first uses the definition of $\sigma_L$ by generalized Seifert surfaces and forms. The second relies on a new extension of $\sigma_L$ from it

Tamás Szidarovszky

A theoretical framework is presented for the computation of rovibrational polaritonic states of a molecule in a lossless infrared (IR) microcavity. In the proposed approach the quantum treatment of the rotational and vibrational motion of the molecule can be formulated using arbitrary approximations. The cavity-induced changes in electronic structure are tre

Lars Becker

A well known conjecture states that constant functions are extremizers of the $L^2 \to L^6$ Tomas-Stein extension inequality for the circle. We prove that functions supported in a $\sqrt{6}/80$-neighbourhood of a pair of antipodal points on $S^1$ satisfy the conjectured sharp inequality. In the process, we make progress on a programm formulated in arXiv:1509

Giuseppe Cannizzaro, Patricia Gonçalves, Ricardo Misturini, Alessandra Occelli

We study the equilibrium fluctuations of an interacting particle system evolving on the discrete ring with $N\in\mathbb N$ points, denoted by $\mathbb T_N$, and with three species of particles that we name $A,B$ and $C$, but such that at each site there is only one particle. We prove that proper choices of density fluctuation fields (that match those from no

R. Davies, O. Absil, G. Agapito, A. Agudo Berbel

ERIS, the Enhanced Resolution Imager and Spectrograph, is an instrument that both extends and enhances the fundamental diffraction limited imaging and spectroscopy capability for the VLT. It replaces two instruments that were being maintained beyond their operational lifetimes, combines their functionality on a single focus, provides a new wavefront sensing

Toru Fuda, Daiju Funakawa, Satoshi Sasayama, Akito Suzuki

In this paper, we derive sufficient conditions for the localization of two-dimensional split-step quantum walks with a strong shift. For this purpose, we analyze the zero points of the function $f$ introduced by Fuda et. al. (Quantum Inf Process 16(8) 203, 2017) and make these zero points explicit. These zeros provide a concrete representation of the eigenva

V. A. Kochelap

A number of novel two-dimensional materials and nanostructures demonstrate complex single-electron energy dispersion, which is called the mexican-hat dispersion. In this paper, we analyze interaction of a pair of electrons with such an energy dispersion. We show that relative motion of the electron pair is of a very peculiar character. For example, the real

Christian Spiering

Cherenkov techniques are widely used in astroparticle experiments. This article reviews the various detection principles and the corresponding experiments, including some of the physics breakthroughs. In particular, it traces the development since the mid of the 1990s, a period when the field took a particularly dynamic development.

Xi Lin, Jens Magelund Tarp, Robin J. Evans

Randomized controlled trials are the gold standard for causal inference and play a pivotal role in modern evidence-based medicine. However, the sample sizes they use are often too limited to draw significant causal conclusions for subgroups that are less prevalent in the population. In contrast, observational data are becoming increasingly accessible in larg

Olli Herrala, Tommi Ekholm, Fabricio Oliveira

Mathematical programming formulations of influence diagrams can bridge the gap between representing and solving decision problems. However, they suffer from both modeling and computational limitations. Aiming to address modeling limitations, we show how to incorporate conditionally observed information within the mathematical programming representation of th

Bo-Hae Im, Hojin Kim, Khac Nhuan Le, Tuan Ngo Dac

In \cite{IKLNDP23} we presented a systematic study of algebra structures of multiple zeta values in positive characteristic introduced by Thakur as analogues of classical multiple zeta values of Euler. In this paper we construct algebra and Hopf algebra structures of alternating multiple zeta values introduced by Harada, extending our previous work. Our resu

Orian Leitersdorf, Yahav Boneh, Gonen Gazit, Ronny Ronen

The Discrete Fourier Transform (DFT) is essential for various applications ranging from signal processing to convolution and polynomial multiplication. The groundbreaking Fast Fourier Transform (FFT) algorithm reduces DFT time complexity from the naive O(n^2) to O(n log n), and recent works have sought further acceleration through parallel architectures such

Louis Mahon, Lei Shah, Thomas Lukasiewicz

Recent years have seen growing interest in learning disentangled representations, in which distinct features, such as size or shape, are represented by distinct neurons. Quantifying the extent to which a given representation is disentangled is not straightforward; multiple metrics have been proposed. In this paper, we identify two failings of existing metric

André de Laire, Guillaume Dujardin, Salvador López-Martínez

The existence and decay properties of dark solitons for a large class of nonlinear nonlocal Gross-Pitaevskii equations with nonzero boundary conditions in dimension one has been established recently in [de Laire and S. L\'opez-Mart\'inez, Comm. Partial Differential Equations, 2022]. Mathematically, these solitons correspond to minimizers of the energy at fix

Namid R. Stillman, Silke Henkes, Roberto Mayor, Gilles Louppe

Many collective systems exist in nature far from equilibrium, ranging from cellular sheets up to flocks of birds. These systems reflect a form of active matter, whereby individual material components have internal energy. Under specific parameter regimes, these active systems undergo phase transitions whereby small fluctuations of single components can lead

Niklas Dahlquist, Akshit Saradagi, George Nikolakopoulos

In this article, we propose a reactive task allocation architecture for a multi-agent system for scenarios where the tasks arrive at random times and are grouped into multiple queues. Two stage tasks are considered where every task has a beginning, an intermediate and a final part, typical in pick-and-drop and inspect-and-report scenarios. A centralized auct

Julian Vill

Given a subspace $U\subset\mathbb{C}[x_1,\dots,x_n]_d$ we consider the closure of the image of the rational map $\mathbb{P}^{n-1}\dashrightarrow\mathbb{P}^{\dim U-1}$ given by $U$. Its coordinate ring is isomorphic to $\bigoplus_{i\ge 0} U^i$ where $U^i$ is the degree $i$ component. We consider the Hilbert function of this algebra in the case where $U$ conta

Sanghyeon Kim, Eunbyung Park

Continuous convolution has recently gained prominence due to its ability to handle irregularly sampled data and model long-term dependency. Also, the promising experimental results of using large convolutional kernels have catalyzed the development of continuous convolution since they can construct large kernels very efficiently. Leveraging neural networks,

Mattis Seehaus, Risheng Pei, Sandra Korte-Kerzel, Stefanie Sandlöbes-Haut

The determination of orientation relationships in dual or multi-phase materials is very important in the field of interface engineering for the design of materials with tailored properties. In this work, a code is developed for the automated and statistical analysis of the orientation relationship of electron backscatter diffraction data. On the example of F

Shiyao Cui, Jiangxia Cao, Xin Cong, Jiawei Sheng

This paper studies the multimodal named entity recognition (MNER) and multimodal relation extraction (MRE), which are important for multimedia social platform analysis. The core of MNER and MRE lies in incorporating evident visual information to enhance textual semantics, where two issues inherently demand investigations. The first issue is modality-noise, w

Marco Caliari, Fabio Cassini

In this manuscript, we propose an efficient, practical and easy-to-implement way to approximate actions of $\varphi$-functions for matrices with $d$-dimensional Kronecker sum structure in the context of exponential integrators up to second order. The method is based on a direction splitting of the involved matrix functions, which lets us exploit the highly e

Kaijian Chen, Peiyu Zhang, Nana Liu, Liu Tan

The self-accelerating beams such as the Airy beam show great potentials in many applications including optical manipulation, imaging and communication. However, their superior features during linear propagation could be easily corrupted by optical nonlinearity or spatial incoherence individually. Here we investigate how the interaction of spatial incoherence

Kristin Courtney

We consider inductive systems of C*-algebras with completely positive contractive connecting maps. We define a condition, called C*-encoding, which is sufficient for the limit of the system to be completely order isomorphic to a C*-algebra and hence guarantees a unique C*-algebra associated to the limit. When the system consists of finite-dimensional C*-alge

Navid Hashemi, Justin Ruths, Jyotirmoy V. Deshmukh

Many real-world systems often involve physical components or operating environments with highly nonlinear and uncertain dynamics. A number of different control algorithms can be used to design optimal controllers for such systems, assuming a reasonably high-fidelity model of the actual system. However, the assumptions made on the stochastic dynamics of the m

Guangtong Zhou, Selasi Kwashie, Yidi Zhang, Michael Bewong

This paper studies the discovery of approximate rules in property graphs. We propose a semantically meaningful measure of error for mining graph entity dependencies (GEDs) at almost hold, to tolerate errors and inconsistencies that exist in real-world graphs. We present a new characterisation of GED satisfaction, and devise a depth-first search strategy to t

Timo Jakobs, Marco Garofalo, Tobias Hartung, Karl Jansen

Hamiltonian simulations of quantum systems require a finite-dimensional representation of the operators acting on the Hilbert space H. Here we give a prescription for gauge links and canonical momenta of an SU(2) gauge theory, such that the matrix representation of the former is diagonal in H. This is achieved by discretising the sphere $S_3$ isomorphic to S

Marlène Careil, Jakob Verbeek, Stéphane Lathuilière

Semantic image synthesis aims to generate photo realistic images given a semantic segmentation map. Despite much recent progress, training them still requires large datasets of images annotated with per-pixel label maps that are extremely tedious to obtain. To alleviate the high annotation cost, we propose a transfer method that leverages a model trained on

H. K. Avetissian, V. A. Sedrakyan, Kh. V. Sedrakian, G. F. Mkrtchian

We consider coherent dynamics of graphene charged carriers exposed to an intense few-cycle linearly polarized laser pulse. The results, obtained by solving the generalized semiconductor Bloch equations numerically in the Hartree-Fock approximation, taking into account many-body Coulomb interaction, demonstrate strong dependence of the valley polarization on

Arshdeep Singh, Mark D. Plumbley

Convolutional neural networks (CNNs) have shown state-of-the-art performance in various applications. However, CNNs are resource-hungry due to their requirement of high computational complexity and memory storage. Recent efforts toward achieving computational efficiency in CNNs involve filter pruning methods that eliminate some of the filters in CNNs based o

S. Rigat, F. Wielonsky

We describe a general method for constructing Heisenberg uniqueness pairs $(\Gamma,\Lambda)$ in the euclidean space $\mathbb{R}^{n}$ based on the study of boundary value problems for partial differential equations. As a result, we show, for instance, that any pair made of the boundary $\Gamma$ of a bounded convex set $\Omega$ and a sphere $\Lambda$ is an Hei

Zhonghao Lyu, Guangxu Zhu, Jie Xu, Bo Ai

With the recent advancements in edge artificial intelligence (AI), future sixth-generation (6G) networks need to support new AI tasks such as classification and clustering apart from data recovery. Motivated by the success of deep learning, the semantic-aware and task-oriented communications with deep joint source and channel coding (JSCC) have emerged as ne

Hugo Rincon Galeana, Ulrich Schmid, Kyrill Winkler, Ami Paz

Consensus is one of the most fundamental problems in distributed computing. This paper studies the consensus problem in a synchronous dynamic directed network, in which communication is controlled by an oblivious message adversary. The question when consensus is possible in this model has already been studied thoroughly in the literature from a combinatorial

Sebatian Forster, Tijn de Vos

In this paper, we bring the techniques of the Laplacian paradigm to the congested clique, while further restricting ourselves to deterministic algorithms. In particular, we show how to solve a Laplacian system up to precision $\epsilon$ in $n^{o(1)}\log(1/\epsilon)$ rounds. We show how to leverage this result within existing interior point methods for solvin

Gesualdo Delfino, Marianna Sorba

Determining the role of initial conditions in the late time evolution is a key issue for the theory of nonequilibrium dynamics of isolated quantum systems. Here we extend the theory of quantum quenches to the case in which before the quench the system is in an excited state. In particular, we show perturbatively in the size of the quench (and for arbitrarily

Yaochen Zhu, Xiangqing Shen, Rui Xia

Personality traits, emotions, and beliefs shape individuals' behavioral choices and decision-making processes. However, for one thing, the affective computing community normally focused on predicting personality traits but overlooks their application in behavior prediction. For another, the multimodal reasoning task emphasized the prediction of future states

Thibault Maho, Seyed-Mohsen Moosavi-Dezfooli, Teddy Furon

The transferability of adversarial examples is a key issue in the security of deep neural networks. The possibility of an adversarial example crafted for a source model fooling another targeted model makes the threat of adversarial attacks more realistic. Measuring transferability is a crucial problem, but the Attack Success Rate alone does not provide a sou

Maryna Ishchenko, Margaryta Sobolenko, Dana Kuvatova, Taras Panamarev

Aims. We estimate the dynamical evolution of the Globular Clusters interaction with the Galactic centre that dynamically changed in the past. Methods. We simulated the orbits of 147 globular clusters over 10 Gyr lookback time using the parallel N-body code phi-GPU. For each globular cluster, we generated 1000 sets of initial data with random proper motions a

W. Li, P. Jönsson, A. M. Amarsi, M. C. Li

As the most abundant element in the universe after hydrogen and helium, oxygen plays a key role in planetary, stellar, and galactic astrophysics. Its abundance is especially influential on stellar structure and evolution, and as the dominant opacity contributor at the base of the Sun's convection zone it is central to the discussion around the solar modellin

Michael Stettler, Alexander Lappe, Nick Taubert, Martin Giese

People can innately recognize human facial expressions in unnatural forms, such as when depicted on the unusual faces drawn in cartoons or when applied to an animal's features. However, current machine learning algorithms struggle with out-of-domain transfer in facial expression recognition (FER). We propose a biologically-inspired mechanism for such transfe

Baptiste Chatelier, Vincent Corlay, Cristina Ciochina, Fallou Coly

User equipment (UE) positioning accuracy is of paramount importance in current and future communications standard. However, traditional methods tend to perform poorly in non line of sight (NLoS) scenarios. As a result, deep learning is a candidate to enhance the UE positioning accuracy in NLoS environments. In this paper, we study the efficiency of deep lear

Vladimir Petrov Kostov

We consider univariate real polynomials with all roots real and with two sign changes in the sequence of their coefficients which are all non-vanishing. One of the changes is between the linear and the constant term. By Descartes' rule of signs, such degree $d$ polynomials have $2$ positive and $d-2$ negative roots. We consider the sequences of the moduli of

Shotaro Yagishita, Jun-ya Gotoh

This paper presents a new approach to selecting knots at the same time as estimating the B-spline regression model. Such simultaneous selection of knots and model is not trivial, but our strategy can make it possible by employing a nonconvex regularization on the least square method that is usually applied. More specifically, motivated by the constraint that

Diego Berti, Andrea Corli, Luisa Malaguti

We investigate a model, inspired by (Johnston et al., Sci. Rep., 7:42134, 2017), to describe the movement of a biological population which consists of isolated and grouped organisms. We introduce biases in the movements and then obtain a scalar reaction-diffusion equation which includes a convective term as a consequence of the biases. We focus on the case t

Taher I. Mayassi, Mohammad N. Abdulrahim

We use $q$-Pascal's triangle to define a family of representations of dimension 6 of the braid group $B_3$ on three strings. Then we give a necessary and sufficient condition for these representations to be irreducible.

Murad Banaji, Balázs Boros, Josef Hofbauer

It is known that rank-two bimolecular mass-action systems do not admit limit cycles. With a view to understanding which small mass-action systems admit oscillation, in this paper we study rank-two networks with bimolecular source complexes but allow target complexes with higher molecularities. As our goal is to find oscillatory networks of minimal size, we f

Elisenda Feliu, Oskar Henriksson, Beatriz Pascual-Escudero

We determine the generic consistency, dimension and nondegeneracy of the zero locus over $\mathbb{C}^*$, $\mathbb{R}^*$ and $\mathbb{R}_{>0}$ of vertically parametrized systems: parametric polynomial systems consisting of linear combinations of monomials scaled by free parameters. These systems generalize sparse systems with fixed monomial support and freely

André Silva, João F. Ferreira, He Ye, Martin Monperrus

Automated program repair is the task of automatically repairing software bugs. A promising direction in this field is self-supervised learning, a learning paradigm in which repair models are trained without commits representing pairs of bug/fix. In self-supervised neural program repair, those bug/fix pairs are generated in some ways. The main problem is to g

Ganesh Subramaniam, Avik De, Tee-How Loo, Yong Kheng Goh

In this study of the modified $f(Q)$ theory of gravity in the spatially flat Friedmann-Lema\^itre-Robertson-Walker (FLRW) spacetime, we explore all the affine connections compatible with the symmetric teleparallel structure; three classes of such connections exist, each involving an unknown time-varying parameter $\gamma(t)$. Assuming ordinary barotropic flu

Ken Yamamoto

In this study, we investigate the lattice angle, which is defined as the angle between two vectors whose components are integers. We focus on the set of angles between a fixed integer vector and other integer vectors. For non-three-dimensional lattices, we proved that this set contains all lattice angles, irrespective of the fixed vector choice. In contrast,

Luigi Appolloni, Giovanni Molica Bisci, Simone Secchi

We study the semilinear equation $-\Delta_g u + V(\sigma) u = f(u)$ on a Cartan-Hadamard manifold ${\cal M}$ of dimension $N \geq 3$, and we prove the existence of a nontrivial solution under suitable assumptions on the potential function $V \in C({\cal M})$. In particular, the decay of $V$ at infinity is allowed, with some restrictions related to the geomet

Birgit C. van Huijgevoort, Chris Verhoek, Roland Tóth, Sofie Haesaert

Most control synthesis methods under temporal logic properties require a model of the system, however, identifying such a model can be a challenging task. In this work, we develop a direct data-driven control synthesis method for temporal logic specifications, which does not require this explicit modeling step, capable of providing certificates for the gener

Yeshwanth Kumar Adimoolam, Charalambos Poullis, Melinos Averkiou

In our study, we conducted a comprehensive analysis of three widely used datasets in the domain of building footprint extraction using deep neural networks: the INRIA Aerial Image Labelling dataset, SpaceNet 2: Building Detection v2, and the AICrowd Mapping Challenge datasets. Our experiments revealed several issues in the AICrowd Mapping Challenge dataset,

Farsad Ahmad, Aeysha Khalique

We show the utility of photon added-then-subtracted (PAS) state and two photon replaced (2PR) state when used in continuous variables measurement device independent quantum key distribution (CV-MDI-QKD) protocol. We report that single and two mode PAS state as well as two mode PR state outperform pure state protocol in the low squeezing and high noise regime

Serhan W. Bahar

Cybersecurity threats and vulnerabilities continue to grow in number and complexity, presenting an increasing challenge for organizations worldwide. Organizations use threat modelling and bug bounty programs to address these threats, which often operate independently. In this paper, we propose a Metric-Based Feedback Methodology (MBFM) that integrates bug bo

Alberto Maistrello, Matteo Agostini, Marco Bigi, Matteo Brombin

SPIDER is the full-scale prototype of the ion source of the ITER Heating Neutral Beam Injector, where negative ions of Hydrogen or Deuterium are produced by a RF generated plasma and accelerated with a set of grids up to ~100 keV. The Power Supply System is composed of high voltage dc power supplies capable of handling frequent grid breakdowns, high current

Nguyen Thi Anh Hang, Hoang Le Truong

In this paper, we will show that the affine cones over any smooth Fano-Mukai fourfold of genus $7$ are flexible.

Mélanie Ducoffe, Maxime Carrere, Léo Féliers, Adrien Gauffriau

As the interest in autonomous systems continues to grow, one of the major challenges is collecting sufficient and representative real-world data. Despite the strong practical and commercial interest in autonomous landing systems in the aerospace field, there is a lack of open-source datasets of aerial images. To address this issue, we present a dataset-lard-

Mainak Bhattacharyya, Ankur Raina

We construct a hybrid quantum-classical Viterbi decoder for the classical error-correcting codes. Viterbi decoding is a trellis-based procedure for maximum likelihood decoding of classical error-correcting codes. In this article, we demonstrate that the quantum approximate optimization algorithm can find any path on the trellis with the minimum Hamming dista

Jae-Hyeon Lee, Chang-Hwan Son

Pest counting, which predicts the number of pests in the early stage, is very important because it enables rapid pest control, reduces damage to crops, and improves productivity. In recent years, light traps have been increasingly used to lure and photograph pests for pest counting. However, pest images have a wide range of variability in pest appearance owi

Marco Matteini, Miha Nemevšek, Lorenzo Ubaldi

We present a fully analytical calculation of the false vacuum decay rate for a self-interacting scalar field in the thin-wall approximation. We obtain the bounce solution, together with the Euclidean action, counter-terms and renormalization group running, and we extract the functional determinant via the Gel'fand-Yaglom theorem. Our procedure is valid for a

Lorenzo Iorio

The perspectives of detecting the general relativistic gravitomagnetic Lense-Thirring effect on the orbits of the Galilean moons of Jupiter induced by the angular momentum ${\boldsymbol{S}}$ of the latter are preliminarily investigated. Numerical integrations over one century show that the expected gravitomagnetic signatures of the directly observable right

Can Yaylali

We use the construction of the stable homotopy category by Khan-Ravi to calculate the integral $T$-equivariant $K$-theory spectrum of a flag variety over an affine scheme, where $T$ is a split torus associated to the flag variety. More precisely, we show that the $T$-equivariant $K$-theory ring spectrum of a flag variety is decomposed into a direct sum of $K

Avraham Aizenbud, Shachar Carmeli, Dmitry Gourevitch, Lev Radzivilovski

We prove that a quotient of a Nash manifold $X$ by a closed equivalence relation $R\subset X\times X$, which is submersive over $X$, yields a Nash manifold $X/R$.

Borja Guerrero Santillan

Machine learning can benefit from causal discovery for interpretation and from causal inference for generalization. In this line of research, a few invariant learning algorithms for out-of-distribution (OOD) generalization have been proposed by using multiple training environments to find invariant relationships. Some of them are focused on causal discovery

Xiaomeng Wu, Yongqing Sun, Akisato Kimura

Most recent methods of deep image enhancement can be generally classified into two types: decompose-and-enhance and illumination estimation-centric. The former is usually less efficient, and the latter is constrained by a strong assumption regarding image reflectance as the desired enhancement result. To alleviate this constraint while retaining high efficie

Linzhi Huang, Mei Wang, Jiahao Liang, Weihong Deng

Although face recognition has made impressive progress in recent years, we ignore the racial bias of the recognition system when we pursue a high level of accuracy. Previous work found that for different races, face recognition networks focus on different facial regions, and the sensitive regions of darker-skinned people are much smaller. Based on this disco

Swetha Bhagwat, Costantino Pacilio, Paolo Pani, Michela Mapelli

Gravitational-wave black-hole spectroscopy provides a unique opportunity to test the strong-field regime of gravity and the nature of the final object formed in the aftermath of a merger. Here we investigate the prospects for black-hole spectroscopy with third-generation gravitational-wave detectors, in particular the Einstein Telescope in different configur

Vasiliy A. Es'kin, Danil V. Davydov, Ekaterina D. Egorova, Alexey O. Malkhanov

A method is presented that allows to reduce a problem described by differential equations with initial and boundary conditions to the problem described only by differential equations. The advantage of using the modified problem for physics-informed neural networks (PINNs) methodology is that it becomes possible to represent the loss function in the form of a

Jan-Hendrik Niemann, Samuel Uram, Sarah Wolf, Nataša Djurdjevac Conrad

Epidemiological models can not only be used to forecast the course of a pandemic like COVID-19, but also to propose and design non-pharmaceutical interventions such as school and work closing. In general, the design of optimal policies leads to nonlinear optimization problems that can be solved by numerical algorithms. Epidemiological models come in differen

Daniel Doerr, Bernard M. E. Moret

In ongoing work to define a principled method for syntenic block discovery and structuring, work based on homology-derived constraints and a generalization of common intervals, we faced a fundamental computational problem: how to determine quickly, among a set of indeterminate strings (strings whose elements consist of subsets of characters), contiguous inte

Indrajith V. S, R. Muthuganesan, R. Sankaranarayanan

In this article, we study quantum coherence of bipartite state from the perspective of weak measurement, which generalizes the notion of coherence relative to measurement. The is being illustrated by computing coherence for the well-known Bell diagonal and Wener states. We have also extended our investigation on quantum correlation measure and uncertainty re

Xinye Xiong, Bingxin Zhou, Yu Guang Wang

Advances in deep learning models have revolutionized the study of biomolecule systems and their mechanisms. Graph representation learning, in particular, is important for accurately capturing the geometric information of biomolecules at different levels. This paper presents a comprehensive review of the methodologies used to represent biological molecules an

Xing Wu, Guangyuan Ma, Peng Wang, Meng Lin

Growing techniques have been emerging to improve the performance of passage retrieval. As an effective representation bottleneck pretraining technique, the contextual masked auto-encoder utilizes contextual embedding to assist in the reconstruction of passages. However, it only uses a single auto-encoding pre-task for dense representation pre-training. This

John Bamberg

Let $\mathcal{C}$ be a 430-cap of $\mathrm{PG}(6,4)$ having two intersection sizes with respect to hyperplanes. We show that no hyperplane of $\mathrm{PG}(6,4)$ intersects $\mathcal{C}$ in a Hill 78-cap. So if it can be shown that the Hill 78-cap of $\mathrm{PG}(5,4)$ is projectively unique, then such a 430-cap does not exist, or equivalently, a two-weight $

Jing Liu, Donglai Wei, Yang Liu, Sipeng Zhang

Text-Based Person Search (TBPS) aims to retrieve target person images from a large-scale gallery using natural language descriptions, posing fundamental challenges in cross-modal representation learning. Existing methods often struggle to bridge the semantic gap between heterogeneous modalities while capturing fine-grained correspondences essential for discr

Jing Xu, Gorka Abad, Stjepan Picek

Backdoor attacks have been demonstrated as a security threat for machine learning models. Traditional backdoor attacks intend to inject backdoor functionality into the model such that the backdoored model will perform abnormally on inputs with predefined backdoor triggers and still retain state-of-the-art performance on the clean inputs. While there are alre

Andrea Palermo, Francesco Becattini

We present the exact form of the spin polarization vector and the spin density matrix of massive and massless free particles of any spin and helicity at general global equilibrium in a relativistic fluid with non-vanishing thermal vorticity, thus extending the known expression at the linear order. The exact form is obtained by means of the analytic continuat

Shreerang Pande, Debarshi Mitra, Apratim Chatterji

Recent experiments have been able to visualise chromosome organization in fast-growing E.coli cells. However, the mechanism underlying the spatio-temporal organization remains poorly understood. We propose that the DNA adopts a specific polymer topology as it goes through its cell cycle. We establish that the emergent entropic forces between polymer segments

Simin Yang, Ze Gao, Reza Hadi Mogavi, Pan Hui

With the advancement of virtual reality (VR) technology, virtual displays have become integral to how museums, galleries, and other tourist destinations present their collections to the public. However, the current lack of immersion in virtual reality displays limits the user's ability to experience and appreciate its aesthetics. This paper presents a case s

Bowen Liu, Yu Chen, Rakesh Chowdary Machineni, Shiyu Liu

Learning-based video compression has been extensively studied over the past years, but it still has limitations in adapting to various motion patterns and entropy models. In this paper, we propose multi-mode video compression (MMVC), a block wise mode ensemble deep video compression framework that selects the optimal mode for feature domain prediction adapti

Wei Tian, Seojeong Lee, Valentyn Panchenko

We generalize the synthetic control (SC) method to a multiple-outcome framework, where the conventional pre-treatment time dimension is supplemented with the extra dimension of related outcomes in computing the SC weights. This generalization improves the reliability of treatment effect estimation, and can be particularly useful for evaluating the effect of

Alexander Söderberg Rousu

We consider a conformal field theory in the presence of a boundary, and explain how two-point correlators of mixed bulk-local operators can be bootstrapped by exploiting the analytical structure of the conformal blocks. This yields the operator product expansion coefficients in either bootstrap channel. We apply this bootstrap technique to an $O(N)$-model in

Kenji Beppu, Kosuke Morikawa

We consider a model identification problem in which an outcome variable contains nonignorable missing values. Statistical inference requires a guarantee of the model identifiability to obtain estimators enjoying theoretically reasonable properties such as consistency and asymptotic normality. Recently, instrumental or shadow variables, combined with the comp