October 2022 arXiv papers — page 117

Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell

Distributional assumptions have been shown to be necessary for the robust learnability of concept classes when considering the exact-in-the-ball robust risk and access to random examples by Gourdeau et al. (2019). In this paper, we study learning models where the learner is given more power through the use of local queries, and give the first distribution-fr

Yossi Arjevani, Michael Field

We study the optimization problem associated with fitting two-layer ReLU neural networks with respect to the squared loss, where labels are generated by a target network. Use is made of the rich symmetry structure to develop a novel set of tools for studying the mechanism by which over-parameterization annihilates spurious minima. Sharp analytic estimates ar

Davide Perrone, Luca Ridolfi, Stefania Scarsoglio

We study the transition to turbulence in a flat plate boundary layer by means of visibility analysis of velocity time-series extracted across the flow domain. By taking into account the mutual visibility of sampled values, visibility graphs are constructed from each time series. The latter are, thus, transformed into a geometrical object, whose main features

Ilya Kuruzov, Alexander Rogozin, Demyan Yarmoshik, Alexander Gasnikov

The saddle-point optimization problems have a lot of practical applications. This paper focuses on such non-smooth problems in decentralized case. This work contains generalization of recently proposed sliding for centralized problem. Through specific penalization method and this sliding we obtain algorithm for non-smooth decentralized saddle-point problems.

Elmer Suarez, Federico Carollo, Igor Lesanovsky, Beatriz Olmos

We investigate experimentally and theoretically the collective coupling between atoms with multilevel ground state manifolds and an optical cavity mode. In our setup the cavity field optically pumps populations among the ground states. The ensuing dynamics can be conveniently described by means of an effective dynamical atom-cavity coupling strength that dep

H. Taguchi, A. Koga, Y. Murakami

We consider the time-dependent thermal pure quantum state method and introduce the efficient scheme to evaluate the change in physical quantities induced by the time-dependent perturbations, which has been proposed in our previous paper [H. Taguchi et al., Phys. Rev. B 105, 125137 (2022)]. Here, we treat the Kitaev model to consider the Majorana-mediated spi

Shunit Truzman, Guy Revach, Nir Shlezinger, Itzik Klein

The Kalman filter (KF) is a widely-used algorithm for tracking the latent state of a dynamical system from noisy observations. For systems that are well-described by linear Gaussian state space models, the KF minimizes the mean-squared error (MSE). However, in practice, observations are corrupted by outliers, severely impairing the KFs performance. In this w

M. J. Luo

Quantum treatment of physical reference frame leads to the Ricci flow of quantum spacetime, which is a quite rigid framework to quantum and renormalization effect of gravity. The theory has a low characteristic energy scale described by a unique constant: the critical density of the universe. At low energy long distance (cosmic or galactic) scale, the theory

Dmitri V. Fursaev

An introduction to generalized thermodynamics of quantum black holes, in the one-loop approximation, is given. The material is aimed at graduate students. The topics include: quantum evaporation of black holes, Euclidean formulation of quantum theory on black hole backgrounds, the Hartle-Hawking-Israel state, generalized entropy of a quantum black hole and i

Yukun Sun, Bo Lei, Junlin Liu, Haonan Huang

With the rapid development of cloud computing, edge computing, and smart devices, computing power resources indicate a trend of ubiquitous deployment. The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect. To overcome these problems and improve network efficiency, a n

Samuel Johnston

Given a log smooth scheme $(X,D)$, and a log \'etale modification $(\tilde{X},\tilde{D}) \rightarrow (X,D)$, we relate the punctured Gromov-Witten theory of $(\tilde{X},\tilde{D})$ to the punctured Gromov-Witten theory of $(X,D)$, generalizing results of Abramovich and Wise in the non-punctured setting in "Birational invariance in log Gromov-Witten Theory".

Michał Czakon, Terry Generet, Alexander Mitov, Rene Poncelet

In this work we derive three sets of non-perturbative fragmentation functions, with uncertainties, for $B$-hadrons, $J/\psi$'s and muons resulting from semileptonic $B$ decays. All three sets are with next-to-next-to leading order accuracy and include next-to-next-to leading logarithmic soft gluon resummation. The novel feature of these new sets is that they

Andrew C. Cullen, Paul Montague, Shijie Liu, Sarah M. Erfani

In response to subtle adversarial examples flipping classifications of neural network models, recent research has promoted certified robustness as a solution. There, invariance of predictions to all norm-bounded attacks is achieved through randomised smoothing of network inputs. Today's state-of-the-art certifications make optimal use of the class output sco

Ben Krause

For $d \geq 2, \ D \geq 1$, let $\mathscr{P}_{d,D}$ denote the set of all degree $d$ polynomials in $D$ dimensions with real coefficients without linear terms. We prove that for any Calder\'{o}n-Zygmund kernel, $K$, the maximally modulated and maximally truncated discrete singular integral operator, \begin{align*} \sup_{P \in \mathscr{P}_{d,D}, \ N} \Big| \s

Giulio Cerbai

The $\sigma$-machine was recently introduced by Cerbai, Claesson and Ferrari as a tool to gain a better insight on the problem of sorting permutations with two stacks in series. It consists of two consecutive stacks, which are restricted in the sense that their content must at all times avoid a certain pattern: a given $\sigma$, in the first stack, and $21$,

Wouter Deleersnyder, David Dudal, Thomas Hermans

Full 3D inversion of time-domain Airborne ElectroMagnetic (AEM) data requires specialists' expertise and a tremendous amount of computational resources, not readily available to everyone. Consequently, quasi-2D/3D inversion methods are prevailing, using a much faster but approximate (1D) forward model. We propose an appraisal tool that indicates zones in the

Jarosław Szewiński, Piotr Bartoszek, Konrad Chmielewski, Tomasz Kowalski

PolFEL stands for Polish Free Electron Laser, the first FEL research infrastructure in Poland. This facility is under development, and it will operate in three wavelength ranges: IR, THz and VUV, using different types of undulators. Machine will be driven by 200 MeV linear superconducting accelerator, which will operate in both, pulsed wave (PW) and continuo

Francesco Lisi, Andrea Michel, Paolo Nepa

Near-field antennas have been successfully adopted in several wireless applications. To exploit the high reconfigurability of array antennas, multiple synthesis techniques for arrays operating in the near-field region have been proposed. Building upon previous works on eigenmode expansions of the radiated fields, two synthesis methods for the excitations of

Dieter Vollhardt

A pedagogical introduction to solving classical and quantum many-body models in infinite spatial dimensions is given. The solution of the Hubbard model obtained in this limit is discussed in detail. It corresponds to a dynamical mean-field theory, which allows to compute the properties of correlated electrons in models and materials.

Ruiyuan Kang, Dimitrios C. Kyritsis, Panos Liatsis

Physics-based inverse modeling techniques are typically restricted to particular research fields, whereas popular machine-learning-based ones are too data-dependent to guarantee the physical compatibility of the solution. In this paper, Self-Validated Physics-Embedding Network (SVPEN), a general neural network framework for inverse modeling is proposed. As i

T. J. Hicken, M. N. Wilson, Z. Salman, S. L. Zhang

Skyrmion-hosting multilayer stacks are promising avenues for applications, although little is known about the depth dependence of the magnetism. We address this by reporting the results of circular dichroic resonant elastic x-ray scattering (CD-REXS), micromagnetic simulations, and low-energy muon-spin rotation (LE-$\mu^+$SR) measurements on a stack comprisi

Naveen Balaji Umasankar

Modular linear differential equations (MLDE) play a significant role in the classification of two-dimensional CFTs, where the modular forms in the equations belonged to the space of $\text{SL}(2,\mathbb{Z})$. A systematic study of the differential equations and their solutions for the Hecke groups $\Gamma_{0}(N)$ and Fricke groups $\Gamma_{0}^{+}(N)$ would b

Yangtian Zhang, Huiyu Cai, Chence Shi, Bozitao Zhong

In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery. This work focuses on blind flexible selfdocking, where we aim to predict the positions, orientations and conformations of docked molecules. Traditional physics-based methods usually suffer from inaccurate scoring functions and high i

Lifeng Han, Gleb Erofeev, Irina Sorokina, Serge Gladkoff

Massively multilingual pre-trained language models (MMPLMs) are developed in recent years demonstrating superpowers and the pre-knowledge they acquire for downstream tasks. This work investigates whether MMPLMs can be applied to clinical domain machine translation (MT) towards entirely unseen languages via transfer learning. We carry out an experimental inve

Maximilian Engelhardt, Carsten Andrich, Alexander Ihlow, Sebastian Giehl

In this paper, we implement a low-latency rapid-prototyping platform for signal processing based on software-defined radios (SDRs) and off-the-shelf PC hardware. This platform allows to evaluate a wide variety of algorithms in real-time environments, supporting new developments in the fields of classical, AI-based, and hybrid signal processing. To accomplish

Federico Brunero, Petros Elia

In this paper, we consider a coded caching scenario where users have heterogeneous interests. Taking into consideration the system model originally proposed by Wang and Peleato, for which the end-receiving users are divided into groups according to their file preferences, we develop a novel information-theoretic converse on the optimal worst-case communicati

Seyed Mohammad Azimi-Abarghouyi, Harpreet S. Dhillon

Inspired by recent applications of point processes to biological nanonetworks, this paper presents a novel variant of a Mat\'ern cluster process (MCP) in which the points located within a certain distance from the cluster centers are removed. We term this new process the MCP with holes at the cluster center (MCP-H, in short). Focusing on the three-dimensiona

Shallu Sharma, Iqbal Kour, Sahil Billawria

In this paper we investigate Arzela Ascoli Theorem in quasi cone metric space, which is a generalization of metric space. We prove some interesting results using forward and backward toplologies, forward and backward continuity and forward and backward totally boundedness. A new notion forward and backward equicontinuity is also introduced.

Heyuan Yao, Zhenhua Song, Baoquan Chen, Libin Liu

In this paper, we introduce ControlVAE, a novel model-based framework for learning generative motion control policies based on variational autoencoders (VAE). Our framework can learn a rich and flexible latent representation of skills and a skill-conditioned generative control policy from a diverse set of unorganized motion sequences, which enables the gener

Kiyuob Jung, Jehan Oh

In this paper, we introduce a new generalized derivative, which we term the specular derivative. We establish the Quasi-Rolles' Theorem, the Quasi-Mean Value Theorem, and the Fundamental Theorem of Calculus in light of the specular derivative. We also investigate various analytic and geometric properties of specular derivatives and apply these properties to

Duksang Lee, Nam Ho-Nguyen, Dabeen Lee

We study the problem of maximizing a continuous DR-submodular function that is not necessarily smooth. We prove that the continuous greedy algorithm achieves an $[(1-1/e)\OPT-\epsilon]$ guarantee when the function is monotone and H\"older-smooth, meaning that it admits a H\"older-continuous gradient. For functions that are non-differentiable or non-smooth, w

Anthony Nixon, Bernd Schulze, Joseph Wall

A bar-joint framework $(G,p)$ is the combination of a finite simple graph $G=(V,E)$ and a placement $p:V\rightarrow \mathbb{R}^d$. The framework is rigid if the only edge-length preserving continuous deformations of the vertices arise from isometries of the space. This article combines two recent extensions of the generic theory of rigid and flexible graphs

Christophe Fluhr, Benoit Dubois, Claudio E. Calosso, Francois Vernotte

We report in this letter the outstanding frequency stability performances of an autonomous cryogenique sapphire oscillator presenting a flicker frequency noise floor below 2e-16 near 1,000 s of integration time and a long term Allan Deviation (ADEV) limited by a random walk process of 1e-18/sqr(tau). The frequency stability qualification at this level called

Moritz F. Richter, Raphael Wiedenmann, Heinz-Peter Breuer

We employ frames consisting of rank-one projectors (i.e. pure quantum states) and their induced informationally complete quantum measurements (IC-POVMs) to represent generally mixed quantum states by quasi-probability distributions. In the case of discrete frames on finite dimensional systems this results in a vector like representation by quasi-probability

David Modiano, Rudy Wijnands

Very fast novae are novae which evolve exceptionally quickly (on timescales of only days). Due to their rapid evolution, very fast novae are challenging to detect and study, especially at early times. Here we report the discovery, which was made as part of our Transient UV Objects project, of a probable very fast nova in the nearby spiral galaxy NGC 300. We

Xin Ren

The left and right $q$-deformed rational numbers were introduced by Bapat, Becker and Licata via regular continued fractions, and they gave a homological interpretation for left and right $q$-deformed rational numbers. In the present paper, we focus on negative continued fractions and defined left $q$-deformed negative continued fractions. We give a formula

Vitalii V. Iudelevich

Each natural number can be associated with some tree graph. Namely, a natural number $n$ can be factorized as $$ n = p_1^{\alpha_1}\ldots p_k^{\alpha_k},$$ where $p_i$ are distinct prime numbers. Since $\alpha_i$ are naturals, they can be factorized in such a manner as well. This process may be continued, building the "factorization tree" until all the top n

A. Graña-González, J. L. Rodríguez-Sánchez, J. Benlliure, G. García-Jiménez

Despite the recent experimental and theoretical progress in the investigation of the nuclear fission process, a complete description still represents a challenge in nuclear physics because it is a very complex dynamical process, whose description involves the coupling between intrinsic and collective degrees of freedom, as well as different quantum-mechanica

Mikhail Gomoyunov

We consider an optimal control problem for a dynamical system described by a Caputo fractional differential equation and a terminal cost functional. We prove that, under certain assumptions, the (non-smooth, in general) value functional of this problem has a property of directional differentiability of order $\alpha$. As an application of this result, we pro

Jerzy Szynka

The subject of this paper is the evolution of the concept of information processing in regular structures based on multi-level processing in nested cellular automata. The essence of the proposed model is a discrete space-time containing nested orthogonal space-times at its points. The factorization of the function describing the global behavior of a system i

Ludovico Tesser, Matteo Acciai, Christian Spånslätt, Juliette Monsel

Nonequilibrium situations where selected currents are suppressed are of interest in fields like thermoelectrics and spintronics, raising the question of how the related noises behave. We study such zero-current charge, spin, and heat noises in a two-terminal mesoscopic conductor. In the presence of voltage, spin and temperature biases, the nonequilibrium (sh

Christian Heppe, Naveen Kumar

The generation of collimated, high brilliance $\gamma$-ray beams from a structured plasma channel target is studied by means of 2D PIC simulations. Simulation results reveal an optimum laser pulse pulse duration of $20\,\text{fs}$, for generating $\gamma$-photon beams of brilliances up to $10^{20}\,\text{s}^{-1}\text{mm}^{-1}\text{mrad}^{-2}\,(0.1\,\%\text{B

Ivar Bengtsson, Anders Forsgren, Albin Fredriksson

Delineating and planning with respect to regions suspected to contain microscopic tumor cells is an inherently uncertain task in radiotherapy. The recently proposed \textit{clinical target distribution} (CTD) is an alternative to the conventional \textit{clinical target volume} (CTV), with initial promise. Previously, using the CTD in planning has primarily

Alexander Dittrich, Jan Schneider, Simon Guist, Nico Gürtler

To approach the level of advanced human players in table tennis with robots, generating varied ball trajectories in a reproducible and controlled manner is essential. Current ball launchers used in robot table tennis either do not provide an interface for automatic control or are limited in their capabilities to adapt speed, direction, and spin of the ball.

Georgi Nakov, Davide Emilio Quadrellaro

We extend the framework of abstract algebraic logic to weak logics, namely logical systems which are not necessarily closed under uniform substitution. We interpret weak logics by algebras expanded with an additional predicate and we introduce a loose and strict version of algebraizability for weak logics. We study this framework by investigating the connect

Herve Hugonnet, Seungwoo Shin, Yongkeun Park

Dielectric tensor tomography reconstructs the three-dimensional dielectric tensors of microscopic objects and provides information about the crystalline structure orientations and principal refractive indices. Because dielectric tensor tomography is based on transmission measurement, it suffers from the missing cone problem, which causes poor axial resolutio

Dalibor Javůrek

The relativity of simultaneity together with definition of a quantum state's collapse result into experimental situations, where a multiple measurements can be taken on an uncollapsed quantum state. A quantum state's collapse space-time distribution is shown in a reference frame of a detector measuring the quantum system and in a reference frame moving relat

Fuying Wang, Yuyin Zhou, Shujun Wang, Varut Vardhanabhuti

Learning medical visual representations directly from paired radiology reports has become an emerging topic in representation learning. However, existing medical image-text joint learning methods are limited by instance or local supervision analysis, ignoring disease-level semantic correspondences. In this paper, we present a novel Multi-Granularity Cross-mo

A. Coclite, G. Pascazio, M. D. de Tullio, P. Decuzzi

In vascular targeted therapies, blood-borne carriers should realize sustained drug release from the luminal side towards the diseased tissue. In this context, such carriers are required to firmly adhere to the vessel walls for a sufficient period of time while resisting force perturbations induced by the blood flow and circulating cells. Here, a hybrid compu

Dan Wei, Tiejun Zhou, Yunqing Huang, Kai Jiang

In this work, we design a multi-category inverse design neural network to map ordered periodic structure to physical parameters. The neural network model consists of two parts, a classifier and Structure-Parameter-Mapping (SPM) subnets. The classifier is used to identify structure, and the SPM subnets are used to predict physical parameters for desired struc

Carolina Fortuna, Din Mušić, Gregor Cerar, Andrej Čampa

Artificial Intelligence (AI) technologies are moving from customized deployments in specific domains towards generic solutions horizontally permeating vertical domains and industries. For instance, decisions on when to perform maintenance of roads or bridges or how to optimize public lighting in view of costs and safety in smart cities are increasingly infor

Thinh Q. Dinh, Son Hoang Dau, Eva Lagunas, Symeon Chatzinotas

Beam Placement (BP) is a well-known problem in Low-Earth Orbit (LEO) satellite communication (SatCom) systems, which can be modelled as an NP-hard clique cover problem. Recently, quantum computing has emerged as a novel technology which revolutionizes how to solve challenging optimization problems by formulating Quadratic Unconstrained Binary Optimization (Q

Tairan He, Yuge Zhang, Kan Ren, Minghuan Liu

A good state representation is crucial to solving complicated reinforcement learning (RL) challenges. Many recent works focus on designing auxiliary losses for learning informative representations. Unfortunately, these handcrafted objectives rely heavily on expert knowledge and may be sub-optimal. In this paper, we propose a principled and universal method f

Md. Rezaul Karim, Hussain Ali, Prinon Das, Mohamed Abdelwaheb

Structured and unstructured data and facts about drugs, genes, protein, viruses, and their mechanism are spread across a huge number of scientific articles. These articles are a large-scale knowledge source and can have a huge impact on disseminating knowledge about the mechanisms of certain biological processes. A knowledge graph (KG) can be constructed by

Rahul Deshpande, Charitha M. de Silva, Ivan Marusic

We present experimental evidence that the superstructures in turbulent boundary layers comprise of smaller, geometrically self-similar coherent motions. The evidence comes from identifying and analyzing instantaneous superstructures from large-scale particle image velocimetry datasets acquired at high Reynolds numbers, capable of capturing streamwise elongat

Pankaj K Mishra, Pushpak Jagtap

This paper considers the tracking control problem for an unknown nonlinear system with time-varying bounded disturbance subjected to a prescribed performance and input constraints. When performance and input constraints are specified simultaneously for such a problem, a trade-off is inevitable. Consequently, a feasibility condition for prescribing performanc

Fatemeh Rahimi, Arash Phirouznia

Non-equilibrium spin-polarized transport properties of antiferromagnetic stanene nanoribbons are theoretically studied under the combining effect of a normal electric field and linearly polarized irradiation based on the tight-binding model at room temperature. Due to the existence of spin-orbit coupling in stanene lattice, applying normal electric field lea

Lukas Prantl, Benjamin Ummenhofer, Vladlen Koltun, Nils Thuerey

We present a novel method for guaranteeing linear momentum in learned physics simulations. Unlike existing methods, we enforce conservation of momentum with a hard constraint, which we realize via antisymmetrical continuous convolutional layers. We combine these strict constraints with a hierarchical network architecture, a carefully constructed resampling s

Yong Wei, Bo Yang, Tailong Zhou

We consider the volume preserving flow of smooth, closed and convex hypersurfaces in the hyperbolic space $\mathbb{H}^{n+1} (n\geq 2)$ with the speed given by arbitrary positive power $\alpha$ of the Gauss curvature. We prove that if the initial hypersurface is convex, then the smooth solution of the flow remains convex and exists for all positive time $t\in

Oskar Laverny, Alessandro Ferriero, Ecaterina Nisipasu

A probability distribution is n-divisible if its nth convolution root exists. While modeling the dependence structure between several (re)insurance losses by an additive risk factor model, the infinite divisibility, that is the $n$-divisibility for all $n \in\mathbb N$, is a very desirable property. Moreover, the capacity to compute the distribution of a pie

Max Spahn, Chadi Salmi, Javier Alonso-Mora

Local motion planning is a heavily researched topic in the field of robotics with many promising algorithms being published every year. However, it is difficult and time-consuming to compare different methods in the field. In this paper, we present localPlannerBench, a new benchmarking suite that allows quick and seamless comparison between local motion plan

Yogesh Verma, Samuel Kaski, Markus Heinonen, Vikas Garg

Generating new molecules is fundamental to advancing critical applications such as drug discovery and material synthesis. Flows can generate molecules effectively by inverting the encoding process, however, existing flow models either require artifactual dequantization or specific node/edge orderings, lack desiderata such as permutation invariance, or induce

Yuchong Sun, Hongwei Xue, Ruihua Song, Bei Liu

Large-scale video-language pre-training has shown significant improvement in video-language understanding tasks. Previous studies of video-language pretraining mainly focus on short-form videos (i.e., within 30 seconds) and sentences, leaving long-form video-language pre-training rarely explored. Directly learning representation from long-form videos and lan

Anish Pal, Riddhideep Biswas, Ritam Pal, Sourav Sarkar

The education sector has suffered a catastrophic setback due to ongoing COVID-pandemic, with classrooms being closed indefinitely. The current study aims to solve the existing dilemma by examining COVID transmission inside a classroom and providing long-term sustainable solutions. In this work, a standard 5m x 3m x 5m classroom is considered where 24 student

Matteo Pedone, Raffaele Argiento, Francesco C. Stingo

Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics, we develop a novel model that flexibly clusters patients with similar predictive characteristics and similar treatment responses; this approach identifies, via pred

Robert A. Wilson

We show how chirality emerges naturally from an embedding of the standard model of particle physics into $E_{8(-24)}$. The well-known argument that there is no chiral theory of fundamental physics in $E_8$ is avoided by implementing chirality not as a property of the complexified Lorentz group, but as a property of the complex representations of the real Lor

Megh Shukla, Roshan Roy, Pankaj Singh, Shuaib Ahmed

Advances in computing have enabled widespread access to pose estimation, creating new sources of data streams. Unlike mock set-ups for data collection, tapping into these data streams through on-device active learning allows us to directly sample from the real world to improve the spread of the training distribution. However, on-device computing power is lim

Jaeho Song, Haechan Jo, Dongwoo Lee

Experimental materials data are heterogeneous and include a variety of metadata for processing and characterization conditions, making the implementation of data-driven approaches for developing novel materials difficult. In this paper, we introduce the Thin-Film Alloy Database (TFADB), a materials data management platform, designed for combinatorially inves

Qi'an Guan, Zhitong Mi, Zheng Yuan

In the present paper, we study the properties of singular Nakano positivity of singular hermitian metrics on holomorphic vector bundles, and establish an optimal $L^2$ extension theorem for holomorphic vector bundles with singular hermitian metrics on weakly pseudoconvex K\"{a}hler manifolds. As applications, we give a necessary condition for the holding of

Qinmengge Li, Matthew T. Patrick, Haihan Zhang, Chachrit Khunsriraksakul

Polygenic risk scores (PRS) have recently received much attention for genetics risk prediction. While successful for the Caucasian population, the PRS based on the minority population suffer from small sample sizes, high dimensionality and low signal-to-noise ratios, exacerbating already severe health disparities. Due to population heterogeneity, direct tran

Ryosuke Sato

It is known that extreme characters of several inductive limits of compact groups exhibit multiplicativity in a certain sense. In the paper, we formulate such multiplicativity for inductive limit quantum groups and provide explicit examples of multiplicative characters in the case of quantum unitary groups. Furthermore, we show a Gaussian fluctuation limit t

Tim Schopf, Daniel Braun, Florian Matthes

In this paper, we consider the task of retrieving documents with predefined topics from an unlabeled document dataset using an unsupervised approach. The proposed unsupervised approach requires only a small number of keywords describing the respective topics and no labeled document. Existing approaches either heavily relied on a large amount of additionally

Laurenţiu Maxim, Mihai Tibăr

Motivated by finding an effective way to compute the algebraic complexity of the nearest point problem for algebraic models, we introduce an efficient method for detecting the limit points of the stratified Morse trajectories in a small perturbation of any polynomial function on a complex affine variety. We compute the multiplicities of these limit points in

Håkon Gryvill, Håkon Tjelmeland

We introduce a computationally efficient variant of the model-based ensemble Kalman filter (EnKF). We propose two changes to the original formulation. First, we phrase the setup in terms of precision matrices instead of covariance matrices, and introduce a new prior for the precision matrix which ensures it to be sparse. Second, we propose to split the state

Jitao Xu, Josep Crego, François Yvon

Non-autoregressive machine translation (NAT) has recently made great progress. However, most works to date have focused on standard translation tasks, even though some edit-based NAT models, such as the Levenshtein Transformer (LevT), seem well suited to translate with a Translation Memory (TM). This is the scenario considered here. We first analyze the vani

Keigo Takeuchi

Orthogonal approximate message-passing (OAMP) is proposed for signal recovery from right-orthogonally invariant linear measurements with spatial coupling. Conventional state evolution is generalized to a unified framework of state evolution for the spatial coupling and long-memory case. The unified framework is used to formulate the so-called Onsager correct

S. M. Troshin, N. E. Tyurin

We consider the symmetry property of the inelastic overlap function and its relation to the reflective scattering mode appearance.

Mikhail V. Volkov

Grammic monoids have recently been introduced by Christian Choffrut in terms of the action of the free monoid over a fixed ordered alphabet $X$ on the set of rows of Young tableaux filled with elements from $X$ via Schensted's insertion. For $X=\{a,b,c\}$ with $a<b<c$, Choffrut has identified the grammic monoid on $X$ with the quotient of the plactic monoid

Neil Robinson, Razyq Nasharuddin, Einar O. Fridjonsson, Michael L. Johns

We demonstrate an unexpected decay-recovery behaviour in the time-dependent $^{1}\mathrm{H}$ NMR relaxation times of water confined within a hydrating porous material. Our observations are rationalised by considering the combined effects of decreasing material pore size and evolving interfacial chemistry, which facilitate a transition between surface-limited

Pedram Bakhtiarifard, Christian Igel, Raghavendra Selvan

Energy consumption from the selection, training, and deployment of deep learning models has seen a significant uptick recently. This work aims to facilitate the design of energy-efficient deep learning models that require less computational resources and prioritize environmental sustainability by focusing on the energy consumption. Neural architecture search

Zixuan Li

Currently, the size of scientific data is growing at an unprecedented rate. Data in the form of tensors exhibit high-order, high-dimensional, and highly sparse features. Although tensor-based analysis methods are very effective, the large increase in data size makes the original tensor impossible to process. Tensor decomposition decomposes a tensor into mult

Hemant Kumar Raut, Qifeng Ruan, Cédric Finet, Vinodkumar Saranathan

The colourful wings of butterflies result from the interaction between light and the intricate chitinous nanostructures on butterflies' scales. This study demonstrates that just by reproducing the chitinous ridges present in butterfly scales (i.e., without any other secondary structure), the entire colour palette is achieved. This result was achieved using a

Leo Ardon, Jared Vann, Deepeka Garg, Tom Spooner

Agent based modelling (ABM) is a computational approach to modelling complex systems by specifying the behaviour of autonomous decision-making components or agents in the system and allowing the system dynamics to emerge from their interactions. Recent advances in the field of Multi-agent reinforcement learning (MARL) have made it feasible to study the equil

Bin Shi, Dehai Luo, Wenqi Zhang

In this paper, we explore optimal disturbances of blockings in the equivalent barotropic atmosphere using the conditional nonlinear optimal perturbation (CNOP) approach. Considering the initial blocking amplitude, the optimal disturbance exhibits a solitary wave-like pattern. As the size increases incrementally, the spatial pattern becomes more concentrated,

Michał Czuba, Piotr Bródka

Investigating the interaction between spreading processes in complex networks is one of the most important challenges in network science. However, whether we would like to know how the information campaign will affect virus spreading or how the advertising campaign of the new iPhone will affect the sales of Samsung phones, we need an environment that will al

Albi Kerbizi, Leif Lönnblad

Quark spin effects in hadronization have been recently included in the PYTHIA 8 event generator for the simulation of the deep inelastic scattering (DIS) process off a polarized nucleon target via the external StringSpinner package. The spin effects can be simulated for the production of pseudoscalar mesons using the string+${}^3P_0$ model of polarized quark

Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic

Denoising diffusion models (DDMs) have shown promising results in 3D point cloud synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii) the ability to output smooth surfaces or meshes. To

Khurram Azeem Hashmi, Alain Pagani, Didier Stricker, Muhammamd Zeshan Afzal

We present a new, simple yet effective approach to uplift video object detection. We observe that prior works operate on instance-level feature aggregation that imminently neglects the refined pixel-level representation, resulting in confusion among objects sharing similar appearance or motion characteristics. To address this limitation, we propose BoxMask,

Yueh-Kao Wu, Ching-Yu Chiu, Yi-Hsuan Yang

This paper proposes a model that generates a drum track in the audio domain to play along to a user-provided drum-free recording. Specifically, using paired data of drumless tracks and the corresponding human-made drum tracks, we train a Transformer model to improvise the drum part of an unseen drumless recording. We combine two approaches to encode the inpu

Ruihao Wang, Jian Qin, Kaiying Li, Yaochen Li

3D lane detection which plays a crucial role in vehicle routing, has recently been a rapidly developing topic in autonomous driving. Previous works struggle with practicality due to their complicated spatial transformations and inflexible representations of 3D lanes. Faced with the issues, our work proposes an efficient and robust monocular 3D lane detection

Lan V. Truong

A generative adversarial network (GAN) is a class of machine learning frameworks designed by Goodfellow et al. in 2014. In the GAN framework, the generative model is pitted against an adversary: a discriminative model that learns to determine whether a sample is from the model distribution or the data distribution. GAN generates new samples from the same dis

Enrico Angelelli, Claudia Archetti, Lorenzo Peirano

Consolidation of loose packages into transport units is a fundamental activity offered by logistics service-providers. Moving the transport units instead of loose packages is faster (with one movement only, multiple packages are loaded instead of having one load operation for each package), safer (chances of damage and loss is reduced) and cheaper. One of th

Xiangjie Yan, Yongpeng Jiang, Chen Chen, Leiliang Gong

There is invariably a trade-off between safety and efficiency for collaborative robots (cobots) in human-robot collaborations. Robots that interact minimally with humans can work with high speed and accuracy but cannot adapt to new tasks or respond to unforeseen changes, whereas robots that work closely with humans can but only by becoming passive to humans,

Chenggong Zhang, Zhilei Liu

The face super-resolution (FSR) task is to reconstruct high-resolution face images from low-resolution inputs. Recent works have achieved success on this task by utilizing facial priors such as facial landmarks. Most existing methods pay more attention to global shape and structure information, but less to local texture information, which makes them cannot r

Markus Knitt, Jakob Schyga, Asan Adamanov, Johannes Hinckeldeyn

Estimating the pose of a pallet and other logistics objects is crucial for various use cases, such as automatized material handling or tracking. Innovations in computer vision, computing power, and machine learning open up new opportunities for device-free localization based on cameras and neural networks. Large image datasets with annotated poses are requir

Ziqing Li, Yan Yang

The DP-coloring is a generalization of the list coloring, introduced by Dvo\v{r}\'{a}k and Postle. Let $\mathcal{H}=(L,H)$ be a cover of a graph $G$ and $P_{DP}(G,\mathcal{H})$ be the number of $\mathcal{H}$-colorings of $G$. The DP color function $P_{DP}(G,m)$ of $G$, introduced by Kaul and Mudrock, is the minimum value of $P_{DP}(G,\mathcal{H})$ where the

Wenzhe Li, Nikolaos Aletras

Graph-based text representation focuses on how text documents are represented as graphs for exploiting dependency information between tokens and documents within a corpus. Despite the increasing interest in graph representation learning, there is limited research in exploring new ways for graph-based text representation, which is important in downstream natu

Sebastian Bahamonde, Johann Chevrier, Jorge Gigante Valcarcel

In the framework of Metric-Affine Gravity, the existing correspondence between the Einstein tensor and the energy-momentum tensor of matter provided by General Relativity is extended towards a post-Riemannian description in terms of the torsion and nonmetricity fields, which are sourced by the spin, dilation and shear currents of matter. In this work, we foc

Genshiro Kitagawa

The goodness of the long-term prediction in the state-space model was evaluated using the squared long-term prediction error. In order to estimate the model parameters suitable for long-term prediction, we devised a modified log-likelihood corresponding to the long-term prediction error variance. Trend models and seasonally adjusted models with and without A

Tai-Yin Chiu, Danna Gurari

Photorealistic style transfer is the task of synthesizing a realistic-looking image when adapting the content from one image to appear in the style of another image. Modern models commonly embed a transformation that fuses features describing the content image and style image and then decodes the resulting feature into a stylized image. We introduce a genera