May 2022 arXiv papers — page 146

Biagio Ricceri

Here is a sample of the results proved in this paper: Let $f:{\bf R}\to {\bf R}$ be a continuous function, let $ρ>0$ and let $ω:[0,ρ[\to [0,+\infty[$ be a continuous increasing function such that $\lim_{ξ\to ρ^-}\int_0^ξω(x)dx=+\infty$. Consider $C^0([0,1])\times C^0([0,1])$ endowed with the norm $$\|(α,β)\|=\int_0^1|α(t)|dt+\int_0^1|β(t)|dt\ .$$ Then, the f

Margherita Doria, Elisa Luciano, Patrizia Semeraro

This paper studies the consequences of capturing non-linear dependence among the covariates that drive the default of different obligors and the overall riskiness of their credit portfolio. Joint default modeling is, without loss of generality, the classical Bernoulli mixture model. Using an application to a credit card dataset we show that, even when Machin

Yoav Bar-Nir

We study the rate of correlation decay in the two-dimensional random-field Ising model at weak field strength $\varepsilon$. We combine elements of the recent proof of exponential decay of correlations with a quantitative refinement of a result of Aizenman--Burchard on the tortuosity of random curves to obtain an upper bound of the form $\exp(\exp(O(1/\varep

X. Chen, T. Gehrmann, E. W. N. Glover, M. Höfer

Isolated photons at hadron colliders are defined by permitting only a limited amount of hadronic energy inside a fixed-size cone around the candidate photon direction. This isolation criterion admits contributions from collinear photon radiation off QCD partons and from parton-to-photon fragmentation processes. We compute the NNLO QCD corrections to isolated

Viet Pham Ngoc, David Tuckey, Herbert Wiklicky

In this paper, we investigate the performances of tunable quantum neural networks in the Quantum Probably Approximately Correct (QPAC) learning framework. Tunable neural networks are quantum circuits made of multi-controlled X gates. By tuning the set of controls these circuits are able to approximate any Boolean functions. This architecture is particularly

Meng Bao, Xiaoquan Xu

Based on the concepts of $\mathbb{R}$-factorizable topological groups and $\mathcal{M}$-factorizable topological groups, we introduce four classes of factorizabilities on topological groups, named $P\mathcal{M}$-factorizabilities, $Pm$-factorizabilities, $S\mathcal{M}$-factorizabilities and $PS\mathcal{M}$-factorizabilities, respectively. Some properties of

Anatole Storck, Ross Church

Observations of binaries containing pairs of neutron stars using the upcoming space-based gravitational wave observatory, LISA, have the potential to improve our understanding of neutron star physics and binary evolution. In this work we assess the effect of changing the model of the Milky Way's kinematics and star formation history on predictions of the

Jinbao Zhu, Songze Li, Jie Li

We study two problems of private matrix multiplication, over a distributed computing system consisting of a master node, and multiple servers that collectively store a family of public matrices using Maximum-Distance-Separable (MDS) codes. In the first problem of Private and Secure Matrix Multiplication (PSMM) from colluding servers, the master intends to co

Philipp Mohr, Gerhard Bauch

Recently, low-resolution LDPC decoders have been introduced that perform mutual information maximizing signal processing. However, the optimal quantization in variable and check nodes requires expensive non-uniform operations. Instead, we propose to use uniform quantization with a simple hardware structure, which reduces the complexity of individual node ope

Grégoire Aufort, Pierre Pudlo, Denis Burgarella

Importance sampling is a Monte Carlo method that introduces a proposal distribution to sample the space according to the target distribution. Yet calibration of the proposal distribution is essential to achieving efficiency, thus the resort to adaptive algorithms to tune this distribution. In the paper, we propose a new adpative importance sampling scheme, n

Hung-yi Lee, Shang-Wen Li, Ngoc Thang Vu

Deep learning has been the mainstream technique in natural language processing (NLP) area. However, the techniques require many labeled data and are less generalizable across domains. Meta-learning is an arising field in machine learning studying approaches to learn better learning algorithms. Approaches aim at improving algorithms in various aspects, includ

Federica Stolf, Antonio Canale

Spatial maps of extreme precipitation are crucial in flood protection. With the aim of producing maps of precipitation return levels, we propose a novel approach to model a collection of spatially distributed time series where the asymptotic assumption, typical of the traditional extreme value theory, is relaxed. We introduce a Bayesian hierarchical model th

Jun-Jie Zhang, Dong-Xiao Zhang, Jian-Nan Chen, Long-Gang Pang

In this study, we explore the inherent trade-off between accuracy and robustness in neural networks, drawing an analogy to the uncertainty principle in quantum mechanics. We propose that neural networks are subject to an uncertainty relation, which manifests as a fundamental limitation in their ability to simultaneously achieve high accuracy and robustness a

Mingle Xu, Sook Yoon, Alvaro Fuentes, Dong Sun Park

Deep learning has been achieving decent performance in computer vision requiring a large volume of images, however, collecting images is expensive and difficult in many scenarios. To alleviate this issue, many image augmentation algorithms have been proposed as effective and efficient strategies. Understanding current algorithms is essential to find suitable

Bowen Jing, Gabriele Corso, Renato Berlinghieri, Tommi Jaakkola

Score-based models generate samples by mapping noise to data (and vice versa) via a high-dimensional diffusion process. We question whether it is necessary to run this entire process at high dimensionality and incur all the inconveniences thereof. Instead, we restrict the diffusion via projections onto subspaces as the data distribution evolves toward noise.

Juntao Huang, Thomas Izgin, Stefan Kopecz, Andreas Meister

In this paper, we perform stability analysis for a class of second and third order accurate strong-stability-preserving modified Patankar Runge-Kutta (SSPMPRK) schemes, which were introduced in [4,5] and can be used to solve convection equations with stiff source terms, such as reactive Euler equations, with guaranteed positivity under the standard CFL condi

Carlos Galindo, Fernando Hernando, Helena Martín-Cruz

We study monomial-Cartesian codes (MCCs) which can be regarded as $(r,\delta)$-locally recoverable codes (LRCs). These codes come with a natural bound for their minimum distance and we determine those giving rise to $(r,\delta)$-optimal LRCs for that distance, which are in fact $(r,\delta)$-optimal. A large subfamily of MCCs admits subfield-subcodes with the

C. Rodenbeck

The neutrino mass experiment KATRIN uses conversion electrons from the 32.2 keV transition of the nuclear isomer $^{\mathrm{83m}}$Kr for calibration. Comparing the measured energies to the appropriate literature values allows for an independent evaluation of the energy scale, but the uncertainties in some of the literature values obtained by gamma spectrosco

Wei Zhao, Shiqi Zhang, Bing Zhou, Bei Wang

Traffic forecasting is essential for the traffic construction of smart cities in the new era. However, traffic data's complex spatial and temporal dependencies make traffic forecasting extremely challenging. Most existing traffic forecasting methods rely on the predefined adjacency matrix to model the Spatio-temporal dependencies. Nevertheless, the road

H. Robert Frost

We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by multiple edge-weighted, potentially directed, graphs over the same set of nodes with each graph representing one layer of the network and no inter-laye

Hsin Chang, Jing-Yang Chang, Yi-Chieh Chang, Yu-Han Chang

We report on a holoscope axion search experiment near $19.6\ {\rm μeV}$ from the TASEH collaboration. The experiment is carried out via a frequency-tunable cavity detector with a volume $V = 0.234\ {\rm liter}$ in a magnetic field $B_0 = 8\ {\rm T}$. With a signal receiver that has a system noise temperature $T_{\rm sys} \cong 2.2\ {\rm K}$ and experiment ti

Tongjun Liu, Cheng-Hung Chi, Jun-Yu Ou, Jie Xu

Despite recent tremendous progress in optical imaging and metrology, the resolution gap between atomic scale transmission electron microscopy and optical techniques has not been closed. Is optical imaging and metrology of nanostructures exhibiting Brownian motion possible with resolution beyond thermal fluctuations? Here we report on an experiment in which t

Ian Williams, Erdal C. Oğuz, Hartmut Löwen, Wilson C. K. Poon

Colloids may be treated as `big atoms' so that they are good models for atomic and molecular systems. Colloidal hard disks are therefore good models for 2d materials and although their phase behavior is well characterized, rheology has received relatively little attention. Here we exploit a novel, particle-resolved, experimental set-up and complementary comp

Qinghui Sun, Sharon Xuesong Wang, Tianjun Gan, Andrew W. Mann

We report the results of our search of planet candidates in Open Clusters and Young Stellar Associations based on the TESS Objects of Interest Catalog. We find one confirmed planet, one promising candidate, one brown dwarf, and three unverified planet candidates in a sample of 1229 Open Clusters from the second Gaia data release. We discuss individual planet

P. F. L. Maxted, N. J. Miller, S. Hoyer, V. Adibekyan

EBLM J0113+31 is moderately bright (V=10.1), metal-poor ([Fe/H]$\approx-0.3$) G0V star with a much fainter M dwarf companion on a wide, eccentric orbit (=14.3 d). We have used near-infrared spectroscopy obtained with the SPIRou spectrograph to measure the semi-amplitude of the M dwarf's spectroscopic orbit, and high-precision photometry of the eclipse an

Balázs Pozsgay, Arthur Hutsalyuk, Levente Pristyák, Gábor Takács

We introduce an integrable spin ladder model and study its exact solution, correlation functions, and entanglement properties. The model supports two particle types (corresponding to the even and odd sub-lattices), such that the scattering phases are constants: particles of the same type scatter as free fermions, whereas the inter-particle phase shift is a c

Dominik Koutný, Laia Ginés, Magdalena Moczała-Dusanowska, Sven Höfling

The quantification of the entanglement present in a physical system is of para\-mount importance for fundamental research and many cutting-edge applications. Currently, achieving this goal requires either a priori knowledge on the system or very demanding experimental procedures such as full state tomography or collective measurements. Here, we demonstrate t

Francois Gieres

For classical relativistic field theory in Minkowski space-time, the addition of a superpotential term to a conserved current density is trivial in the sense that it does not modify the local conservation law nor change the conserved charge, though it may allow us to obtain a current density with some improved properties. The addition of a total derivative t

Viktoria Heimann, Andreas Spruck, André Kaup

The demand for high-resolution point clouds has increased throughout the last years. However, capturing high-resolution point clouds is expensive and thus, frequently replaced by upsampling of low-resolution data. Most state-of-the-art methods are either restricted to a rastered grid, incorporate normal vectors, or are trained for a single use case. We propo

Alex Shtoff

Model training algorithms which observe a small portion of the training set in each computational step are ubiquitous in practical machine learning, and include both stochastic and online optimization methods. In the vast majority of cases, such algorithms typically observe the training samples via the gradients of the cost functions the samples incur. Thus,

Chengdong Li, James Binney

We investigate the structure of our Galaxy's young stellar disc by fitting the distribution functions (DFs) of a new family to five-dimensional Gaia data for a sample of $47\,000$ OB stars. Tests of the fitting procedure show that the young disc's DF would be strongly constrained by Gaia data if the distribution of Galactic dust were accurately known

G. Mountrichas, V. Buat, G. Yang, M. Boquien

We use $\sim 1800$ X-ray Active Galactic Nuclei (AGN) in the eROSITA Final Equatorial-Depth Survey (eFEDS), that span over two orders of magnitude in X-ray luminosity, $\rm L_{X,2-10keV} \approx 10^{43-45}\,ergs^{-1}$, and compare their star-formation rate (SFR) relative to that of non-AGN star-forming systems, at $\rm 0.5<z<1.5$. For that purpose, we compil

Dennis Reddyhoff

This essay seeks to tie together thoughts on the political economy of academia, the inequities in access to the academic means of production and decolonial practice in data empowerment. To demonstrate this I will provide a brief analysis of the neo-colonial, extractive practices of the Western Academy, introduce concepts around decolonial AI practice and the

Mingshuai Chen, Joost-Pieter Katoen, Lutz Klinkenberg, Tobias Winkler

We study discrete probabilistic programs with potentially unbounded looping behaviors over an infinite state space. We present, to the best of our knowledge, the first decidability result for the problem of determining whether such a program generates exactly a specified distribution over its outputs (provided the program terminates almost surely). The class

Chris Lu, Timon Willi, Christian Schroeder de Witt, Jakob Foerster

In general-sum games, the interaction of self-interested learning agents commonly leads to collectively worst-case outcomes, such as defect-defect in the iterated prisoner&#39;s dilemma (IPD). To overcome this, some methods, such as Learning with Opponent-Learning Awareness (LOLA), shape their opponents&#39; learning process. However, these methods are myopi

Oswin Krause, Anasua Chatterjee, Ferdinand Kuemmeth, Evert van Nieuwenburg

We introduce an algorithm that is able to find the facets of Coulomb diamonds in quantum dot arrays. We simulate these arrays using the constant-interaction model, and rely only on one-dimensional raster scans (rays) to learn a model of the device using regularized maximum likelihood estimation. This allows us to determine, for a given charge state of the de

Shenglong Zhou, Geoffrey Ye Li

Federated learning has shown its advances recently but is still facing many challenges, such as how algorithms save communication resources and reduce computational costs, and whether they converge. To address these critical issues, we propose a hybrid federated learning algorithm (FedGiA) that combines the gradient descent and the inexact alternating direct

Sean Parker, Sami Alabed, Eiko Yoneki

Training deep learning models takes an extremely long execution time and consumes large amounts of computing resources. At the same time, recent research proposed systems and compilers that are expected to decrease deep learning models runtime. An effective optimisation methodology in data processing is desirable, and the reduction of compute requirements of

J van Dongen, L Prokhorov, S J Cooper, M A Barton

Control noise is a limiting factor in the low-frequency performance of the LIGO gravitational wave detectors. In this paper we model the effects of using new sensors called HoQIs to control the suspension resonances. We show if we were to use HoQIs, instead of the standard shadow sensors, we can suppress resonance peaks up to tenfold more while simultaneousl

Willian T. Lunardi, Martin Andreoni Lopez, Jean-Pierre Giacalone

As the number of heterogenous IP-connected devices and traffic volume increase, so does the potential for security breaches. The undetected exploitation of these breaches can bring severe cybersecurity and privacy risks. Anomaly-based \acp{IDS} play an essential role in network security. In this paper, we present a practical unsupervised anomaly-based deep l

Thomas L. Carroll, Joseph D. Hart

A reservoir computer is a type of dynamical system arranged to do computation. Typically, a reservoir computer is constructed by connecting a large number of nonlinear nodes in a network that includes recurrent connections. In order to achieve accurate results, the reservoir usually contains hundreds to thousands of nodes. This high dimensionality makes it d

Jacob Imola, Takao Murakami, Kamalika Chaudhuri

Subgraph counting is fundamental for analyzing connection patterns or clustering tendencies in graph data. Recent studies have applied LDP (Local Differential Privacy) to subgraph counting to protect user privacy even against a data collector in social networks. However, existing local algorithms suffer from extremely large estimation errors or assume multi-

Alexander Mordvintsev, Ettore Randazzo, Craig Fouts

Modeling the ability of multicellular organisms to build and maintain their bodies through local interactions between individual cells (morphogenesis) is a long-standing challenge of developmental biology. Recently, the Neural Cellular Automata (NCA) model was proposed as a way to find local system rules that produce a desired global behaviour, such as growi

Léonie Canet

Turbulence is a complex nonlinear and multi-scale phenomenon. Although the fundamental underlying Navier-Stokes equations have been known for two centuries, it remains extremely challenging to extract from them the statistical properties of turbulence. Therefore, for practical purpose, a sustained effort has been devoted to obtaining some effective descripti

Philip Dörr, Thomas Kahle

We investigate extreme values of Mahonian and Eulerian distributions arising from counting inversions and descents of random elements of finite Coxeter groups. To this end, we construct a triangular array of either distribution from a sequence of Coxeter groups with increasing ranks. To avoid degeneracy of extreme values, the number of i.i.d. samples $k_n$ i

Jin Dai, Theodora Ioannidou, Antti Niemi

In this paper, a novel discrete algebra is presented which follows by combining the SU(2) Lie-Poisson bracket with the discrete Frenet equation. Physically, the construction describes a discrete piecewise linear string in R3. The starting point of our derivation is the discrete Frenet frame assigned at each vertix of the string. Then the link vector that con

Henna Kokkonen, Lauri Lovén, Naser Hossein Motlagh, Abhishek Kumar

Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on edge AI for resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the

Chethan Krishnan, Jude Pereira

We study the asymptotic symmetries of Einstein gravity in flat space. Instead of Bondi gauge, we work with the recently introduced special double null gauge, in which $\mathscr{I}^{+}$ and $\mathscr{I}^{-}$ are approached along null directions. We find four new functions worth of asymptotic diffeomorphisms beyond the familiar supertranslations and superrotat

Marco Bernardo, Claudio A. Mezzina

Causal reversibility blends reversibility and causality for concurrent systems. It indicates that an action can be undone provided that all of its consequences have been undone already, thus making it possible to bring the system back to a past consistent state. Time reversibility is instead considered in the field of stochastic processes, mostly for efficie

Simo Linkola, Christian Guckelsberger, Tomi Männistö, Anna Kantosalo

Which factors influence the human assessment of creativity exhibited by a computational system is a core question of computational creativity (CC) research. Recently, the system&#39;s embodiment has been put forward as such a factor, but empirical studies of its effect are lacking. To this end, we propose an experimental framework which isolates the effect o

Ran Zmigrod, Tim Vieira, Ryan Cotterell

Significance testing -- especially the paired-permutation test -- has played a vital role in developing NLP systems to provide confidence that the difference in performance between two systems (i.e., the test statistic) is not due to luck. However, practitioners rely on Monte Carlo approximation to perform this test due to a lack of a suitable exact algorith

Chao Bian, Yawen Zhou, Chao Qian

Subset selection, which aims to select a subset from a ground set to maximize some objective function, arises in various applications such as influence maximization and sensor placement. In real-world scenarios, however, one often needs to find a subset which is robust against (i.e., is good over) a number of possible objective functions due to uncertainty,

Daniel Bogdoll, Enrico Eisen, Maximilian Nitsche, Christin Scheib

Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads. Nevertheless, the performance of their perception systems is strongly dependent on the quality of the utilized training data. As these usually only cover a fraction of all object classes an autonomous driving system will face, such systems

Miguel Cárcamo, Anna M. M. Scaife, Emma L. Alexander, J. Patrick Leahy

The reconstruction of Faraday depth structure from incomplete spectral polarization radio measurements using the RM Synthesis technique is an under-constrained problem requiring additional regularisation. In this paper we present cs-romer: a novel object-oriented compressed sensing framework to reconstruct Faraday depth signals from spectro-polarization radi

Varun Babbar, Umang Bhatt, Adrian Weller

Research on human-AI teams usually provides experts with a single label, which ignores the uncertainty in a model&#39;s recommendation. Conformal prediction (CP) is a well established line of research that focuses on building a theoretically grounded, calibrated prediction set, which may contain multiple labels. We explore how such prediction sets impact exp

Klaas Landsman

Supplementing earlier literature by e.g. Tipler, Clarke, & Ellis (1980), Israel (1987), Thorne, (1994), Earman (1999), Senovilla & Garfinkle (2015), Curiel (2019ab), and Landsman (2021ab), I provide a historical and conceptual analysis of Penrose&#39;s path-breaking 1965 singularity (or incompleteness) theorem. The emphasis is on the nature and historical or

Axel Fünfhaus, Thilo Kopp, Elias Lettl

Chern numbers can be calculated within a frame of vortex fields related to phase conventions of a wave function. In a band protected by gaps the Chern number is equivalent to the total number of flux carrying vortices. In the presence of topological defects like Dirac cones this method becomes problematic, in particular if they lack a well-defined winding nu

Niamh Farrell, Caroline Lassueur

We compute the trivial source character tables (also called species tables of the trivial source ring) of the infinite family of finite groups $\text{SL}_{2}(q)$ for $q$ even, over a large enough field $k$ of positive characteristic ${\ell}$ not dividing $q$. This article is a continuation of our article Trivial Source Character Tables of $\text{SL}_{2}(q)$

Nathalie Ysard, Marc-Antoine Miville-Deschênes, Laurent Verstraete, Anthony Peter Jones

Context. Excess microwave emission, commonly known as anomalous microwave emission (AME), is now routinely detected in the Milky Way. Although its link with the rotation of interstellar (carbonaceous) nano-grains seems to be relatively well established at cloud scales, large-scale observations show a lack of correlation between the different tracers of nano-

Zied Ben Houidi, Dario Rossi

Boosted by deep learning, natural language processing (NLP) techniques have recently seen spectacular progress, mainly fueled by breakthroughs both in representation learning with word embeddings (e.g. word2vec) as well as novel architectures (e.g. transformers). This success quickly invited researchers to explore the use of NLP techniques to other fields, s

Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan

Contrastively trained language-image models such as CLIP, ALIGN, and BASIC have demonstrated unprecedented robustness to multiple challenging natural distribution shifts. Since these language-image models differ from previous training approaches in several ways, an important question is what causes the large robustness gains. We answer this question via a sy

Kwokwai Chan

This is a write-up of the author's invited talk at the Eighth International Congress of Chinese Mathematicians (ICCM) held at Beijing in June 2019. We give a survey on joint works with Naichung Conan Leung and Ziming Nikolas Ma where we study how tropical objects arise from asymptotic analysis of the Maurer-Cartan equation for deformation of complex structur

Zhicheng Zhang, Sha Wang, Jun Wang

Noise-like pulses (NLP) are extremely sought after in many fields. Here, we experimentally and numerically investigated the generation of noise-like pulses in an all-normal-dispersion fiber laser with weak spectrum filtering. With the insertion of the grating as a tunable spectrum filter, the laser operates at a stable dissipative soliton state with a 3.84 p

Abraham Loeb

I study seven novel observational tests of general relativity. First, I show that a gravitational wave pulse from a major merger of massive black holes at the Galactic center induces a permanent increase in the Earth-Moon separation. Second, I show that General Relativity sets an absolute upper limit on the energy flux observed from a cosmological source as

Jianing Zhao, Xiang Yin, Shaoyuan Li

In this paper, we investigate property verification problems in partially-observed discrete-event systems (DES). Particularly, we are interested in verifying observational properties that are related to the information-flow of the system. Observational properties considered here include diagnosability, predictability, detectability and opacity, which have dr

Alain Connes, Caterina Consani

We prove a Riemann-Roch theorem of an entirely novel nature for divisors on the Arakelov compactification of the algebraic spectrum of the integers. This result relies on the introduction of three key concepts: the cohomologies (attached to a divisor), their integer dimension, and Serre duality. These notions directly extend their classical counterparts for

Junfeng Yin, Nan Li, Ning Zheng

A class of restarted randomized surrounding methods are presented to accelerate the surrounding algorithms by restarted techniques for solving the linear equations. Theoretical analysis prove that the proposed method converges under the randomized row selection rule and the expectation convergence rate is also addressed. Numerical experiments further demonst

Jean-Luc Baril, Helmut Prodinger

Łukasiewicz paths are lattice paths in $\Bbb{N}^2$ starting at the origin, ending on the $x$-axis, and consisting of steps in the set $\{(1,k), k\geq -1\}$. We give generating function and exact value for the number of $n$-length prefixes (resp. suffixes) of these paths ending at height $k\geq 0$ with a given type of step. We make a similar study for prefixe

Aljosha Köcher, Lasse Beers, Alexander Fay

Being able to quickly integrate new equipment and functions into an existing plant is a major goal for both discrete and process manufacturing. But currently, these two industry domains use different approaches to achieve this goal. While the Module Type Package (MTP) is getting more and more adapted in practical applications of process manufacturing, so-cal

Zhenguang Liu, Sifan Wu, Chejian Xu, Xiang Wang

One compelling application of artificial intelligence is to generate a video of a target person performing arbitrary desired motion (from a source person). While the state-of-the-art methods are able to synthesize a video demonstrating similar broad stroke motion details, they are generally lacking in texture details. A pertinent manifestation appears as dis

James J. Cusick

This paper presents concepts and methods to support preparing software and system releases to production. Keywords: Operational Readiness Review, ORR, IT Services, IT Operations, ITIL, Process Engineering, Reliability, Availability, Software Architecture, Cloud Computing, Networking, Site Reliability Engineering, DevOps, Agile Methods, Quality, Defect Preven

Tobias Breiten, Karl Kunisch

The long time behavior and detailed convergence analysis of Langevin equations has received increased attention over the last years. Difficulties arise from a lack of coercivity, usually termed hypocoercivity, of the underlying kinetic Fokker-Planck operator which is a consequence of the partially deterministic nature of a second order stochastic differentia

Maria Hellgren, Damian Contant, Thomas Pitts, Michele Casula

The high-pressure II-III phase transition in solid hydrogen is investigated using the random phase approximation and diffusion Monte Carlo. Good agreement between the methods is found confirming that an accurate treatment of exchange and correlation increases the transition pressure by more than 100 GPa with respect to semilocal density functional approximat

Jeevesh Juneja, Ritu Agarwal

We analyze the Knowledge Neurons framework for the attribution of factual and relational knowledge to particular neurons in the transformer network. We use a 12-layer multi-lingual BERT model for our experiments. Our study reveals various interesting phenomena. We observe that mostly factual knowledge can be attributed to middle and higher layers of the netw

Patricia Bouyer, Antonio Casares, Mickael Randour, Pierre Vandenhove

In two-player games on graphs, the simplest possible strategies are those that can be implemented without any memory. These are called positional strategies. In this paper, we characterize objectives recognizable by deterministic B\"uchi automata (a subclass of omega-regular objectives) that are half-positional, that is, for which the protagonist can always

Genly Leon, A. Paliathanasis, P. G. L. Leach

Using the singularity analysis, we investigate the integrability properties and existence of analytic solutions in $f\left( R\right)$-cosmology. Specifically, for some power-law $f\left( R\right) $-theories of particular interest, we apply the ARS algorithm to prove if the field equations possess the Painlev\'{e} property. Constraints for the free parameters

Hugo Thimonier, Fabrice Popineau, Arpad Rimmel, Bich-Liên Doan

As with many other tasks, neural networks prove very effective for anomaly detection purposes. However, very few deep-learning models are suited for detecting anomalies on tabular datasets. This paper proposes a novel methodology to flag anomalies based on TracIn, an influence measure initially introduced for explicability purposes. The proposed methods can

Dmitry Kleinbock, Mishel Skenderi

The present paper is a sequel to [Monatsh.~Math.\ {\bf 194} (2021), 523--554] in which results of that paper are generalized so that they hold in the setting of inhomogeneous Diophantine approximation. Given any integers $n \geq 2$ and $\ell \geq 1$, any ${\pmb ξ} = \left(ξ_1, \dots , ξ_\ell \right) \in \mathbb{R}^\ell$, and any homogeneous function \linebre

Shaswat Mohanty, Anirudh Vijay, Shailesh Deshpande

Urban metabolism is an active field of research that deals with the estimation of emissions and resource consumption from urban regions. The analysis could be carried out through a manual surveyor by the implementation of elegant machine learning algorithms. In this exploratory work, we estimate the water consumption by the buildings in the region captured b

Sahin Buyukdagli

The Derjaguin-Landau-Verywey-Overbeek (DLVO) theory has been a remarkably accurate framework for the characterization of macromolecular stability in water solvent. In view of its solvent-implicit nature neglecting the electrostatics of water molecules with non-negligible charge structure and concentration, the precision of the DLVO formalism is somewhat puzz

Xiaoyu Pan, Jiaming Mai, Xinwei Jiang, Dongxue Tang

We present a learning algorithm that uses bone-driven motion networks to predict the deformation of loose-fitting garment meshes at interactive rates. Given a garment, we generate a simulation database and extract virtual bones from simulated mesh sequences using skin decomposition. At runtime, we separately compute low- and high-frequency deformations in a

Sanjar Shaymatov, Naresh Dadhich

In this paper, we wish to investigate the weak cosmic censorship conjecture (WCCC) for the non black hole object, Buchdahl star and test its validity. It turns out that the extremal limit for the star is over-extremal for black hole, $Q^2/M^2 \leq 9/8 >1$; i.e., it could have $9/8 \geq Q^2/M^2 > 1$. By carrying out both linear and non-linear perturbations, w

Hao Ouyang, Jun-Bao Wu

We construct for the first time Drukker-Trancanelli (DT) type fermionic Bogomolnyi-Prasad-Sommerfield (BPS) Wilson loops in four-dimensional $\mathcal{N}=2$ superconformal $SU(N)\times SU(N)$ quiver theory and $\mathcal{N}=4$ super Yang-Mills theory. The connections of these fermionic BPS Wilson loops have a supermatrix structure. We construct timelike BPS W

Sebastian Müller, Andreas Penzkofer, Nikita Polyanskii, Jonas Theis

The Unspent Transaction Output (UTXO) model is commonly used in the field of Distributed Ledger Technology (DLT) to transfer value between participants. One of its advantages is that it allows parallel processing of transactions, as independent transactions can be added in any order. This property of order invariance and parallelisability has potential benef

Tuomas Kärnä, Joseph G. Wallwork, Stephan C. Kramer

Calibration of unknown model parameters is a common task in many ocean model applications. We present an adjoint-based optimization of an unstructured mesh shallow water model for the Baltic Sea. Spatially varying bottom friction parameter is tuned to minimize the misfit with respect to tide gauge sea surface height (SSH) observations. A key benefit of adjoi

Peng Chen, Changsong Deng, Rene Schilling, Lihu Xu

We propose two Euler-Maruyama (EM) type numerical schemes in order to approximate the invariant measure of a stochastic differential equation (SDE) driven by an $α$-stable Lévy process ($1<α<2$): an approximation scheme with the $α$-stable distributed noise and a further scheme with Pareto-distributed noise. Using a discrete version of Duhamel&#39;s principl

Ayano Nakai-Kasai, Tadashi Wadayama

Motivated by emerging technologies for energy efficient analog computing and continuous-time processing, this paper proposes continuous-time minimum mean squared error estimation for multiple-input multiple-output (MIMO) systems based on an ordinary differential equation. Mean squared error (MSE) is a principal detection performance measure of estimation met

S. X. Wang, Z. K. Huang, W. Q. Wen, W. L. Ma

High precision spectroscopy of the low-lying dielectronic resonances in fluorine-like nickel ions were determined by employing the merged electron-ion beam at the heavy-ion storage ring CSRm. The measured dielectronic resonances are identified by comparing with the most recent relativistic calculation utilizing the FAC code. The first resonance at about 86 m

Freddy Torres-Payoma, Diana Herrera, Karla Triana, Laura Neira-Quintero

In this paper, we calculate the gravitational acceleration by using simple pendulums within the designated World Pendulum Alliance: a network constituted by fourteen institutions in eight countries, each provided by a pendulum that can be accessed remotely via the Internet. As a pedagogical option for remote laboratory experiences, we show how to access them

DRHBc Mass Table Collaboration, Cong Pan, Myung-Ki Cheoun, Yong-Beom Choi

The aim of this work is to extend the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) based on the point-coupling density functionals to odd-$A$ and odd-odd nuclei and examine its applicability by taking odd-$A$ Nd isotopes as examples. In the DRHBc theory, the densities and potentials with axial deformation are expanded in terms of Lege

Joonsuk Huh

Exact calculation and even multiplicative error estimation of matrix permanent are challenging for both classical and quantum computers. Regarding the permanents of random Gaussian matrices, the additive error estimation is closely linked to boson sampling, and achieving multiplicative error estimation requires exponentially many samplings. Our newly develop

Michael Ulrich, Sascha Braun, Daniel Köhler, Daniel Niederlöhner

This paper presents novel hybrid architectures that combine grid- and point-based processing to improve the detection performance and orientation estimation of radar-based object detection networks. Purely grid-based detection models operate on a bird&#39;s-eye-view (BEV) projection of the input point cloud. These approaches suffer from a loss of detailed in

Ritabrata Bhattacharya

In this work computation of the renormalised mass at two loop order for the NS sector of heterotic string theory is attempted. We first implement the vertical integration prescription for choosing a section avoiding the spurious poles due to the presence of a required number of picture changing operators. As a result the relevant amplitude on genus 2 Riemann

Marko Znidaric

We study unitary evolution of bipartite entanglement in a circuit with nearest-neighbor random gates. Deriving a compact non-unitary description of purity dynamics on qudits we find a sudden transition in the purity relaxation rate the origin of which is in the underlying boundary localized eigenmodes -- the skin effect. We provide the full solution of the p

Junu Jeong, Sungwoo Youn, Jihn E. Kim

The invisible axion is a well-motivated hypothetical particle which could address two fundamental questions in modern physics - the CP symmetry problem in the strong interactions and the dark matter mystery of our universe. The plausible mass (frequency) range of the QCD axion as a dark matter candidate spans from ueV to meV (O(GHz) to O(THz)). The axion hal

Zhou-Zheng Kang, Rong-Cao Yang

A generalized inhomogeneous higher-order nonlinear Schrodinger (GIHNLS) equation for the Heisenberg ferromagnetic spin chain system in (1+1)-dimensions under zero boundary condition at infinity is taken into account. The spectral analysis is first performed to generate a related matrix Riemann-Hilbert problem on the real axis. Then, through solving the resul

Xin Lin, Changxing Ding, Yibing Zhan, Zijian Li

Scene graph generation (SGG) aims to detect objects and predict their pairwise relationships within an image. Current SGG methods typically utilize graph neural networks (GNNs) to acquire context information between objects/relationships. Despite their effectiveness, however, current SGG methods only assume scene graph homophily while ignoring heterophily. A

Chuan-Chi Wang, Chun-Yen Ho, Chia-Heng Tu, Shih-Hao Hung

Particle Swarm Optimization (PSO) is a stochastic technique for solving the optimization problem. Attempts have been made to shorten the computation times of PSO based algorithms with massive threads on GPUs (graphic processing units), where thread groups are formed to calculate the information of particles and the computed outputs for the particles are aggr

Yun Li, Zhe Liu, Lina Yao, Jessica J. M. Monaghan

EEG-based tinnitus classification is a valuable tool for tinnitus diagnosis, research, and treatments. Most current works are limited to a single dataset where data patterns are similar. But EEG signals are highly non-stationary, resulting in model&#39;s poor generalization to new users, sessions or datasets. Thus, designing a model that can generalize to ne

Julian Dolby, Jason Tsay, Martin Hirzel

Machine learning in practice often involves complex pipelines for data cleansing, feature engineering, preprocessing, and prediction. These pipelines are composed of operators, which have to be correctly connected and whose hyperparameters must be correctly configured. Unfortunately, it is quite common for certain combinations of datasets, operators, or hype