May 2022 arXiv papers — page 6

Xing Wang, Alvin Lim

In this paper, we design and implement a reliable broadcast algorithm over a VANET for supporting multi-hop forwarding of vehicle sensor and control packets that will enable vehicles to platoon with each other in order to form a road train behind the lead truck. In particular, we use multipoint relays (MPRs) for packet transmission, which leads to more effic

L. T. Lehmann, J. -F. Donati

Insights on stellar surface large-scale magnetic field topologies are usually drawn by applying Zeeman-Doppler-Imaging (ZDI) to the observed spectropolarimetric time series. However, ZDI requires experience for reliable results to be reached and is based on a number of prior assumptions that may not be valid, e.g., when the magnetic topology is evolving on t

Mohamed Maghenem, Elena Panteley, Antonio Loria

We analyze networked heterogeneous nonlinear systems, with diffusive coupling and interconnected over a generic static directed graph. Due to the network's hetereogeneity, complete synchronization is impossible, in general, but an emergent dynamics arises. This may be characterized by two dynamical systems evolving in two time-scales. The first, "slow", corr

Alessia Ciccotelli, Robert B. Appleby, Francesco Cerutti, Kacper Bilko

The LHCb (Large Hadron Collider beauty) experiment at CERN aims at achieving a significantly higher luminosity than originally planned by means of two major upgrades: the Upgrade I that took place during the Long Shutdown 2 (LS2) and the Upgrade II foreseen for LS4. Such an increase in instantaneous and integrated luminosity with respect to the design values

Olivier Herscovici-Schiller, Fabien Gachet, Jocelyn Couetdic, Luc Meyer

Ionospheric effects degrade the quality of radar data, which are critical for the precision of the satellite ephemeris produced by space surveillance systems; this degradation is especially noticeable for radars such as GRAVES that operate in the very high frequency range. This article presents a simple and effective method to correct for ionospheric effects

Andrei T. Patrascu

Neural networks are being used to improve the probing of the state spaces of many particle systems as approximations to wavefunctions and in order to avoid the recurring sign problem of quantum monte-carlo. One may ask whether the usual classical neural networks have some actual hidden quantum properties that make them such suitable tools for a highly couple

Matteo Paoluzzi, Demian Levis, Ignacio Pagonabarraga

Living materials such as biological tissues or bacterial colonies are collections of heterogeneous entities of different sizes, capable of autonomous motion, and often capable of cooperating. Such a degree of complexity brings to collective motion on large scales. However, how the competition between geometrical frustration, autonomous motion, and the tenden

Ali Bereyhi, Saba Asaad, Chongjun Ouyang, Ralf R. Müller

This work extends the concept of channel hardening to multi-antenna systems that are aided by intelligent reflecting surfaces (IRSs). For fading links between a multi-antenna transmitter and a single-antenna receiver, we derive an accurate approximation for the distribution of the input-output mutual information when the number of reflecting elements grows l

Ivan Bartulović

In $1998$, Connes and Moscovici defined the cyclic cohomology of Hopf algebras. In $2010$, Khalkhali and Pourkia proposed a braided generalization: to any Hopf algebra $H$ in a braided category $\mathcal B$, they associate a paracocyclic object in $\mathcal B$. In this paper we explicitly compute the powers of the paracocyclic operator of this paracocyclic o

Pavel Khrapov, Veronika Nikishina

The paper considers the generalized Ising model in the $2\times2\times\infty$ strip with a Hamiltonian invariant with respect to the central axis of rotation through the angle $\pi/2$, which includes all possible multiplicative interactions of an even number of spins in the unit cube.The exact value of free energy and heat capacity in the thermodynamic limit

Wallace Moreira Bessa, Max Suell Dutra, Edwin Kreuzer

Electrohydraulic servosystems are widely employed in industrial applications such as robotic manipulators, active suspensions, precision machine tools and aerospace systems. They provide many advantages over electric motors, including high force to weight ratio, fast response time and compact size. However, precise control of electrohydraulic actuated system

Yu Wang, An Zhang, Xiang Wang, Yancheng Yuan

Learning causal structure from observational data is a fundamental challenge in machine learning. However, the majority of commonly used differentiable causal discovery methods are non-identifiable, turning this problem into a continuous optimization task prone to data biases. In many real-life situations, data is collected from different environments, in wh

Gert Vercleyen, Joost Slingerland

We present a method to generate all fusion rings of a specific rank and multiplicity. This method was used to generate exhaustive lists of fusion rings up to order 9 for several multiplicities. We introduce a class of non-commutative fusion rings based on a group with transitive action on a set. This generalizes the Tambara-Yamagami (TY) and Haagerup- Izumi

Michael T. Craig, Jan Wohland, Laurens P. Stoop, Alexander Kies

Energy system models underpin decisions by energy system planners and operators. Energy system modelling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modelling has started to form that aims to better integrate energy system models

Daichi Nakamura, Takumi Bessho, Masatoshi Sato

A striking feature of non-Hermitian systems is the presence of two different types of topology. One generalizes Hermitian topological phases, and the other is intrinsic to non-Hermitian systems, which are called line-gap topology and point-gap topology, respectively. Whereas the bulk-boundary correspondence is a fundamental principle in the former topology,

Jacques Demongeot, Quentin Griette, Yvon Maday, Pierre Magal

During an epidemic, the infectiousness of infected individuals is known to depend on the time since the individual was infected, that is called the age of infection. Here we study the parameter identifiability of the Kermack-McKendrick model with age of infection which takes into account this dependency. By considering a single cohort of individuals, we show

L. Papadimitriou, P. Manganas, A. Ranella, E. Stratakis

Unlike other tissue types, the nervous tissue extends to a wide and complex environment that provides a plurality of different biochemical and topological stimuli which in turn define the functions of that tissue. As a consequence of such complexity, the traditional transplantation therapeutic methods are quite ineffective; therefore, the restoration of peri

Leonardo Biliotti

Let $G$ be a connected semisimple noncompact real Lie group and let $\rho: G \longrightarrow \mathrm{SL}(V)$ be a representation on a finite dimensional vector space $V$ over $\mathbb R$, with $\rho(G)$ closed in $\mathrm{SL}(V)$. Identifying $G$ with $\rho(G)$, we assume there exists a $K$-invariant scalar product $\mathtt g$ such that $G=K\exp(\mathfrak p)

Martin Kutrib, Andreas Malcher, Carlo Mereghetti, Beatrice Palano

An iterated uniform finite-state transducer (IUFST) runs the same length-preserving transduction, starting with a sweep on the input string and then iteratively sweeping on the output of the previous sweep. The IUFST accepts the input string by halting in an accepting state at the end of a sweep. We consider both the deterministic (IUFST) and nondeterministi

Graham W. Pulford

Expectation maximisation (EM) is usually thought of as an unsupervised learning method for estimating the parameters of a mixture distribution, however it can also be used for supervised learning when class labels are available. As such, EM has been applied to train neural nets including the probabilistic radial basis function (PRBF) network or shared kernel

Manjinder Singh, Alexander E. I. Brownlee, David Cairns

Metaheuristic search algorithms look for solutions that either maximise or minimise a set of objectives, such as cost or performance. However most real-world optimisation problems consist of nonlinear problems with complex constraints and conflicting objectives. The process by which a GA arrives at a solution remains largely unexplained to the end-user. A po

Nitya Sathyavageeswaran, Roy D. Yates, Anand D. Sarwate, Narayan Mandayam

A source generates time-stamped update packets that are sent to a server and then forwarded to a monitor. This occurs in the presence of an adversary that can infer information about the source by observing the output process of the server. The server wishes to release updates in a timely way to the monitor but also wishes to minimize the information leaked

Stephan Schlegel, Joachim Saur

Io's movement relative to the plasma in Jupiter's magnetosphere creates Alfv\'en waves propagating along the magnetic field lines which are partially reflected along their path. These waves are the root cause for auroral emission, which is subdivided into the Io Footprint (IFP), its tail and leading spot. New observations of the Juno spacecraft by Mura et al

Martin Hoefer, Lisa Wilhelmi

Financial network games model payment incentives in the context of networked liabilities. In this paper, we advance the understanding of incentives in financial networks in two important directions: minimal clearing (arising, e.g., as a result of sequential execution of payments) and seniorities (i.e., priorities over debt contracts). We distinguish between

Marco Antonio Stranisci, Simona Frenda, Eleonora Ceccaldi, Valerio Basile

Despite the large number of computational resources for emotion recognition, there is a lack of data sets relying on appraisal models. According to Appraisal theories, emotions are the outcome of a multi-dimensional evaluation of events. In this paper, we present APPReddit, the first corpus of non-experimental data annotated according to this theory. After d

Wietse M. Boon, Alessio Fumagalli

A solution technique is proposed for flows in porous media that guarantees local conservation of mass. We first compute a flux field to balance the mass source and then exploit exact co-chain complexes to generate a solenoidal correction. A reduced basis method based on proper orthogonal decomposition is employed to construct the correction and we show that

Mizuki Tani, Kakeru Sasaki, Yasushi Shinohara, Kenichi L. Ishikawa

We theoretically investigate the optical energy absorption of crystalline silicon subject to dual-color femtosecond laser pulses, using the time-dependent density functional theory (TDDFT). We employ the modified Becke-Johnson (mBJ) exchange-correlation potential which reproduces the experimental direct bandgap energy $E_g$. We consider situations where the

Avinandan Bose, Arunesh Sinha, Tien Mai

Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of fractionals, non-convex optimization which is used for decision making in prominent areas such as facility location and security games. In contrast to

Alexander Nedergaard, Matthew Cook

Exploration in high-dimensional, continuous spaces with sparse rewards is an open problem in reinforcement learning. Artificial curiosity algorithms address this by creating rewards that lead to exploration. Given a reinforcement learning algorithm capable of maximizing rewards, the problem reduces to finding an optimization objective consistent with explora

J. Kluson

In this short note we present analysis of the tachyon kink solution in the canonical formalism. We also find Hamiltonian for simplified system of Dp-brane and anti-Dp-brane.

Nitin Kumar Chidambaram, Elba Garcia-Failde, Alessandro Giacchetto

We construct and study various properties of a negative spin version of the Witten $ r $-spin class. By taking the top Chern class of a certain vector bundle on the moduli space of twisted spin curves that parametrises $ r $-th roots of the anticanonical bundle, we construct a non-semisimple cohomological field theory (CohFT) that we call the Theta class $ \

Diego A. Lopez

It is known that Shintani zeta functions, which generalise multiple zeta functions, extend to meromorphic functions with poles on affine hyperplanes. We refine this result in showing that the poles lie on hyperplanes parallel to the facets of certain convex polyhedra associated to the defining matrix for the Shintani zeta function. Explicitly, the latter are

Daiki Chijiwa, Shin'ya Yamaguchi, Atsutoshi Kumagai, Yasutoshi Ida

Few-shot learning for neural networks (NNs) is an important problem that aims to train NNs with a few data. The main challenge is how to avoid overfitting since over-parameterized NNs can easily overfit to such small dataset. Previous work (e.g. MAML by Finn et al. 2017) tackles this challenge by meta-learning, which learns how to learn from a few data by us

Dong Liu, Weihua Deng

Anomalous diffusions are ubiquitous in nature, whose functional distributions are governed by the backward Feynman-Kac equation. In this paper, the local discontinuous Galerkin (LDG) method is used to solve the 2D backward Feynman-Kac equation in a rectangular domain. The spatial semi-discrete LDG scheme of the equivalent form (obtained by Laplace transform)

Tobias Uelwer, Sebastian Konietzny, Stefan Harmeling

Phase retrieval is the problem of reconstructing images from magnitude-only measurements. In many real-world applications the problem is underdetermined. When training data is available, generative models allow optimization in a lower-dimensional latent space, hereby constraining the solution set to those images that can be synthesized by the generative mode

Christopher Linderälv, Joakim Hagel, Samuel Brem, Ermin Malic

Moir\'e superlattices serve as a playground for emerging phenomena, such as localization of band states, superconductivity, and localization of excitons. These superlattices are large and are often modeled in the zero angle limit, which obscures the effect of finite twist angles. Here, by means of first-principles calculations we quantify the twist-angle dep

Zixiang Ren, Xianxin Song, Yuan Fang, Ling Qiu

This paper studies the multi-antenna multicast channel with integrated sensing and communication (ISAC), in which a multi-antenna base station (BS) sends common messages to a set of single-antenna communication users (CUs) and simultaneously estimates the parameters of an extended target via radar sensing. We investigate the fundamental performance limits of

Matteo Zecchin, Marios Kountouris, David Gesbert

Decentralized learning algorithms empower interconnected devices to share data and computational resources to collaboratively train a machine learning model without the aid of a central coordinator. In the case of heterogeneous data distributions at the network nodes, collaboration can yield predictors with unsatisfactory performance for a subset of the devi

H. S. Vieira, V. B. Bezerra, C. R. Muniz, M. S. Cunha

We examine the interaction between massless scalar fields and the gravitational field generated by a black hole solution that was recently obtained in the consistent well-defined 4-dimensional Einstein-Gauss-Bonnet gravity with a cosmological constant. In order to do this, we calculate quasibound state frequencies of scalar fields for the spherically symmetr

Emanuele Marconato, Andrea Passerini, Stefano Teso

There is growing interest in concept-based models (CBMs) that combine high-performance and interpretability by acquiring and reasoning with a vocabulary of high-level concepts. A key requirement is that the concepts be interpretable. Existing CBMs tackle this desideratum using a variety of heuristics based on unclear notions of interpretability, and fail to

Alexis L. Quintana, Nicholas J. Wright

The proper motions (PMs) of OB stars in Cygnus have recently been found to exhibit two large-scale kinematic patterns suggestive of expansion. We perform a 3D traceback on these OB stars, the newly-identified OB associations and related open clusters in the region. We find that there are two groups of stars, associations and clusters and that they were each

Takuya Katagiri, Masashi Kimura

We study quasinormal modes (QNMs) of massive Klein-Gordon fields in static Ba\~{n}ados-Teitelboim-Zanelli (BTZ) black holes in terms of ladder operators constructed from spacetime conformal symmetries. Because the BTZ spacetime is locally isometric to the three-dimensional anti-de Sitter spacetime, ladder operators, which map a solution of the massive Klein-

Minseok Seo, Jeongwon Ryu, Kwangjin Yoon

In this paper, SIA_Track is presented which is developed by a research team from SI Analytics. The proposed method was built from pre-existing detector and tracker under the tracking-by-detection paradigm. The tracker we used is an online tracker that merely links newly received detections with existing tracks. The core part of our method is training procedu

Eunbi Choi, Yongrae Jo, Joel Jang, Minjoon Seo

Recent works have shown that attaching prompts to the input is effective at conditioning Language Models (LM) to perform specific tasks. However, prompts are always included in the input text during inference, thus incurring substantial computational and memory overhead. Also, there is currently no straightforward method of utilizing prompts that are longer

Wei Mao, Miaomiao Liu, Mathieu Salzmann

We introduce the task of action-driven stochastic human motion prediction, which aims to predict multiple plausible future motions given a sequence of action labels and a short motion history. This differs from existing works, which predict motions that either do not respect any specific action category, or follow a single action label. In particular, addres

Jingru Fu, Antonios Tzortzakakis, José Barroso, Eric Westman

Analyzing and predicting brain aging is essential for early prognosis and accurate diagnosis of cognitive diseases. The technique of neuroimaging, such as Magnetic Resonance Imaging (MRI), provides a noninvasive means of observing the aging process within the brain. With longitudinal image data collection, data-intensive Artificial Intelligence (AI) algorith

J. Bukhari, A. G. Somanagoudar, L. Hou, O. Herrera

Greenhouse gas, produced from various industries such as Power, Manufacturing, Transport, Chemical, or Agriculture, is the major source of global warming. While the transport industry is among the top three major contributors, accounting for 16.2% of global emissions. To counter this, many countries are responding actively to achieve net or absolute zero-emi

Fakhrielddine Bader, Mostafa Bendahmane, Mazen Saad, Raafat Talhouk

We present a novel microscopic tridomain model describing the electrical activity in cardiac tissue with dynamical gap junctions. The microscopic tridomain system consists of three PDEs modeling the tissue electrical conduction in the intra-and extra-cellular domains, supplemented by a nonlinear ODE system for the dynamics of the ion channels and the gap jun

Evangelos Matsinos

Compared in this work are a few sets of results, obtained from the pion-nucleon ($\pi N$) data at low energy (pion laboratory kinetic energy up to $100$ MeV) on the basis of the modelling of the $s$- and $p$-wave $K$-matrix elements (or of their reciprocal) via simple polynomials. The fitted values and uncertainties of the model parameters, as well as the co

Theodoros Nakas

In the context of this dissertation, we study the emergence of black-string and black-hole solutions in the framework of five-dimensional braneworld models. The main motivation for studying such theories stems from the Randall-Sundrum model, which was formulated by Lisa Randall and Raman Sundrum back in 1999. We investigate thoroughly the physical characteri

Ivan Ivec, Ivana Vojnović

We develop a new method for stochastic optimization using the Bayesian statistics approach. More precisely, we optimize parameters of chess engines as those data are available to us, but the method should apply to all situations where we want to optimize a certain gain/loss function which has no analytical form and thus cannot be measured directly but only b

Shintaro Murakami, Yohei Tachiya

Let $(i,j)\in \mathbb{N}\times \mathbb{N}_{\geq2}$ and $S_{i,j}$ be an infinite subset of positive integers including all prime numbers in some arithmetic progression. In this paper, we prove the linear independence over $\mathbb{Q}$ of the numbers \[ 1, \quad \sum_{n\in S_{i,j}}^{}\frac{a_{i,j}(n)}{b^{in^j}},\quad (i,j)\in \mathbb{N}\times \mathbb{N}_{\geq2

Rémi Goudey, Claude Le Bris

We consider an homogenization problem for the second order elliptic equation $- \Delta u^{\varepsilon} + \dfrac{1}{\varepsilon} V(./\varepsilon) u^{\varepsilon} + \nu u^{\varepsilon} =f$ when the highly oscillatory potential $V$ belongs to a particular class of non-periodic potentials. We show the existence of an adapted corrector and prove the convergence o

Alp Öktem, Rodolfo Zevallos, Yasmin Moslem, Güneş Öztürk

We develop machine translation and speech synthesis systems to complement the efforts of revitalizing Judeo-Spanish, the exiled language of Sephardic Jews, which survived for centuries, but now faces the threat of extinction in the digital age. Building on resources created by the Sephardic community of Turkey and elsewhere, we create corpora and tools that

Kazuki Nakamura, Eiichiro Uchino, Noriaki Sato, Ayano Araki

Early disease detection and prevention methods based on effective interventions are gaining attention. Machine learning technology has enabled precise disease prediction by capturing individual differences in multivariate data. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that co

Navin Kumar Chandra, Shubham Sharma, Saptarshi Basu, Aloke Kumar

Droplet atomization through aerobreakup is omnipresent in various natural and industrial processes. Atomization of Newtonian droplets is a well-studied area; however, non-Newtonian droplets have received less attention despite their frequent encounters. By subjecting polymeric droplets of different concentrations to the induced airflow behind a moving shock

Hanna Sai, Xiaofeng Wang, Nancy Elias-Rosa, Yi Yang

Early-time radiative signals from type Ia supernovae (SNe Ia) can provide important constraints on the explosion mechanism and the progenitor system. We present observations and analysis of SN 2019np, a nearby SN Ia discovered within 1-2 days after the explosion. Follow-up observations were conducted in optical, ultraviolet, and near-infrared bands, covering

Satoshi Tsutsui, Weijia Mao, Sijing Lin, Yunyi Zhu

Rendering scenes with a high-quality human face from arbitrary viewpoints is a practical and useful technique for many real-world applications. Recently, Neural Radiance Fields (NeRF), a rendering technique that uses neural networks to approximate classical ray tracing, have been considered as one of the promising approaches for synthesizing novel views from

Jordan Serres

We study stability of the eigenvalues of the generator of a one dimensional reversible diffusion process satisfying some natural conditions. The proof is based on Stein's method. In particular, these results are applied to the Normal distribution (via the Ornstein-Uhlenbeck process), to Gamma distributions (via the Laguerre process) and to Beta distributions

Suvankar Dutta, Gurmeet Singh Punia

The connection between the bulk and the boundary first law of thermodynamics in adS space has been discussed in generic higher derivative gravity. String theory corrections to the supergravity render higher derivative terms in the bulk action, proportional to different powers of string theory parameter $\alpha'$. A variation in the cosmological constant indu

Yurui Ming, Cuihuan Du, Chin-Teng Lin

Autoencoder can give rise to an appropriate latent representation of the input data, however, the representation which is solely based on the intrinsic property of the input data, is usually inferior to express some semantic information. A typical case is the potential incapability of forming a clear boundary upon clustering of these representations. By enco

Maxime Clenet, E Ferchichi, Jamal Najim

We study the equilibria of a large Lokta-Volterra system of coupled differential equations in the case where the interaction coefficients form a large random matrix. In the case where this random matrix follows an elliptic model , we study the existence of a (componentwise) positive equilibrium and describe a phase transition for the matrix normalization.If

Houssam Boukhecham

We prove that a $C^1$ hyperbolic map whose differential is regular enough has an SRB measure. The precise regularity condition is weaker than H{\"o}lder and was mentionned by various authors through the developement of expanding and uniformly hyperbolic dynamics.

Mauri J. Valtonen, Lankeswar Dey, S. Zola, S. Ciprini

OJ 287 is a BL Lacertae type quasar in which the active galactic nucleus (AGN) outshines the host galaxy by an order of magnitude. The only exception to this may be at minimum light when the AGN activity is so low that the host galaxy may make quite a considerable contribution to the photometric intensity of the source. Such a dip or a fade in the intensity

Mao Zhang, Huai-Ming Yu, Haidong Yuan, Xiaoguang Wang

Quantum parameter estimation promises a high-precision measurement in theory, however, how to design the optimal scheme in a specific scenario, especially under a practical condition, is still a serious problem that needs to be solved case by case due to the existence of multiple mathematical bounds and optimization methods. Depending on the scenario conside

Adam Gabriel Dobrakowski, Andrzej Pacuk, Piotr Sankowski, Marcin Mucha

This paper introduces the concept of traffic-fingerprints, i.e., normalized 24-dimensional vectors representing a distribution of daily traffic on a web page. Using k-means clustering we show that similarity of traffic-fingerprints is related to the similarity of profitability time patterns for ads shown on these pages. In other words, these fingerprints are

Juan A. Barceló, Carlos Castro, Fabricio Macià, Cristóbal J. Meroño

In this work we illustrate a number of properties of the Born approximation in the three-dimensional Calder\'on inverse conductivity problem by numerical experiments. The results are based on an explicit representation formula for the Born approximation recently introduced by the authors. We focus on the particular case of radial conductivities in the ball $

Jérôme Bolte, Edouard Pauwels, Samuel Vaiter

Differentiation along algorithms, i.e., piggyback propagation of derivatives, is now routinely used to differentiate iterative solvers in differentiable programming. Asymptotics is well understood for many smooth problems but the nondifferentiable case is hardly considered. Is there a limiting object for nonsmooth piggyback automatic differentiation (AD)? Do

Xiaolei Zhang, Hwankoo Kim, Wei Qi

In this paper, we prove that a finitely embedded $R$-module $M$ is Artinian if and only if for every prime ideal $\mathfrak{p}$ of $R$ with $(0:_RM)\subseteq \mathfrak{p}$, there exists a submodule $N^\mathfrak{p}$ of $M$ such that $M/N^\mathfrak{p}$ is finitely embedded and $M[\mathfrak{p}]\subseteq N^\mathfrak{p}\subseteq (0:_M\mathfrak{p})$.

Sosuke Kobayashi, Eiichi Matsumoto, Vincent Sitzmann

Emerging neural radiance fields (NeRF) are a promising scene representation for computer graphics, enabling high-quality 3D reconstruction and novel view synthesis from image observations. However, editing a scene represented by a NeRF is challenging, as the underlying connectionist representations such as MLPs or voxel grids are not object-centric or compos

Tao She, Chunxiang Wang

We study the spectra of mixed graphs about its Hermitian adjacency matrix of the second kind (i.e. N-matrix) introduced by Mohar [1]. We extend some results and define one new Hermitian adjacency matrix, and the entry corresponding to an arc from $u$ to $v$ is equal to the $k$-th( or the third) root of unity, i.e. ${\omega} = cos(2{\pi}/k) + \textbf{i} \ sin

Xiaolei Zhang, Hwankoo Kim, Wei Qi

Parkash and Kour obtained a new version of Cohen's theorem for Noetherian modules, which states that a finitely generated $R$-module $M$ is Noetherian if and only if for every prime ideal $\mathfrak{p}$ of $R$ with Ann$(M)\subseteq \mathfrak{p}$, there exists a finitely generated submodule $N^\mathfrak{p}$ of $M$ such that $\mathfrak{p} M\subseteq N^\mathfra

Yating Ma, Zhichao Lian

With the development of technology rapidly, applications of convolutional neural networks have improved the convenience of our life. However, in image classification field, it has been found that when some perturbations are added to images, the CNN would misclassify it. Thus various defense methods have been proposed. The previous approach only considered ho

Armin W. Thomas, Christopher Ré, Russell A. Poldrack

Deep learning (DL) models find increasing application in mental state decoding, where researchers seek to understand the mapping between mental states (e.g., perceiving fear or joy) and brain activity by identifying those brain regions (and networks) whose activity allows to accurately identify (i.e., decode) these states. Once a DL model has been trained to

Alexander Tyurin, Peter Richtárik

We present a new method that includes three key components of distributed optimization and federated learning: variance reduction of stochastic gradients, partial participation, and compressed communication. We prove that the new method has optimal oracle complexity and state-of-the-art communication complexity in the partial participation setting. Regardles

A. T. Sutinjo, D. C. X. Ung, M. Sokolowski

We present the system equivalent flux density (SEFD) expressions for all four Stokes parameters: I, Q, U, V. The expressions were derived based on our derivation of SEFD I (for Stokes I) and subsequent extensions of that work to phased array and multipole interferometers. The key to the derivation of the SEFD Q, U, V expressions is to recognize that the nois

Nihat Sadik Deger, Henning Samtleben

Three-dimensional Yang-Mills theory allows for a deformation quadratic in the field strengths which can not be integrated to a local action without auxiliary fields. Yet, its covariant divergence consistently vanishes after iterating the equation, realizing a spin-1 analogue of `minimal massive gravity', which has been dubbed `third way consistent'. In this

Chean Fei Shee, Seiichi Uchida

A multi-layer image is more valuable than a single-layer image from a graphic designer's perspective. However, most of the proposed image generation methods so far focus on single-layer images. In this paper, we propose MontageGAN, which is a Generative Adversarial Networks (GAN) framework for generating multi-layer images. Our method utilized a two-step app

Georgios Mylonas, Dimitrios Amaxilatis, Ioannis Chatzigiannakis

The COVID-19 pandemic has brought profound change in the daily lives of a large part of the global population during 2020 and 2021. Such changes were mirrored in aspects such as changes to the overall energy consumption, or long periods of sustained inactivity inside public buildings. At the same time, due to the large proliferation of IoT, sensors and smart

Marvin Nipper, Andreas Ostermaier, Jochen Theis

Sustainability reporting enables investors to make informed decisions and is hoped to facilitate the transition to a green economy. The European Union's taxonomy regulation enacts rules to discern sustainable activities and determine the resulting green revenue, whose disclosure is mandatory for many companies. In an experiment, we explore how this standardi

Stefan Schweter, Luisa März, Katharina Schmid, Erion Çano

Compared to standard Named Entity Recognition (NER), identifying persons, locations, and organizations in historical texts constitutes a big challenge. To obtain machine-readable corpora, the historical text is usually scanned and Optical Character Recognition (OCR) needs to be performed. As a result, the historical corpora contain errors. Also, entities lik

T S Mahesh, Priya Batra, M. Harshanth Ram

Quantum controls realize the unitary or nonunitary operations employed in quantum computers, quantum simulators, quantum communications, and other quantum information devices. They implement the desired quantum dynamics with the help of electric, magnetic, or electromagnetic control fields. Quantum optimal control (QOC) deals with designing an optimal contro

Wenlin Zhuang, Jinwei Qi, Peng Zhang, Bang Zhang

Due to the increasing demand in films and games, synthesizing 3D avatar animation has attracted much attention recently. In this work, we present a production-ready text/speech-driven full-body animation synthesis system. Given the text and corresponding speech, our system synthesizes face and body animations simultaneously, which are then skinned and render

Weikai Chen, Cheng Lin, Weiyang Li, Bo Yang

Recent advances in learning 3D shapes using neural implicit functions have achieved impressive results by breaking the previous barrier of resolution and diversity for varying topologies. However, most of such approaches are limited to closed surfaces as they require the space to be divided into inside and outside. More recent works based on unsigned distanc

Mingxing Xu, Chenglin Li, Wenrui Dai, Siheng Chen

Pooling and unpooling are two essential operations in constructing hierarchical spherical convolutional neural networks (HS-CNNs) for comprehensive feature learning in the spherical domain. Most existing models employ downsampling-based pooling, which will inevitably incur information loss and cannot adapt to different spherical signals and tasks. Besides, t

Greg Ashton, Noam Bernstein, Johannes Buchner, Xi Chen

We review Skilling's nested sampling (NS) algorithm for Bayesian inference and more broadly multi-dimensional integration. After recapitulating the principles of NS, we survey developments in implementing efficient NS algorithms in practice in high-dimensions, including methods for sampling from the so-called constrained prior. We outline the ways in which N

Tony Tohme, Dehong Liu, Kamal Youcef-Toumi

Identifying the mathematical relationships that best describe a dataset remains a very challenging problem in machine learning, and is known as Symbolic Regression (SR). In contrast to neural networks which are often treated as black boxes, SR attempts to gain insight into the underlying relationships between the independent variables and the target variable

Dennis Rieber, Moritz Reiber, Oliver Bringmann, Holger Fröning

The process of optimizing the latency of DNN operators with ML models and hardware-in-the-loop, called auto-tuning, has established itself as a pervasive method for the deployment of neural networks. From a search space of loop-optimizations, the candidate providing the best performance has to be selected. Performance of individual configurations is evaluate

Johannes Konig, David I. Stern, Richard S. J. Tol

We compute confidence intervals for recursive impact factors, that take into account that some citations are more prestigious than others, as well as for the associated ranks of journals, applying the methods to the population of economics journals. The Quarterly Journal of Economics is clearly the journal with greatest impact, the confidence interval for it

Youngsik Yoon, Jinhwan Nam, Hyojeong Yun, Jaeho Lee

We consider a practical scenario of machine unlearning to erase a target dataset, which causes unexpected behavior from the trained model. The target dataset is often assumed to be fully identifiable in a standard unlearning scenario. Such a flawless identification, however, is almost impossible if the training dataset is inaccessible at the time of unlearni

Jean-Yves Welschinger

From the topological viewpoint, Morse shellings of finite simplicial complexes are {\it pinched} handle decompositions and extend the classical shellings. We prove that every discrete Morse function on a finite simplicial complex induces Morse shellings on its second barycentric subdivision whose critical tiles-or pinched handles-are in oneto-one corresponde

Emre Can Kuran, Umut Kuran, Mehmet Bilal Er

Contrast enhancement is very important in terms of assessing images in an objective way. Contrast enhancement is also significant for various algorithms including supervised and unsupervised algorithms for accurate classification of samples. Some contrast enhancement algorithms solve this problem by addressing the low contrast issue. Mean and variance based

Tamem Al-shorman, Malik Bataineh, Melis Bolat, Bayram Ali Ersoy

In this study, we introduce graded pseudo weakly prime submodules of G-graded R-modules, which are an extension of graded weakly prime ideals over G-graded rings. On the graded spectrum of graded pseudo weakly prime submodules, we investigate the Zariski topology. Different aspects of this topological space are investigated, and they are linked to the algebr

Songze Li, Sizai Hou, Baturalp Buyukates, Salman Avestimehr

We consider a foundational unsupervised learning task of $k$-means data clustering, in a federated learning (FL) setting consisting of a central server and many distributed clients. We develop SecFC, which is a secure federated clustering algorithm that simultaneously achieves 1) universal performance: no performance loss compared with clustering over centra

Hariprasad Manjunath, Sivaram Ambikasaran

This article looks at the eigenvalues of magic squares generated by the MATLAB's magic($n$) function. The magic($n$) function constructs doubly even ($n = 4k$) magic squares, singly even ($n = 4k+2$) magic squares and odd ($n = 2k+1$) magic squares using different algorithms. The doubly even magic squares are constructed by a criss-cross method that involves

Khoi Nguyen, Sinisa Todorovic

This paper addresses incremental few-shot instance segmentation, where a few examples of new object classes arrive when access to training examples of old classes is not available anymore, and the goal is to perform well on both old and new classes. We make two contributions by extending the common Mask-RCNN framework in its second stage -- namely, we specif

Pei Liu, Yanjie Zhao, Haipeng Cai, Mattia Fazzini

Fragmentation is a serious problem in the Android ecosystem. This problem is mainly caused by the fast evolution of the system itself and the various customizations independently maintained by different smartphone manufacturers. Many efforts have attempted to mitigate its impact via approaches to automatically pinpoint compatibility issues in Android apps. U

Songbai Guo, Yuling Xue, Xiliang Li, Zuohuan Zheng

A coronavirus disease 2019 (COVID-19) model with quarantine and standard incidence rate is first developed, then a novel analysis approach for finding the ultimate lower bound of COVID-19 infectious individuals is proposed, which means that the COVID-19 pandemic is uniformly persistent if the control reproduction number $\mathcal{R}_{c}>1$. This approach can

Ganesh Pokharel, Brenden Ortiz, Juan Chamorro, Paul Sarte

RV6Sn6 (R=rare earth) compounds are appealing materials platforms for exploring the interplay between R-site magnetism and nontrivial band topology associated with the nonmagnetic vanadium-based kagome network. Here we present the synthesis and characterization of the kagome metal TbV6Sn6 via single-crystal x-ray diffraction, magnetization, transport, and he

Kiyoshi Kanazawa, Hideki Takayasu, Misako Takayasu

The two-body stochastic dealer model is revisited to provide an exact solution to the average order-book profile using the kinetic approach. The dealer model is a microscopic financial model where individual traders make decisions on limit-order prices stochastically and then reach agreements on transactions. In the literature, this model was solved for seve