May 2022 arXiv papers — page 11

Andrea Munaro, Shizhou Yang

Mim-width and sim-width are among the most powerful graph width parameters, with sim-width more powerful than mim-width, which is in turn more powerful than clique-width. While several $\mathsf{NP}$-hard graph problems become tractable for graph classes whose mim-width is bounded and quickly computable, no algorithmic applications of boundedness of sim-width

Sabine Frittella, Ondrej Majer, Sajad Nazari

Belief and plausibility are weaker measures of uncertainty than that of probability. They are motivated by the situations when full probabilistic information is not available. However, information can also be contradictory. Therefore, the framework of classical logic is not necessarily the most adequate. Belnap-Dunn logic was introduced to reason about incom

Vadim Gubaidulin, Vladimir Kornilov, Oliver Boine-Frankenheim, Elias Métral

A pulsed electron lens produces a betatron tune shift along a hadron bunch as a function of the longitudinal coordinates, which is a longitudinal detuning. An example of transverse detuning are the tune shifts due to octupole magnets. This paper considers a pulsed electron lens as a measure to mitigate the head-tail instabilities. Using a detailed analytical

Zheyuan Li, Jiguo Cao

The selection of smoothing parameter is central to the estimation of penalized splines. The best value of the smoothing parameter is often the one that optimizes a smoothness selection criterion, such as generalized cross-validation error (GCV) and restricted likelihood (REML). To correctly identify the global optimum rather than being trapped in an undesire

Jihan Yang, Shaoshuai Shi, Runyu Ding, Zhe Wang

Despite substantial progress in 3D object detection, advanced 3D detectors often suffer from heavy computation overheads. To this end, we explore the potential of knowledge distillation (KD) for developing efficient 3D object detectors, focusing on popular pillar- and voxel-based detectors.In the absence of well-developed teacher-student pairs, we first stud

Christina Psaroudaki, Christos Panagopoulos

We derive the quantization of magnetic helicity in the solid-state and demonstrate tunable macroscopic quantum tunneling, coherence, and oscillation for a skyrmion spin texture stabilized in frustrated magnets. We also discuss the parameter space for the experimental realization of quantum effects. Typically, for a skyrmion of 5 nm radius, quantum tunneling

Song Li, Zhuang Li, Fei Wang, Jin Min Yang

We propose to jointly explain the electron/muon $g-2$ anomalies in the framework of anomaly mediated SUSY breaking (AMSB) scenario. Two Yukawa deflected AMSB models are proposed and discussed in depth: one with lepton-specific interactions and the other one with messenger-matter interactions. Both models are found to be able to jointly explain the anomalies

Shreyas Gawde, Shruti Patil, Satish Kumar, Pooja Kamat

Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of machinery. Majority of these machines comprise rotating components and are called rotating machines. The engineers' top priority is to maintain these critical machines to reduce the unplanned shutdown and increase the useful life of machinery. Predictive maintenance

Rucha Shinde, Shruti Patil, Ketan Kotecha, Vidyasagar Potdar

Healthcare systems are increasingly incorporating Artificial Intelligence into their systems, but it is not a solution for all difficulties. AI's extraordinary potential is being held back by challenges such as a lack of medical datasets for training AI models, adversarial attacks, and a lack of trust due to its black box working style. We explored how block

Enguerrand Lavigne Bon

This paper is devoted to the semiclassical analysis of the spectrum of the Dirichlet-Pauli operator on an annulus. We assume that the magnetic field is strictly positive and radial. We give an explicit asymptotic expansion at the first order of the lowest eigenvalues of this operator in the semiclassical limit. In particular, we exhibit the Aharonov-Bohm eff

Sumit Haldar, Sk Saniur Rahaman, Manoranjan Kumar

The Berezinskii-Kosterlitz-Thouless (BKT) transition in magnetic system is an intriguing phenomena and an accurate estimation of the BKT transition temperature has been a long-standing problem. In this work we explore the anisotropic classical Heisenberg XY and XXZ models with ferromagnetic exchange on a square lattice and antiferromagnetic exchange on a tri

Thu Nguyen, Hoang Thien Ly, Michael Alexander Riegler, Pål Halvorsen

Missing data is a commonly occurring problem in practice. Many imputation methods have been developed to fill in the missing entries. However, not all of them can scale to high-dimensional data, especially the multiple imputation techniques. Meanwhile, the data nowadays tends toward high-dimensional. Therefore, in this work, we propose Principal Component An

Nicolas-Domenic Reiter, Andreas Gerhardus, Jakob Runge

Complex dynamical systems are prevalent in many scientific disciplines. In the analysis of such systems two aspects are of particular interest: 1) the temporal patterns along which they evolve and 2) the underlying causal mechanisms. Time-series representations like discrete Fourier and wavelet transforms have been widely applied in order to obtain insights

Francesco Antonio Denisi

We show that Kov\'acs' result on the cone of curves of a K3 surface generalizes to any projective irreducible holomorphic symplectic manifold $X$. In particular, we show that if $\rho(X)\geq 3$, the pseudo-effective cone $\overline{\mathrm{Eff}(X)}$ is either circular or equal to $\overline{\sum_{E}\mathbf{R}^{\geq 0} [E]}$, where the sum runs over the prime

Bacco Manlio, Delmastro Franca, Ferro Erina, Gotta Alberto

This work presents an innovative, multidisciplinary and cost-effective ecosystem of ICT solutions able to collect, process and distribute geo-referenced information about the influence of pollution and micro-climatic conditions on the quality of life in Smart Cities. The system has been developed and experimentally evaluated in the framework of the research

Zhanpeng Zhou, Wen Shen, Huixin Chen, Ling Tang

In this paper, we prove the effects of the BN operation on the back-propagation of the first and second derivatives of the loss. When we do the Taylor series expansion of the loss function, we prove that the BN operation will block the influence of the first-order term and most influence of the second-order term of the loss. We also find that such a problem

Clemens Heistracher, Anahid Jalali, Jürgen Schneeweiss, Klaudia Kovacs

Coating chambers create thin layers that improve the mechanical and optical surface properties in jewelry production using physical vapor deposition. In such a process, evaporated material condensates on the walls of such chambers and, over time, causes mechanical defects and unstable processes. As a result, manufacturers perform extensive maintenance proced

Maria Tantalo, Massimo Dall'Ora, Giuseppe Bono, Peter B. Stetson

We present accurate and deep multi-band ($g,r,i$) photometry of the Local Group dwarf irregular galaxy NGC 6822. The images were collected with wide field cameras at 2m/4m- (INT,CTIO,CFHT) and 8m-class telescopes (SUBARU) covering a 2 square degrees FoV across the center of the galaxy. We performed PSF photometry of $\approx$7,000 CCD images and the final ca

Amirkeivan Mohtashami, Martin Jaggi, Sebastian Stich

When training neural networks, it has been widely observed that a large step size is essential in stochastic gradient descent (SGD) for obtaining superior models. However, the effect of large step sizes on the success of SGD is not well understood theoretically. Several previous works have attributed this success to the stochastic noise present in SGD. Howev

Ryuta Arisaka, Ryoma Nakai, Yusuke Kawamoto, Takayuki Ito

From daily discussions to marketing ads to political statements, information manipulation is rife. It is increasingly more important that we have the right set of tools to defend ourselves from manipulative rhetoric, or fallacies. Suitable techniques to automatically identify fallacies are being investigated in natural language processing research. However,

Marcel Khalifa, Rotem Ben-Hur, Ronny Ronen, Orian Leitersdorf

Aligning the entire genome of an organism is a compute-intensive task. Pre-alignment filters substantially reduce computation complexity by filtering potential alignment locations. The base-count filter successfully removes over 68% of the potential locations through a histogram-based heuristic. This paper presents FiltPIM, an efficient design of the basecou

Yuhang Wang, Li Wang, Yanjie Yang, Yilin Zhang

Since fake news poses a serious threat to society and individuals, numerous studies have been brought by considering text, propagation and user profiles. Due to the data collection problem, these methods based on propagation and user profiles are less applicable in the early stages. A good alternative method is to detect news based on text as soon as they ar

Raven Beutner, David Carral, Bernd Finkbeiner, Jana Hofmann

We study satisfiability for HyperLTL with a $\forall^*\exists^*$ quantifier prefix, known to be highly undecidable in general. HyperLTL can express system properties that relate multiple traces (so-called hyperproperties), which are often combined with trace properties that specify functional behavior on single traces. Following this conceptual split, we fir

Simon F Muller-Cleve, Vittorio Fra, Lyes Khacef, Alejandro Pequeno-Zurro

Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches have reached outstanding accuracies in such tasks, but their implementation on conventional embedded solutions is still very computationally and energy expensive. Tactile sensing in robotic applicatio

Alexandre Girard, H. Harry Asada

Vehicle power-trains use a variable transmission (multiple gear-ratios) to minimize motor size and maximize efficiency while meeting a wide-range of operating points. Robots could similarly benefit from variable transmission to save weight and improve energy efficiency; leading to potentially groundbreaking improvements for mobile and wearable robotic system

Dmitry Kovalev, Aleksandr Beznosikov, Ekaterina Borodich, Alexander Gasnikov

We study structured convex optimization problems, with additive objective $r:=p + q$, where $r$ is ($\mu$-strongly) convex, $q$ is $L_q$-smooth and convex, and $p$ is $L_p$-smooth, possibly nonconvex. For such a class of problems, we proposed an inexact accelerated gradient sliding method that can skip the gradient computation for one of these components whi

Keyan Nasseri, Chandan Singh, James Duncan, Aaron Kornblith

Machine learning in high-stakes domains, such as healthcare, faces two critical challenges: (1) generalizing to diverse data distributions given limited training data while (2) maintaining interpretability. To address these challenges, we propose an instance-weighted tree-sum method that effectively pools data across diverse groups to output a concise, rule-

B. J. De Bortoli, M. C. Parisi, L. P. Bassino, D. Geisler

We derived radial velocities and CaT metallicity of more than 150 red giants stars in six SMC star clusters and their surrounding fields, with the instrument GMOS on GEMINI-S. The mean cluster radial velocity and metallicity were obtained with mean errors of 2.2 km\,s$^{-1}$ and 0.03 dex, while the mean field metallicities have a mean error of 0.13 dex. We a

Oliver Withington, Laurissa Tokarchuk

The past decade has seen a rapid increase in the level of research interest in procedural content generation (PCG) for digital games, and there are now numerous research avenues focused on new approaches for driving and applying PCG systems. An area in which progress has been comparatively slow is the development of generalisable approaches for comparing alt

Dragan S. Rakić, Martin Z. Ljubenović

The classes of 1MP-inverses and MP1-inverses are recently introduced classes of generalized inverses of complex matrix. Actually, they coincide with the classes of $\{1,2,3\}$ and $\{1,2,4\}$ inverses, respectively. We consider these inverses in the context of a ring with involution and prove that their most important characterizations and properties remain

Prashant K. Jha, J. Tinsley Oden

In this work, a Bayesian model calibration framework is presented that utilizes goal-oriented a-posterior error estimates in quantities of interest (QoIs) for classes of high-fidelity models characterized by PDEs. It is shown that for a large class of computational models, it is possible to develop a computationally inexpensive procedure for calibrating para

Xu Cheng, Hao Zhang, Yue Xin, Wen Shen

This paper mathematically derives an analytic solution of the adversarial perturbation on a ReLU network, and theoretically explains the difficulty of adversarial training. Specifically, we formulate the dynamics of the adversarial perturbation generated by the multi-step attack, which shows that the adversarial perturbation tends to strengthen eigenvectors

H. Gfrerer, M. Mandlmayr, J. V. Outrata, J. Valdman

In the paper, a variant of the \ssstar Newton method is developed for the numerical solution of generalized equations, in which the multi-valued part is a so-called SCD (subspace containing derivative) mapping. Under a rather mild regularity requirement, the method exhibits (locally) superlinear convergence behavior. From the main conceptual algorithm, two i

Hamid Bostani, Zhengyu Zhao, Zhuoran Liu, Veelasha Moonsamy

Machine Learning (ML) promises to enhance the efficacy of Android Malware Detection (AMD); however, ML models are vulnerable to realistic evasion attacks--crafting realizable Adversarial Examples (AEs) that satisfy Android malware domain constraints. To eliminate ML vulnerabilities, defenders aim to identify susceptible regions in the feature space where ML

Jie Chen, Weiqi Liu, Zhizhong Huang, Junbin Gao

The performance of GNNs degrades as they become deeper due to the over-smoothing. Among all the attempts to prevent over-smoothing, residual connection is one of the promising methods due to its simplicity. However, recent studies have shown that GNNs with residual connections only slightly slow down the degeneration. The reason why residual connections fail

Linjie Xu, Jorge Hurtado-Grueso, Dominic Jeurissen, Diego Perez Liebana

Strategy video games challenge AI agents with their combinatorial search space caused by complex game elements. State abstraction is a popular technique that reduces the state space complexity. However, current state abstraction methods for games depend on domain knowledge, making their application to new games expensive. State abstraction methods that requi

Yves Sibony, Boyuan Liu, Charlotte Simmonds, Georges Meynet

Context: Population III (Pop III) stars may be fast rotating. An expected consequence of fast rotation is strong internal mixing that deeply affects their evolutionary tracks in the Hertzsprung-Russell diagram and hence their ionising power. aims: We investigate the impact on the ionising power of Pop III stars in an extreme case of internal mixing, the one

Imad Aouali, Branislav Kveton, Sumeet Katariya

A contextual bandit is a popular framework for online learning to act under uncertainty. In practice, the number of actions is huge and their expected rewards are correlated. In this work, we introduce a general framework for capturing such correlations through a mixed-effect model where actions are related through multiple shared effect parameters. To explo

J. P. Santos Pires, Simão M. João, Aires Ferreira, B. Amorim

We present the first theoretical study of transport properties of Weyl semimetals with point defects. Focusing on a class of time-reversal symmetric Weyl lattice models, we show that dilute lattice vacancies induce a finite density of quasi-localized states at and near the nodal energy, causing strong modifications to the low-energy spectrum. This generates

Álvaro Sáiz, José-Enrique García-Ramos, José Miguel Arias, Lucas Lamata

A digital quantum simulation for the extended Agassi model is proposed using a quantum platform with eight trapped ions. The extended Agassi model is an analytically solvable model including both short range pairing and long range monopole-monopole interactions with applications in nuclear physics and in other many-body systems. In addition, it owns a rich p

Ali Chouman, Dimitrios Michael Manias, Abdallah Shami

Wireless networks, in the fifth-generation and beyond, must support diverse network applications which will support the numerous and demanding connections of today's and tomorrow's devices. Requirements such as high data rates, low latencies, and reliability are crucial considerations and artificial intelligence is incorporated to achieve these requirements

Atasi Chakraborty, Kamal Das, Subhajit Sinha, Pratap Chandra Adak

Nonlinear anomalous Hall effect is the Berry curvature dipole induced second-order Hall voltage or temperature difference in response to a longitudinal electric field or temperature gradient. These are the prominent Hall responses in time reversal symmetric systems. Here, we investigate the family of second-order nonlinear anomalous Hall effects, the electri

M. Alkhateeb, A. Matzkin

We investigate Klein tunneling through finite potential barriers with space-time resolved solutions to relativistic quantum field equations. We find that no particle actually tunnels through a finite supercritical barrier, even in the case of resonant tunneling. The transmission is instead mediated by modulations in pair production rates, at each edge of the

Anna Ivagnes, Giovanni Stabile, Andrea Mola, Traian Iliescu

In this paper, we develop data-driven closure/correction terms to increase the pressure and velocity accuracy of reduced order models (ROMs) for fluid flows. Specifically, we propose the first pressure-based data-driven variational multiscale ROM, in which we use the available data to construct closure/correction terms for both the momentum equation and the

Rohith Mahadevan, Sam Richard, Kishore Harshan Kumar, Jeevitha Murugan

The upsurge of real estate involves a variety of factors that have got influenced by many domains. Indeed, the unrecognized sector that would affect the economy for which regulatory proposals are being drafted to keep this in control is the payday loans. This research paper revolves around the impact of payday loans in the real estate market. The research pa

Yangze Zhou, Gitta Kutyniok, Bruno Ribeiro

This work provides the first theoretical study on the ability of graph Message Passing Neural Networks (gMPNNs) -- such as Graph Neural Networks (GNNs) -- to perform inductive out-of-distribution (OOD) link prediction tasks, where deployment (test) graph sizes are larger than training graphs. We first prove non-asymptotic bounds showing that link predictors

Theodore R. Gull, D. John Hillier, Henrik Hartman, Michael F. Corcoran

FUV spectra of Eta Car, recorded across two decades with HST/STIS, document multiple changes in resonant lines caused by dissipating extinction in our line of sight. The FUV flux has increased nearly ten-fold which has led to increased ionization of the multiple shells within the Homunculus and photo-destruction of molecular hydrogen. Comparison of observed

Ennio Bilancini, Leonardo Boncinelli, Eugenio Vicario

In pairwise interactions assortativity in cognition means that pairs where both decision-makers use the same cognitive process are more likely to occur than what happens under random matching. In this paper we study both the mechanisms determining assortativity in cognition and its effects. In particular, we analyze an applied model where assortativity in co

Yongqi Zhang, Zhanke Zhou, Quanming Yao, Xiaowen Chu

Due to the popularity of Graph Neural Networks (GNNs), various GNN-based methods have been designed to reason on knowledge graphs (KGs). An important design component of GNN-based KG reasoning methods is called the propagation path, which contains a set of involved entities in each propagation step. Existing methods use hand-designed propagation paths, ignor

Vinod Raman, Ambuj Tewari

Boosting is a fundamental approach in machine learning that enjoys both strong theoretical and practical guarantees. At a high-level, boosting algorithms cleverly aggregate weak learners to generate predictions with arbitrarily high accuracy. In this way, boosting algorithms convert weak learners into strong ones. Recently, Brukhim et al. extended boosting t

Mingshuai Dong, Xiuli Yu

Grasp detection in a cluttered environment is still a great challenge for robots. Currently, the Transformer mechanism has been successfully applied to visual tasks, and its excellent ability of global context information extraction provides a feasible way to improve the performance of robotic grasp detection in cluttered scenes. However, the insufficient in

Amjad Ali, Muhammad Hamraz, Naz Gul, Dost Muhammad Khan

kNN based ensemble methods minimise the effect of outliers by identifying a set of data points in the given feature space that are nearest to an unseen observation in order to predict its response by using majority voting. The ordinary ensembles based on kNN find out the k nearest observations in a region (bounded by a sphere) based on a predefined value of

Bettina Eska, Jasmin Niess, Florian Müller

There is a recommended body posture and hand position for playing every musical instrument, allowing efficient and quick movements without blockage. Due to humans' limited cognitive capabilities, they struggle to concentrate on several things simultaneously and thus sometimes lose the correct position while playing their instrument. Incorrect positions when

M. Biassoni, C. Brofferio, S. Dell'Oro, L. Gironi

The response of particle detectors to different types of radiation is not necessarily identical and, in some cases, neglecting this behavior can lead to a misinterpretation of the acquired data. While commercial radioactive sources are in general suitable to investigate the response to $\beta$'s and $\gamma$'s, in the case of $\alpha$'s the need for custom-m

Yu Cheng, John J. Tobin, Yao-Lun Yang, Merel L. R. van't Hoff

We present ALMA band 6/7 (1.3 mm/0.87 mm) and VLA Ka band (9 mm) observations toward NGC 2071 IR, an intermediate-mass star forming region. We characterize the continuum and associated molecular line emission towards the most luminous protostars, i.e., IRS1 and IRS3, on ~100 au (0. 2") scales. IRS1 is partly resolved in millimeter and centimeter continuum, w

M. Anbarloei

Let R be a Krasner (m,n)-hyperring and S be an n-ary multiplicative subset of R. The purpose of this paper is to introduce the notion of n-ary S-prime hyperideals as a new expansion of n-ary prime hyperideals. Several properties and characterizations concerning n-ary S-prime hyperideals are presented. The stability of this new concept with respect to various

Domenico De Guglielmo, Francesco Restuccia, Giuseppe Anastasi, Marco Conti

Many analytical models have been proposed for evaluating the performance of event-driven 802.15.4 Wireless Sensor Networks (WSNs), in Non-Beacon Enabled (NBE) mode. However, existing models do not provide accurate analysis of large-scale WSNs, due to tractability issues and/or simplifying assumptions. In this paper, we propose a new approach called Event Cha

Qingzhai Fan, Jiahui Wang

We show that the following divisible properties of the ${\rm C^*}$-algebras in a class $\mathcal{P}$ are inherited by simple unital ${\rm C^*}$-algebras in the class of asymptotically tracially in $\mathcal{P}$: $(1)$ $m$-almost divisible, $(2)$ weakly $(m, n)$-divisible.

Francisco Kordon, Thierry Lambre

We compute the center and the Lie algebra of outer derivations of a familiy of algebras of differential operators associated to hyperplane arrangements of the affine space A 3. The results are completed for 4-braid arrangements and for reflection arrangements associated to the wreath product of a cyclic group with the symmetric group S 3. To achieve this we

Lena Mondrejevski, Ioanna Miliou, Annaclaudia Montanino, David Pitts

Although Machine Learning (ML) can be seen as a promising tool to improve clinical decision-making for supporting the improvement of medication plans, clinical procedures, diagnoses, or medication prescriptions, it remains limited by access to healthcare data. Healthcare data is sensitive, requiring strict privacy practices, and typically stored in data silo

Alexis Front, Djahid Oucheriah, Christine Mottet, Hakim Amara

At the nanoscale, materials exhibit unique properties that differ greatly from those of the bulk state. In the case of Ag$_x$Pt$_{1-x}$ nanoalloys, we aimed to study the solid-liquid transition of nanoparticles of different sizes and compositions. This system is particularly interesting since Pt has a high melting point (2041 K compare to 1035 K for Ag) whic

Aladji Babacar Niang, Akym Adekpedjou, Harouna Sangaré, Gane Samb Lo

The investigation asymptotic limits on associated data mainly focused on limit theorems of summands of associated data and on the related invariance principles. In a series of papers, we are going to set the general frame of the theory by considering an arbitrary infinitely decomposable (divisible) limit law for summands and study the associated functional l

Matthew Badger, Alyssa Genschaw

For every $n\geq 2$, Bourgain's constant $b_n$ is the largest number such that the (upper) Hausdorff dimension of harmonic measure is at most $n-b_n$ for every domain in $\mathbb{R}^n$ on which harmonic measure is defined. Jones and Wolff (1988) proved that $b_2=1$. When $n\geq 3$, Bourgain (1987) proved that $b_n>0$ and Wolff (1995) produced examples showin

Leonardo Cella, Karim Lounici, Massimiliano Pontil

Meta-learning seeks to build algorithms that rapidly learn how to solve new learning problems based on previous experience. In this paper we investigate meta-learning in the setting of stochastic linear bandit tasks. We assume that the tasks share a low dimensional representation, which has been partially acquired from previous learning tasks. We aim to leve

Marco Badran, Manuel del Pino

We consider the magnetic Ginzburg-Landau equations in $\mathbb{R}^4$ $$ \begin{cases} -\varepsilon^2(\nabla-iA)^2u = \frac{1}{2}(1-|u|^{2})u,\\ \varepsilon^2 d^*dA = \langle(\nabla-iA)u,iu\rangle \end{cases} $$ formally corresponding to the Euler-Lagrange equations for the energy functional $$ E(u,A)=\frac{1}{2}\int_{\mathbb{R}^4}|(\nabla-iA)u|^{2}+\varepsil

Adrien Dubouloz

We consider fibrations by affine lines on smooth affine surfaces obtained as complements of smooth rational curves $B$ in smooth projective surfaces $X$ defined over an algebraically closed field of characteristic zero. We observe that except for two exceptions, these surfaces $X \setminus B$ admit infinitely many families of $\mathbb{A}^1$-fibrations over t

Loreto Pescosolido, Marco Conti, Andrea Passarella

Offloading data traffic from Infrastructure-to-Device (I2D) to Device-to-Device (D2D) communications is a powerful tool for reducing congestion, energy consumption, and spectrum usage of mobile cellular networks. Prior network-level studies on D2D data offloading focus on high level performance metrics as the offloading efficiency, and take into account the

Prosenjit Bose, Vida Dujmović, Hussein Houdrouge, Mehrnoosh Javarsineh

$\DeclareMathOperator{\chicen}{\chi_{\mathrm{cen}}}\DeclareMathOperator{\chilin}{\chi_{\mathrm{lin}}}$ A centred colouring of a graph is a vertex colouring in which every connected subgraph contains a vertex whose colour is unique and a \emph{linear colouring} is a vertex colouring in which every (not-necessarily induced) path contains a vertex whose colour

Jérôme Denis, François Damanet, John Martin

In recent years, artificial neural networks (ANNs) have become an increasingly popular tool for studying problems in quantum theory, and in particular entanglement theory. In this work, we analyse to what extent ANNs can accurately predict the geometric measure of entanglement of symmetric multiqubit states using only a limited number of Wehrl moments (momen

Christian Tomani, Daniel Cremers

We show that utilizing attribution maps for training neural networks can improve regularization of models and thus increase performance. Regularization is key in deep learning, especially when training complex models on relatively small datasets. In order to understand inner workings of neural networks, attribution methods such as Layer-wise Relevance Propag

Wen-Yuan Ai, Juan S. Cruz, Bjorn Garbrecht, Carlos Tamarit

While $CP$ violation has never been observed in the strong interactions, the QCD Lagrangian admits a $CP$-odd topological interaction proportional to the so called $\theta$ angle, which weighs the contributions to the partition function from different topological sectors. The observational bounds are usually interpreted as demanding a severe tuning of $\thet

Sebastien Court

The aim of this paper is to design a feedback operator for stabilizing in infinite time horizon a system modeling the interactions between a viscous incompressible fluid and the deformation of a soap bubble. The latter is represented by an interface separating a bounded domain of $\mathbb{R}^2$ into two connected parts filled with viscous incompressible flui

Guilherme Grams, Jerome Margueron, Rahul Somasundaram, Nicolas Chamel

We present a detailed analysis of three models predicting the properties of non-uniform matter in the crust of neutron stars: the compressible liquid-drop model, the fourth order Extended Thomas Fermi (ETF) method, and ETF plus Strutinsky integral (ETFSI) correction. The former treats the nuclear clusters as uniform hard spheres, the second takes into accoun

Nicholas Schreck, Manuel Wiesenfarth

In the linear mixed model (LMM), the simultaneous assessment and comparison of dispersion relevance of explanatory variables associated with fixed and random effects remains an important open practical problem. Based on the restricted maximum likelihood equations in the variance components form of the LMM, we prove a proper decomposition of the sum of square

Mark Baum, Minmin Fu, Stephen Bourguet

Over its multibillion-year history, the Earth has experienced a wide range of climates. The long-term climate is controlled by the atmospheric carbon dioxide concentration, which is regulated by marine sequestration through chemical weathering. This chemical weathering sink is strongly linked to the distribution and composition of the continents. However, th

Oleg Stepanyuk, Kamen Kozarev, Mohamed Nedal

Coronal Mass Ejections (CMEs) influence the interplanetary environment over vast distances in the solar system by injecting huge clouds of fast solar plasma and energetic particles (SEPs). A number of fundamental questions remain about how SEPs are produced, but current understanding points to CME-driven shocks and compressions in the solar corona. At the sa

Hernan C. Vazquez, J. Andres Diaz Pace, Claudia Marcos, Santiago Vidal

The selection of software technologies is an important but complex task. We consider developers of JavaScript (JS) applications, for whom the assessment of JS libraries has become difficult and time-consuming due to the growing number of technology options available. A common strategy is to browse software repositories via search engines (e.g., NPM, or Googl

Luca Rizzi, Francesco Zucconi

Given a semistable fibration $f\colon X\to B$ we introduce a correspondence between foliations $\mathcal{F}$ on $X$ and local systems $\mathbb{L}$ on $B$. Building up on this correspondence we find conditions that give maximal rationally connected fibrations in terms of data on the foliation. We prove the Castelnuovo-de Franchis theorem in the case of $p$-fo

Xuan Zhang, Necdet Serhat Aybat, Mert Gürbüzbalaban

We propose a new stochastic method SAPD+ for solving nonconvex-concave minimax problems of the form $\min\max\mathcal{L}(x,y)=f(x)+\Phi(x,y)-g(y)$, where $f,g$ are closed convex and $\Phi(x,y)$ is a smooth function that is weakly convex in $x$, (strongly) concave in $y$. Let $\delta^2$ denote the variance bound for the unbiased stochastic oracle used within

Di Jin, Luzhi Wang, Yizhen Zheng, Xiang Li

Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering. As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly le

Ulrik Skre Fjordholm, Markus Musch, Andrey Pilipenko

We study the zero-noise limit for autonomous, one-dimensional ordinary differential equations with discontinuous right-hand sides. Although the deterministic equation might have infinitely many solutions, we show, under rather general conditions, that the sequence of stochastically perturbed solutions converges to a unique distribution on classical solutions

Yuchong Kang, Xiaoyun Ma, Jing Fu, Kun Yang

Two-dimensional boron monolayer (borophene) stands out from the two-dimensional atomic layered materials due to its structural flexibility, tunable electronic and mechanical properties from a large number of allotropic materials. The stability of pristine borophene polymorphs could possibly be improved via hydrogenation with atomic hydrogen (referred to as b

Jiehua Zhang, Zhuo Su, Li Liu

Face recognition is one of the most active tasks in computer vision and has been widely used in the real world. With great advances made in convolutional neural networks (CNN), lots of face recognition algorithms have achieved high accuracy on various face datasets. However, existing face recognition algorithms based on CNNs are vulnerable to noise. Noise co

Anton Bovier, Adrien Schertzer

20 years ago, Bovier, Kurkova, and L\"owe [5] proved a central limit theorem (CLT) for the fluctuations of the free energy in the p-spin version of the Sherrington-Kirkpatrick model of spin glasses at high temperatures. In this paper we improve their results in two ways. First, we extend the range of temperatures to cover the entire regime where the quenched

Rajat K. Dey

The distribution of the muon content of highly inclined Monte Carlo cosmic ray showers is affected by the influence of Earth's geomagnetic field. It is found that the shapes of the positive and negative muon distributions get affected/modified by the influence of the Earth's geomagnetic field. Such a correlation between the earth's geomagnetic activity and t

Andrea Munari

We focus on a slotted ALOHA system without feedback, in which nodes transmit time-stamped updates to a common gateway. Departing from the classical generate-at-will model, we assume that each transmitter may not always have fresh information to deliver, and tackle the fundamental question of whether sending stale packets can be beneficial from an age-of-info

Yuchen He, Chihao Zhang

The problem of bandit with graph feedback generalizes both the multi-armed bandit (MAB) problem and the learning with expert advice problem by encoding in a directed graph how the loss vector can be observed in each round of the game. The mini-max regret is closely related to the structure of the feedback graph and their connection is far from being fully un

Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin

Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise similarities and therefore reduce the complexity of graph methods. Although widely applied in large-scale applications, existing approaches do not pay sufficient attention to establishing correct correspondences between the anchor sets across views. To be specific, anch

Lu Wang, Mokhtar Bozorg, Mohammad Rayati, Rachid Cherkaoui

The concept of Energy Hub (EH) has been emerged to accommodate renewable energy sources in a multi-energy system to deploy the synergies between electricity and other energy sources. However, the market mechanisms for the integration of the EHs into the energy markets are not sufficiently elaborated. This paper proposes a flow-based two-level distributed tra

L. Stefanello, S. Trappeniers

L. N. Childs defined a bi-skew brace to be a skew brace such that if we swap the role of the two operations, then we find again a skew brace. In this paper, we give a systematic analysis of bi-skew braces. We study nilpotency and solubility, and connections between bi-skew braces and set-theoretic solutions of the Yang--Baxter equation. Further, we deal with

Amartya Bose

Recent work has shown that it is possible to circumvent the calculation of the spectral density and directly calculate the coefficients of the discretized influence functionals using data from classical trajectory simulations. However, the accuracy of this procedure depends on the validity of the high temperature approximation. In this work, an alternative d

Weiyi Gong, Tao Sun, Hexin Bai, Shah Tanvir ur Rahman Chowdhury

In a data-driven paradigm, machine learning (ML) is the central component for developing accurate and universal exchange-correlation (XC) functionals in density functional theory (DFT). It is well known that XC functionals must satisfy several exact conditions and physical constraints, such as density scaling, spin scaling, and derivative discontinuity. In t

M. Anbarloei

The formation of rings of fractions and the associated process of localization are the most important technical tools in commutative algebra. Krasner (m,n)-hyperrings are a generalization of (m,n)-ring. Let R be a commutative Krasner (m,n)-hyperring. The aim of this research work is to introduced the concept of fractions generated by R and then investigate t

Abhishek Ghosh, Vivek Tewary

In this article, we obtain hessian estimates for Kolmogorov-Fokker-Planck operators in non-divergence form in several Banach function spaces. Our approach relies on a representation formula and newly developed sparse domination techniques in Harmonic Analysis. Our result when restricted to weighted Lebesgue spaces yields sharp quantitative hessian estimates

Bingxin Zhou, Xuebin Zheng, Yu Guang Wang, Ming Li

Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction. Given the non-Euclidean structural property of graphs, preserving the original graph data's similarity relationship in the embedded space needs specific tools and a similarity metric. This paper develops a new grap

Zhuan Ge, Teng Man, Herbert E. Huppert, Sergio Anders Galindo-Torres

In this letter, we investigate the transient rheological behavior of immersed granular flows using both experiments of submerged granular column collapses and corresponding numerical simulations. The simulations are performed with the lattice-Boltzmann method (LBM) coupled with the discrete element method (DEM) and provide a significant amount of data of the

Thomas Bläsius, Philipp Fischbeck

The number one criticism of average-case analysis is that we do not actually know the probability distribution of real-world inputs. Thus, analyzing an algorithm on some random model has no implications for practical performance. At its core, this criticism doubts the existence of external validity, i.e., it assumes that algorithmic behavior on the somewhat

Marc Carrascosa-Zamacois, Lorenzo Galati-Giordano, Anders Jonsson, Giovanni Geraci

Wi-Fi 7 is already in the making, and Multi-Link Operation (MLO) is one of the main features proposed in its correspondent IEEE 802.11be amendment. MLO will allow devices to coordinate multiple radio interfaces to access separate channels through a single association, aiming for improved throughput, network delay, and overall spectrum reuse efficiency. In th

Changmin Yu, David Mguni, Dong Li, Aivar Sootla

Efficient reinforcement learning (RL) involves a trade-off between "exploitative" actions that maximise expected reward and "explorative'" ones that sample unvisited states. To encourage exploration, recent approaches proposed adding stochasticity to actions, separating exploration and exploitation phases, or equating reduction in uncertainty with reward. Ho

Valerio Arnaboldi, Mattia G. Campana, Franca Delmastro, Elena Pagani

The current availability of interconnected portable devices, and the advent of the Web 2.0, raise the problem of supporting anywhere and anytime access to a huge amount of content, generated and shared by mobile users. In this work we propose a novel framework for pervasive social networks, called Pervasive PLIERS (pPLIERS), able to discover and select, in a