May 2022 arXiv papers — page 4

Christopher Bockel-Rickermann

The internet has changed the way we live, work and take decisions. As it is the major modern resource for research, detailed data on internet usage exhibits vast amounts of behavioral information. This paper aims to answer the question whether this information can be facilitated to predict future returns of stocks on financial capital markets. In an empirica

Daniela Di Donato

Recently, in the metric spaces, Le Donne and the author introduced the so-called intrinsically Lipschitz sections. The main aim of this note is to adapt Cheeger theory for the classical Lipschitz constants in our new context. More precisely, we define the intrinsic Cheeger energy from $L^2(Y,\R^s)$ to $[0,+\infty],$ where $(Y,d_Y,\mm)$ is a metric measure sp

Liangrui Pan, Zhichao Feng, Shaoliang Peng

Computational pathology is part of precision oncology medicine. The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinical practice improves cancer treatment plans, treatment cycles, and cure rates, and helps doctors open up innovative approaches to patient prognosis. In the p

Anna Di Natale, David Garcia

Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents. These lists are often generated by applying computational lexicon expansion methods to small, manually-curated sets of root words. Despite the wide use of this approach, we still lack an exhaustive comp

Mahan Mj, Parthanil Roy, Sourav Sarkar

We show that any stationary symmteric $\alpha$-stable ($S\alpha S$) random field indexed by a countable amenable group $G$ is weakly mixing if and only if it is generated by a null action, extending works of Samorodnitsky and Wang-Roy-Stoev for abelian groups to all amenable groups. This enables us to improve significantly the domain of a recently discovered

Qiancheng Fu, Qingshan Xu, Yew-Soon Ong, Wenbing Tao

Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view reconstruction. However, one key challenge remains: existing approaches lack explicit multi-view geometry constraints, hence usually fail to generate geometry consistent surface reconstruction. To address this challenge, we propose geometry-consistent neural imp

Hu Zhang, Chendong Jin, Ruqian Lian, Peng-Lai Gong

The Datta-Das-type spin field-effect transistor, using a two-dimensional electron gas in a semiconductor heterostructure as a channel, plays a key role in spintronics. Here, we theoretically present a type of spin field-effect transistor based on massless birefringent Dirac fermions in polar Dirac semimetals. The manipulation of spin arises from the existenc

Yashas Joshi, Charalambos Poullis

Modern Redirected Walking (RDW) techniques significantly outperform classical solutions. Nevertheless, they are often limited by their heavy reliance on eye-tracking hardware embedded within the VR headset to reveal redirection opportunities. We propose a novel RDW technique that leverages the temporary blindness induced due to saccades for redirection. Howe

David Wiedemann, Malte A. Peter

We consider the homogenisation of a coupled reaction-diffusion process in a porous medium with evolving microstructure. A concentration-dependent reaction rate at the interface of the pores with the solid matrix induces a concentration-dependent evolution of the domain. Hence, the evolution is fully coupled with the reaction-diffusion process. In order to pa

Jouni Parkkonen, Frédéric Paulin

We extend formulae of Mertens and Mirsky on the asymptotic behaviour of the standard Euler function to the Euler functions of principal rings of integers of imaginary quadratic number fields, giving versions in angular sector and with congruences.

Tuhin Malik, Constança Providência

The possible signatures of the presence of hyperons inside neutron stars are discussed within a Bayesian inference framework applied to a set of models based on a density-dependent relativistic mean-field description of hadronic matter. Nuclear matter properties, hypernuclei properties, and observational information are used to constrain the models. General

A. N. Matveeva, I. A. Zobkalo, A. Sazonov, M. Meven

Structural, magnetic and multiferroic properties of single crystals of Dy$_{1-x}$Ho$_x$MnO$_3$ ($x = 0, 0.2$) were investigated by the different methods of polarized and classical neutron diffraction and macroscopic methods in order to determine the effect of Ho doping on the magneto-electric behavior of the title compounds. It is shown that substitution by

Farhad Nawaz, Melkior Ornik

Missions for autonomous systems often require agents to visit multiple targets in complex operating conditions. This work considers the problem of visiting a set of targets in minimum time by a team of non-communicating agents in a Markov decision process (MDP). The single-agent problem is at least NP-complete by reducing it to a Hamiltonian path problem. We

Aida Abiad, Hidde Koerts

The $k$-independence number of a graph, $\alpha_k(G)$, is the maximum size of a set of vertices at pairwise distance greater than $k$, or alternatively, the independence number of the $k$-th power graph $G^k$. Although it is known that $\alpha_k(G)=\alpha(G^k)$, this, in general, does not hold for most graph products, and thus the existing bounds for $\alpha

J. A. N. Bruin, R. R. Claus, Y. Matsumoto, J. Nuss

The layered honeycomb magnet $\alpha$-RuCl$_3$ has been suggested to exhibit a field-induced quantum spin liquid state, in which the reported large thermal Hall effect close to the half-quantized value still remains a subject of debate. Recently, oscillatory structures of the magnetothermal conductivity were reported and interpreted as quantum oscillations o

Tianhao Wu, Fangcheng Zhong, Andrea Tagliasacchi, Forrester Cole

Given a monocular video, segmenting and decoupling dynamic objects while recovering the static environment is a widely studied problem in machine intelligence. Existing solutions usually approach this problem in the image domain, limiting their performance and understanding of the environment. We introduce Decoupled Dynamic Neural Radiance Field (D$^2$NeRF),

Can Wang, Weihua Deng, Xiangong Tang

In this paper, we are concerned with the numerical solution for the two-dimensional time fractional Fokker-Planck equation with tempered fractional derivative of order $\alpha$. Although some of its variants are considered in many recent numerical analysis papers, there are still some significant differences. Here we first provide the regularity estimates of

Simon Dahan, Logan Z. J. Williams, Abdulah Fawaz, Daniel Rueckert

The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design limitations resulting in poor modelling of long-range associations, as the generalisation of convolutions to irregular surfaces is non-trivial. Recent state-of-the-art performance of

Alejandra Duque-Torres, Dietmar Pfahl, Claus Klammer, Stefan Fischer

Metamorphic testing (TM) examines the relations between inputs and outputs of test runs. These relations are known as metamorphic relations (MR). Currently, MRs are handpicked and require in-depth knowledge of the System Under Test (SUT), as well as its problem domain. As a result, the identification and selection of high-quality MRs is a challenge. \citeaut

Dimitrios Tzarouchis, Mario Junior Mencagli, Brian Edwards, Nader Engheta

Performing analog computations with metastructures is an emerging wave-based paradigm for solving mathematical problems. For such devices, one major challenge is their reconfigurability, especially without the need for a priori mathematical computations or computationally-intensive optimization. Their equation-solving capabilities are applied only to matrice

Hidde Fokkema, Rianne de Heide, Tim van Erven

Different users of machine learning methods require different explanations, depending on their goals. To make machine learning accountable to society, one important goal is to get actionable options for recourse, which allow an affected user to change the decision $f(x)$ of a machine learning system by making limited changes to its input $x$. We formalize th

Pengkun Liu, Jinding Xing, Ruoxin Xiong, Pingbo Tang

Early identification of on-site hazards is crucial for accident prevention in the construction industry. Currently, the construction industry relies on experienced safety advisors (SAs) to identify site hazards and generate mitigation measures to guide field workers. However, more than half of the site hazards remain unrecognized due to the lack of field exp

Rushil Anirudh, Rick Archibald, M. Salman Asif, Markus M. Becker

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are g

Kathleen Yang, Salman Salamatian, Rafael G. L . D'Oliveira, Muriel Medard

In the wideband regime, the performance of many of the popular modulation schemes such as code division multiple access and orthogonal frequency division multiplexing falls quickly without channel state information. Obtaining the amount of channel information required for these techniques to work is costly and difficult, which suggests the need for schemes w

Wen Chang, Sibylle Schroll

In this paper, using the correspondence of gentle algebras and dissections of marked surfaces, we study full exceptional sequences in the perfect derived category $\mathsf{K^b(A)}$ of a gentle algebra $\mathsf{A}$. We show that full exceptional sequences in $\mathsf{K^b(A)}$ exist if and only if the associated marked surface has no punctures and has at least

David Gerard-Varet, Sameer Iyer, Yasunori Maekawa

We establish linearized well-posedness of the Triple-Deck system in Gevrey-$\frac32$ regularity in the tangential variable, under concavity assumptions on the background flow. Due to the recent result \cite{DietertGV}, one cannot expect a generic improvement of the result of \cite{IyerVicol} to a weaker regularity class than real analyticity. Our approach ex

Ronika Sarkar, Suraj S. Hegde, Awadhesh Narayan

Symmetry-protected spectral topology in non-Hermitian systems has interesting manifestations such as dynamically anomalous chiral currents and skin effect. We study the interplay between symmetries and disorder in a paradigmatic model for spectral topology - the non-reciprocal Su-Schrieffer-Heeger model. We numerically study the effect of disorder in on-site

Marnix Suilen, Thiago D. Simão, David Parker, Nils Jansen

Markov decision processes (MDPs) are formal models commonly used in sequential decision-making. MDPs capture the stochasticity that may arise, for instance, from imprecise actuators via probabilities in the transition function. However, in data-driven applications, deriving precise probabilities from (limited) data introduces statistical errors that may lead

Amaury Micheli, Scott Robertson

In a 1D Bose gas, there is no non-trivial scattering channel involving three Bogoliubov quasiparticles that conserves both energy and momentum. Nevertheless, we show that such 3-wave mixing processes (Beliaev and Landau damping) account for their decay via interactions with thermal fluctuations. Within an appropriate time window where the Fermi Golden Rule i

Teemu Pennanen, Ari-Pekka Perkkiö

This paper studies duality and optimality conditions for general convex stochastic optimization problems. The main result gives sufficient conditions for the absence of a duality gap and the existence of dual solutions in a locally convex space of random variables. It implies, in particular, the necessity of scenario-wise optimality conditions that are behin

Zhengyao Jiang, Tianjun Zhang, Robert Kirk, Tim Rocktäschel

The successes of deep Reinforcement Learning (RL) are limited to settings where we have a large stream of online experiences, but applying RL in the data-efficient setting with limited access to online interactions is still challenging. A key to data-efficient RL is good value estimation, but current methods in this space fail to fully utilise the structure

Juliette Millet, Ioana Chitoran, Ewan Dunbar

Our native language influences the way we perceive speech sounds, affecting our ability to discriminate non-native sounds. We compare two ideas about the influence of the native language on speech perception: the Perceptual Assimilation Model, which appeals to a mental classification of sounds into native phoneme categories, versus the idea that rich, fine-g

Amit Saha, Turbasu Chatterjee, Anupam Chattopadhyay, Amlan Chakrabarti

In some quantum algorithms, arithmetic operations are of utmost importance for resource estimation. In binary quantum systems, some efficient implementation of arithmetic operations like, addition/subtraction, multiplication/division, square root, exponential and arcsine etc. have been realized, where resources are reported as a number of Toffoli gates or T

Tal Daniel, Aviv Tamar

We propose a new representation of visual data that disentangles object position from appearance. Our method, termed Deep Latent Particles (DLP), decomposes the visual input into low-dimensional latent ``particles'', where each particle is described by its spatial location and features of its surrounding region. To drive learning of such representations, we

Tobias Graß

The presence of a bias field, encoding some information about the target state, can enhance the performance of quantum optimization methods. Here we investigate the effect of such a bias field on the outcome of quantum annealing sampling, at the example of the exact cover problem. The sampling is carried out on a D-Wave machine, and different bias configurat

Juliette Millet, Ewan Dunbar

Self-supervised models for speech processing form representational spaces without using any external labels. Increasingly, they appear to be a feasible way of at least partially eliminating costly manual annotations, a problem of particular concern for low-resource languages. But what kind of representational spaces do these models construct? Human perceptio

Alain Mazzolo, Cécile Monthus

When the unconditioned process is a diffusion process $X(t)$ of drift $\mu(x)$ and of diffusion coefficient $D=1/2$, the local time $A(t)= \int_{0}^{t} d\tau \delta(X(\tau)) $ at the origin $x=0$ is one of the most important time-additive observable. We construct various conditioned processes $[X^*(t),A^*(t)]$ involving the local time $A^*(T)$ at the time ho

Yuning Zhang, Jiayang Wu, Yunyi Yang, Yang Qu

We experimentally demonstrate enhanced self-phase modulation (SPM) in silicon nitride (Si3N4) waveguides integrated with 2D graphene oxide (GO) films. GO films are integrated onto Si3N4 waveguides using a solution-based, transfer-free coating method that enables precise control of the film thickness. Detailed SPM measurements are carried out using both picos

Yoichi Takeda

Photospheric abundances of C, N, O, and Na were determined by applying the synthetic spectrum-fitting technique to 34 snap-shot high-dispersion spectra of 22 RR Lyr stars covering a metallicity range of -1.8 <[Fe/H] < 0.0, with an aim of investigating the mixing mechanism in the interior of low-mass giant stars by examining the abundance anomalies of these e

Tommaso Franzini, Charles A. S. Young

In this work we will use a general procedure to construct higher local Hamiltonians for the affine $\mathfrak{sl}_2$ Gaudin model. We focus on the first non-trivial example, the quartic Hamiltonians. We show by direct calculation that the quartic Hamiltonians commute amongst themselves and with the quadratic Hamiltonians which define the model. We go on to i

Artem Moskalev, Ivan Sosnovik, Volker Fischer, Arnold Smeulders

The standard approach to contrastive learning is to maximize the agreement between different views of the data. The views are ordered in pairs, such that they are either positive, encoding different views of the same object, or negative, corresponding to views of different objects. The supervisory signal comes from maximizing the total similarity over positi

M. Gąsiorek

We continue the Coxeter spectral analysis of finite connected posets $I$ that are non-negative in the sense that their symmetric Gram matrix $G_I:=\frac{1}{2}(C_I + C_I^{tr})\in\mathbb{M}_{m}(\mathbb{Q})$ is positive semi-definite of rank $n\geq 0$, where $C_I\in\mathbb{M}_m(\mathbb{Z})$ is the incidence matrix of $I$ encoding the relation $\preceq_I$. We ex

Iknoor Singh, Yue Li, Melissa Thong, Carolina Scarton

This paper describes the second-placed system on the leaderboard of SemEval-2022 Task 8: Multilingual News Article Similarity. We propose an entity-enriched Siamese Transformer which computes news article similarity based on different sub-dimensions, such as the shared narrative, entities, location and time of the event discussed in the news article. Our sys

Florian M. Arnold, Tsai-Jung Liu, Agnieszka Kuc, Thomas Heine

In cove-edged zigzag graphene nanoribbons (ZGNR-C), one terminal CH group per length unit is removed on each zigzag edge, forming a regular pattern of coves which controls their electronic structure. Based on three structural parameters that unambiguously characterize the atomistic structure of ZGNR-C, we present a scheme that classifies their electronic sta

Zequn Lv, Ervin Győri, Zhen He, Nika Salia

We resolve a conjecture of Cox and Martin by determining asymptotically for every $k\ge 2$ the maximum number of copies of $C_{2k}$ in an $n$-vertex planar graph.

Arthur Jacot, Eugene Golikov, Clément Hongler, Franck Gabriel

We study the loss surface of DNNs with $L_{2}$ regularization. We show that the loss in terms of the parameters can be reformulated into a loss in terms of the layerwise activations $Z_{\ell}$ of the training set. This reformulation reveals the dynamics behind feature learning: each hidden representations $Z_{\ell}$ are optimal w.r.t. to an attraction/repuls

Donggyu Kim, Minseok Shin

This paper introduces novel volatility diffusion models to account for the stylized facts of high-frequency financial data such as volatility clustering, intra-day U-shape, and leverage effect. For example, the daily integrated volatility of the proposed volatility process has a realized GARCH structure with an asymmetric effect on log-returns. To further ex

Elizeu França, Douglas Finamore

The $n$-torus is the the unique closed manifold supporting a set of $n$ linearly independent closed $1$-forms. In this paper we improve on this result and show that the torus is the unique closed $n$-dimensional manifold supporting a linearly independent set consisting of $(n-1)$ closed $1$-forms whose product determines a non-zero cohomological class.

Michael Khanevsky

Let $\Sigma$ be a compact surface of genus $g \geq 1$ equipped with an area form. We construct eggbeater Hamiltonian diffeomorphisms which lie arbitrarily far in the Hofer metric from the set of autonomous Hamiltonians. This result is already known for $g \geq 2$ (our argument provides an alternative, very simple construction compared to previous publication

Lei Xie, S. H. Song, Yonina C. Eldar, Khaled B. Letaief

With the development of innovative applications that demand accurate environment information, e.g., autonomous driving, sensing becomes an important requirement for future wireless networks. To this end, integrated sensing and communication (ISAC) provides a promising platform to exploit the synergy between sensing and communication, where perceptive mobile

Jafar Moradicheghamahi, Gerard Fortuny, Josep M. López, Joan Herrero

Cardiopulmonary resuscitation (CPR) is an emergency procedure performed on patients during cardiac and respiratory arrest. This procedure externally activates the cardiac and respiratory systems via the delivery of chest compression and artificial ventilation. As the main purpose of CPR is to recirculate the blood flow, prediction of the myocardium behavior

Yoichi Takeda

The mass (M) of a star can be evaluated from its spectroscopically determined effective temperature (T _eff) and metallicity ([Fe/H]) along with the luminosity (L; derived from parallax), while comparing them with grids of theoretical evolutionary tracks. It has been argued, however, that such a track-based mass (M_trk) may tend to be overestimated for the c

Pierre Laforgue, Andrea Della Vecchia, Nicolò Cesa-Bianchi, Lorenzo Rosasco

We introduce and analyze AdaTask, a multitask online learning algorithm that adapts to the unknown structure of the tasks. When the $N$ tasks are stochastically activated, we show that the regret of AdaTask is better, by a factor that can be as large as $\sqrt{N}$, than the regret achieved by running $N$ independent algorithms, one for each task. AdaTask can

Cheng-Ying Tsai

Recently a mechanism of storage ring operation based on the steady-state microbunching has been proposed and investigated, which contains a laser cavity modulator providing the longitudinal focusing for the circulating microbunches. In this paper we analyze the impact of the coherent undulator radiation on the longitudinal single-bunch multi-turn collective

Akash Kundu, Jarosław A. Miszczak

Nonclassical phenomena can be enhanced by introducing $q$-deformation in optomechanical systems. This motivates investigation of the optical response in a $q$-deformed linearly coupled optomechanical system. The system consists of two deformed cavities that are linearly coupled to the motion of mechanical mirrors, and the cavities are coupled to each other b

Pierre Popineau, François Baccelli

This paper studies a multiclass spatial birth-and-death (SBD) processes on a compact region of the Euclidean plane modeling wireless interactions. In this model, users arrive at a constant rate and leave at a rate function of the interference created by other users in the network. The novelty of this work lies in the addition of service differentiation, insp

Naoyuki Kanomata, Akifumi Sako

We find the exact solutions of the $\Phi_{2}^{3}$ finite matrix model (Grosse-Wulkenhaar model). In the $\Phi_{2}^{3}$ finite matrix model, multipoint correlation functions are expressed as $G_{|a_{1}^{1}\ldots a_{N_{1}}^{1}|\ldots|a_{1}^{B}\ldots a_{N_{B}}^{B}|}$. The $\displaystyle \sum_{i=1}^{B}N_{i}$-point function denoted by $G_{|a_{1}^{1}\ldots a_{N_{1

Angel Reina Ramirez, V. K. Magas, L. P. Csernai, D. Strottman

We present a Generalized Effective String Rope Model (GESRM) for the description of the initial state of relativistic heavy ion collisions. We start from the Effective String Rope Model (ESRM) and take into account fluctuations in the initial state following the Glauber Monte Carlo approach. Results from symmetric Au+Au collisions at different impact paramet

K. Biazzo, V. D'Orazi, S. Desidera, D. Turrini

Exoplanetary properties depend on stellar properties: to know the planet with accuracy and precision it is necessary to know the star as accurately and precisely as possible. Our immediate aim is to characterize in a homogeneous and accurate way a sample of 27 transiting planet-hosting stars observed within the GAPS program. We determined stellar parameters

Siqiao Xue, Chao Qu, Xiaoming Shi, Cong Liao

Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud. In recent works, Reinforcement Learning (RL) has been introduced as a promising approach to learn the resource management policies to guide the scalin

Shao-Long Chen, Wen-wen Jiang, Ze-Kun Liu

We extend the contents of the standard model (SM) by introducing TeV-scale scalar leptoquarks to generate neutrino masses and explain some current observed deviations from the SM predictions, including the anomalous magnetic moments of charged leptons (electron and muon) and $B$-physics anomalies ($R_{K^{(*)}}$ and $R_{D^{(*)}}$). The model consists of $\tex

C. Swastik, Ravinder K. Banyal, Mayank Narang, P. Manoj

The imprints of stellar nucleosynthesis and chemical evolution of the galaxy can be seen in different stellar populations, with older generation stars showing higher $\alpha$-element abundances while the later generations becoming enriched with iron-peak elements. The evolutionary connections and chemical characteristics of circumstellar disks, stars, and th

Naciye Celebi, Qingzhong Liu, Muhammed Karatoprak

Recently, image manipulation has achieved rapid growth due to the advancement of sophisticated image editing tools. A recent surge of generated fake imagery and videos using neural networks is DeepFake. DeepFake algorithms can create fake images and videos that humans cannot distinguish from authentic ones. (GANs) have been extensively used for creating real

S. Mohsen J. Khadem, Rainer Klages, Sabine H. L. Klapp

This paper is concerned with the stochastic thermodynamics of non-equilibrium Gaussian processes that can exhibit anomalous diffusion. In the systems considered, the noise correlation function is not necessarily related to friction. Thus, there is no conventional fluctuation-dissipation relation (FDR) of the second kind and no unique way to define a temperat

Sunghan Ro, Yong Woon Kim

We consider a problem of finding a target located in a finite $d$-dimensional domain, using $N$ independent random walkers, when partial information on the target location is given as a probability distribution. When $N$ is large, the first-passage time sensitively depends on the initial searcher distribution, which invokes the question of what is the optima

T. Thebault, W. Knafo, M. Vališka, G. Lapertot

Multiple unconventional superconducting phases are suspected to be driven by magnetic fluctuations in the heavy-fermion paramagnet UTe$_2$, and a challenge is to identify the signatures of the electronic correlations, including the magnetic fluctuations, in the bulk physical quantities. Here, we investigate thoroughly the anisotropy of the electrical resisti

Nadia Mazza

In this paper we investigate some properties of the Burnside ring of a profinite group as defined in \cite{ds}. We introduce the notion of the crossed Burnside ring of a profinite FC-group, and generalise some results from finite to profinite (FC-)groups. In our investigations, we also obtain results on profinite FC-groups which may be of independent interes

H. D. Rosales, F. A. Gómez Albarracín, K. Guratinder, V. Tsurkan

We theoretically and experimentally study the stability of the unconventional fractional antiferromagnetic skyrmion lattice (AF-SkL) in Mn$_2$S$_4$ spinel under magnetic fields applied along the $[$1-10$]$ crystal direction. By performing numerical Monte Carlo simulations for the minimal effective spin model that we proposed in Ref. [S. Gao, et al., Nature 5

G. Uhlrich

The calculation of one-loop Wilson coefficients for general Beyond the Standard Model (BSM) scenarios is a technical challenge often addressed by doing long and error prone analytical calculations by hand. Several software programs already provide squared amplitude calculations at the loop-level, but few of them are also able to derive general loop-level Wil

Vadim Gorin, Jiaoyang Huang

We introduce dynamical versions of loop (or Dyson-Schwinger) equations for large families of two--dimensional interacting particle systems, including Dyson Brownian motion, Nonintersecting Bernoulli/Poisson random walks, $\beta$--corners processes, uniform and Jack-deformed measures on Gelfand-Tsetlin patterns, Macdonald processes, and $(q,\kappa)$-distribut

Lorenzo Pacchiardi, Ritabrata Dutta

Bayesian Likelihood-Free Inference methods yield posterior approximations for simulator models with intractable likelihood. Recently, many works trained neural networks to approximate either the intractable likelihood or the posterior directly. Most proposals use normalizing flows, namely neural networks parametrizing invertible maps used to transform sample

Jose L. Gómez, Gabriel Villalonga, Antonio M. López

Semantic image segmentation is a central and challenging task in autonomous driving, addressed by training deep models. Since this training draws to a curse of human-based image labeling, using synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies to address an unsupervised dom

Alejandra Duque-Torres, Dietmar Pfahl, Rudolf Ramler, Claus Klammer

Metamorphic Testing (MT) addresses the test oracle problem by examining the relations between inputs and outputs of test executions. Such relations are known as Metamorphic Relations (MRs). In current practice, identifying and selecting suitable MRs is usually a challenging manual task, requiring a thorough grasp of the SUT and its application domain. Thus,

Dagoberto Contreras, Fiona McCarthy, Matthew C. Johnson

The kinetic Sunyaev Zel'dovich (kSZ) effect, cosmic microwave background (CMB) temperature anisotropies induced by the scattering of CMB photons from free electrons, will be measured by near-term CMB experiments at high significance. By combining CMB temperature anisotropies with a tracer of structure, such as a galaxy redshift survey, previous literature in

Francesco Petiziol, André Eckardt

Considering the example of superconducting circuits, we show how Floquet engineering can be combined with reservoir engineering for the controlled preparation of target states. Floquet engineering refers to the control of a quantum system by means of time-periodic forcing, typically in the high-frequency regime, so that the system is governed effectively by

Alexander Henkes, Henning Wessels

Multiscale simulations are demanding in terms of computational resources. In the context of continuum micromechanics, the multiscale problem arises from the need of inferring macroscopic material parameters from the microscale. If the underlying microstructure is explicitly given by means of microCT-scans, convolutional neural networks can be used to learn t

Jose Barrientos, Adolfo Cisterna, David Kubiznak, Julio Oliva

Theories of non-linear electrodynamics naturally describe deviations from Maxwell's theory in the strong field regime. Among these, of special interest is the recently discovered ModMax electrodynamics, which is a unique 1-parametric generalization of Maxwell's theory that possesses the conformal invariance as well as the electromagnetic duality. In this pap

Marc Kesseböhmer, Aljoscha Niemann, Sanguo Zhu

We provide a complete picture of the upper quantization dimension in terms of the R\'enyi dimension by proving that the upper quantization dimension $\bar{D}_{r}(\nu)$ of order $r>0$ for an arbitrary compactly supported Borel probability measure $\nu$ is given by its R\'enyi dimension at the point $q_{r}$ where the $L^{q}$-spectrum of $\nu$ and the line thro

Masahiro Kadosawa, Masaaki Nakamura, Yukinori Ohta, Satoshi Nishimoto

We introduce spiral boundary conditions (SBCs) as a useful tool for handling the shape of finite-size periodic clusters. Using SBCs, a lattice model for more than two dimensions can be exactly projected onto a one-dimensional (1D) periodic chain with translational invariance. Hence, the existing 1D techniques such as density-matrix renormalization group (DMR

Eduardo Maria Polli, Juan Luis Gonzalo, Camilla Colombo

This paper presents an analytical model for collision probability assessments between de-orbiting or injecting space objects and satellite constellations. Considering the first to be subjected to a continuous tangential acceleration, its spiraling motion would result in a series of close approaches in the proximity of a constellation. The proposed methodolog

Xia Chen, Aurélien Deya, Jian Song, Samy Tindel

We study a wave equation in dimension $d\in \{1,2\}$ with a multiplicative space-time Gaussian noise. The existence and uniqueness of the Stratonovich solution is obtained under some conditions imposed on the Gaussian noise. The strategy is to develop some Strichartz type estimates for the wave kernel in weighted Besov spaces, by which we can prove the wellp

Mads J. Ahlebæk, Mads S. Peters, Wei-Chih Huang, Mads T. Frandsen

We present a simple but novel hybrid approach to hyperspectral data cube reconstruction from computed tomography imaging spectrometry (CTIS) images that sequentially combines neural networks and the iterative Expectation Maximization (EM) algorithm. We train and test the ability of the method to reconstruct data cubes of $100\times100\times25$ and $100\times

Linhui Gu, Abdusalam Abdukerim, Zihao Bo, Wei Chen

Compared with the signature of dark matter elastic scattering off nuclei, the absorption of fermionic dark matter by nuclei opens up a new searching channel for light dark matter with a characteristic monoenergetic signal. In this Letter, we explore the $95.0$-day data from the PandaX-4T commissioning run and report the first dedicated searching results of t

Daniel Jodlbauer, Ulrich Langer, Thomas Wick, Walter Zulehner

We consider the widely used continuous $\mathcal{Q}_{k}$-$\mathcal{Q}_{k-1}$ quadrilateral or hexahedral Taylor-Hood elements for the finite element discretization of the Stokes and generalized Stokes systems in two and three spatial dimensions. For the fast solution of the corresponding symmetric, but indefinite system of finite element equations, we propos

Andrea Bontempelli, Stefano Teso, Katya Tentori, Fausto Giunchiglia

Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the same performance as black-box models without compromising transparency. ProtoPNets compute predictions based on similarity to class-specific part-prototypes learned to recognize parts of training examples, making it easy to faithfully determine what examples are respon

Mark Boss, Andreas Engelhardt, Abhishek Kar, Yuanzhen Li

Inverse rendering of an object under entirely unknown capture conditions is a fundamental challenge in computer vision and graphics. Neural approaches such as NeRF have achieved photorealistic results on novel view synthesis, but they require known camera poses. Solving this problem with unknown camera poses is highly challenging as it requires joint optimiz

Philipp Haindl, Thomas Hoch, Javier Dominguez, Julen Aperribai

As AI-enabled software systems become more prevalent in smart manufacturing, their role shifts from a reactive to a proactive one that provides context-specific support to machine operators. In the context of an international research project, we develop an AI-based software platform that shall facilitate the collaboration between human operators and manufac

Masashi Tokuda, Ryoya Nakamura, Masaki Maeda, Yasuhiro Niimi

We studied resistance oscillations in two types of superconducting mesoscopic Nb rings. In a simple superconducting ring device, a resistance oscillation with a period of the quantized magnetic flux h/2e was clearly observed. On the other hand, in a micro-bridge ring device where two-narrow parts are embedded in parallel and work as superconductor-normal met

Itay Eilat, Ben Finkelshtein, Chaim Baskin, Nir Rosenfeld

Strategic classification studies learning in settings where users can modify their features to obtain favorable predictions. Most current works focus on simple classifiers that trigger independent user responses. Here we examine the implications of learning with more elaborate models that break the independence assumption. Motivated by the idea that applicat

Martin Vastl, Jonáš Kulhánek, Jiří Kubalík, Erik Derner

Many real-world problems can be naturally described by mathematical formulas. The task of finding formulas from a set of observed inputs and outputs is called symbolic regression. Recently, neural networks have been applied to symbolic regression, among which the transformer-based ones seem to be the most promising. After training the transformer on a large

Utku Ozbulak, Manvel Gasparyan, Shodhan Rao, Wesley De Neve

Predictions made by deep neural networks were shown to be highly sensitive to small changes made in the input space where such maliciously crafted data points containing small perturbations are being referred to as adversarial examples. On the other hand, recent research suggests that the same networks can also be extremely insensitive to changes of large ma

Alessandro Daniele, Luciano Serafini

In the recent past, there has been a growing interest in Neural-Symbolic Integration frameworks, i.e., hybrid systems that integrate connectionist and symbolic approaches to obtain the best of both worlds. In this work we focus on a specific method, KENN (Knowledge Enhanced Neural Networks), a Neural-Symbolic architecture that injects prior logical knowledge

Martin Humenberger, Yohann Cabon, Noé Pion, Philippe Weinzaepfel

Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two purposes: (1) provide an approximate pose estimate or (2) determine which parts of the scene are potentially

Soroush Ebadian, Anson Kahng, Dominik Peters, Nisarg Shah

A voting rule decides on a probability distribution over a set of m alternatives, based on rankings of those alternatives provided by agents. We assume that agents have cardinal utility functions over the alternatives, but voting rules have access to only the rankings induced by these utilities. We evaluate how well voting rules do on measures of social welf

Dagui Chen, Qi Yan, Chunjie Chen, Zhenzhe Zheng

A contemporary feed application usually provides blended results of organic items and sponsored items~(ads) to users. Conventionally, ads are exposed at fixed positions. Such a static exposure strategy is inefficient due to ignoring users' personalized preferences towards ads. To this end, adaptive ad exposure has become an appealing strategy to boost the ov

Hiren Kakkad, Anderson Kendi Kohara, Piotr Kotko

We turn high energy elastic scattering of hadrons into an initial value problem using an evolution equation based on the Regge Field Theory, which has a form of the complex nonlinear reaction-diffusion equation, with time being played by the logarithm of energy. The initial conditions are provided by the data-driven models for the real and imaginary parts of

Xihe Hu

The bootstrap is a technique recently developed to get energy eigenvalues of bound states and correlation functions. There are three crucial steps - recursive equations, positivity constraints, search space. We calculate recursive equations of many representative quantum mechanics systems, such as polynomial potential, exponential potential, Yukawa potential

Alex Shamis, Peter Pietzuch, Antoine Delignat-Lavaud, Andrew Paverd

Marketplaces for machine learning (ML) models are emerging as a way for organizations to monetize models. They allow model owners to retain control over hosted models by using cloud resources to execute ML inference requests for a fee, preserving model confidentiality. Clients that rely on hosted models require trustworthy inference results, even when models

Atsushi Ito, Ching-Jui Lai, Sz-Sheng Wang

We describe explicitly the chamber structure of the movable cone for a general complete intersection Calabi--Yau threefold in a non-split $(n + 4)$-dimensional $\mathbb{P}^{n}$-ruled Fano manifold of index $n + 1$ and Picard number two. Moreover, all birational minimal models of such Calabi--Yau threefolds are found whose number is finite.

Jin Hu, Zhe Xu

A mutilated model is constructed to approximate the collision term of spin Boltzmann equation that incorporates newly appearing collisional invariants i.e, the total angular momentum. With recourse to degenerate perturbation theory, the dispersion relations of hydrodynamic modes are formulated, among which spin modes are responsible for spin equilibration. W