January 2022 arXiv papers — page 4

Laura Bishop, Carlos Cancino-Chacón, Werner Goebl

In this chapter, we focus on two main categories of visual interaction: body gestures and gaze direction. Our focus on body gestures is motivated by research showing that gesture patterns often change during joint action tasks to become more predictable (van der Wel et al., 2016). Moreover, coordination sometimes emerges between musicians at the level of bod

Kailash C. Sahu, Jay Anderson, Stefano Casertano, Howard E. Bond

We report the first unambiguous detection and mass measurement of an isolated stellar-mass black hole (BH). We used the Hubble Space Telescope (HST) to carry out precise astrometry of the source star of the long-duration (t_E~270 days), high-magnification microlensing event MOA-2011-BLG-191/OGLE-2011-BLG-0462 (hereafter designated as MOA-11-191/OGLE-11-462),

N. A. Carella

Let $x\geq 1$ be a large integer, and let $\mu:\mathbb{N}\longrightarrow\{-1,0,1\}$ be the Mobius function. This article proposes an effective asymptotic result for the autocorrelation function $\sum_{n \leq x} \mu(n) \mu(n+t) =O\left( e^{-c\sqrt{\log x}}\right) $, where $t\ne 0$ be a small fixed integer, and $c>0$ is a constant.

Maxime Debiossac, Martin Luc Rosinberg, Eric Lutz, Nikolai Kiesel

Causality is an important assumption underlying nonequilibrium generalizations of the second law of thermodynamics known as fluctuation relations. We here experimentally study the nonequilibrium statistical properties of the work and of the entropy production for an optically trapped, underdamped nanoparticle continuously subjected to a time-delayed feedback

Nicolas Borghini, Marc Borrell, Hendrik Roch

We investigate the early time development of the anisotropic transverse flow and spatial eccentricities of a fireball with various particle-based transport approaches using a fixed initial condition. In numerical simulations ranging from the quasi-collisionless case to the hydrodynamic regime, we find that the onset of $v_n$ and of related measures of anisot

Fabian Brauneis, Timothy G Backert, Simeon I Mistakidis, Mikhail Lemeshko

We investigate the ground-state properties of weakly repulsive one-dimensional bosons in the presence of an attractive zero-range impurity potential. First, we derive mean-field solutions to the problem on a finite ring for the two asymptotic cases: (i) all bosons are bound to the impurity and (ii) all bosons are in a scattering state. Moreover, we derive th

Chryssis Georgiou, Nicolas Nicolaou, Andria Trigeorgi

Data availability is one of the most important features in distributed storage systems, made possible by data replication. Nowadays data are generated rapidly and the goal to develop efficient, scalable and reliable storage systems has become one of the major challenges for high performance computing. In this work, we develop a dynamic, robust and strongly c

Tristan Gomez, Thomas Fréour, Harold Mouchère

Due to the black-box nature of deep learning models, there is a recent development of solutions for visual explanations of CNNs. Given the high cost of user studies, metrics are necessary to compare and evaluate these different methods. In this paper, we critically analyze the Deletion Area Under Curve (DAUC) and Insertion Area Under Curve (IAUC) metrics pro

K. Topolnicki, T. Bold

High energy physics experiments, in particular experiments at the LHC, require the reconstruction of charged particle trajectories. Methods of reconstructing such trajectories have been known for decades, yet the applications at High Luminosity LHC require this reconstruction to be fast enough to be suitable for online event filtering. A particle traversing

Xi Zhou, Qinghao Ye, Xiaolin Yang, Jiakuan Chen

Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multi-modal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite N

Double Chooz Collaboration, H. de Kerret, Y. Abe, C. Aberle

This article describes the setup and performance of the near and far detectors in the Double Chooz experiment. The electron antineutrinos of the Chooz nuclear power plant were measured in two identically designed detectors with different average baselines of about 400 m and 1050 m from the two reactor cores. Over many years of data taking the neutrino signal

Xuan Kien Phung

We study the class of asynchronous non-uniform cellular automata (ANUCA) over an arbitrary group universe with multiple local transition rules. We introduce the notion of stable injectivity, stable reversibility, stable post-surjectivity and investigate several dynamical properties of such automata. In particular, we establish the equivalence between reversi

Sayan Goswami

Erd\H{o}s-Ginzburg-Ziv theorem says that if there are 2n-1 number is given, then there are n numbers such that their sum is divided by n. We will connect this theorem with the Ramsey theoretic large sets and will prove an infinitary version of this theorem. In our proof we will use the methods of ultrafilters. But one may proceed using methods of Topological

José A. Iglesias, Gwenael Mercier

The use of the fractional Laplacian in image denoising and regularization of inverse problems has enjoyed a recent surge in popularity, since for discontinuous functions it can behave less aggressively than methods based on $H^1$ norms, while being linear and computable with fast spectral numerical methods. In this work, we examine denoising and linear inver

Odysseas Moschidis, Angelos Markos, Theodore Chadjipadelis

Clustering of mixed-type datasets can be a particularly challenging task as it requires taking into account the associations between variables with different level of measurement, i.e., nominal, ordinal and/or interval. In some cases, hierarchical clustering is considered a suitable approach, as it makes few assumptions about the data and its solution can be

Philipp Oberdiek, Gernot A. Fink, Matthias Rottmann

We present an approach to quantifying both aleatoric and epistemic uncertainty for deep neural networks in image classification, based on generative adversarial networks (GANs). While most works in the literature that use GANs to generate out-of-distribution (OoD) examples only focus on the evaluation of OoD detection, we present a GAN based approach to lear

Niklas Gard, Anna Hilsmann, Peter Eisert

In this paper, we present a multi-object 6D detection and tracking pipeline for potentially similar and non-textured objects. The combination of a convolutional neural network for object classification and rough pose estimation with a local pose refinement and an automatic mismatch detection enables direct application in real-time AR scenarios. A new network

Simon Allais

We prove an extension of the homology version of the Hofer-Zehnder conjecture proved by Shelukhin to the weighted projective spaces which are symplectic orbifolds. In particular, we prove that if the number of fixed points counted with their isotropy order as multiplicity of a non-degenerate Hamiltonian diffeomorphism of such a space is larger than the minim

Chandresh Pravin, Ivan Martino, Giuseppe Nicosia, Varun Ojha

We identify fragile and robust neurons of deep learning architectures using nodal dropouts of the first convolutional layer. Using an adversarial targeting algorithm, we correlate these neurons with the distribution of adversarial attacks on the network. Adversarial robustness of neural networks has gained significant attention in recent times and highlights

Elías Portolés, Shuichi Iwakiri, Giulia Zheng, Peter Rickhaus

Magic-angle twisted bilayer graphene (MATBG) hosts a number of correlated states of matter that can be tuned by electrostatic doping. Superconductivity has drawn considerable attention and the mechanism behind it is a topic of active discussion. MATBG has been experimentally characterized by numerous transport and scanning-probe experiments. The material has

P. Hadrava, M. Cabezas, G. Djurašević, J. Garcés

Context. The eclipsing close binary UU Cas is an interacting massive double-periodic system with a gainer star partly hidden in an accretion disk. Aims. In order to study the physics of the accretion process in greater detail, along with the structure and dynamics of the circumstellar matter in the system, we supplement our previous results obtained from pho

G. Morello, H. Parviainen, F. Murgas, E. Pallé

Context. Exoplanets with orbital periods of less than one day are known as ultra-short period (USP) planets. They are relatively rare products of planetary formation and evolution processes, but especially favourable for characterisation with current planet detection methods. At the time of writing, 125 USP planets have already been confirmed. Aims. Our aim

William Kengne

We consider the model selection problem for a large class of time series models, including, multivariate count processes, causal processes with exogenous covariates. A procedure based on a general penalized contrast is proposed. Some asymptotic results for weak and strong consistency are established. The non consistency issue is addressed, and a class of pen

Iasson Karafyllis, Filippos Vokos, Miroslav Krstic

We solve the output-feedback stabilization problem for a tank with a liquid modeled by the viscous Saint-Venant PDE system. The control input is the acceleration of the tank and a Control Lyapunov Functional methodology is used. The measurements are the tank position and the liquid level at the tank walls. The control scheme is a combination of a state feedb

Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Pavan Tummala

Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding t

Erman Isik, Yasemin Kara, Ekin Ozman

In this paper, we prove results about solutions of the Diophantine equation $x^p+y^p=z^3$ over various number fields using the modular method. Firstly, by assuming some standard modularity conjecture we prove an asymptotic result for general number fields of narrow class number one satisfying some technical conditions. Secondly, we show that there is an expl

Jorge Lerendegui-Marco, Javier Balibrea-Correa, Víctor Babiano-Súarez, Ion Ladarescu

In this work, we report on the advantageous aspects of the i-TED Compton imager for proton-range monitoring, based on the results of the first Monte Carlo study of its applicability to this field. i-TED is an array of Compton cameras, that have been designed for neutron-capture nuclear physics experiments, which are characterized by $\gamma$-ray energies spa

Mary Scott, Graham Cormode, Carsten Maple

Advances in communications, storage and computational technology allow significant quantities of data to be collected and processed by distributed devices. Combining the information from these endpoints can realize significant societal benefit but presents challenges in protecting the privacy of individuals, especially important in an increasingly regulated

Stephan Gärttner, Peter Knabner, Nadja Ray

Two-scale models pose a promising approach in simulating reactive flow and transport in evolving porous media. Classically, homogenized flow and transport equations are solved on the macroscopic scale, while effective parameters are obtained from auxiliary cell problems on possibly evolving reference geometries (micro-scale). Despite their perspective succes

P. Chakrabarti, A. Wildeis, M. Hartmann, R. Brandt

X-ray diffraction with high spatial resolution is commonly used to characterize (poly-)crystalline samples with, for example, respect to local strain, residual stress, grain boundaries and texture. However, the investigation of highly absorbing samples or the simultaneous assessment of high-Z materials by X-ray fluorescence have been limited due to the utili

Giovanni Luca Torrisi, Michele Garetto, Emilio Leonardi

We analyze the bootstrap percolation process on the stochastic block model (SBM), a natural extension of the Erd\H{o}s--R\'{e}nyi random graph that incorporates the community structure observed in many real systems. In the SBM, nodes are partitioned into two subsets, which represent different communities, and pairs of nodes are independently connected with a

Oren Fivel

This thesis applies Floquet theory to analyze linear periodic time-varying (LPTV) systems, represented by a system of ordinary differential equations (ODEs) that depend on a time variable t and have a matrix of coefficients with period T>0. The transition matrix of an LPTV system represented by a square periodic-function matrix A(t)=A(t+T) can be expressed a

Matias Berdakin, German Soldano, Franco P. Bonafé, Varlamova Liubov

The harnessing of plasmon-induced hot carriers promises to open new avenues for the development of clean energies and chemical catalysis. The extraction of carriers before thermalization and recombination is of primordial importance to obtain appealing conversion yields. Here, hot carrier injection in the paradigmatic Au-TiO$_{2}$ system is studied by means

Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun

Generative flow networks (GFlowNets) are a method for learning a stochastic policy for generating compositional objects, such as graphs or strings, from a given unnormalized density by sequences of actions, where many possible action sequences may lead to the same object. We find previously proposed learning objectives for GFlowNets, flow matching and detail

Elliot Kaplan

Let $T$ be an o-minimal theory extending the theory of real closed ordered fields. An $H_T$-field is a model $K$ of $T$ equipped with a $T$-derivation such that the underlying ordered differential field of $K$ is an $H$-field. We study $H_T$-fields and their extensions. Our main result is that if $T$ is power bounded, then every $H_T$-field $K$ has either ex

Mark Levene

A novel yet simple extension of the symmetric logistic distribution is proposed by introducing a skewness parameter. It is shown how the three parameters of the ensuing skew logistic distribution may be estimated using maximum likelihood. The skew logistic distribution is then extended to the skew bi-logistic distribution to allow the modelling of multiple w

Samuel Hurault, Arthur Leclaire, Nicolas Papadakis

Plug-and-Play (PnP) methods solve ill-posed inverse problems through iterative proximal algorithms by replacing a proximal operator by a denoising operation. When applied with deep neural network denoisers, these methods have shown state-of-the-art visual performance for image restoration problems. However, their theoretical convergence analysis is still inc

Laurent Saloff-Coste, Sophie Uluatam

Consider the problem of approximating a given probability distribution on the cube $[0,1]^n$ via the use of a square lattice discretization with mesh-size $1/N$ and the Metropolis algorithm. Here the dimension $n$ is fixed and we focus for the most part on the case $n=2$. In order to understand the speed of convergence of such a procedure, one needs to contr

Elena Celledoni, Andrea Leone, Davide Murari, Brynjulf Owren

Recently, there has been an increasing interest in modelling and computation of physical systems with neural networks. Hamiltonian systems are an elegant and compact formalism in classical mechanics, where the dynamics is fully determined by one scalar function, the Hamiltonian. The solution trajectories are often constrained to evolve on a submanifold of a

Michael A. Allen, Kenneth Edwards

We consider two families of Pascal-like triangles that have all ones on the left side and ones separated by $m-1$ zeros on the right side. The $m=1$ cases are Pascal's triangle and the two families also coincide when $m=2$. Members of the first family obey Pascal's recurrence everywhere inside the triangle. We show that the $m$-th triangle can also be obtain

Seung-Il Choi, Young-Hun Kim, Sun-Young Nam, Young-Tak Oh

Let $n$ be a nonnegative integer. For each composition $\alpha$ of $n$, Berg $\textit{et al.}$ introduced a cyclic indecomposable $H_n(0)$-module $\mathcal{V}_\alpha$ with a dual immaculate quasisymmetric function as the image of the quasisymmetric characteristic. In this paper, we study $\mathcal{V}_\alpha$'s from the homological viewpoint. To be precise, w

Hao Max Sun

We give a conjectural construction of Bridgeland stability conditions on the derived category of fibred threefolds. The construction depends on a conjectural Bogomolov-Gieseker type inequality for certain stable complexes. It can be considered as a relative version of the construction of Bayer, Macr\`i and Toda. We prove the conjectural Bogomolov-Gieseker ty

Quanlong Wang, Richie Yeung, Mark Koch

ZX-calculus has proved to be a useful tool for quantum technology with a wide range of successful applications. Most of these applications are of an algebraic nature. However, other tasks that involve differentiation and integration remain unreachable with current ZX techniques. Here we elevate ZX to an analytical perspective by realising differentiation and

Tilman Klaeger, Konstantin Merker

While lots of research has been conducted on the architecture of Industrial Internet of Things (IIoT) systems, concepts of structuring their development processes are missing. Therefore, we propose a holistic approach supporting organizations in rapid development of IIoT systems. It includes the structuring of the development process into multiple projects s

Lukas Stankevičius, Mantas Lukoševičius, Jurgita Kapočiūtė-Dzikienė, Monika Briedienė

Due to the fast pace of life and online communications and the prevalence of English and the QWERTY keyboard, people tend to forgo using diacritics, make typographical errors (typos) when typing in other languages. Restoring diacritics and correcting spelling is important for proper language use and the disambiguation of texts for both humans and downstream

F. Feringa, G. E. W. Bauer, B. J. van Wees

Van der Waals materials are a new platform to study two-dimensional systems, including magnetic order. Since the number of spins is relatively small, measuring the magnetization is challenging. Here we report spin Hall magnetoresistance (SMR) up to room temperature caused by the magnetic surface texture of exfoliated flakes of magnetic van der Waals material

Rohan Sawhney, Dario Seyb, Wojciech Jarosz, Keenan Crane

Partial differential equations (PDEs) with spatially-varying coefficients arise throughout science and engineering, modeling rich heterogeneous material behavior. Yet conventional PDE solvers struggle with the immense complexity found in nature, since they must first discretize the problem -- leading to spatial aliasing, and global meshing/sampling that is c

Ádám Kovács, Kinga Gémes, Eszter Iklódi, Gábor Recski

We present POTATO, a task- and languageindependent framework for human-in-the-loop (HITL) learning of rule-based text classifiers using graph-based features. POTATO handles any type of directed graph and supports parsing text into Abstract Meaning Representations (AMR), Universal Dependencies (UD), and 4lang semantic graphs. A streamlit-based user interface

Archit Agarwal, Meghali Garg, Bibekananda Maji

In 1916, Riesz proved that the Riemann hypothesis is equivalent to the bound $\sum_{n=1}^\infty \frac{\mu(n)}{n^2} \exp\left( - \frac{x}{n^2} \right) = O_{\epsilon} \left( x^{-\frac{3}{4} + \epsilon} \right)$, as $x \rightarrow\infty$, for any $\epsilon >0$. Around the same time, Hardy and Littlewood gave another equivalent criteria for the Riemann hypothesi

Soumitra Ghara, Surjit Kumar, Gadadhar Misra, Paramita Pramanick

Let $\mathcal U(d)$ be the group of $d\times d$ unitary matrices. We find conditions to ensure that a $\mathcal U(d)$-homogeneous $d$-tuple $\boldsymbol T$ is unitarily equivalent to multiplication by the coordinate functions on some reproducing kernel Hilbert space $\mathcal H_K(\mathbb B_d, \mathbb C^n) \subseteq \mbox{\rm Hol}(\mathbb B_d, \mathbb C^n)$,

Mnacho Echenim, Nicolas Peltier

A proof procedure, in the spirit of the sequent calculus, is proposed to check the validity of entailments between Separation Logic formulas combining inductively defined predicates denoted structures of bounded tree width and theory reasoning. The calculus is sound and complete, in the sense that a sequent is valid iff it admits a (possibly infinite) proof

K. France, B. Fleming, A. Youngblood, J. Mason

The Extreme-ultraviolet Stellar Characterization for Atmospheric Physics and Evolution (ESCAPE) mission is an astrophysics Small Explorer employing ultraviolet spectroscopy (EUV: 80 - 825 \AA\ and FUV: 1280 - 1650 \AA) to explore the high-energy radiation environment in the habitable zones around nearby stars. ESCAPE provides the first comprehensive study of

Allan Sacha Brun, Antoine Strugarek, Quentin Noraz, Barbara Perri

We use the ASH code to model the convective dynamo of solar-type stars. Based on a series of 15 3-D MHD simulations spanning 4 bins in rotation and mass, we show what mechanisms are at work in these stellar dynamos with and without magnetic cycles and how global stellar parameters affect the outcome. We also derive scaling laws for the differential rotation

Tom Hess, Ron Visbord, Sivan Sabato

We propose a new algorithm for k-means clustering in a distributed setting, where the data is distributed across many machines, and a coordinator communicates with these machines to calculate the output clustering. Our algorithm guarantees a cost approximation factor and a number of communication rounds that depend only on the computational capacity of the c

Alexandre L. M. Levada

Random field models are mathematical structures used in the study of stochastic complex systems. In this paper, we compute the shape operator of Gaussian random field manifolds using the first and second fundamental forms (Fisher information matrices). Using Markov Chain Monte Carlo techniques, we simulate the dynamics of these random fields and compute the

Alberto Martín Santa Daría, Gustavo Avila, Edit Mátyus

Vibrational states of the formic acid molecule are converged using the GENIUSH-Smolyak approach and the potential energy surface taken from [D. Tew and W. Mizukami, J. Phys. Chem. A 120, 9815 (2016)]. The quantum nuclear motion is described by using the $cis$-$trans$ torsional coordinate and eight curvilinear normal coordinates defined with respect to an ins

Alireza Akbari, Peter Thalmeier

We investigate the quasiparticle excitations in the FFLO- type helical state of a superconductor with inversion-symmetry breaking and strong Rashba spin-orbit coupling. We restrict to a state with single finite momentum of Cooper pairs in the helical phase that is determined by minimization of the condensation energy. We derive the dependence of quasiparticl

Edit Mátyus, Dávid Ferenc

A variational procedure is described for the computation of the non-adiabatic mass-correction tensor applicable for multi-dimensional electronic manifolds. The 30-year-old computations of Wolniewicz, Dressler, and their co-workers are appended with the computed vibronic mass-correction functions corresponding to the $EF$-$GK$-$H\bar{H}$-$S5$-$S6$ $^1\Sigma_\

Iacopo Giordano

We prove a classification of additive polynomial superfunctors, which allows us to compute some extensions of a superfunctor of the form $F \circ A$ where $F$ is a classical polynomial functor and $A$ is additive. We get a formula which relates these extensions to the classical ones of $F$. A possible generalisation is conjectured at the end.

Reza Rawassizadeh, Yi Rong

Mobile and wearable technologies have promised significant changes to the healthcare industry. Although cutting-edge communication and cloud-based technologies have allowed for these upgrades, their implementation and popularization in low-income countries have been challenging. We propose "ODSearch", an On-device Search framework equipped with a natural lan

Joanna Mikolajewska, Andrzej A. Zdziarski, Janusz Ziolkowski, Manuel A. P. Torres

We estimate the parameters of the donor of the accreting black-hole binary MAXI J1820+070. The measured values of the binary period, rotational and radial velocities and constraints on the orbital inclination imply the donor is a subgiant with the mass of $M_2\approx 0.49^{+0.10}_{-0.10}M_\odot$ and the radius of $R_2\approx 1.19^{+0.08}_{-0.08}R_\odot$. We

Kohta Hatakeyama, Konstantinos Anagnostopoulos, Takehiro Azuma, Mitsuaki Hirasawa

The type IIB matrix model has been proposed as a non-perturbative definition of superstring theory since 1996. We study a simplified model that describes the late time behavior of the type IIB matrix model non-perturbatively using Monte Carlo methods, and we use the complex Langevin method to overcome the sign problem. We investigate a scenario where the spa

Vladimir A. Koutvitsky, Eugene M. Maslov

Applying the perturbative approach to geodesic equations, we study motion of the test particles in time-dependent spherically symmetric spacetimes created by oscillating dark matter. Assuming the weakness of the gravitational field, we derive general formulas that describe infinite trajectories of the test particles and determine the total deflection angle i

Jon Lachmann, Geir Storvik, Florian Frommlet, Aliaksadr Hubin

It is common practice to use Laplace approximations to compute marginal likelihoods in Bayesian versions of generalised linear models (GLM). Marginal likelihoods combined with model priors are then used in different search algorithms to compute the posterior marginal probabilities of models and individual covariates. This allows performing Bayesian model sel

Youn Jue Bae, Jue Wang, Allen Scheie, Junwen Xu

Two-dimensional (2D) magnetic semiconductors feature both tightly-bound excitons with large oscillator strength and potentially long-lived coherent magnons due to the presence of bandgap and spatial confinement. While magnons and excitons are energetically mismatched by orders of magnitude, their coupling can lead to efficient optical access to spin informat

Youcef Askoura, Mohammed Sbihi

We prove a conditional expectation bang-bang principle. Based on properties of the conditional expectation vector measure, we establish that the conditional expectation of a set-valued mapping coincides with the conditional expectation of the set of selections of its extreme points part. As a by-product, we obtain straightforwardly a purification principle.

Daniel Bershatsky, Aleksandr Mikhalev, Alexandr Katrutsa, Julia Gusak

In modern neural networks like Transformers, linear layers require significant memory to store activations during backward pass. This study proposes a memory reduction approach to perform backpropagation through linear layers. Since the gradients of linear layers are computed by matrix multiplications, we consider methods for randomized matrix multiplication

Peican Zhu, Xin Hou, Keke Tang, Zhen Wang

Along with the flourish of the information age, massive amounts of data are generated day by day. Due to the large-scale and high-dimensional characteristics of these data, it is often difficult to achieve better decision-making in practical applications. Therefore, an efficient big data analytics method is urgently needed. For feature engineering, feature s

Clément Cancès, Claire Chainais-Hillairet, Benoît Merlet, Federica Raimondi

We are interested in a reduced model for corrosion of iron, in which ferric cations and electrons evolve in a fixed oxide layer subject to a self-consistent electrostatic potential. Reactions at the boundaries are modeled thanks to Butler-Volmer formulas, whereas the boundary conditions on the electrostatic potential model capacitors located at the interface

Emilio Dorigatti, Jann Goschenhofer, Benjamin Schubert, Mina Rezaei

Positive-unlabeled learning (PUL) aims at learning a binary classifier from only positive and unlabeled training data. Even though real-world applications often involve imbalanced datasets where the majority of examples belong to one class, most contemporary approaches to PUL do not investigate performance in this setting, thus severely limiting their applic

Ammar Alsaffar, Steffen Kieß, Kaicong Sun, Sven Simon

In computed tomography (CT) reconstruction, scattering causes server quality degradation of the reconstructed CT images by introducing streaks and cupping artifacts which reduce the detectability of low contrast objects. Monte Carlo (MC) simulation is considered as the most accurate approach for scatter estimation. However, the existing MC estimators are com

Saeed Hadadan, Matthias Zwicker

We introduce Differentiable Neural Radiosity, a novel method of representing the solution of the differential rendering equation using a neural network. Inspired by neural radiosity techniques, we minimize the norm of the residual of the differential rendering equation to directly optimize our network. The network is capable of outputting continuous, view-in

AmirHosein Sadeghimanesh, Matthew England

We are concerned with the problem of decomposing the parameter space of a parametric system of polynomial equations, and possibly some polynomial inequality constraints, with respect to the number of real solutions that the system attains. Previous studies apply a two step approach to this problem, where first the discriminant variety of the system is comput

E. Lellouch, R. Moreno, D. Bockelée-Morvan, N. Biver

The recently announced Oort-cloud comet C/2014 UN271 (Bernardinelli-Bernstein) is remarkable in at least three respects: (i) it was discovered inbound as far as ~29 au from the Sun (with prediscovery images up to ~34 au); (ii) it showed cometary activity already at almost 24 au; and (iii) its nuclear magnitude (Hr ~ 8.0) indicates an exceptionally large obje

Pei-Lun Tseng

We consider the notions of operator-valued infinitesimal (OVI) free independence, OVI Boolean independence, and OVI monotone independence. For each notion of OVI independence, we introduce the corresponding infinitesimal transforms, and then we show that the transforms satisfy certain multiplicative property. Additionally, we extend the concept of $t$-coeffi

Pol Heuschling, Christian Partmann, Christian Fidler

We present a novel method for including the impact of massive neutrinos in cold dark matter N-body simulations. Our approach is compatible with widely employed Newtonian N-body codes and relies on only three simple modifications. First, we use commonly employed backscaling initial conditions, based on the cold dark matter plus baryon power spectrum instead o

Daniel Gerth

Linear non-compact operators are difficult to study because they do not exist in the finite dimensional world. Recently, Math\'{e} and Hofmann studied the singular values of the compact composition of the non-compact Hausdorff moment operator and the compact integral operator and found credible arguments, but no strict proof, that those singular values fall

Alexander Kubanek

Hexagonal boron nitride is an emerging two-dimensional material with far-reaching applications in fields like nanophotonics or nanomechanics. Its layered architecture plays a key role for new materials such as Van der Waals heterostructures. The layered structure has also unique implications for hosted, optically active defect centers. A very special type of

Igor D. Kaganovich, Edward A. Startsev, Hong Qin, Erik Gilson

For the successful generation of ion-beam-driven high energy density matter and heavy ion fusion energy, intense ion beams must be transported and focused onto a target with small spot size. One of the successful approaches to achieve this goal is to accelerate and transport intense ion charge bunches in an accelerator and then focus the charge bunches balli

Philippe Weinzaepfel, Thomas Lucas, Diane Larlus, Yannis Kalantidis

Methods that combine local and global features have recently shown excellent performance on multiple challenging deep image retrieval benchmarks, but their use of local features raises at least two issues. First, these local features simply boil down to the localized map activations of a neural network, and hence can be extremely redundant. Second, they are

Teja Mannepalli, Aurobinda Routray

Source localization using EEG is important in diagnosing various physiological and psychiatric diseases related to the brain. The high temporal resolution of EEG helps medical professionals assess the internal physiology of the brain in a more informative way. The internal sources are obtained from EEG by an inversion process. The number of sources in the br

Mikhail E. Belkin, Leonid Zhukov, Alexander S. Sigov

A new cost-efficient concept to realize a real-time monitoring of quality-of-service metrics and other service data in 5G and beyond access network using a separate return channel based on a vertical cavity surface emitting laser in the optical injection locked mode that simultaneously operates as an optical transmitter and as a resonant cavity enhanced phot

Tommaso Salvatori, Luca Pinchetti, Beren Millidge, Yuhang Song

Training with backpropagation (BP) in standard deep learning consists of two main steps: a forward pass that maps a data point to its prediction, and a backward pass that propagates the error of this prediction back through the network. This process is highly effective when the goal is to minimize a specific objective function. However, it does not allow tra

Jérémie Klinger, Raphaël Voituriez, Olivier Bénichou

We derive a universal, exact asymptotic form of the splitting probability for symmetric continuous jump processes, which quantifies the probability $ \pi_{0,\underline{x}}(x_0)$ that the process crosses $x$ before 0 starting from a given position $x_0\in[0,x]$ in the regime $x_0\ll x$. This analysis provides in particular a fully explicit determination of th

Yiming Li, Haoxiang Zhong, Xingjun Ma, Yong Jiang

Visual object tracking (VOT) has been widely adopted in mission-critical applications, such as autonomous driving and intelligent surveillance systems. In current practice, third-party resources such as datasets, backbone networks, and training platforms are frequently used to train high-performance VOT models. Whilst these resources bring certain convenienc

Giovanni Abbiendi

The MUonE experiment has been proposed to measure the differential cross section of $\mu e$ elastic scattering, by colliding the 160 GeV muons of the CERN M2 beam with atomic electrons of thin target plates. From a very precise measurement of the shape one can achieve a competitive determination of the leading hadronic contribution to the muon magnetic momen

Luca Pasqualini, Gianluca Amato, Marco Fantozzi, Rosa Gini

In the last years, the DeepMind algorithm AlphaZero has become the state of the art to efficiently tackle perfect information two-player zero-sum games with a win/lose outcome. However, when the win/lose outcome is decided by a final score difference, AlphaZero may play score-suboptimal moves because all winning final positions are equivalent from the win/lo

A. Lamberts, E. Puchwein, C. Pfrommer, P. Chang

The intergalactic medium (IGM) acts like a calorimeter recording energy injection by cosmic structure formation, shocks and photoheating from stars and active galactic nuclei. It was recently proposed that spatially inhomogeneous TeV-blazars could significantly heat up the underdense IGM, resulting in patches of both cold and warm IGM around $z\simeq2-3$. Th

Gu Zhang, Igor V. Gornyi, Christian Spånslätt

We study delta-T noise -- excess charge noise at zero voltage but finite temperature bias -- for weak tunneling in 1D interacting systems. We show that the sign of the delta-T noise is generically determined by the nature of the dominating tunneling process (more specifically, its scaling dimension). We clarify the relation between the sign of delta-T noise

Amin Hosseinkhani, Guido Burkard

We develop the theory of single-electron silicon spin qubit relaxation in the presence of a magnetic field gradient. Such field gradients are routinely generated by on-chip micromagnets to allow for electrically controlled quantum gates on spin qubits. We build on a valley-dependent envelope function theory that enables the analysis of the electron wave func

Tiancheng Jin, Tal Lancewicki, Haipeng Luo, Yishay Mansour

The standard assumption in reinforcement learning (RL) is that agents observe feedback for their actions immediately. However, in practice feedback is often observed in delay. This paper studies online learning in episodic Markov decision process (MDP) with unknown transitions, adversarially changing costs, and unrestricted delayed bandit feedback. More prec

C. Oancea, C. Bălan, J. Pivec, C. Granja

This work aims to characterize ultra high dose rate pulses (UHDpulse) electron beams using the hybrid semiconductor pixel detector. The Timepix3 (TPX3) ASIC chip was used to measure the composition, spatial, time, and spectral characteristics of the secondary radiation fields from pulsed 15 to 23 MeV electron beams. The challenge is to develop a single compa

Tal Lancewicki, Aviv Rosenberg, Yishay Mansour

We study cooperative online learning in stochastic and adversarial Markov decision process (MDP). That is, in each episode, $m$ agents interact with an MDP simultaneously and share information in order to minimize their individual regret. We consider environments with two types of randomness: \emph{fresh} -- where each agent's trajectory is sampled i.i.d, an

Sander Beckers

Although standard Machine Learning models are optimized for making predictions about observations, more and more they are used for making predictions about the results of actions. An important goal of Explainable Artificial Intelligence (XAI) is to compensate for this mismatch by offering explanations about the predictions of an ML-model which ensure that th

Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung

Neural implicit fields are quickly emerging as an attractive representation for learning based techniques. However, adopting them for 3D shape modeling and editing is challenging. We introduce a method for $\mathbf{E}$diting $\mathbf{I}$mplicit $\mathbf{S}$hapes $\mathbf{T}$hrough $\mathbf{P}$art $\mathbf{A}$ware $\mathbf{G}$enera$\mathbf{T}$ion, permuted in

Xiaorong Wang, Xiaodi Zhang

In this paper, we consider the numerical approximation for a diffuse interface model of the two-phase incompressible inductionless magnetohydrodynamics problem. This model consists of Cahn-Hilliard equations, Navier-Stokes equations and Poisson equation. We propose a linear and decoupled finite element method to solve this highly nonlinear and multi-physics

Emilia Anna Alfano, Luisa Fattorusso, Lubomira Softova

We deal with nonlinear systems of parabolic type satisfying component-wise structural conditions. The nonlinear terms are Carath\'eodory maps having controlled growth with respect to the solution and the gradient and the data are in anisotropic Lebesgue spaces. Under these assumptions we obtain essential boundedness of the weak solutions.

Jakub Kabat

We provide a classification result on nearly free arrangements of lines in the complex projective plane with nodes and triple points.

Mingfu Xue, Shifeng Ni, Yinghao Wu, Yushu Zhang

Recent researches demonstrate that Deep Neural Networks (DNN) models are vulnerable to backdoor attacks. The backdoored DNN model will behave maliciously when images containing backdoor triggers arrive. To date, existing backdoor attacks are single-trigger and single-target attacks, and the triggers of most existing backdoor attacks are obvious thus are easy

Siddharth Madan, Parth Bambhaniya

Naked singularities form during the gravitational collapse of inhomogeneous matter clouds. The final nature of the singularity depends on the initial conditions of the matter properties and types of matter profiles. These naked singularities can also be divided into two types: null-like and timelike singularities. The spacelike singularity of the Schwarzschi