July 2022 arXiv papers — page 150

Yuhao Huang, Xin Yang, Xiaoqiong Huang, Jiamin Liang

Deep segmentation models often face the failure risks when the testing image presents unseen distributions. Improving model robustness against these risks is crucial for the large-scale clinical application of deep models. In this study, inspired by human learning cycle, we propose a novel online reflective learning framework (RefSeg) to improve segmentation

Yuxin Zou, Haoran Dou, Yuhao Huang, Xin Yang

Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide complete anatomy with the addition of coronal plane. However, manually navigating SPs in 3D US is laborious and biased due to the orientation variability and huge search space. In this

Jiamin Liang, Xin Yang, Yuhao Huang, Kai Liu

Ultrasound (US) is widely used for its advantages of real-time imaging, radiation-free and portability. In clinical practice, analysis and diagnosis often rely on US sequences rather than a single image to obtain dynamic anatomical information. This is challenging for novices to learn because practicing with adequate videos from patients is clinically unprac

Oliver Lloyd, Yi Liu, Tom Gaunt

Embedding knowledge graphs into low-dimensional spaces is a popular method for applying approaches, such as link prediction or node classification, to these databases. This embedding process is very costly in terms of both computational time and space. Part of the reason for this is the optimisation of hyperparameters, which involves repeatedly sampling, by

Sherzod R. Otajonov, Eduard N. Tsoy, Fatkhulla Kh. Abdullaev

Modulational instability of a uniform two-dimensional binary Bose-Einstein condensate (BEC) in the presence of quantum fluctuations is studied. The analysis is based on the coupled Gross-Pitaevskii equations. It is shown that quantum fluctuations can induce instability when the BEC density is below a threshold. The dependence of the growth rate of modulation

Skralan Hosteaux, Luciano Rodiguez, Stefaan Poedts

This paper studies ICMEs detected by both Voyager spacecraft during propagation from 1 to 10 AU, with observations from 1977 to 1980. ICMEs are detected by using several signatures in the in-situ data, the primary one being the low measured to expected proton temperature ratio. We found 21 events common to both spacecraft and study their internal structure i

B. Das, P. Chandra, V. Petit

Recently a large number of hot magnetic stars have been discovered to produce auroral radio emission by the process of electron cyclotron maser emission (ECME). Such stars have been given the name of Main-sequence Radio Pulse emitters (MRPs). The phenomenon characterizing MRPs is very similar to that exhibited by planets like the Jupiter. However, one import

Thomas Budzinski, Nicolas Curien, Bram Petri

It is a well-known result due to Bollobas that the maximal Cheeger constant of large $d$-regular graphs cannot be close to the Cheeger constant of the $d$-regular tree. We prove analogously that the Cheeger constant of closed hyperbolic surfaces of large genus is bounded from above by $2/\pi \approx 0.63...$ which is strictly less than the Cheeger constant o

Jorge A. Mendez, Alborz Geramifard, Mohammad Ghavamzadeh, Bing Liu

Learning task-oriented dialog policies via reinforcement learning typically requires large amounts of interaction with users, which in practice renders such methods unusable for real-world applications. In order to reduce the data requirements, we propose to leverage data from across different dialog domains, thereby reducing the amount of data required from

Saeed Mahdisoltani, Ramin Golestanian

The classical Coulomb gas model has served as one of the most versatile frameworks in statistical physics, connecting a vast range of phenomena across many different areas. Nonequilibrium generalisations of this model have so far been studied much more scarcely. With the abundance of contemporary research into active and driven systems, one would naturally e

M. Giulietti, A. Lapi, M. Massardi, M. Behiri

We present high-resolution ($\lesssim0.1$arcsec) ALMA observations of the strongly-lensed galaxy HATLASJ113526.2-01460 at redshift $z\sim3.1$ discovered in the Gama 12$^{\rm th}$ field of the Herschel-ATLAS survey. The gravitationally lensed system is remarkably peculiar in that neither the background source nor the foreground lens show a clearly detected op

Antonio L. Maroto, Alfredo D. Miravet

In this work we make the observation that the gravitational leptogenesis mechanism can be implemented without invoking new axial couplings in the inflaton sector. We show that in the perturbed Robertson-Walker background emerging after inflation, the spacetime metric itself breaks parity symmetry and generates a non-vansihing Pontryagin density which can pro

Charlotte Micheloud, Fadoua Balabdaoui, Leonhard Held

We study a statistical framework for replicability based on a recently proposed quantitative measure of replication success, the sceptical $p$-value. A recalibration is proposed to obtain exact overall Type-I error control if the effect is null in both studies and additional bounds on the partial and conditional Type-I error rate, which represent the case wh

Min-Sheng Lin

A dominating set of a graph is a set of vertices such that every vertex not in the set has at least one neighbor in the set. The problem of counting dominating sets is #P-complete for chordal graphs but solvable in polynomial time for its subclass of interval graphs. The complexity status of the corresponding problem is still undetermined for directed path g

Neeraj Jain, Patricio A. Muñoz, Jörg Büchner

Hybrid-kinetic simulations describe ion-scale kinetic phenomena in space plasmas by considering ions kinetically, i.e. as particles, while electrons are modelled as a fluid. Most of the existing hybrid-kinetic codes neglect the electron mass (see chapter 3) for a simplified calculation of the electromagnetic fields. There are, however, situations in which de

Jorge A. Mendez, Shashank Shivkumar, Eric Eaton

Methods for learning from demonstration (LfD) have shown success in acquiring behavior policies by imitating a user. However, even for a single task, LfD may require numerous demonstrations. For versatile agents that must learn many tasks via demonstration, this process would substantially burden the user if each task were learned in isolation. To address th

Antonio Montanaro, Diego Valsesia, Enrico Magli

Inverse problems consist in reconstructing signals from incomplete sets of measurements and their performance is highly dependent on the quality of the prior knowledge encoded via regularization. While traditional approaches focus on obtaining a unique solution, an emerging trend considers exploring multiple feasibile solutions. In this paper, we propose a m

Jingxiao Ma, Sherief Reda

Approximate computing is an emerging computing paradigm that offers improved power consumption by relaxing the requirement for full accuracy. Since real-world applications may have different requirements for design accuracy, one trend of approximate computing is to design runtime quality-configurable circuits, which are able to operate under different accura

Botond Fazekas, Guilherme Aresta, Dmitrii Lachinov, Sophie Riedl

Optical coherence tomography (OCT) is a non-invasive 3D modality widely used in ophthalmology for imaging the retina. Achieving automated, anatomically coherent retinal layer segmentation on OCT is important for the detection and monitoring of different retinal diseases, like Age-related Macular Disease (AMD) or Diabetic Retinopathy. However, the majority of

Javad Asadollahi, Somayeh Sadeghi, Hipolito Treffinger

The main theme of this paper is to study $\tau$-tilting subcategories in an abelian category $\mathscr{A}$ with enough projective objects. We introduce the notion of $\tau$-cotorsion torsion triples and show a bijection between the collection of $\tau$-cotorsion torsion triples in $\mathscr{A}$ and the collection of $\tau$-tilting subcategories of $\mathscr{

Anoop Kini, Amit Kumar Choudhary, Dominic Hohs, Andreas Jansche

The Fe14Nd2B-based permanent magnets are technologically sought-after for energy conversion due to their unparalleled high energy product (520 kJ/m3). For such 14:2:1 phases of different compositions, determining the magnetization from the measured magnetic moment is often bottlenecked by lack of mass density. We present a machine learning (ML) mass density

Yang Liu, Nan Liang, Xiaoyao Xie, Zunli Yuan

An improved Amati correlation was constructed in (ApJ 931 (2022) 50) by us recently. In this paper, we further study constraints on the $\Lambda$CDM and $w$CDM models from the gamma ray bursts (GRBs) standardized with the standard and improved Amati correlations, respectively. By using the Pantheon type Ia supernova sample to calibrate the latest A220 GRB da

G. Rebora, J. Rech, D. Ferraro, T. Jonckheere

The current fluctuations due to a temperature bias, i.e. the delta-$T$ noise, allow one to access properties of strongly interacting systems which cannot be addressed by the usual voltage-induced noise. In this work, we study the full delta-$T$ noise between two different fractional quantum Hall edge states, with filling factors $(\nu_L,\nu_R)$ in the Laughl

Matteo Gardini, Piergiacomo Sabino

In this article we focus on the pricing of exchange options when the dynamic of logprices follows either the well-known variance gamma or the recent variance gamma++ process introduced in Gardini et al [19]. In particular, for the former model we can derive a Margrabe's type formula whereas, for the latter one we can write an "integral free" formula. Further

Thibault Sohier, Pedro M. M. C. de Melo, Zeila Zanolli, Matthieu Jean Verstraete

The transport and optical properties of semiconducting transition metal dichalcogenides around room temperature are dictated by electron-phonon scattering mechanisms within a complex, spin-textured and multi-valley electronic landscape. The relative positions of the valleys are critical, yet they are sensitive to external parameters and very difficult to det

Andrew R. Booker, Michael Farmer, Min Lee

We extend Venkatesh's proof of the converse theorem for classical holomorphic modular forms to arbitrary level and character. The method of proof, via the Petersson trace formula, allows us to treat arbitrary degree 2 gamma factors of Selberg class type.

Niklas Schlomberg, Hanjo Thiele, Jens Vygen

We devise constant-factor approximation algorithms for finding as many disjoint cycles as possible from a certain family of cycles in a given planar or bounded-genus graph. Here disjoint can mean vertex-disjoint or edge-disjoint, and the graph can be undirected or directed. The family of cycles under consideration must satisfy two properties: it must be uncr

Sanat Ramesh, Vinkle Srivastav, Deepak Alapatt, Tong Yu

The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amounts of annotated data, imposing a prohibitively high cost; especially in the clinical domain. Self-Supervised Le

Jingda Wu, Wenhui Huang, Niels de Boer, Yanghui Mo

Decision-making is critical for lane change in autonomous driving. Reinforcement learning (RL) algorithms aim to identify the values of behaviors in various situations and thus they become a promising pathway to address the decision-making problem. However, poor runtime safety hinders RL-based decision-making strategies from complex driving tasks in practice

Vitalii Makogin, Evgeny Spodarev

We use the concept of excursions for the prediction of random variables without any moment existence assumptions. To do so, an excursion metric on the space of random variables is defined which appears to be a kind of a weighted $L^1$-distance. Using equivalent forms of this metric and the specific choice of excursion levels, we formulate the prediction prob

Guanxing Fu, Ulrich Horst, Xiaonyu Xia

We consider a mean-field control problem with c\`adl\`ag semimartingale strategies arising in portfolio liquidation models with transient market impact and self-exciting order flow. We show that the value function depends on the state process only through its law, and that it is of linear-quadratic form and that its coefficients satisfy a coupled system of n

Xuan Zheng, Kerstin Eder, Tim Blackmore

Novel test selectors used in simulation-based verification have been shown to significantly accelerate coverage closure regardless of the number of coverage holes. This paper presents a configurable and highly-automated framework for novel test selection based on neural networks. Three configurations of this framework are tested with a commercial signal proc

Zhukov Petr, Glushchenko Anton, Fomin Andrey

Considering high-temperature heating, the equations of transient heat conduction model require an adaptation, i.e. the dependence of thermophysical parameters of the model on the temperature is to be identified for each specific material to be heated. This problem is most often solved by approximation of the tabular data on the measurements of the required p

Enrico Barausse, Miguel Bezares, Marco Crisostomi, Guillermo Lara

We point out that the initial-value (Cauchy) problem for self-interacting vector fields presents the same well-posedness issues as for first-order derivative self-interacting scalar fields (often referred to as $k$-essence). For the latter, suitable strategies have been employed in the last few years to successfully evolve the Cauchy problem at the level of

Elias R. Most, Alexander Haber, Steven P. Harris, Ziyuan Zhang

In nuclear matter in isolated neutron stars, the flavor content (e.g., proton fraction) is subject to weak interactions, establishing flavor ($\beta$-)equilibrium. However, there can be deviations from this equilibrium during the merger of two neutron stars. We study the resulting out-of-equilibrium dynamics during the collision by incorporating direct and m

Jing Li, Boqiang Lü, Xue Wang

The full compressible Navier-Stokes system describing the motion of a viscous, compressible, heat-conductive, and Newtonian polytropic fluid is studied in a three-dimensional simply connected bounded domain with smooth boundary having a finite number of two-dimensional connected components. For the initial-boundary-value problem with slip boundary conditions

Orlando Luongo, Marco Muccino

We propose how to calibrate long gamma-ray burst (GRB) correlations employing intermediate redshift data sets, instead of limiting to $z\simeq0$ catalogs. To do so, we examine the most updated observational Hubble data (OHD) and baryonic acoustic oscillations (BAO). We exploit the model-independent technique of B\'ezier polynomial interpolation, alleviating

Anita Bagri, Anupam Jana, Gyanendra Panchal, Rakhul Raj

Magnetoelastic and magnetoelectric coupling in the artificial multiferroic heterostructures facilitate valuable features for device applications such as magnetic field sensors and electric write magnetic-read memory devices. In a ferromagnetic/ferroelectric heterostructures, the strain mediated coupling exploits piezoelectricity/electrostriction in ferroelec

Mohammad Farhat, Pierre Auclair-Desrotour, Gwenaël Boué, Jacques Laskar

Due to tidal interactions in the Earth-Moon system, the spin of the Earth slows down and the Moon drifts away. This recession of the Moon is now measured with great precision, but it has been realized, more than fifty years ago, that simple tidal models extrapolated back in time lead to an age of the Moon that is by far incompatible with the geochronological

Udaysinh T. Bhosale

Higher-order spacing ratios are investigated analytically using a Wigner-like surmise for Gaussian ensembles of random matrices. For $k$-th order spacing ratio $(r^{(k)}$, $k>1)$ the matrix of dimension $2k+1$ is considered. A universal scaling relation for this ratio, known from earlier numerical studies, is proved in the asymptotic limits of $r^{(k)}\right

Christian Liedl, Sebastian Pucher, Philipp Schneeweiss, Leonid P. Yatsenko

Quantum emitters with a $\Lambda$-type level structure enable numerous protocols and applications in quantum science and technology. Understanding and controlling their dynamics is, therefore, one of the central research topics in quantum optics. Here, we drive two-photon Rabi oscillations between the two ground states of cesium atoms and observe the associa

Jonghun Kwak, Jungyu Ahn, Jinho Lee, Sungwoo Park

The finance industry has adopted machine learning (ML) as a form of quantitative research to support better investment decisions, yet there are several challenges often overlooked in practice. (1) ML code tends to be unstructured and ad hoc, which hinders cooperation with others. (2) Resource requirements and dependencies vary depending on which algorithm is

Gustavo S. Vicente, Rudnei O. Ramos, Leila L. Graef

The effective approach in Loop Quantum Cosmology (LQC) has provided means to obtain predictions for observable quantities in LQC models. While an effective dynamics in LQC has been extensively considered in different scenarios, a robust demonstration of the validity of effective descriptions for the perturbative level still requires further attention. The co

T. C. Fujita, H. Ito, M. Kawasaki

Pyrochlore oxides $A_2B_2$O$_7$ have been a fruitful playground for condensed matter physics because of the unique geometry in the crystal structure. Especially focusing on the $A$-site tetrahedral sub-lattice, in particular, pyrochlore oxides $A_2B_2$O$_7$ ($A$ = Sn, Pb and $B$ = Nb, Ta), recent theoretical studies predict the emergence of the "quasi-flat b

Amruta Desai, Shubham Sharma, Prince A. Ganai

We examine the thermodynamics of a regular charged black hole (RCB) added with corrections due to massive gravity and thermal fluctuations at quantum level. We then derive the expressions for all the relevant thermodynamic quantities such as entropy, Hawkings temperature, internal energy, Gibbs free energy, corrected to first order. We also briefly discuss t

Maria Heitmeier, Yu-Ying Chuang, R. Harald Baayen

Trial-to-trial effects have been found in a number of studies, indicating that processing a stimulus influences responses in subsequent trials. A special case are priming effects which have been modelled successfully with error-driven learning (Marsolek, 2008), implying that participants are continuously learning during experiments. This study investigates w

Jorge A. Mendez, Harm van Seijen, Eric Eaton

Humans commonly solve complex problems by decomposing them into easier subproblems and then combining the subproblem solutions. This type of compositional reasoning permits reuse of the subproblem solutions when tackling future tasks that share part of the underlying compositional structure. In a continual or lifelong reinforcement learning (RL) setting, thi

Yanick Schraner, Christian Scheller, Michel Plüss, Manfred Vogel

We present an in-depth evaluation of four commercially available Speech-to-Text (STT) systems for Swiss German. The systems are anonymized and referred to as system a-d in this report. We compare the four systems to our STT model, referred to as FHNW from hereon after, and provide details on how we trained our model. To evaluate the models, we use two STT da

Hip Kuen Chong, Daniel T. Wise

We study a family of finitely generated residually finite groups. These groups are doubles $F_2*_H F_2$ of a rank-$2$ free group $F_2$ along an infinitely generated subgroup $H$. Varying $H$ yields uncountably many groups up to isomorphism.

Guilherme Delfino, Weslei B. Fontana, Pedro R. S. Gomes, Claudio Chamon

In this work we propose a $\mathbb{Z}_N$ clock model which is exactly solvable on the lattice. We find exotic properties for the low-energy physics, such as UV/IR mixing and excitations with restricted mobility, that resemble fractonic physics from higher dimensional models. We then study the continuum descriptions for the lattice system in two distinct regi

Junnosuke Okajima, Masaki Ito, Yuka Iga

This study experimentally investigated the influence of heat transfer from a heated hydrofoil on cavitating flow to understand the evaporation phenomenon under high-heat-flux and high-speed conditions. A temperature difference was generated between the hydrofoil and mainstream by installing an aluminum nitride heater in a NACA0015 hydrofoil fabricated from c

Henry W. Lin, Juan Maldacena, Liza Rozenberg, Jieru Shan

We study correlation functions for extremal supersymmetric black holes. It is necessary to take into account the strongly coupled nature of the boundary supergraviton mode. We consider the case with ${\cal N}=2$ supercharges which is the minimal amount of supersymmetry needed to give a large ground state degeneracy, separated from the continuum. Using the ex

Henry W. Lin, Juan Maldacena, Liza Rozenberg, Jieru Shan

We study the gravitational description of extremal supersymmetric black holes. We point out that the $AdS_2$ near horizon geometry can be used to compute interesting observables, such as correlation functions of operators. In this limit, the Hamiltonian is zero and correlation functions are time independent. We discuss some possible implications for the grav

Enrico Formenti, Luca Mariot

We address the enumeration of coprime polynomial pairs over $\F_2$ where both polynomials have a nonzero constant term, motivated by the construction of orthogonal Latin squares via cellular automata. To this end, we leverage on Benjamin and Bennett's bijection between coprime and non-coprime pairs, which is based on the sequences of quotients visited by dil

Jeremy Rigney, Gavin Ramsay, Eoin P. Carley, J. Gerry Doyle

Solar radio emission at low frequencies (<1 GHz) can provide valuable information on processes driving flares and coronal mass ejections (CMEs). Radio emission has been detected from active M dwarf stars, suggestive of much higher levels of activity than previously thought. Observations of active M dwarfs at low frequencies can provide information on the emi

Omprakash Atale

Inspired by the work of C. Mortici [1] and A. Laforgia et. al [2] we have established some new Tur\'an-type inequalities for k-polygamma function and p-k-polygamma function.

Simon Thomä, Grit Walther, Maximilian Schiffer

We study piecewise affine policies for multi-stage adjustable robust optimization (ARO) problems with non-negative right-hand side uncertainty. First, we construct new dominating uncertainty sets and show how a multi-stage ARO problem can be solved efficiently with a linear program when uncertainty is replaced by these new sets. We then demonstrate how solut

Tomas Scagliarini, Davide Nuzzi, Yuri Antonacci, Luca Faes

O-information is an information-theoretic metric that captures the overall balance between redundant and synergistic information shared by groups of three or more variables. To complement the global assessment provided by this metric, here we propose the gradients of the O-information as low-order descriptors that can characterise how high-order effects are

Robin Bartlett, Misja F. A. Steinmetz

We give an explicit formulation of the weight part of Serre's conjecture for GL_2 using Kummer theory. This avoids any reference to p-adic Hodge theory. The key inputs are a description of the reduction modulo p of crystalline extensions in terms of certain "G_K-Artin-Scheier cocycles" and a result of Abrashkin which describes these cocycles in terms of Kumm

Jorge F. Lazo, Chun-Feng Lai, Sara Moccia, Benoit Rosa

Navigation inside luminal organs is an arduous task that requires non-intuitive coordination between the movement of the operator's hand and the information obtained from the endoscopic video. The development of tools to automate certain tasks could alleviate the physical and mental load of doctors during interventions, allowing them to focus on diagnosis an

Theodor Cheslerean-Boghiu, Felix C. Hofmann, Manuel Schultheiß, Franz Pfeiffer

Deep learning based solutions are being succesfully implemented for a wide variety of applications. Most notably, clinical use-cases have gained an increased interest and have been the main driver behind some of the cutting-edge data-driven algorithms proposed in the last years. For applications like sparse-view tomographic reconstructions, where the amount

Joanna L. Karczmarek, Harold C. Steinacker

We consider a solution of a IKKT-type matrix model which can be considered as a 1+1-dimensional space-time with Minkowski signature and a Big Bounce-like singularity. A suitable $i\varepsilon$ regularization of the Lorentzian matrix integral is proposed, which leads to the standard $i\varepsilon$-prescription for the effective field theory. In particular, th

M. Anbarloei

Krasner F^{(m,n)}-hyperring were introduced and investigated by Farshi and Davvaz. In this paper, our purpose is to define and characterize three classes of F-hyperideals in a Krasner F^{(m,n)}-hyperring, namely, prime F-hyperideals, maximal F-hyperideals and primary F-hyperideals.

Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo, Kuzman Ganchev

The ability to convey relevant and faithful information is critical for many tasks in conditional generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal hallucinations and fail to correctly cover important details. In this work, we advocate planning as a useful intermediate representation for rendering conditional generati

Tianxiang Liu, Ting Kei Pong, Akiko Takeda

We consider a class of sparsity-inducing optimization problems whose constraint set is regularizer-compatible, in the sense that, the constraint set becomes easy-to-project-onto after a coordinate transformation induced by the sparsity-inducing regularizer. Our model is general enough to cover, as special cases, the ordered LASSO model and its variants with

Samuel D. Slöetjes, Björgvin Hjörvarsson, Vassilios Kapaklis

We analyse the thermal fluctuations of magnetization textures in two stray field coupled elements, forming mesospins. To this end, the energy landscape associated with the thermal dynamics of the textures is mapped out and asymmetric energy barriers are identified. These barriers are modified by changing the gap that separates the mesospins. Moreover, the co

David Fischer, Péter Györgyi

In this paper we consider the coupled task scheduling problem with exact delay times on a single machine with the objective of minimizing the total completion time of the jobs. We provide constant-factor approximation algorithms for several variants of this problem that are known to be NP-hard, while also proving NP-hardness for two variants whose complexity

Nicolas Brisebarre, Bruno Salvy

Six families of generalized hypergeometric series in a variable $x$ and an arbitrary number of parameters are considered. Each of them is indexed by an integer $n$. Linear recurrence relations in $n$ relate these functions and their product by the variable $x$. We give explicit factorizations of these equations as products of first order recurrence operators

Samuel Horváth

Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., mobile phone devices, with an incentive to collaborate in solving a machine learning problem coordinated by a central server. FL was proposed in 2016 by Kone\v{c}n\'{y} et al. and McMahan et al. as a viable privacy-preserving alternative to traditional centrali

Emanuele Francazi, Marco Baity-Jesi, Aurelien Lucchi

Data imbalance is a common problem in machine learning that can have a critical effect on the performance of a model. Various solutions exist but their impact on the convergence of the learning dynamics is not understood. Here, we elucidate the significant negative impact of data imbalance on learning, showing that the learning curves for minority and majori

Yuanchang Lin, Xuguang Liu, Chengming Bai

We establish a bialgebra theory for differential algebras, called differential antisymmetric infinitesimal (ASI) bialgebras by generalizing the study of ASI bialgebras to the context of differential algebras, in which the derivations play an important role. They are characterized by double constructions of differential Frobenius algebras as well as matched p

Martin Burger, Alex Rossi

In this paper we provide a novel approach to the analysis of kinetic models for label switching, which are used for particle systems that can randomly switch between gradient flows in different energy landscapes. Besides problems in biology and physics, we also demonstrate that stochastic gradient descent, the most popular technique in machine learning, can

Marco Bonacini, Riccardo Cristoferi, Ihsan Topaloglu

We consider a class of attractive-repulsive energies, given by the sum of two nonlocal interactions with power-law kernels, defined over sets with fixed measure. It has recently been proved by R. Frank and E. Lieb that the ball is the unique (up to translation) global minimizer for sufficiently large mass. We focus on the issue of the stability of the ball,

G. Pérez-Callejo, M. Bailly-Grandvaux, R. Florido, C. A. Walsh

Magnetization of inertial confinement implosions is a promising means of improving their performance, owing to the potential reduction of energy losses within the target and mitigation of hydrodynamic instabilities. In particular, cylindrical implosions are useful for studying the influence of a magnetic field thanks to their axial symmetry. Here we present

Benjamin Merlin Bumpus, Bart M. P. Jansen, Jari J. H. de Kroon

We investigate preprocessing for vertex-subset problems on graphs. While the notion of kernelization, originating in parameterized complexity theory, is a formalization of provably effective preprocessing aimed at reducing the total instance size, our focus is on finding a non-empty vertex set that belongs to an optimal solution. This decreases the size of t

Pierre Fromholz, Mikheil Tsitsishvili, Matteo Votto, Marcello Dalmonte

Dressed Rydberg atoms in optical lattices are a promising platform for the quantum simulation of intriguing phenomena emerging in strongly interacting systems. Relevant to such a setup, we investigate the phase diagram of hard-core bosons in a triangular ladder with next-to-nearest-neighbor interaction along each leg and nearest-neighbors interactions withou

Loring W. Tu

The classical Lefschetz fixed point theorem states that the number of fixed points, counted with multiplicity $\pm 1$, of a smooth map $f$ from a manifold $M$ to itself can be calculated as the alternating sum $\sum (-1)^k \textrm{ tr } f^*|_{H^k(M)}$ of the trace of the induced homomorphism in cohomology. In 1964, at a conference in Woods Hole, Shimura conj

Naiyuan Liu, Xiaohan Wang, Xiaobo Li, Yi Yang

In this report, we present the ReLER@ZJU-Alibaba submission to the Ego4D Natural Language Queries (NLQ) Challenge in CVPR 2022. Given a video clip and a text query, the goal of this challenge is to locate a temporal moment of the video clip where the answer to the query can be obtained. To tackle this task, we propose a multi-scale cross-modal transformer an

Ziye Lu, Xinyu Yang, Lin Huang, Xiyu Chen

Metal thio(seleno)phosphates MPX$_3$ have attracted considerable attentions with wide spanned band gaps and rich magnetic properties. In this series, two neighboring members MnPS$_3$ and NiPS$_3$ differ in magnetic atoms, magnetic easy axes, spin anisotropy, as well as nearest-neighbor magnetic interactions. The competition between these components may cause

Nils Werner, Tim Zeitz

We study efficient and exact shortest path algorithms for routing on road networks with realistic traffic data. For navigation applications, both current (i.e., live) traffic events and predictions of future traffic flows play an important role in routing. While preprocessing-based speedup techniques have been employed successfully to both settings individua

Chenyu Qiu, Bangyu Wu, Meng Li, Hui Yang

Semblance velocity analysis is a crucial step in seismic data processing. To avoid the huge time-cost when performed manually, some deep learning methods are proposed for automatic semblance velocity picking. However, the application of existing deep learning methods is still restricted by the shortage of labels in practice. In this letter, we propose an att

Soham Das, Ceyhun Eksin

We consider the control of decentralized learning dynamics for agents in an anti-coordination network game. In the anti-coordination network game, there is a preferred action in the absence of neighbors' actions, and the utility an agent receives from the preferred action decreases as more of its neighbors select the preferred action, potentially causing the

Michał Siemaszko, Adam Buraczewski, Bertrand Le Saux, Magdalena Stobińska

Time series prediction is essential for human activities in diverse areas. A common approach to this task is to harness Recurrent Neural Networks (RNNs). However, while their predictions are quite accurate, their learning process is complex and, thus, time and energy consuming. Here, we propose to extend the concept of RRNs by including continuous-variable q

Amuthan A. Ramabathiran, Prabhu Ramachandran

There has been a growing interest in the use of Deep Neural Networks (DNNs) to solve Partial Differential Equations (PDEs). Despite the promise that such approaches hold, there are various aspects where they could be improved. Two such shortcomings are (i) their computational inefficiency relative to classical numerical methods, and (ii) the non-interpretabi

Fraser Daly

We discuss Stein's method for approximation by the stationary distribution of a single-birth Markov chain, in conjunction with stochastic monotonicity and similar assumptions. We use bounds on the increments of the solution of Poisson's equation for such a process. Applications include rates of convergence to stationarity, and bounding the total variation di

Pierluigi Colli, Gianni Gilardi, Andrea Signori, Jürgen Sprekels

In this paper, we investigate optimal control problems for a nonlinear state system which constitutes a version of the Caginalp phase field system modeling nonisothermal phase transitions with a nonconserved order parameter that takes thermal memory into account. The state system, which is a first-order approximation of a thermodynamically consistent system,

Simone Mastrogiovanni, Astrid Lamberts, Rahul Srinivasan, Tristan Bruel

With its last observing run, the LIGO, Virgo, and KAGRA collaboration has detected almost one hundred gravitational waves from compact binary coalescences. A common approach to studying the population properties of the observed binaries is to use phenomenological models to describe the spin, mass, and redshift distributions. More recently, with the aim of pr

Lars Grüne, Lisa Krügel, Matthias A. Müller

We consider nonlinear model predictive control (MPC) with multiple competing cost functions. This leads to the formulation of multiobjective optimal control problems (MO OCPs). Since the design of MPC algorithms for directly solving multiobjective problems is rather complicated, particularly if terminal conditions shall be avoided, we use an indirect approac

Nicholas Kah Yean Low, Andrew Melatos

One form of long-term behavior revealed by opinion dynamics simulations is intermittency, where an individual cycles between eras of stable, constant beliefs and turbulent, fluctuating beliefs, for example when inferring the political bias of a media organization. We explore this phenomenon by building an idealized network of Bayesian learners, who infer the

Alexander Bigalke, Lasse Hansen, Mattias P. Heinrich

Recent deep learning-based methods for medical image registration achieve results that are competitive with conventional optimization algorithms at reduced run times. However, deep neural networks generally require plenty of labeled training data and are vulnerable to domain shifts between training and test data. While typical intensity shifts can be mitigat

Zeshun Shi, Jeroen Bergers, Ken Korsmit, Zhiming Zhao

Data tampering is often considered a severe problem in industrial applications as it can lead to inaccurate financial reports or even a corporate security crisis. A correct representation of data is essential for companies' core business processes and is demanded by investors and customers. Traditional data audits are performed through third-party auditing s

Federico Binda, Hiroki Kato, Alberto Vezzani

We give a proof of the $p$-adic weight monodromy conjecture for scheme-theoretic complete intersections in projective smooth toric varieties. The strategy is based on Scholze's proof in the $\ell$-adic setting, which we adapt using homotopical results developed in the context of rigid analytic motives.

Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers, Enda Howley

Many real-world problems contain multiple objectives and agents, where a trade-off exists between objectives. Key to solving such problems is to exploit sparse dependency structures that exist between agents. For example, in wind farm control a trade-off exists between maximising power and minimising stress on the systems components. Dependencies between tur

Moritz Herrmann, Florian Pfisterer, Fabian Scheipl

Outlier or anomaly detection is an important task in data analysis. We discuss the problem from a geometrical perspective and provide a framework that exploits the metric structure of a data set. Our approach rests on the manifold assumption, i.e., that the observed, nominally high-dimensional data lie on a much lower dimensional manifold and that this intri

J. Bodensteiner, M. Heida, M. Abdul-Masih, D. Baade

While the number of stellar-mass black holes detected in X-rays or as gravitational wave sources is steadily increasing, the known population remains orders of magnitude smaller than predicted by stellar evolution theory. A significant fraction of stellar-mass black holes is expected to hide in X-ray-quiet binaries where they are paired with a "normal" star.

Kok Wee Song, Vladimir I Fal'ko

We build a microscopic model to study the intra- and inter-layer superexchange due to electrons hopping in chromium trihalides ($\mathrm{CrX}_3$, X= Cl, Br, and I). In evaluating the superexchange, we identify the relevant intermediate excitations in the hopping. In our study, we find that the intermediate hole-pairs excitations in the $p$-orbitals on X ion

Pedro Miguel M. C. de Melo, Joao C. de Abreu, Bogdan Guster, Matteo Giantomassi

The electronic structure of condensed matter can be significantly affected by the electron-phonon interaction, leading to important phenomena such as electrical resistance, superconductivity or the formation of polarons. This interaction is often neglected in band structure calculations but can have a strong impact on band gaps or optical spectra. Commonly u

Eric Bergshoeff, Kevin van Helden, Johannes Lahnsteiner, Luca Romano

We discuss the generalized Newton-Cartan geometries that can serve as gravitational background fields for particles and strings. In order to enable us to define affine connections that are invariant under all the symmetries of the structure group, we describe torsionful geometries with independent torsion tensors. A characteristic feature of the non-Lorentzi

Robert Pawlus, Stefan Breuer, Martin Virte

We propose and demonstrate a technique to control the balance between the two amplitudes of a dual-wavelength laser based on a phase-controlled optical feedback. The feedback cavity length is adjusted to achieve a relative phase shift between the desired emission wavelengths, introducing a boost in gain for one wavelength while the other wavelength experienc

Nikolai Dokuchaev

The paper presents linear integral predictors for continuous time high-frequency signals with a a finite spectrum gap. The predictors are based on approximation of a complex valued periodic exponential (complex sinusoid) by rational polynomials.