October 2020 arXiv papers — page 89

Stephen Chung

Nearly all state-of-the-art deep learning algorithms rely on error backpropagation, which is generally regarded as biologically implausible. An alternative way of training an artificial neural network is through treating each unit in the network as a reinforcement learning agent, and thus the network is considered as a team of agents. As such, all units can

Marcus Gualtieri, Robert Platt

We consider robotic pick-and-place of partially visible, novel objects, where goal placements are non-trivial, e.g., tightly packed into a bin. One approach is (a) use object instance segmentation and shape completion to model the objects and (b) use a regrasp planner to decide grasps and places displacing the models to their goals. However, it is critical f

Frank Tavares, Denise Buckner, Dana Burton, Jordan McKaig

We recommend that the planetary science and space exploration community engage in a robust reevaluation concerning the ethics of how future crewed and uncrewed missions to the Moon and Mars will interact with those planetary environments. This should occur through a process of community input, with emphasis on how such missions can resist colonial structures

Ekta Sood, Simon Tannert, Philipp Mueller, Andreas Bulling

A lack of corpora has so far limited advances in integrating human gaze data as a supervisory signal in neural attention mechanisms for natural language processing(NLP). We propose a novel hybrid text saliency model(TSM) that, for the first time, combines a cognitive model of reading with explicit human gaze supervision in a single machine learning framework

Bernhard Heim, Markus Neuhauser

We attach to normalized (non-vanishing) arithmetic functions $g$ and $h$ recursively defined polynomials. Let $P_0^{g,h}(x):=1$. Then \begin{equation} P_n^{g,h}(x) := \frac{x}{h(n)} \sum_{k=1}^{n} g(k) \, P_{n-k}^{g,h}(x). \end{equation} For special $g$ and $h$, we obtain the D'Arcais polynomials, which are equal to the coefficients of the $-z$th powers

Ewain Gwynne, Joshua Pfeffer, Scott Sheffield

Recent works have shown that there is a canonical way to to assign a metric (distance function) to a Liouville quantum gravity (LQG) surface for any parameter $γ\in (0,2)$. We establish a strong confluence property for LQG geodesics, which generalizes a result proven by Angel, Kolesnik and Miermont for the Brownian map. Using this property, we also establish

Lan-Hsuan Huang, Dan A. Lee, Raquel Perales

We prove results on intrinsic flat convergence of points---a concept first explored by Sormani in \cite{Sormani-AA}. In particular, we discuss compatibility with Gromov-Hausdorff convergence of points---a concept first described by Gromov in \cite{Gromov-poly}. We apply these results to the problem of stability of the positive mass theorem in mathematical re

Grigorii Trofimiuk, Peter Trifonov

A decoding algorithm for polar (sub)codes with binary $2^t\times 2^t$ polarization kernels is presented. It is based on the window processing (WP) method, which exploits the linear relationship of the polarization kernels and the Arikan matrix. This relationship enables one to compute the kernel input symbols probabilities by computing the probabilities of s

Yuhang Wang, Sruthi Murlidaran, David A. Pearlman

The COVID-19 pandemic has led to unprecedented efforts to identify drugs that can reduce its associated morbidity/mortality rate. Computational chemistry approaches hold the potential for triaging potential candidates far more quickly than their experimental counterparts. These methods have been widely used to search for small molecules that can inhibit crit

Gabriel Berzunza Ojeda, Cecilia Holmgren

Aldous, Evans and Pitman (1998) studied the behavior of the fragmentation process derived from deleting the edges of a uniform random tree on $n$ labelled vertices. In particular, they showed that, after proper rescaling, the above fragmentation process converges as $n \rightarrow \infty$ to the fragmentation process of the Brownian CRT obtained by cutting-d

François Delduc, Sylvain Lacroix, Konstantinos Sfetsos, Konstantinos Siampos

In the study of integrable non-linear $σ$-models which are assemblies and/or deformations of principal chiral models and/or WZW models, a rational function called the twist function plays a central role. For a large class of such models, we show that they are one-loop renormalizable, and that the renormalization group flow equations can be written directly i

Hannah Bast, Matthias Hertel, Mostafa M. Mohamed

We consider the following tokenization repair problem: Given a natural language text with any combination of missing or spurious spaces, correct these. Spelling errors can be present, but it's not part of the problem to correct them. For example, given: "Tispa per isabout token izaionrep air", compute "Tis paper is about tokenizaion repair&#3

Viktor Novičenko, Irmantas Ratas

Weakly coupled limit cycle oscillators can be reduced into a system of weakly coupled phase models. These phase models are helpful to analyze the synchronization phenomena. For example, a phase model of two oscillators has a one-dimensional differential equation for the evolution of the phase difference. The existence of fixed points determines frequency-loc

Nicolás P. Müller, Juan Ignacio Polanco, Giorgio Krstulovic

The velocity circulation, a measure of the rotation of a fluid within a closed path, is a fundamental observable in classical and quantum flows. It is indeed a Lagrangian invariant in inviscid classical fluids. In quantum flows, circulation is quantized, taking discrete values that are directly related to the number and the orientation of thin vortex filamen

Peter Belcak

The LL(finite) parsing strategy for parsing of LL(k) grammars where k needs not to be known is presented. The strategy parses input in linear time, uses arbitrary but always minimal lookahead necessary to disambiguate between alternatives of nonterminals, and it is optimal in the number of lookahead terminal scans performed. Modifications to the algorithm ar

Michael F. Zimmer

The purpose of this paper is to improve upon existing variants of gradient descent by solving two problems: (1) removing (or reducing) the plateau that occurs while minimizing the cost function, (2) continually adjusting the learning rate to an "ideal" value. The approach taken is to approximately solve for the learning rate as a function of a trust

Gavin S. Hartnett, Raffaele Vardavas, Lawrence Baker, Michael Chaykowsky

Network data is increasingly being used in quantitative, data-driven public policy research. These are typically very rich datasets that contain complex correlations and inter-dependencies. This richness both promises to be quite useful for policy research, while at the same time posing a challenge for the useful extraction of information from these datasets

Fabian Ying, Neave O'Clery

Since the outbreak of COVID-19 in early March 2020, UK supermarkets have implemented different policies to reduce the virus transmission in stores to protect both customers and staff, such as restricting the maximum number of customers in a store, changes to the store layout, or enforcing a mandatory face covering policy. To quantitatively assess these mitig

Shuxi Zeng, Serge Assaad, Chenyang Tao, Shounak Datta

Causal inference, or counterfactual prediction, is central to decision making in healthcare, policy and social sciences. To de-bias causal estimators with high-dimensional data in observational studies, recent advances suggest the importance of combining machine learning models for both the propensity score and the outcome function. We propose a novel scalab

Vladislav Lialin, Rahul Goel, Andrey Simanovsky, Anna Rumshisky

Currently used semantic parsing systems deployed in voice assistants can require weeks to train. Datasets for these models often receive small and frequent updates, data patches. Each patch requires training a new model. To reduce training time, one can fine-tune the previously trained model on each patch, but naive fine-tuning exhibits catastrophic forgetti

Matthew Ceko, Silvia M. C. Pagani, Rob Tijdeman

The reconstruction of an unknown function $f$ from its line sums is the aim of discrete tomography. However, two main aspects prevent reconstruction from being an easy task. In general, many solutions are allowed due to the presence of the switching functions. Even when uniqueness conditions are available, results about the NP-hardness of reconstruction algo

William T. Emond, Yu-tin Huang, Uri Kol, Nathan Moynihan

Electric-magnetic duality, the Newman-Janis shift, and the double copy all act by elementary operations on three-point amplitudes. At the same time, they generate a network of interesting classical solutions spanning from the Coulomb charge via the dyon to the Kerr-Taub-NUT spacetime. We identify the amplitudes corresponding to each of these solutions, worki

Philipp F. M. Baumann, Torsten Hothorn, David Rügamer

Learning the cumulative distribution function (CDF) of an outcome variable conditional on a set of features remains challenging, especially in high-dimensional settings. Conditional transformation models provide a semi-parametric approach that allows to model a large class of conditional CDFs without an explicit parametric distribution assumption and with on

Erwann Martin, Maxence Ernoult, Jérémie Laydevant, Shuai Li

Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge. Equilibrium Propagation is a promising alternative to backpropagation as it only involves local computations, but hardware-oriented studies have so far focused on rate-based netwo

Cecilia Jarne

Training neural networks to perform different tasks is relevant across various disciplines. In particular, Recurrent Neural Networks (RNNs) are of great interest in Computational Neuroscience. Open-source frameworks dedicated to Machine Learning, such as Tensorflow and Keras have produced significant changes in the development of technologies that we current

Fan Bao, Chongxuan Li, Kun Xu, Hang Su

Score matching (SM) provides a compelling approach to learn energy-based models (EBMs) by avoiding the calculation of partition function. However, it remains largely open to learn energy-based latent variable models (EBLVMs), except some special cases. This paper presents a bi-level score matching (BiSM) method to learn EBLVMs with general structures by refo

L. Schoeffel, C. Baldenegro, H. Hamdaoui, S. Hassani

Under certain running conditions, the CERN Large Hadron Collider (LHC) can be considered as a photon-photon collider. Indeed, in proton-proton, proton-ion, ion-ion collisions, when incoming particles pass very close to each other in very peripheral collisions, the incoming protons or ions remain almost intact and continue their path along the beam axis. Then

Frederik Garbe, Robert Hancock, Jan Hladký, Maryam Sharifzadeh

We develop a limit theory of Latin squares, paralleling the recent limit theories of dense graphs and permutations. We introduce a notion of density, an appropriate version of the cut distance, and a space of limit objects - so-called Latinons. Key results of our theory are the compactness of the limit space and the equivalence of the topologies induced by t

Aditya Gangrade, Anil Kag, Venkatesh Saligrama

We propose a novel method for selective classification (SC), a problem which allows a classifier to abstain from predicting some instances, thus trading off accuracy against coverage (the fraction of instances predicted). In contrast to prior gating or confidence-set based work, our proposed method optimises a collection of class-wise decoupled one-sided emp

Chin-Yao Chang, Eric Jones, Yiyun Yao, Peter Graf

Quantum computing is emerging as a new computing resource that could be superior to conventional computing for certain classes of optimization problems. However, in principle, most existing approaches to quantum optimization are intended to solve unconstrained binary programming problems, while mixed-integer linear programming is of most interest in practice

X. C. Zheng, H. J. A. Röttgering, P. N. Best, A. van der Wel

The morphology of quiescent galaxies has been found to be correlated with the activity of their central super massive black hole. In this work, we use data from the first data release of the LOFAR Two$-$Metre Sky Survey (LoTSS DR1) and the Sloan Digital Sky Survey Data Release 7 (SDSS DR7) to select more than 15 000 quiescent galaxies at $z<0.3$ to investiga

Mingchao Liu, Michael Gomez, Dominic Vella

We study elastic snap-through induced by a control parameter that evolves dynamically. In particular, we study an elastic arch subject to an end-shortening that evolves linearly with time, i.e. at a constant rate. For large end-shortening the arch is bistable but, below a critical end-shortening, the arch becomes monostable. We study when and how the arch tr

Evgeny Akhmedov

Neutrino flavour transmutations in nonuniform matter are described by a Schrödinger-like evolution equation with coordinate-dependent potential. In all the derivations of this equation it is assumed that the potential, which is due to coherent forward scattering of neutrinos on matter constituents, is a continuous function of coordinate that changes slowly o

Joseph Cho, Wayne Rossman, Tomoya Seno

We connect certain continuous motions of discrete planar curves resulting in semi-discrete potential Korteweg-de Vries (mKdV) equation with Darboux transformations of smooth planar curves. In doing so, we define infinitesimal Darboux transformations that include the aforementioned motions, and also give an alternate geometric interpretation for establishing

Xupin Zhang, Hanjia Lyu, Jiebo Luo

The COVID-19 pandemic has affected people&#39;s lives around the world on an unprecedented scale. We intend to investigate hoarding behaviors in response to the pandemic using large-scale social media data. First, we collect hoarding-related tweets shortly after the outbreak of the coronavirus. Next, we analyze the hoarding and anti-hoarding patterns of over

Jian-Song Hong, Ting-Fung Jeffrey Poon, Long Zhang, Xiong-Jun Liu

Symmetry-protected topological superconductors (TSCs) can host multiple Majorana zero modes (MZMs) at their edges or vortex cores, while whether the Majorana braiding in such systems is non-Abelian in general remains an open question. Here we uncover in theory the unitary symmetry-protected non-Abelian statisitcs of MZMs and propose the experimental realizat

Huangjun Zhu

Classical information can be completely hidden in the correlations of bipartite quantum systems. However, it is impossible to hide or mask all quantum information according to the no-hiding and no-masking theorems derived recently. Here we show that any set of informationally complete quantum states is neither hidable nor maskable, thereby strengthening the

Monjed H. Samuh, Anas M. Salhab, Ahmed H. Abd El-Malek

This work studies and optimizes the performance of reconfigurable intelligent surface (RIS)-aided networks in Nakagami-m fading environment. First, accurate closed-form approximations for the channel distributions are derived. Then, closed-form formulas for the system outage probability, average symbol error probability (ASEP), and the channel capacity are o

Timotej Hrga, Janez Povh

We present MADAM, a parallel semidefinite based exact solver for Max-Cut, a problem of finding the cut with maximum weight in a given graph. The algorithm uses branch and bound paradigm that applies alternating direction method of multipliers as the bounding routine to solve the basic semidefinite relaxation strengthened by a subset of hypermetric inequaliti

Nikolaus Lorenz, Lorenzo Festa, Lea-Marina Steinert, Christian Gross

Single neutral atoms trapped in optical tweezers and laser-coupled to Rydberg states provide a fast and flexible platform to generate configurable atomic arrays for quantum simulation. The platform is especially suited to study quantum spin systems in various geometries. However, for experiments requiring continuous trapping, inhomogeneous light shifts induc

Zhenkun Li, Fan Ye

This paper establishes a new technique that enables us to access some fundamental structural properties of instanton Floer homology. As an application, we establish, for the first time, a relation between the instanton Floer homology of a $3$-manifold or a null-homologous knot inside a $3$-manifold and the Heegaard diagram of that $3$-manifold or knot. We fu

Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren

Fine-tuned pre-trained language models (LMs) have achieved enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the fine-tuning stage. We study the problem of fine-tuning pre-trained LMs using only weak supervision, without any labeled data. This problem is challenging because the high capacity of

Vojtěch Havlena, Ondřej Lengál

This paper provides several optimizations of the rank-based approach for complementing Büchi automata. We start with Schewe&#39;s theoretically optimal construction and develop a set of techniques for pruning its state space that are key to obtaining small complement automata in practice. In particular, the reductions (except one) have the property that they

Vincent Dumont, Verónica Rodríguez Tribaldos, Jonathan Ajo-Franklin, Kesheng Wu

Moving loads such as cars and trains are very useful sources of seismic waves, which can be analyzed to retrieve information on the seismic velocity of subsurface materials using the techniques of ambient noise seismology. This information is valuable for a variety of applications such as geotechnical characterization of the near-surface, seismic hazard eval

Tri Vu, Anthony DiSpirito, Daiwei Li, Zixuan Zhang

Photoacoustic microscopy (PAM) is an emerging imaging method combining light and sound. However, limited by the laser&#39;s repetition rate, state-of-the-art high-speed PAM technology often sacrifices spatial sampling density (i.e., undersampling) for increased imaging speed over a large field-of-view. Deep learning (DL) methods have recently been used to im

Didier Poilblanc, Matthieu Mambrini, Fabien Alet

Within the tensor network framework, the (positive) thermal density operator can be approximated by a double layer of infinite Projected Entangled Pair Operator (iPEPO) coupled via ancilla degrees of freedom. To investigate the thermal properties of the spin-1/2 Heisenberg model on the square lattice, we introduce a family of fully spin-$SU(2)$ and lattice-$

Gjalt Huppes, Ruben Huele

How may exposure risks to SARS-CoV-2 be assessed quantitatively? The material metabolism approach of Industrial Ecology can be applied to the mass flows of these virions by their numbers, as a key step in the analysis of the current pandemic. Several transmission routes of SARS-2 from emission by a person to exposure of another person have been modelled and

Matteo Magoni, Paolo P. Mazza, Igor Lesanovsky

We explore the relaxation dynamics of elementary spin clusters of a kinetically constrained spin system. Inspired by experiments with Rydberg lattice gases, we focus on the situation in which an excited spin leads to a &#34;facilitated&#34; excitation of a neighboring spin. We show that even weak interactions that extend beyond nearest neighbors can have a d

Rupesh Kumar, Francesco Mazzoncini, Hao Qin, Romain Alléaume

Inspired by the methodology used for classical cryptographic hardware, we consider the use of attack ratings in the context of QKD security evaluation. To illustrate the relevance of this approach, we conduct an experimental vulnerability assessment of CV-QKD against saturation attacks, for two different attack strategies. The first strategy relies on induci

Nils Schöneberg, Matteo Lucca, Deanna C. Hooper

Measuring spectral distortions (SDs) of the cosmic microwave background (CMB) will provide new constraints on previously unexplored scales of the primordial power spectrum, allowing us to extend the probed parameter space by several orders of magnitude in $k$-space, which could have significant implications in the context of primordial black holes and gravit

Joseph Patterson, Jonathan Kemp, Berto Monard, Gordon Myers

We present a study of the orbital light curves of the recurrent nova IM Normae since its 2002 outburst. The broad &#34;eclipses&#34; recur with a 2.46 hour period, which increases on a timescale of 1.28(16)x10^6 years. Under the assumption of conservative mass-transfer, this suggests a rate near 10^-7 M_sol/year, and this agrees with the estimated /accretion

Bardia Doosti, Ching-Hui Chen, Raviteja Vemulapalli, Xuhui Jia

Mutual gaze detection, i.e., predicting whether or not two people are looking at each other, plays an important role in understanding human interactions. In this work, we focus on the task of image-based mutual gaze detection, and propose a simple and effective approach to boost the performance by using an auxiliary 3D gaze estimation task during the trainin

Adeem Aslam, Zubair Khalid, Jason D. McEwen

We present a framework for the optimal filtering of spherical signals contaminated by realizations of an additive, zero-mean, uncorrelated and anisotropic noise process on the sphere. Filtering is performed in the wavelet domain given by the scale-discretized wavelet transform on the sphere. The proposed filter is optimal in the sense that it minimizes the m

Rossano Sannipoli

In this paper we study some properties of the torsion function with Robin boundary conditions. Here we write the shape derivative of the $L^{\infty}$ and $L^p$ norms, for $p\ge 1$, of the torsion function, seen as a functional on a bounded simply connected open set $Ω\subset \mathbb{R}^n$, and prove that the balls are critical shapes for these functionals, w

Xiao Luo, Daqing Wu, Zeyu Ma, Chong Chen

Recently, hashing is widely used in approximate nearest neighbor search for its storage and computational efficiency. Most of the unsupervised hashing methods learn to map images into semantic similarity-preserving hash codes by constructing local semantic similarity structure from the pre-trained model as the guiding information, i.e., treating each point p

Zaccharie Ramzi, Philippe Ciuciu, Jean-Luc Starck

We present a new neural network, the XPDNet, for MRI reconstruction from periodically under-sampled multi-coil data. We inform the design of this network by taking best practices from MRI reconstruction and computer vision. We show that this network can achieve state-of-the-art reconstruction results, as shown by its ranking of second in the fastMRI 2020 cha

Alina Ene, Huy L. Nguyen

We develop new adaptive algorithms for variational inequalities with monotone operators, which capture many problems of interest, notably convex optimization and convex-concave saddle point problems. Our algorithms automatically adapt to unknown problem parameters such as the smoothness and the norm of the operator, and the variance of the stochastic evaluat

Miguel Arduengo, Adrià Colomé, Júlia Borràs, Luis Sentis

Learning from Demonstration (LfD) is a paradigm that allows robots to learn complex manipulation tasks that can not be easily scripted, but can be demonstrated by a human teacher. One of the challenges of LfD is to enable robots to acquire skills that can be adapted to different scenarios. In this paper, we propose to achieve this by exploiting the variation

Claire Mathieu, Michel de Rougemont

We study the detection and the reconstruction of a large very dense subgraph in a social graph with $n$ nodes and $m$ edges given as a stream of edges, when the graph follows a power law degree distribution, in the regime when $m=O(n. \log n)$. A subgraph $S$ is very dense if it has $\Omega(|S|^2)$ edges. We uniformly sample the edges with a Reservoir of siz

Bingzhao Zhu, Uisub Shin, Mahsa Shoaran

Neural interfaces capable of multi-site electrical recording, on-site signal classification, and closed-loop therapy are critical for the diagnosis and treatment of neurological disorders. However, deploying machine learning algorithms on low-power neural devices is challenging, given the tight constraints on computational and memory resources for such devic

Fabio van Dissel, Evangelos I. Sfakianakis

Oscillons are long-lived, spatially localized field configurations, which are supported by attractive non-linearities in the scalar potential. We study oscillons comprised of multiple interacting fields, each having an identical potential with quadratic, quartic and sextic terms. We consider quartic interaction terms of either attractive or repulsive nature.

Yanghao Zhang, Wenjie Ruan, Fu Wang, Xiaowei Huang

The previous study has shown that universal adversarial attacks can fool deep neural networks over a large set of input images with a single human-invisible perturbation. However, current methods for universal adversarial attacks are based on additive perturbation, which cause misclassification when the perturbation is directly added to the input images. In

M. Girfoglio, L. Scandurra, F. Ballarin, G. Infantino

Reduced order modeling (ROM) techniques are numerical methods that approximate the solution of parametric partial differential equation (PDE) by properly combining the high-fidelity solutions of the problem obtained for several configurations, i.e. for several properly chosen values of the physical/geometrical parameters characterizing the problem. In this c

Vincent P. Michal, Benjamin Venitucci, Yann-Michel Niquet

Holes confined in semiconductor nanostructures realize qubits where the quantum mechanical spin is strongly mixed with the quantum orbital angular momentum. The remarkable spin-orbit coupling allows for fast all electrical manipulation of such qubits. We study an idealization of a CMOS device where the hole is strongly confined in one direction (thin film ge

Tim Laux, Yuning Liu

In this work, we study the nematic-isotropic phase transition based on the dynamics of the Landau--De Gennes theory of liquid crystals. At the critical temperature, the Landau--De Gennes bulk potential favors the isotropic phase and nematic phase equally. When the elastic coefficient is much smaller than that of the bulk potential, a scaling limit can be der

Sebastian Schrom, Stephan Hasler, Jürgen Adamy

Domain Adaptation (DA) is a highly relevant research topic when it comes to image classification with deep neural networks. Combining multiple source domains in a sophisticated way to optimize a classification model can improve the generalization to a target domain. Here, the difference in data distributions of source and target image datasets plays a major

Jing Li, Valerio Olevano

We check the ab initio GW approximation and Bethe-Salpeter equation (BSE) many-body methodology against the exact solution benchmark of the hydrogen molecule H$_2$ ground state and excitation spectrum, and in comparison with the configuration interaction (CI) and time-dependent Hartree-Fock methods. The comparison is made on all the states we could unambiguo

Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta

Reinforcement learning algorithms are widely used in domains where it is desirable to provide a personalized service. In these domains it is common that user data contains sensitive information that needs to be protected from third parties. Motivated by this, we study privacy in the context of finite-horizon Markov Decision Processes (MDPs) by requiring info

Zexu Pan, Ruijie Tao, Chenglin Xu, Haizhou Li

Speaker extraction algorithm relies on the speech sample from the target speaker as the reference point to focus its attention. Such a reference speech is typically pre-recorded. On the other hand, the temporal synchronization between speech and lip movement also serves as an informative cue. Motivated by this idea, we study a novel technique to use speech-l

Víctor Mayoral-Vilches, Irati Abad-Fernández, Martin Pinzger, Stefan Rass

The reuse of technologies and inherent complexity of most robotic systems is increasingly leading to robots with wide attack surfaces and a variety of potential vulnerabilities. Given their growing presence in public environments, security research is increasingly becoming more important than in any other area, specially due to the safety implications that r

Mingshuo Ding, Yinghao Ma

Despite recent achievements of deep learning automatic music generation algorithms, few approaches have been proposed to evaluate whether a single-track music excerpt is composed by automatons or Homo sapiens. To tackle this problem, we apply a masked language model based on ALBERT for composers classification. The aim is to obtain a model that can suggest t

Run-Qiu Yang

This paper argues that the weak cosmic censorship hypothesis implies that the Schwarzschild black hole has maximal entropy in all stationary black holes of fixing temperature, or equivalently, to store a same amount of information the Schwarzschild black hole has highest temperature. It then gives the independent mathematical proofs for 4-dimensional general

Christian Bäuml, Lorenz Bauriedl, Magdalena Marganska, Milena Grifoni

We demonstrate long-range superconducting correlations in a several micrometer-long carbon nanotube encapsulated in a van der Waals stack between hBN and NbSe$_2$. We show that a substantial supercurrent flows through the nanotube section beneath the NbSe$_2$ crystal as well as through the 2 $μ$m-long section not in contact with it. As expected for supercond

Riccardo Passeggeri

Consider a class of probability distributions which is dense in the space of all probability distributions on $\mathbb{R}^{d}$ with respect to weak convergence, for every $d\in\mathbb{N}$. Then, we construct various explicit classes of continuous (cádlág) processes which are dense in the space of all continuous (cádlág) processes with respect to convergence

Michal Kloc, Daniel Šimsa, Filip Hanák, Petra Ruth Kaprálová-Žďánská

The dynamics of a quantum system following a sudden, highly non-adiabatic change of its control parameter (quantum quench) is studied with quasiclassical techniques. Recent works have shown, using exact quantum mechanical approach, that equilibration after quantum quench exhibits specific features in the presence of excited-state quantum phase transitions. I

T. D. Kitching, A. C. Deshpande, P. L. Taylor

In this paper we derive a full expression for the propagation of weak lensing shape measurement biases into cosmic shear power spectra including the effect of missing data. We show using simulations that terms higher than first order in bias parameters can be ignored and the impact of biases can be captured by terms dependent only on the mean of the multipli

Cheng Perng Phoo, Bharath Hariharan

Most few-shot learning techniques are pre-trained on a large, labeled &#34;base dataset&#34;. In problem domains where such large labeled datasets are not available for pre-training (e.g., X-ray, satellite images), one must resort to pre-training in a different &#34;source&#34; problem domain (e.g., ImageNet), which can be very different from the desired tar

Junyi Zhu, Matthew Blaschko

Federated learning frameworks have been regarded as a promising approach to break the dilemma between demands on privacy and the promise of learning from large collections of distributed data. Many such frameworks only ask collaborators to share their local update of a common model, i.e. gradients with respect to locally stored data, instead of exposing thei

Max Okraschevski, Niklas Buerkle, Rainer Koch, Hans-Joerg Bauer

Weakly-compressible particle-based discretization methods, utilized for the solution of the subsonic Navier-Stokes equation, are gaining increasing popularity in the fluid dynamics community. One of the most popular among these methods is the weakly-compressible smoothed particle hydrodynamics (WCSPH). Since the dynamics of a single numerical particle is det

Gilad Asharov, Wei-Kai Lin, Elaine Shi

We consider the classical problem of sorting an input array containing $n$ elements, where each element is described with a $k$-bit comparison-key and a $w$-bit payload. A long-standing open problem is whether there exist $(k + w) \cdot o(n \log n)$-sized boolean circuits for sorting. We show that one can overcome the $n\log n$ barrier when the keys to be so

Carlo Scarpa

We present an infinite-dimensional hyperkähler reduction that extends the classical moment map picture of Fujiki and Donaldson for the scalar curvature of Kähler metrics. We base our approach on an explicit construction of hyperkähler metrics due to Biquard and Gauduchon. The construction is motivated by how one can derive Hitchin&#39;s equations for harmoni

Mattia Cafasso, Tom Claeys, Giulio Ruzza

We study a family of unbounded solutions to the Korteweg-de Vries equation which can be constructed as log-derivatives of deformed Airy kernel Fredholm determinants, and which are connected to an integro-differential version of the second Painlev\'e equation. The initial data of the Korteweg-de Vries solutions are well-defined for $x>0$, but not for $x<0$, w

Sebastian Raimondo, Barbara Benigni, Manlio De Domenico

During COVID-19, draconian countermeasures forbidding non-essential human activities have been adopted worldwide, providing an unprecedented setup for testing sustainability policies. We unravel causal relationships among 16 environmental conditions and human activity variables and argue that, despite a measurable decrease in NO2 concentration due to human a

Han Hsu, Christian P. Crisostomo, Wenzhong Wang, Zhongqing Wu

Ferromagnesite (Mg,Fe)CO3, also referred to as magnesiosiderite at high iron concentration, is a solid solution of magnesite (MgCO3) and siderite (FeCO3). Ferromagnesite is believed to enter the Earth&#39;s lower mantle via subduction and is considered a major carbon carrier in the Earth&#39;s lower mantle, playing a key role in the Earth&#39;s deep carbon c

Jian-Rong Zhang

Motivated by the LHCb&#39;s new observation of structures in the $J/ψ$-pair invariant mass spectrum, for which could be classified as possible $cc\bar{c}\bar{c}$ tetraquark candidates, we systematically study $0^{+}$ fully-charmed tetraquark states through QCD sum rules. Making the development of calculation techniques to fourfold heavy hadronic systems, fou

Weijie Yu, Chen Xu, Jun Xu, Liang Pang

One approach to matching texts from asymmetrical domains is projecting the input sequences into a common semantic space as feature vectors upon which the matching function can be readily defined and learned. In real-world matching practices, it is often observed that with the training goes on, the feature vectors projected from different domains tend to be i

Charis Ganotaki, Tomas Persson

We consider dynamical systems $(X,T,μ)$ which have exponential decay of correlations for either Hölder continuous functions or functions of bounded variation. Given a sequence of balls $(B_n)_{n=1}^\infty$, we give sufficient conditions for the set of eventually always hitting points to be of full measure. This is the set of points $x$ such that for all larg

Michael Reitz, Claudiu Genes

We discuss Floquet engineering of dissipative molecular systems through periodic driving of an infrared-active vibrational transition, either directly or via a cavity mode. Following a polaron quantum Langevin equations approach, we derive correlation functions and stationary quantities showing strongly modified optical response of the infrared-dressed molec

Jorge Fuenzalida, Armin Hochrainer, Gabriela Barreto Lemos, Evelyn A. Ortega

Quantum imaging with undetected photons is a recently introduced technique that goes significantly beyond what was previously possible. In this technique, images are formed without detecting the light that interacted with the object that is imaged. Given this unique advantage over the existing imaging schemes, it is now of utmost importance to understand its

Antonis Sidiropoulos, Zoe Doulgeri

In this work, a novel Dynamic Movement Primitive (DMP) formulation is proposed which supports reversibility, i.e. backwards reproduction of a learned trajectory. Apart from sharing all favourable properties of the original DMP, decoupling the teaching of position and velocity profiles and bidirectional drivability along the encoded path are also supported. O

Thomas Kosch

In today&#39;s society, our cognition is constantly influenced by information intake, attention switching, and task interruptions. This increases the difficulty of a given task, adding to the existing workload and leading to compromised cognitive performances. The human body expresses the use of cognitive resources through physiological responses when confro

Parul Jain, Subhash Mahapatra

We study holographic aspects of mixed state entanglement measures in various large $N$ top-down as well as bottom-up confining models. For the top-down models, we consider wrapped $D3$ and $D4$ branes gravity solutions whereas, for the bottom-up confining model, the Einstein-Maxwell-dilaton gravity solution is considered. We study entanglement entropy, mutua

Ali Kashefi, Davis Rempe, Leonidas J. Guibas

We present a novel deep learning framework for flow field predictions in irregular domains when the solution is a function of the geometry of either the domain or objects inside the domain. Grid vertices in a computational fluid dynamics (CFD) domain are viewed as point clouds and used as inputs to a neural network based on the PointNet architecture, which l

Michele Santacatterina

Covariate balance is crucial in obtaining unbiased estimates of treatment effects in observational studies. Methods based on inverse probability weights have been widely used to estimate treatment effects with observational data. Machine learning techniques have been proposed to estimate propensity scores. These techniques however target accuracy instead of

Janet Rafner, Lotte Philipsen, Sebastian Risi, Joel Simon

Human-computer image generation using Generative Adversarial Networks (GANs) is becoming a well-established methodology for casual entertainment and open artistic exploration. Here, we take the interaction a step further by weaving in carefully structured design elements to transform the activity of ML-assisted imaged generation into a catalyst for large-sca

Roger Moser, James Roberts

We prove partial regularity of weakly stationary harmonic maps with (partially) free boundary data on manifolds where the domain metric may degenerate or become singular along the free boundary at the rate $d^α$ for the distance function $d$ from the boundary.

Rui Zhang, Masaaki Imaizumi, Bernhard Schölkopf, Krikamol Muandet

We investigate a simple objective for nonlinear instrumental variable (IV) regression based on a kernelized conditional moment restriction (CMR) known as a maximum moment restriction (MMR). The MMR objective is formulated by maximizing the interaction between the residual and the instruments belonging to a unit ball in a reproducing kernel Hilbert space (RKH

Piotr Wozniak

In this paper we prove the higher Sobolev regularity of minimisers for convex integral functionals evaluated on linear differential operators of order one. This intends to generalise the already existing theory for the cases of full and symmetric gradients to the entire class of $\mathbb{C}$-elliptic operators therein including the trace-free symmetric gradi

F. N. Womack, D. P. Young, D. A. Browne, G. Catelani

We report the high-field superconducting properties of thin, disordered Re films via magneto-transport and tunneling density of states measurements. Films with thicknesses in the range of 9 nm to 3 nm had normal state sheet resistances of $\sim$0.2 k$Ω$ to $\sim$1 k$Ω$ and corresponding transition temperatures in the range of 6 K to 3 K. Tunneling spectra we

Hamzeh Keshavarzi

Let $0<p<\infty$, $β>-1$, and $Ω$ be a strongly pseudoconvex bounded domain with a smooth boundary in $\mathbb{C}^n$. We will study the interpolation problem for weighted Bergman spaces $A^p_β(Ω)$. In the case, $1\leq p<\infty$, and $β> \max \{n(2p-1)-1, n(2q-1)-1\}$, where $q$ is the conjugate exponent of $p$ (let $q=1$, for $p=1$), we show that a sequence