January 2022 arXiv papers — page 14

Zijian Liu, Ta Duy Nguyen, Alina Ene, Huy L. Nguyen

In this paper, we study the finite-sum convex optimization problem focusing on the general convex case. Recently, the study of variance reduced (VR) methods and their accelerated variants has made exciting progress. However, the step size used in the existing VR algorithms typically depends on the smoothness parameter, which is often unknown and requires tun

Stanislav Morozov, Nikolai Zamarashkin, Eugene Tyrtyshnikov

The low-rank matrix approximation problem is ubiquitous in computational mathematics. Traditionally, this problem is solved in spectral or Frobenius norms, where the accuracy of the approximation is related to the rate of decrease of the singular values of the matrix. However, recent results indicate that this requirement is not necessary for other norms. In

Martin Bertran, Walter Talbott, Nitish Srivastava, Joshua Susskind

Learning generalizeable policies from visual input in the presence of visual distractions is a challenging problem in reinforcement learning. Recently, there has been renewed interest in bisimulation metrics as a tool to address this issue; these metrics can be used to learn representations that are, in principle, invariant to irrelevant distractions by meas

Alessio Belfiglio, Orlando Luongo, Stefano Mancini

We investigate entanglement production by inhomogeneous perturbations over a homogeneous and isotropic cosmic background, demonstrating that the interplay between quantum and geometric effects can have relevant consequences on entanglement entropy, with respect to homogeneous scenarios. To do so, we focus on a conformally coupled scalar field and discuss how

Marta Milewska, Remco van der Hofstad, Bert Zwart

In this paper, we draw attention to a promising yet slightly underestimated measure of variability - the Gini coefficient. We describe two new ways of defining and interpreting this parameter. Using our new representations, we compute the Gini index for a few probability distributions and describe it in more detail for the negative binomial distribution. We

Ritu Nehra, Dibyendu Roy

The multipartite non-Hermitian Su-Schrieffer-Heeger model is explored as a prototypical example of one-dimensional systems with several sublattice sites for unveiling intriguing insulating and metallic phases with no Hermitian counterparts. These phases are characterized by composite cyclic loops of multiple complex-energy bands encircling single or multiple

Jiachen Sun, Qingzhao Zhang, Bhavya Kailkhura, Zhiding Yu

Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications. However, their robustness against corruptions is less studied. In this paper, we present ModelNet40-C, the first comprehensive benchmark on 3D point cloud corruption robustness, consisting of 15 common and realistic corruptions. Ou

Jan Sbierski

This paper establishes a mathematical proof of the blue-shift instability at the sub-extremal Kerr Cauchy horizon for the linearised vacuum Einstein equations. More precisely, we exhibit conditions on the $s=+2$ Teukolsky field, consisting of suitable integrated upper and lower bounds on the decay along the event horizon, that ensure that the Teukolsky field

Jonathan Luk, Sung-Jin Oh, Yakov Shlapentokh-Rothman

Motivated by the strong cosmic censorship conjecture, we study the linear scalar wave equation in the interior of subextremal strictly charged Reissner-Nordstr\"om black holes by analyzing a suitably-defined "scattering map" at $0$ frequency. The method can already be demonstrated in the case of spherically symmetric scalar waves on Reissner-Nordstr\"om: we

Runtian Zhai, Chen Dan, Zico Kolter, Pradeep Ravikumar

Empirical risk minimization (ERM) is known in practice to be non-robust to distributional shift where the training and the test distributions are different. A suite of approaches, such as importance weighting, and variants of distributionally robust optimization (DRO), have been proposed to solve this problem. But a line of recent work has empirically shown

J. J. Esteve-Paredes, J. J. Palacios

We present a comprehensive study of the velocity operator, $\hat{\boldsymbol{v}}=\frac{i}{\hbar} [\hat{H},\hat{\boldsymbol{r}}]$, when used in crystalline solids calculations. The velocity operator is key to the evaluation of a number of physical properties and its computation, both from a practical and fundamental perspective, has been a long-standing debat

Alexander Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov

Gradient-free/zeroth-order methods for black-box convex optimization have been extensively studied in the last decade with the main focus on oracle calls complexity. In this paper, besides the oracle complexity, we focus also on iteration complexity, and propose a generic approach that, based on optimal first-order methods, allows to obtain in a black-box fa

Ming Zhou, Jingxiao Chen, Ying Wen, Weinan Zhang

Policy Space Response Oracle methods (PSRO) provide a general solution to learn Nash equilibrium in two-player zero-sum games but suffer from two drawbacks: (1) the computation inefficiency due to the need for consistent meta-game evaluation via simulations, and (2) the exploration inefficiency due to finding the best response against a fixed meta-strategy a

Jingyun Liang, Jiezhang Cao, Yuchen Fan, Kai Zhang

Video restoration (e.g., video super-resolution) aims to restore high-quality frames from low-quality frames. Different from single image restoration, video restoration generally requires to utilize temporal information from multiple adjacent but usually misaligned video frames. Existing deep methods generally tackle with this by exploiting a sliding window

Karthik Sivarama Krishnan, Koushik Sivarama Krishnan

Neuromorphic vision-based sensors are gaining popularity in recent years with their ability to capture Spatio-temporal events with low power sensing. These sensors record events or spikes over traditional cameras which helps in preserving the privacy of the subject being recorded. These events are captured as per-pixel brightness changes and the output data

Jean-Baptiste Bouvier, Melkior Ornik

Actuator malfunctions may have disastrous consequences for systems not designed to mitigate them. We focus on the loss of control authority over actuators, where some actuators are uncontrolled but remain fully capable. To counteract the undesirable outputs of these malfunctioning actuators, we use real-time measurements and redundant actuators. In this sett

Mohammad Hatami, Markus Leinonen, Zheng Chen, Nikolaos Pappas

We consider a resource-constrained IoT network, where multiple users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor. The edge node serves users' requests by deciding whether to command the corresponding sensor to send a fresh status update or retrieve t

Matteo Castiglioni, Diodato Ferraioli, Nicola Gatti, Alberto Marchesi

Most of the economic reports forecast that almost half of the worldwide market value unlocked by AI over the next decade (up to 6 trillion USD per year) will be in marketing&sales. In particular, AI will enable the optimization of more and more intricate economic settings, in which multiple different activities need to be jointly automated. This is the case

Patricia Alonso Ruiz, Fabrice Baudoin

Motivated by recent developments in the theory of bounded variation functions on nested fractals, this paper studies the exact asymptotics of functionals related to the total variation measure associated with unions of $n$-complexes. The oscillatory behavior observed implies the non-uniqueness of BV measures in this setting.

Maike Herkenrath, Till Fluschnik, Francesco Grothe, Leon Kellerhals

Choosing the placement of wildlife crossings (i.e., green bridges) to reconnect animal species' fragmented habitats is among the 17 goals towards sustainable development by the UN. We consider the following established model: Given a graph whose vertices represent the fragmented habitat areas and whose weighted edges represent possible green bridge locations

Frederik Garbe, Jan Hladký, Matas Šileikis, Fiona Skerman

We introduce a class of random graph processes, which we call flip processes. Each such process is given by a rule which is a function $\mathcal{R}:\mathcal{H}_k\rightarrow \mathcal{H}_k$ from all labeled $k$-vertex graphs into itself ($k$ is fixed). The process starts with a given $n$-vertex graph $G_0$. In each step, the graph $G_i$ is obtained by sampling

Maximilian Boeker, Aleksandra Urman

TikTok currently is the fastest growing social media platform with over 1 billion active monthly users of which the majority is from generation Z. Arguably, its most important success driver is its recommendation system. Despite the importance of TikTok's algorithm to the platform's success and content distribution, little work has been done on the empirical

Danilo Beli, Matheus Inguaggiato Nora Rosa, Carlos De Marqui, Massimo Ruzzene

In this paper, we present numerical and experimental evidence of directional wave behavior, i.e. beaming and diffraction, along high-order rotational symmetries of quasicrystalline elastic metamaterial plates. These structures are obtained by growing pillars on an elastic plate following a particular rotational symmetry arrangement, such as 8-fold and 10-fol

Aman Verma, Vibhav Prakash Singh

Brain Tumors are abnormal mass of clustered cells penetrating regions of brain. Their timely identification and classification help doctors to provide appropriate treatment. However, Classifi-cation of Brain Tumors is quite intricate because of high-intra class similarity and low-inter class variability. Due to morphological similarity amongst various MRI-Sl

Zdzislaw Burda, Malgorzata J. Krawczyk, Krzysztof Kulakowski

We discuss a cellular automaton simulating the process of reaching Heider balance in a fully connected network. The dynamics of the automaton is defined by a deterministic, synchronous and global update rule. The dynamics has a very rich spectrum of attractors including fixed points and limit cycles, the length and number of which change with the size of the

Varish Mulwad, Andrew Crapo, Vijay S. Kumar, James Jobin

Scientific models hold the key to better understanding and predicting the behavior of complex systems. The most comprehensive manifestation of a scientific model, including crucial assumptions and parameters that underpin its usability, is usually embedded in associated source code and documentation, which may employ a variety of (potentially outdated) progr

Aidan Rocke

In this article we show that the Erd\H{o}s-Kac theorem, which informally states that the number of prime divisors of very large integers converges to a normal distribution, has an elegant proof via Algorithmic Information Theory.

Marco Frasca, Anish Ghoshal, Nobuchika Okada

We use some exact results in the scalar field theory to revise the analysis by Coleman and Callan about the false vacuum decay and propose a simple non-perturbative formalism. We introduce exact Green's function which incorporates non-perturbative corrections in the strong coupling regimes of the theory. The solution of the scalar field theory involves Jacob

Kimberley Parsons Trommler, Matthias Hafner, Wolfgang Kellerer, Peter Merz

Although 5G (Fifth Generation) mobile technology is still in the rollout phase, research and development of 6G (Sixth Generation) wireless have already begun. This paper is an introduction to 6G wireless networks, covering the main drivers for 6G, some of the expected use cases, some of the technical challenges in 6G, example areas that will require research

Haixin Sun, Minh-Quan Dao, Vincent Fremont

Event-based cameras can overpass frame-based cameras limitations for important tasks such as high-speed motion detection during self-driving cars navigation in low illumination conditions. The event cameras' high temporal resolution and high dynamic range, allow them to work in fast motion and extreme light scenarios. However, conventional computer vision me

Sung-Soo Kim, Xing-Yue Wei

We propose refined topological vertex formalism for 5-brane systems with ON-planes by introducing a new vertex associated with reflection over an ON-plane, which gives rise to new vertex and edge factors. We test our proposal against various 5d $\mathcal{N}=1$ gauge theories which can be realized as 5-brane webs with ON-planes, which include $D$-type quiver

Krzysztof Rusek, Piotr Boryło, Piotr Jaglarz, Fabien Geyer

We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths. The GNN-based algorithm is trained only with random graphs generated with the Barab\'asi-Albert model. Even though, the obtained test

Patrick Eggenberger, Sylvia Ekström, Cyril Georgy, Sébastien Martinet

Context: Grids of stellar models, computed with the same physical ingredients, allow one to study the impact of a given physics on a broad range of initial conditions and are a key ingredient for modeling the evolution of galaxies. Aims: We present a grid of single star models for masses between 0.8 and 120 $M_\odot$, with and without rotation for a mass fra

Stanisław M. Dubiel, Jan Żukrowski

Sigma-phase Fe0.525Cr0.455Ni0.020 alloy was studied by means of M\"ossbauer spectrometry in the temperature range of 5-293 K. The average center shift, <CS>, determined from the recorded M\"ossbauer spectra was shown to significantly deviate from the Debye model in the temperature range below ca. 50 K i.e. in the magnetic state of the sample. The deviation i

Maria Chiara Fiorentino, Francesca Pia Villani, Mariachiara Di Cosmo, Emanuele Frontoni

Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. Despite a large number of survey papers already present in this field, most of them are focusing on a broader area of medical-image analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 14

Tara Kalsi, Alessandro Romito, Henning Schomerus

We investigate the measurement-induced entanglement transition in quantum circuits built upon Dyson's three circular ensembles (circular unitary, orthogonal, and symplectic ensembles; CUE, COE and CSE). We utilise the established model of a one-dimensional circuit evolving under alternating local random unitary gates and projective measurements performed wit

Xu Feng, Guo-Yang Li, Antoine Ramier, Amira M. Eltony

Traveling-wave optical coherence elastography (OCE) is a promising technique to measure the stiffness of biological tissues. While OCE has been applied to relatively homogeneous samples, tissues with significantly varying elasticity through depth pose a challenge, requiring depth-resolved measurement with sufficient resolution and accuracy. Here, we develop

H. Bonet, A. Bonhomme, C. Buck, K. Fülber

We report first constraints on neutrino electromagnetic properties from neutrino-electron scattering using data obtained from the CONUS germanium detectors, i.e. an upper limit on the effective neutrino magnetic moment and an upper limit on the effective neutrino millicharge. The electron antineutrinos are emitted from the 3.9 GW$_\mathrm{th}$ reactor core o

HM Chan, ST Tsou

The standard model (SM) is viewed as a variation on the Yang-Mills theory with gauge symmetry $u(1) \times su(2) \times su(3)$, in which the flavour symmetry is framed and to which 3 generations of quarks and leptons are appended as inputs from experiment. The framed standard model (FSM) is then a further variation on the SM in which the colour symmetry is a

Sabrina Müller, Daniel Braun

We study the sensitivity of a Mach-Zehnder interferometer that contains in addition to the phase shifter a non-linear element. By including both elements in a cavity or a loop that the light transverses many times, a non-linear kicked version of the interferometer arises. We study its sensitivity as function of the phase shift, the kicking strength, the maxi

M. V. Dolgopolik

In this two-part study, we develop a general theory of the so-called exact augmented Lagrangians for constrained optimization problems in Hilbert spaces. In contrast to traditional nonsmooth exact penalty functions, these augmented Lagrangians are continuously differentiable for smooth problems and do not suffer from the Maratos effect, which makes them espe

Enrico Bertuzzo, Andre Scaffidi, Marco Taoso

We consider a dark sector containing a pair of almost degenerate states coupled to the Standard Model through a dark photon mediator. This set-up constitutes a simple realization of the inelastic dark matter scenario. The heaviest dark state is long-lived, in the limit of a small kinetic mixing among the dark photon and the Standard Model hypercharge gauge b

Ana Contreras-Santos, Alexander Knebe, Frazer Pearce, Roan Haggar

Using the data set of The Three Hundred project, i.e. a suite of 324 hydrodynamical resimulations of cluster-sized haloes, we study galaxy cluster mergers and their effect on colour and luminosity changes of their brightest cluster galaxies (BCG). We track the main progenitor of each halo at z=0 and search for merger situations based on its mass accretion hi

Benedikt Stufler

We survey some properties of Gromov--Hausdorff--Prokhorov convergent sequences $(\mathsf{X}_n, d_{\mathsf{X}_n}, \nu_{\mathsf{X}_n})_{n \ge 1}$ of random compact metric spaces equipped with Borel probability measures. We formalize that if the limit is almost surely non-atomic, then for large $n$ each open ball in $\mathsf{X}_n$ with small radius must have sm

Frederik Benzing

Second-order optimizers are thought to hold the potential to speed up neural network training, but due to the enormous size of the curvature matrix, they typically require approximations to be computationally tractable. The most successful family of approximations are Kronecker-Factored, block-diagonal curvature estimates (KFAC). Here, we combine tools from

Chaima Ghribi, Elie Cali, Christian Hirsch, Benedikt Jahnel

Device-to-device (D2D) communications is one of the key emerging technologies for the fifth generation (5G) networks and beyond. It enables direct communication between mobile users and thereby extends coverage for devices lacking direct access to the cellular infrastructure and hence enhances network capacity. D2D networks are complex, highly dynamic and wi

Laurine Bénéteau, Jérémie Chalopin, Victor Chepoi, Yann Vaxès

The median of a graph $G$ with weighted vertices is the set of all vertices $x$ minimizing the sum of weighted distances from $x$ to the vertices of $G$. For any integer $p\ge 2$, we characterize the graphs in which, with respect to any non-negative weights, median sets always induce connected subgraphs in the $p$th power $G^p$ of $G$. This extends some char

Axel Böhm

We investigate a structured class of nonconvex-nonconcave min-max problems exhibiting so-called \emph{weak Minty} solutions, a notion which was only recently introduced, but is able to simultaneously capture different generalizations of monotonicity. We prove novel convergence results for a generalized version of the optimistic gradient method (OGDA) in this

G. Abdellaoui, S. Abe, J. H. Adams, D. Allard

Compilation of papers presented by the JEM-EUSO Collaboration at the 37th International Cosmic Ray Conference (ICRC), held on July 12-23, 2021 (online) in Berlin, Germany.

Alexander Korotin, Vage Egiazarian, Lingxiao Li, Evgeny Burnaev

Wasserstein barycenters have become popular due to their ability to represent the average of probability measures in a geometrically meaningful way. In this paper, we present an algorithm to approximate the Wasserstein-2 barycenters of continuous measures via a generative model. Previous approaches rely on regularization (entropic/quadratic) which introduces

Emine Celik, Eric Olson

We study a discrete-in-time data-assimilation algorithm based on nudging through a time-delayed feedback control in which the observational measurements have been contaminated by a Gaussian noise process. In the context of the two-dimensional incompressible Navier-Stokes equations we prove the expected value of the square-error between the approximating solu

Yixuan Wang, Simon Zhan, Zhilu Wang, Chao Huang

In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties (e.g., safety, stability) under the learned controller. However, as existing methods typically apply formal verification \emph{after} the controller has been learned, it is sometimes difficult to obtain any certificate, even after

Ibrahim Abdelaziz, Julian Dolby, Kavitha Srinivas

Recently, dynamically typed languages, such as Python, have gained unprecedented popularity. Although these languages alleviate the need for mandatory type annotations, types still play a critical role in program understanding and preventing runtime errors. An attractive option is to infer types automatically to get static guarantees without writing types. E

László Csató

If the final position of a team is already secured independently of the outcomes of the remaining games in a round-robin tournament, it might play with little enthusiasm. This is detrimental to attendance and can inspire collusion and match-fixing. We demonstrate that tie-breaking rules might affect the occurrence of such a situation. Its probability is quan

Avik Pal, Alan Edelman, Christopher Rackauckas

Implicit models separate the definition of a layer from the description of its solution process. While implicit layers allow features such as depth to adapt to new scenarios and inputs automatically, this adaptivity makes its computational expense challenging to predict. In this manuscript, we increase the "implicitness" of the DEQ by redefining the method i

Tim Brune

The emergence of domain walls is a well-known problem in Majoron models for neutrino mass generation. Here, we present extensions of the Majoron model by right-handed doublets and triplets that prevent domain walls from arising. These extensions are highly interesting in the context of Leptogenesis as they impact the conversion of a lepton asymmetry to a bar

Chen Wang, Ziyang Lu, Zhaojun Lan, Gennian Ge

Motivated by applications in DNA-based storage, we study explicit encoding and decoding schemes of binary strings satisfying locally balanced constraints, where the $(\ell,\delta)$-locally balanced constraint requires that the weight of any consecutive substring of length $\ell$ is between $\frac{\ell}{2}-\delta$ and $\frac{\ell}{2}+\delta$. In this paper we

Dimuthu Wannipurage, Indrajit Deb, Eroma Abeysinghe, Sudhakar Pamidighantam

Fragment Molecular Orbital (FMO) calculations provide a framework to speed up quantum mechanical calculations and so can be used to explore structure-energy relationships in large and complex biomolecular systems. These calculations are still onerous, especially when applied to large sets of molecules. Therefore, cyberinfrastructure that provides mechanisms

M. Pérez, O. I. Abbate, J. Lipovetzky, F. Alcalde Bessia

In this paper we present a method for obtention of neutron images with Commercial-Off-The-Shelf (COTS) CMOS image sensors through the activation of indium foils. This detection method has been designed specifically for the acquisition of thermal and epitermal neutron images in mixed beams with a high gamma flux, and also for the study of high radioactive sam

M. Alamgir Hossain, Sam Pimentel, John M. Stockie

We implement a data assimilation framework for integrating ice surface and terminus position observations into a numerical ice-flow model. The model uses the well-known shallow shelf approximation (SSA) coupled to a level set method to capture ice motion and changes in the glacier geometry. The level set method explicitly tracks the evolving ice-atmosphere a

Gabriele Casciaro, Francesco Ferrari, Daniele Lagomarsino Oneto, Andrea Lira-Loarca

All numerical weather prediction models used for the wind industry need to produce their forecasts starting from the main synoptic hours 00, 06, 12, and 18 UTC, once the analysis becomes available. The six-hour latency time between two consecutive model runs calls for strategies to fill the gap by providing new accurate predictions having, at least, hourly f

Shuyuan Huyan, Juan Schmidt, Elena Gati, Ruidan Zhong

The honeycomb antiferromagnet BaCo2(AsO4)2, in which small in-plane magnetic fields (H1 = 0.26 T and H2 = 0.52 T at T = 1.8 K < TN = 5.4 K) induce two magnetic phase transitions, has attracted attention as a possible candidate material for the realization of Kitaev physics based on the 3d element Co2+. Here, we report on the change of the transition temperat

S. Derkachov, A. P. Isaev, L. Shumilov

A convenient integral representation for zig-zag four-point and two-point planar Feynman diagrams relevant to the bi-scalar D-dimensional fishnet field theory is obtained. This representation gives a possibility to evaluate exactly diagrams of the zig-zag series in special cases. In particular, we give a fairly simple proof of the Broadhurst-Kreimer conjectu

Mingyu Cai, Erfan Aasi, Calin Belta, Cristian-Ioan Vasile

Model-free continuous control for robot navigation tasks using Deep Reinforcement Learning (DRL) that relies on noisy policies for exploration is sensitive to the density of rewards. In practice, robots are usually deployed in cluttered environments, containing many obstacles and narrow passageways. Designing dense effective rewards is challenging, resulting

Ziyad Benomar, Chaima Ghribi, Elie Cali, Alexander Hinsen

This paper presents a new multi-agent model for simulating malware propagation in device-to-device (D2D) 5G networks. This model allows to understand and analyze mobile malware-spreading dynamics in such highly dynamical networks. Additionally, we present a theoretical study to validate and benchmark our proposed approach for some basic scenarios that are le

Jack Kelly, Carlo Berzuini, Bernard Keavney, Maciej Tomaszewski

With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology and genetics. This paper aims to introduce and review the available methods for building large-scale causal molecular networks that have been developed in the pas

Richard Pates

Networks constructed out of resistors, inductors, capacitors and transformers form a compelling subclass of simple models. Models constructed out of these basic elements are frequently used to explain phenomena in large-scale applications, from inter-area oscillations in power systems, to the transient behaviour of optimisation algorithms. Furthermore they c

Oleg Ivrii

In this paper, we study meromorphic functions on a domain $\Omega \subset \mathbb{C}$ whose image has finite spherical area, counted with multiplicity. The paper is composed of two parts. In the first part, we show that the limit of a sequence of meromorphic functions is naturally defined on $\Omega$ union a tree of spheres. In the second part, we show that

Emad Ibrahim, Rickard Nilsson, Jaap van de Beek

We propose a novel reconfigurable intelligent surface (RIS) encoded information transmission scheme for a line-of-sight environment. A RIS fed with data modulates the information on impinging waves emitted from an external source in the states of polarization (SoP) of the scattered waves by performing a novel differential polarization shift keying. In partic

Federico Camerlenghi, Emanuele Dolera, Stefano Favaro, Edoardo Mainini

Posterior contractions rates (PCRs) strengthen the notion of Bayesian consistency, quantifying the speed at which the posterior distribution concentrates on arbitrarily small neighborhoods of the true model, with probability tending to 1 or almost surely, as the sample size goes to infinity. Under the Bayesian nonparametric framework, a common assumption in

S. Rasoul Etesami

We consider a subclass of $n$-player stochastic games, in which players have their own internal state/action spaces while they are coupled through their payoff functions. It is assumed that players' internal chains are driven by independent transition probabilities. Moreover, players can receive only realizations of their payoffs, not the actual functions, a

Sibasish Banerjee, Pietro Longhi, Mauricio Romo

This paper studies a notion of enumerative invariants for stable $A$-branes, and discusses its relation to invariants defined by spectral and exponential networks. A natural definition of stable $A$-branes and their counts is provided by the string theoretic origin of the topological $A$-model. This is the Witten index of the supersymmetric quantum mechanics

Sean Deyo

We present a directed percolation inverse problem for diode networks: Given information about which pairs of nodes allow current to percolate from one to the other, can one find a configuration of diodes consistent with the observed currents? We implement a divide-and-concur iterative projection method for solving the problem and demonstrate the supremacy of

Rizwanul Alam, George Siopsis, Rebekah Herrman, James Ostrowski

We introduce a method to solve the MaxCut problem efficiently based on quantum imaginary time evolution (QITE). We employ a linear Ansatz for unitary updates and an initial state involving no entanglement, as well as an imaginary-time-dependent Hamiltonian interpolating between a given graph and a subgraph with two edges excised. We apply the method to thous

Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev

We present a novel neural-networks-based algorithm to compute optimal transport maps and plans for strong and weak transport costs. To justify the usage of neural networks, we prove that they are universal approximators of transport plans between probability distributions. We evaluate the performance of our optimal transport algorithm on toy examples and on

Silvia Severini, Ayyoob Imani, Philipp Dufter, Hinrich Schütze

Parallel corpora are ideal for extracting a multilingual named entity (MNE) resource, i.e., a dataset of names translated into multiple languages. Prior work on extracting MNE datasets from parallel corpora required resources such as large monolingual corpora or word aligners that are unavailable or perform poorly for underresourced languages. We present CLC

Vicente Atal, Jose J. Blanco-Pillado, Albert Sanglas, Nikolaos Triantafyllou

Black holes binaries coming from a distribution of primordial black holes might exhibit a large merger rate up to large redshifts. Using a phenomenological model for the merger rate, we show that changes in its slope, up to redshifts $z\sim 4$, are constrained by current limits on the amplitude of the stochastic gravitational wave background from LIGO/Virgo

John F. Donoghue

I show that quantum corrections due to a massive particle generates a non-local term in the gravitational effective action which is of zeroth order in the derivative expansion, much like the cosmological constant. It carries a fixed coefficient which is very much larger than the cosmological constant, and which cannot be fine-tuned. The interaction is active

Mihaela Gaman, Lida Ghadamiyan, Radu Tudor Ionescu, Marius Popescu

An important preliminary step of optical character recognition systems is the detection of text rows. To address this task in the context of historical data with missing labels, we propose a self-paced learning algorithm capable of improving the row detection performance. We conjecture that pages with more ground-truth bounding boxes are less likely to have

Pierre Descombes

In this paper we prove a toric localization formula in cohomological Donaldson Thomas theory. Consider a -1-shifted symplectic algebraic space with a C* action leaving the -1-shifted symplectic form invariant. This includes the moduli space of stable sheaves or complexes of sheaves on a Calabi-Yau threefold with a C*-invariant Calabi-Yau form, or the interse

Guido L. A. Kusters, Paul van der Schoot, Cornelis Storm

Recently, Van der Kooij and co-workers recognised three distinct, transient regimes in the dynamics of electrically-deforming liquid crystal networks [Van der Kooij et al., Nat. Commun. 10, 1 (2019)]. Based on a Landau-theoretical framework, which encompasses spatially resolved information, we interpret these regimes: initially, the response is dominated by

C. Wetterich

Inflation and quintessence can both be described by a single scalar field. The cosmic time evolution of this cosmon field realizes a crossover from the region of an ultraviolet fixed point in the infinite past to an infrared fixed point in the infinite future. This amounts to a transition from early inflation to late dynamical dark energy, with intermediate

Thomas W. Mitchel, Noam Aigerman, Vladimir G. Kim, Michael Kazhdan

M\"obius transformations play an important role in both geometry and spherical image processing - they are the group of conformal automorphisms of 2D surfaces and the spherical equivalent of homographies. Here we present a novel, M\"obius-equivariant spherical convolution operator which we call M\"obius convolution, and with it, develop the foundations for M

Siddhartha Datta, Nigel Shadbolt

Malicious agents in collaborative learning and outsourced data collection threaten the training of clean models. Backdoor attacks, where an attacker poisons a model during training to successfully achieve targeted misclassification, are a major concern to train-time robustness. In this paper, we investigate a multi-agent backdoor attack scenario, where multi

Surajit Kalita, Lupamudra Sarmah

In recent years, the idea of sub- and super-Chandrasekhar limiting mass white dwarfs (WDs), which are potential candidates to produce under- and over-luminous type Ia supernovae, respectively, has been a key interest in the scientific community. Although researchers have proposed different models to explain these peculiar objects, modified theories of Einste

I. Gezer, Ł. Wyrzykowski, P. Zieliński, G. Marton

Gravitational microlensing method is a powerful method to detect isolated black holes in the Milky Way. During a microlensing event brightness of the source increases and this feature is used by many photometric surveys to alert on potential events. A typical microlensing event shows a characteristic light curve, however, some outbursting variable stars may

Maged Shoman, Ana Tsui Moreno

The growth of ridehailing (RH) companies over the past few years has affected urban mobility in numerous ways. Despite widespread claims about the benefits of such services, limited research has been conducted on the topic. This paper assesses the willingness of Munich transportation users to pay for RH services. Realizing the difficulty of obtaining data di

Vineel Pratap, Awni Hannun, Gabriel Synnaeve, Ronan Collobert

We develop an algorithm which can learn from partially labeled and unsegmented sequential data. Most sequential loss functions, such as Connectionist Temporal Classification (CTC), break down when many labels are missing. We address this problem with Star Temporal Classification (STC) which uses a special star token to allow alignments which include all poss

Valentina Vacca, Timothy Shimwell, Richard A. Perley, Federica Govoni

The galaxy cluster Abell 523 (A523) hosts an extended diffuse synchrotron source historically classified as a radio halo. Its radio power at 1.4 GHz makes it one of the most significant outliers in the scaling relations between observables derived from multi-wavelength observations of galaxy clusters: it has a morphology that is different and offset from the

Q. S. Feng, R. Aboushelbaya, M. W. Mayr, W. P. Wang

Laser beams carrying orbital angular momentum (OAM) provide an additional degree of freedom and have found wide applications ranging from optical communications and optical manipulation to quantum information. The efficient generation and operation of ultra-intense OAM beams is a big challenge that has to be met, currently setting a limit to the potential ap

Aleksandr Beznosikov, Alexander Gasnikov, Karina Zainulina, Alexander Maslovskiy

In this paper, we present a unified analysis of methods for such a wide class of problems as variational inequalities, which includes minimization problems and saddle point problems. We develop our analysis on the modified Extra-Gradient method (the classic algorithm for variational inequalities) and consider the strongly monotone and monotone cases, which c

Agustín Vallejo, Jorge I. Zuluaga, Germán Chaparro

On April 13, 2029, asteroid Apophis will pass within six Earth radii ($\sim$31000 km above surface), in the closest approach of this asteroid in recorded history. This event provides unique scientific opportunities to study the asteroid, its orbit, and surface characteristics at an exceptionally close distance. In this paper we perform a synthetic geometrica

Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo Rezende

It is common practice in deep learning to represent a measurement of the world on a discrete grid, e.g. a 2D grid of pixels. However, the underlying signal represented by these measurements is often continuous, e.g. the scene depicted in an image. A powerful continuous alternative is then to represent these measurements using an implicit neural representatio

H. K. Vedantham, J. R. Callingham, T. W. Shimwell, A. O. Benz

The empirical relationship between the non-thermal 5GHz radio luminosity and the soft X-ray luminosity of active stellar coronae, canonically called the G\"udel-Benz relationship (G\"udel & Benz 1993), has been a cornerstone of stellar radio astronomy as it explicitly ties the radio emission to the coronal heating mechanisms. The relationship extends from mi

Jingyi Chen, Guozhen Su, Jincan Chen, Shanhe Su

Quantum coherence associated with the superpositions of two different sets of eigenbasis vectors has been regarded as essential in thermodynamics. It is found that coherent factors can be determined by writing observables as an expansion in the basis vectors of the systemic density operator and Hamiltonian. We reveal the roles of coherence in finite-time the

Philip T Gressman

This paper establishes a necessary and sufficient condition for $L^p$-boundedness of a class of multilinear functionals which includes both the Brascamp-Lieb inequalities and generalized Radon transforms associated to algebraic incidence relations. The testing condition involves bounding the average of an inverse power of certain Jacobian-type quantities alo

Rachel Arredondo, Ofri Dar, Kylon Chiang, Arielle Blonder

Seagrass meadows are twice as efficient as forests at capturing and storing carbon, but over the last two decades they have been disappearing due to human activities. We take a nature-centered design approach using contextual inquiry and iterative participatory designs methods to consolidate knowledge from the marine and material sciences to industrial desig

Jordi Ferrer Orri, Tiarnan A. S. Doherty, Duncan Johnstone, Sean M. Collins

The interaction of high-energy electrons and X-ray photons with soft semiconductors such as halide perovskites is essential for the characterisation and understanding of these optoelectronic materials. Using nano-probe diffraction techniques, which can investigate physical properties on the nanoscale, we perform studies of the interaction of electron and X-r

Leyang Zhang, Zhi-Qin John Xu, Tao Luo, Yaoyu Zhang

In recent years, understanding the implicit regularization of neural networks (NNs) has become a central task in deep learning theory. However, implicit regularization is itself not completely defined and well understood. In this work, we attempt to mathematically define and study implicit regularization. Importantly, we explore the limitations of a common a

M. Boglione, M. Diefenthaler, S. Dolan, L. Gamberg

We introduce a new phenomenological tool based on momentum region indicators to guide the analysis and interpretation of semi-inclusive deep-inelastic scattering measurements. The new tool, referred to as "affinity", is devised to help visualize and quantify the proximity of any experimental kinematic bin to a particular hadron production region, such as tha