November 2022 arXiv papers — page 11

Apostolos I. Rikos, Themistoklis Charalambous, Christoforos N. Hadjicostis, Karl H. Johansson

We consider the problems of computing the average degree and the size of a given network in a distributed fashion under quantized communication. We present two distributed algorithms which rely on quantized operation (i.e., nodes process and transmit quantized messages), and are able to calculate the exact solutions in a finite number of steps. Furthermore,

Victor David, Raphaël Fournier-S'niehotta, Nicolas Travers

We aim at improving reasoning on inconsistent and uncertain data. We focus on knowledge-graph data, extended with time intervals to specify their validity, as regularly found in historical sciences. We propose principles on semantics for efficient Maximum A-Posteriori inference on the new Temporal Markov Logic Networks (TMLN) which extend the Markov Logic Ne

Ankit Vishnubhotla

We investigate $\mathbb{R}^n$ as the additive group with the Euclidean topology to give a description of $S(\mathbb{R}^n)$, the phase space of the universal ambit of $\mathbb{R}^n$ and $M(\mathbb{R}^n)$, the phase space of the universal minimal dynamical system, in terms of $M(\mathbb{Z}^n)$, the phase space of universal minimal flow of $\mathbb{Z}^n$. This

Manel Baradad, Chun-Fu Chen, Jonas Wulff, Tongzhou Wang

Learning image representations using synthetic data allows training neural networks without some of the concerns associated with real images, such as privacy and bias. Existing work focuses on a handful of curated generative processes which require expert knowledge to design, making it hard to scale up. To overcome this, we propose training with a large data

T. B. Watson, Z. E. Musielak

In this letter we seek to redress lingering misconceptions pertaining to the physicality of the chiral phase of Dirac bi-spinor fields. Demonstrably, the most general first-order partial differential equation for spinor wavefunctions that can be obtained in Minkowski spacetime is the Dirac-like equation which leaves both the mass and chiral angles as free pa

Catherine Bruce, Xiong Jin

In this paper we extend Hochman and Shmerkin's projection theorem to product measures of Mandelbrot cascades acting on ergodic measures imaged through canonical mappings of one-dimensional iterated function systems without any separation conditions. Consequently we extend Furstenberg sumset theorem to images of subshifts on symbolic spaces, and to Mandelbrot

Adrian L. H. Lam, Jean-Luc Margot, Emily Whittaker, Nathan Myhrvold

We used 22 $\mu$m (W4) Wide-field Infrared Survey Explorer (WISE) observations of 4420 asteroids to analyze lightcurves and determined spin period estimates for 1929 asteroids. We fit second-order Fourier models at a large number of trial frequencies to the W4 data and analyzed the resulting periodograms. We initially excluded rotational frequencies exceedin

Lipei Du, Chun Shen, Sangyong Jeon, Charles Gale

Using a (3+1)-dimensional hybrid framework with parametric initial conditions, we study the rapidity-dependent directed flow $v_1(y)$ of identified particles, including pions, kaons, protons, and lambdas in heavy-ion collisions. Cases involving Au+Au collisions are considered, performed at $\sqrt{s_{\rm NN}}$ ranging from 7.7 to 200 GeV. The dynamics in the

Robert Huben, S. Kaliszewski, Nadia S. Larsen, John Quigg

We prove a version of the result in the title that makes use of maximal coactions in the context of discrete groups. Earlier Gauge-Invariant Uniqueness theorems for $C^*$-algebras associated to $P$-graphs and similar $C^*$-algebras exploited a property of coactions known as normality. In the present paper, the view point is that maximal coactions provide a m

Vera M. Balmer, Sophia V. Kuhn, Rafael Bischof, Luis Salamanca

For conceptual design, engineers rely on conventional iterative (often manual) techniques. Emerging parametric models facilitate design space exploration based on quantifiable performance metrics, yet remain time-consuming and computationally expensive. Pure optimisation methods, however, ignore qualitative aspects (e.g. aesthetics or construction methods).

Leron Borsten, Hyungrok Kim, Branislav Jurčo, Tommaso Macrelli

Colour-kinematics duality is a remarkable property of Yang-Mills theory. Its validity implies a relation between gauge theory and gravity scattering amplitudes, known as double copy. Albeit fully established at the tree level, its extension to the loop level is conjectural. Lifting the on-shell, scattering amplitudes-based description to the level of action

Jose Beltrán Jiménez, Dario Bettoni, Philippe Brax

We consider a family of non-linear theories of electromagnetism that interpolate between Born-Infeld at small distances and ModMax at large distances. These models are duality invariant and feature a $K-$mouflage screening in the Born-Infeld regime. We focus on computing the static perturbations around a point-like screened charge in terms of two decoupled s

Sarah Samorodnitsky, Chris H. Wendt, Eric F. Lock

Understanding of the pathophysiology of obstructive lung disease (OLD) is limited by available methods to examine the relationship between multi-omic molecular phenomena and clinical outcomes. Integrative factorization methods for multi-omic data can reveal latent patterns of variation describing important biological signal. However, most methods do not prov

Farzan Byramji

We study the deterministic query complexity of Boolean functions on slices of the hypercube. The $k^{th}$ slice $\binom{[n]}{k}$ of the hypercube $\{0,1\}^n$ is the set of all $n$-bit strings with Hamming weight $k$. We show that there exists a function on the balanced slice $\binom{[n]}{n/2}$ requiring $n - O(\log \log n)$ queries. We give an explicit funct

Yifei Zhang, Sourav Kumar Ukil, Andrei Sperila, Serban Sabau

This paper addresses the end-to-end sample complexity bound for learning in closed loop the state estimator-based robust H2 controller for an unknown (possibly unstable) Linear Time Invariant (LTI) system, when given a fixed state-feedback gain. We build on the results from Ding et al. (1994) to bridge the gap between the parameterization of all state-estima

César Rosales

In a round ball $B\subset\mathbb{R}^{n+1}$ endowed with an $O(n+1)$-invariant metric we consider a radial function that weights volume and area. We prove that a compact two-sided hypersurface in $B$ which is stable capillary in weighted sense and symmetric about some line containing the center of $B$ is homeomorphic to a closed $n$-dimensional disk. When com

Zafar Iqbal, Usman Mahmood, Zening Fu, Sergey Plis

Data scarcity is a notable problem, especially in the medical domain, due to patient data laws. Therefore, efficient Pre-Training techniques could help in combating this problem. In this paper, we demonstrate that a model trained on the time direction of functional neuro-imaging data could help in any downstream task, for example, classifying diseases from h

Ankush Girdhar, Vinod Ashokan, Rajesh O. Sharma, N. D. Drummond

We use the variational quantum Monte Carlo (VMC) method to study the wire width ($b$) and electron density ($r_\text{s}$) dependences of the ground-state properties of quasi-one-dimensional paramagnetic electron fluids. The onset of a quasi-Wigner crystal phase is known to depend on electron density, and the crossover occurs in the low density regime. We stu

Nikolay Kozyrev

In this paper the harmonic superspace action of the tensor multiplet of $N=(1,0)$, $d=6$ supersymmetry is constructed which in the bosonic limit reduces to the known Pasti-Sorokin-Tonin action for the self-dual tensor field. The action involves, besides the potential containing the dynamical fields, also an auxiliary tensor multiplet and a set of analytic su

Dario Di Pinto, Giulia Dileo

We introduce and study a special class of almost contact metric manifolds, which we call anti-quasi-Sasakian (aqS). Among the class of transversely K\"ahler almost contact metric manifolds $(M,\varphi, \xi,\eta,g)$, quasi-Sasakian and anti-quasi-Sasakian manifolds are characterized, respectively, by the $\varphi$-invariance and the $\varphi$-anti-invariance

Sudhakar Prasad

We calculate the quantum Fisher information (QFI) for estimating, using a circular imaging aperture, the two-dimensional location of a point source against a uniformly bright disk of known center and radius in the ideal photon-counting limit. We present both a perturbative calculation of the QFI in powers of the background-to-source brightness ratio and a nu

V. E. Kuzmichev, V. V. Kuzmichev

The Hubble tension is analyzed in the framework of quantum cosmological approach. It is found that there arises a new summand in the expression for the total energy density stipulated by the quantum Bohm potential. This additional energy density acts similarly to a stiff matter component, modifying the expansion history of the early universe and decaying wit

Arman Oganisian, Kelly D. Getz, Todd A. Alonzo, Richard Aplenc

We develop a Bayesian semi-parametric model for the estimating the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML). The data consist of a subset of patients enrolled in the phase III AAML1031 clinical trial in which patients move through a sequence of four treatment courses. At each course, t

Alexnader Zapryagaev

B\"uchi arithmetics BA_n, n >= 2, are extensions of Presburger arithmetic with an unary functional symbol V_n(x) denoting the largest power of n that divides x. Definability of a set in BA_n is equivalent to its recognizability by a finite automaton receiving numbers in their n-ary expansion. We show that B\"uchi arithmetics BA_n and BA_m are bi-interpretabl

Katherine Goldman

Let $A_\Gamma$ be an Artin group with defining graph $\Gamma$. We introduce the notion of $A_\Gamma$ being extra-large relative to a family of arbitrary parabolic subgroups. This generalizes a related notion of $A_\Gamma$ being extra-large relative to two parabolic subgroups, one of which is always large type. Under this new condition, we show that $A_\Gamma

H. M. Courtois, A. Dupuy, D. Guinet, G. Baulieu

This article publicly releases three-dimensional reconstructions of the local Universe gravitational field below z=0.8 that were computed using the CosmicFlows-4 catalog of 56,000 galaxy distances and its sub-sample of 1,008 type Ia supernovae distances. The article also provides measurements of the growth rate of structure using the pairwise correlation of

Oliver O'Brien, Sergii Strelchuk

Fermion-to-qubit mappings play a crucial role in representing fermionic interactions on a quantum computer. Efficient mappings translate fermionic modes of a system to qubit interactions with a high degree of locality while using few auxiliary resources. We present a family of locality-preserving fermion-to-qubit mappings that require fewer auxiliary qubits

H. M. Courtois, R. van de Weygaert, M. Aubert, D. Pomarède

Context. The present study addresses a key question related to our understanding of the relation between void galaxies and their environment: the relationship between luminous and dark matter in and around voids. Aims. To explore the extent to which local Universe voids are empty of matter, we study the full (dark+luminous) matter content of seven nearby cos

Johan Klarbring, Utkarsh Singh, Sergei I. Simak, Igor A. Abrikosov

A family of magnetic halide double perovskites (HDPs) have recently attracted attention due to their potential to broaden application areas of halide double perovskites into e.g. spintronics. Up to date the theoretical modelling of these systems have relied on primitive approximations to the density functional theory (DFT). In this paper, we study structural

Mara Pistellato, Filippo Bergamasco

The vast majority of Shape-from-Polarization (SfP) methods work under the oversimplified assumption of using orthographic cameras. Indeed, it is still not well understood how to project the Stokes vectors when the incoming rays are not orthogonal to the image plane. We try to answer this question presenting a geometric model describing how a general projecti

Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen

Approximating radiance fields with volumetric grids is one of promising directions for improving NeRF, represented by methods like Plenoxels and DVGO, which achieve super-fast training convergence and real-time rendering. However, these methods typically require a tremendous storage overhead, costing up to hundreds of megabytes of disk space and runtime memo

Srivatsan Krishnan, Natasha Jaques, Shayegan Omidshafiei, Dan Zhang

Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. As the systems grow in complexity, fine-tuning architectural parameters across multiple sub-systems (e.g., datapath, memory blocks in different hierarchies, interconnects, compiler optimization, etc.) quickly results

Yuga Iguchi, Alexandros Beskos, Matthew M. Graham

This work aims at making a comprehensive contribution in the general area of parametric inference for discretely observed diffusion processes. Established approaches for likelihood-based estimation invoke a time-discretisation scheme for the approximation of the intractable transition dynamics of the Stochastic Differential Equation (SDE) model over finite t

Torsha Moitra, Lukas Konecny, Marius Kadek, Angel Rubio

First principle theoretical modeling of out-of-equilibrium processes observed in attosecond pump-probe transient absorption spectroscopy (TAS) triggering pure electron dynamics remains a challenging task, specially for heavy elements and/or core excitations containing fingerprints of scalar and spin-orbit relativistic effects. To address this, we formulate a

Jay Baptista, Robyn Sanderson, Dan Huber, Andrew Wetzel

The shape and orientation of dark matter (DM) halos are sensitive to the micro-physics of the DM particle, yet in many mass models, the symmetry axes of the Milky Way's DM halo are often assumed to be aligned with the symmetry axes of the stellar disk. This is well-motivated for the inner DM halo but not for the outer halo. We use zoomed cosmological-baryoni

Hadeel Saadany, Catherine Breslin, Constantin Orăsan, Sophie Walker

Transcription of legal proceedings is very important to enable access to justice. However, speech transcription is an expensive and slow process. In this paper we describe part of a combined research and industrial project for building an automated transcription tool designed specifically for the Justice sector in the UK. We explain the challenges involved i

Marissa D'Alonzo, Rebecca Russell

Knowledge of the symmetries of reinforcement learning (RL) systems can be used to create compressed and semantically meaningful representations of a low-level state space. We present a method of automatically detecting RL symmetries directly from raw trajectory data without requiring active control of the system. Our method generates candidate symmetries and

Feng Shao, Guolei Zhong

Let $f:Y\to X$ be a finite morphism between Fano manifolds $Y$ and $X$ such that the Fano index of $X$ is greater than 1. On the one hand, when both $X$ and $Y$ are fourfolds of Picard number 1, we show that the degree of $f$ is bounded in terms of $X$ and $Y$ unless $X\cong\mathbb{P}^4$; hence, such $X$ does not admit any non-isomorphic surjective endomorph

Simon Apers, Stephen Piddock

We study an elementary Markov process on graphs based on electric flow sampling (elfs). The elfs process repeatedly samples from an electric flow on a graph. While the sinks of the flow are fixed, the source is updated using the electric flow sample, and the process ends when it hits a sink vertex. We argue that this process naturally connects to many key qu

Lukas Konecny, Stanislav Komorovsky, Jan Vicha, Kenneth Ruud

X-ray absorption spectroscopy (XAS) has gained popularity in recent years as it probes matter with high spatial and elemental sensitivity. However, the theoretical modelling of XAS is a challenging task since XAS spectra feature a fine structure due to scalar (SC) and spin-orbit (SO) relativistic effects, in particular near L and M absorption edges. While fu

Luca Cocconi, Jacob Knight, Connor Roberts

We identify generic protocols achieving optimal power extraction from a single active particle subject to continuous feedback control under the assumption that its spatial trajectory, but not its instantaneous self-propulsion force, is accessible to direct observation. Our Bayesian approach draws on the Onsager-Machlup path integral formalism and is exemplif

Wenbin Zhao, Sangwook Ryu, Chun Shen, Björn Schenke

We present (3+1)D dynamical simulations of asymmetric nuclear collisions at the Relativistic Heavy Ion Collider (RHIC). Employing a dynamical initial state model coupled to (3+1)D viscous relativistic hydrodynamics, we explore the rapidity dependence of anisotropic flow in the RHIC small system scan at 200 GeV center of mass energy. We calibrate parameters t

G. A. Tsolias, Robert J. Decker, A. Demirkaya, T. J. Alexander

In the present work we explore the competition of quadratic and quartic dispersion in producing kink-like solitary waves in a model of the nonlinear Schr{\"o}dinger type bearing cubic nonlinearity. We present the first 6 families of multikink solutions and explore their bifurcations as the strength of the quadratic dispersion is varied. We reveal a rich bifu

Gwanghyun Kim, Se Young Chun

Recent 3D generative models have achieved remarkable performance in synthesizing high resolution photorealistic images with view consistency and detailed 3D shapes, but training them for diverse domains is challenging since it requires massive training images and their camera distribution information. Text-guided domain adaptation methods have shown impressi

Agrim Gupta, Sajjad Nassirpour, Manideep Dunna, Eamon Patamasing

With the turn of new decade, wireless communications face a major challenge on connecting many more new users and devices, at the same time being energy efficient and minimizing its carbon footprint. However, the current approaches to address the growing number of users and spectrum demands, like traditional fully digital architectures for Massive MIMO, dema

Stanley Nicholson, David Minh, Robert Eisenberg

We applied coherence analysis to molecular dynamics simulations of the plant protein crambin, a thionin storage protein found in Abyssinian cabbage. Coherence analysis was developed by engineers to identify linear interactions, without statistical assumptions. Coherence is greater than 0.9 between the displacement of oxygen and nitrogen atoms of H bonds in a

Tess Bouis

We introduce the notion of a $p$-Cartier smooth algebra. It generalises that of a smooth algebra and includes valuation rings over a perfectoid base. We give several characterisations of $p$-Cartier smoothness in terms of prismatic cohomology, and deduce a comparison theorem between syntomic and \'etale cohomologies under this hypothesis.

F. B. M. dos Santos

We establish constraints on $f(T)$ gravity by considering the possibility of a scenario that supports a phantom crossing of the equation of state parameter $\omega_{DE}$. After determining the viable parameter space of the model, while checking the impact on the background dynamics, we perform an analysis to obtain constraints on cosmological parameters and

Marjan Celikik, Jacek Wasilewski, Sahar Mbarek, Pablo Celayes

A large number of empirical studies on applying self-attention models in the domain of recommender systems are based on offline evaluation and metrics computed on standardized datasets, without insights on how these models perform in real life scenarios. Moreover, many of them do not consider information such as item and customer metadata, although deep-lear

Md. Sabir Ali, Shashi C. L. Srivastava

We investigate the level spacing distribution for three ensembles of real symmetric matrices having additional structural constraint to reduce the number of independent entries to only $ (n+1)/2 $ in contrast to the $ n(n+1)/2 $ for a real symmetric matrix of size $ n \times n $. We derive all the results analytically exactly for the $ 3\times 3 $ matrices a

D. V. Khveshchenko

This note addresses the problem of computing fermion propagators in a broad variety of strongly correlated systems that can be mapped onto the theory of fermions coupled to an (over)damped bosonic mode. A number of the previously applied approaches and their results are reviewed, including the conventional diagrammatic resummation and eikonal technique, as w

Emre Anakok, Pierre Barbillon, Colin Fontaine, Elisa Thebault

The structure of a bipartite interaction network can be described by providing a clustering for each of the two types of nodes. Such clusterings are outputted by fitting a Latent Block Model (LBM) on an observed network that comes from a sampling of species interactions in the field. However, the sampling is limited and possibly uneven. This may jeopardize t

Premjeet Singh, Shefali Waldekar, Md Sahidullah, Goutam Saha

This work analyzes the constant-Q filterbank-based time-frequency representations for speech emotion recognition (SER). Constant-Q filterbank provides non-linear spectro-temporal representation with higher frequency resolution at low frequencies. Our investigation reveals how the increased low-frequency resolution benefits SER. The time-domain comparative an

Malte Knüppel, Fabian Krüger, Marc-Oliver Pohle

Calibration tests based on the probability integral transform (PIT) are routinely used to assess the quality of univariate distributional forecasts. However, PIT-based calibration tests for multivariate distributional forecasts face various challenges. We propose two new types of tests based on proper scoring rules, which overcome these challenges. They aris

Sharan Banagiri, Zoheyr Doctor, Vicky Kalogera, Chase Kimball

In recent years, a handful of ``dark" binaries have been discovered with a non-luminous compact object. Astrometry and radial velocity measurements of the bright companion allow us to measure the post-supernova orbital elements of such a binary. In this paper, we develop a statistical formalism to use such measurements to infer the pre-supernova orbital elem

Ishita Vohra, Meher Shashwat Nigam, Aryan Sakaria, Amey Kudari

The pandemic required efficient allocation of public resources and transforming existing ways of societal functions. To manage any crisis, governments and public health researchers exploit the information available to them in order to make informed decisions, also defined as situational awareness. Gathering situational awareness using social media has been f

Aleksandra Suwalska, Joanna Tobiasz, Wojciech Prazuch, Marek Socha

The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often

Camille Eloy, Olaf Hohm, Henning Samtleben

We consider the target space theory of bosonic and heterotic string theory to first order in $\alpha'$ compactified to three dimensions, using a formulation that is manifestly T-duality invariant under ${\rm O}(d,d,\mathbb{R})$ with $d=23$ and $d=7$, respectively. While the two-derivative supergravity exhibits a symmetry enhancement to the U-duality group ${

Christoph Dlapa, Johannes M. Henn, Fabian J. Wagner

In recent years, differential equations have become the method of choice to compute multi-loop Feynman integrals. Whenever they can be cast into canonical form, their solution in terms of special functions is straightforward. Recently, progress has been made in understanding the precise canonical form for Feynman integrals involving elliptic polylogarithms.

Runjia Li, Yang Yu, Charlie Haywood

In this paper, we address the problem of blind deblurring with high efficiency. We propose a set of lightweight deep-wiener-network to finish the task with real-time speed. The Network contains a deep neural network for estimating parameters of wiener networks and a wiener network for deblurring. Experimental evaluations show that our approaches have an edge

Mehdi Ouadfel, Michael De San Féliciano, Cecilia Herrero, Samy Merabia

Thermo-osmotic flows, generated at liquid-solid interfaces by thermal gradients, can be used to produce electric currents from waste heat on charged surfaces. The two key parameters controlling the thermo-osmotic current are the surface charge and the interfacial enthalpy excess due to liquid-solid interactions. While it has been shown that the contribution

Changhao Li, Japinder Nijjer, Luyi Feng, Qiuting Zhang

Living active collectives have evolved with remarkable self-patterning ability to meet the physical and biological constraints for growth and survival. However, how complex multicellular patterns emerge from a single founder cell remains elusive. Here, by recourse to an agent-based model, we track the three-dimensional (3D) morphodynamics and cell orientatio

Marjan Celikik, Matthias Kirmse, Timo Denk, Pierre Gagliardi

Over the past years, fashion-related challenges have gained a lot of attention in the research community. Outfit generation and recommendation, i.e., the composition of a set of items of different types (e.g., tops, bottom, shoes, accessories) that go well together, are among the most challenging ones. That is because items have to be both compatible amongst

Robin Smeyers, Lucian Covaci, Milorad V. Milošević

When using hexagonal boron-nitride (hBN) as a substrate for graphene, the resulting moir\'e pattern creates secondary Dirac points. By encapsulating a multilayer graphene within aligned hBN sheets the controlled moir\'e stacking may offer even richer benefits. Using advanced tight-binding simulations on atomistically-relaxed heterostructures, here we show th

Gayane Chilingaryan, Hovhannes Tamoyan, Ani Tevosyan, Nelly Babayan

We discover a robust self-supervised strategy tailored towards molecular representations for generative masked language models through a series of tailored, in-depth ablations. Using this pre-training strategy, we train BARTSmiles, a BART-like model with an order of magnitude more compute than previous self-supervised molecular representations. In-depth eval

Paola Vargas, Miguel Angel Moreles, Joaquin Peña, Adriana Monroy

In this work, we test the performance of Peak glucose concentration ($A$) and average of glucose removal rates ($\alpha$), as normoglycemia and dysglycemia indices on a population monitored at the Mexico General Hospital between the years 2017 - 2019. A total of 1911 volunteer patients at the Mexico General Hospital are considered. 1282 female patients age r

Maxim Kharitonov

Continuum models are particularly appealing for theoretical studies of bound states, due to simplicity of their bulk Hamiltonians. The main challenge on this path is a systematic description of the boundary, which comes down to determining proper boundary conditions (BCs). BCs are a consequence of the fundamental principle of quantum mechanics: norm conserva

Frederic Hummel, Peter Schmelcher, Matthew T. Eiles

Ultralong-range Rydberg molecules provide an exciting testbed for molecular physics at exaggerated scales. In the so-called trilobite and butterfly Rydberg molecules, the Born-Oppenheimer approximation can fail due to strong non-adiabatic couplings arising from the combination of radial oscillations and rapid energy variations in the adiabatic potential ener

Xi Jia, Joseph Bartlett, Wei Chen, Siyang Song

Unsupervised image registration commonly adopts U-Net style networks to predict dense displacement fields in the full-resolution spatial domain. For high-resolution volumetric image data, this process is however resource-intensive and time-consuming. To tackle this problem, we propose the Fourier-Net, replacing the expansive path in a U-Net style network wit

Jonas Hall, Armin Nurkanovic, Florian Messerer, Moritz Diehl

In this paper we introduce an open-source software package written in C++ for efficiently finding solutions to quadratic programming problems with linear complementarity constraints. These problems arise in a wide range of applications in engineering and economics, and they are challenging to solve due to their structural violation of standard constraint qua

Daren B. H. Cline, Tailen Hsing

Let $S_n$ be the sum of independent random variables with distribution $F$. Under the assumption that $-\log(1-F(x))$ is slowly varying, conditions for $$ \lim_{n\to\infty}\sup_{s\ge t_n}\left|{P[S_n>s]\over n(1-F(s))}-1\right| =0 $$ are given. These conditions extend and strengthen a series of previous results. Additionally, a connection with subexponential

João P. S. Maurício de Carvalho, Alexandre A. Rodrigues

There are few adapted SIR models in the literature that combine vaccination and logistic growth. In this article, we study bifurcations of a SIR model where the class of Susceptible individuals grows logistically and has been subject to constant vaccination. We explicitly prove that the endemic equilibrium is a codimension two singularity in the parameter sp

S. Konstantinou-Rizos

The 4-simplex equation is a higher-dimensional analogue of Zamolodchikov's tetrahedron equation and the Yang--Baxter equation which are two of the most fundamental equations of mathematical physics. In this paper, we introduce a method for constructing 4-simplex maps, namely solutions to the set-theoretical 4-simplex equation, using Lax matrix refactorisatio

Boris Albrecht, Constantin Dalyac, Lucas Leclerc, Luis Ortiz-Gutiérrez

Using a quantum processor to embed and process classical data enables the generation of correlations between variables that are inefficient to represent through classical computation. A fundamental question is whether these correlations could be harnessed to enhance learning performances on real datasets. Here, we report the use of a neutral atom quantum pro

Zhi-Feng Liu, Chao Chen, Jia-Min Xu, Zi-Mo Cheng

Two-photon Hong-Ou-Mandel (HOM) interference is a fundamental quantum effect with no classical counterpart. The exiting researches on two-photon interference were mainly limited in one degree of freedom (DoF), hence it is still a challenge to realize the quantum interference in multiple DoFs. Here we demonstrate the HOM interference between two hyper-entangl

Felix Grezes, Thomas Allen, Sergi Blanco-Cuaresma, Alberto Accomazzi

The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing. At ADASS 2021, we introduced astroBERT, a machine learning language model tailored to the text used in astronomy papers in ADS.

Turcan Tuna, Julian Nubert, Yoshua Nava, Shehryar Khattak

Modern robotic systems are required to operate in challenging environments, which demand reliable localization under challenging conditions. LiDAR-based localization methods, such as the Iterative Closest Point (ICP) algorithm, can suffer in geometrically uninformative environments that are known to deteriorate point cloud registration performance and push o

Franco Golfieri Madriaga, Ariel Pacetti, Lucas Villagra Torcomian

Let $f$ and $g$ be two different newforms without complex multiplication having the same coefficient field. The main result of the present article proves that a congruence between the Galois representations attached to $f$ and to $g$ for a large prime $p$ implies an isomorphism between the endomorphism algebras of the abelian varieties $A_f$ and $A_g$ attach

Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Ankit Pensia

We study the fundamental task of outlier-robust mean estimation for heavy-tailed distributions in the presence of sparsity. Specifically, given a small number of corrupted samples from a high-dimensional heavy-tailed distribution whose mean $\mu$ is guaranteed to be sparse, the goal is to efficiently compute a hypothesis that accurately approximates $\mu$ wi

Yiyan Li, Lidong Song, Yi Hu, Hanpyo Lee

This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event. The inputs are time series load data before and after the inpainting period together with explanatory variables (e.g., weather data). We propose a Generat

João P. Moutinho, Duarte Magano, Bruno Coutinho

Link prediction methods use patterns in known network data to infer which connections may be missing. Previous work has shown that continuous-time quantum walks can be used to represent path-based link prediction, which we further study here to develop a more optimized quantum algorithm. Using a sampling framework for link prediction, we analyze the query ac

Heike Wehrheim, Lara Bargmann, Brijesh Dongol

Modern processors such as ARMv8 and RISC-V allow executions in which independent instructions within a process may be reordered. To cope with such phenomena, so called promising semantics have been developed, which permit threads to read values that have not yet been written. Each promise is a speculative update that is later validated (fulfilled) by an actu

Yang Yuan

With infinitely many high-quality data points, infinite computational power, an infinitely large foundation model with a perfect training algorithm and guaranteed zero generalization error on the pretext task, can the model be used for everything? This question cannot be answered by the existing theory of representation, optimization or generalization, becau

Andrea Armaroli, Guillaume Dujardin, Alexandre Kudlinski, Arnaud Mussot

We study modulational instability in a dispersion-managed system where the sign of the group-velocity dispersion is changed at uniformly distributed random distances around a reference length. An analytical technique is presented to estimate the instability gain from the linearized nonlinear Schr{\"o}dinger equation, which is also solved numerically. The com

Zeyu Zhou, Hsing-Ta Chen, Joseph E. Subotnik, Abraham Nitzan

The interplay between collective optical response and molecular static and dynamic disorder is studied using simple effective Hamiltonians for an ensemble of two-level emitters inside and outside a single-mode cavity. We model environmental disorder by randomly modulating the molecular transition frequencies and the coupling between the emitters and the elec

Sophie Laplante, Loris Perez, Sylvie Tissot, Lou Vettier

We introduce a visual representation of qubits to assist in explaining quantum computing to a broad audience. The representation follows from physical devices that we developed to explain superposition, entanglement, measurement, phases, interference, and quantum gates. We describe how this representation can be used to explain teleportation and the Bennett-

Tobias Simonsen Folden, Fynn Jerome Aschmoneit

This review article presents a summary of the main categories of models developed for modeling cavitation, a multiphase phenomenon in which a fluid locally experiences phase change due to a drop in ambient pressure. The most common approaches to modeling cavitation along with the most common modifications to said approaches due to other effects of cavitating

Sean Greenaway, Francesco Petiziol, Hongzheng Zhao, Florian Mintert

We experimentally investigate the viability of a variational quantum gate optimization protocol informed by the underlying physical Hamiltonian of fixed-frequency transmon qubits. The utility of the scheme is demonstrated through the successful experimental optimization of two and three qubit quantum gates tailored on the native cross-resonance interaction.

Lucas C. F. Ferreira, Daniel F. Machado

In this paper we consider the incompressible inhomogeneous Navier-Stokes equations in the whole space with dimension $n\geq 3$. We present local and global well-posedness results in a new framework for inhomogeneous fluids, namely Besov-Morrey spaces $\mathcal{N}_{p,q,r}^{s}$ that are Besov spaces based on Morrey ones. In comparison with the previous works i

CMS Collaboration, TOTEM Collaboration

A search is performed for exclusive high-mass $\gamma\gamma$ $\to$ WW and $\gamma\gamma$ $\to$ ZZ production in proton-proton collisions using intact forward protons reconstructed in near-beam detectors, with both weak bosons decaying into boosted and merged jets. The analysis is based on a sample of proton-proton collisions collected by the CMS and TOTEM ex

Fu Xing Long, Diederick Vermetten, Bas van Stein, Anna V. Kononova

Benchmarking is a key aspect of research into optimization algorithms, and as such the way in which the most popular benchmark suites are designed implicitly guides some parts of algorithm design. One of these suites is the black-box optimization benchmarking (BBOB) suite of 24 single-objective noiseless functions, which has been a standard for over a decade

Maham Misbah, Misha Urooj Khan, Zhaohui Yang, Zeeshan Kaleem

Technological advancements have normalized the usage of unmanned aerial vehicles (UAVs) in every sector, spanning from military to commercial but they also pose serious security concerns due to their enhanced functionalities and easy access to private and highly secured areas. Several instances related to UAVs have raised security concerns, leading to UAV de

Enes Altinisik, Safa Messaoud, Husrev Taha Sencar, Sanjay Chawla

Adversarial training has been empirically shown to be more prone to overfitting than standard training. The exact underlying reasons still need to be fully understood. In this paper, we identify one cause of overfitting related to current practices of generating adversarial samples from misclassified samples. To address this, we propose an alternative approa

Christopher Reale, Rebecca Russell

Hidden parameters are latent variables in reinforcement learning (RL) environments that are constant over the course of a trajectory. Understanding what, if any, hidden parameters affect a particular environment can aid both the development and appropriate usage of RL systems. We present an unsupervised method to map RL trajectories into a feature space wher

Maximilian Weiherer, Finn Klein, Bernhard Egger

To date, the comparison of Statistical Shape Models (SSMs) is often solely performance-based, carried out by means of simplistic metrics such as compactness, generalization, or specificity. Any similarities or differences between the actual shape spaces can neither be visualized nor quantified. In this paper, we present a new method to qualitatively compare

Malte C. Tichy, Illia Babounikau, Nikolas Wolke, Stefan Ulbrich

Forecast quality should be assessed in the context of what is possible in theory and what is reasonable to expect in practice. Often, one can identify an approximate upper bound to a probabilistic forecast's sharpness, which sets a lower, not necessarily achievable, limit to error metrics. In retail forecasting, a simple, but often unconquerable sharpness li

Runyu Ding, Jihan Yang, Chuhui Xue, Wenqing Zhang

Open-vocabulary scene understanding aims to localize and recognize unseen categories beyond the annotated label space. The recent breakthrough of 2D open-vocabulary perception is largely driven by Internet-scale paired image-text data with rich vocabulary concepts. However, this success cannot be directly transferred to 3D scenarios due to the inaccessibilit

Denis Leahy, Traian Craiciu, Joseph Postma

The bulge of M31 is of interest in the context of the nature of galactic bulges and how their structure relates to bulge formation mechanisms and their subsequent evolution. With the UVIT instrument on AstroSat, we have observed the bulge of M31 in five far ultraviolet (FUV) and near ultraviolet (NUV) filters at 1" spatial resolution. Models for the luminosi

Edoardo Spadetto, Pier Herman Theodoor Philipsen, Arno Förster, Lucas Visscher

Pair atomic density fitting (PADF) is a promising strategy to reduce the scaling with system size of quantum chemical methods for the calculation of the correlation energy like the direct random phase approximation (RPA) or second-order M{\o}ller-Plesset perturbation theory (MP2). PADF can however introduce large errors in correlation energies as the two-ele

Sohan Rudra, Saksham Goel, Anirban Santara, Claudio Gentile

Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object. Object-nav has been extensively studied by the Embodied-AI community, but most solutions are often restricted to considering static objects (e.g., television, fridge, etc.). We propose a modular framework for object-nav that is able to efficiently search ind