February 2021 arXiv papers — page 12

Yuanjian Li, A. Hamid Aghvami

Integrating unmanned aerial vehicles (UAVs) into existing cellular networks encounters lots of challenges, among which one of the most striking concerns is how to achieve harmonious coexistence of aerial transceivers, inter alia, UAVs, and terrestrial user equipments (UEs). In this paper, a cellular-connected UAV network is focused, where multiple UAVs recei

Qipin Chen, Wenrui Hao, Juncai He

In this paper, we develop a new neural network family based on power series expansion, which is proved to achieve a better approximation accuracy in comparison with existing neural networks. This new set of neural networks embeds the power series expansion (PSE) into the neural network structure. Then it can improve the representation ability while preservin

Chenyang Yuan, Pablo A. Parrilo

We study the problem of maximizing the geometric mean of $d$ low-degree non-negative forms on the real or complex sphere in $n$ variables. We show that this highly non-convex problem is NP-hard even when the forms are quadratic and is equivalent to optimizing a homogeneous polynomial of degree $O(d)$ on the sphere. The standard Sum-of-Squares based convex re

Dan Soriano, Eli Ben-Michael, Peter J. Bickel, Avi Feller

Assessing sensitivity to unmeasured confounding is an important step in observational studies, which typically estimate effects under the assumption that all confounders are measured. In this paper, we develop a sensitivity analysis framework for balancing weights estimators, an increasingly popular approach that solves an optimization problem to obtain weig

K. Ammari, F. Shel, L. Tebou

In this paper, we examine regularity and stability issues for two damped abstract elastic systems. The damping involves the average velocity and a fractional power $θ$, with $θ$ in $[-1,1]$, of the principal operator. The matrix operator defining the damping mechanism for the coupled system is degenerate. First, we prove that for $θ$ in $(1/2,1]$, the underl

Pieter Spaas

We show that the stabilization of any countable ergodic p.m.p. equivalence relation which is not Schmidt, i.e. admits no central sequences in its full group, always gives rise to a stable equivalence relation with a unique stable decomposition, providing the first non-strongly ergodic such examples. In the proof, we moreover establish a new local characteriz

M. S. Bahovadinov, D. V. Kurlov, B. L. Altshuler, G. V. Shlyapnikov

We study effects of disorder on eigenstates of 1D two-component fermions with infinitely strong Hubbard repulsion. We demonstrate that the spin-independent (potential) disorder reduces the problem to the one-particle Anderson localization taking place at arbitrarily weak disorder. In contrast, a random magnetic field can cause reentrant many-body localizatio

Judith Zimmermann, Kathrin Baeumler, Michael Loecher, Tyler E. Cork

The analysis of quantitative hemodynamics and luminal pressure may add valuable information to aid treatment strategies and prognosis for aortic dissections. This work directly compared in vitro 4D-flow magnetic resonance imaging (MRI), catheter-based pressure measurements, and computational fluid dynamics that integrated fluid-structure interaction (CFD FSI

Tatyana Zaitseva, Yuri Malykhin, Konstantin Ryutin

We consider the problem of the uniform (in $L_\infty$) recovery of ridge functions $f(x)=φ(\langle a,x\rangle)$, $x\in B_2^n$, using noisy evaluations $y_1\approx f(x^1),\ldots,y_N\approx f(x^N)$. It is known that for classes of functions $φ$ of finite smoothness the problem suffers from the curse of dimensionality: in order to provide good accuracy for the

Maria Dimakopoulou, Zhimei Ren, Zhengyuan Zhou

During online decision making in Multi-Armed Bandits (MAB), one needs to conduct inference on the true mean reward of each arm based on data collected so far at each step. However, since the arms are adaptively selected--thereby yielding non-iid data--conducting inference accurately is not straightforward. In particular, sample averaging, which is used in th

Noel Csomay-Shanklin, Maegan Tucker, Min Dai, Jenna Reher

Experimental demonstration of complex robotic behaviors relies heavily on finding the correct controller gains. This painstaking process is often completed by a domain expert, requiring deep knowledge of the relationship between parameter values and the resulting behavior of the system. Even when such knowledge is possessed, it can take significant effort to

Sean Fancher, Eleni Katifori

Transport networks are typically optimized, either by evolutionary pressures in biological systems or by human design in engineered structures. In the case of systems such as the animal vasculature, the transport of fluids is hindered by the inherent viscous resistance to flow while being kept in a dynamic state by the pulsatile nature of the heart and elast

David Chiang, Alexander M. Rush, Boaz Barak

We propose a notation for tensors with named axes, which relieves the author, reader, and future implementers of machine learning models from the burden of keeping track of the order of axes and the purpose of each. The notation makes it easy to lift operations on low-order tensors to higher order ones, for example, from images to minibatches of images, or f

Ethan A. Brooks, Janarthanan Rajendran, Richard L. Lewis, Satinder Singh

Learning to flexibly follow task instructions in dynamic environments poses interesting challenges for reinforcement learning agents. We focus here on the problem of learning control flow that deviates from a strict step-by-step execution of instructions -- that is, control flow that may skip forward over parts of the instructions or return backward to previ

Heinz H. Bauschke, Shambhavi Singh, Xianfu Wang

The problem of finding a best approximation pair of two sets, which in turn generalizes the well known convex feasibility problem, has a long history that dates back to work by Cheney and Goldstein in 1959. In 2018, Aharoni, Censor, and Jiang revisited this problem and proposed an algorithm that can be used when the two sets are finite intersections of halfs

Manuel Dubinsky, César Massri, Gabriel Taubin

Consider a connected graph $G$ and let $T$ be a spanning tree of $G$. Every edge $e \in G-T$ induces a cycle in $T \cup \{e\}$. The intersection of two distinct such cycles is the set of edges of $T$ that belong to both cycles. We consider the problem of finding a spanning tree that has the least number of such non-empty intersections.

Fernando Zanferrari Morais, Gabriel Matheus de Almeida, Leizer Pinto, Kleber Vieira Cardoso

The fifth-generation mobile evolution enables several transformations on Next Generation Radio Access Networks (NG-RAN). The RAN protocol stack is splitting into eight possible disaggregated options combined into three network units, i.e., Central, Distributed, and Radio. Besides that, further advances allow the RAN software to be virtualized on top of gener

Chirag Agarwal, Himabindu Lakkaraju, Marinka Zitnik

As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes important to ensure that these representations are fair and stable. In this work, we establish a key connection between counterfactual fairness and stability and leverage it to propose a novel framework, NIFTY (uNIfying Fairness and

Huichen Li, Linyi Li, Xiaojun Xu, Xiaolu Zhang

Gradient estimation and vector space projection have been studied as two distinct topics. We aim to bridge the gap between the two by investigating how to efficiently estimate gradient based on a projected low-dimensional space. We first provide lower and upper bounds for gradient estimation under both linear and nonlinear projections, and outline checkable

Sankha Narayan Guria, Jeffrey S. Foster, David Van Horn

In recent years, researchers have explored component-based synthesis, which aims to automatically construct programs that operate by composing calls to existing APIs. However, prior work has not considered efficient synthesis of methods with side effects, e.g., web app methods that update a database. In this paper, we introduce RbSyn, a novel type- and effec

Yves-Laurent Kom Samo

We propose a novel estimator of the mutual information between two ordinal vectors $x$ and $y$. Our approach is inductive (as opposed to deductive) in that it depends on the data generating distribution solely through some nonparametric properties revealing associations in the data, and does not require having enough data to fully characterize the true joint

M. Cristina Rabello Soares, Frédéric Baudin, Vanessa G. Teixeira

Several attempts have been made to observe whether solar flares excite acoustic modes since Charles Wolff suggested this possibility almost fifty years ago. We look for the impact of flares on the amplitude of solar acoustic modes and other effects that are also affecting the mode amplitude. Solar acoustic mode amplitudes are known to be sensitive to magneti

Yixin Lin, Austin S. Wang, Eric Undersander, Akshara Rai

Manipulation tasks, like loading a dishwasher, can be seen as a sequence of spatial constraints and relationships between different objects. We aim to discover these rules from demonstrations by posing manipulation as a classification problem over a graph, whose nodes represent task-relevant entities like objects and goals, and present a graph neural network

Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo

Across machine learning, the use of curricula has shown strong empirical potential to improve learning from data by avoiding local optima of training objectives. For reinforcement learning (RL), curricula are especially interesting, as the underlying optimization has a strong tendency to get stuck in local optima due to the exploration-exploitation trade-off

Hong Sun, Vincenzo De Florio

The population of the elderly people has kept increasing rapidly over the world in the past decades. Solutions that are able to effectively support the elderly people to live independently at their home are thus urgently needed. Ambient assisted living (AAL) aims to provide products and services with ambient intelligence to build a safe environment around pe

Zhuolin Yang, Zhaoxi Chen, Tiffany Cai, Xinyun Chen

Adversarial examples have appeared as a ubiquitous property of machine learning models where bounded adversarial perturbation could mislead the models to make arbitrarily incorrect predictions. Such examples provide a way to assess the robustness of machine learning models as well as a proxy for understanding the model training process. Extensive studies try

Alireza Karduni, Ryan Wesslen, Douglas Markant, Wenwen Dou

Images are an indispensable part of the news content we consume. Highly emotional images from sources of misinformation can greatly influence our judgements. We present two studies on the effects of emotional facial images on users' perception of bias in news content and the credibility of sources. In study 1, we investigate the impact of happy and angry

Federico Cattorini, Bruno Giacomazzo, Francesco Haardt, Monica Colpi

We perform the first suite of fully general relativistic magnetohydrodynamic simulations of spinning massive black hole binary mergers. We consider binary black holes with spins of different magnitudes aligned to the orbital angular momentum, which are immersed in a hot, magnetized gas cloud. We investigate the effect of the spin and degree of magnetization

Jens O. Andersen

Magnetic catalysis is the enhancement of a condensate due to the presence of an external magnetic field. Magnetic catalysis at $T=0$ is a robust phenomenon in low-energy theories and models of QCD as well as in lattice simulations. We review the underlying physics of magnetic catalysis from both perspectives. The quark-meson model is used as a specific examp

Nils T. Basse

We study the global, i.e. radially averaged, high Reynolds number (asymptotic) scaling of streamwise turbulence intensity squared defined as ${I^2=\overline{u^2}/U^2}$, where $u$ and $U$ are the fluctuating and mean velocities, respectively (overbar is time averaging). The investigation is based on the mathematical abstraction that the logarithmic region in

Naoya Takeishi, Alexandros Kalousis

Integrating physics models within machine learning models holds considerable promise toward learning robust models with improved interpretability and abilities to extrapolate. In this work, we focus on the integration of incomplete physics models into deep generative models. In particular, we introduce an architecture of variational autoencoders (VAEs) in wh

Miloš Savić, Jasna Atanasijević, Dušan Jakovetić, Nataša Krejić

Big data methods are becoming an important tool for tax fraud detection around the world. Unsupervised learning approach is the dominant framework due to the lack of label and ground truth in corresponding data sets although these methods suffer from low interpretability. HUNOD, a novel hybrid unsupervised outlier detection method for tax evasion risk manage

Robert Boltje, Deniz Yılmaz

We give sufficient conditions on $p$-blocks of $p$-nilpotent groups over $\mathbb{F}_p$ to be splendidly Rickard equivalent and $p$-permutation equivalent to their Brauer correspondents. The paper also contains Galois descent results on $p$-permutation modules and $p$-permutation equivalences that hold for arbitrary groups.

Ufuk Altun, Gunes Karabulut Kurt, Enver Ozdemir

In conventional communication systems, any interference between two communicating points is regarded as unwanted noise since it distorts the received signals. On the other hand, allowing simultaneous transmission and intentionally accepting the interference of signals and even benefiting from it have been considered for a range of wireless applications. As p

Anna Vershynina

We present a genuine coherence measure based on a quasi-relative entropy as a difference between quasi-entropies of the dephased and the original states. The measure satisfies non-negativity and monotonicity under genuine incoherent operations (GIO). It is strongly monotone under GIO in two- and three-dimensions, or for pure states in any dimension, making i

Sourav Chatterjee

Consider a deterministically growing surface of any dimension, where the growth at a point is an arbitrary nonlinear function of the heights at that point and its neighboring points. Assuming that this nonlinear function is monotone, invariant under the symmetries of the lattice, equivariant under constant shifts, and twice continuously differentiable, it is

Teodoro Alamo, Daniel G. Reina, Pablo Millán Gata, Victor M. Preciado

This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases.

Michael A. Hedderich, Lukas Lange, Dietrich Klakow

Distant supervision allows obtaining labeled training corpora for low-resource settings where only limited hand-annotated data exists. However, to be used effectively, the distant supervision must be easy to gather. In this work, we present ANEA, a tool to automatically annotate named entities in texts based on entity lists. It spans the whole pipeline from

Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski

A popular assumption for out-of-distribution generalization is that the training data comprises sub-datasets, each drawn from a distinct distribution; the goal is then to "interpolate" these distributions and "extrapolate" beyond them -- this objective is broadly known as domain generalization. A common belief is that ERM can interpolate but

Aaron Calderon, James Farre

We extend Mirzakhani's conjugacy between the earthquake and horocycle flows to a bijection, demonstrating conjugacies between these flows on all strata and exhibiting an abundance of new ergodic measures for the earthquake flow. The structure of our map indicates a natural extension of the earthquake flow to an action of the the upper-triangular subgroup

Zhen Lin, Nicholas Huang, Camille Avestruz, W. L. Kimmy Wu

Galaxy clusters identified from the Sunyaev Zel'dovich (SZ) effect are a key ingredient in multi-wavelength cluster-based cosmology. We present a comparison between two methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding and a method using Convolutional Neural Networks (CNN). We further implement and show resu

Stephanie Tonnesen, Jeremiah P. Ostriker

The simplest scheme for predicting real galaxy properties after performing a dark matter simulation is to rank order the real systems by stellar mass and the simulated systems by halo mass and then simply assume monotonicity - that the more massive halos host the more massive galaxies. This has had some success, but we study here if a better motivated and mo

R. M. González Delgado, L. A. Díaz-García, A. de Amorim, G. Bruzual

J-PAS will soon start imaging 8000 deg2 of the northern sky with its unique set of 56 filters (R $\sim$ 60). Before, we observed 1 deg2 on the AEGIS field with an interim camera with all the J-PAS filters. With this data (miniJPAS), we aim at proving the scientific potential of J-PAS to identify and characterize the galaxy populations with the goal of perfor

M. P. Bento

In a model with more than one scalar doublet, the parameter space encloses both physical and unphysical information. Invariant theory provides a detailed description of the counting and characterization of the physical parameter space. The Hilbert series for the 3HDM is computed for the first time using partition analysis, in particular Omega calculus, givin

M. M. Guzzo, L. J. F. Leite, S. W. P. Novelo, O. L. G. Peres

It has been commonly assumed that pseudoscalar contributions to the leptonic decay of charged mesons, like pions and kaons, is strongly constrained due to the helicity suppression present in the ratio $R_{l/l'} = Γ(P \rightarrow lν[γ])/Γ(P \rightarrow l'ν[γ])$, where $P$ are the charged pseudoscalar meson and $l,l' = e,μ,τ$. Here we show that if

Tomohiro Hashizume, Gregory Bentsen, Sebastian Weber, Andrew J. Daley

Fast scramblers are dynamical quantum systems that produce many-body entanglement on a timescale that grows logarithmically with the system size $N$. We propose and investigate a family of deterministic, fast scrambling quantum circuits realizable in near-term experiments with arrays of neutral atoms. We show that three experimental tools -- nearest-neighbou

Aseem Paranjape, Ravi K. Sheth

We study the radial acceleration relation (RAR) between the total ($a_{\rm tot}$) and baryonic ($a_{\rm bary}$) centripetal acceleration profiles of central galaxies in the cold dark matter (CDM) paradigm. We analytically show that the RAR is intimately connected with the physics of the quasi-adiabatic relaxation of dark matter in the presence of baryons in

P. V. F. Edelmann, L. Horst, J. P. Berberich, R. Andrassy

Accurate simulations of flows in stellar interiors are crucial to improving our understanding of stellar structure and evolution. Because the typically slow flows are merely tiny perturbations on top of a close balance between gravity and the pressure gradient, such simulations place heavy demands on numerical hydrodynamics schemes. We demonstrate how discre

Sudhir R. Ghorpade, Rakhi Pratihar, Tovohery H. Randrianarisoa

We consider a $q$-analogue of abstract simplicial complexes, called $q$-complexes, and discuss the notion of shellability for such complexes. It is shown that $q$-complexes formed by independent subspaces of a $q$-matroid are shellable. Further, we explicitly determine the homology of $q$-complexes corresponding to uniform $q$-matroids. We also outline some

Juan Esteban Rodríguez Camargo

We give a new proof of Faltings's p-adic Eichler-Shimura decomposition of the modular curves via BGG methods and the Hodge-Tate period map. The key property is the relation between the Tate module and the Faltings extension, which was already used in the original proof. Then, we construct overconvergent Eichler-Shimura (ES) maps for the modular curves pr

Sitan Chen, Jerry Li, Ryan O'Donnell

We revisit the basic problem of quantum state certification: given copies of unknown mixed state $ρ\in\mathbb{C}^{d\times d}$ and the description of a mixed state $σ$, decide whether $σ= ρ$ or $\|σ- ρ\|_{\mathsf{tr}} \ge ε$. When $σ$ is maximally mixed, this is mixedness testing, and it is known that $Ω(d^{Θ(1)}/ε^2)$ copies are necessary, where the exact ex

Sebastian Munoz

We study the existence of classical solutions to a broad class of local, first order, forward-backward Extended Mean Field Games systems, that includes standard Mean Field Games, Mean Field Games with congestion, and mean field type control problems. We work with a strictly monotone cost that may be fully coupled with the Hamiltonian, which is assumed to hav

A. Caragea, D. G. Lee, J. Maly, G. Pfander

Until recently, applications of neural networks in machine learning have almost exclusively relied on real-valued networks. It was recently observed, however, that complex-valued neural networks (CVNNs) exhibit superior performance in applications in which the input is naturally complex-valued, such as MRI fingerprinting. While the mathematical theory of rea

Ana de Almeida Borges, Joost J. Joosten

Vardanyan's Theorems state that $\mathsf{QPL}(\mathsf{PA})$ - the quantified provability logic of Peano Arithmetic - is $Π^0_2$ complete, and in particular that this already holds when the language is restricted to a single unary predicate. Moreover, Visser and de Jonge generalized this result to conclude that it is impossible to computably axiomatize th

Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan

We present a method that synthesizes novel views of complex scenes by interpolating a sparse set of nearby views. The core of our method is a network architecture that includes a multilayer perceptron and a ray transformer that estimates radiance and volume density at continuous 5D locations (3D spatial locations and 2D viewing directions), drawing appearanc

Kenneth Borup, Lars N. Andersen

Knowledge distillation is classically a procedure where a neural network is trained on the output of another network along with the original targets in order to transfer knowledge between the architectures. The special case of self-distillation, where the network architectures are identical, has been observed to improve generalization accuracy. In this paper

Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl

How do we build a general and broad object detection system? We use all labels of all concepts ever annotated. These labels span diverse datasets with potentially inconsistent taxonomies. In this paper, we present a simple method for training a unified detector on multiple large-scale datasets. We use dataset-specific training protocols and losses, but share

Mehdi Abdi

A compact scheme for the preparation of macroscopic multipartite entanglement is proposed and analyzed. In this scheme the vibrational modes of a mechanical resonator constitute continuous variable (CV) subsystems that entangle to each other as a result of their interaction with a two-level system (TLS). By properly driving the TLS, we show that a selected s

Jeffrey Galkowski, David Lafontaine, Euan A. Spence, Jared Wunsch

Over the last ten years, results from [Melenk-Sauter, 2010], [Melenk-Sauter, 2011], [Esterhazy-Melenk, 2012], and [Melenk-Parsania-Sauter, 2013] decomposing high-frequency Helmholtz solutions into "low"- and "high"-frequency components have had a large impact in the numerical analysis of the Helmholtz equation. These results have been proved

CMS Collaboration

Jet production in lead-lead (PbPb) and proton-proton (pp) collisions at a nucleon-nucleon center-of-mass energy of 5.02 TeV is studied with the CMS detector at the LHC, using PbPb and pp data samples corresponding to integrated luminosities of 404 $μ$b$^{-1}$ and 27.4 pb$^{-1}$, respectively. Jets with different areas are reconstructed using the anti-$k_\mat

Jia-Huei Jiang, Jian-Sheng Wang

This erratum aims to correct 1) the wrong expressions, 2) some typographical errors, 3) some erroneous points made in discussion of the disparity of heat flux ratios between our full RPA model and the local conductivity model, and 4) the lower bound of the characteristic distance scale comparable to the graphene thermal length, appearing in the original pape

Marion Jeannin

Let G be a reductive group over an algebraically closed field k of separably good characteristic p>0 for G. Under these assumptions a Springer isomorphism from the reduced nilpotent scheme of the Lie algebra of G to the reduced unipotent scheme of G always exists. This allows to integrate any p-nilpotent element of Lie(G) into a unipotent element of G. One s

Natan T'Joens, Jasper De Bock

We consider three different types of global uncertainty models for discrete-time stochastic processes: measure-theoretic upper expectations, game-theoretic upper expectations and axiomatic upper expectations. The last two are known to be identical. We show that they coincide with measure-theoretic upper expectations on two distinct domains: monotone pointwis

Bruno Brito, Michael Everett, Jonathan P. How, Javier Alonso-Mora

Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the environment conditions are continuously changing. Local trajectory optimization methods, such as model predictive control (MPC), can deal with those changes but require global guidance,

Panayotis Smyrnelis

We establish a comparison principle providing accurate upper bounds for the modulus of vector valued minimizers of an energy functional, associated when the potential is smooth, to elliptic gradient systems. Our assumptions are very mild: we assume that the potential is lower semicontinuous, and satisfies a monotonicity condition in a neighborhood of its min

Isaac A. Spiegel

Hybrid systems have steadily grown in popularity over the last few decades because they ease the task of modeling complicated nonlinear systems. Legged locomotion, robotic manipulation, and additive manufacturing are representative examples of systems benefiting from hybrid modeling. They are also prime examples of repetitive processes; gait cycles in walkin

Emmanuel Abbe, Shuangping Li, Allan Sly

We consider the symmetric binary perceptron model, a simple model of neural networks that has gathered significant attention in the statistical physics, information theory and probability theory communities, with recent connections made to the performance of learning algorithms in Baldassi et al. '15. We establish that the partition function of this mode

Apostolos Chalkis, Vissarion Fisikopoulos, Marios Papachristou, Elias Tsigaridas

We introduce Reflective Hamiltonian Monte Carlo (ReHMC), an HMC-based algorithm, to sample from a log-concave distribution restricted to a convex body. We prove that, starting from a warm start, the walk mixes to a log-concave target distribution $π(x) \propto e^{-f(x)}$, where $f$ is $L$-smooth and $m$-strongly-convex, within accuracy $\varepsilon$ after $\

Chloe Ya Gao, David T. Limmer

We introduce a thermodynamically consistent, minimal stochastic model for complementary logic gates built with field-effect transistors. We characterize the performance of such gates with tools from information theory and study the interplay between accuracy, speed, and dissipation of computations. With a few universal building blocks, such as the NOT and NA

Momina Masood, Marriam Nawaz, Khalid Mahmood Malik, Ali Javed

Easy access to audio-visual content on social media, combined with the availability of modern tools such as Tensorflow or Keras, open-source trained models, and economical computing infrastructure, and the rapid evolution of deep-learning (DL) methods, especially Generative Adversarial Networks (GAN), have made it possible to generate deepfakes to disseminat

Richárd Balka, Márton Elekes, Viktor Kiss

We say that $E$ is a microset of the compact set $K\subset \mathbb{R}^d$ if there exist sequences $λ_n\geq 1$ and $u_n\in \mathbb{R}^d$ such that $(λ_n K + u_n ) \cap [0,1]^d$ converges to $E$ in the Hausdorff metric, and moreover, $E \cap (0, 1)^d \neq \emptyset$. The main result of the paper is that for a non-empty set $A\subset [0,d]$ there is a compact s

Mao-Jun Yan, Fang-Zheng Peng, Mario Sánchez Sánchez, Manuel Pavon Valderrama

Early speculations about the existence of heavy hadron molecules were grounded on the idea that light-meson exchanges forces could lead to binding. In analogy to the deuteron, the light-mesons usually considered include the pion, sigma, rho and omega, but not the axial meson $a_1(1260)$. Though it has been argued in the past that the coupling of the axial me

Richard S. J. Tol

I propose a new conceptual framework to disentangle the impacts of weather and climate on economic activity and growth: A stochastic frontier model with climate in the production frontier and weather shocks as a source of inefficiency. I test it on a sample of 160 countries over the period 1950-2014. Temperature and rainfall determine production possibilitie

Pietro Lombardi, Maja Colautti, Rocco Duquennoy, Ghulam Murtaza

Single molecules in solid-state matrices have been proposed as sources of single-photon Fock states back 20 years ago. Their success in quantum optics and in many other research fields stems from the simple recipes used in the preparation of samples, with hundreds of nominally identical and isolated molecules. Main challenges as of today for their applicatio

Fei Gao, Joannis Papavassiliou, Jan M. Pawlowski

We present a comprehensive study of the quark sector of $2+1$ flavour QCD, based on a self-consistent treatment of the coupled system of Schwinger-Dyson equations for the quark propagator and the full quark-gluon vertex. The individual form factors of the quark-gluon vertex are expressed in a special tensor basis obtained from a set of gauge-invariant operat

Machiel van Frankenhuijsen, Clayton Moore Williams

Using ultraproduct techniques we define a nonstandard Minkowski dimension which exists for all bounded sets and which has the property that $\dim(A\times B)=\dim(A)+\dim(B).$ That is, our new dimension is product-summable. To illustrate our theorem we generalize an example of Falconer's to show that the standard upper Minkowski dimension, as well as the

ATLAS Collaboration

The ATLAS experiment at the Large Hadron Collider (LHC) employs a trigger system consisting of a first-level hardware trigger (L1) and a software-based high-level trigger. The L1 muon trigger system selects muon candidates, assigns them to the correct LHC bunch crossing and classifies them into one of six transverse-momentum threshold classes. The L1 muon tr

Richárd Balka, Márton Elekes, Viktor Kiss

The lower dimension $\dim_L$ is the dual concept of the Assouad dimension. As it fails to be monotonic, Fraser and Yu introduced the modified lower dimension $\dim_{ML}$ by making the lower dimension monotonic with the simple formula $\dim_{ML} X=\sup\{\dim_L E: E\subset X\}$. As our first result we prove that the modified lower dimension is finitely stable

CMS Collaboration

The first evidence for X(3872) production in relativistic heavy ion collisions is reported. The X(3872) production is studied in lead-lead (PbPb) collisions at a center-of-mass energy of $\sqrt{s_\mathrm{NN}} =$ 5.02 TeV per nucleon pair, using the decay chain X(3872) $\to$ J$/ψ\, π^+π^- \to$ $μ^+μ^-π^+π^-$. The data were recorded with the CMS detector in 20

Furkan Sezer, Hossein Khazaei, Ceyhun Eksin

We consider linear-quadratic Gaussian (LQG) games in which players have quadratic payoffs that depend on the players' actions and an unknown payoff-relevant state, and signals on the state that follow a Gaussian distribution conditional on the state realization. An information designer decides the fidelity of information revealed to the players in order

Michael Dymond, Vojtěch Kaluža

We introduce a hierachy of equivalence relations on the set of separated nets of a given Euclidean space, indexed by concave increasing functions $ϕ\colon (0,\infty)\to(0,\infty)$. Two separated nets are called $ϕ$-displacement equivalent if, roughly speaking, there is a bijection between them which, for large radii $R$, displaces points of norm at most $R$

Juan Sebastian Bruzzone, Robert Weryk, Diego Janches, Carsten Baumann

We present observations of the sudden outburst of the $α$ Carinid meteor shower recorded with the Southern Argentina Agile MEteor Radar-Orbital System (SAAMER-OS) near the South Toroidal sporadic region. The outburst peaked between 21 UT and 22 UT on October 14, 2020 and lasted 7 days $(199^{\circ}\leqλ_{\odot}\leq 205^{\circ})$ with a mean Sun-centered geoc

Gregory W. Benton, Wesley J. Maddox, Sanae Lotfi, Andrew Gordon Wilson

With a better understanding of the loss surfaces for multilayer networks, we can build more robust and accurate training procedures. Recently it was discovered that independently trained SGD solutions can be connected along one-dimensional paths of near-constant training loss. In this paper, we show that there are mode-connecting simplicial complexes that fo

Christina Kaiser, Oskar J. Sandberg, Nasim Zarrabi, Wei Li

In crystalline semiconductors, absorption onset sharpness is characterized by temperature dependent Urbach energies. These energies quantify the static, structural disorder causing localized exponential-tail states, and dynamic disorder from electron-phonon scattering. Applicability of this exponential-tail model to disordered solids has been long debated. N

Amuthan A. Ramabathiran, Prabhu Ramachandran

We introduce a class of Sparse, Physics-based, and partially Interpretable Neural Networks (SPINN) for solving ordinary and partial differential equations (PDEs). By reinterpreting a traditional meshless representation of solutions of PDEs we develop a class of sparse neural network architectures that are partially interpretable. The SPINN model we propose h

Costantino Budroni, Adán Cabello, Otfried Gühne, Matthias Kleinmann

A central result in the foundations of quantum mechanics is the Kochen-Specker theorem. In short, it states that quantum mechanics is in conflict with classical models in which the result of a measurement does not depend on which other compatible measurements are jointly performed. Here compatible measurements are those that can be implemented simultaneously

Rita Parada Ramos, Patrícia Pereira, Helena Moniz, Joao Paulo Carvalho

Deep neural networks have achieved state-of-the-art results in various vision and/or language tasks. Despite the use of large training datasets, most models are trained by iterating over single input-output pairs, discarding the remaining examples for the current prediction. In this work, we actively exploit the training data, using the information from near

Piotr Lebiedowicz

We present first predictions of the cross sections and differential distributions for the exclusive reaction $pp \to pp K^{*0} \bar{K}^{*0}$ contributing to the $K^{+} K^{-} π^{+} π^{-}$ channel. The amplitudes for the reaction are formulated within the nonperturbative tensor-pomeron approach. We consider separately the $f_{2}(1950)$ $s$-channel exchange mec

Waqas Ali, Muhammad Saleem, Bin Yao, Aidan Hogan

RDF has seen increased adoption in recent years, prompting the standardization of the SPARQL query language for RDF, and the development of local and distributed engines for processing SPARQL queries. This survey paper provides a comprehensive review of techniques and systems for querying RDF knowledge graphs. While other reviews on this topic tend to focus

Andrey A Popov, Adrian Sandu

Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ensemble Kalman filter (MFEnKF) recently developed by the authors combines a full-order physical model and a hiera

Anupam K. Singh, Sanjay Singh, Dhananjai Pandey

Precursor phenomena observed prior to the martensite phase transition plays a critical role towards the understanding of important technological properties of shape memory and magnetic shape memory alloys (MSMAs). The premartensite (PM) phase, considered as the precursor state of the martensite is proven to be a thermodynamically stable phase recently (Natur

Alexander V. Gramolin, Rebecca L. Russell

A puzzling discrepancy exists between the values of the proton charge radius obtained using different experimental techniques: elastic electron-proton scattering and spectroscopy of electronic and muonic hydrogen. The proton radius is defined through the slope of the electric form factor, $G_E(Q^2)$, at zero four-momentum transfer, which is inaccessible in s

Ryan G. McClarren, James A. Rossmanith, Minwoo Shin

The thermal radiative transfer (TRT) equations form an integro-differential system that describes the propagation and collisional interactions of photons. Computing accurate and efficient numerical solutions TRT are challenging for several reasons, the first of which is that TRT is defined on a high-dimensional phase. In order to reduce the dimensionality of

Ewoud Vissers, Stijn Poelman, Camiel Op de Beeck, Kasper Van Gasse

Integrated semiconductor mode-locked lasers have shown promise in many applications and are readily fabricated using generic InP photonic integration platforms. However, the passive waveguides offered in such platforms have relatively high linear and nonlinear losses that limit the performance of these lasers. By extending such lasers with, for example, an e

Rodrigo Nogueira, Zhiying Jiang, Jimmy Lin

The ability to perform arithmetic tasks is a remarkable trait of human intelligence and might form a critical component of more complex reasoning tasks. In this work, we investigate if the surface form of a number has any influence on how sequence-to-sequence language models learn simple arithmetic tasks such as addition and subtraction across a wide range o

Junchao Zhang, Jed Brown, Satish Balay, Jacob Faibussowitsch

PetscSF, the communication component of the Portable, Extensible Toolkit for Scientific Computation (PETSc), is designed to provide PETSc's communication infrastructure suitable for exascale computers that utilize GPUs and other accelerators. PetscSF provides a simple application programming interface (API) for managing common communication patterns in s

Josiah N. Purdum, Zhong-Yi Lin, Bryce T. Bolin, Kritti Sharma

We observed Episodically Active Asteroid (6478) Gault in 2020 with multiple telescopes in Asia and North America and have found that it is no longer active after its recent outbursts at the end of 2018 and start of 2019. The inactivity during this apparation allowed us to measure the absolute magnitude of Gault of H_r = 14.63 +/- 0.02, G_r = 0.21 +/- 0.02 fr

Jack Sutton, Golnaz Shahtahmassebi, Haroldo V. Ribeiro, Quentin S. Hanley

We investigated daily COVID-19 cases and deaths in the 337 lower tier local authority regions in England and Wales to better understand how the disease propagated over a 15-month period. Population density scaling models revealed residual variance and skewness to be sensitive indicators of the dynamics of propagation. Lockdowns and schools reopening triggere

Sebastian Franke, Juanjuan Ren, Marten Richter, Andreas Knorr

We demonstrate a fundamental breakdown of the photonic spontaneous emission (SE) formula derived from Fermi's golden rule, in absorptive and amplifying media, where one assumes the SE rate scales with the local photon density of states, an approach often used in more complex, semiclassical nanophotonics simulations. Using a rigorous quantization of the m

Noriyoshi Fukaya, Masayuki Hayashi

We consider the following nonlinear Schrödinger equation with derivative: \begin{equation} iu_t =-u_{xx} -i |u|^{2}u_x -b|u|^4u , \quad (t,x) \in \mathbb{R}\times\mathbb{R}, \ b \in\mathbb{R}. \end{equation} If $b=0$, this equation is a gauge equivalent form of the well-known derivative nonlinear Schrödinger (DNLS) equation. The soliton profile of DNLS satis