arXiv papers from December 2021

Adrien Boyer, Jean-Claude Picaud

We introduce the Riesz operator in the context of Gromov hyperbolic groups in order to investigate a one parameter family of non unitary boundary Hilbertian representations of hyperbolic groups. We prove asymptotic Schur's relations, the latter being the main result of this paper. Up to normalization, the Riesz operator plays the role in the context of hyper

Wolfgang Bietenholz

Srinivasa Ramanujan was a great self-taught Indian mathematician, who died a century ago, at the age of only 32, one year after returning from England. Among his numerous achievements is the assignment of sensible, finite values to divergent series, which correspond to Riemann's $\zeta$-function with negative integer arguments. He hardly left any explanation

Vivek Subramanian, Dhanasekar Sundararaman

Neural machine translation (NMT) systems aim to map text from one language into another. While there are a wide variety of applications of NMT, one of the most important is translation of natural language. A distinguishing factor of natural language is that words are typically ordered according to the rules of the grammar of a given language. Although many a

Ronald E. Robertson, Jon Green, Damian J. Ruck, Katherine Ognyanova

If popular online platforms systematically expose their users to partisan and unreliable news, they could potentially contribute to societal issues like rising political polarization. This concern is central to the echo chamber and filter bubble debates, which critique the roles that user choice and algorithmic curation play in guiding users to different onl

Jian Yan, Xianyang Zhang

Motivated by the increasing use of kernel-based metrics for high-dimensional and large-scale data, we study the asymptotic behavior of kernel two-sample tests when the dimension and sample sizes both diverge to infinity. We focus on the maximum mean discrepancy (MMD) using isotropic kernel, including MMD with the Gaussian kernel and the Laplace kernel, and t

Nimit S. Sohoni, Maziar Sanjabi, Nicolas Ballas, Aditya Grover

While neural networks have shown remarkable success on classification tasks in terms of average-case performance, they often fail to perform well on certain groups of the data. Such group information may be expensive to obtain; thus, recent works in robustness and fairness have proposed ways to improve worst-group performance even when group labels are unava

N. Karjanto

This article provides brief guidance for a successful graduate teaching assistantship at our school. Although the components mentioned in this article are primarily aimed at mathematics courses designated as the "Basic Science and Mathematics" (BSM) modules with multiple sections, the principle can also be applied and adapted to other courses and institution

A. Arbey, M. Battaglia, A. Djouadi, F. Mahmoudi

The study of the Higgs boson properties offers compelling perspectives for testing the effects of physics beyond the Standard Model and has deep implications for the LHC program and future colliders. Accurate determinations of the Higgs boson properties can provide us with a distinctively precise picture of the Higgs sector, set tight bounds, and predict ran

James O. Chibueze, M. Caleb, L. Spitler, H. Ashkar

We report on a search for persistent radio emission from the one-off Fast Radio Burst (FRB) 20190714A, as well as from two repeating FRBs, 20190711A and 20171019A, using the MeerKAT radio telescope. For FRB 20171019A we also conducted simultaneous observations with the High Energy Stereoscopic System (H.E.S.S.) in very high energy gamma rays and searched for

Noirrit Kiran Chandra, Abhra Sarkar, John F. de Groot, Ying Yuan

The availability of electronic health records (EHR) has opened opportunities to supplement increasingly expensive and difficult to carry out randomized controlled trials (RCT) with evidence from readily available real world data. In this paper, we use EHR data to construct synthetic control arms for treatment-only single arm trials. We propose a novel nonpar

Afia Fairoose Abedin, Amirul Islam Al Mamun, Rownak Jahan Nowrin, Amitabha Chakrabarty

In recent times, a large number of people have been involved in establishing their own businesses. Unlike humans, chatbots can serve multiple customers at a time, are available 24/7 and reply in less than a fraction of a second. Though chatbots perform well in task-oriented activities, in most cases they fail to understand personalized opinions, statements o

Santanu Mondal, Tanmoy Bhattacharya, Rajan Gupta, Bálint Joó

We present our recent high precision calculations (Phys. Rev. D102 (2020) no.5, 054512 and JHEP 04 (2021) 044, JHEP 21 (2020) 004) of the first moment of nucleon isovector polarized, unpolarized and transversity distributions, i.e., momentum fraction, helicity and transversity moment, respectively. We use the standard method for the calculation of these mome

A. A. Araújo Filho

This work is devoted to study the behavior of massless particles within the context of curved spacetime. In essence, we investigate the consequences of the scale factor $C(\eta)$ of the Friedmann-Robertson-Walker metric in the Einstein-aether formalism to study photon-like particles. To do so, we consider the system within the canonical ensemble formalism in

Xiaoyang Shi, Hang Xiao, Weifeng Liu, Xi Chen

The distributed consensus mechanism is the backbone of the rapidly developing blockchain network. Blockchain platforms consume vast amounts of electricity based on the current consensus mechanism of Proof of Work. Here, we point out an advanced consensus mechanism named Proof of Stake that can eliminate the extensive energy consumption of the current PoW-bas

Xiuzhen Ye, Iñaki Esnaola, Samir M. Perlaza, Robert F. Harrison

Sparse stealth attack constructions that minimize the mutual information between the state variables and the observations are proposed. The attack construction is formulated as the design of a multivariate Gaussian distribution that aims to minimize the mutual information while limiting the Kullback-Leibler divergence between the distribution of the observat

Niels Borne, Amine Laaroussi

Given a scheme over a field endowed with a strict normal crossings divisor, we define strongly parabolic connections, consistently with the current terminology for Higgs bundles. When the weights are rational with prescribed denominators, we show that strongly parabolic connections correspond to holomorphic connections on the corresponding stack of roots. We

Samaa Gazzaz, Vishal Chakraborty, Faisal Nawab

Emerging edge applications require both a fast response latency and complex processing. This is infeasible without expensive hardware that can process complex operations -- such as object detection -- within a short time. Many approach this problem by addressing the complexity of the models -- via model compression, pruning and quantization -- or compressing

Ian Charlesworth, Brent Nelson

We establish several properties of the free Stein dimension, an invariant for finitely generated unital tracial $*$-algebras. We give formulas for its behaviour under direct sums and tensor products with finite dimensional algebras. Among a given set of generators, we show that (approximate) algebraic relations produce (non-approximate) bounds on the free St

Gianfranco Liberona, David Salas, Léonard von Niederhäusern

For drivers in ride-hailing companies, allocation within the city is paramount to get matched with rides. This decision depends on many factors, where some of them (such as demand and allocation of others) are unknown for the drivers, but are available for the company. In this work, we investigate whether it is beneficial or not for the ride-hailing company

Bogdan Alexandru Stoica, Swarup K. Sahoo, James R. Larus, Vikram S. Adve

Dynamic program slicing can significantly reduce the code developers need to inspect by narrowing it down to only a subset of relevant program statements. However, despite an extensive body of research showing its usefulness, dynamic slicing is still short from production-level use due to the high cost of runtime instrumentation. As an alternative, we propos

Xinke Deng, Junyi Geng, Timothy Bretl, Yu Xiang

This paper proposes a category-level 6D object pose and shape estimation approach iCaps, which allows tracking 6D poses of unseen objects in a category and estimating their 3D shapes. We develop a category-level auto-encoder network using depth images as input, where feature embeddings from the auto-encoder encode poses of objects in a category. The auto-enc

Serguei Barannikov, Ilya Trofimov, Nikita Balabin, Evgeny Burnaev

Comparison of data representations is a complex multi-aspect problem that has not enjoyed a complete solution yet. We propose a method for comparing two data representations. We introduce the Representation Topology Divergence (RTD), measuring the dissimilarity in multi-scale topology between two point clouds of equal size with a one-to-one correspondence be

Yangjun Ruan, Yann Dubois, Chris J. Maddison

Machine learning systems often experience a distribution shift between training and testing. In this paper, we introduce a simple variational objective whose optima are exactly the set of all representations on which risk minimizers are guaranteed to be robust to any distribution shift that preserves the Bayes predictor, e.g., covariate shifts. Our objective

Junichi Okamoto, Sajad Mirmohammadi

We investigate the microscopic dynamics at the initial stage of photoinduced phase transitions in tetrathiafulvalene-$p$-chloranil by exact diagonalization. We first show that the one-dimensional extended Peierls-Hubbard model exhibits a neutral phase with small ionicity and negligible dimerization and an ionic phase with moderate ionicity and dimerization.

M. M. Rahman, E. Malaia, A. C. Gurbuz, D. J. Griffin

RF sensors have been recently proposed as a new modality for sign language processing technology. They are non-contact, effective in the dark, and acquire a direct measurement of signing kinematic via exploitation of the micro-Doppler effect. First, this work provides an in depth, comparative examination of the kinematic properties of signing as measured by

Shaul Zemel

The Vahlen group gives a way for presenting the hyperbolic space of every dimension of a group acting via M\"{o}bius transformations. As Vahlen groups and paravector Vahlen groups are now defined over any field of characteristic different from 2, we establish analogous spaces on which they operate transitively as M\"{o}bius transformations, by defining appro

Carlo Marinelli

We obtain estimates on the first-order Malliavin derivative of mild solutions, evaluated at fixed points in time and space, to a class of parabolic dissipative stochastic PDEs on bounded domain of $\mathbb{R}^d$. In particular, such equations are driven by multiplicative Wiener noise and the nonlinear drift term is the superposition operator associated to a

Toby Godwin, Georgios Rizos, Alice Baird, Najla D. Al Futaisi

Despite advances in deep algorithmic music generation, evaluation of generated samples often relies on human evaluation, which is subjective and costly. We focus on designing a homogeneous, objective framework for evaluating samples of algorithmically generated music. Any engineered measures to evaluate generated music typically attempt to define the samples

Elena Soto-Calvo, Fiona R. Simmons, Anne-Marie Adams, Hannah N. Francis

This study examines the longitudinal relationships between home learning experiences and early number skills. The counting, number transcoding and calculation skills of 274 children were assessed in the penultimate term of preschool (Mage=4:0). Prior to these assessments, parents completed questionnaires that surveyed the frequency of the children's home lea

I. I. Mokhov

Frequency of warm and cold winters in the North Eurasian regions is analyzed from long-term data, depending on El Nino phenomena of different types. Frequencies of extremely warm and extremely cold winters for North Eurasian regions in different phases of El Ni\~no phenomena are compared. Potential predictability of anomalous winters in the El Ni\~no, La Ni\

F. H. B. Somhorst, R. van der Meer, M. Correa Anguita, R. Schadow

One of the core questions of quantum physics is how to reconcile the unitary evolution of quantum states, which is information-preserving and time-reversible, with evolution following the second law of thermodynamics, which, in general, is neither. The resolution to this paradox is to recognize that global unitary evolution of a multi-partite quantum state c

Giorgio Busoni

The extreme conditions in Neutron Stars make them ideal test facilities for fundamental interactions. A Neutron Star can capture Dark Matter via scattering. As a result of the scattering, Dark Matter kinetic energy is transferred to the star. An observational consequence of this can be the warming of old neutron stars to near-infrared temperatures. Different

Jahed Abedi, Luís Felipe Longo Micchi, Niayesh Afshordi

Being arguably the most massive binary black hole merger event observed to date, GW190521 deserves special attention. The exceptionally loud ringdown of this merger makes it an ideal candidate to search for gravitational wave echoes, a proposed smoking gun for the quantum structure of black hole horizons. We perform an unprecedented multi-pronged search for

Wyatt Adams, Anindya Ghoshroy, Durdu O. Guney

Imaging is indispensable for nearly every field of science, engineering, technology, and medicine. However, measurement noise and stochastic distortions pose fundamental limits to accessible spatiotemporal information despite impressive tools such as SIM, PALM/STORM, and STED microscopy. How to combat this challenge ideally has been an open question for deca

Thang T. Q. Lê, Adam S. Sikora

We develop a theory of stated SL(n)-skein modules, $S_n(M,N),$ of 3-manifolds $M$ marked with intervals $N$ in their boundaries. They consist of linear combinations of $n$-webs with ends in $N$, considered up to skein relations inspired by the relations of the Reshetikhin-Turaev theory. We prove that cutting $M$ along a disk resulting in a $3$-manifold $M'$

Chenghao Yang, Hongyuan Mei, Jason Eisner

The neural Hawkes process (Mei & Eisner, 2017) is a generative model of irregularly spaced sequences of discrete events. To handle complex domains with many event types, Mei et al. (2020a) further consider a setting in which each event in the sequence updates a deductive database of facts (via domain-specific pattern-matching rules); future events are then c

Zejiang Hou, Sun-Yuan Kung

Vision transformers (ViT) have recently attracted considerable attentions, but the huge computational cost remains an issue for practical deployment. Previous ViT pruning methods tend to prune the model along one dimension solely, which may suffer from excessive reduction and lead to sub-optimal model quality. In contrast, we advocate a multi-dimensional ViT

Abhiram Iyer, Karan Grewal, Akash Velu, Lucas Oliveira Souza

A key challenge for AI is to build embodied systems that operate in dynamically changing environments. Such systems must adapt to changing task contexts and learn continuously. Although standard deep learning systems achieve state of the art results on static benchmarks, they often struggle in dynamic scenarios. In these settings, error signals from multiple

Michał Jóźwikowski

We study the geometry of the second-order expansion of the extended end-point map for the sub-Riemannian geodesic problem. Translating the geometric reality into equations we derive new second-order necessary optimality conditions in sub-Riemannian Geometry. In particular, we find an ODE for velocity of an abnormal sub-Riemannian geodesics. It allows to divi

David W. Ash

Research in combinatorics has often explored the asymmetric simple exclusion process (ASEP). The ASEP, inspired by examples from statistical mechanics, involves particles of various species moving around a lattice. With the traditional ASEP particles of a given species can move but do not change species. In this paper a new combinatorial formalism, the DASEP

Angeliki Kamoutsi, Goran Banjac, John Lygeros

We consider large-scale Markov decision processes (MDPs) with an unknown cost function and employ stochastic convex optimization tools to address the problem of imitation learning, which consists of learning a policy from a finite set of expert demonstrations. We adopt the apprenticeship learning formalism, which carries the assumption that the true cost fun

Ole Christensen, Marzieh Hasannasab

Dynamical sampling deals with representations of a frame $\{ f_k \}_{k=1}^\infty$ as an orbit $\{ T^n \varphi \}_{n=0}^\infty$ of a linear and possibly bounded operator $T$ acting on the underlying Hilbert space. It is known that the desire of boundedness of the operator $T$ puts severe restrictions on the frame $\{ f_k \}_{k=1}^\infty$. The purpose of the p

Mathieu Dumberry

We present a model of the Cassini state of Mercury that comprises an inner core, a fluid core and a mantle. Our model includes inertial and gravitational torques between interior regions, and viscous and electromagnetic (EM) coupling at the boundaries of the fluid core. We show that the coupling between Mercury's interior regions is sufficiently strong that

Melissa M Fuentes

We consider a problem proposed by Linial and Wilf to determine the structure of graphs that allows the maximum number of $q$-colorings among graphs with $n$ vertices and $m$ edges. Let $T_r(n)$ denote the Tur\'{a}n graph - the complete $r$-partite graph on $n$ vertices with partition sizes as equal as possible. We prove that for all odd integers $q\geq 5$ an

Thanasis Karakasis, Eleftherios Papantonopoulos, Zi-Yu Tang, Bin Wang

We consider a $f(R)$ gravity theory in $(2+1)$-dimensions with a self-interacting scalar field non-minimally coupled to gravity. Without specifying the form of the $f(R)$ function, solving the field equations we find that the Ricci scalar receives a non-linear correction term which breaks the conformal invariance and leads to a massless black hole solution.

Ahmet Gungor, Muhammad Saif Ullah Khalid, Arman Hemmati

The unsteady hydrodynamics of two in-phase pitching foils arranged in side-by-side (parallel) configurations is examined for a range of Strouhal number and separation distance. Three distinct vortex patterns are identified in the Strohual number-separation distance phase maps, which include separated wake, merged wake, and transitional-merged wake. Furthermo

Ethan Cotterill, Nathan Pflueger, Naizhen Zhang

The {\it Weierstrass semigroup} of pole orders of meromorphic functions in a point $p$ of a smooth algebraic curve $C$ is a classical object of study; a celebrated problem of Hurwitz is to characterize which semigroups ${\rm S} \subset \mathbb{N}$ with finite complement are {\it realizable} as Weierstrass semigroups ${\rm S}= {\rm S}(C,p)$. In this note, we

Marco Benedetti, Enrico Ventura, Enzo Marinari, Giancarlo Ruocco

The Hebbian unlearning algorithm, i.e. an unsupervised local procedure used to improve the retrieval properties in Hopfield-like neural networks, is numerically compared to a supervised algorithm to train a linear symmetric perceptron. We analyze the stability of the stored memories: basins of attraction obtained by the Hebbian unlearning technique are found

Paul Syverson

We provide a rough sketch of a simple system design for exposure notification of COVID-19 infections based on copresence at cluster events -- locations and times where a threshold number of tested-positive (TP) individuals were present. Unlike other designs, such as DP3T or the Apple-Google exposure-notification system, this design does not track or notify b

Walter H. Baron

We explore the role of the dilaton field on higher derivative supergravity within the framework of Double Field Theory and use it to fix the Lorentz non covariant field redefinitions connecting the metric and dilaton fields with the duality multiplets.

Thomas Huckans, Peter Stine

As is common with the collection of astronomical data, signals are frequently dominated by noise. However, when performing FTs of light curves, re-binning data can improve the signal-to-noise ratio (SNR) at lower frequencies. Using data collected from the Kepler space telescope, we sequentially re-binned data three times to investigate the SNR improvement of

Simone Giannerini, Greta Goracci, Anders Rahbek

We consider bootstrap-based testing for threshold effects in non-linear threshold autoregressive (TAR) models. It is well-known that classic tests based on asymptotic theory tend to be oversized in the case of small, or even moderate sample sizes, or when the estimated parameters indicate non-stationarity, as often witnessed in the analysis of financial or c

Randy S. Conklin, Niayesh Afshordi

The existence of black hole horizons has not been strictly proven observationally, and indeed it may not be possible to do so. However, alternatives may be established by the observation of gravitational wave echoes that probe possible near-horizon structure. These echoes are proposed to be generated in exotic compact objects that are horizonless and feature

P. F. Pacchiarotti

The Countable Telescope Conjecture arose in the framework of stable homotopy theory, as a tool conceived to study the chromatic filtration. It turned out, however, to trigger extremely fertile research within the framework of Module Categories. The project aims at presenting an almost self-contained review of the recent work of Saroch on the Countable Telesc

Jeff Murugan

The study of 2-dimensional surfaces of constant curvature constitutes a beautiful branch of geometry with well-documented ties to the mathematical physics of integrable systems. A lesser known, but equally fascinating, fact is its connection to 2-dimensional gravity; specifically Jackiw-Teitelboim (JT) gravity, where the connection manifests through a coordi

Ufuk Aydemir, Jing Ren

With the recent progress in observations of astrophysical black holes, it has become more important to understand in detail the physics of strongly gravitating horizonless objects. If the objects identified in the observations are indeed horizonless and ultracompact, high curvature effects may become important, and their explorations may be intimately relate

Olivier Lennon

Q-balls -- whether in the single-field or multi-field context -- are usually studied in theories containing only one stabilising symmetry. However, this is not the most general scenario. In this paper, we study a class of theories with multiple symmetries. We consider both the traditional thin- and thick-wall limits of these theories, deriving sufficient con

Swetha Bhagwat, Costantino Pacilio, Enrico Barausse, Paolo Pani

Measuring the quasi-normal mode~(QNM) spectrum emitted by a perturbed black-hole~(BH) --~also known as BH spectroscopy~-- provides an excellent opportunity to test the predictions of general relativity in the strong-gravity regime. We investigate the prospects and precision of BH spectroscopy in massive binary black hole ringdowns, one of the primary science

Sanaea C. Rose, Smadar Naoz, Re'em Sari, Itai Linial

Most stellar evolution models predict that black holes (BHs) should not exist above approximately $50-70$ M$_\odot$, the lower limit of the pair-instability mass gap. However, recent LIGO/Virgo detections indicate the existence of BHs with masses at and above this threshold. We suggest that massive BHs, including intermediate mass black holes (IMBHs), can fo

Y. T. Yan, C. Henkel, K. M. Menten, Y. Gong

Molecular maser lines are signposts of high-mass star formation, probing excitation and kinematics of very compact regions in the close environment of young stellar objects and providing useful targets for trigonometric parallax measurements. Only a few NH$_{3}$ (9,6) masers were known so far, and their origin is still poorly understood. Here we aim to find

Yuzhu Wang, Bo Yang

Neutral excitations in fractional quantum Hall (FQH) fluids define the incompressibility of topological phases, a species of which can show graviton-like behaviors and are thus called the graviton modes (GMs). Here, we develop the microscopic theory for multiple GMs in FQH fluids and show explicitly that they are associated with the geometric fluctuation of

S. Cerci, D. Sunar Cerci, D. Lazic, G. Landsberg

We describe a proposal to add a set of very forward detectors to the CMS experiment for the high-luminosity era of the Large Hadron Collider to search for beyond the standard model long-lived particles, such as dark photons, heavy neutral leptons, axion-like particles, and dark Higgs bosons. The proposed subsystem is called FACET for Forward-Aperture CMS ExT

Lakshya Bhardwaj, Simone Giacomelli, Max Hubner, Sakura Schafer-Nameki

A relative theory is a boundary condition of a higher-dimensional topological quantum field theory (TQFT), and carries a non-trivial defect group formed by mutually non-local defects living in the relative theory. Prime examples are 6d N=(2,0) theories that are boundary conditions of 7d TQFTs, with the defect group arising from surface defects. In this paper

Tim Johnston, Sotirios Sabanis

In recent years tamed schemes have become an important technique for simulating SDEs and SPDEs whose continuous coefficients display superlinear growth. The taming method, which involves curbing the growth of the coefficients as a function of stepsize, has so far however not been adapted to preserve the monotonicity of the coefficients. This has arisen as an

Nicholas J. Irons, Meyer Scetbon, Soumik Pal, Zaid Harchaoui

Triangular flows, also known as Kn\"{o}the-Rosenblatt measure couplings, comprise an important building block of normalizing flow models for generative modeling and density estimation, including popular autoregressive flow models such as real-valued non-volume preserving transformation models (Real NVP). We present statistical guarantees and sample complexit

Iddo Drori, Sarah Zhang, Reece Shuttleworth, Leonard Tang

We demonstrate that a neural network pre-trained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates new questions at a human level. We automatically synthesize programs using few-shot learning and OpenAI's Codex transformer and execute them to solve course problems at 81% automatic accuracy. We curate a new d

Sichun Sun, Xing-Yu Yang, Yun-Long Zhang

The coherent oscillation of ultralight dark matter in the mass regime around $10^{-23}$ eV induces changes in gravitational potential with the frequency in the nanohertz range. This effect is known to produce a monochromatic signal in the pulsar timing residuals. Here we discuss a multifield scenario that produces a wide spectrum of frequencies, such that th

Florian Nortier

Particle physics models with extra dimensions of space (EDS's) and branes shed new light on electroweak and flavor hierarchies with a rich TeV scale phenomenology. This article highlights new model building issues with EDS's and branes, arising in the framework of weakly nonlocal field theories. It is shown that a brane-localized field is still delocalized i

Fan Zhou, Ping Li, Cun-Hui Zhang

Let $\bx_j = \btheta +\bep_j, j=1,...,n$, be observations of an unknown parameter $\btheta$ in a Euclidean or separable Hilbert space $\scrH$, where $\bep_j$ are noises as random elements in $\scrH$ from a general distribution. We study the estimation of $f(\btheta)$ for a given functional $f:\scrH\rightarrow \RR$ based on $\bx_j$'s. The key element of our a

Nana Geraldine Cabo Bizet, Yulier Jiménez Santana, Roberto Santos Silva

We consider a U(1) Gauged Linear Sigma Model (GLSM) with (2,2) supersymmetry, leading to a susy vacua of the resolved conifold. It possesses the non-Abelian global symmetry SU(2)xSU(2). A non-Abelian T-duality can be constructed which can be described by gauging the global non-Abelian symmetry. This leads to a dual action, in terms of the dual model Kaehler

Shashank Ranjan, Corey Toler-Franklin

We propose a 3-D material style transfer framework for reconstructing invisible (or faded) appearance properties in complex natural materials. Our algorithm addresses the technical challenge of transferring appearance properties from one object to another of the same material when both objects have intricate, noncorresponding color patterns. Eggshells, exosk

Xingbang Cui, Liping Zhang

The theory of eigenvalues and eigenvectors is one of the fundamental and essential components in tensor analysis. Computing the dominant eigenpair of an essentially nonnegative tensor is an important topic in tensor computation because of the critical applications in network resource allocations. In this paper, we consider the aforementioned topic and there

Helmut Lenzing, Hagen Meltzer, Shiquan Ruan

This present paper is devoted to the study of a class of Nakayama algebras $N_n(r)$ given by the path algebra of the equioriented quiver $\mathbb{A}_n$ subject to the nilpotency degree $r$ for each sequence of $r$ consecutive arrows. We show that the Nakayama algebras $N_n(r)$ for certain pairs $(n,r)$ can be realized as endomorphism algebras of tilting obje

Edward Shuryak, Ismail Zahed

This is the third paper on hadronic light front wave functions (LFWFs). We derive a light front Hamiltonian from first principles using the key features of the QCD vacuum at low resolution. In the first approximation, it gives transverse oscillator and longitudinal harmonic modes and yields the correct Regge trajectories. For heavy quarkonia, we compare its

Masoumeh Ebrahimzadeh, Kazem Haghnejad Azar

Let $X$ be an ordered vector space. The net $\{x_\alpha\}\subseteq X$ is semi unbounded order convergent to $x$ (in symbol $x_\alpha\xrightarrow{suo}x$), if there is a net $\{y_\beta\}$, possibly over a different index set, such that $y_\beta \downarrow 0$ and for every $\beta$ there exists $\alpha_0$ such that $\{\{\pm(x_\alpha - x)\}^u,y\}^l\subseteq \{y_\

Shri Prakash Dwivedi

Graph matching is the process of computing the similarity between two graphs. Depending on the requirement, it can be exact or inexact. Exact graph matching requires a strict correspondence between nodes of two graphs, whereas inexact matching allows some flexibility or tolerance during the graph matching. In this chapter, we describe an approximate inexact

Evren Özarslan, Magnus Herberthson

In a recent work, a method for the magnetic resonance (MR) measurement of the true diffusion propagator was introduced, which was subsequently implemented and validated for free diffusion on a benchtop MR scanner. Here, we provide a brief theoretical description of the method and discuss various experimental regimes.

M. Tacu, D. Bénisti

This paper provides a complete self-consistent nonlinear theory for electron plasma waves, within the framework of the adiabatic approximation. The theory applies whatever the variations of the wave amplitude, provided that they are slow enough, and it is also valid when the plasma is inhomogeneous and non stationary. Moreover, it accounts for: (i) the geome

Jiaming Wang, Weishi Yuan, Philip M. Singer, Rebecca W. Smaha

We use $^{79}$Br nuclear quadrupole resonance (NQR) to demonstrate that ultra slow lattice dynamics set in below the temperature scale set by the Cu-Cu super-exchange interaction $J$~($\simeq160$~K) in the kagome lattice Heisenberg antiferromagnet Zn-barlowite. The lattice completely freezes below 50~K, and $^{79}$Br NQR lineshapes become twice broader due t

Denis R. Candido, Michael E. Flatté

Surface electric (charge) noise influences spin defects due to fluctuation of the surface charge density and also the electrostatic potential at the crystal surface. Surprisingly, the two-point correlation function of both the charged particles' positions and the surface electrostatic potential strongly influences the power of the polynomial decay of the ele

Hassan Al-Zoubi

In this paper, we firstly investigate some relations regarding the first and the second Laplace operators corresponding to the third fundamental form III of a surface in the Euclidean space E3. Besides, we introduce the finite Chen type surfaces of revolution with nonvanishing Gauss curvature with respect to the third fundamental form. We present a special c

Teng Fei, Duong H. Phong, Sebastien Picard, Xiangwen Zhang

In this paper the dynamical stability of the Type IIA flow with no source near its stationary points is established. These stationary points had been shown previously by the authors to be Ricci-flat K\"ahler metrics on Calabi-Yau 3-folds. The dynamical stability of the Type IIA flow is then applied to prove the stability under symplectic deformations of the

Samin Yeasar Arnob, Riyasat Ohib, Sergey Plis, Doina Precup

Deep Reinforcement Learning (RL) is a powerful framework for solving complex real-world problems. Large neural networks employed in the framework are traditionally associated with better generalization capabilities, but their increased size entails the drawbacks of extensive training duration, substantial hardware resources, and longer inference times. One w

Samin Yeasar Arnob, Riashat Islam, Doina Precup

We hypothesize that empirically studying the sample complexity of offline reinforcement learning (RL) is crucial for the practical applications of RL in the real world. Several recent works have demonstrated the ability to learn policies directly from offline data. In this work, we ask the question of the dependency on the number of samples for learning from

Jakob Heiss, Josef Teichmann, Hanna Wutte

In practice, multi-task learning (through learning features shared among tasks) is an essential property of deep neural networks (NNs). While infinite-width limits of NNs can provide good intuition for their generalization behavior, the well-known infinite-width limits of NNs in the literature (e.g., neural tangent kernels) assume specific settings in which

Ioannis Dalianis, George P. Kodaxis

We investigate the cosmology of mini Primordial Black Holes (PBHs) produced by large density perturbations that collapse during a stiff fluid domination phase. Such a phase can be realized by a runaway-inflaton model that crosses an inflection point or a sharp feature at the last stage of inflation. Mini PBHs evaporate promptly and reheat the early universe.

Jiaqi Ma, Xingjian Zhang, Qiaozhu Mei

Multinomial Logit (MNL) is one of the most popular discrete choice models and has been widely used to model ranking data. However, there is a long-standing technical challenge of learning MNL from many real-world ranking data: exact calculation of the MNL likelihood of \emph{partial rankings} is generally intractable. In this work, we develop a scalable meth

Subhodeep Dey, Anoop Mutneja, Smarajit Karmakar

Active glassy systems can be thought of as simple model systems that imitate complex biological systems. Sometimes, it becomes crucial to estimate the amount of the activity present in such biological systems, such as predicting the progression rate of the cancer cells or the healing time of the wound. In this work, we study a model active glassy system to u

Laurent Freidel, Daniele Pranzetti, Ana-Maria Raclariu

In this paper we extract from a large-$r$ expansion of the vacuum Einstein's equations a dynamical system governing the time evolution of an infinity of higher-spin charges. Upon integration, we evaluate the canonical action of these charges on the gravity phase space. The truncation of this action to quadratic order and the associated charge conservation la

Aritra Chakravorty, William S. Cleveland, Patrick J. Wolfe

Designing scalable estimation algorithms is a core challenge in modern statistics. Here we introduce a framework to address this challenge based on parallel approximants, which yields estimators with provable properties that operate on the entirety of very large, distributed data sets. We first formalize the class of statistics which admit straightforward ca

Sílvia Casacuberta, Esra Suel, Seth Flaxman

In this paper we introduce a new problem within the growing literature of interpretability for convolution neural networks (CNNs). While previous work has focused on the question of how to visually interpret CNNs, we ask what it is that we care to interpret, that is, which layers and neurons are worth our attention? Due to the vast size of modern deep learni

Kun Wang, Pengfu Tian, Jingya Zhu

In this study, we explore the detectability of heavy Higgs bosons in the $pp \to b\bar{b}H/A \to b\bar{b}t\bar{t}$ channel at a 100 TeV hadron collider within the semi-constrained Next-to-Minimal Supersymmetric Standard Model (NMSSM). We calculate their production cross sections and decay branching ratios, comparing these with simulation results from existin

Feng Feng, Yu Jia, Deshan Yang

The universal fragmentation functions of gluon into the flavored quarkonia $B_c$ and (polarized) $B_c^*$ are computed within NRQCD factorization framework, at the lowest order in velocity expansion and strong coupling constant. It is mandatory to invoke the DGLAP renormalization program to render the NRQCD short-distance coefficients UV finite in a point-wis

Thibault Lahire

This technical report is devoted to explaining how the actor loss of soft actor critic is obtained, as well as the associated gradient estimate. It gives the necessary mathematical background to derive all the presented equations, from the theoretical actor loss to the one implemented in practice. This necessitates a comparison of the reparameterization tric

Aixin Pi, Ye Zhang, Yan He, Chih-Chun Chien

The ensemble geometric phase (EGP) has been proposed as a topological indicator for finite-temperatures systems. The ensemble Wilson loop, or the transfer matrix, contains the crucial information in the EGP construction. We propose a proxy index and a proxy EGP directly from the transfer matrix and apply them to time-reversal invariant topological insulators

Rohit Bhat, Shranav Palakurthi, Naman Tiwari

We present Tracer Tokens, a hardware token of privacy-preserving contact tracing utilizing Exposure Notification \cite{GAEN} protocol. Through subnetworks, we show that any disease spread by proximity can be traced such as seasonal flu, cold, regional strains of COVID-19, or Tuberculosis. Further, we show this protocol to notify $n^n$ users in parallel, prov

Tommaso Goldhirsch, Urs Lang

The concept of Gromov hyperbolicity manifests itself in many different ways. With only mild assumptions on the underlying metric space, the spectrum of equivalent properties includes various thin triangle conditions, the stability of quasi-geodesics (the Morse lemma), a linear isoperimetric filling inequality for closed curves, and a sub-quadratic isoperimet

Camilla Nobili

In most results concerning bounds on the heat transport in the Rayleigh-B\'{e}nard convection problem no-slip boundary conditions for the velocity field are assumed. Nevertheless it is debatable, whether these boundary conditions reflect the behavior of the fluid at the boundary. This problem is important in theoretical fluid mechanics as well as in industri

Juan Carlos Nuño, Francisco J. Muñoz

We study some properties of binary sequences generated by random substitutions of constant length. Specifically, assuming the alphabet $\{0,1\}$, we consider the following asymmetric substitution rule of length $k$: $0 \to \langle 0, 0, \ldots,0\rangle$ and $1 \to \langle Y_1, Y_2, \ldots, Y_k \rangle$, where $Y_i$ is a Bernoulli random variable with paramet

Saeed Vatankhah, Rosemary A. Renaut, Xingguo Huang, Kevin Mickus

A fast algorithm for the large-scale joint inversion of gravity and magnetic data is developed. It uses a nonlinear Gramian constraint to impose correlation between density and susceptibility of reconstructed models. The global objective function is formulated in the space of the weighted parameters, but the Gramian constraint is implemented in the original