October 2020 arXiv papers — page 18

Jonathan San Miguel, Arpit Dua, Dominic Williamson

We introduce the subsystem symmetry-preserving real-space entanglement renormalization group and apply it to study bifurcating flows generated by linear and fractal subsystem symmetry-protected topological phases in two spatial dimensions. We classify all bifurcating fixed points that are given by subsystem symmetric cluster states with two qubits per unit c

Sownak Bose, Abraham Loeb

We use the IllustrisTNG (TNG) cosmological, hydrodynamical simulations of galaxy formation to measure the velocity dispersion profiles of dark matter and star particles in Milky Way-mass, galaxy group, and cluster-scale dark matter halos. The mean profile calculated from both dark and luminous tracers are similar in shape, exhibiting a large degree of halo-t

Kassandra R. Anderson, Dong Lai

The large spin-orbit misalignments in the DI Herculis stellar binary system have resolved the decades-long puzzle of the anomalously slow apsidal precession rate, but raise new questions regarding the origin of the obliquities. This paper investigates obliquity evolution in stellar binaries hosting modestly-inclined circumbinary disks. As the disk and binary

Lei Chen, Zhengdao Chen, Joan Bruna

From the perspective of expressive power, this work compares multi-layer Graph Neural Networks (GNNs) with a simplified alternative that we call Graph-Augmented Multi-Layer Perceptrons (GA-MLPs), which first augments node features with certain multi-hop operators on the graph and then applies an MLP in a node-wise fashion. From the perspective of graph isomo

Q. D. Zhou, S. Yamada, P. Robbe, D. Charlet

Belle II is a new-generation B-factory experiment, dedicated to exploring new physics beyond the standard model of elementary particles in the flavor sector. Belle~II started data-taking in April 2018, using a synchronous data acquisition (DAQ) system based on pipelined trigger flow control. The Belle II DAQ system is designed to handle a 30-kHz trigger rate

Kyle Aitken, Vinay V. Ramasesh, Ankush Garg, Yuan Cao

Despite the widespread application of recurrent neural networks (RNNs) across a variety of tasks, a unified understanding of how RNNs solve these tasks remains elusive. In particular, it is unclear what dynamical patterns arise in trained RNNs, and how those patterns depend on the training dataset or task. This work addresses these questions in the context o

Zu-Jian Ying

We study the ground state (GS) and excitation gap of anisotropic quantum Rabi model (QRM) which connects the fundamental QRM and the Jaynes-Cummings model (JCM). While the GS has a second-order quantum phase transition (QPT) in the low frequency limit, turning on finite frequencies we shed a novel light on the phase diagram to illuminate a fine structure of

Ida Syafiza M. Isa, Taisir E. H. El-Gorashi, Mohamed O. I. Musa, J. M. H. Elmirghani

In this paper, a 1+1 server protection scheme is considered where two servers, a primary and a secondary processing server are used to serve ECG monitoring applications concurrently. The infrastructure is designed to be resilient against server failure under two scenarios related to the geographic location of primary and secondary servers and resilient again

David Rubin

Recently, the Nancy Grace Roman Space Telescope (Roman) Project raised the possibility of adding another filter to Roman. Based on the Filter Working Group's recommendations, this filter may be a K-band filter, extending significantly redder than the current-reddest F184. Among other scientific possibilities, this K filter raises the possibility of measuring

Elizabeth Fons, Paula Dawson, Xiao-jun Zeng, John Keane

Data augmentation methods in combination with deep neural networks have been used extensively in computer vision on classification tasks, achieving great success; however, their use in time series classification is still at an early stage. This is even more so in the field of financial prediction, where data tends to be small, noisy and non-stationary. In th

Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani

In suitably initialized wide networks, small learning rates transform deep neural networks (DNNs) into neural tangent kernel (NTK) machines, whose training dynamics is well-approximated by a linear weight expansion of the network at initialization. Standard training, however, diverges from its linearization in ways that are poorly understood. We study the re

Lukas Graf, Sudip Jana, Manfred Lindner, Werner Rodejohann

We discuss a mechanism of neutrinoless double beta decay, where neutrinos of different flavours come into play. This is realized by effective flavour-violating scalar interactions. As one consequence, we find that within the normal mass ordering the neutrino effective mass may no longer vanish due to contributions from other flavours. We evaluate the necessa

Puskar Mondal

Here we prove the linear stability of a family of `$n+1$'-dimensional Friedmann Lema\^{i}tre Robertson Walker (FLRW) cosmological models of general relativity. We show that the solutions to the linearized Einstein-Euler field equations around a class of FLRW metrics with compact spatial topology (negative Einstein spaces and in particular hyperbolic for $n=3

Ken Ono, Robert Schneider, Ian Wagner

In earlier work generalizing a 1977 theorem of Alladi, the authors proved a partition-theoretic formula to compute arithmetic densities of certain subsets of the positive integers $\mathbb N$ as limiting values of $q$-series as $q\to \zeta$ a root of unity (instead of using the usual Dirichlet series to compute densities), replacing multiplicative structures

Nicola Andreas Söker, Sven Auschra, Viktor Holubec, Klaus Kroy

Active-particle suspensions exhibit distinct polarization-density patterns in activity landscapes, even without anisotropic particle interactions. Such polarization without alignment forces is at work in motility-induced phase separation and betrays intrinsic microscopic activity to mesoscale observers. Using stable long-term confinement of a single hot micr

Juan C. Henao-Londono, Sebastian M. Krause, Thomas Guhr

Recent research on the response of stock prices to trading activity revealed long lasting effects, even across stocks of different companies. These results imply non-Markovian effects in price formation and when trading many stocks at the same time, in particular trading costs and price correlations. How the price response is measured depends on data set and

Roberto de A. Capistrano Filho, Thiago Yukio Tanaka

The main goal of this manuscript is to prove the existence of insensitizing controls for the fourth-order dispersive nonlinear Schr\"odinger equation with cubic nonlinearity. To obtain the main result we prove a null controllability property for a coupled fourth-order Schr\"odinger cascade type system with zero-order coupling which is equivalent to the insen

Alex B. Grilo, Kathrin Hövelmanns, Andreas Hülsing, Christian Majenz

The random oracle model (ROM) enjoys widespread popularity, mostly because it tends to allow for tight and conceptually simple proofs where provable security in the standard model is elusive or costly. While being the adequate replacement of the ROM in the post-quantum security setting, the quantum-accessible random oracle model (QROM) has thus far failed to

Marcel Hansmann, David Krejcirik

We discuss abstract Birman-Schwinger principles to study spectra of self-adjoint operators subject to small non-self-adjoint perturbations in a factorised form. In particular, we extend and in part improve a classical result by Kato which ensures spectral stability. As an application, we revisit known results for Schr\"odinger and Dirac operators in Euclidea

Adarsh Subbaswamy, Roy Adams, Suchi Saria

As the use of machine learning in high impact domains becomes widespread, the importance of evaluating safety has increased. An important aspect of this is evaluating how robust a model is to changes in setting or population, which typically requires applying the model to multiple, independent datasets. Since the cost of collecting such datasets is often pro

Reginald J. Caginalp, Samuel Leutheusser

We numerically analyze the complexity of unitary time-evolution and precursor operators in one- and two-qubit systems using the framework of Nielsen complexity geometry. We find that, as expected, the complexities of unitary time evolution operators grow linearly with time, at least initially, for both the one- and two-qubit cases. The precursor operators di

Ky Ho, Yun-Ho Kim, Patrick Winkert, Chao Zhang

In this paper we prove the boundedness and H\"older continuity of quasilinear elliptic problems involving variable exponents for a homogeneous Dirichlet and a nonhomogeneous Neumann boundary condition, respectively. The novelty of our work is the fact that we allow critical growth even on the boundary and so we close the gap in the papers of Fan-Zhao [Nonlin

Mateo Casariego, Yasser Omar, Mikel Sanz

Quantum-enhanced, idler-free sensing protocol to measure the response of a target object to the frequency of a probe in a noisy and lossy scenario is proposed. In this protocol, a target with frequency-dependent reflectivity embedded in a thermal bath is considered. The aim is to estimate the parameter $\lambda = \eta(\omega_2)-\eta(\omega_1)$, since it cont

Rosa Valiante, Monica Colpi, Raffaella Schneider, Alberto Mangiagli

Third Generation ground based Gravitational Wave Interferometers, like the Einstein Telescope (ET), Cosmic Explorer (CE), and the Laser Interferometer Space Antenna (LISA) will detectcoalescing binary black holes over a wide mass spectrum and across all cosmic epochs. We track the cosmological growth of the earliest light and heavy seeds that swiftly transit

Fridtjof Brauns, Jacob Halatek, Erwin Frey

Spatial organization of proteins in cells is important for many biological functions. In general, the nonlinear, spatially coupled models for protein-pattern formation are only accessible to numerical simulations, which has limited insight into the general underlying principles. To overcome this limitation, we adopt the setting of two diffusively coupled, we

S. Carolan, A. A. Vidotto, C. Villarreal D'Angelo, G. Hazra

We use 3D hydrodynamics simulations followed by synthetic line profile calculations to examine the effect increasing the strength of the stellar wind has on observed Ly-$\alpha$ transits of a Hot Jupiter (HJ) and a Warm Neptune (WN). We find that increasing the stellar wind mass-loss rate from 0 (no wind) to 100 times the solar mass-loss rate value causes re

Chenyang Xu

We propose a finite generation conjecture for the valuation which computes the stability threshold of a log Fano pair. We also initiate a degeneration strategy of attacking the conjecture.

Matthias Thamm, Bernd Rosenow

We study coherent electronic transport through a Coulomb blockaded superconducting Rashba wire in the co-tunneling regime between conductance resonances. By varying an external Zeeman field the wire can be tuned into a topological regime, where non-local transport through Majorana zero modes is the dominant mechanism. We model coherent transport in the co-tu

J. E. Owen, I. F. Shaikhislamov, H. Lammer, L. Fossati

The discovery of thousands of highly irradiated, low-mass, exoplanets has led to the idea that atmospheric escape is an important process that can drive their evolution. Of particular interest is the inference from recent exoplanet detections that there is a large population of low mass planets possessing significant, hydrogen dominated atmospheres, even at

Mehrdad Fazli, Kamran Kowsari, Erfaneh Gharavi, Laura Barnes

Activity recognition using built-in sensors in smart and wearable devices provides great opportunities to understand and detect human behavior in the wild and gives a more holistic view of individuals' health and well being. Numerous computational methods have been applied to sensor streams to recognize different daily activities. However, most methods are u

Vishal Sunder, Eric Fosler-Lussier

Utterance classification performance in low-resource dialogue systems is constrained by an inevitably high degree of data imbalance in class labels. We present a new end-to-end pairwise learning framework that is designed specifically to tackle this phenomenon by inducing a few-shot classification capability in the utterance representations and augmenting da

Xinyuan Dou, Guangbin Ren, Irene Sabadini, Ting Yang

In the literature on slice analysis in the hypercomplex setting, there are two main approaches to define slice regular functions in one variable: one consists in requiring that the restriction to any complex plane is holomorphic (with the same complex structure of the complex plane), the second one makes use of stem and slice functions. So far, in the settin

Sihan Zeng, Thinh T. Doan, Justin Romberg

We study a decentralized variant of stochastic approximation, a data-driven approach for finding the root of an operator under noisy measurements. A network of agents, each with its own operator and data observations, cooperatively find the fixed point of the aggregate operator over a decentralized communication graph. Our main contribution is to provide a f

Sarah Millholland, Christopher Spalding

Small, rocky planets have been found orbiting in extreme proximity to their host stars, sometimes down to only $\sim 2$ stellar radii. These ultra-short-period planets (USPs) likely did not form in their present-day orbits, but rather migrated from larger initial separations. While tides are the probable cause of this migration, the tidal source has remained

Hao-Chiang Shao, Ya-Jen Cheng, Meng-Yun Duh, Chia-Wen Lin

Recently, falsified images have been found in papers involved in research misconducts. However, although there have been many image forgery detection methods, none of them was designed for molecular-biological experiment images. In this paper, we proposed a fast blind inquiry method, named FBI$_{GEL}$, for integrity of images obtained from two common sorts o

Gopal Bhatta, Radim Pánis, Zdeněk Stuchlík

Linear time series analysis, mainly the Fourier transform based methods, has been quite successful in extracting information contained in the ever-modulating light curves (Lcs) of active galactic nuclei, and thereby contribute in characterizing the general features of supermassive black hole systems. In particular, the statistical properties of $\gamma$-ray

Giorgio Fabbro, Vladimir Golkov, Thomas Kemp, Daniel Cremers

Modern text-to-speech systems are able to produce natural and high-quality speech, but speech contains factors of variation (e.g. pitch, rhythm, loudness, timbre)\ that text alone cannot contain. In this work we move towards a speech synthesis system that can produce diverse speech renditions of a text by allowing (but not requiring) explicit control over th

Mihyun Kang, Michael Missethan

Let $P(n,m)$ be a graph chosen uniformly at random from the class of all planar graphs on vertex set $\left\{1, \ldots, n\right\}$ with $m=m(n)$ edges. We show that in the sparse regime, when $\limsup_{n \to \infty} m/n<1$, with high probability the maximum degree of $P(n,m)$ takes at most two different values.

Josef Rucska, James Wadsley

We present numerical simulations of dust clumping and planetesimal formation initiated by the streaming instability with self-gravity. We examine the variability in the planetesimal formation process by employing simulation domains with large radial and azimuthal extents and a novel approach of re-running otherwise identical simulations with different random

Alberto Barbado, Óscar Corcho

Identifying anomalies in the fuel consumption of the vehicles of a fleet is a crucial aspect for optimizing consumption and reduce costs. However, this information alone is insufficient, since fleet operators need to know the causes behind anomalous fuel consumption. We combine unsupervised anomaly detection techniques, domain knowledge and interpretable Mac

Sana Riaz, Fahd Ahmed Khan, Sajid Saleem, Qasim Zeeshan Ahmed

In this letter, a new power allocation scheme is proposed to improve the reliability of cooperative non-orthogonal multiple access (CO-NOMA). The strong user is allocated the maximum power, whereas the weak user is allocated the minimum power. This power allocation alters the decoding sequence along with the signal-to-interference plus noise ratio (SINR), at

Vytautas Dūdėnas, Maximilian Löschner

We show how the renormalization constant of the Higgs vacuum expectation value, fixed by a tadpole condition, is responsible for gauge dependences in various definitions of parameters in the $R_{\xi}$-gauge. Then we show the relationship of this renormalization constant to the Fleischer-Jegerlehner (FJ) scheme, which is used to avoid these gauge dependences.

Noushin Hajarolasvadi, Miguel Arjona Ramírez, Hasan Demirel

Synthesizing realistic data samples is of great value for both academic and industrial communities. Deep generative models have become an emerging topic in various research areas like computer vision and signal processing. Affective computing, a topic of a broad interest in computer vision society, has been no exception and has benefited from generative mode

Shi Cheng

We study the mirror symmetry of abelian 3d $\mathcal{N}=2$ theories with mixed Chern-Simons levels by turning them into $\mathcal{T}_{A,N}$ theories that are defined as $N$ copies of $U(1)-[1]$ theory coupled together by mixed Chern-Simons levels $k_{ij}$. We find that $\mathcal{T}_{A,N}$ theories have many mirror dual theories with different mixed CS levels

Michael Dymond

Kalu\v{z}a, Kopeck\'a and the author have shown that the best Lipschitz constant for mappings taking a given $n^{d}$-element set in the integer lattice $\mathbb{Z}^{d}$, with $n\in \mathbb{N}$, surjectively to the regular $n$ times $n$ grid $\left\{1,\ldots,n\right\}^{d}$ may be arbitrarily large. However, there remain no known, non-trivial asymptotic bounds

James D. Sadler, Hui Li

We show that, under Braginskii magneto-hydrodynamics, anti-parallel gradients in average ion charge state and electron temperature can be unstable to the growth of self-generated magnetic fields. The instability is analogous to the field-generating thermomagnetic instability, although it is driven by the collisional thermal force magnetic source term rather

Alejandro Gallo, Felix Hummel, Andreas Irmler, Andreas Grüneis

We present an implementation of equation of motion coupled-cluster singles and doubles (EOM-CCSD) theory using periodic boundary conditions and a plane wave basis set. Our implementation of EOM-CCSD theory is applied to study $F$-centers in alkaline earth oxides employing a periodic supercell approach. The convergence of calculated electronic excitation ener

Federico Franzoni, Vanesa Daza

The Bitcoin P2P network is at the core of all communications between clients. The reachable part of this network has been explored and analyzed by numerous studies. Unreachable nodes, however, are, in most part, overlooked. Nonetheless, they are a relevant part of the network and play an essential role in the propagation of messages. In this paper, we focus

Benjamin D. Smith, Logan W. Cooke, Lindsay J. LeBlanc

As a first approximation beyond linearity, the nonlinear Schr\"odinger equation (NLSE) reliably describes a broad class of physical systems. Though numerical solutions of this model are well-established, these methods can be computationally complex. In this paper, we showcase a code development approach, demonstrating how computational time can be significan

Selwyn Simsek, Florian Mintert

The framework of quantum invariants is an elegant generalization of adiabatic quantum control to control fields that do not need to change slowly. Due to the unavailability of invariants for systems with more than one spatial dimension, the benefits of this framework have not yet been exploited in multi-dimensional systems. We construct a multi-dimensional G

M. Tarik Altuncu, Sophia N. Yaliraki, Mauricio Barahona

Production of news content is growing at an astonishing rate. To help manage and monitor the sheer amount of text, there is an increasing need to develop efficient methods that can provide insights into emerging content areas, and stratify unstructured corpora of text into `topics' that stem intrinsically from content similarity. Here we present an unsupervi

Himanshu B. Mishra, Prem Singh, Abhishek K. Prasad, Rohit Budhiraja

This work proposes a superimposed pilot (SP)-based channel estimation and data detection framework for orthogonal time-frequency space (OTFS) scheme, wherein low-powered pilots are superimposed on to data symbols in the delay-Doppler domain. We propose two channel estimation and data detection designs for SP-OTFS systems which, unlike the existing OTFS desig

Yin-Zhe Ma, Yan Gong, Tilman Troster, Ludovic Van Waerbeke

We confront the universal pressure profile (UPP) proposed by~\citet{Arnaud10} with the recent measurement of the cross-correlation function of the thermal Sunyaev-Zeldovich (tSZ) effect from Planck and weak gravitational lensing measurement from the Red Cluster Sequence lensing survey (RCSLenS). By using the halo model, we calculate the prediction of $\xi^{y

Shuting Shen, Junwei Lu

In this paper, we propose an inferential framework testing the general community combinatorial properties of the stochastic block model. Instead of estimating the community assignments, we aim to test the hypothesis on whether a certain community property is satisfied. For instance, we propose to test whether a given set of nodes belong to the same community

Jianfeng Lu, Cody Murphey, Stefan Steinerberger

We study the problem of predicting highly localized low-lying eigenfunctions $(-\Delta +V) \phi = \lambda \phi$ in bounded domains $\Omega \subset \mathbb{R}^d$ for rapidly varying potentials $V$. Filoche & Mayboroda introduced the function $1/u$, where $(-\Delta + V)u=1$, as a suitable regularization of $V$ from whose minima one can predict the location of

Julie Decaup

We study algebraic and topological properties of subsets of preorders on a group. In particular we study properties of the composition of two preorders, generalize a topological theorem of \cite{S} in the case of standard orders and show the same theorem in the case of standard preorders. We also show a property of standard valuations.

Ivan Smiljanic, Ivanka Bozovic Jelisavcic, Goran Kacarevic, Natasa Vukasinovic

The very forward region is one of the most challenging regions to instrument at a future $e^+e^-$ collider. At CEPC, machine-detector interface includes, among others, a calorimeter dedicated for precision measurement of the integrated luminosity at a per mill level or better. Here we review a feasibility of such precision, from the point of view systematic

Huihui Zhang, Yan Lin, Xiao Yu

In this paper, the authors prove the boundedness of commutators generated by the weighted Hardy operator on weighted $\lambda$-central Morrey space with the weight $\omega$ satisfying the doubling condition. Moreover, the authors give the characterization for the weighted $\lambda$-central Campanato space by introducing a new kind of operator which is relate

Victor Abu-Marrul, Rafael Martinelli, Silvio Hamacher, Irina Gribkovskaia

In this paper, we address a variant of a batch scheduling problem with identical parallel machines and non-anticipatory family setup times to minimize the total weighted completion time. We developed an ILS and a GRASP matheuristics to solve the problem using a constructive heuristic and two MIP-based neighborhood searches, considering two batch scheduling m

Tomasz Korbak, Julian Zubek, Joanna Rączaszek-Leonardi

Compositionality is an important explanatory target in emergent communication and language evolution. The vast majority of computational models of communication account for the emergence of only a very basic form of compositionality: trivial compositionality. A compositional protocol is trivially compositional if the meaning of a complex signal (e.g. blue ci

Masashi Aiko, Shinya Kanemura, Mariko Kikuchi, Kentarou Mawatari

We discuss a possibility that the parameter space of the two Higgs doublet model is significantly narrowed down by considering the synergy between direct searches for additional Higgs bosons at the LHC and its luminosity upgraded operation and precision measurements of the Higgs boson properties at future electron-positron colliders such as the International

Divya Kanapram, Pablo Marin-Plaza, Lucio Marcenaro, David Martin

The evolution of Intelligent Transportation System in recent times necessitates the development of self-driving agents: the self-awareness consciousness. This paper aims to introduce a novel method to detect abnormalities based on internal cross-correlation parameters of the vehicle. Before the implementation of Machine Learning, the detection of abnormaliti

Dimitra N. Panou, Martin Reczko

Despite considerable progress, ab initio protein structure prediction remains suboptimal. A crowdsourcing approach is the online puzzle video game Foldit, that provided several useful results that matched or even outperformed algorithmically computed solutions. Using Foldit, the WeFold crowd had several successful participations in the Critical Assessment of

Geondo Park, June Yong Yang, Sung Ju Hwang, Eunho Yang

Neural networks embedded in safety-sensitive applications such as self-driving cars and wearable health monitors rely on two important techniques: input attribution for hindsight analysis and network compression to reduce its size for edge-computing. In this paper, we show that these seemingly unrelated techniques conflict with each other as network compress

Andrew Bunting, Caroline Terquem

We calculate the conversion from non-adiabatic, non-radial oscillations tidally induced by a hot Jupiter on a star to observable spectroscopic and photometric signals. Models with both frozen convection and an approximation for a perturbation to the convective flux are discussed. Observables are calculated for some real planetary systems to give specific pre

Ryan Steed, Aylin Caliskan

Recent advances in machine learning leverage massive datasets of unlabeled images from the web to learn general-purpose image representations for tasks from image classification to face recognition. But do unsupervised computer vision models automatically learn implicit patterns and embed social biases that could have harmful downstream effects? We develop a

Nikolay V. Golubev, Jiří Vaníček, Alexander I. Kuleff

Tracing ultrafast processes induced by interaction of light with matter is often very challenging. In molecular systems, the initially created electronic coherence becomes damped by the slow nuclear rearrangement on a femtosecond timescale which makes real-time observations of electron dynamics in molecules particularly difficult. In this work, we report an

Ana Laura Frapiccini, Dario Marcelo Mitnik

In this work, we present a theoretical study of the photoionization for atomic hydrogen confined in onion fullerene compared with the bare H atom and the single fullerene case. We obtained the expected confinement resonances for the integrated probabilities, finding different trends for the main peak and the first ATI peak integrations. We perform these calc

Steven G. Murray, Bradley Greig, Andrei Mesinger, Julian B. Muñoz

This brief code paper presents a new Python-wrapped version of the popular 21cm cosmology simulator, 21cmFAST. The new version, v3+, maintains the same core functionality of previous versions of 21cmFAST, but features a simple and intuitive interface, and a great deal more flexibility. This evolution represents the work of a formalized collaboration, and the

An Zhao, Krishna Subramani, Paris Smaragdis

The Short-Time Fourier Transform (STFT) has been a staple of signal processing, often being the first step for many audio tasks. A very familiar process when using the STFT is the search for the best STFT parameters, as they often have significant side effects if chosen poorly. These parameters are often defined in terms of an integer number of samples, whic

Daoji Huang

We give bijective proofs of Monk's rule for Schubert and double Schubert polynomials computed with bumpless pipe dreams. In particular, they specialize to bijective proofs of transition and cotransition formulas of Schubert and double Schubert polynomials, which can be used to establish bijections with ordinary pipe dreams.

Samuel Rivera, Catherine A. Best, Hyungwook Yim, Dirk B. Walther

Visual categorization and learning of visual categories exhibit early onset, however the underlying mechanisms of early categorization are not well understood. The main limiting factor for examining these mechanisms is the limited duration of infant cooperation (10-15 minutes), which leaves little room for multiple test trials. With its tight link to visual

Pintu Bhunia, Kallol Paul, Raj Kumar Nayak

We give new inequalities for $A$-operator seminorm and $A$-numerical radius of semi-Hilbertian space operators and show that the inequalities obtained here generalize and improve on the existing ones. Considering a complex Hilbert space $\mathcal{H}$ and a non-zero positive bounded linear operator $A$ on $\mathcal{H},$ we show with among other seminorm inequ

Ananyo Maitra

I show that two-dimensional nematic order in an active fluid film can be stable and long-ranged if exchange of particles and momentum with an ambient three-dimensional fluid is allowed. Number-conserving films present an instability, with a fastest-growing mode independent of activity at high activities or low concentrations. Motility can stabilize long-rang

Rui Wang, Yu-Gang Ma, R. Wada, Lie-Wen Chen

The machine-learning techniques have shown their capability for studying phase transitions in condensed matter physics. Here, we employ the machine-learning techniques to study the nuclear liquid-gas phase transition. We adopt an unsupervised learning and classify the liquid and gas phases of nuclei directly from the final state raw experimental data of heav

D. Bhakta, K. P. Mooley, A. Corsi, A. Balasubramanian

We present a sensitive search with the Karl G. Jansky Very Large Array (VLA) for the radio counterpart of the gravitational wave candidate S191216ap, classified as a binary black hole merger, and suggested to be a possible multi-messenger event, based on the detection of a high energy neutrino and a TeV photon. We carried out a blind search at C band (4--8 G

Xiu Su, Shan You, Tao Huang, Hongyan Xu

To deploy a well-trained CNN model on low-end computation edge devices, it is usually supposed to compress or prune the model under certain computation budget (e.g., FLOPs). Current filter pruning methods mainly leverage feature maps to generate important scores for filters and prune those with smaller scores, which ignores the variance of input batches to t

Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andrew Brock

The instability of Generative Adversarial Network (GAN) training has frequently been attributed to gradient descent. Consequently, recent methods have aimed to tailor the models and training procedures to stabilise the discrete updates. In contrast, we study the continuous-time dynamics induced by GAN training. Both theory and toy experiments suggest that th

S. N. Sajadi, Robert B. Mann, N. Riazi, Saeed Fakhry

We obtain analytical approximate black hole solutions for higher derivative gravity in the presence of Maxwell electromagnetic source. We construct near horizon and asymptotic solutions and then use these to obtain an approximate analytic solution using a continued fraction method to get a complete solution. We compute the thermodynamic quantities and check

M. Mirzargar, R. Scapellato

The power graph P(G) of a group G is a graph with vertex set G, where two vertices u and v are adjacent if and only if one is the power of the other. In this paper, we raise and study the following question: For which natural numbers n every two groups of order n with isomorphic power graphs are isomorphic? In particular, we determine prove that all such n a

David J. Jackson

The central arguments for a generalised form of proper time as the basis for a unified theory, accounting for the Standard Model of particle physics and encompassing all four forces of nature including a consistent `quantum gravity', are presented. After first outlining a set of criteria to be ideally met by any proposal for a comprehensive unification schem

Henri Roesch

In the first part of this paper, we extend the result of Li-Wang on the linearized embedding problem to a compact manifold of arbitrary dimension. Using this, we then show that any metric perturbation of a embedded $n$-sphere is also isometrically embedded up to a solution of the homogenous Codazzi equation, irrespective of the ambient geometry. In the secon

Usman Naseem, Imran Razzak, Shah Khalid Khan, Mukesh Prasad

Word representation has always been an important research area in the history of natural language processing (NLP). Understanding such complex text data is imperative, given that it is rich in information and can be used widely across various applications. In this survey, we explore different word representation models and its power of expression, from the c

Unal Ertan

We have extended the analytical model proposed earlier to estimate the inner disk radius of accreting neutron stars in the strong-propeller (SP) phase, and the conditions for the transitions between the strong and weak propeller (WP) phases (Ertan 2017, 2018) to the WP (accretion with spin-down) and the spin-up (SU) phases, and the torque reversals during th

Zihao Liang, Wanxin Jin, Shaoshuai Mou

This paper proposes an inverse optimal control method which enables a robot to incrementally learn a control objective function from a collection of trajectory segments. By saying incrementally, it means that the collection of trajectory segments is enlarged because additional segments are provided as time evolves. The unknown objective function is parameter

Jared Sigurd Johansen, Thomas Victor Ilyevsky, Jeffrey Mark Siskind

State-of-the-art natural-language-driven autonomous-navigation systems generally lack the ability to operate in real unknown environments without crutches, such as having a map of the environment in advance or requiring a strict syntactic structure for natural-language commands. Practical artificial-intelligent systems should not have to depend on such prior

Daniel Barcelona-Pons, Pedro García-López

Serverless computing has seen a myriad of work exploring its potential. Some systems tackle Function-as-a-Service (FaaS) properties on automatic elasticity and scale to run highly-parallel computing jobs. However, they focus on specific platforms and convey that their ideas can be extrapolated to any FaaS runtime. An important question arises: do all FaaS pl

Abhin Shah, Devavrat Shah, Gregory W. Wornell

We consider learning a sparse pairwise Markov Random Field (MRF) with continuous-valued variables from i.i.d samples. We adapt the algorithm of Vuffray et al. (2019) to this setting and provide finite-sample analysis revealing sample complexity scaling logarithmically with the number of variables, as in the discrete and Gaussian settings. Our approach is app

Jonas Kastberg Hinrichsen, Jesper Bengtson, Robbert Krebbers

Message passing is a useful abstraction for implementing concurrent programs. For real-world systems, however, it is often combined with other programming and concurrency paradigms, such as higher-order functions, mutable state, shared-memory concurrency, and locks. We present Actris: a logic for proving functional correctness of programs that use a combinat

Daichi Kitahara

The short-time Fourier transform (STFT) usually computes the same number of frequency components as the frame length while overlapping adjacent time frames by more than half. As a result, the number of components of a spectrogram matrix becomes more than twice the signal length, and hence STFT is hardly used for signal compression. In addition, even if we mo

Yanbo Xu, Cao Xiao, Jimeng Sun

Counterfactual prediction is about predicting outcome of the unobserved situation from the data. For example, given patient is on drug A, what would be the outcome if she switch to drug B. Most of existing works focus on modeling counterfactual outcome based on static data. However, many applications have time-varying confounding effects such as multiple tre

Changpeng Shao, Jin-Peng Liu

Many eigenvalue problems arising in practice are often of the generalized form $A\x=\lambda B\x$. One particularly important case is symmetric, namely $A, B$ are Hermitian and $B$ is positive definite. The standard algorithm for solving this class of eigenvalue problems is to reduce them to Hermitian eigenvalue problems. For a quantum computer, quantum phase

Grant Merz, Zach Meisel

The thermal structure of accreting neutron stars is affected by the presence of urca nuclei in the neutron star crust. Nuclear isobars harboring urca nuclides can be produced in the ashes of Type I X-ray bursts, but the details of their production have not yet been explored. Using the code {\tt MESA}, we investigate urca nuclide production in a one-dimension

Xingchen Song, Zhiyong Wu, Yiheng Huang, Chao Weng

Non-autoregressive (NAR) transformer models have achieved significantly inference speedup but at the cost of inferior accuracy compared to autoregressive (AR) models in automatic speech recognition (ASR). Most of the NAR transformers take a fixed-length sequence filled with MASK tokens or a redundant sequence copied from encoder states as decoder input, they

Almut Burchard, Galia Dafni, Ryan Gibara

We derive bounds on the mean oscillation of the decreasing rearrangement $f^*$ on $\mathbb{R}_+$ in terms of the mean oscillation of $f$ on a suitable measure space $X$. In the special case of a doubling metric measure space, the bound depends only on the doubling constant.

Bahareh Azad, Farhang Loran, Ali Mostafazadeh

We study the scattering of low-energy massless and massive minimally coupled scalar fields by an asymptotically flat traversable wormhole. We provide a comprehensive treatment of this problem offering analytic expressions for the transmission and reflection amplitudes of the corresponding effective potential and the absorption cross section of the wormhole.

Disson dos Prazeres, Edgard A. Pimentel, Giane C. Rampasso

We establish the density of the partial regularity result in the class of continuous viscosity solutions. Given a fully nonlinear equation, we prove the existence of a sequence entitled to the partial regularity result, approximating its solutions. Distinct conditions on the operator driving the equation lead to density in different topologies. Our findings

Andrew Bailey, Mark D. Plumbley

Depression is a large-scale mental health problem and a challenging area for machine learning researchers in detection of depression. Datasets such as Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WOZ) have been created to aid research in this area. However, on top of the challenges inherent in accurately detecting depression, biases in datasets ma

Vung Pham, Chau Pham, Tommy Dang

The road is vital for many aspects of life, and road maintenance is crucial for human safety. One of the critical tasks to allow timely repair of road damages is to quickly and efficiently detect and classify them. This work details the strategies and experiments evaluated for these tasks. Specifically, we evaluate Detectron2's implementation of Faster R-CNN

Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra

We study online learning in unknown Markov games, a problem that arises in episodic multi-agent reinforcement learning where the actions of the opponents are unobservable. We show that in this challenging setting, achieving sublinear regret against the best response in hindsight is statistically hard. We then consider a weaker notion of regret by competing w