January 2019 arXiv papers — page 26

Sian Jin, Sheng Di, Xin Liang, Jiannan Tian

DNNs have been quickly and broadly exploited to improve the data analysis quality in many complex science and engineering applications. Today's DNNs are becoming deeper and wider because of increasing demand on the analysis quality and more and more complex applications to resolve. The wide and deep DNNs, however, require large amounts of resources, signific

Crystal L. Martin, Stephanie H. Ho, Glenn G. Kacprzak, Christopher W. Churchill

We present observations of 50 pairs of redshift z ~ 0.2 star-forming galaxies and background quasars. These sightlines probe the circumgalactic medium (CGM) out to half the virial radius, and we describe the circumgalactic gas kinematics relative to the reference frame defined by the galactic disks. We detect halo gas in MgII absorption, measure the equivale

Jamel Benameur, Mariem Bennaceur

In this paper we study the incompressible Navier-Stokes equations in $L^2(\mathbb R^3)\cap\mathcal X^{-1}(\mathbb R^3)$. In the global existence case, we establish that if the solution $u$ is in the space $C(\mathbb R^+,L^2\cap\mathcal X^{-1})$, then for $\sigma>-3/2$ the decay of $\|u(t)\|_{\mathcal X^\sigma}$ is at least of the order of $t^{-\frac{\sigma+\

Zaid Ahsan, Harry Dankowicz, Jan Sieber

This paper generalizes a previously-conceived, continuation-based optimization technique for scalar objective functions on constraint manifolds to cases of periodic and quasiperiodic solutions of delay-differential equations. A Lagrange formalism is used to construct adjoint conditions that are linear and homogenous in the unknown Lagrange multipliers. As a

James Garrett, Nataša Jonoska, Hwee Kim, Masahico Saito

We initiate an algebraic approach to study DNA origami structures by associating an element from a monoid to each structure. We identify two types of basic building blocks and describe an DNA origami structure with their composition. These building blocks are taken as generators of a monoid, called origami monoid, and, motivated by the well studied Temperley

Alexander S. Glasser, Hong Qin

In the following work, we pedagogically develop 5-vector theory, an evolution of scalar field theory that provides a stepping stone toward a Poincar\'e-invariant lattice gauge theory. Defining a continuous flat background via the four-dimensional Cartesian coordinates $\{x^a\}$, we `lift' the generators of the Poincar\'e group so that they transform only the

Alexander S. Glasser, Hong Qin

The following work demonstrates the viability of Poincar\'e symmetry in a discrete universe. We develop the technology of the discrete principal Poincar\'e bundle to describe the pairing of (1) a hypercubic lattice `base manifold' labeled by integer vertices-denoted $\{\mathbf{n}\}=\{(n_t,n_x,n_y,n_z)\}$-with (2) a Poincar\'e structure group. We develop latt

Yusuke Ide, Norio Konno, Etsuo Segawa

In this paper, we consider an extended coined Szegedy model and discuss the existence of the point spectrum of induced quantum walks in terms of recurrence properties of the underlying birth and death process. We obtain that if the underlying random walk is not null recurrent, then the point spectrum exists in the induced quantum walks. As an application, we

Michael Altschuler, Michael Bloodgood

During active learning, an effective stopping method allows users to limit the number of annotations, which is cost effective. In this paper, a new stopping method called Predicted Change of F Measure will be introduced that attempts to provide the users an estimate of how much performance of the model is changing at each iteration. This stopping method can

Toni Karvonen, Simo Särkkä, Chris. J. Oates

Bayesian cubature provides a flexible framework for numerical integration, in which a priori knowledge on the integrand can be encoded and exploited. This additional flexibility, compared to many classical cubature methods, comes at a computational cost which is cubic in the number of evaluations of the integrand. It has been recently observed that fully sym

Zahra Sadeghi

How do humans distinguish between general categories of objects? In a number of semantic category deficits, patients are good at making broad categorization but are unable to remember fine and specific details. It has been well accepted that general information about concepts is more robust to damages related to semantic memory. Results from patients with se

Gustavo Garrigós, Andreas Seeger, Tino Ullrich

We study Schauder basis properties for the Haar system in Besov spaces $B^s_{p,q}(\mathbb{R}^d)$. We give a complete description of the limiting cases, obtaining various positive results for $q\leq \min\{1,p\}$, and providing new counterexamples in other situations. The study is based on suitable estimates of the dyadic averaging operators $\mathbb{E}_N$; in

John Cotrina, Abderrahim Hantoute, Anton Svensson

A quasi-equilibrium problem is an equilibrium problem where the constraint set does depend on the reference point. It generalizes important problems such as quasi-variational inequalities and generalized Nash equilibrium problems. We study the existence of equilibria on unbounded sets under a coerciveness condition adapted from one specific for quasi-variati

Wei Hu, Nathaniel Trask, Xiaozhe Hu, Wenxiao Pan

We present a scheme implementing an a posteriori refinement strategy in the context of a high-order meshless method for problems involving point singularities and fluid-solid interfaces. The generalized moving least squares (GMLS) discretization used in this work has been previously demonstrated to provide high-order compatible discretization of the Stokes a

Andrei Popescu-Belis

This review paper discusses how context has been used in neural machine translation (NMT) in the past two years (2017-2018). Starting with a brief retrospect on the rapid evolution of NMT models, the paper then reviews studies that evaluate NMT output from various perspectives, with emphasis on those analyzing limitations of the translation of contextual phe

Patrick J. Strohbeen, Dongxue Du, Chenyu Zhang, Estiaque H. Shourov

We report the molecular beam epitaxial growth, structure, and electronic measurements of single-crystalline LaAuSb films on Al$_2$O$_3$ (0001) substrates. LaAuSb belongs to a broad family of hexagonal $ABC$ intermetallics in which the magnitude and sign of layer buckling have strong effects on properties, e.g., predicted hyperferroelecticity, polar metallici

Yi Shi, Yalin E. Sagduyu, Kemal Davaslioglu, Jason H. Li

As online systems based on machine learning are offered to public or paid subscribers via application programming interfaces (APIs), they become vulnerable to frequent exploits and attacks. This paper studies adversarial machine learning in the practical case when there are rate limitations on API calls. The adversary launches an exploratory (inference) atta

Jose A. Magpantay

I work out the general expressions for the first relativistic correction of order $ \dfrac{1}{c^2} $ to thermodynamics. The starting point is the relativistic Hamiltonian that I derived in a previous paper, which I expanded to powers of $ \dfrac{1}{c^2} $ to derive a local (in time) Hamiltonian. Limiting to the first relativistic correction, I worked out in

Alexander Liefke, Vincent Marciniak, Uwe Janoske, Hanno Gottschalk

We consider the evaluation of manufacturing variations to the aerodynamic performace of turbine vanes using the adjoint method. The empirical data is based on 102 white light scans from casted parts. We compare expensive calculations by the finite disfference method with cheap adjoint calculations and we find high correlations.

Yang Li, Kimball A. Milton, Xin Guo, Gerard Kennedy

We calculate the Casimir forces in two configurations, namely, three parallel dielectric slabs and a dielectric slab between two perfectly conducting plates, where the dielectric materials are dispersive and inhomogeneous in the direction perpendicular to the interfaces. A renormalization scheme is proposed consisting of subtracting the effect of one interfa

W. Töws, G. M. Pastor

The ultrafast demagnetization (UFD) dynamics of itinerant ferromagnets is theoretically investigated as a function of the characteristics of the initial laser excitation. A many-body pd-band Hamiltonian is considered which takes into account hybridizations, Coulomb interactions, spin-orbit interactions and the coupling to the laser field on the same electron

Parvin Nazari, Davoud Ataee Tarzanagh, George Michailidis

Adaptive gradient-based optimization methods such as \textsc{Adagrad}, \textsc{Rmsprop}, and \textsc{Adam} are widely used in solving large-scale machine learning problems including deep learning. A number of schemes have been proposed in the literature aiming at parallelizing them, based on communications of peripheral nodes with a central node, but incur h

Hankui Peng, Nicos Pavlidis, Idris Eckley, Ioannis Tsalamanis

In this paper we consider the problem of clustering collections of very short texts using subspace clustering. This problem arises in many applications such as product categorisation, fraud detection, and sentiment analysis. The main challenge lies in the fact that the vectorial representation of short texts is both high-dimensional, due to the large number

Kai Sheng Tai, Peter Bailis, Gregory Valiant

How can prior knowledge on the transformation invariances of a domain be incorporated into the architecture of a neural network? We propose Equivariant Transformers (ETs), a family of differentiable image-to-image mappings that improve the robustness of models towards pre-defined continuous transformation groups. Through the use of specially-derived canonica

Huda Alamri, Vincent Cartillier, Abhishek Das, Jue Wang

We introduce the task of scene-aware dialog. Our goal is to generate a complete and natural response to a question about a scene, given video and audio of the scene and the history of previous turns in the dialog. To answer successfully, agents must ground concepts from the question in the video while leveraging contextual cues from the dialog history. To be

Loïc Chaumont, Jacek Małecki

We provide integral formulae for the Laplace transform of the entrance law of the reflected excursions for symmetric L\'evy processes in terms of their characteristic exponent. For subordinate Brownian motions and stable processes we express the density of the entrance law in terms of the generalized eigenfunctions for the semigroup of the process killed whe

Nelson Darkwah Oppong, Luis Riegger, Oscar Bettermann, Moritz Höfer

We report on the experimental observation of multiorbital polarons in a two-dimensional Fermi gas of $^{173}\mathrm{Yb}$ atoms formed by mobile impurities in the metastable $^3\mathrm{P}_0$ orbital and a Fermi sea in the ground-state $^1\mathrm{S}_0$ orbital. We spectroscopically probe the energies of attractive and repulsive polarons close to an orbital Fes

M. M. Hedman, P. D. Nicholson

Density waves in Saturn's rings are usually tightly wrapped spiral patterns generated by resonances with either Saturn's moons or structures inside the planet. However, between the Barnard and Bessel Gaps in the Cassini Division (i.e. between 120,240 and 120,300 km from Saturn's spin axis), there are density variations that appear to form an axisymmetric den

Bartholomeus Machielse, Stefan Bogdanovic, Srujan Meesala, Scarlett Gauthier

Photon-mediated coupling between distant matter qubits may enable secure communication over long distances, the implementation of distributed quantum computing schemes, and the exploration of new regimes of many-body quantum dynamics. Nanophotonic devices coupled to solid-state quantum emitters represent a promising approach towards realization of these goal

Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota

Recent years have seen the rise of Deep Learning (DL) techniques applied to source code. Researchers have exploited DL to automate several development and maintenance tasks, such as writing commit messages, generating comments and detecting vulnerabilities among others. One of the long lasting dreams of applying DL to source code is the possibility to automa

David Hoffman, Tom Ilmanen, Francisco Martín, Brian White

The purpose of these notes is to provide an introduction to those who want to learn more about translating solitons for the mean curvature flow in $\mathbb{R}^3$, particularly those which are complete graphs over domains in $\mathbb{R}^2$. In this paper we describe a full classification of complete translating graphs in $\mathbb{R}^3$.

Uri Hadar, Jingbo Liu, Yury Polyanskiy, Ofer Shayevitz

We characterize the communication complexity of the following distributed estimation problem. Alice and Bob observe infinitely many iid copies of $\rho$-correlated unit-variance (Gaussian or $\pm1$ binary) random variables, with unknown $\rho\in[-1,1]$. By interactively exchanging $k$ bits, Bob wants to produce an estimate $\hat\rho$ of $\rho$. We show that

Hyunuk Kim, Inho Hong, Woo-Sung Jung

In the era of big science, countries allocate big research and development budgets to large scientific facilities that boost collaboration and research capability. A nuclear fusion device called the "tokamak" is a source of great interest for many countries because it ideally generates sustainable energy expected to solve the energy crisis in the future. Her

Christopher Lee, Prashant Shrivastava, Varun Vaidya

We develop an effective field theory (EFT) framework to perform an analytic calculation for energy correlator observables computed on groomed heavy-quark jets. A soft-drop grooming algorithm is applied to a jet initiated by a massive quark to minimize soft contamination effects such as pile-up and multi-parton interactions. We specifically consider the two-p

Samuel N. Quinn, Juliette C. Becker, Joseph E. Rodriguez, Sam Hadden

We report the Transiting Exoplanet Survey Satellite ($TESS$) detection of a multi-planet system orbiting the $V=10.9$ K0 dwarf TOI 125. We find evidence for up to five planets, with varying confidence. Three high signal-to-noise transit signals correspond to sub-Neptune-sized planets ($2.76$, $2.79$, and $2.94\ R_{\oplus}$), and we statistically validate the

Ernest Ma

The standard model of quarks and leptons is extended to include the gauge symmetry $U(1)_\chi$ which comes from $SO(10) \to SU(5) \times U(1)_\chi$. The radiative generation of Dirac neutrino masses through dark matter is discussed in two examples. One allows for light Dirac fermion dark matter. The other allows for self-interacting scalar dark matter with a

Ryan Marcus, Olga Papaemmanouil

Integrating machine learning into the internals of database management systems requires significant feature engineering, a human effort-intensive process to determine the best way to represent the pieces of information that are relevant to a task. In addition to being labor intensive, the process of hand-engineering features must generally be repeated for ea

Adithya Murali, Lucas Peña, Christof Löding, P. Madhusudan

We propose a novel logic, called Frame Logic (FL), that extends first-order logic (with recursive definitions) using a construct Sp(.) that captures the implicit supports of formulas -- the precise subset of the universe upon which their meaning depends. Using such supports, we formulate proof rules that facilitate frame reasoning elegantly when the underlyi

Angela Zhang, Po-Yu Kao, Ronald Sahyouni, Ashutosh Shelat

Normal Pressure Hydrocephalus (NPH) is one of the few reversible forms of dementia, Due to their low cost and versatility, Computed Tomography (CT) scans have long been used as an aid to help diagnose intracerebral anomalies such as NPH. However, no well-defined and effective protocol currently exists for the analysis of CT scan-based ventricular, cerebral m

Raphael Arkady Meyer, Jean Honorio

We propose a novel combination of optimization tools with learning theory bounds in order to analyze the sample complexity of optimal kernel sum classifiers. This contrasts the typical learning theoretic results which hold for all (potentially suboptimal) classifiers. Our work also justifies assumptions made in prior work on multiple kernel learning. As a by

Oscar García-Prada

This paper is a survey on the role of Higgs bundle theory in the study of higher Teichm\"uller spaces. Recall that the Teichm\"uller space of a compact surface can be identified with a certain connected component of the moduli space of representations of the fundamental group of the surface into $\mathrm{PSL}(2,{\mathbb{R}})$. Higher Teichm\"uller spaces cor

Sebastian Goldt, Madhu S. Advani, Andrew M. Saxe, Florent Krzakala

Deep neural networks achieve stellar generalisation on a variety of problems, despite often being large enough to easily fit all their training data. Here we study the generalisation dynamics of two-layer neural networks in a teacher-student setup, where one network, the student, is trained using stochastic gradient descent (SGD) on data generated by another

Zhengbo Zou, Semiha Ergan

According to the World Bank, more than half of the world's population now lives in cities, creating burdens on the degraded city infrastructures and driving up the demand for new ones. Construction sites are abundant in already dense cities and have unavoidable impacts on surrounding environments and residents. However, such impacts were rarely quantified an

Scott W. McIntosh, Robert J. Leamon, Ricky Egeland, Mausumi Dikpati

We observe the abrupt end of solar activity cycles at the Sun's equator by combining almost 140 years of observations from ground and space. These "terminator" events appear to be very closely related to the onset of magnetic activity belonging to the next sunspot cycle at mid-latitudes and the polar-reversal process at high-latitudes. Using multi-scale trac

Stefano Martinazzi

It is now a whole year since Lightning Network (LN) has been launched on the Bitcoi's mainnet. LN has been claimed as the solution for several of Bitcoin's weaknesses such as its difficulty to scale in number of transactions per second and its expensiveness for relative small amount exchanged. LN is based upon a network of micro-channels opened and closed by

Arjun Pakrashi, Bidyut B. Chaudhuri

Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amount of time, various meta-heuristic approaches and their hybridization, sometimes with K-Means technique, have been employed. A Kalman Filtering based heuristic approach called Heuristic Kalman Algo

Fernando E. Vargas, José L. Lanata, Guillermo Abramson, Marcelo N. Kuperman

We present a study on the rock art of Northern Patagonia based on network analysis and communities detection. We unveil a significant aggregation of archaeological sites, linked by common rock art motifs that turn out to be consistent with their geographical distribution and archaeological background of hunter-gatherer stages of regional peopling and land us

Avary Kolasinski, Weizhang Huang

A surface moving mesh method is presented for general surfaces with or without explicit parameterization. The method can be viewed as a nontrivial extension of the moving mesh partial differential equation method that has been developed for bulk meshes and demonstrated to work well for various applications. The main challenges in the development of surface m

Linbo Shao, Smarak Maity, Lu Zheng, Lue Wu

Phonons at gigahertz frequencies interact with electrons, photons, and atomic systems in solids, and therefore have extensive applications in signal processing, sensing, and quantum technologies. Surface acoustic wave (SAW) resonators that confine surface phonons can play a crucial role in such integrated phononic systems due to small mode size, low dissipat

Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama

In computer vision applications, such as domain adaptation (DA), few shot learning (FSL) and zero-shot learning (ZSL), we encounter new objects and environments, for which insufficient examples exist to allow for training "models from scratch," and methods that adapt existing models, trained on the presented training environment, to the new scenario are requ

David van Dijk, Daniel Burkhardt, Matthew Amodio, Alex Tong

Archetypal analysis is a data decomposition method that describes each observation in a dataset as a convex combination of "pure types" or archetypes. These archetypes represent extrema of a data space in which there is a trade-off between features, such as in biology where different combinations of traits provide optimal fitness for different environments.

Simon Cho

Using the category of metric spaces as a template, we develop a metric analogue of the categorical semantics of classical/intuitionistic logic, and show that the natural notion of predicate in this "continuous semantics" is equivalent to the a priori separate notion of predicate in continuous logic, a logic which is independently well-studied by model theori

Nestor Armesto, Paul R. Newman, Wojciech Slominski, Anna M. Stasto

We analyse the possibilities for the study of inclusive diffraction offered by future electron--proton/nucleus colliders in the TeV regime, the Large Hadron-electron Collider as an upgrade of the HL-LHC and the Future Circular Collider in electron-hadron mode. Compared to $ep$ collisions at HERA, we find an extension of the available kinematic range in $x$ b

Norbert Polat

Peano partial cubes are the bipartite graphs whose geodesic interval spaces are (closed) join spaces. They are the partial cubes all of whose finite convex subgraphs have a pre-hull number which is at most 1. Special Peano partial cubes are median graphs, cellular bipartite graphs and netlike partial cubes. Analogous properties of these graphs are satisfied

Lawrence Rudnick

Magnetic field strengths in objects ranging from HII regions to cosmological large scale structure can be estimated using dense grids of Rotation Measures (RMs) from polarized background radio structures. Upcoming surveys on the SKA and its precursors will dramatically increase the number N of background sources. However, detectable magnetic field strengths

Mario Coccia

Technological parasitism is a new theory to explain the evolution of technology in society. In this context, this study proposes a model to analyze the interaction between a host technology (system) and a parasitic technology (subsystem) to explain evolutionary pathways of technologies as complex systems. The coefficient of evolutionary growth of the model h

Brendan D. O'Brien, Carlos F. Da Silva Costa, Sergey Klimenko

Measurements of multiple harmonic modes in the gravitational wave signals from binary black hole events could provide an accurate test of general relativity, however they have never been observed before. The sub-dominant modes, other than the main (l = 2, m = 2) mode, are weak in amplitude and thus difficult to detect in a single event at the current sensiti

Filippo Cellini, Francesco Lavini, Claire Berger, Walt de Heer

The study of the nanomechanics of graphene $-$ and other 2D materials $-$ has led to the discovery of exciting new properties in 2D crystals, such as their remarkable in-plane stiffness and out of plane flexibility, as well as their unique frictional and wear properties at the nanoscale. Recently, nanomechanics of graphene has generated renovated interest fo

Patrick Rall, Daniel Liang, Jeremy Cook, William Kretschmer

We present novel algorithms to estimate outcomes for qubit quantum circuits. Notably, these methods can simulate a Clifford circuit in linear time without ever writing down stabilizer states explicitly. These algorithms outperform previous noisy near-Clifford techniques for most circuits. We identify a large class of input states that can be efficiently simu

Felipe Almeida, Geraldo Xexéo

This work lists and describes the main recent strategies for building fixed-length, dense and distributed representations for words, based on the distributional hypothesis. These representations are now commonly called word embeddings and, in addition to encoding surprisingly good syntactic and semantic information, have been proven useful as extra features

Zhenxun Zhuang, Ashok Cutkosky, Francesco Orabona

Stochastic Gradient Descent (SGD) has played a central role in machine learning. However, it requires a carefully hand-picked stepsize for fast convergence, which is notoriously tedious and time-consuming to tune. Over the last several years, a plethora of adaptive gradient-based algorithms have emerged to ameliorate this problem. They have proved efficient

Ning Chen, Tao Han, Shufang Su, Wei Su

Future precision measurements of the Standard Model (SM) parameters at the proposed Z-factories and Higgs factories may have significant impacts on new physics beyond the Standard Model (BSM). We illustrate this by focusing on the Type-II two Higgs doublet model. A multi-variable global fitting is performed with full one loop contributions to relevant coupli

Neel Doshi, Kaushik Jayaram, Benjamin Goldberg, Zachary Manchester

Planning locomotion trajectories for legged microrobots is challenging because of their complex morphology, high frequency passive dynamics, and discontinuous contact interactions with their environment. Consequently, such research is often driven by time-consuming experimental methods. As an alternative, we present a framework for systematically modeling, p

T. I. Mogilyuk, P. D. Grigoriev

We develop the theory of transverse magnetoresistance in layered quasi-two-dimensional metals. Using the Kubo formula and harmonic expansion, we calculate intralayer conductivity in a magnetic field perpendicular to conducting layers. The analytical expressions for the amplitudes and phases of magnetic quantum oscillations (MQO) and of the so-called slow osc

Yuchen Xie, Richard Byrd, Jorge Nocedal

The classical convergence analysis of quasi-Newton methods assumes that the function and gradients employed at each iteration are exact. In this paper, we consider the case when there are (bounded) errors in both computations and establish conditions under which a slight modification of the BFGS algorithm with an Armijo-Wolfe line search converges to a neigh

Youness Dendane, Fabio Petrillo, Hamid Mcheick, Souhail Ben Ali

Today, we have to deal with many data (Big data) and we need to make decisions by choosing an architectural framework to analyze these data coming from different area. Due to this, it become problematic when we want to process these data, and even more, when it is continuous data. When you want to process some data, you have to first receive it, store it, an

Paul B. Slater

We employ a quasirandom methodology, recently developed by Martin Roberts, to estimate the separability probabilities, with respect to the Bures (minimal monotone/statistical distinguishability) measure, of generic two-qubit and two-rebit states. This procedure, based on generalized properties of the golden ratio, yielded, in the course of almost seventeen b

Ming Li, Michael Mills, Prateek Puri, Alexander Petrov

An important physical process unique to neutral-ion systems is the charge-transfer (CT) reaction. Here, we present measurements of and models for CT processes between co-trapped ultracold Ca atoms and Yb ions under well-controlled conditions. The theoretical analysis reveals the existence of three reaction mechanisms when lasers from a magneto-optical trap (

Roy Adams, Yuelong Ji, Xiaobin Wang, Suchi Saria

Measurement error in observational datasets can lead to systematic bias in inferences based on these datasets. As studies based on observational data are increasingly used to inform decisions with real-world impact, it is critical that we develop a robust set of techniques for analyzing and adjusting for these biases. In this paper we present a method for es

Filippo Cellini, Yang Gao, Elisa Riedo

During conventional nanoindentation measurements, the indentation depths are usually larger than 1-10 nm, which hinders the ability to study ultra-thin films ($<$ 10 nm) and supported atomically thin two-dimensional (2D) materials. Here, we discuss the development of modulated \r{A}-indentation to achieve sub-\r{A} indentation depths during force-indentation

Linyuan Lu, Zhiyu Wang

For a fixed set of positive integers $R$, we say $\mathcal{H}$ is an $R$-uniform hypergraph, or $R$-graph, if the cardinality of each edge belongs to $R$. An $R$-graph $\mathcal{H}$ is \emph{covering} if every vertex pair of $\mathcal{H}$ is contained in some hyperedge. For a graph $G=(V,E)$, a hypergraph $\mathcal{H}$ is called a \textit{Berge}-$G$, denoted

Dara Gold, Steven Rosenberg

Gradient descent, or negative gradient flow, is a standard technique in optimization to find minima of functions. Many implementations of gradient descent rely on discretized versions, i.e., moving in the gradient direction for a set step size, recomputing the gradient, and continuing. In this paper, we present an approach to manifold learning where gradient

Abhinav Jangda, Bobby Powers, Emery Berger, Arjun Guha

All major web browsers now support WebAssembly, a low-level bytecode intended to serve as a compilation target for code written in languages like C and C++. A key goal of WebAssembly is performance parity with native code; previous work reports near parity, with many applications compiled to WebAssembly running on average 10% slower than native code. However

Hao Wang, Lei Chen

The prevailing models for advancing dynamic contact angle are under intensive debates, and the fitting performances are far from satisfying in practice. The present study proposes a model based on the recent understanding of the multi-scale structure and the local friction at the contact line. The model has unprecedented fitting performance for dynamic conta

Michael A. Pajkos, Sean M. Couch, Kuo-Chuan Pan, Evan P. O'Connor

We explore the influence of progenitor mass and rotation on the gravitational-wave (GW) emission from core-collapse supernovae, during the postbounce, preexplosion, accretion-phase. We present the results from 15 two-dimensional (2D) neutrino radiation-hydrodynamic simulations from initial stellar collapse to $\sim$300 ms after core bounce. We examine the fe

Björn Barz, Joachim Denzler

Two things seem to be indisputable in the contemporary deep learning discourse: 1. The categorical cross-entropy loss after softmax activation is the method of choice for classification. 2. Training a CNN classifier from scratch on small datasets does not work well. In contrast to this, we show that the cosine loss function provides significantly better perf

Jeffrey. H. Dinitz, Richard Games, Robert Roth

The generalized taxicab number $T(n,m,t)$ is equal to the smallest number that is the sum of $n$ positive $m$th powers in $t$ ways. This definition is inspired by Ramanujan's observation that $1729 = 1^3+ 12^3 =9^3 + 10^3 $ is the smallest number that is the sum of two cubes in two ways and thus $1729= T(2,3,2)$. In this paper we prove that for any given pos

Kohtaroh Miura

Lattice QCD (LQCD) studies for the hadron vacuum polarization (HVP) and its contribution to the muon anomalous magnetic moment (muon g-2) are reviewed. There currently exists more than 3-sigma deviations in the muon g-2 between the BNL experiment with 0.5 ppm precision and the Standard Model (SM) predictions, where the latter relies on the QCD dispersion rel

Flavien Léger, Wuchen Li

We study generalized Hopf-Cole transformations motivated by the Schr\"odinger bridge problem, which can be seen as a boundary value Hamiltonian system on the Wasserstein space. We prove that generalized Hopf-Cole transformations are symplectic submersions in the Wasserstein symplectic geometry. Many examples, including a Hopf-Cole transformation for the shal

Théo Coulin, Maxence Detante, William Mouchère, Fabio Petrillo

In Software Engineering, early detection of architectural issues is key. It helps mitigate the risk of poor performance, and lowers the cost of repairing these issues. Metrics give a quick overview of the project which helps designers with the detection of flaws or degradation in their architecture. Even though studies unveiled architectural metrics more tha

Guillaume Bellec, Franz Scherr, Elias Hajek, Darjan Salaj

The way how recurrently connected networks of spiking neurons in the brain acquire powerful information processing capabilities through learning has remained a mystery. This lack of understanding is linked to a lack of learning algorithms for recurrent networks of spiking neurons (RSNNs) that are both functionally powerful and can be implemented by known bio

Subhroneel Chakrabarti, Deepali Mishra, Yogesh K. Srivastava, Amitabh Virmani

The generalised Garfinkle-Vachaspati transform (GGV) introduced in our previous work (arXiv:1808.04981) is a powerful technique to add hair modes to a class of known D1-D5 solutions. The richest and most interesting examples of D1-D5 geometries involve the dilaton and this calls for an extension of the procedure to accommodate for the presence of the dilaton

Julaiti Alafate, Yoav Freund

State-of-the-art implementations of boosting, such as XGBoost and LightGBM, can process large training sets extremely fast. However, this performance requires that the memory size is sufficient to hold a 2-3 multiple of the training set size. This paper presents an alternative approach to implementing the boosted trees, which achieves a significant speedup o

Renata Kallosh, Yusuke Yamada

We construct the simplest inflationary $\alpha$-attractor models in supergravity: it has only one scalar, the inflaton. There is no sinflaton since the inflaton belongs to an orthogonal nilpotent superfield where the sinflaton depends on fermion bilinears. When the local supersymmetry is gauge-fixed, these models have only one single real scalar (the inflato

L. O. McNeill, Rosemary A. Mardling, B. Müller

We study the effect of tidal forcing on gravitational wave signals from tidally relaxed white dwarf pairs in the LISA, DECIGO and BBO frequency band ($0.1-100\,{\rm mHz}$). We show that for stars not in hydrostatic equilibrium (in their own rotating frames), tidal forcing will result in energy and angular momentum exchange between the orbit and the stars, th

Daniel M. Siegel

The first neutron star (NS) merger observed by advanced LIGO and Virgo, GW170817, and its fireworks of electromagnetic counterparts across the entire electromagnetic spectrum marked the beginning of multi-messenger astronomy and astrophysics with gravitational waves. The ultraviolet, optical, and near-infrared emission was consistent with being powered by th

Pedro L. S. Lopes, V. L. Quito, Bo Han, Jeffrey C. Y. Teo

Through a theoretical coupled wire model, we construct strongly correlated electronic \emph{integer} quantum Hall states. As a distinguishing feature, these states support electric and thermal Hall transport violating the Wiedemann-Franz law as $\left(\kappa_{xy}/\sigma_{xy}\right)/\left[\left(\pi^{2}k_{B}^{2}T\right)/3e^{2}\right]<1$.We propose a new Abelia

Kevin Qian, Zachary Eldredge, Wenchao Ge, Guido Pagano

We generalize past work on quantum sensor networks to show that, for $d$ input parameters, entanglement can yield a factor $\mathcal O(d)$ improvement in mean squared error when estimating an analytic function of these parameters. We show that the protocol is optimal for qubit sensors, and conjecture an optimal protocol for photons passing through interferom

Christopher D. Wiens, Trey V. Wenger, Panayiotis Tzanavaris, Kelsey E. Johnson

We use the outputs of a semi-analytical model of galaxy formation run on the Millennium Simulation to investigate the prevalence of 3D compact groups (CGs) of galaxies from $z = 11$ to 0. Our publicly available code identifies CGs using the 3D galaxy number density, the mass ratio of secondary+tertiary to the primary member, mass density in a surrounding she

P. De Vis, A. Jones, S. Viaene, V. Casasola

Observations of evolution in the dust-to-metal ratio allow us to constrain the dominant dust processing mechanisms. In this work, we present a study of the dust-to-metal and dust-to-gas ratios in a sub-sample of ~500 DustPedia galaxies. Using literature and MUSE emission line fluxes, we derived gas-phase metallicities (oxygen abundances) for over 10000 indiv

Robert Feldmann, Claude-André Faucher-Giguère, Dušan Kereš

Properties of galaxies vary systematically with the mass of their parent dark matter halos. This basic galaxy - halo connection shows a fair amount of scatter whose origin is not fully understood. Here, we study how differences in the halo assembly history affect central galaxies in low mass (M_halo < 10^12 M_sun) halos at z=2-6 with the help of the MassiveF

Marcel Balsiger, Thomas Becher, Ding Yu Shao

Starting from a factorization theorem in effective field theory, we present resummed results for two non-global observables: the invariant-mass distribution of jets and the energy distribution outside jets. Our results include the full next-to-leading-order corrections to the hard, jet and soft functions and are implemented in a parton-shower framework which

Yuting Hu, Yidun Wan

Entanglement entropy in topologically ordered matter phases has been computed extensively using various methods. In this paper, we study the entanglement entropy of topological phases in two-spaces from a new perspective---the perspective of quasiparticle fluctuations. In this picture, the entanglement spectrum of a topologically ordered system is identified

P. D. Grigoriev, T. I. Mogilyuk

The interplay between angular and quantum magnetoresistance oscillations in quasi-two-dimensional metals leads to the angular oscillations of the amplitude of quantum oscillations. This effect becomes pronounced in high magnetic field, when the simple factorization of the angular and quantum oscillations is not valid. The amplitude of quantum magnetoresistan

Pavel Safronov

We introduce quantum versions of Manin pairs and Manin triples and define quantum moment maps in this context. This provides a framework that incorporates quantum moment maps for actions of Lie algebras and quantum groups for any quantum parameter. We also show how our quantum moment maps degenerate to known classical versions of moment maps and describe the

E. Zubizarreta Casalengua, J. C. López Carreño, F. P. Laussy, E. del Valle

We show how the photon statistics emitted by a large variety of light-matter systems under weak coherent driving can be understood, to lowest order in the driving, in the framework of an admixture of (or interference between) a squeezed state and a coherent state, with the resulting state accounting for all bunching and antibunching features. One can further

Thierry Combot

Let $H$ be a hyperexponential function in $n$ variables $x=(x_1,\dots,x_n)$ with coefficients in a field $\mathbb{K}$, $[\mathbb{K}:\mathbb{Q}] <\infty$, and $\omega$ a rational differential $1$-form. Assume that $H\omega$ is closed and $H$ transcendental. We prove using Schanuel conjecture that there exist a univariate function $f$ and multivariate rational

V. V. Ryzhikov

This work contains the following results: the trajectory fullness of the homoclinic groups, their connections with factors, K-property, weak multiple mixing; the ergodicity of the weakly homoclinic group for Gauss and Poisson actions; the triviality of the weakly homoclinic group for classes of rank-1 actions. Some open problems are discussed.

D. Jack

Using the recently published Gaia second data release which includes measurements of the mean radial velocity of about 7.2 million stars, we performed a systematic comparison with other existing radial velocity catalogues in order to search for variations in the radial velocity measurements, with the goal that detected differences may indicate that these sta

Alessio Corti, Giulia Gugiatti

For all k>0 integer, we consider the regularised I-function of the family of del Pezzo surfaces of degree 8k+4 in P(2,2k+1,2k+1, 4k+1), first constructed by Johnson and Koll\'ar. We show that this function, which is of hypergeometric type, is a period of an explicit pencil of curves. Thus the pencil is a candidate LG mirror of the family of del Pezzo surface