April 2019 arXiv papers — page 119

Jiameng Zhang, Xue Li, Yong Zou, Shuguang Guan

We study synchronization in a system of Stuart-Landau oscillators with frequency-weighted coupling. For three typical unimodal frequency distributions, namely, the Lorentzian, the triangle, and the uniform, we found that the first-order transition occurs when the frequency distribution is relatively compact, while the synchronization transition is continuous

Jessica R. Lu, Casey Lam, Will Dawson, B. Scott Gaudi

One of the key phases of stellar evolution that remains poorly understood is stellar death. We lack a predictive model for how a star of a given mass explodes and what kind of remnant it leaves behind (i.e. the initial-final mass relation, IFMR). Progress has been limited due to the difficulty in finding and weighing black holes and neutron stars in large nu

Xin Li, Chao Ma, Baoyuan Wu, Zhenyu He

Existing deep trackers mainly use convolutional neural networks pre-trained for generic object recognition task for representations. Despite demonstrated successes for numerous vision tasks, the contributions of using pre-trained deep features for visual tracking are not as significant as that for object recognition. The key issue is that in visual tracking

Jessica R. Lu, Casey Y. Lam, Michael Medford, William Dawson

Gravitational microlensing is one of the few means of finding primordial black holes (PBHs), if they exist. Recent LIGO detections of 30 Msun black holes have re-invigorated the search for PBHs in the 10-100 Msun mass regime. Unfortunately, individual PBH microlensing events cannot easily be distinguished from stellar lensing events from photometry alone. Ho

Asa Eagle, Takumi Kato, Yuichiro Minato

To solve tiling puzzles, such as "pentomino" or "tetromino" puzzles, we need to find the correct solutions out of numerous combinations of rotations or piece locations. Solving this kind of combinatorial optimization problem is a very difficult problem in computational science, and quantum computing is expected to play an important role in th

Matthias F. M. Lutz, Xiao-Yu Guo, Yonggoo Heo

We discuss the convergence properties of chiral expansions for the pseudoscalar and vector charmed meson masses based on the chiral SU(3) Lagrangian. Conventional expansion strategies as formulated in terms of bare meson masses are shown to suffer from poor convergence properties. This changes once the expansion is set up in terms of on-shell masses. We find

Murat Yessenov, Basanta Bhaduri, H. Esat Kondakci, Monjurul Meem

Space-time (ST) wave packets are coherent pulsed beams that propagate diffraction-free and dispersion-free by virtue of tight correlations introduced between their spatial and temporal spectral degrees of freedom. Less is known of the behavior of incoherent ST fields that maintain the spatio-temporal spectral structure of their coherent wave-packet counterpa

Xiangyun Meng, Nathan Ratliff, Yu Xiang, Dieter Fox

End-to-end learning for autonomous navigation has received substantial attention recently as a promising method for reducing modeling error. However, its data complexity, especially around generalization to unseen environments, is high. We introduce a novel image-based autonomous navigation technique that leverages in policy structure using the Riemannian Mo

Raymond Chan, Hongfei Yang, Tieyong Zeng

Image segmentation is an important task in the domain of computer vision and medical imaging. In natural and medical images, intensity inhomogeneity, i.e. the varying image intensity, occurs often and it poses considerable challenges for image segmentation. In this paper, we propose an efficient variational method for segmenting images with intensity inhomog

Hootan Alavizadeh, Hooman Alavizadeh, Dong Seong Kim, Julian Jang-Jaccard

Cloud service providers offer their customers with on-demand and cost-effective services, scalable computing, and network infrastructures. Enterprises migrate their services to the cloud to utilize the benefit of cloud computing such as eliminating the capital expense of their computing need. There are security vulnerabilities and threats in the cloud. Many

Line Roald, Daniel K. Molzahn

In many power system optimization problems, we observe that only a small fraction of the line flow constraints ever become active at the optimal solution, despite variations in the load profile and generation costs. This observation has far-reaching implications not only for power system optimization, but also for the practical long-term planning, operation,

Muhammad Usman, Yi Z. Wong, Charles D. Hill, Lloyd C. L. Hollenberg

Atomic level qubits in silicon are attractive candidates for large-scale quantum computing, however, their quantum properties and controllability are sensitive to details such as the number of donor atoms comprising a qubit and their precise location. This work combines machine learning techniques with million-atom simulations of scanning-tunnelling-microsco

Shang-Wu Bin, Xin-You Lü, Tai-Shuang Yin, Gui-Lei Zhu

Mass sensing connects the mass variation to a frequency shift of a mechanical oscillator, whose limitation is determined by its mechanical frequency resolution. Here we propose a method to enlarge a minute mechanical frequency shift, which is smaller than the linewidth of the mechanical oscillator, into a huge frequency shift of the normal mode. Explicitly,

Yshai Avishai, Keisuke Totsuka, Naoto Nagaosa

The matrix-valued Aharonov-Casher phase factor $F_{\text{AC}}$ (related to the c-number Aharonov-Casher phase $λ_{\text{AC}}$) plays an important role in the physics of mesoscopic systems in which spin-orbit coupling is relevant. Yet, its relation to experimental observables is rather elusive. Based on the SU(2)-gauge-invariant formulation of the Schroedinge

Cheng Tang

We present Matrix Krasulina, an algorithm for online k-PCA, by generalizing the classic Krasulina's method (Krasulina, 1969) from vector to matrix case. We show, both theoretically and empirically, that the algorithm naturally adapts to data low-rankness and converges exponentially fast to the ground-truth principal subspace. Notably, our result suggests

Sze-Teng Liong, Y. S. Gan, Danna Zheng, Shu-Meng Lic

Owing to the development and advancement of artificial intelligence, numerous works were established in the human facial expression recognition system. Meanwhile, the detection and classification of micro-expressions are attracting attentions from various research communities in the recent few years. In this paper, we first review the processes of a conventi

Ya Li, Xinmei Tian, Tongliang Liu, Dacheng Tao

Multitask learning (MTL) aims to learn multiple tasks simultaneously through the interdependence between different tasks. The way to measure the relatedness between tasks is always a popular issue. There are mainly two ways to measure relatedness between tasks: common parameters sharing and common features sharing across different tasks. However, these two t

Guoxing Ji

Let $\mathfrak A$ be a maximal subdiagonal algebra in a $σ$-finite von Neumann algebra $\mathcal M$. If every right invariant subspace of $\mathfrak A$ in the non-commutative Hardy space $H^2$ is of Beurling type, then we say $\mathfrak A$ to be type 1. We determine generators of these algebras and consider a Riesz type factorization theorem for the non-comm

Xue Dong He, Moris S. Strub, Thaleia Zariphopoulou

We introduce the concept of forward rank-dependent performance processes, extending the original notion to forward criteria that incorporate probability distortions. A fundamental challenge is how to reconcile the time-consistent nature of forward performance criteria with the time-inconsistency stemming from probability distortions. For this, we first propo

Kai Liao

Assuming the $Λ$CDM model, the CMB and BAO observations indicate a very flat Universe. Model-independent measurements are therefore worth studying. Time delays measured in lensed quasars provide the time delay distances. When compared with the luminosity distances from Supernova Ia observation, the measurements can provide the curvature information under the

Wei-Ming Yao

Particle Identification (PID) plays a key role in heavy flavor physics in high-energy physics experiments. However, its impact on Higgs physics is still not clear. In this note, we will explore some of the potential of PID to improve the identification of heavy-flavour jets by using identified charged Kaons in addition to the traditional vertexing informatio

Wei Li, Xiang Xiao, Huilong Xu

Van der Waals heterostructures formed by stacking of various two-dimensional materials are promising in electronic applications. However, the performances of most reported electronic devices based on van der Waals heterostructures are far away from existing technologies'. Here we report high performance heterostructure devices based on vertically stacked

Makoto Kawashima

Let $m\ge 2$ be an integer, $K$ an algebraic number field and $α\in K\setminus \{0,-1\}$ with sufficiently small absolute value. In this article, we provide a new lower bound for linear form in $1,{\rm{log}}(1+α),\ldots,{\rm{log}}^{m-1}(1+α)$ with algebraic integer coefficients in both complex and $p$-adic cases (see Theorem $2.1$ and Theorem $2.4$). Especia

Nathalia Nascimento, Carlos Lucena, Paulo Alencar, Carlos Juliano Viana

Multiagent Systems (MASs) involve different characteristics, such as autonomy, asynchronous and social features, which make these systems more difficult to understand. Thus, there is a lack of procedures guaranteeing that multiagent systems would behave as desired. Further complicating the situation is the fact that current agent-based approaches may also in

Jian-Guo Zhang, Pengcheng Zou, Zhao Li, Yao Wan

Nowadays, more and more customers browse and purchase products in favor of using mobile E-Commerce Apps such as Taobao and Amazon. Since merchants are usually inclined to describe redundant and over-informative product titles to attract attentions from customers, it is important to concisely display short product titles on limited screen of mobile phones. To

Wenzhe Liu, Bo Wang, Yiwen Zhang, Jiajun Wang

Bound states in the continuum in periodic photonic systems like photonic crystal slabs are proved to be accompanied by vortex polarization singularities on the photonic bands in the momentum space. The winding structures of polarization states not only widen the field of topological physics but also show great potential that such systems could be applied in

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

In this paper, we propose a resilient energy efficient and fog computing infrastructure for health monitoring applications. We design the infrastructure to be resilient against server failures under two scenarios; without geographical constraints and with geographical constraints. We consider a heart monitoring application where patients send their 30-second

Shawn X. Cui, Kevin T. Tian, Jennifer F. Vasquez, Zhenghan Wang

It is an open question if there are leakage-free entangling Fibonacci braiding gates. We provide evidence to the conjecture for the negative in this paper. We also found a much simpler protocol to generate approximately leakage-free entangling Fibonacci braiding gates than existing algorithms in the literature.

Shaoying Wang, Haijiang Lai, Yifan Yang, Jian Yin

Deep-networks-based hashing has become a leading approach for large-scale image retrieval, which learns a similarity-preserving network to map similar images to nearby hash codes. The pairwise and triplet losses are two widely used similarity preserving manners for deep hashing. These manners ignore the fact that hashing is a prediction task on the list of b

Anirban Bhattacharjee, Yogesh Barve, Shweta Khare, Shunxing Bao

With the proliferation of machine learning (ML) libraries and frameworks, and the programming languages that they use, along with operations of data loading, transformation, preparation and mining, ML model development is becoming a daunting task. Furthermore, with a plethora of cloud-based ML model development platforms, heterogeneity in hardware, increased

Hirshikesh, C Jansari, K Kannan, RK Annabattula

An adaptive phase field method is proposed for crack propagation in brittle materials under quasi-static loading. The adaptive refinement is based on the recovery type error indicator, which is combined with the quadtree decomposition. Such a decomposition leads to elements with hanging nodes. Thanks to the polygonal finite element method, the elements with

Han Qin, Kit Riehle, Haozhen Zhao

Web traffic is a valuable data source, typically used in the marketing space to track brand awareness and advertising effectiveness. However, web traffic is also a rich source of information for cybersecurity monitoring efforts. To better understand the threat of malicious cyber actors, this study develops a methodology to monitor and evaluate web activity u

Yusuke Kamogawa, Joji Nasu, Akihisa Koga

We study a ferromagnetic instability in a doped single-band Hubbard model by means of dynamical mean-field theory with the continuous-time quantum Monte Carlo simulations. Examining the effect of the strong correlations in the system on the hypercubic and Bethe lattice, we find that the ferromagnetically ordered state appears in the former, while it does not

Fusheng Wei, Han Qin, Shi Ye, Haozhen Zhao

Predictive coding has been widely used in legal matters to find relevant or privileged documents in large sets of electronically stored information. It saves the time and cost significantly. Logistic Regression (LR) and Support Vector Machines (SVM) are two popular machine learning algorithms used in predictive coding. Recently, deep learning received a lot

Peter Gronvall, Nathaniel Huber-Fliflet, Jianping Zhang, Robert Keeling

Protecting privileged communications and data from disclosure is paramount for legal teams. Unrestricted legal advice, such as attorney-client communications or litigation strategy. are vital to the legal process and are exempt from disclosure in litigations or regulatory events. To protect this information from being disclosed, companies and outside counsel

Rishi Chhatwal, Peter Gronvall, Nathaniel Huber-Fliflet, Robert Keeling

In today's legal environment, lawsuits and regulatory investigations require companies to embark upon increasingly intensive data-focused engagements to identify, collect and analyze large quantities of data. When documents are staged for review the process can require companies to dedicate an extraordinary level of resources, both with respect to human

Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber

Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural networks to learn directly from examples of buggy programs and their fixes. In this work, we consider a recently identified class of bugs called variable-misuse bugs. The state-of-the-art

Rishi Chhatwal, Nathaniel Huber-Fliflet, Robert Keeling, Jianping Zhang

One type of machine learning, text classification, is now regularly applied in the legal matters involving voluminous document populations because it can reduce the time and expense associated with the review of those documents. One form of machine learning - Active Learning - has drawn attention from the legal community because it offers the potential to ma

Rishi Chhatwal, Nathaniel Huber-Fliflet, Robert Keeling, Jianping Zhang

Predictive coding, once used in only a small fraction of legal and business matters, is now widely deployed to quickly cull through increasingly vast amounts of data and reduce the need for costly and inefficient human document review. Previously, the sole front-end input used to create a predictive model was the exemplar documents (training data) chosen by

Yoshimasa Matsuno

In a previous paper (Matsuno 2011 {\it J. Phys. A: Math. Theore.} {\bf 44} 495202), we have presented a determinantal expression of the bright $N$-soliton solution for a multi-component modified nonlinear Schrödinger (NLS) system with zero boundary conditions. The present paper provides the dark $N$-soliton solution for the same system of equations with plan

Naveen Somasunderam

We derive an inequality on the discrepancy of sequences on the ring of $p$-adic integers $\ZZ_p$ using techniques from Fourier analysis. The inequality is used to obtain an upper bound on the discrepancy of the sequence $α_n = na +b$, where $a$ and $b$ are elements of $\ZZ_p$. This is a $p$-adic analogue of the classical LeVeque inequality on the circle grou

Kelsey M. Funkhouser, William Martinez, Rachel Henderson, Marcos Caballero

Recently, there have been several national calls to emphasize physics practices and skills within laboratory courses. In this paper, we describe the redesign and implementation of a two-course sequence of algebra-based physics laboratories at Michigan State University called Design Analysis Tools and Apprenticeship (DATA) Lab. The large-scale course transfor

Chang Sik Lee, Dong Eui Chang

An energy based approach for stabilizing a mechanical system has offered a simple yet powerful control scheme. However, since it does not impose such strong constraints on parameter space of the controller, finding appropriate parameter values for an optimal controller is known to be hard. This paper intends to generate an optimal energy-based controller for

Timothy Verstraeten, Ann Nowe, Jonathan Keller, Yi Guo

Wind farms are an indispensable driver toward renewable and nonpolluting energy resources. However, as ideal sites are limited, placement in remote and challenging locations results in higher logistics costs and lower average wind speeds. Therefore, it is critical to increase the reliability of the turbines to reduce maintenance costs. Robust implementation

T. Mühlpfordt, L. Roald, V. Hagenmeyer, T. Faulwasser

As the share of renewables in the grid increases, the operation of power systems becomes more challenging. The present paper proposes a method to formulate and solve chance-constrained optimal power flow while explicitly considering the full nonlinear AC power flow equations and stochastic uncertainties. We use polynomial chaos expansion to model the effects

Keaton Hamm, Longxiu Huang

This article discusses a useful tool in dimensionality reduction and low-rank matrix approximation called the CUR decomposition. Various viewpoints of this method in the literature are synergized and are compared and contrasted; included in this is a new characterization of exact CUR decompositions. A novel perturbation analysis is performed on CUR approxima

Yongli Zhu, Chengxi Liu, Renchang Dai, Guangyi Liu

A placement problem for multiple Battery Energy Storage System (BESS) units is formulated towards power system transient voltage stability enhancement in this paper. The problem is solved by the Cross-Entropy (CE) optimization method. A simulation-based approach is adopted to incorporate higher-order dynamics and nonlinearities of generators and loads. The o

Brittany Froese Hamfeldt

It is well known that the quadratic-cost optimal transportation problem is formally equivalent to the second boundary value problem for the Monge-Ampère equation. Viscosity solutions are a powerful tool for analysing and approximating fully nonlinear elliptic equations. However, we demonstrate that this nonlinear elliptic equation does not satisfy a comparis

Valentina V. Tarasova, Vasily E. Tarasov

The article discusses a generalization of model of economic growth with constant pace, which takes into account the effects of dynamic memory. Memory means that endogenous or exogenous variable at a given time depends not only on their value at that time, but also on their values at previous times. To describe the dynamic memory we use derivatives of non-int

Aqfil Mu'tazili, Hendra Gunawan

In this article, we compute Von Neumann-Jordan constant, James constant, and Dunkl-Williams constant for small Morrey spaces. Our approach can also be seen as an alternative way in computing the three constants for the (classical) Morrey spaces. In addition, we prove constructively that Morrey spaces are not uniformly non-octahedral.

Jin W. Kim, Prashant G. Mehta

The purpose of this paper is to review and highlight some connections between the problem of nonlinear smoothing and optimal control of the Liouville equation. The latter has been an active area of recent research interest owing to work in mean-field games and optimal transportation theory. The nonlinear smoothing problem is considered here for continuous-ti

Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, Matt Coler

A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence o

Stephen Ro, Eric R. Coughlin, Eliot Quataert

Core-collapse supernovae span a wide range of energies, from much less than to much greater than the binding energy of the progenitor star. As a result, the shock wave generated from a supernova explosion can have a wide range of Mach numbers. In this paper, we investigate the propagation of shocks with arbitrary initial strengths in polytropic stellar envel

H. Vivas

The magnetic Hartree Fock ground state stability for a two-dimensional interacting electron system with Rashba-type coupling is studied by implementing the standard many body Green's function formalism. The externally applied electrical field $\mathbf{E}$ enters into the Hamiltonian model through a local gauge-type transformation $\sim E_{j}\mathcal{A}_{

G. Dias Pais, Srikumar Ramalingam, Venu Madhav Govindu, Jacinto C. Nascimento

We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans. Given a set of 3D point correspondences, we build a deep neural network to address the following two challenges: (i) classification of the point correspondences into inliers/outliers, and (ii) regression of the motion parameters that align the scans into a common refere

Tobias Melson, Daniel Kresse, H. -Thomas Janka

We present a carefully designed, systematic study of the angular resolution dependence of simulations with the Prometheus-Vertex neutrino-hydrodynamics code. Employing a simplified neutrino heating-cooling scheme in the Prometheus hydrodynamics module allows us to sample the angular resolution between 4 degrees and 0.5 degrees. With a newly-implemented stati

Muhammad Usman Riaz, Hamdan Awan, Chun Tung Chou

This work builds on our earlier work on designing demodulators for diffusion-based molecular communications using a Markovian approach. The demodulation filters take the form of an ordinary differential equation (ODE) which computes the log-posteriori probability of observing a transmission symbol given the continuous history of receptor activities. A limita

Jeremy Nixon, Mike Dusenberry, Ghassen Jerfel, Timothy Nguyen

Overconfidence and underconfidence in machine learning classifiers is measured by calibration: the degree to which the probabilities predicted for each class match the accuracy of the classifier on that prediction. How one measures calibration remains a challenge: expected calibration error, the most popular metric, has numerous flaws which we outline, and t

Aparna Balagopalan, Ksenia Shkaruta, Jekaterina Novikova

Automatic Speech Recognition (ASR) is a critical component of any fully-automated speech-based dementia detection model. However, despite years of speech recognition research, little is known about the impact of ASR accuracy on dementia detection. In this paper, we experiment with controlled amounts of artificially generated ASR errors and investigate their

Javier Roulet, Liang Dai, Tejaswi Venumadhav, Barak Zackay

We introduce an algorithm for placing template waveforms for the search of compact binary mergers in gravitational wave interferometer data. We exploit the smooth dependence of the amplitude and unwrapped phase of the frequency-domain waveform on the parameters of the binary. We group waveforms with similar amplitude profiles and perform a singular value dec

Scott E. Kruger, John M. Greene

The properties of two local reference frames based on the magnetic field and the current density are investigated for magnetized plasmas in toroidal geometry with symmetric angle. The magnetic field-based local frame of reference has been well-studied for example by Dewar and colleagues [Phys. Fluids 27, 1723 (1984)] An analogous frame based on the current d

Emilien Dupont, Arnaud Doucet, Yee Whye Teh

We show that Neural Ordinary Differential Equations (ODEs) learn representations that preserve the topology of the input space and prove that this implies the existence of functions Neural ODEs cannot represent. To address these limitations, we introduce Augmented Neural ODEs which, in addition to being more expressive models, are empirically more stable, ge

Daniel Jakubovitz, Miguel R. D. Rodrigues, Raja Giryes

Transfer learning is a very important tool in deep learning as it allows propagating information from one "source dataset" to another "target dataset", especially in the case of a small number of training examples in the latter. Yet, discrepancies between the underlying distributions of the source and target data are commonplace and are known

Hideki Maeda, Cristian Martinez

We present higher-dimensional generalizations of the Buchdahl and Janis-Robinson-Winicour transformations which generate static solutions in the Einstein-Maxwell system with a massless scalar field. While the former adds a nontrivial scalar field to a vacuum solution, the latter generates a charged solution from a neutral one with the same scalar field. Appl

Oskar Ljungqvist, Niclas Evestedt, Daniel Axehill, Marcello Cirillo

Maneuvering a general 2-trailer with a car-like tractor in backward motion is a task that requires significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path-following control solution that can be used to automatically plan and execute difficult parking and obsta

Rosario Scalise, Jesse Thomason, Yonatan Bisk, Siddhartha Srinivasa

We use static object data to improve success detection for stacking objects on and nesting objects in one another. Such actions are necessary for certain robotics tasks, e.g., clearing a dining table or packing a warehouse bin. However, using an RGB-D camera to detect success can be insufficient: same-colored objects can be difficult to differentiate, and re

Chiwoo Park, Peihua Qiu, Jennifer Carpena-Núñez, Rahul Rao

Selecting input variables or design points for statistical models has been of great interest in adaptive design and active learning. Motivated by two scientific examples, this paper presents a strategy of selecting the design points for a regression model when the underlying regression function is discontinuous. The first example we undertook was for the pur

Jihong Ma, Kai Sun, Stefano Gonella

In this work, we investigate theoretically and demonstrate experimentally the existence of valley-Hall edge states in the in-plane dynamics of honeycomb lattices with bi-valued strut thickness. We exploit these states to achieve non-trivial waveguiding of optical modes that is immune to backscattering from sharp corners. We also present how different types o

Becca Thomases, Robert D. Guy

Simulations of undulatory swimming in viscoelastic fluids with large amplitude gaits show concentration of polymer elastic stress at the tips of the swimmers.We use a series of related theoretical investigations to probe the origin of these concentrated stresses. First the polymer stress is computed analytically at a given oscillating extensional stagnation

Matthew Giamou, Filip Maric, Valentin Peretroukhin, Jonathan Kelly

Estimating unknown rotations from noisy measurements is an important step in SfM and other 3D vision tasks. Typically, local optimization methods susceptible to returning suboptimal local minima are used to solve the rotation averaging problem. A new wave of approaches that leverage convex relaxations have provided the first formal guarantees of global optim

Kazim Buyukboduk, Ryotaro Sakamoto

The primary goal of this article is to study $p$-adic Beilinson conjectures in the presence of exceptional zeros for Artin motives over CM fields. In more precise terms, we address a question raised by Hida and Tilouine on the order of vanishing of Katz $p$-adic $L$-functions associated to CM fields, by means of a leading term formula we prove in terms of Ru

Karel Mundnich, Brandon M. Booth, Benjamin Girault, Shrikanth Narayanan

Human annotations serve an important role in computational models where the target constructs under study are hidden, such as dimensions of affect. This is especially relevant in machine learning, where subjective labels derived from related observable signals (e.g., audio, video, text) are needed to support model training and testing. Current research trend

Keck Array, BICEP2 Collaborations, :, P. A. R. Ade

Precision measurements of cosmic microwave background (CMB) polarization require extreme control of instrumental systematics. In a companion paper we have presented cosmological constraints from observations with the BICEP2 and Keck Array experiments up to and including the 2015 observing season (BK15), resulting in the deepest CMB polarization maps to date

Benjamin Peet

This exploratory paper considers the notion of a covering of a configuration and $G$-coverings which are coverings that are quotients under a semi-regular group action. We consider prime configurations, those which cannot $G$-cover other configurations, before considering orbiconfigurations. These are a generalized notion of a configuration in the spirit of

Eugene Vorontsov, Pavlo Molchanov, Christopher Beckham, Jan Kautz

Often in medical imaging, it is prohibitively challenging to produce enough boundary annotations to train deep neural networks for accurate tumor segmentation. We propose the use of weak labels about whether an image presents tumor or whether it is absent to extend training over images that lack these annotations. Specifically, we propose a semi-supervised f

Di Lao, Chandra Raman, C. A. R. Sá de Melo

We propose the creation of artificial nematic-orbit coupling in spin-1 Bose-Einstein condensates, in analogy to spin-orbit coupling. Using a suitably designed microwave chip, the quadratic Zeeman shift, normally uniform in space, can be made to be spatio-temporally varying, leading to a coupling between spatial and nematic degrees of freedom. A phase diagram

C. Ashall, P. Hoeflich, E. Y. Hsiao, M. M. Phillips

Our recent work demonstrates a correlation between the high-velocity blue edge, $v_{edge}$, of the iron-peak Fe/Co/Ni $H$-band emission feature and the optical light curve shape of normal, transitional and sub-luminous type Ia Supernovae (SNe Ia). We explain this correlation in terms of SN Ia physics. $v_{edge}$ corresponds to the sharp transition between th

S. O. Kepler, Ingrid Pelisoli, Detlev Koester, Nicole Reindl

White dwarfs carry information on the structure and evolution of the Galaxy, especially through their luminosity function and initial-to-final mass relation. Very cool white dwarfs provide insight into the early ages of each population. Examining the spectra of all stars with $3σ$ proper motion in the Sloan Digital Sky Survey Data Release 14, we report the c

David Aulicino, Chaya Norton

We prove that there are no Shimura-Teichmüller curves generated by genus five translation surfaces, thereby completing the classification of Shimura-Teichmüller curves in general. This was conjectured by Möller in his original work introducing Shimura-Teichmüller curves. Moreover, the property of being a Shimura-Teichmüller curve is equivalent to having comp

Jillian Tang, Reid D. Landes

N-of-1 trials allow inference between two treatments given to a single individual. Most often, clinical investigators analyze an individual's N-of-1 trial data with usual t-tests or simple nonparametric methods. These simple methods do not account for serial correlation in repeated observations coming from the individual. Existing methods accounting for

Ming Lu, Weiqiang Wang

Recently the authors initiated an $\imath$Hall algebra approach to (universal) $\imath$quantum groups arising from quantum symmetric pairs. In this paper we construct and study BGP type reflection functors which lead to isomorphisms of the $\imath$Hall algebras associated to acyclic $\imath$quivers. For Dynkin quivers, these symmetries on $\imath$Hall algebr

Madeline A. Marshall, Simon J. Mutch, Yuxiang Qin, Gregory B. Poole

We study the sizes, angular momenta and morphologies of high-redshift galaxies using an update of the Meraxes semi-analytic galaxy evolution model. Our model successfully reproduces a range of observations from redshifts $z=0$-$10$. We find that the effective radius of a galaxy disc scales with UV luminosity as $R_e\propto L_{\textrm{UV}}^{0.33}$ at $z=5$-$1

Timo Schick, Hinrich Schütze

Learning high-quality embeddings for rare words is a hard problem because of sparse context information. Mimicking (Pinter et al., 2017) has been proposed as a solution: given embeddings learned by a standard algorithm, a model is first trained to reproduce embeddings of frequent words from their surface form and then used to compute embeddings for rare word

Peter Senchyna, Daniel P. Stark, Jacopo Chevallard, Stéphane Charlot

Ultraviolet (UV) observations of local star-forming galaxies have begun to establish an empirical baseline for interpreting the rest-UV spectra of reionization-era galaxies. However, existing high-ionization emission line measurements at $z>6$ ($\mathrm{W_{C IV, 0}} \gtrsim 20$ Å) are uniformly stronger than observed locally ($\mathrm{W_{C IV, 0}} \lesssim 2

Alexander van Renen, Lukas Vogel, Viktor Leis, Thomas Neumann

I/O latency and throughput is one of the major performance bottlenecks for disk-based database systems. Upcoming persistent memory (PMem) technologies, like Intel's Optane DC Persistent Memory Modules, promise to bridge the gap between NAND-based flash (SSD) and DRAM, and thus eliminate the I/O bottleneck. In this paper, we provide one of the first perfo

Rajan Singh, Arnab Sarkar, Chitres Guria, Ryan J. T. Nicholl

High quality factor mechanical resonators have shown great promise in developing classical or quantum technologies. Simultaneously, progress has been made in developing controlled mechanical nonlinearity. Here we combine these two directions of progress in a single platform consisting of coupled Silicon Nitride (SiNx) and graphene mechanical resonators. We s

Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu

Majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases. In this paper, we study the complem

Evan Angelico, Jonathan Eisch, Andrey Elagin, Henry Frisch

We propose to use a higher-frequency RF bunch structure for the primary proton beam on target and precision timing to select different energy and flavor spectra from a wide-band neutrino beam, based on the relative arrival times of the neutrinos with respect to the RF bunch structure. This `stroboscopic' approach is complementary to techniques that selec

Juan M. Torres-Rincon, Edward Shuryak

The scalar-isoscalar mode of QCD becomes lighter/nearly massless close to the chiral transition/second-order critical point. From nuclear physics we know that this mode is the main responsible for the attractive part of the nucleon-nucleon potential at inter-particle distances of 1-2 fm. Therefore one expects that close to the critical point there is a long-

Dawei Wang

In this paper, we study the capacity dimension of the boundary of $CAT(0)$ spaces. We first compare the two metrics on the boundary of a hyperbolic $CAT(0)$ space, i.e., the visual metric and the conical metric, and prove that they give the same capacity dimension of the boundary. Then we study the capacity dimension of the boundary of buildings, which is an

Arman Cohan, Waleed Ammar, Madeleine van Zuylen, Field Cady

Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature. We propose structural scaffolds, a multitask model to incorporate structural information of scientific papers into citations

Lucian Beznea, Iulian Cîmpean, Michael Röckner

We develop a general method for extending Markov processes to a larger state space such that the added points form a polar set. The so obtained extension is an improvement on the standard trivial extension in which case the process is made stuck in the added points, and it renders a new technique of constructing extended solutions to S(P)DEs from all startin

Eric Lehman, Jay DeYoung, Regina Barzilay, Byron C. Wallace

How do we know if a particular medical treatment actually works? Ideally one would consult all available evidence from relevant clinical trials. Unfortunately, such results are primarily disseminated in natural language scientific articles, imposing substantial burden on those trying to make sense of them. In this paper, we present a new task and corpus for

Calvin Kuo, Jodie Sheffels, Michael Fanton, Ina Bianca Yu

It has long been suggested that neck muscle strength and anticipatory cocontraction can decrease head motions during head impacts. Here, we quantify the relative angular impulse contributions of neck soft tissue to head stabilization using a musculoskeletal model with Hill-type muscles and rate-dependent ligaments. We simulated sagittal extension and lateral

Priya Malpani, Kishore Thapliyal, Nasir Alam, Anirban Pathak

Quantum phase properties of photon added and subtracted displaced Fock states (and a set of quantum states which can be obtained as the limiting cases of these states) are investigated from a number of perspectives, and it is shown that the quantum phase properties are dependent on the quantum state engineering operations performed. Specifically, the analyti

Bari Maqbool, M. Sneha Prakash, R. Misra, J. S. Yadav

We report the results from analysis of six observations of Cygnus X-1 by Large Area X-ray Proportional Counters (LAXPC) and Soft X-ray Telescope (SXT) on-board AstroSat, when the source was in the hard spectral state as revealed by the broad band spectra. The spectra obtained from all the observations can be described by a single temperature Comptonizing reg

David A. B. Miller

This is a tutorial introduction to the functional analysis mathematics needed in many physical problems, such as in waves in continuous media. Functional analysis takes us beyond finite matrices, allowing us to work with infinite sets of continuous functions. It resolves important issues, such as whether, why and how we can practically reduce such problems t

Dorottya Demszky, Nikhil Garg, Rob Voigt, James Zou

We provide an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force. We quantify these aspects with existing lexical methods, and propose clustering of tweet embeddings as a means to identify salient topics for analysis across events; human evaluations show that ou

Mauricio Bustamante

The astrophysical neutrinos recently discovered by the IceCube neutrino telescope have the highest detected neutrino energies --- from TeV to PeV --- and travel the longest distances --- up to a few Gpc, the size of the observable Universe. These features make them naturally attractive probes of fundamental particle-physics properties, possibly tiny in size,

Angkun Wu, Sarang Gopalakrishnan, J. H. Pixley

We study the Anderson orthogonality catastrophe, and the corresponding x-ray edge problem, in systems that are at a localization transition driven by a deterministic quasiperiodic potential. Specifically, we address how the ground state of the Aubry-Andre model, at its critical point, responds to an instantaneous local quench. At this critical point, both th