January 2019 arXiv papers — page 30

Lei Shi, Jinquan Wei, Kazuhiro Yanagi, Takeshi Saito

Double-walled carbon nanotubes (DWCNTs) combined the advantages of multi-walled (MW-) and single-walled (SW-) CNTs can be obtained by transforming the precursors (e.g. fullerene, ferrocene) into thin inner CNTs inside SWCNTs as templates. However, this method is limited since the DWCNT yield is strongly influenced by the filling efficiency (depending on the

Palash Dey

Studying complexity of various bribery problems has been one of the main research focus in computational social choice. In all the models of bribery studied so far, the briber has to pay every voter some amount of money depending on what the briber wants the voter to report and the briber has some budget at her disposal. Although these models successfully ca

Trung Le, Dinh Phung

Previous work has questioned the conditions under which the decision regions of a neural network are connected and further showed the implications of the corresponding theory to the problem of adversarial manipulation of classifiers. It has been proven that for a class of activation functions including leaky ReLU, neural networks having a pyramidal structure

Logan Martel, Edward Newell, Drew Margolin, Derek Ruths

On the topic of journalistic integrity, the current state of accurate, impartial news reporting has garnered much debate in context to the 2016 US Presidential Election. In pursuit of computational evaluation of news text, the statements (attributions) ascribed by media outlets to sources provide a common category of evidence on which to operate. In this pap

Kyriakos Keremedis, Eliza Wajch

A metric space $\mathbf{X}$ is called densely complete if there exists a dense set $D$ in $\mathbf{X}$ such that every Cauchy sequence of points of $D $ converges in $\mathbf{X}$. One of the main aims of this work is to prove that the countable axiom of choice, $\mathbf{CAC}$ for abbreviation, is equivalent with the following statements:\smallskip (i) Every

Harsh Gupta, Seo Taek Kong, R. Srikant, Weina Wang

Boltzmann exploration is widely used in reinforcement learning to provide a trade-off between exploration and exploitation. Recently, in (Cesa-Bianchi et al., 2017) it has been shown that pure Boltzmann exploration does not perform well from a regret perspective, even in the simplest setting of stochastic multi-armed bandit (MAB) problems. In this paper, we

Yan Zhang, Qingyang Du, Chuangtang Wang, Takian Fakhrul

Integrated optical isolators have been a longstanding challenge for photonic integrated circuits (PIC). An ideal integrated optical isolator for PIC should be made by a monolithic process, have a small footprint, exhibit broadband and polarization-diverse operation, and be compatible with multiple materials platforms. Despite significant progress, the optica

Nima Tajbakhsh, Yufei Hu, Junli Cao, Xingjian Yan

We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data. The underlying idea is to assign artificial labels to abundantly available unlabeled medical images and, through a process known as surrogate supervision, pre-train a deep neural network mode

Laura Graesser, Kyunghyun Cho, Douwe Kiela

In this work, we propose a computational framework in which agents equipped with communication capabilities simultaneously play a series of referential games, where agents are trained using deep reinforcement learning. We demonstrate that the framework mirrors linguistic phenomena observed in natural language: i) the outcome of contact between communities is

Hatef Monajemi, Riccardo Murri, Eric Jonas, Percy Liang

Modern data science research can involve massive computational experimentation; an ambitious PhD in computational fields may do experiments consuming several million CPU hours. Traditional computing practices, in which researchers use laptops or shared campus-resident resources, are inadequate for experiments at the massive scale and varied scope that we now

Zdenek Sekanina

Intrinsically faint comets in nearly-parabolic orbits with perihelion distances much smaller than 1 AU exhibit strong propensity for suddenly disintegrating at a time not long before perihelion, as shown by Bortle (1991). Evidence from available observations of such comets suggests that the disintegration event usually begins with an outburst and that the de

Fenglong Qu, Bo Zhang, Haiwen Zhang

This paper is concerned with direct and inverse scattering by a locally perturbed infinite plane (called a locally rough surface in this paper) on which a Neumann boundary condition is imposed. A novel integral equation formulation is proposed for the direct scattering problem which is defined on a bounded curve (consisting of a bounded part of the infinite

Bernard Deconinck, Jeremy Upsal

We examine the stability of the elliptic solutions of the focusing nonlinear Schr\"odinger equation (NLS) with respect to subharmonic perturbations. Using the integrability of NLS, we discuss the spectral stability of the elliptic solutions, establishing that solutions of smaller amplitude are stable with respect to larger classes of perturbations. We show t

Nathan Duignan, Holger Dullin

This paper serves as a first foray on regularisation for planar vector fields. Motivated by singularities in celestial mechanics, the block regularisation of a generic class of degenerate singularities is studied. The paper is concerned with asymptotic properties of the transition map between a section before and after the singularity. Block regularisation i

Jason Crann, Matthias Neufang

We prove that a locally compact group has the approximation property (AP), introduced by Haagerup-Kraus, if and only if a non-commutative Fej\'{e}r theorem holds for the associated $C^*$- or von Neumann crossed products. As applications, we answer three open problems in the literature. Specifically, we show that any locally compact group with the AP is exact

Masahiro Manago, Shunsaku Kitagawa, Kenji Ishida, Kazuhiko Deguchi

A $^{59}$Co nuclear quadrupole resonance (NQR) was performed on a single-crystalline ferromagnetic (FM) superconductor UCoGe under pressure. The FM phase vanished at a critical pressure $P_c$, and the NQR spectrum just below $P_c$ showed phase separation of the FM and paramagnetic (PM) phases below Curie temperature $T_{\textrm{Curie}}$, suggesting first-ord

Sameh Y. Elnaggar, Gregory N. Milford

Using the time periodic ABCD parameters, an expression for the dispersion relation of space-time modulated structures is obtained. The relation is valid for general structures even when the spatial granularity is comparable to the operating and modulation wavelengths. In the limit of infinitesimal unit cell, the dispersion relation reduces identically to its

Roberto Fernández, Manuel González-Navarrete, Eugene Pechersky, Anatoly Yambartsev

We study a ferromagnetic Ising model with a staggered cell-board magnetic field previously proposed for image processing [Maruani et al., Markov Processes Relat. Fields 1 (1995) \cite{MPS}]. We complement previous results on the existence of phase transitions at low temperature [Gonz\'alez-Navarrete et al., J. Stat. Phys. 162 (2016)] by determining bounds to

Zhenhai Chang, Caiyan Jia, Xianjun Yin, Yimei Zheng

Many real-world networks known as attributed networks contain two types of information: topology information and node attributes. It is a challenging task on how to use these two types of information to explore structural regularities. In this paper, by characterizing potential relationship between link communities and node attributes, a principled statistic

Rabia Hameed, Ghulam Mustafa

In this article, we present a new method to construct a family of (2N+2)-point binary subdivision schemes with one tension parameter where N is a non-negative integer. The construction of the family of schemes is based on repeated local translation of points by certain displacement vectors. Therefore, the refinement rules of a (2N+2)-point scheme for N=M are

Will Trojak, Rob Watson, Ashley Scillitoe, Paul G. Tucker

Theoretical methods are developed to understand the effect of non-uniform grids on Flux Reconstruction (FR) in multi-dimensions. The analysis reveals that the same effect of expanding and contracting grids is seen in two dimensions as in one dimension. Namely, that expansions cause instability and contractions cause excess dissipation. Subsequent numerical e

Lihui Weng, Wei Liu

In this paper, the Milstein method is used to approximate invariant measures of stochastic differential equations with commutative noise. The decay rate of the transition probability kernel generated by the Milstein method to the unique invariant measure of the method is observed to be exponential with respect to the time variable. The convergence rate of th

Giacomo Como

Resilience has become a key aspect in the design of contemporary infrastructure networks. This comes as a result of ever-increasing loads, limited physical capacity, and fast-growing levels of interconnectedness and complexity due to the recent technological advancements. The problem has motivated a considerable amount of research within the last few years,

Jon Chaika

This is a straightforward generalization Section 2 of arXiv:1805.11167. It shows that for a residual set of transformations in the space of measure preserving transformations, with the weak topology, any self-joining defines a Markov operator that is a strong operator topology limit of convex combinations of powers of the unitary operator given by the transf

Ralph L. Cohen

In 1995 the author, Jones, and Segal introduced the notion of "Floer homotopy theory". The proposal was to attach a (stable) homotopy type to the geometric data given in a version of Floer homology. More to the point, the question was asked, "When is the Floer homology isomorphic to the (singular) homology of a naturally occuring (pro)spectrum defined from t

Sourjya Dutta, C. Nicolas Barati, Aditya Dhananjay, David A. Ramirez

Due to the heavy reliance of millimeter-wave (mmWave) wireless systems on directional links, beamforming (BF) with high-dimensional arrays is essential for cellular systems in these frequencies. How to perform the array processing in a power efficient manner is a fundamental challenge. Analog and hybrid BF require fewer analog-to-digital and digital-to-analo

Vincent Roma, Jade Powell, Ik Siong Heng, Ray Frey

The next generation of gravitational wave detectors will improve the detection prospects for gravitational waves from core-collapse supernovae. The complex astrophysics involved in core-collapse supernovae pose a significant challenge to modeling such phenomena. The Supernova Model Evidence Extractor (SMEE) attempts to capture the main features of gravitatio

Daniel Hauer, Jose M. Mazon

In this paper, we develop a functional analytical theory for establishing that mild solutions of first-order Cauchy problems involving homogeneous operators of order zero are strong solutions; in particular, the first-order time derivative satisfies a global regularity estimate depending only on the initial value and the positive time. We apply those results

Israel Quiros

Scalar fields have played an important role in the development of the fundamental theories of physics as well as in other branches of physics such as gravitation and cosmology. For a long time these escaped detection until 2012 year when the Higgs boson was observed for the first time. Since then alternatives to the general theory of relativity like the Bran

Dmitry Kovalev, Samuel Horvath, Peter Richtarik

The stochastic variance-reduced gradient method (SVRG) and its accelerated variant (Katyusha) have attracted enormous attention in the machine learning community in the last few years due to their superior theoretical properties and empirical behaviour on training supervised machine learning models via the empirical risk minimization paradigm. A key structur

Poojan Oza, Vishal M. Patel

We present a novel Convolutional Neural Network (CNN) based approach for one class classification. The idea is to use a zero centered Gaussian noise in the latent space as the pseudo-negative class and train the network using the cross-entropy loss to learn a good representation as well as the decision boundary for the given class. A key feature of the propo

Shuyi Chen, Xiqing Liu, Tianyu Zhao, Hsiao-Hwa Chen

Ultra-dense network (UDN) is one of the enabling technologies to achieve 1000-fold capacity increase in 5G communication systems, and the application of joint transmission (JT) is an effective method to deal with severe inter-cell interferences in UDNs. However, most works done for performance analysis on JT schemes in the literature were based largely on si

Alexey Kroshnin, Darina Dvinskikh, Pavel Dvurechensky, Alexander Gasnikov

We study the complexity of approximating Wassertein barycenter of $m$ discrete measures, or histograms of size $n$ by contrasting two alternative approaches, both using entropic regularization. The first approach is based on the Iterative Bregman Projections (IBP) algorithm for which our novel analysis gives a complexity bound proportional to $\frac{mn^2}{\v

Shoko Sakai, Jessica R. Lu, Andrea Ghez, Siyao Jia

Precision measurements of the stars in short-period orbits around the supermassive black hole at the Galactic Center are now being used to constrain general relativistic effects, such as the gravitational redshift and periapse precession. One of the largest systematic uncertainties in the measured orbits has been errors in the astrometric reference frame, wh

Youngjun Ahn, Anastasios Pateras, Samuel D. Marks, Han Xu

Above-bandgap optical illumination induces a transformation from tilted tetragonal-like (T-like) and rhombohedral-like (R-like) phases to an untilted T-like phase in compressively strained BiFeO3. Optical excitation leads to an out-of-plane lattice expansion in the T-like phase. The transformation proceeds in regions with boundaries between the T-like and ti

Mike Behrisch, Edith Vargas-García

Motivated by reconstruction results by Rubin, we introduce a new reconstruction notion for permutation groups, transformation monoids and clones, called automatic action compatibility, which entails automatic homeomorphicity. We further give a characterization of automatic homeomorphicity for transformation monoids on arbitrary carriers with a dense group of

Stanislaw Makarchuk, Vasco C. Braz, Nuno A. M. Araújo, Lena Ciric

How motile bacteria move near a surface is a problem of fundamental biophysical interest and is key to the emergence of several phenomena of biological, ecological and medical relevance, including biofilm formation. Solid boundaries can strongly influence a cell's propulsion mechanism, thus leading many flagellated bacteria to describe long circular trajecto

W. D'Arcy Kenworthy, Dan Scolnic, Adam Riess

We use the largest sample to date of spectroscopic SN Ia distances and redshifts to look for evidence in the Hubble diagram of large scale outflows caused by local voids suggested to exist at z<0.15. Our sample combines data from the Pantheon sample with the Foundation survey and the most recent release of lightcurves from the Carnegie Supernova Project to c

Ashutosh Patri, Guillaume Lavigne, Christophe Caloz

We present a novel type of Suppressed-Order Diffraction Grating(SODG). An SODG is a diffraction grating whose diffraction orders have all been suppressed except one that is selected to provide electromagnetic deflection. The proposed SODG is a transmissive grating that exhibits high-efficiency refraction-like deflection for all angles, including large angles

Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine

Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary. Such methods perform single-objective optimization on some simple consolidation of the losses, e.g. an arithmetic average. In this work,

David Tamayo, J. Alberto Vazquez

The dark energy component of the universe still remains as a mystery, however, several papers based on observational data have shown that its equation of state may have an oscillatory behaviour. In this paper, we provide a general description for the dark-energy equation-of-state $w(z)$ in the form of Fourier series. This description generalises some previou

Kevin O'Keeffe, Paolo Santi, Brandon Wang, Carlo Ratti

We study a new random search process: the \textit{taxi-drive}. The motivation for this process comes from urban sensing, in which sensors are mounted on moving vehicles such as taxis, allowing urban environments to be opportunistically monitored. Inspired by the movements of real taxis, the taxi-drive is composed of both random and regular parts; passengers

Casey Brinkman, Dipanjan Mitra, Joanna Rankin

The phenomenon of profile mode switching in pulsars, where the stable average pulse profile changes to another stable state on the timescale of a pulsar's period, remains poorly understood. We sought to understand how pulsars undergo profile mode switching through a comparative analysis of the polarization and geometry of the two different profile modes of P

M. A. Cordiner, M. Y. Palmer, M. de Val-Borro, S. B. Charnley

The Atacama Large Millimeter/submillimeter Array (ALMA) is a powerful tool for high-resolution mapping of comets, but the main interferometer (comprised of 50x12-m antennas) is insensitive to the largest coma scales due to a lack of very short baselines. In this work, we present a new technique employing ALMA autocorrelation data (obtained simultaneously wit

L. Kovalcinova, A. Taranto, L. Kondic

We consider two (2D) and three (3D) dimensional granular systems exposed to compression, and ask what is the influence of the number of physical dimensions on the properties of the interaction networks that spontaneously form as these systems evolve. The study is carried out based on discrete element simulations of frictional disks in 2D and spheres in 3D. T

Ting Lin

One of the primary goals of the RHIC spin program is to determine the spin-dependent gluon distribution, $\Delta g(x)$, of the proton. The measurements of the 2009 longitudinal double-helicity asymmetry, $A_{LL}$, for mid-rapidity inclusive jet and $\pi^{0}$ production place strong constraints on $\Delta g(x)$ and, for the first time, find evidence for non-z

Benjamin Koch, Enrique Muñoz

Gauge theories with general covariance are particularly reluctant to quantization. We discuss the example of the Hamiltonian formulation of the relativistic point particle that, despite its apparent simplicity, is of crucial importance since a number of point particle systems can be cast into this form on a higher dimensional Rindler background, as recently

Siddhant Das, Markus Nöth, Detlef Dürr

It is well known that orthodox quantum mechanics does not make unambiguous predictions for the statistics in arrival time (or time-of-flight) experiments. Bohmian mechanics (or de Broglie-Bohm theory) offers a distinct conceptual advantage in this regard, owing to the well defined concepts of point particles and trajectories embedded in this theory. We revis

Jose Manuel Flores-Camacho, Jorge Puebla, Florent Auvray, Alfonso Lastras-Martinez

The formation of novel two-dimensional electron gas (2DEG) with high mobility in metal/amorphous interfaces has motivated an ongoing debate regarding the formation and novel characteristics of these 2DEGs. Here we report an optical study, based on infrared spectroscopic ellipsometry, of nonmagnetic metal and amorphous semiconducting oxide (Cu/Bi$_2$O$_3$) in

K. L. Cristiano, D. A. Triana, A. Estupiñán

We present this work like software tool developed in Python, based on a methodology to obtain the electric field produced by n charges. The tool was developed and implemented in courses of electromagnetism and laboratory in three institutions of higher education. The aim for this work is to incorporate information and communication technologies (ICTs) at the

Xu Qian, Zheng Qu, Peter Richtárik

We study the problem of minimizing the average of a very large number of smooth functions, which is of key importance in training supervised learning models. One of the most celebrated methods in this context is the SAGA algorithm. Despite years of research on the topic, a general-purpose version of SAGA---one that would include arbitrary importance sampling

Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern

Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we study a version of constrained SC in which we try to incorporate the fairness notion proposed by Chierichetti et al. (2017). According to this notion, a clustering is fair if every demographic group is approximately proportionally represented in each cluster. To this

Anton A. Minnekhanov, Andrey V. Emelyanov, Dmitry A. Lapkin, Kristina E. Nikiruy

In this paper, the resistive switching and neuromorphic behavior of memristive devices based on parylene, a polymer both low-cost and safe for the human body, is comprehensively studied. The Metal/Parylene/ITO sandwich structures were prepared by means of the standard gas phase surface polymerization method with different top active metal electrodes (Ag, Al,

Jason Arnold, Boris Glavic, Ioan Raicu

HRDBMS is a novel distributed relational database that uses a hybrid model combining the best of traditional distributed relational databases and Big Data analytics platforms such as Hive. This allows HRDBMS to leverage years worth of research regarding query optimization, while also taking advantage of the scalability of Big Data platforms. The system uses

Robert C. Williamson, Aditya Krishna Menon

Ensuring that classifiers are non-discriminatory or fair with respect to a sensitive feature (e.g., race or gender) is a topical problem. Progress in this task requires fixing a definition of fairness, and there have been several proposals in this regard over the past few years. Several of these, however, assume either binary sensitive features (thus preclud

V. Krokos, G. Pashos, A. N. Spyropoulos, G. Kokkoris

The growing need for creating surfaces with specific wetting properties, such as superhyrdophobic behavior, asks for novel methods for their efficient design. In this work, a fast computational method for the evaluation of patterned superhyrdophobic surfaces is introduced. The hydrophobicity of a surface is quantified in energy terms through an objective fun

E. V. Calman, L. H. Fowler-Gerace, L. V. Butov, D. E. Nikonov

Indirect excitons (IX) in semiconductor heterostructures are bosons, which can cool below the temperature of quantum degeneracy and can be effectively controlled by voltage and light. IX quantum Bose gases and IX devices were explored in GaAs heterostructures where an IX range of existence is limited to low temperatures due to low IX binding energies. IXs in

Peng Chen, Keyi Wu, Joshua Chen, Thomas O'Leary-Roseberry

We propose a fast and scalable variational method for Bayesian inference in high-dimensional parameter space, which we call projected Stein variational Newton (pSVN) method. We exploit the intrinsic low-dimensional geometric structure of the posterior distribution in the high-dimensional parameter space via its Hessian (of the log posterior) operator and per

Hyungtae Lee, Sungmin Eum, Heesung Kwon

We address two questions for training a convolutional neural network (CNN) for hyperspectral image classification: i) is it possible to build a pre-trained network? and ii) is the pre-training effective in furthering the performance? To answer the first question, we have devised an approach that pre-trains a network on multiple source datasets that differ in

Johannes Flake, Robert Laugwitz

We explicitly compute a monoidal subcategory of the monoidal center of Deligne's interpolation category Rep(S_t), for t not necessarily a natural number, and we show that this subcategory is a ribbon category. For t=n, a natural number, there exists a functor onto the braided monoidal category of modules over the Drinfeld double of S_n which is essentially s

Melody Xuan Lim, Kieran A. Murphy, Heinrich M. Jaeger

The properties of small clusters depend dramatically on the interactions between their constituent particles. However, it remains challenging to design and tune the interactions between macroscopic particles, such as in a granular material. Here, we use acoustic levitation to trap macroscopic grains and induce forces between them. Our main results show that

Alexander E. Litvak, Konstantin Tikhomirov, Nicole Tomczak-Jaegermann

Let $A$ be an $n\times n$ random matrix with i.i.d. entries of zero mean, unit variance and a bounded subgaussian moment. We show that the condition number $s_{\max}(A)/s_{\min}(A)$ satisfies the small ball probability estimate $${\mathbb P}\big\{s_{\max}(A)/s_{\min}(A)\leq n/t\big\}\leq 2\exp(-c t^2),\quad t\geq 1,$$ where $c>0$ may only depend on the subga

Lawrence Chan, Dylan Hadfield-Menell, Siddhartha Srinivasa, Anca Dragan

Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences. Such approaches can fail when people are themselves learning about what they want. In this work, we introduce the assis

Heidi Goodson

The classic way to write down Pascal's triangle leads to entries in alternating rows being vertically aligned. In this paper, we prove a linear dependence on vertically aligned entries in Pascal's triangle. Furthermore, we give an application of this dependence to morphisms between hyperelliptic curves.

Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso

Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. However, so far, reinforcement learning resear

Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort

Scaling end-to-end reinforcement learning to control real robots from vision presents a series of challenges, in particular in terms of sample efficiency. Against end-to-end learning, state representation learning can help learn a compact, efficient and relevant representation of states that speeds up policy learning, reducing the number of samples needed, a

Bo Pang, Zhong-Ping Jiang

This paper studies the infinite-horizon adaptive optimal control of continuous-time linear periodic (CTLP) systems. A novel value iteration (VI) based off-policy ADP algorithm is proposed for a general class of CTLP systems, so that approximate optimal solutions can be obtained directly from the collected data, without the exact knowledge of system dynamics.

Christopher Grimm, Satinder Singh

We present a novel method for learning a set of disentangled reward functions that sum to the original environment reward and are constrained to be independently obtainable. We define independent obtainability in terms of value functions with respect to obtaining one learned reward while pursuing another learned reward. Empirically, we illustrate that our me

Chun-Hao Chang, Mingjie Mai, Anna Goldenberg

Imagine a patient in critical condition. What and when should be measured to forecast detrimental events, especially under the budget constraints? We answer this question by deep reinforcement learning (RL) that jointly minimizes the measurement cost and maximizes predictive gain, by scheduling strategically-timed measurements. We learn our policy to be dyna

Dalia Terhesiu

We obtain Krickeberg mixing for a class of Z extensions of Gibbs Markov semiflows with roof function and displacement function not in L2, where previous methods have not been employed. This is done via a 'smooth tail' estimate for the isomorphic suspension flow.

C. S. Froning, A. Kowalski, K. France, R. O. Parke Loyd

As part of the Mega MUSCLES Hubble Space Telescope (HST) Treasury program, we obtained time-series ultraviolet spectroscopy of the M2.5V star, GJ~674. During the FUV monitoring observations, the target exhibited several small flares and one large flare (E_FUV = 10^{30.75} ergs) that persisted over the entirety of a HST orbit and had an equivalent duration >3

Sezgin Sucu

In this article, we give a sequence of operators for producing an approximation result. The relation between the rate of approximation of sequence operators including Dunkl variant of exponential function with first and second-order modulus of continuity are shown. A specific application of sequence of operators which include Gould-Hopper type polynomials is

Michael Burr, Kisun Lee, Anton Leykin

We develop algorithms for certifying an approximation to a nonsingular solution of a square system of equations built from univariate analytic functions. These algorithms are based on the existence of oracles for evaluating basic data about the input analytic functions. One approach for certification is based on alpha-theory while the other is based on the K

Josep Àlvarez Montaner

We provide a Hochster type formula for the local cohomology modules of binomial edge ideals. As a consequence we obtain a simple criterion for the Cohen-Macaulayness of these ideals and we describe their Castelnuovo-Mumford regularity and their Hilbert series. We also prove a conjecture of Conca, De Negri and Gorla relating the graded components of the local

Richard Meyes, Melanie Lu, Constantin Waubert de Puiseau, Tobias Meisen

Ablation studies have been widely used in the field of neuroscience to tackle complex biological systems such as the extensively studied Drosophila central nervous system, the vertebrate brain and more interestingly and most delicately, the human brain. In the past, these kinds of studies were utilized to uncover structure and organization in the brain, i.e.

Stanisław Migórski, Paweł Szafraniec

In this paper we prove the existence and uniqueness of the weak solution for a dynamic thermoviscoelastic problem which describes frictional contact between a body and a foundation. We employ the nonlinear constitutive viscoelastic law with a long-term memory, which include the thermal effects and consider the general nonmonotone and multivalued subdifferent

Keita Nii

We propose a "chiral" version of the Kutasov-Schwimmer duality in a 3d $\mathcal{N}=2$ $SU(N)$ gauge theory with $F$ fundamental matters, $\bar{F}$ anti-fundamental matters and an adjoint matter $X$ with a tree-level superpotential $W= \mathrm{tr} \, X^{k+1}$. The theory exhibits a rich structure of the baryonic and (dressed) Coulomb branch operators. At fir

Kevin McGoff, Sayan Mukherjee, Andrew Nobel

In this paper we consider a Bayesian framework for making inferences about dynamical systems from ergodic observations. The proposed Bayesian procedure is based on the Gibbs posterior, a decision theoretic generalization of standard Bayesian inference. We place a prior over a model class consisting of a parametrized family of Gibbs measures on a mixing shift

Ch. Helling, P. Gourbin, P. Woitke, V. Parmentier

WASP-18b is an utra-hot Jupiter with a temperature difference of upto 2500K between day and night. Such giant planets begin to emerge as planetary laboratory for understanding cloud formation and gas chemistry in well-tested parameter regimes in order to better understand planetary mass loss and for linking observed element ratios to planet formation and evo

Carlos Villalpando, Sujoy K. Modak

The most obvious obstacle behind a direct test of Quantum Gravity (QG) is its energy scale ($10^{19}$ GeV), which remains well outside of any human made machine. The next best possible approach is to provide indirect tests on effective theories of QG which can be performed in a lower energy scale. This paper is aimed in this direction, and shows a promising

Fan Zhang, Lei Zou, Li Zeng, Xiangyang Gou

A streaming graph is a graph formed by a sequence of incoming edges with time stamps. Unlike static graphs, the streaming graph is highly dynamic and time related. In the real world, the high volume and velocity streaming graphs such as internet traffic data, social network communication data and financial transfer data are bringing challenges to the classic

Hui Lin, Chengyu Shi, Brian Wang, Maria F. Chan

Radiation therapy of thoracic and abdominal tumors requires incorporating the respiratory motion into treatments. To precisely account for the patient respiratory motions and predict the respiratory signals, a generalized model for predictions of different types of respiratory motions is desired. The aim of this study is to explore the feasibility of develop

Roberto Brenes, Madeleine Laitz, Joel Jean, Dane W. deQuilettes

Photon recycling is required for a solar cell to achieve an open-circuit voltage ($V_{OC}$) and power conversion efficiency (PCE) approaching the Shockley-Queisser theoretical limit. In metal halide perovskite solar cells, the achievable performance gains from photon recycling remain uncertain due to high variability in perovskite material quality and the no

Paweł Szafraniec

In this paper we prove the existence and regularity of a solution to a two-dimensional system of evolutionary hemivariational inequalities which describes the Boussinesq model with nonmonotone friction and heat flux. We use the time retardation and regularization technique, combined with a regularized Galerkin method, and recent results from the theory of he

Niraj Kumar, Gwendolyn M. Cramer, Seyed Alireza Zamani Dahaj, Bala Sundaram

Phenotypic heterogeneity in cancer cells is widely observed and is often linked to drug resistance. In several cases, such heterogeneity in drug sensitivity of tumors is driven by stochastic and reversible acquisition of a drug tolerant phenotype by individual cells even in an isogenic population. Accumulating evidence further suggests that cell-fate transit

Harry I. Ringermacher, Lawrence R. Mead

We observe damped temporal oscillations in the scale factor at a dominant frequency of ~ 7 cycles/Hubble-time in the Pantheon Compilation of 1048 type Ia supernovae (SNe). The residual oscillations observed in the Pantheon data closely matches and reaffirms our initial observation of oscillations from earlier SNe data (primarily SNLS3, 2011) at 2-sigma confi

Chris Alberti, Kenton Lee, Michael Collins

This technical note describes a new baseline for the Natural Questions. Our model is based on BERT and reduces the gap between the model F1 scores reported in the original dataset paper and the human upper bound by 30% and 50% relative for the long and short answer tasks respectively. This baseline has been submitted to the official NQ leaderboard at ai.goog

T. Shang, D. J. Gawryluk, J. A. T. Verezhak, E. Pomjakushina

The binary Re$_{1-x}$Mo$_x$ alloys, known to cover the full range of solid solutions, were successfully synthesized and their crystal structures and physical properties investigated via powder x-ray diffraction, electrical resistivity, magnetic susceptibility, and heat capacity. By varying the Re/Mo ratio we explore the full Re$_{1-x}$Mo$_x$ binary phase dia

Alireza Behtash, Syo Kamata, M. Martinez, Haosheng Shi

In relativistic kinetic theory, the one-particle distribution function is approximated by an asymptotic perturbative power series in Knudsen number which is divergent. For the Bjorken flow, we expand the distribution function in terms of its moments and study their nonlinear evolution equations. The resulting coupled dynamical system can be solved for each m

Vladimir Kozlov, Alexander Nazarov

We prove a comparison theorem for super- and sub-solutions with non-vanishing gradients to semilinear PDEs provided a nonlinearity $f$ is $L^p$ function with $p > 1$. The proof is based on a strong maximum principle for solutions of divergence type elliptic equations with VMO leading coefficients and with lower order coefficients from a Kato class. An applic

Sikai Qu, Armand M. Makowski

The multiplicative attribute graph (MAG) model was introduced by Kim and Leskovec as a mathematically tractable model of certain classes of real-world networks. It is an instance of hidden graph models, and implements the plausible idea that network structure is collectively shaped by attributes individually associated with nodes. These authors have studied

Khashayar Asadi, Pengyu Chen, Kevin Han, Tianfu Wu

Camera-equipped unmanned vehicles (UVs) have received a lot of attention in data collection for construction monitoring applications. To develop an autonomous platform, the UV should be able to process multiple modules (e.g., context-awareness, control, localization, and mapping) on an embedded platform. Pixel-wise semantic segmentation provides a UV with th

Sylwia Cichacz, Zsolt Tuza

Let $\overrightarrow{G}$ be a directed graph with no component of orderless than~$3$, and let $\Gamma$ be a finite Abelian group such that $|\Gamma|\geq 4|V(\overrightarrow{G})|$ or if $|V(\overrightarrow{G})|$ is large enough with respect to an arbitrarily fixed $\varepsilon>0$ then $|\Gamma|\geq (1+\varepsilon)|V(\overrightarrow{G})|$. We show that there e

Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern

In data summarization we want to choose $k$ prototypes in order to summarize a data set. We study a setting where the data set comprises several demographic groups and we are restricted to choose $k_i$ prototypes belonging to group $i$. A common approach to the problem without the fairness constraint is to optimize a centroid-based clustering objective such

Christopher M. J. Osborne, John A. Armstrong, Lyndsay Fletcher

During a solar flare, it is believed that reconnection takes place in the corona followed by fast energy transport to the chromosphere. The resulting intense heating strongly disturbs the chromospheric structure, and induces complex radiation hydrodynamic effects. Interpreting the physics of the flaring solar atmosphere is one of the most challenging tasks i

Aditya Kumar Pathak, Priyankit Acharya, Dilpreet Kaur, Rakesh Chandra Balabantaray

The parallel corpus for multilingual NLP tasks, deep learning applications like Statistical Machine Translation Systems is very important. The parallel corpus of Hindi-English language pair available for news translation task till date is of very limited size as per the requirement of the systems are concerned. In this work we have developed an automatic par

Jiancheng Lyu, Shuai Zhang, Yingyong Qi, Jack Xin

ShuffleNet is a state-of-the-art light weight convolutional neural network architecture. Its basic operations include group, channel-wise convolution and channel shuffling. However, channel shuffling is manually designed empirically. Mathematically, shuffling is a multiplication by a permutation matrix. In this paper, we propose to automate channel shuffling

Motaz Alfarraj, Ghassan AlRegib

Reservoir characterization involves the estimation petrophysical properties from well-log data and seismic data. Estimating such properties is a challenging task due to the non-linearity and heterogeneity of the subsurface. Various attempts have been made to estimate petrophysical properties using machine learning techniques such as feed-forward neural netwo

Matthew Kahle

This survey article collects a few of my favorite open problems of Branko Gr\"{u}nbaum.

Mengke Lian, Fabrizio Carpi, Christian Häger, Henry D. Pfister

We consider the weighted belief-propagation (WBP) decoder recently proposed by Nachmani et al. where different weights are introduced for each Tanner graph edge and optimized using machine learning techniques. Our focus is on simple-scaling models that use the same weights across certain edges to reduce the storage and computational burden. The main contribu