arXiv papers from January 2019

Kyungjae Lee, Sungyub Kim, Sungbin Lim, Sungjoon Choi

In this paper, we present a new class of Markov decision processes (MDPs), called Tsallis MDPs, with Tsallis entropy maximization, which generalizes existing maximum entropy reinforcement learning (RL). A Tsallis MDP provides a unified framework for the original RL problem and RL with various types of entropy, including the well-known standard Shannon-Gibbs

K. L. Yang, J. M. Zhang

The eigenstates and eigenenergies of a toy model, which arose in idealizing a local quenched tight-binding model in a previous publication [Zhang and Yang, EPL 114, 60001 (2016)], are solved analytically. This enables us to study its dynamics in a different way. This model can serve as a good exercise in quantum mechanics at the undergraduate level.

Xiang Zhang, Nicholas Woolsey, Mingyue Ji

We consider a cache-aided interference network which consists of a library of $N$ files, $K_T$ transmitters and $K_R$ receivers (users), each equipped with a local cache of size $M_T$ and $M_R$ files respectively, and connected via a discrete-time additive white Gaussian noise channel. Each receiver requests an arbitrary file from the library. The objective

M. Cepeda, S. Gori, P. Ilten, M. Kado

The discovery of the Higgs boson in 2012, by the ATLAS and CMS experiments, was a success achieved with only a percent of the entire dataset foreseen for the LHC. It opened a landscape of possibilities in the study of Higgs boson properties, Electroweak Symmetry breaking and the Standard Model in general, as well as new avenues in probing new physics beyond

Rui Paiva, Eduardo Palmeira, Regivan Santiago, Benjamin Bedregal

Overlap functions were introduced as class of bivariate aggregation functions on [0, 1] to be applied in image processing. This paper has as main objective to present appropriates definitions of overlap functions considering the scope of lattices and introduced a more general definition, called of quasi-overlaps, which arise of abolishes the continuity condi

Ryan Marcus, Olga Papaemmanouil

Query performance prediction, the task of predicting the latency of a query, is one of the most challenging problem in database management systems. Existing approaches rely on features and performance models engineered by human experts, but often fail to capture the complex interactions between query operators and input relations, and generally do not adapt

C. Sinan Güntürk, Weilin Li

We show that the method of distributed noise-shaping beta-quantization offers superior performance for the problem of spectral super-resolution with quantization whenever there is redundancy in the number of measurements. More precisely, if the (integer) oversampling ratio $\lambda$ is such that $\lfloor M/\lambda\rfloor - 1\geq 4/\Delta$, where $M$ denotes

Chad Middleton, Bret A. Brouse, Scott D. Jackson

We examine the time evolution of the D=d+4 dimensional Einstein field equations subjected to a flat Robertson-Walker metric where the 3D and higher-dimensional scale factors are allowed to evolve at different rates. We find the exact solution to these equations for a single fluid component, which yields two limiting regimes offering the 3D scale factor as a

Jacques Pienaar

Modern approaches to causal modeling give a central role to interventions, which require the active input of an observer and introduces an explicit `causal arrow of time'. Causal models typically adopt a mechanistic interpretation, according to which the direction of the causal arrow is intrinsic to the process being studied. Here we investigate whether the

Young Jin Kim, Ping-Han Chu, Igor Savukov, Shaun Newman

Exotic spin-dependent interactions between fermions have recently attracted attention in relation to theories beyond the Standard Model. The exotic interactions can be mediated by hypothetical fundamental bosons which may explain several unsolved mysteries in physics. Here we expand this area of research by probing an exotic parity-odd spin- and velocity-dep

Amir Ahmad, Shehroz S. Khan

Mixed datasets consist of both numeric and categorical attributes. Various k-means-based clustering algorithms have been developed for these datasets. Generally, these algorithms use random partition as a starting point, which tends to produce different clustering results for different runs. In this paper, we propose, initKmix, a novel algorithm for finding

Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara, Volkan Cevher

We propose a stochastic gradient framework for solving stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need to be satisfied almost surely. We use smoothing and homotopy techniques to handle constraints without the need for matrix-valued projections. We show for our stochastic gradient alg

Zhaoyang Xu, Faranak Sobhani, Carlos Fernandez Moro, Qianni Zhang

We present a novel neural network architecture, US-Net, for robust nuclei instance segmentation in histopathology images. The proposed framework integrates the nuclei detection and segmentation networks by sharing their outputs through the same foundation network, and thus enhancing the performance of both. The detection network takes into account the high-l

Chandrasekhar Roychoudhuri, Narasimha S. Prasad

Sensors are measuring tools. In any measurement, we have at least two different kinds of interactants. We never know all there are to know about any one of these interactants and the interaction processes that are mostly invisible. Yet, our engineering innovation driven evolution is persisting for over five million years. It is then important to articulate e

Qingnan An, Zhichao Liu, Yuanhang Zhang

In this paper, a classification is given of real rank zero $C^*$-algebras that can be expressed as inductive limits of a sequence of a subclass of Elliott-Thomsen algebras $\mathcal{C}$.

Franco Vargas Pallete

In the following article we discuss Delaunay triangulations for a point cloud on an embedded surface in $\mathbb{R}^3$. We give sufficient conditions on the point cloud to show that the diagonal switch algorithm finds an embedded Delaunay triangulation.

Takahiro Ohgoe, Motoaki Hirayama, Takahiro Misawa, Kota Ido

Understanding physics of high-$T_c$ cuprate superconductors remains one of the important problems in materials science. Though a number of diverse theories argue about the superconductivity and competing orders, ab initio and quantitative understanding is lacking. Here, we reproduce the experimental phase diagram of HgBa$_2$CuO$_{4+y}$ by solving its ab init

L. Camarillo-Rodriguez, Z. J. Waldman, I. Orosz, J. Stein

Patients diagnosed with epilepsy experience cognitive dysfunction that may be due to a transient cognitive/memory impairment (TCI/TMI) caused by spontaneous epileptiform spikes. We asked in a cohort of 166 adult patients with medically refractory focal epilepsy if spikes in specific neuroanatomical regions during verbal episodic memory encoding would signifi

Yusuke Yamada

We discuss $N=2\to N=1$ reduction in four dimensional conformal supergravity. In particular, we keep the off-shell structure of supermultiplets (except hypermultiplets). As we will show, starting with (almost) off-shell conformal supergravity makes the procedure simpler than that from $N=2$ Poincar\'e supergravity, which makes it easier to show the correspon

Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos

Analogical reasoning has been a principal focus of various waves of AI research. Analogy is particularly challenging for machines because it requires relational structures to be represented such that they can be flexibly applied across diverse domains of experience. Here, we study how analogical reasoning can be induced in neural networks that learn to perce

Kunal Relia, Zhengyi Li, Stephanie H. Cook, Rumi Chunara

We study malicious online content via a specific type of hate speech: race, ethnicity and national-origin based discrimination in social media, alongside hate crimes motivated by those characteristics, in 100 cities across the United States. We develop a spatially-diverse training dataset and classification pipeline to delineate targeted and self-narration o

Clara Nellist

The newest results from the ATLAS Collaboration for the production of a top-quark pair in association with a $Z$ or $W$ boson, and for the production of four top quarks, are summarised in these proceedings. The measurements were performed with 36.1 fb$^{-1}$ of proton-proton collision data from the Large Hadron Collider at a centre-of-mass energy of 13 TeV.

Biswajit Santra, John P. Perdew

Semi-local density functionals for the exchange-correlation energy of a many-electron system cannot be exact for all one-electron densities. In 1981, Perdew and Zunger (PZ) subtracted the fully-nonlocal self-interaction error orbital-by-orbital, making the corrected functional exact for all collections of separated one-electron densities, and making no corre

Alfredo Iorio, Pablo Pais

We show that, by going beyond the low-energy approximation for which the dispersion relations of graphene are linear, the corresponding emergent field theory is a specific generalization a Dirac field theory. The generalized Dirac Hamiltonians one obtains are those compatible with specific generalizations of the uncertainty principle. We also briefly comment

Gerardo Cardona, Alain Sarlette, Pierre Rouchon

We address the standard quantum error correction using the three-qubit bit-flip code, yet in continuous-time. This entails rendering a target manifold of quantum states globally attractive. Previous feedback designs could feature spurious equilibria, or resort to discrete kicks pushing the system away from these equilibria to ensure global asymptotic stabili

Ariel Arza, Pierre Sikivie

Electromagnetic radiation with angular frequency equal to half the axion mass stimulates the decay of cold dark matter axions and produces an echo, i.e. faint electromagnetic radiation traveling in the opposite direction. We propose to search for axion dark matter by sending out to space a powerful beam of microwave radiation and listening for its echo. We e

Da Li, Jianshu Zhang, Yongxin Yang, Cong Liu

Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domains with different statistics than a set of known training domains. The simple approach of aggregating data from all source domains and training a single deep neural network end-to-end on all the data provides a surprisingly strong baseli

Yanni Georghiades, Steven Flolid, Sriram Vishwanath

Over the past five years, the rewards associated with mining Proof-of-Work blockchains have increased substantially. As a result, miners are heavily incentivized to design and utilize Application Specific Integrated Circuits (ASICs) that can compute hashes far more efficiently than existing general purpose hardware. Currently, it is difficult for most users

Giacomo Gradenigo, Fabrizio Antenucci, Luca Leuzzi

We present here for the first time a unifying perspective for the lack of equipartition in non-linear ordered systems and the low temperature phase-space fragmentation in disordered systems. We demonstrate that they are just two manifestation of the same underlying phenomenon: ergodicity breaking. Inspired by recent experiments, suggesting that lasing in opt

K. Kawagoe, Y. Miura, I. Sekiya, T. Suehara

The technological prototype of the CALICE highly granular silicon-tungsten electromagnetic calorimeter (SiW-ECAL) was tested in a beam at DESY in 2017. The setup comprised seven layers of silicon sensors. Each layer comprised four sensors, with each sensor containing an array of 256 $5.5\times5.5$ mm$^2$ silicon PIN diodes. The four sensors covered a total a

T. Csörgő, R. Pasechnik, A. Ster

A novel model-independent L\'evy imaging method is employed for reconstruction of the elastic $pp$ and $p\bar p$ scattering amplitudes at low and high energies. The four-momentum transfer $t$ dependent elastic slope $B(t)$, the nuclear phase $\phi(t)$ as well as the excitation function of the shadow profile $P(b)$ have been extracted from data at ISR, Tevatr

John Susice

We show that for any regular cardinal $\kappa$, $\square_{\kappa, 2}$ is consistent with "all $\kappa^+$-Aronszajn trees are special." By a result of Shelah and Stanley this is optimal in the sense that $\square_{\kappa, 2}$ may not be strengthened to $\square_{\kappa}$. Using methods of Golshani and Hayut we obtain our consistency result simultaneously for

Per Kristian Lehre, Dirk Sudholt

We propose a new black-box complexity model for search algorithms evaluating $\lambda$ search points in parallel. The parallel unary unbiased black-box complexity gives lower bounds on the number of function evaluations every parallel unary unbiased black-box algorithm needs to optimise a given problem. It captures the inertia caused by offspring populations

M. R. Hall, D. W. Barbadian, T. Baugher, A. Lepailleur

Detection of nuclear-decay $\gamma$ rays provides a sensitive thermometer of nova nucleosynthesis. The most intense $\gamma$-ray flux is thought to be annihilation radiation from the $\beta^+$ decay of $^{18}$F, which is destroyed prior to decay by the $^{18}$F($p$,$\alpha$)$^{15}$O reaction. Estimates of $^{18}$F production had been uncertain, however, beca

Lin Xie, Nils Thieme, Ruslan Krenzler, Hanyi Li

Robotic mobile fulfillment systems (RMFSs) are a new type of warehousing system, which has received more attention recently, due to increasing growth in the e-commerce sector. Instead of sending pickers to the inventory area to search for and pick the ordered items, robots carry shelves (called "pods") including ordered items from the inventory area to picki

Enrico Au-Yeung, Greg Zanotti

In datasets where the number of parameters is fixed and the number of samples is large, principal component analysis (PCA) is a powerful dimension reduction tool. However, in many contemporary datasets, when the number of parameters is comparable to the sample size, PCA can be misleading. A closely related problem is the following: is it possible to recover

Eric Clarkson

We derive a connection between performance of estimators the performance of the ideal observer on related detection tasks. Specifically we show how Shannon Information for the task of detecting a change in a parameter is related to the Fisher Information and the Bayesian Fisher Information. We have previously shown that this Shannon Information is related vi

Valery Alexeev, Wenfei Liu

We derive simple formulas for the basic numerical invariants of a singular surface with Picard number one obtained by blowups and contractions of the four-line configuration in the plane. As an application, we establish the smallest positive volume and the smallest accumulation point of volumes of log canonical surfaces obtained in this way.

Iago A Carvalho

The experimental evaluation of algorithms results in a large set of data which generally do not follow a normal distribution or are not heteroscedastic. Besides, some of its entries may be missing, due to the inability of an algorithm to find a feasible solution until a time limit is met. Those characteristics restrict the statistical evaluation of computati

Kyle Luther, H. Sebastian Seung

In the deep metric learning approach to image segmentation, a convolutional net densely generates feature vectors at the pixels of an image. Pairs of feature vectors are trained to be similar or different, depending on whether the corresponding pixels belong to same or different ground truth segments. To segment a new image, the feature vectors are computed

Eric Clarkson

The van Trees inequality relates the Ensemble Mean Squared Error of an estimator to a Bayesian version of the Fisher Information. The Ziv-Zakai inequality relates the Ensemble Mean Squared Error of an estimator to the Minimum Probability of Error for the task of detecting a change in the parameter. In this work we complete this circle by deriving an inequali

Emily Dinan, Varvara Logacheva, Valentin Malykh, Alexander Miller

We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots. Some key takeaways from the competition are: (i) pretrained Transformer variants are currently the best performing models on this task, (ii) but to improve performance on multi-turn conversations with humans, future system

Emanuele Fabbiani, Andrea Marziali, Giuseppe De Nicolao

Gas demand is made of three components: Residential, Industrial, and Thermoelectric Gas Demand. Herein, the one-day-ahead prediction of each component is studied, using Italian data as a case study. Statistical properties and relationships with temperature are discussed, as a preliminary step for an effective feature selection. Nine "base forecasters" are im

Shaoming Guo, Joris Roos, Andreas Seeger, Po-Lam Yung

Let $H^{(u)}$ be the Hilbert transform along the parabola $(t, ut^2)$ where $u\in \mathbb R$. For a set $U$ of positive numbers consider the maximal function $\mathcal{H}^U \!f= \sup\{|H^{(u)}\! f|: u\in U\}$. We obtain an (essentially) optimal result for the $L^p$ operator norm of $\mathcal{H}^U$ when $2<p<\infty$. The results are proved for families of Hil

Thomas Roy, Tom B. Jönsthövel, Christopher Lemon, Andrew J. Wathen

In petroleum reservoir simulation, the industry standard preconditioner, the constrained pressure residual method (CPR), is a two-stage process which involves solving a restricted pressure system with Algebraic Multigrid (AMG). Initially designed for isothermal models, this approach is often used in the thermal case. However, it does not have a specific trea

I. V. Anikin

We develop an approach based on the light-cone sum rules at the leading order of $\alpha_S$ to calculate the gravitational form factors $A(t)$ and $B(t)$ for the valence quark combinations in nucleon. Within the proposed model, the predictions for the gravitational form factor $D(t)$ ($D$-term contributions) have been presented. Comparison with the experimen

Greg Olmschenk, Hao Tang, Zhigang Zhu

Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks is less effect

Victor Churchill, Anne Gelb

Image reconstruction based on an edge-sparsity assumption has become popular in recent years. Many methods of this type are capable of reconstructing nearly perfect edge-sparse images using limited data. In this paper, we present a method to improve the accuracy of a suboptimal image resulting from an edge-sparsity image reconstruction method when compressed

Christopher Tran, Elena Zheleva

The causal effect of a treatment can vary from person to person based on their individual characteristics and predispositions. Mining for patterns of individual-level effect differences, a problem known as heterogeneous treatment effect estimation, has many important applications, from precision medicine to recommender systems. In this paper we define and st

Peter James Levens, Nicolas Labrosse

Observations of the Mg II h and k lines in solar prominences with IRIS reveal a wide range of line shapes from simple non-reversed profiles to typical double-peaked reversed profiles with many other complex line shapes possible. The physical conditions responsible for this variety are not well understood. Our aim is to understand how physical conditions insi

Yifan Zhou, Dániel Apai, Ben W. P. Lew, Glenn Schneider

Directly-imaged planetary-mass companions offer unique opportunities in atmospheric studies of exoplanets. They share characteristics of both brown dwarfs and transiting exoplanets, therefore, are critical for connecting atmospheric characterizations for these objects. Rotational phase mapping is a powerful technique to constrain the condensate cloud propert

Pragathi Praveena, Guru Subramani, Bilge Mutlu, Michael Gleicher

Human demonstrations are important in a range of robotics applications, and are created with a variety of input methods. However, the design space for these input methods has not been extensively studied. In this paper, focusing on demonstrations of hand-scale object manipulation tasks to robot arms with two-finger grippers, we identify distinct usage paradi

Fathi Haggui, Abdessami Jalled

We study holomorphic curves $f:\C\longrightarrow \C^3$ avoiding four complex hyperplanes and a real subspace of real dimension four or five in $\C^3$. We show that the projection of $f$ into the complex projective space $\C P^2$ is not necessarily constant.

D. O. Cook, J. C. Lee, A. Adamo, H. Kim

We present the star cluster catalogs for 17 dwarf and irregular galaxies in the $HST$ Treasury Program "Legacy ExtraGalactic UV Survey" (LEGUS). Cluster identification and photometry in this subsample are similar to that of the entire LEGUS sample, but special methods were developed to provide robust catalogs with accurate fluxes due to low cluster statistic

Hanspeter Fischer, Jacob D. Garcia

Given a path-connected space $X$ and $H\leq\pi_1(X,x_0)$, there is essentially only one construction of a map $p_H:(\widetilde{X}_H,\widetilde{x}_0)\rightarrow(X,x_0)$ with connected and locally path-connected domain that can possibly have the following two properties: $(p_{H})_{\#}\pi_1(\widetilde{X}_H,\widetilde{x}_0)=H$ and $p_H$ has the unique lifting pr

Geoffrey Wolfer, Aryeh Kontorovich

We exhibit an efficient procedure for testing, based on a single long state sequence, whether an unknown Markov chain is identical to or $\varepsilon$-far from a given reference chain. We obtain nearly matching (up to logarithmic factors) upper and lower sample complexity bounds for our notion of distance, which is based on total variation. Perhaps surprisin

Hao Sheng, Xiaozhe Wang

In this paper, a novel non-intrusive probabilistic power flow (PPF) analysis method based on the low-rank approximation (LRA) is proposed, which can accurately and efficiently estimate the probabilistic characteristics (e.g., mean, variance, probability density function) of the PPF solutions. This method aims at building up a statistically-equivalent surroga

Liubin Pan, Paolo Padoan, Åke Nordlund

We examine the accuracy of spatial derivatives computed from numerical simulations of supersonic turbulence. Two sets of simulations, carried out using a finite-volume code that evolves the hydrodynamic equations with an approximate Riemann solver and a finite-difference code that solves the Navier-Stokes equations, are tested against a number of criteria ba

Yifeng Tao, Chunhui Cai, William W. Cohen, Xinghua Lu

Cancers are mainly caused by somatic genomic alterations (SGAs) that perturb cellular signaling systems and eventually activate oncogenic processes. Therefore, understanding the functional impact of SGAs is a fundamental task in cancer biology and precision oncology. Here, we present a deep neural network model with encoder-decoder architecture, referred to

Inhee Lee, Franz G. Utermohlen, Kyusung Hwang, Daniel Weber

We lay the foundation for determining the microscopic spin interactions in two-dimensional (2D) ferromagnets by combining angle-dependent ferromagnetic resonance (FMR) experiments on high quality CrI$_3$ single crystals with theoretical modeling based on symmetries. We discover that the Kitaev interaction is the strongest in this material with $K \sim -5.2$

Yu. D. Panov, V. A. Ulitko, K. S. Budrin, A. A. Chikov

We consider the competition of magnetic and charge ordering in high-Tc cuprates within the framework of the simplified static 2D spin-pseudospin model. This model is equivalent to the 2D dilute antiferromagnetic (AFM) Ising model with charged impurities. We present the mean-field results for the system under study and make a brief comparison with classical M

Rakshith Sharma Srinivasa, Mark A. Davenport, Justin Romberg

We study the problem of actively imaging a range-limited far-field scene using an antenna array. We describe how the range limit imposes structure in the measurements across multiple wavelengths. This structure allows us to introduce a novel trade-off: the number of spatial array measurements (i.e., beams that have to be formed) can be reduced significantly

J. Erik Baxter

In this work, we find new static, spherically symmetric, dyonic, globally regular exact solutions to $\mathfrak{su}(\infty)$ Einstein-Yang-Mills theory with a negative cosmological constant $\Lambda$, in the regime that $|\Lambda|$ is very large. In this regime, we also prove that dyonic globally regular solutions may be uniquely characterised by a countably

James Beare, Matthew Nugent, Murray Wilson, Yipeng Cai

We present muon spin rotation and relaxation (muSR) measurements as well as demagnetising field corrected magnetisation measurements on polycrystalline samples of the noncentrosymmetric superconductor BeAu. From muSR measurements in a transverse field, we determine that BeAu is a type-I superconductor with Hc = 256 Oe, amending the previous understanding of

Mehul P. Makwana, Richard Craster, Sebastien Guenneau

We create a passive wave splitter, created purely by geometry, to engineer three-way beam splitting in electromagnetism in transverse electric polarisation. We do so by considering arrangements of Indium Phosphide dielectric pillars in air, in particular we place several inclusions within a cell that is then extended periodically upon a square lattice. Hexag

Nidham Gazagnadou, Robert M. Gower, Joseph Salmon

Recently it has been shown that the step sizes of a family of variance reduced gradient methods called the JacSketch methods depend on the expected smoothness constant. In particular, if this expected smoothness constant could be calculated a priori, then one could safely set much larger step sizes which would result in a much faster convergence rate. We fil

Juan Pablo Velasquez-Rodriguez

In this paper we use Riesz spectral Theory and Gershgorin Theory to obtain explicit information concerning the spectrum of pseudo-differential operators defined on the unit circle $\mathbb{T} := \mathbb{R}/ 2 \pi \mathbb{ Z}$. For symbols in the H\"ormander class $S^m_{1 , 0} (\mathbb{T} \times \mathbb{Z})$, we provide a sufficient and necessary condition to

Alberto Enciso, Arick Shao, Bruno Vergara

We establish a new family of Carleman inequalities for wave operators on cylindrical spacetime domains containing a potential that is critically singular, diverging as an inverse square on all the boundary of the domain. These estimates are sharp in the sense that they capture both the natural boundary conditions and the natural $H^1$-energy. The proof is ba

Yue Cao, Yang Shen

Motivation: Ab initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this field, there is no docking method available for rigorous uncertainty quantification (UQ) of its solution quality (e.g. interface RMSD or iRMSD). Results: We introduce a novel algo

Dafydd Gibbon, Xuewei Lin

Speech rhythms have been dealt with in three main ways: from the introspective analyses of rhythm as a correlate of syllable and foot timing in linguistics and applied linguistics, through analyses of durations of segments of utterances associated with consonantal and vocalic properties, syllables, feet and words, to models of rhythms in speech production an

Ivan Duchemin, Xavier Blase

We explore a separable resolution-of-the-identity formalism built on quadratures over limited sets of real-space points designed for all-electron calculations. Our implementation preserves in particular the use of common atomic orbitals and their related auxiliary basis sets. The set up of the present density fitting scheme, i.e. the calculation of the syste

V. V. Baran, D. S. Delion

We study the description of single-species and isovector pairing correlations in the framework of the projected-BCS (PBCS) and the Quartet Condensation Model (QCM) from a particle-hole perspective and we introduce the representation of the QCM quartet condensate state in terms of particle-hole excitations with respect to the Hartree-Fock state. We also prese

Christian Espíndola

Given a regular cardinal $\kappa$ such that $\kappa^{<\kappa}=\kappa$ (e.g., if the Generalized Continuum Hypothesis holds), we develop a proof system for classical infinitary logic that includes heterogeneous quantification (i.e., infinite alternate sequences of quantifiers) within the language $\mathcal{L}_{\kappa^+, \kappa}$, where there are conjunctions

Sreenath K. Manikandan, Francesco Giazotto, Andrew N. Jordan

We propose a solid state refrigeration technique based on repeated adiabatic magnetization/demagnetization cycles of a superconductor which acts as the working substance. The gradual cooling down of a substrate (normal metal) in contact with the working substance is demonstrated for different initial temperatures of the substrate. Excess heat is given to a h

Justin Chan, Thomas Rea, Shyamnath Gollakota, Jacob E. Sunshine

Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide. Rapid diagnosis and initiation of cardiopulmonary resuscitation (CPR) is the cornerstone of therapy for victims of cardiac arrest. Yet a significant fraction of cardiac arrest victims have no chance of survival because they experience an unwitnessed event, often in the privacy of th

Bibin Francis, Manoj Mathew, Muthuvel Arigovindan

We consider the problem of reconstructing 2D images from randomly under-sampled confocal microscopy samples. The well known and widely celebrated total variation regularization, which is the L1 norm of derivatives, turns out to be unsuitable for this problem; it is unable to handle both noise and under-sampling together. This issue is linked with the notion

Peter Eastman, Vijay S. Pande

We train a neural network to predict chemical toxicity based on gene expression data. The input to the network is a full expression profile collected either in vitro from cultured cells or in vivo from live animals. The output is a set of fine grained predictions for the presence of a variety of pathological effects in treated animals. When trained on the Op

Hedda A. Gressel, Camille Bonvin, Marco Bruni, David Bacon

We present a full-sky derivation of weak lensing observables in the Post-Friedmann (PF) formalism. Weak lensing has the characteristic of mixing small scales and large scales since it is affected by inhomogeneities integrated along the photon trajectory. With the PF formalism, we develop a modelling of lensing observables which encompasses both leading order

Theodore W. Burkhardt

In the simplest model of single-file diffusion, $N$ point particles wander on a segment of the $x$ axis of length $L$, with hard core interactions, which prevent passing, and with overdamped Brownian dynamics, $\lambda\dot{x}=\eta(t)$, where $\eta(t)$ has the form of Gaussian white noise with zero mean. In 1965 Harris showed that in the limit $N\to\infty$, $

Yuesong Shen, Tao Wu, Csaba Domokos, Daniel Cremers

Probabilistic graphical models are traditionally known for their successes in generative modeling. In this work, we advocate layered graphical models (LGMs) for probabilistic discriminative learning. To this end, we design LGMs in close analogy to neural networks (NNs), that is, they have deep hierarchical structures and convolutional or local connections be

Elijah Paul, Gleb Pogudin, William Qin, Reinhard Laubenbacher

Boolean networks are a popular modeling framework in computational biology to capture the dynamics of molecular networks, such as gene regulatory networks. It has been observed that many published models of such networks are defined by regulatory rules driving the dynamics that have certain so-called canalizing properties. In this paper, we investigate the d

Martin Virte, Francesco Ferranti

When studying the dynamics of Vertical-Cavity Surface-Emitting Lasers, and their polarization properties, the spin-flip model appears to be the simplest model qualitatively reproducing all dynamical features that have been observed experimentally. Nonetheless, because of the fast time-scale of the spin-relaxation processes, the specific role and the importan

Audrey Sedal, Alan Wineman, R Brent Gillespie, C David Remy

Successful soft robot modeling approaches appearing in recent literature have been based on a variety of distinct theories, including traditional robotic theory, continuum mechanics, and machine learning. Though specific modeling techniques have been developed for and validated against already realized systems, their strengths and weaknesses have not been ex

Michael D. Iannacone, Robert A. Bridges

Metrics and frameworks to quantifiably assess security measures have arisen from needs of three distinct research communities - statistical measures from the intrusion detection and prevention literature, evaluation of cyber exercises, e.g.,red-team and capture-the-flag competitions, and economic analyses addressing cost-versus-security tradeoffs. In this pa

Javier Bernal

As shape analysis of the form presented in Srivastava and Klassen's textbook 'Functional and Shape Data Analysis' is intricately related to Lebesgue integration and absolute continuity, it is advantageous to have a good grasp of the latter two notions. Accordingly, in these notes we review basic concepts and results about Lebesgue integration and absolute co

Bouchra R. Nasri, Bruno N. Remillard

In this paper, we find necessary and sufficient conditions so that copula-based conditional distributions of a response variable with respect to covariates, are ordered with respect to the simple stochastic order introduced by Lehmann. These conditions do not depend on the marginal distributions of the random variables. As a result, we have conditions to ens

David B. Stein, Michael J. Shelley

An important class of fluid-structure problems involve the dynamics of ordered arrays of immersed, flexible fibers. While specialized numerical methods have been developed to study fluid-fiber systems, they become infeasible when there are many, rather than a few, fibers present, nor do these methods lend themselves to analytical calculation. Here, we introd

Shouvik Chatterjee, Shoaib Khalid, Hadass S. Inbar, Aranya Goswami

Observation of large non-saturating magnetoresistance in rare-earth monopnictides has raised enormous interest in understanding the role of its electronic structure. Here, by a combination of molecular-beam epitaxy, low-temperature transport, angle-resolved photoemssion spectroscopy, and hybrid density functional theory we have unveiled the bandstructure of

François Landais, Frederic Schmidt, Shaun Lovejoy

Current technology is not able to map the topography of rocky exoplanets, simply because the objects are too faint and far away to resolve them. Nevertheless, indirect effect of topography should be soon observable thanks to photometry techniques, and the possibility of detecting specular reflections. In addition, topography may have a strong effect on Earth

Shuyi Shen, Bernardo A. Huberman, Lin Cheng, Gee-Kung Chang

We experimentally demonstrate a novel scheme of power loading based on portfolio theory for millimeter-wave small-cell densification. By exploiting the statistical characteristics of interference, this approach improves the average throughput by 91% and reduces the variance.

Andrija Petrović, Mladen Nikolić, Miloš Jovanović, Boris Delibašić

Gaussian conditional random fields (GCRF) are a well-known used structured model for continuous outputs that uses multiple unstructured predictors to form its features and at the same time exploits dependence structure among outputs, which is provided by a similarity measure. In this paper, a Gaussian conditional random fields model for structured binary cla

Euclid Collaboration, N. Martinet, T. Schrabback, H. Hoekstra

In modern weak-lensing surveys, the common approach to correct for residual systematic biases in the shear is to calibrate shape measurement algorithms using simulations. These simulations must fully capture the complexity of the observations to avoid introducing any additional bias. In this paper we study the importance of faint galaxies below the observati

Ashok Deb, Luca Luceri, Adam Badawy, Emilio Ferrara

One of the hallmarks of a free and fair society is the ability to conduct a peaceful and seamless transfer of power from one leader to another. Democratically, this is measured in a citizen population's trust in the electoral system of choosing a representative government. In view of the well documented issues of the 2016 US Presidential election, we conduct

Nicolò Maccaferri

We review both the fundamental aspects and the applications of functional magneto-optic and opto-magnetic metamaterials displaying collective and coupling effects on the nanoscale, where the concepts of optics and magnetism merge to produce unconventional phenomena. The use of magnetic materials instead of the usual noble metals allows for an additional degr

V. A. De Lorenci, C. C. H. Ribeiro

Quantum vacuum fluctuations of the electromagnetic field in empty space seem not to produce observable effects over the motion of a charged test particle. However, when a change in the background vacuum state is implemented, as for instance when a conducting boundary is introduced, dispersions of the particle velocity may occur. As a consequence, besides the

David Melhart, Ahmad Azadvar, Alessandro Canossa, Antonios Liapis

Is it possible to predict the motivation of players just by observing their gameplay data? Even if so, how should we measure motivation in the first place? To address the above questions, on the one end, we collect a large dataset of gameplay data from players of the popular game Tom Clancy's The Division. On the other end, we ask them to report their levels

Robert Cardona, Eva Miranda, Daniel Peralta-Salas

Tichler proved that a manifold admitting a smooth closed one-form fibers over a circle. More generally a manifold admitting $k$ independent closed one-forms fibers over a torus $T^k$. In this article we explain a version of this construction for manifolds with boundary using the techniques of $b$-calculus. We explore new applications of this idea to Fluid Dy

Hedi Ben-younes, Rémi Cadene, Nicolas Thome, Matthieu Cord

Multimodal representation learning is gaining more and more interest within the deep learning community. While bilinear models provide an interesting framework to find subtle combination of modalities, their number of parameters grows quadratically with the input dimensions, making their practical implementation within classical deep learning pipelines chall

P. J. Vallely, M. A. Tucker, B. J. Shappee, J. S. Brown

One observational prediction for Type Ia supernova (SNe Ia) explosions produced through white dwarf-white dwarf collisions is the presence of bimodal velocity distributions for the $^{56}$Ni decay products, although this signature can also be produced by an off-center ignition in a delayed detonation explosion. These bimodal velocity distributions can manife

Rebecca Nealon, Christophe Pinte, Richard Alexander, Daniel Mentiplay

Three-dimensional hydrodynamic numerical simulations have demonstrated that the structure of a protoplanetary disc may be strongly affected by a planet orbiting in a plane that is misaligned to the disc. When the planet is able to open a gap, the disc is separated into an inner, precessing disc and an outer disc with a warp. In this work, we compute infrared