May 2019 arXiv papers — page 3

Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Felix Li

Reinforcement learning (RL) for robotics is challenging due to the difficulty in hand-engineering a dense cost function, which can lead to unintended behavior, and dynamical uncertainty, which makes exploration and constraint satisfaction challenging. We address these issues with a new model-based reinforcement learning algorithm, Safety Augmented Value Esti

Tiantian Shi, Duo Pan, Jingbiao Chen

The residual cavity-pulling effect limits further narrowing of linewidth in dual-wavelength (DW) good-bad-cavity active optical clocks (AOCs). In this paper, we for the first time experimentally realize the cavity-length stabilization of the 1064/1470 nm DW-AOCs by utilizing the phase locking technique of two independent 1064 nm good-cavity lasers. The frequ

Yuan Gong, Boyang Li, Christian Poellabauer, Yiyu Shi

In recent years, many efforts have demonstrated that modern machine learning algorithms are vulnerable to adversarial attacks, where small, but carefully crafted, perturbations on the input can make them fail. While these attack methods are very effective, they only focus on scenarios where the target model takes static input, i.e., an attacker can observe t

Yixiao Zhou, Martin Asplund, Remo Collet

The amplitude of solar p-mode oscillations is governed by stochastic excitation and mode damping, both of which take place in the surface convection zone. However, the time-dependent, turbulent nature of convection makes it difficult to self-consistently study excitation and damping processes through the use of traditional one-dimensional hydrostatic models.

D. Tseluiko, M. Alesemi, T. -S. Lin, U. Thiele

We consider the Cahn-Hilliard (CH) equation with a Burgers-type convective term that is used as a model of coarsening dynamics in laterally driven phase-separating systems. In the absence of driving, it is known that solutions to the standard CH equation are characterized by an initial stage of phase separation into regions of one phase surrounded by the oth

Shah Rukh Qasim, Hassan Mahmood, Faisal Shafait

Document structure analysis, such as zone segmentation and table recognition, is a complex problem in document processing and is an active area of research. The recent success of deep learning in solving various computer vision and machine learning problems has not been reflected in document structure analysis since conventional neural networks are not well

Rongrong Hu, Dmitri R. Yakovlev, Pan Liang, Gang Qiang

Coherent spin dynamics in colloidal CdSe quantum dots (QDs) typically show two spin components with different Larmor frequencies, whose origin is an open question. We exploit the photocharging approach to identify their origin and find that surface states play a key role in the appearance of the spin signals. By controlling the photocharging with electron or

Jordan DuBeau, Keith A. Kearnes

We construct a Jónsson algebra of cardinality $ω_1$ in the variety of Jónsson-Tarski algebras.

Todd Hylton

A thermodynamically motivated neural network model is described that self-organizes to transport charge associated with internal and external potentials while in contact with a thermal reservoir. The model integrates techniques for rapid, large-scale, reversible, conservative equilibration of node states and slow, small-scale, irreversible, dissipative adapt

Sam Kumar, Yuncong Hu, Michael P Andersen, Raluca Ada Popa

As the Internet of Things (IoT) emerges over the next decade, developing secure communication for IoT devices is of paramount importance. Achieving end-to-end encryption for large-scale IoT systems, like smart buildings or smart cities, is challenging because multiple principals typically interact indirectly via intermediaries, meaning that the recipient of

Zakaria Mhammedi, Peter D. Grunwald, Benjamin Guedj

We present a new PAC-Bayesian generalization bound. Standard bounds contain a $\sqrt{L_n \cdot \KL/n}$ complexity term which dominates unless $L_n$, the empirical error of the learning algorithm's randomized predictions, vanishes. We manage to replace $L_n$ by a term which vanishes in many more situations, essentially whenever the employed learning algor

Huajun Gong, Changyou Wang, Xiaotao Zhang

In this paper, inspired by the seminal work by Caffarelli-Kohn-Nirenberg \cite{CKN} on the incompressible Navier-Stokes equation, we establish the existence of a suitable weak solution to the Navier-Stokes-Planck-Nernst-Poisson equation in dimension three, which is shown to be smooth away from a closed set whose $1$-dimensional parabolic Hausdorff measure is

Clay Cordova, Daniel S. Freed, Ho Tat Lam, Nathan Seiberg

We extend our earlier work on anomalies in the space of coupling constants to four-dimensional gauge theories. Pure Yang-Mills theory (without matter) with a simple and simply connected gauge group has a mixed anomaly between its one-form global symmetry (associated with the center) and the periodicity of the $θ$-parameter. This anomaly is at the root of man

Nitya Raviprakash, Sonam Damani, Ankush Chatterjee, Meghana Joshi

The handicraft industry is a strong pillar of Indian economy which provides large-scale employment opportunities to artisans in rural and underprivileged communities. However, in this era of globalization, diverse modern designs have rendered traditional designs old and monotonous, causing an alarming decline of handicraft sales. For this age-old industry to

Murad Qasaimeh, Kristof Denolf, Jack Lo, Kees Vissers

Developing high performance embedded vision applications requires balancing run-time performance with energy constraints. Given the mix of hardware accelerators that exist for embedded computer vision (e.g. multi-core CPUs, GPUs, and FPGAs), and their associated vendor optimized vision libraries, it becomes a challenge for developers to navigate this fragmen

Maolin Shi, Wei Sun, Xueguan Song, Hongyou Li

Support vector regression (SVR) has been widely used to reduce the high computational cost of computer simulation. SVR assumes the input parameters have equal sample sizes, but unequal sample sizes are often encountered in engineering practices. To solve this issue, a new prediction approach based on SVR, namely as high-low-level SVR approach (HL-SVR) is pro

Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistub

We consider the stochastic multi-armed bandit problem and the contextual bandit problem with historical observations and pre-clustered arms. The historical observations can contain any number of instances for each arm, and the pre-clustering information is a fixed clustering of arms provided as part of the input. We develop a variety of algorithms which inco

Rukmal Weerawarana, Yiyi Zhu, Yuzhen He

Market sectors play a key role in the efficient flow of capital through the modern Global economy. We analyze existing sectorization heuristics, and observe that the most popular - the GICS (which informs the S&P 500), and the NAICS (published by the U.S. Government) - are not entirely quantitatively driven, but rather appear to be highly subjective and root

Jiawei Zhang

In this paper, we will provide an introduction to the brain structure and function. Brain is an astonishing living organ inside our heads, weighing about 1.5kg, consisting of billions of tiny cells. The brain enables us to sense the world around us (to touch, to smell, to see and to hear, etc.), to think and to respond to the world as well. The main obstacle

Victor E Hansen

This paper introduces the Contextual Evaluation Model (CEM), a novel method for knowledge representation and manipulation. The CEM differs from existing models in that it integrates facts, patterns and sequences into a single contextual framework. V5, an implementation of the model is presented and demonstrated with multiple annotated examples. The paper inc

Johar M. Ashfaque

In this note, we show that the $μ$-oscillator does not lead to the Fibonacci sequence as claimed in \cite{Marinho:2019zny} since $[n]^μ=n$ in the limit $μ\rightarrow 0$. Thus we obtain the sequence $[0]^μ=0, 1, 2,...$. We only obtain the Fibonacci sequence when the $q$-deformation is associated to the $μ$-deformation via the basis number $$\lim_{μ\rightarrow

Mahouton Norbert Hounkonnou, Fridolin Melong

This paper addresses a theory of R(p,q)-deformed combinatorics in discrete probability. It mainly focuses on R(p,q)-deformed factorials, binomial coefficients, Vandermonde's formula, Cauchy's formula, binomial and negative binomial formulae, factorial and binomial moments, and Stirling numbers. Moreover, the R(p,q)-Stirling numbers of the second kind

Bonggun Shin, Julien Hogan, Andrew B. Adams, Raymond J. Lynch

Electronic Health Records (EHRs) have been heavily used to predict various downstream clinical tasks such as readmission or mortality. One of the modalities in EHRs, clinical notes, has not been fully explored for these tasks due to its unstructured and inexplicable nature. Although recent advances in deep learning (DL) enables models to extract interpretabl

Himan Abdollahpouri

One of the most essential parts of any recommender system is personalization-- how acceptable the recommendations are from the user's perspective. However, in many real-world applications, there are other stakeholders whose needs and interests should be taken into account. In this work, we define the problem of multistakeholder recommendation and we focu

Shumon Koga, Iasson Karafyllis, Miroslav Krstic

This paper presents results for the sampled-data boundary feedback control to the Stefan problem. The Stefan problem represents a liquid-solid phase change phenomenon which describes the time evolution of a material's temperature profile and the interface position. First, we consider the sampled-data control for the one-phase Stefan problem by assuming t

Mayukh Das, Devendra Singh Dhami, Yang Yu, Gautam Kunapuli

Recently, deep models have had considerable success in several tasks, especially with low-level representations. However, effective learning from sparse noisy samples is a major challenge in most deep models, especially in domains with structured representations. Inspired by the proven success of human guided machine learning, we propose Knowledge-augmented

Gregory Plumb, Maruan Al-Shedivat, Eric Xing, Ameet Talwalkar

Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, which lack guarantees about their explanation quality. We propose an alternative to these approaches by directly regularizing a black-box model for interp

Ramamonjy Andriamifidisoa, Rufine Marius Lalasoa, Toussaint Joseph Rabeherimanana

A closer look at linear recurring sequences allowed us to define the multiplication of a univariate polynomial and a sequence, viewed as a power series with another variable, resulting in another sequence. Extending this operation, one gets the multiplication of matrices of multivariate polynomials and vectors of powers series. A dynamical system, according

Nick Firoozye, Adriano Koshiyama

Dynamic trading strategies, in the spirit of trend-following or mean-reversion, represent an only partly understood but lucrative and pervasive area of modern finance. Assuming Gaussian returns and Gaussian dynamic weights or signals, (e.g., linear filters of past returns, such as simple moving averages, exponential weighted moving averages, forecasts from A

Bálint Kaszás, Tímea Haszpra, Mátyás Herein

Using an intermediate complexity climate model (Planet Simulator), we investigate the so-called Snowball Earth transition. For certain values of the solar constant, the climate system allows two different stable states: one of them is the Snowball Earth, covered by ice and snow, and the other one is today's climate. In our setup, we consider the case whe

Andrew Lobb

We show that the torus knot $T_{4,9}$ bounds a smooth Möbius band in the $4$-ball, giving a counterexample to Batson's non-orientable analogue of Milnor's conjecture on the smooth slice genera of torus knots.

Bernd Finkbeiner, Christopher Hahn, Marvin Stenger, Leander Tentrup

We present RVHyper, a runtime verification tool for hyperproperties. Hyperproperties, such as non-interference and observational determinism, relate multiple computation traces with each other. Specifications are given as formulas in the temporal logic HyperLTL, which extends linear-time temporal logic (LTL) with trace quantifiers and trace variables. RVHype

Joonatan Jalonen, Jarkko Kari

Cellular automata are topological dynamical systems. We consider the problem of deciding whether two cellular automata are conjugate or not. We also consider deciding strong conjugacy, that is, conjugacy by a map that commutes with the shift maps. We show that the following two sets of pairs of one-dimensional one-sided cellular automata are recursively inse

Reynald Lercier, Elisa Lorenzo García, Christophe Ritzenthaler

Let C/K: F = 0 be a smooth plane quartic over a complete discrete valuation field K. In a previous paper the authors togetehr with Q. Liu give various characterizations of the reduction (i.e. non-hyperelliptic genus 3 curve, hyperelliptic genus 3 curve or bad) of the stable model of C: in terms of the existence of a special plane quartic model and in terms o

Payam Ghassemi, Sumeet Sanjay Lulekar, Souma Chowdhury

This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to "when" to sample and "how many" samples to add (to preserve the credibility of the optimization sea

Oscar J. Romero

Automatic service composition in mobile and pervasive computing faces many challenges due to the complex nature of the environment. Common approaches address service composition from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host's mobil

Bharti Arora, J. C. Pandey, M. De Becker

We investigated the long-term behaviour in X-rays of the colliding wind binary WR 25, using archival data obtained with Suzaku, Swift, XMM-Newton, and NuSTAR spanning over ~16 years. Our analysis reveals phase-locked variations repeating consistently over many consecutive orbits, in agreement with an X-ray emission fully explained by thermal emission from th

Joshua W. E. Farrell

In 1655, John Wallis whilst at the University of Oxford discovered the famous and beautiful formula for pi, now known as Wallis' Product. Since then, several analogous formulae have been discovered generalising the original. One more modern proof of the Wallis Product and its relatives directly uses the Gamma Function. This short paper will use similar t

Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang

Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying relation between entities is pre-determined. However, the explicit graph structure (relation) does not necessari

Hongfu Liu, Zhiqiang Tao, Zhengming Ding

Consensus clustering fuses diverse basic partitions (i.e., clustering results obtained from conventional clustering methods) into an integrated one, which has attracted increasing attention in both academic and industrial areas due to its robust and effective performance. Tremendous research efforts have been made to thrive this domain in terms of algorithms

Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen

Recently, a method [7] was proposed to generate contrastive explanations for differentiable models such as deep neural networks, where one has complete access to the model. In this work, we propose a method, Model Agnostic Contrastive Explanations Method (MACEM), to generate contrastive explanations for \emph{any} classification model where one is able to \e

Tomáš Musil

In this paper we compare structure of Czech word embeddings for English-Czech neural machine translation (NMT), word2vec and sentiment analysis. We show that although it is possible to successfully predict part of speech (POS) tags from word embeddings of word2vec and various translation models, not all of the embedding spaces show the same structure. The in

B. Fernandes, T. Montmerle, T. Santos-Silva, J. Gregorio-Hetem

The origin of the arc-shaped Sh2-296 nebula is still unclear. Mainly due to its morphology, the nebula has been suggested to be a 0.5 Myr-old supernova remnant (SNR) that could be inducing star formation in the CMa OB1 association. We aim to show, for the first time, that the nebula is part of a large, shell-like structure, which we have designated the ``CMa

Krishnendu Chatterjee, Laura Schmid, Stefan Schmid

The Price of Anarchy (PoA) is a well-established game-theoretic concept to shed light on coordination issues arising in open distributed systems. Leaving agents to selfishly optimize comes with the risk of ending up in sub-optimal states (in terms of performance and/or costs), compared to a centralized system design. However, the PoA relies on strong assumpt

Hsiao-Ping Hsu, Kurt Kremer

Polymer melts undergoing large deformation by uniaxial elongation are studied by molecular dynamics simulations of bead-spring chains in melts. Applying a primitive path analysis to strongly deformed polymer melts, the role of topological constrains in highly entangled polymer melts is investigated and quantified. We show that the over-all, large scale confo

Gautham Krishna Gudur, Prahalathan Sundaramoorthy, Venkatesh Umaashankar

Various health-care applications such as assisted living, fall detection etc., require modeling of user behavior through Human Activity Recognition (HAR). HAR using mobile- and wearable-based deep learning algorithms have been on the rise owing to the advancements in pervasive computing. However, there are two other challenges that need to be addressed: firs

Jakob Suchan, Mehul Bhatt, Srikrishna Varadarajan

We demonstrate the need and potential of systematically integrated vision and semantics} solutions for visual sensemaking (in the backdrop of autonomous driving). A general method for online visual sensemaking using answer set programming is systematically formalised and fully implemented. The method integrates state of the art in (deep learning based) visua

Shuai Liu, Jie Li, Kochise C. Bennett, Brad Ganoe

We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data representation of a molecule. We define multiple channels that describe different spatial resolutions for each atom type that utilizes cropping, pooling, and concatenation to create a

Hao Wang, Linlin Zong, Bing Liu, Yan Yang

Beyond existing multi-view clustering, this paper studies a more realistic clustering scenario, referred to as incomplete multi-view clustering, where a number of data instances are missing in certain views. To tackle this problem, we explore spectral perturbation theory. In this work, we show a strong link between perturbation risk bounds and incomplete mul

Wojciech Czernous, Tomasz Szarek

It is known that Iterated Function Systems generated by orientation preserving homeomorphisms of the unit interval admit a unique invariant measure on $(0,1)$. The setup for this result is the positivity of Lyapunov exponents at both fixed points and the minimality of the induced action. With the additional requirement of continuous differentiability of maps

Bonggun Shin, Hao Yang, Jinho D. Choi

Recent advances in deep learning have facilitated the demand of neural models for real applications. In practice, these applications often need to be deployed with limited resources while keeping high accuracy. This paper touches the core of neural models in NLP, word embeddings, and presents a new embedding distillation framework that remarkably reduces the

Diab W. Abueidda, Mohammad Almasri, Rami Ammourah, Umberto Ravaioli

In this paper, we develop a convolutional neural network model to predict the mechanical properties of a two-dimensional checkerboard composite quantitatively. The checkerboard composite possesses two phases, one phase is soft and ductile while the other is stiff and brittle. The ground-truth data used in the training process are obtained from finite element

Luis Riera, Koray Ozcan, Jennifer Merickel, Mathew Rizzo

In this paper, we present a novel model to detect lane regions and extract lane departure events (changes and incursions) from challenging, lower-resolution videos recorded with mobile cameras. Our algorithm used a Mask-RCNN based lane detection model as pre-processor. Recently, deep learning-based models provide state-of-the-art technology for object detect

Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang

With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks differ significantly from other deep learning networks due to their need to handle categorical features and are not well studied or understood. In this paper, we develop a state-o

Muhammad A. Masood, Finale Doshi-Velez

Standard reinforcement learning methods aim to master one way of solving a task whereas there may exist multiple near-optimal policies. Being able to identify this collection of near-optimal policies can allow a domain expert to efficiently explore the space of reasonable solutions. Unfortunately, existing approaches that quantify uncertainty over policies a

C. Flannigan, C. D. Tan, J. F. Scott

Previous studies of Barkhausen noise in PZT have been limited to the energy spectrum (slew rate response voltages versus time), showing agreement with avalanche models; in barium titanate other exponents have been measured acoustically, but only at ambient temperatures. In the present study we report the Omori exponent (-0.95$\pm$0.03) for aftershocks in PZT

Varun Srivastava, Stefan Ballmer, Duncan A. Brown, Chaitanya Afle

We optimize the third-generation gravitational-wave detector to maximize the range to detect core-collapse supernovae. Based on three-dimensional simulations for core-collapse and the corresponding gravitational-wave waveform emitted, the corresponding detection range for these waveforms is limited to within our galaxy even in the era of third-generation det

Kent Bonsma-Fisher, Weng-Kian Tham, Hugo Ferretti, Aephraim Steinberg

As the separation between two emitters is decreased below the Rayleigh limit, the information that can be gained about their separation using traditional imaging techniques, photon counting in the image plane, reduces to nil. Assuming the sources are of equal intensity, Rayleigh's "curse" can be alleviated by making phase-sensitive measurements i

Dali Wang, Zheng Lu, Zhirong Bao

The detection of cell shape changes in 3D time-lapse images of complex tissues is an important task. However, it is a challenging and tedious task to establish a comprehensive dataset to improve the performance of deep learning models. In the paper, we present a deep learning approach to augment 3D live images of the Caenorhabditis elegans embryo, so that we

Logan Lebanoff, Kaiqiang Song, Franck Dernoncourt, Doo Soon Kim

When writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence. However, the mechanisms behind the selection of one or multiple source sentences remain poorly understood. Sentence fusion assumes multi-sentence input; yet sentence selection methods only work with single sentences and not combinati

Yalin E. Sagduyu, Yi Shi, Tugba Erpek

Machine learning finds rich applications in Internet of Things (IoT) networks such as information retrieval, traffic management, spectrum sensing, and signal authentication. While there is a surge of interest to understand the security issues of machine learning, their implications have not been understood yet for wireless applications such as those in IoT s

Ruben Becker, Federico Corò, Gianlorenzo D'Angelo, Hugo Gilbert

The personalization of our news consumption on social media has a tendency to reinforce our pre-existing beliefs instead of balancing our opinions. This finding is a concern for the health of our democracies which rely on an access to information providing diverse viewpoints. To tackle this issue from a computational perspective, Garimella et al. (NIPS'1

Benjamin M. Case, Evan M. Haithcock, Renu C. Laskar

A set $S\subseteq V$ is $α$-dominating if for all $v\in V-S$, $|N(v) \cap S | \geq α|N(v)|.$ The $α$-domination number of $G$ equals the minimum cardinality of an $α$-dominating set $S$ in $G$. Since being introduced by Dunbar, et al. in 2000, $α$-domination has been studied for various graphs and a variety of bounds have been developed. In this paper, we pr

Sangwoo Cho, Logan Lebanoff, Hassan Foroosh, Fei Liu

The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data. These obstacles prevent encoder-decoder models from being used directly, but optimization-based methods such as determinantal point processes (DPPs) are known to handle them well. In this paper we see

Md Ahsanul Abeed, Supriyo Bandyopadhyay

Nanomagnets with small shape anisotropy energy barriers on the order of the thermal energy have unstable magnetization that fluctuates randomly in time. They have recently emerged as promising hardware platforms for stochastic computing and machine learning because the random magnetization states can be harnessed for probabilistic bits. Here, we have studied

Ahmad Sabihi

In this paper, we present a novel method to draw a circle tangent to three given circles lying on a plane. Using the analytic geometry and inversion (reflection) theorems, the center and radius of the inversion circle are obtained. Inside any one of the three given circles, a circle of the similar radius and concentric with its own corresponding original cir

S. L. Guglielmino, P. Romano, B. Ruiz Cobo, F. Zuccarello

Recent observations of the solar photosphere revealed the presence of elongated filamentary bright structures inside sunspot umbrae, called umbral filaments (UFs). These features differ in morphology, magnetic configuration, and evolution from light bridges that are usually observed to intrude in sunspots. To characterize an UF observed in the umbra of the g

Andrew King, Rebecca Nealon

We consider black hole - galaxy coevolution using simple analytic arguments. We focus on the fact that several supermassive black holes are known with masses significantly larger than suggested by the $M - σ$ relation, sometimes also with rather small stellar masses. We show that these are likely to have descended from extremely compact `blue nugget' gal

R. R. de Carvalho, A. P. Costa, T. C. Moura, A. L. B. Ribeiro

This paper is the third of a series in which we investigate the discrimination between Gaussian (G) and Non-Gaussian (NG) clusters, based on the velocity distribution of the member galaxies. We study a sample of 177 groups from the Yang catalog in the redshift interval of 0.03 $\le$ z $\le$ 0.1 and masses $\ge$ 10$^{14} \rm M_{\odot}$. Examining the projecte

Jozef Barunik, Cathy Yi-Hsuan Chen, Jan Vecer

We propose how to quantify high-frequency market sentiment using high-frequency news from NASDAQ news platform and support vector machine classifiers. News arrive at markets randomly and the resulting news sentiment behaves like a stochastic process. To characterize the joint evolution of sentiment, price, and volatility, we introduce a unified continuous-ti

Martin Klein, Lyudmila Balakireva, Harihar Shankar

Services and applications based on the Memento Aggregator can suffer from slow response times due to the federated search across web archives performed by the Memento infrastructure. In an effort to decrease the response times, we established a cache system and experimented with machine learning models to predict archival holdings. We reported on the experim

Andrea Tosatto, Tilman Weckesser, Spyros Chatzivasileiadis

This report describes a modified version of the IEEE 3-area RTS '96 Test Case for time series analysis. This test case was originally developed to investigate the impact of the introduction of losses in the market clearing, thus the main application of this system is DC Optimal Power Flow (OPF) studies. A fourth area is included in the system. In each sn

Fahime Sadat Mirhosseini, Andrea Pizzo, Luca Sanguinetti, Aliakbar Tadaion

This work considers the uplink of a Massive MIMO network wherein the base stations (BSs) are randomly deployed according to a homogenous Poisson point process of intensity $λ$. Each BS is equipped with $M$ antennas and serves $K$ user equipments. A rigorous stochastic geometry framework with a multi-slope path loss model and pilot-based channel estimation is

Amir Behjat, Krushang Gabani, Souma Chowdhury

Cooperative autonomous approaches to avoiding collisions among small Unmanned Aerial Vehicles (UAVs) is central to safe integration of UAVs within the civilian airspace. One potential online cooperative approach is the concept of reciprocal actions, where both UAVs take pre-trained mutually coherent actions that do not require active online coordination (the

Zheng Tracy Ke, Lingzhou Xue, Fan Yang

We consider the problem of decomposing a large covariance matrix into the sum of a low-rank matrix and a diagonally dominant matrix, and we call this problem the "Diagonally-Dominant Principal Component Analysis (DD-PCA)". DD-PCA is an effective tool for designing statistical methods for strongly correlated data. We showcase the use of DD-PCA in two

Kunal Swami, Kaushik Raghavan, Nikhilanj Pelluri, Rituparna Sarkar

Recent deep learning based approaches have outperformed classical stereo matching methods. However, current deep learning based end-to-end stereo matching methods adopt a generic encoder-decoder style network with skip connections. To limit computational requirement, many networks perform excessive down sampling, which results in significant loss of useful l

Víctor Valls, George Iosifidis, Douglas J. Leith, Leandros Tassiulas

This paper addresses Online Convex Optimization (OCO) problems where the constraints have additive perturbations that (i) vary over time and (ii) are not known at the time to make a decision. Perturbations may not be i.i.d. generated and can be used to model a time-varying budget or commodity in resource allocation problems. The problem is to design a policy

Colin Cherry, George Foster

Simultaneous machine translation attempts to translate a source sentence before it is finished being spoken, with applications to translation of spoken language for live streaming and conversation. Since simultaneous systems trade quality to reduce latency, having an effective and interpretable latency metric is crucial. We introduce a variant of the recentl

Xufeng Zhang, Kun Ding, Dafei Jin, Xianjing Zhou

Exceptional points (EPs) are singularities of energy levels in non-Hermitian systems. In this Letter, we demonstrate the surface of EPs on a magnon polariton platform composed of coupled magnons and microwave photons. Our experiments show that EPs form a three-dimensional exceptional surface (ES) when the system is tuned in a four-dimensional synthetic space

Li Deng, Shuo Zhang, Krisztian Balog

Tables contain valuable knowledge in a structured form. We employ neural language modeling approaches to embed tabular data into vector spaces. Specifically, we consider different table elements, such caption, column headings, and cells, for training word and entity embeddings. These embeddings are then utilized in three particular table-related tasks, row p

Mohammadali Asadi, Alexander Brandt, Robert H. C. Moir, Marc Moreno Maza

We discuss the parallelization of algorithms for solving polynomial systems symbolically by way of triangular decomposition. Algorithms for solving polynomial systems combine low-level routines for performing arithmetic operations on polynomials and high-level procedures which produce the different components (points, curves, surfaces) of the solution set. T

Harbir Antil, Deepanshu Verma, Mahamadi Warma

This paper introduces the notion of state constraints for optimal control problems governed by fractional elliptic PDEs of order $s \in (0,1)$. There are several mathematical tools that are developed during the process to study this problem, for instance, the characterization of the dual of the fractional order Sobolev spaces and well-posedness of fractional

Elan Rosenfeld, Santosh Vempala, Manuel Blum

Password users frequently employ passwords that are too simple, or they just reuse passwords for multiple websites. A common complaint is that utilizing secure passwords is too difficult. One possible solution to this problem is to use a password schema. Password schemas are deterministic functions which map challenges (typically the website name) to respons

Andrzej Bedychaj, Przemysław Spurek, Łukasz Struskim, Jacek Tabor

Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent. In this paper we present Multiple-weighted Independent Component Analysis (MWeICA) algorithm, a new ICA method which is based on approximate diagonalization of weighted covariance matrices. Our

Heinrich Jiang, Maya Gupta

We present a new active sampling method we call min-margin which trains multiple learners on bootstrap samples and then chooses the examples to label based on the candidates' minimum margin amongst the bootstrapped models. This extends standard margin sampling in a way that increases its diversity in a supervised manner as it arises from the model uncert

Kathryn Volk, Renu Malhotra

We present numerical simulations of giant planet migration in our solar system and examine how the speed of planetary migration affects inclinations in the resulting population of small bodies (test particles) scattered outward and subsequently captured into Neptune's 3:2 mean motion resonance (the Plutinos) as well as the hot classical Kuiper belt popul

Armin Tavakoli, Géraldine Haack, Nicolas Brunner, Jonatan Bohr Brask

The generation of genuine multipartite entangled states is challenging in practice. Here we explore a new route to this task, via autonomous entanglement engines which use only incoherent coupling to thermal baths and time-independent interactions. We present a general machine architecture, which allows for the generation of a broad range of multipartite ent

Toshiyasu Arai, David Fernández-Duque, Stanley Wainer, Andreas Weiermann

The classical Goodstein process gives rise to long but finite sequences of natural numbers whose termination is not provable in Peano arithmetic. In this manuscript we consider a variant based on the Ackermann function. We show that Ackermannian Goodstein sequences eventually terminate, but this fact is not provable using predicative means.

James MacDonald, Véronique Petit

It has been suggested that the absence of macroturbulence in the atmosphere of NGC 1624 - 2 is due its strong magnetic field (the strongest known for a massive O star) suppressing convection in its outer layers, removing the mechanism thought responsible for the observed macroturbulence in stars with lower field strengths. Here, we develop and apply a criter

Marios Mattheakis, Matthias Maier, Wei Xi Boo, Efthimios Kaxiras

Plasmonic crystals are a class of optical metamaterials that consist of engineered structures at the sub-wavelength scale. They exhibit optical properties that are not found under normal circumstances in nature, such as negative-refractive-index and epsilon-near-zero (ENZ) behavior. Graphene-based plasmonic crystals present linear, elliptical, or hyperbolic

Rubab Amin, Jonathan George, Shuai Sun, Thomas Ferreira de Lima

Recently integrated optics has become an intriguing platform for implementing machine learning algorithms and inparticular neural networks. Integrated photonic circuits can straightforwardly perform vector-matrix multiplicationswith high efficiency and low power consumption by using weighting mechanism through linear optics. Although,this can not be said for

Jon Hakkila, Robert D. Preece

We demonstrate how pulse structures in Short gamma-ray bursts (SGRBs), coupled with observations of GRB/GW 170817A, constrain the geometries of dying HMXRB systems composed of merging neutron stars.

Bryan O'Gorman

We present a conceptually clear and algorithmically useful framework for parameterizing the costs of tensor network contraction. Our framework is completely general, applying to tensor networks with arbitrary bond dimensions, open legs, and hyperedges. The fundamental objects of our framework are rooted and unrooted contraction trees, which represent classes

Kazunori Asakura, Kiyoshi Hayashida, Takashi Hanasaka, Tomoki Kawabata

X-ray polarimetry in astronomy has not been exploited well, despite its importance. The recent innovation of instruments is changing this situation. We focus on a complementary MOS (CMOS) pixel detector with small pixel size and employ it as an x-ray photoelectron tracking polarimeter. The CMOS detector we employ is developed by GPixel Inc., and has a pixel

Antonios Tsokaros, Milton Ruiz, Vasileios Paschalidis, Stuart L. Shapiro

We perform long-term simulations of spinning binary neutron stars, with our highest dimensionless spin being $χ\sim 0.32$. To assess the importance of spin during the inspiral we vary the spin, and also use two equations of state, one that consists of plain nuclear matter and produces compact stars (SLy), and a hybrid one that contains both nuclear and quark

Carlos Blanco, M. Sten Delos, Adrienne L. Erickcek, Dan Hooper

If the dark matter is part of a hidden sector with only very feeble couplings to the Standard Model, the lightest particle in the hidden sector will generically be long-lived and could come to dominate the energy density of the universe prior to the onset of nucleosynthesis. During this early matter-dominated era, density perturbations will grow more quickly

L. E. Abramson, G. B. Brammer, K. B. Schmidt, T. Treu

We present a catalogue of 22755 objects with slitless, optical, Hubble Space Telescope (HST) spectroscopy from the Grism Lens-Amplified Survey from Space (GLASS). The data cover $\sim$220 sq. arcmin to 7-orbit ($\sim$10 ks) depth in 20 parallel pointings of the Advanced Camera for Survey's G800L grism. The fields are located 6' away from 10 massive g

Ignacio Martín-Navarro, Glenn van de Ven, Akın Yıldırım

The formation and assembly process of massive galaxies is a combination of two phases: an initial in-situ-dominated one followed by an ex-situ-dominated evolution. Separating these two contributions is therefore crucial to understand the baryonic cycle within massive halos. A recently discovered population of so-called naked red nuggets, galaxies that shortc

Daniel Lam

I propose a new approach to free-form cluster lens modeling that is inspired by the JPEG image compression method. This approach is motivated specifically by the need for accurate modeling of high-magnification regions in galaxy clusters. Existing modeling methods may struggle in these regions due to their limited flexibility in the parametrization of the le

Hsiao-Wen Chen, Erin Boettcher, Sean D. Johnson, Fakhri S. Zahedy

This paper reports the discovery of spatially-extended line-emitting nebula, reaching to ~100 physical kpc (pkpc) from a damped Lyα absorber (DLA) at z_DLA=0.313 along the sightline toward QSO PKS1127-145 (z_QSO=1.188). This DLA was known to be associated with a galaxy group of dynamical mass M_group ~3e12 M_sun, but its physical origin remained ambiguous. N