April 2019 arXiv papers — page 53

Xuanting Ji, Yan Liu, Xin-Meng Wu

We study the chiral vortical conductivity in a holographic Weyl semimetal model, which describes a topological phase transition from the strongly coupled topologically nontrivial phase to a trivial phase. We focus on the temperature dependence of the chiral vortical conductivity where the mixed gauge-gravitational anomaly plays a crucial role. After a proper

Sunny Sanyal, Dapeng Wu, Boubakr Nour

Based on the dominant paradigm, all the wearable IoT devices used in the healthcare sector also known as the internet of medical things (IoMT) are resource constrained in power and computational capabilities. The IoMT devices are continuously pushing their readings to the remote cloud servers for real-time data analytics, that causes faster drainage of the d

Shintaro Akamine, Masaaki Umehara, Kotaro Yamada

Calabi's Bernstein-type theorem asserts that a zero mean curvature entire graph in Lorentz-Minkowski space $\boldsymbol L^3$ which admits only space-like points is a space-like plane. Using the fluid mechanical duality between minimal surfaces in Euclidean 3-space $\boldsymbol E^3$ and maximal surfaces in Lorentz-Minkowski space $\boldsymbol L^3$, we giv

Alexander W. Bray, David Freeman, Sebastian Eckart, Anatoli S. Kheifets

We consider the process of high-order harmonics generation (HHG) in the xenon atom enhanced by the inter-shell correlation between the valence 5p and inner 4d shells. We derive the HHG spectrum from a numerical solution of the one-electron time-dependent Schrödinger equation multiplied by the enhancement factor taken as the photoionization cross-sections rat

Benjamin Jaye, Galyna V. Livshyts, Grigoris Paouris, Peter Pivovarov

In this note we study a conjecture of Madiman and Wang which predicted that the generalized Gaussian distribution minimizes the Rényi entropy of the sum of independent random variables. Through a variational analysis, we show that the generalized Gaussian fails to be a minimizer for the problem.

Yi-Jun Chang, Thatchaphol Saranurak

An $(ε,ϕ)$-expander decomposition of a graph $G=(V,E)$ is a clustering of the vertices $V=V_{1}\cup\cdots\cup V_{x}$ such that (1) each cluster $V_{i}$ induces subgraph with conductance at least $ϕ$, and (2) the number of inter-cluster edges is at most $ε|E|$. In this paper, we give an improved distributed expander decomposition. Specifically, we construct a

Binxuan Huang, Kathleen M. Carley

In this paper, we study the problem of node representation learning with graph neural networks. We present a graph neural network class named recurrent graph neural network (RGNN), that address the shortcomings of prior methods. By using recurrent units to capture the long-term dependency across layers, our methods can successfully identify important informa

Brenden M. Lake, Steven T. Piantadosi

Machine learning has made major advances in categorizing objects in images, yet the best algorithms miss important aspects of how people learn and think about categories. People can learn richer concepts from fewer examples, including causal models that explain how members of a category are formed. Here, we explore the limits of this human ability to infer c

Anna Rodionova, Kirill Antonov, Arina Buzdalova, Carola Doerr

We analyze the performance of the 2-rate $(1+λ)$ Evolutionary Algorithm (EA) with self-adjusting mutation rate control, its 3-rate counterpart, and a $(1+λ)$~EA variant using multiplicative update rules on the OneMax problem. We compare their efficiency for offspring population sizes ranging up to $λ=3,200$ and problem sizes up to $n=100,000$. Our empirical

Honghui Liu, Askar B. Abdikamalov, Dimitry Ayzenberg, Cosimo Bambi

We continue exploring the constraining capabilities of X-ray reflection spectroscopy to test the Kerr-nature of astrophysical black holes and we present the results of our analysis of two NuSTAR observations of Cygnus X-1 in the soft state. We find that the final measurement can strongly depend on the assumption of the intensity profile. We conclude that Cyg

Ivailo Hartarsky, Marco Baity-Jesi, Riccardo Ravasio, Alain Billoire

We study the evolution of the maximum energy $E_\max(t)$ reached between time $0$ and time $t$ in the dynamics of simple models with glassy energy landscapes, in instant quenches from infinite temperature to a target temperature $T$. Through a detailed description of the activated dynamics, we are able to describe the evolution of $E_\max(t)$ from short time

Rasmus Backholm, Tatiana A. Bubba, Camille Bélanger-Champagne, Tapio Helin

The International Atomic Energy Agency (IAEA) has recently approved passive gamma emission tomography (PGET) as a method for inspecting spent nuclear fuel assemblies (SFAs), an important aspect of international nuclear safeguards which aim at preventing the proliferation of nuclear weapons. The PGET instrument is essentially a single photon emission computed

Yacine Jernite, Kavya Srinet, Jonathan Gray, Arthur Szlam

We propose a large scale semantic parsing dataset focused on instruction-driven communication with an agent in Minecraft. We describe the data collection process which yields additional 35K human generated instructions with their semantic annotations. We report the performance of three baseline models and find that while a dataset of this size helps us train

Koen Ruymbeek, Wim Vanroose

Computed Tomography is a powerful imaging technique that allows non-destructive visualization of the interior of physical objects in different scientific areas. In traditional reconstruction techniques the object of interest is mostly considered to be static, which gives artefacts if the object is moving during the data acquisition. In this paper we present

Andrew Durden

Fluorescence microscopy has led to impressive quantitative models and new insights gained from richer sets of biomedical imagery. However, there is a dearth of rigorous and established bioimaging strategies for modeling spatiotemporal behavior of diffuse, subcellular components such as mitochondria or actin. In many cases, these structures are assessed by ha

Guillaume Noyel, R Thomas, S Iles, G Bhakta

In order to estimate a registration model of eye fundus images made of an affinity and two radial distortions, we introduce an estimation criterion based on an error between the vessels. In [1], we estimated this model by minimising the error between characteristics points. In this paper, the detected vessels are selected using the circle and ellipse equatio

Zohaib Khan, Faisal Shafait, Ajmal Mian

We propose the construction of a prototype scanner designed to capture multispectral images of documents. A standard sheet-feed scanner is modified by disconnecting its internal light source and connecting an external multispectral light source comprising of narrow band light emitting diodes (LED). A document is scanned by illuminating the scanner light guid

Sascha Wirges, Johannes Gräter, Qiuhao Zhang, Christoph Stiller

We present a self-supervised approach to estimate flow in camera image and top-view grid map sequences using fully convolutional neural networks in the domain of automated driving. We extend existing approaches for self-supervised optical flow estimation by adding a regularizer expressing motion consistency assuming a static environment. However, as this ass

Guillaume Noyel, Michel Jourlin

In order to create an image segmentation method robust to lighting changes, two novel homogeneity criteria of an image region were studied. Both were defined using the Logarithmic Image Processing (LIP) framework whose laws model lighting changes. The first criterion estimates the LIP-additive homogeneity and is based on the LIP-additive law. It is theoretic

Alberto Tarable, Francisco J. Escribano

Anytime reliable communication systems are needed in contexts where the property of vanishing error probability with time is critical. This is the case of unstable real time systems that are to be controlled through the transmission and processing of remotely sensed data. The most successful anytime reliable transmission systems developed so far are based on

Zulifqar Ali, Ijaz Hussain, Muhammad Faisal, Hafiza Mamona Nazir

These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the countrys environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and detection of drought can provide guidance t

Changliang Zhou

The paper is concerned about a sharp form of Anisotropic Moser-Trudinger inequality which involves $L^{n}$ norm. Let \begin{equation*} λ_{1}(Ω) = \inf_{u\in W_0^{1,n}(Ω),u\not\equiv 0} ||F(\nabla u)||_{L^n(Ω)}^n / ||u||_{L^n(Ω)}^n \end{equation*} be the first eigenvalue associated with $n$-Finsler-Laplacian. using blowing up analysis, we obtain that \begin{e

Izaak D. Neveln, Amoolya Tirumalai, Simon Sponberg

Movement in biology is often achieved with distributed control of coupled subcomponents, e.g. muscles and limbs. Coupling could range from weak and local, i.e. decentralized, to strong and global, i.e. centralized. We developed a model-free measure of centralization that compares information shared between control signals and both global and local states. A

Izzat Qaralleh, Farrukh Mukhamedov

In this paper, we introduce Volterra evolution algebras which are evolution algebras whose structural matrices are described by skew symmetric matrices. A main result of the present paper gives a connection between such kind of algebras with ergodicities of Volterra quadratic stochastic operators. Furthermore, some of properties of the considered algebras su

G. Modanese

In systems with non-local potentials or other kinds of non-locality, the Landauer-Büttiker formula of quantum transport leads to replace the usual gauge-invariant current density $\textbf{J}$ with a current $\textbf{J}^{ext}$ which has a non-local part and coincides with the current of the extended Aharonov-Bohm electrodynamics. It follows that the electroma

Görkem Paçacı, David Johnson, Steve McKeever, Andreas Hamfelt

By their nature, the composition of black box models is opaque. This makes the ability to generate explanations for the response to stimuli challenging. The importance of explaining black box models has become increasingly important given the prevalence of AI and ML systems and the need to build legal and regulatory frameworks around them. Such explanations

Tong Mu, Karan Goel, Emma Brunskill

In many cases an intelligent agent may want to learn how to mimic a single observed demonstrated trajectory. In this work we consider how to perform such procedural learning from observation, which could help to enable agents to better use the enormous set of video data on observation sequences. Our approach exploits the properties of this setting to increme

Yuwei Zhang, Xin Wu, Chenyang Gu, Yueqi Xie

This is a method report for the Kaggle data competition 'Predict future sales'. In this paper, we propose a rather simple approach to future sales predicting based on feature engineering, Random Forest Regressor and ensemble learning. Its performance turned out to exceed many of the conventional methods and get final score 0.88186, representing root

Vivienne V. Forrester

Despite the benefits of school management information systems (SMIS), the concept of data-driven school culture failed to materialize for many educational institutions. Challenges posed by the quality of data in the big data era have prevented many schools from realizing the real potential of the SMIS. The paper analyses the uses, features, and inhibiting fa

Veronica J. Berrocal, Yawen Guan, Amanda Muyskens, Haoyu Wang

A typical problem in air pollution epidemiology is exposure assessment for individuals for which health data are available. Due to the sparsity of monitoring sites and the limited temporal frequency with which measurements of air pollutants concentrations are collected (for most pollutants, once every 3 or 6 days), epidemiologists have been moving away from

Thierry Boy de la Tour, Rachid Echahed

We address the problem of defining graph transformations by the simultaneous application of direct transformations even when these cannot be applied independently of each other. An algebraic approach is adopted, with production rules of the form $L\xleftarrow{l}K \xleftarrow{i} I \xrightarrow{r} R$, called weak spans. A parallel coherent transformation is in

Sebastian F Ruf, Magnus Egerstedt, Jeff S. Shamma

We consider the notion of herdability, a set-based reachability condition, which asks whether the state of a system can be controlled to be element-wise larger than a non-negative threshold. First a number of foundational results on herdability of a continuous time, linear time invariant system are presented. These show that the herdability of a linear syste

Yi Zheng, Peng Huang, Anqi Huang, Jinye Peng

In a practical CVQKD system, the optical attenuator can adjust the Gaussian-modulated coherent states and the local oscillator signal to an optimal value for guaranteeing the security of the system and optimizing the performance of the system. However, the performance of the optical attenuator may deteriorate due to the intentional and unintentional damage o

Prashant Anand, Ajeet Kumar Singh, Siddharth Srivastava, Brejesh Lall

The recent advances in deep learning are mostly driven by availability of large amount of training data. However, availability of such data is not always possible for specific tasks such as speaker recognition where collection of large amount of data is not possible in practical scenarios. Therefore, in this paper, we propose to identify speakers by learning

Jonathan Brisch, Holger Kantz

We propose a dynamical mechanism for a scale dependent error growth rate, by the introduction of a class of hierarchical models. The coupling of time scales and length scales is motivated by atmospheric dynamics. This model class can be tuned to exhibit a scale dependent error growth rate in the form of a power law, which translates in power law error growth

Jurjen R. Helmus, Seyla Wachlin, Igna Vermeulen, Mike H. Lees

Governments and cities around the world are currently facing rapid growth in the use of Electric Vehicles and therewith the need for Charging Infrastructure. For these cities, the struggle remains how to further roll out charging infrastructure in the most efficient way, both in terms of cost and use. Forecasting models are not able to predict more long-term

Christian Parkinson, Kevin Huynh, Deanna Needell

Matrix completion is a classical problem in data science wherein one attempts to reconstruct a low-rank matrix while only observing some subset of the entries. Previous authors have phrased this problem as a nuclear norm minimization problem. Almost all previous work assumes no explicit structure of the matrix and uses uniform sampling to decide the observed

Matthew Purri, Jia Xue, Kristin Dana, Matthew Leotta

Material recognition methods use image context and local cues for pixel-wise classification. In many cases only a single image is available to make a material prediction. Image sequences, routinely acquired in applications such as mutliview stereo, can provide a sampling of the underlying reflectance functions that reveal pixel-level material attributes. We

Shahar Mendelson, Grigoris Paouris

Recovery procedures in various application in Data Science are based on \emph{stable point separation}. In its simplest form, stable point separation implies that if $f$ is "far away" from $0$, and one is given a random sample $(f(Z_i))_{i=1}^m$ where a proportional number of the sample points may be corrupted by noise, that information is still enou

Niko Nikolay, Noah Mendelson, Ersan Özelci, Bernd Sontheimer

Single photon emitters in two-dimensional materials are promising candidates for future generation of quantum photonic technologies. In this work, we experimentally determine the quantum efficiency (QE) of single photon emitters (SPE) in few-layer hexagonal boron nitride (hBN). We employ a metal hemisphere that is attached to the tip of an atomic force micro

Reazul H. Russel, Tianyi Gu, Marek Petrik

Optimism about the poorly understood states and actions is the main driving force of exploration for many provably-efficient reinforcement learning algorithms. We propose optimism in the face of sensible value functions (OFVF)- a novel data-driven Bayesian algorithm to constructing Plausibility sets for MDPs to explore robustly minimizing the worst case expl

Pedro J. Zufiria, David Pastor-Escuredo, Luis Ubeda Medina, Miguel A. Hernandez Medina

Social vulnerability is defined as the capacity of individuals and social groups to respond to any external stress placed on their livelihoods and wellbeing. Mobility and migrations are relevant when assessing vulnerability since the movements of a population reflect on their livelihoods, coping strategies and social safety nets. Although in general migratio

Nikhita Vedula, Nedim Lipka, Pranav Maneriker, Srinivasan Parthasarathy

Detecting and identifying user intent from text, both written and spoken, plays an important role in modelling and understand dialogs. Existing research for intent discovery model it as a classification task with a predefined set of known categories. To generailze beyond these preexisting classes, we define a new task of \textit{open intent discovery}. We in

Sanket S. Kalamkar, Martin Haenggi

The meta distribution (MD) of the signal-to-interference ratio (SIR) provides fine-grained reliability performance in wireless networks modeled by point processes. In particular, for an ergodic point process, the SIR MD yields the distribution of the per-link reliability for a target SIR. Here we reveal that the SIR MD has a second important application, whi

Vitor Possebom

I analyze treatment effects in situations when agents endogenously select into the treatment group and into the observed sample. As a theoretical contribution, I propose pointwise sharp bounds for the marginal treatment effect (MTE) of interest within the always-observed subpopulation under monotonicity assumptions. Moreover, I impose an extra mean dominance

Arkady Molev-Shteiman, Xiao-Feng Qi, Laurence Mailaender, Narayan Prasad

When a quantizer input signal is the sum of the desired signal and input white noise, the quantization error is a function of total input signal. Our new equivalent model splits the quantization error into two components: a non-linear distortion (NLD) that is a function of only the desired part of input signal (without noise), and an equivalent out-put white

Xiao Lin, Josep R. Casas, Montse Pardàs

We propose a novel 3D segmentation method for RBGD stream data to deal with 3D object segmentation task in a generic scenario with frequent object interactions. It mainly contributes in two aspects, while being generic and not requiring initialization: firstly, a novel tree structure representation for the point cloud of the scene is proposed. Then, a dynami

Saurav Islam, Semonti Bhattacharyya, Hariharan Nhalil, Mitali Banerjee

Implementing topological insulators as elementary units in quantum technologies requires a comprehensive understanding of the dephasing mechanisms governing the surface carriers in these materials, which impose a practical limit to the applicability of these materials in such technologies requiring phase coherent transport. To investigate this, we have perfo

Guanxiong Liu, Issa Khalil, Abdallah Khreishah

Neural Network classifiers have been used successfully in a wide range of applications. However, their underlying assumption of attack free environment has been defied by adversarial examples. Researchers tried to develop defenses; however, existing approaches are still far from providing effective solutions to this evolving problem. In this paper, we design

Omer Rahman, Erdong Wang, Ilan Ben-Zvi, Jyoti Biswas

Charge lifetime of strained superlattice GaAs photocathodes in DC guns is limited by ion back bombardment. It needs to be improved at least an order of magnitude to meet the requirements for future colliders such as Electron-Ion Collider (EIC). In this work, we propose and present simulation results for an offset anode scheme to increase charge lifetime in D

Joseph M. Lukens, Hsuan-Hao Lu, Bing Qi, Pavel Lougovski

We propose an electro-optic approach for transparent optical networking, in which frequency channels are actively transformed into any desired mapping in a wavelength-multiplexed environment. Based on electro-optic phase modulators and Fourier-transform pulse shapers, our all-optical frequency processor (AFP) is examined numerically for the specific operatio

Lucas Esclapez, Valentina Ricchiuti, John B. Bell, Marcus S. Day

We propose an algorithm for low Mach number reacting flows subjected to electric field that includes the chemical production and transport of charged species. This work is an extension of a multi-implicit spectral deferred correction (MISDC) algorithm designed to advance the conservation equations in time at scales associated with advective transport. The fa

Behzad Ghazanfari, Fatemeh Afghah, Kayvan Najarian, Sajad Mousavi

The high rate of false alarms in intensive care units (ICUs) is one of the top challenges of using medical technology in hospitals. These false alarms are often caused by patients' movements, detachment of monitoring sensors, or different sources of noise and interference that impact the collected signals from different monitoring devices. In this paper,

Mhd Hasan Sarhan, Abouzar Eslami, Nassir Navab, Shadi Albarqouni

Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning a disentangled feature representation is important for a more compact and explainable representation of the data. In this paper, we introduce a novel adversarial variational autoe

Yuxin Chen, Huiying Li, Steven Nagels, Zhijing Li

Recent works have explained the principle of using ultrasonic transmissions to jam nearby microphones. These signals are inaudible to nearby users, but leverage "hardware nonlinearity" to induce a jamming signal inside microphones that disrupts voice recordings. This has great implications on audio privacy protection. In this work, we gain a deeper u

Jeonghee Rho, Danny Milisavljevic, Arkaprabha Sarangi, Raffaella Margutti

Whether supernovae are a significant source of dust has been a long-standing debate. The large quantities of dust observed in high-redshift galaxies raise a fundamental question as to the origin of dust in the Universe since stars cannot have evolved to the AGB dust-producing phase in high-redshift galaxies. In contrast, supernovae occur within several milli

Junsik Kim, Tae-Hyun Oh, Seokju Lee, Fei Pan

In daily life, graphic symbols, such as traffic signs and brand logos, are ubiquitously utilized around us due to its intuitive expression beyond language boundary. We tackle an open-set graphic symbol recognition problem by one-shot classification with prototypical images as a single training example for each novel class. We take an approach to learn a gene

Jie You, Mosharaf Chowdhury

Geo-distributed analytics (GDA) frameworks transfer large datasets over the wide-area network (WAN). Yet existing frameworks often ignore the WAN topology. This disconnect between WAN-bound applications and the WAN itself results in missed opportunities for cross-layer optimizations. In this paper, we present Terra to bridge this gap. Instead of decoupled WA

Negin Musavi

Predicting the outcomes of integrating Unmanned Aerial Systems (UAS) into the National Aerospace (NAS) is a complex problem which is required to be addressed by simulation studies before allowing the routine access of UAS into the NAS. This thesis focuses on providing 2D and 3D simulation frameworks using a game theoretical methodology to evaluate integratio

Dan Edidin, Ryan Richey

We study the Fulton-Macpherson Chow cohomology of affine toric varieties. In particular, we prove that the Chow cohomology vanishes in positive degree. We prove an analogous result for the operational $K$-theory defined by Anderson and Payne.

Aleksandra Borówka

Using quaternionic Feix--Kaledin construction we provide a local classification of quaternion-Kähler metrics with a rotating $S^1$-symmetry with the fixed point set submanifold $S$ of maximal possible dimension. For any Kähler manifold $S$ equipped with a line bundle with a unitary connection of curvature proportional to the Kähler form we explicitly constru

Abraham C. -L. Chian, Suzana S. A. Silva, Erico L. Rempel, Milan Gošić

The quiet Sun exhibits a wealth of magnetic activities that are fundamental for our understanding of solar and astrophysical magnetism. The magnetic fields in the quiet Sun are observed to evolve coherently, interacting with each other to form distinguished structures as they are advected by the horizontal photospheric flows. We study coherent structures in

Chensheng Wu, Shaomin Chen, T. N. Chang, Xiang Gao

We present the results of a detailed theoretical study which meets the spatial and temporal criteria of the Debye-Huckel (DH) approximation on the variation of the transition energies as well as the oscillator strengths for the ${2p^53d\ ^1P_1\rightarrow2p^6\ ^1S_0}$ (3C line) and the ${2p^53d\ ^3D_1\rightarrow2p^6\ ^1S_0}$ (3D line) transitions of the Ne-li

Pak-Hin Li

Let $Q$ be a finite type quiver i.e. ADE Dynkin quiver. Denote by $Λ$ its preprojective algebra. It is known that there are finitely many indecomposable $Λ$-modules if and only if $Q$ is of type $A_1,A_2,A_3,A_4$. In this paper, extending Lusztig's construction of $U\frak{n}_+$, we study an algebra generated by these indecomposable submodules. It turns o

Emiliano Dall'Anese, Andrea Simonetto, Andrey Bernstein

This paper leverages a framework based on averaged operators to tackle the problem of tracking fixed points associated with maps that evolve over time. In particular, the paper considers the Krasnosel'skii-Mann method in a settings where: (i) the underlying map may change at each step of the algorithm, thus leading to a "running" implementation o

Yutaka Nagashima

Mechanized theorem proving is becoming the basis of reliable systems programming and rigorous mathematics. Despite decades of progress in proof automation, writing mechanized proofs still requires engineers' expertise and remains labor intensive. Recently, researchers have extracted heuristics of interactive proof development from existing large proof co

Moises S. Santos, Paulo R. Protachevicz, Kelly C. Iarosz, Iberê L. Caldas

Chimera states are spatiotemporal patterns in which coherence and incoherence coexist. We observe the coexistence of synchronous (coherent) and desynchronous (incoherent) domains in a neuronal network. The network is composed of coupled adaptive exponential integrate-and-fire neurons that are connected by means of chemical synapses. In our neuronal network,

Geovane G. A. de Souza, Hugo Natal da Luz

In this work we characterized a X-ray position sensitive gaseous detector based in a triple stack of gas electron multipliers (GEM). The readout circuit is divided in 256 strips for each dimension and using a resistive chain interconnecting the strips, we are able to reconstruct the radiation interaction points by resistive charge division. The detector achi

Marten Klein, Christian Zenker, Heiko Schmidt

Small-scale effects of turbulent mixing are numerically investigated by applying the map-based, stochastic, one-dimensional turbulence (ODT) model to confined planar jets. The model validation is carried out for the momentum transport by comparing ODT results to available reference data for the bulk Reynolds numbers $Re=20\,000$ and $40\,000$. Various pointw

Luis F. R. Lucas, Chris J. Skipper, Anna M. M. Scaife

Wide-field imaging has become a major challenge for modern radio astronomy, which uses high sensitivity acquisition systems that deal with huge amounts of data. In this paper we investigate a fast wide-field imaging solution based on the w-projection algorithm, which is intended for modern astronomy systems. The core idea of the proposed method is to reduce

Zhenyu Zhang, Stéphane Lathuilière, Andrea Pilzer, Nicu Sebe

In this work, we tackle the problem of online adaptation for stereo depth estimation, that consists in continuously adapting a deep network to a target video recordedin an environment different from that of the source training set. To address this problem, we propose a novel Online Meta-Learning model with Adaption (OMLA). Our proposal is based on two main c

Stavros Theodorakis, Nikolas Charalambous

The collapse of attractive Bose-Einstein condensates in a box with tunable interatomic interactions was studied experimentally recently. Not only were remarkably stable remnant condensates observed, but furthermore they often seem to involve two stable plateaus. We suggest that these plateaus correspond in fact to two minima of the energy, the attractive ato

Hieu Quang Nguyen, Abdul Hasib Rahimyar, Xiaodi Wang

The task of predicting future stock values has always been one that is heavily desired albeit very difficult. This difficulty arises from stocks with non-stationary behavior, and without any explicit form. Hence, predictions are best made through analysis of financial stock data. To handle big data sets, current convention involves the use of the Moving Aver

Uğur Teğin, Eirini Kakkava, Babak Rahmani, Demetri Psaltis

In this Letter, we demonstrate, to the best of our knowledge, the first spatiotemporally mode-locked fiber laser with self-similar pulse evolution. The multimode fiber oscillator generates parabolic amplifier similaritons at 1030 nm with 90 mW average power, 2.3 ps duration, and 37.9 MHz repetition rate. Remarkably, we observe experimentally a near-Gaussian

Vittorio Penna, Andrea Richaud

We investigate the phase separation mechanism of bosonic binary mixtures in spatially-fragmented traps, evidencing the emergence of phases featuring a different degree of mixing. The analysis is initially carried out by means of a semiclassical approach which transparently shows the occurrence of critical phenomena. These predictions are actually corroborate

Sucheta Majumdar

Starting with the light-cone Hamiltonian for gravity, we perform a field redefinition that reveals a hidden symmetry in four dimensions, namely the Ehlers $SL(2,R)$ symmetry. The field redefinition, which is non-local in space but local in time, acts as a canonical transformation in the Hamiltonian formulation keeping the Poisson bracket relations unaltered.

Jingjing Bu, Afshin Mesbahi, Mehran Mesbahi

This work presents a fairly complete account on various topological and metrical aspects of feedback stabilization for single-input-single-output (SISO) continuous and discrete time linear-time-invariant (LTI) systems. In particular, we prove that the set of stabilizing output feedback gains for a SISO system with n states has at most $\lceil{\frac{n}{2}}\rc

Raffaele Pascale, James Binney, Carlo Nipoti, Lorenzo Posti

A new family of self-consistent DF-based models of stellar systems is explored. The stellar component of the models is described by a distribution function (DF) depending on the action integrals, previously used to model the Fornax dwarf spheroidal galaxy (dSph). The stellar component may cohabit with either a dark halo, also described by a DF, or with a mas

Samuel J. Holo, Edwin S. Kite, Stuart J. Robbins

The dynamics of Mars' obliquity are believed to be chaotic, and the historical ~3.5 Gyr (late-Hesperian onward) obliquity probability density function (PDF) is high uncertain and cannot be inferred from direct simulation alone. Obliquity is also a strong control on post-Noachian Martian climate, enhancing the potential for equatorial ice/snow melting and

Lucrezia Cossetti

Several recent papers have focused their attention in proving the correct analogue to the Lieb-Thirring inequalities for non self-adjoint operators and in finding bounds on the distribution of their eigenvalues in the complex plane. This paper provides some improvement in the state of the art in this topic. Precisely, we address the question of finding quant

Ji Lin, Chuang Gan, Song Han

Neural network quantization is becoming an industry standard to efficiently deploy deep learning models on hardware platforms, such as CPU, GPU, TPU, and FPGAs. However, we observe that the conventional quantization approaches are vulnerable to adversarial attacks. This paper aims to raise people's awareness about the security of the quantized models, an

John J. Tobin, Stella Offner, Patrick Sheehan, Zhi-Yun Li

Knowledge of protostellar evolution has been revolutionized with the advent of surveys at near-infrared to submillimeter wavelengths. This has enabled the bolometric luminosities and bolometric temperatures (traditional protostellar evolution diagnostics) to be measured for large numbers of protostars. However, further progress is difficult without knowing t

John J. Tobin, Marina Kounkel, Stella Offner, Patrick Sheehan

Significant advances have been made over the past decade in the characterization of multiple protostar systems, enabled by the Karl G. Jansky Very Large Array (VLA), high-resolution infrared observations with the Hubble Space Telescope, and ground-based facilities. To further understand the mechanism(s) of multiple star formation, a combination of statistics

Jesse Bublitz, Joel H. Kastner, Miguel Santander-García, Valentin Bujarrabal

Certain planetary nebulae contain shells, filaments, or globules of cold gas and dust whose heating and chemistry are likely driven by UV and X-ray emission from their central stars and from wind-collision-generated shocks. We present the results of a survey of molecular line emission in the 88-236 GHz range from nine nearby (<1.5 kpc) planetary nebulae span

D. Cormier, N. P. Abel, S. Hony, V. Lebouteiller

The sensitive infrared telescopes, Spitzer and Herschel, have been used to target low-metallicity star-forming galaxies, allowing us to investigate the properties of their interstellar medium (ISM) in unprecedented detail. Interpretation of the observations in physical terms relies on careful modeling of those properties. We have employed a multiphase approa

Avishai Dekel, Sharon Lapiner, Yohan Dubois

We address the origin of the golden mass and time for galaxy formation and the onset of rapid black-hole growth. The preferred dark-halo mass of ~$10^{12}M_\odot$ is translated to a characteristic epoch, z~2, at which the typical forming halos have a comparable mass. We put together a coherent picture based on existing and new simple analytic modeling and co

Rebecca K. Leane, Tracy R. Slatyer

Statistical evidence has previously suggested that the Galactic Center GeV Excess (GCE) originates largely from point sources, and not from annihilating dark matter. We examine the impact of unmodeled source populations on identifying the true origin of the GCE using non-Poissonian template fitting (NPTF) methods. In a proof-of-principle example with simulat

Nathaniel Craig, Isabel Garcia Garcia, Seth Koren

We explore the prospects for bounding the weak scale using the weak gravity conjecture (WGC), addressing the hierarchy problem by violating the expectations of effective field theory. Building on earlier work by Cheung and Remmen, we construct models in which a super-extremal particle satisfying the electric WGC for a new Abelian gauge group obtains some of

Sebastian Fiedlschuster

IceCube is a neutrino observatory at Earth&#39;s South Pole that uses glacial ice as detector medium. Secondary particles from neutrino interactions produce Cherenkov light, which is detected by an array of photo detectors deployed within the ice. In distinction from the glacial bulk ice, hole ice is the refrozen water in the drill holes around the detector

Lambert Schomaker

This technical report describes a practical field test on word-image classification in a very large collection of more than 300 diverse handwritten historical manuscripts, with 1.6 million unique labeled images and more than 11 million images used in testing. Results indicate that several deep-learning tests completely failed (mean accuracy 83%). In the test

Yizeng Li, Yongcheng Qi

Empirical likelihood is a well-known nonparametric method in statistics and has been widely applied in statistical inference. The method has been employed by Lu and Peng (2002) to constructing confidence intervals for the tail index of a heavy-tailed distribution. It is demonstrated in Lu and Peng (2002) that the empirical likelihood-based confidence interva

Mohamed Hamroun, Sonia Lajmi

Videos clips became the most important and prominent multimedia document to illustrate the rituals process of Hajj and Umrah. Therefore, it is necessary to develop a system to facilitate access to information related to the duties, the pillars, the stages and the prayers. In this paper present a new project accomplishing a search engine in a large video data

Antoine Marnat

Using the variational principle in parametric geometry of numbers, we compute the Hausdorff and packing dimension of Diophantine sets related to exponents of Diophantine approximation, and their intersections. In particular, we extend a result of Jarník and Besicovitch to intermediate exponents.

Soichiro Fujii

We investigate an enriched-categorical approach to a field of discrete mathematics. The main result is a duality theorem between a class of enriched categories (called $\overline{\mathbb{Z}}$- or $\overline{\mathbb{R}}$-categories) and that of what we call ($\overline{\mathbb{Z}}$- or $\overline{\mathbb{R}}$-) extended L-convex sets. We introduce extended L-

Xuelong Li, Quanmao Lu, Yongsheng Dong, Dacheng Tao

Cluster analysis plays a very important role in data analysis. In these years, cluster ensemble, as a cluster analysis tool, has drawn much attention for its robustness, stability, and accuracy. Many efforts have been done to combine different initial clustering results into a single clustering solution with better performance. However, they neglect the stru

Youjun Deng, Hongyu Liu, Wing-Yan Tsui

We are concerned with the inverse problem of identifying magnetic anomalies with varing parameters beneath the Earth using geomagnetic monitoring. Observations of the change in Earth&#39;s magnetic field--the secular variation--provide information about the anomalies as well as their variations. In this paper, we rigorously establish the unique recovery resu

Jiang Cong, Yu Zong-Wen, Wang Xiang-Bin

In the original round-robin differential-phase-shift (RRDPS) quantum key distribution and its improved method, the photon-number-resolving detectors are must for the security. We present a RRDPS protocol with yes-no detectors only. We get the upper bounds of mutual information of Alice and Eve, and Bob and Eve, and the formula of key rate. Our main idea is t

Dmitry Chistikov, Mikhail Vyalyi

Consider the following one-player game. Take a well-formed sequence of opening and closing brackets. As a move, the player can pair any opening bracket with any closing bracket to its right, erasing them. The goal is to re-pair (erase) the entire sequence, and the complexity of a strategy is measured by its width: the maximum number of nonempty segments of s

Samineh Bagheri, Wolfgang Konen, Thomas Bäck

Real-world optimization problems often have expensive objective functions in terms of cost and time. It is desirable to find near-optimal solutions with very few function evaluations. Surrogate-assisted optimizers tend to reduce the required number of function evaluations by replacing the real function with an efficient mathematical model built on few evalua

J. Jesús Aguilera-Verdugo, Félix Driencourt-Mangin, Judith Plenter, Selomit Ramírez-Uribe

We present the first comprehensive analysis of the unitarity thresholds and anomalous thresholds of scattering amplitudes at two loops and beyond based on the loop-tree duality, and show how non-causal unphysical thresholds are locally cancelled in an efficient way when the forest of all the dual on-shell cuts is considered as one. We also prove that soft an