July 2019 arXiv papers — page 20

Hui Yu, Hailiang Liu

For a class of convection-diffusion equations with variable diffusivity, we construct third order accurate discontinuous Galerkin (DG) schemes on both one and two dimensional rectangular meshes. The DG method with an explicit time stepping can well be applied to nonlinear convection-diffusion equations. It is shown that under suitable time step restrictions,

Chao Lu, Yi Bu, Xianlei Dong, Jie Wang

The number of publications and the number of citations received have become the most common indicators of scholarly success. In this context, scientific writing increasingly plays an important role in scholars' scientific careers. To understand the relationship between scientific writing and scientific impact, this paper selected 12 variables of linguistic c

Amin Babadi, Kourosh Naderi, Perttu Hämäläinen

Animation and machine learning research have shown great advancements in the past decade, leading to robust and powerful methods for learning complex physically-based animations. However, learning can take hours or days, especially if no reference movement data is available. In this paper, we propose and evaluate a novel combination of techniques for acceler

Cesar Cuenca, Vadim Gorin, Grigori Olshanski

We introduce and study a new family of $q$-translation-invariant determinantal point processes on the two-sided $q$-lattice. We prove that these processes are limits of the $q$-$zw$ measures, which arise in the $q$-deformation of harmonic analysis on $U(\infty)$, and express their correlation kernels in terms of Jacobi theta functions. As an application, we

Chuanjian Liu, Yunhe Wang, Kai Han, Chunjing Xu

Exploring deep convolutional neural networks of high efficiency and low memory usage is very essential for a wide variety of machine learning tasks. Most of existing approaches used to accelerate deep models by manipulating parameters or filters without data, e.g., pruning and decomposition. In contrast, we study this problem from a different perspective by

J. Tapia, H. González, R. Rubiano

In this work we obtain Planck's blackbody spectrum from the thermal scalar radiation contained in a resonant cavity of volume V in the context of classical mechanics, which provides the classical zero-point electromagnetic radiation in terms of a scale parameter that depends on geometric properties of the enclosure and electrical magnitudes. The scale parame

Tao Sun, Roberto Barrio, Marcos Rodriguez, Hao Jiang

In image processing, Total Variation (TV) regularization models are commonly used to recover blurred images. One of the most efficient and popular methods to solve the convex TV problem is the Alternating Direction Method of Multipliers (ADMM) algorithm, recently extended using the inertial proximal point method. Although all the classical studies focus on o

Paul Tarau

We derive a Prolog theorem prover for an Intuitionistic Epistemic Logic by starting from the sequent calculus {\bf G4IP} that we extend with operator definitions providing an embedding in intuitionistic propositional logic ({\bf IPC}). With help of a candidate definition formula generator, we discover epistemic operators for which axioms and theorems of Arte

Kai Han, Yunhe Wang, Han Shu, Chuanjian Liu

This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm. Existing vanilla CNNs cannot be straightforwardly applied to handle multi-attribute data because of the larger label space as well as the attribute entanglement and correlations. W

Chaojin Qing, Bin Cai, Qingyao Yang, Jiafan Wang

Massive multiple-input multiple-output (MIMO) with frequency division duplex (FDD) mode is a promising approach to increasing system capacity and link robustness for the fifth generation (5G) wireless cellular systems. The premise of these advantages is the accurate downlink channel state information (CSI) fed back from user equipment. However, conventional

Haidong Zhu, Jialin Shi, Ji Wu

Deep learning methods have achieved promising performance in many areas, but they are still struggling with noisy-labeled images during the training process. Considering that the annotation quality indispensably relies on great expertise, the problem is even more crucial in the medical image domain. How to eliminate the disturbance from noisy labels for segm

Benedetto Militello

The degenerate Landau-Zener-Majorana-St\"uckelberg model consists of two degenerate energy levels whose energies vary with time and in the presence of an interaction which couples the states of the two levels. In the adiabatic limit, it allows for the populations transfer from states of one level to states of the other level. The presence of an interaction w

Zhiyi Zhang, Yu Guan, Xinyu Ma, Lixia Zhang

As Personally Identifiable Information (PII) data sharing among multiple parties becomes increasingly common, so does the potential for data leakage. As required by new data protection regulations and laws, when PII leakage occurs, one must be able to reliably identify the leaking sources. Existing solutions utilize watermark technologies or data object allo

Binghui Chen, Weihong Deng

In zero-shot image retrieval (ZSIR) task, embedding learning becomes more attractive, however, many methods follow the traditional metric learning idea and omit the problems behind zero-shot settings. In this paper, we first emphasize the importance of learning visual discriminative metric and preventing the partial/selective learning behavior of learner in

Ming-Xun Deng, Yan-Yan Yang, Wei Luo, R. Ma

Recently, a class of Dirac semimetals, such as \textrm{Na}$_{\mathrm{3}}% $\textrm{Bi} and \textrm{Cd}$_{\mathrm{2}}$\textrm{As}$_{\mathrm{3}}$, are discovered to carry $\mathbb{Z}_{2}$ monopole charges. We present an experimental mechanism to realize the $\mathbb{Z}_{2}$ anomaly in regard to the $\mathbb{Z}_{2}$ topological charges, and propose to probe it

Pengyu Zhao, Ansheng You, Yuanxing Zhang, Jiaying Liu

360{\deg} images are usually represented in either equirectangular projection (ERP) or multiple perspective projections. Different from the flat 2D images, the detection task is challenging for 360{\deg} images due to the distortion of ERP and the inefficiency of perspective projections. However, existing methods mostly focus on one of the above representati

S. A. Hussain, B. Dey, N. Mehta

The Langmuir-Blodgett (LB) technique is a way of making supra-molecular assembly in ultrathin films with a controlled layered structure and crystal parameter, which have many envisioned technological applications for optical and molecular electronic devices as well as signal processing and transformation. Probably LB technique is the best method to manipulat

Richarj Mondal, Yuki Aihara, Yuta Saito, Paul Fons

Chalcogenide superlattices (SL), formed by the alternate stacking of GeTe and Sb$_{2}$Te$_{3}$ layers, also referred to as interfacial phase change memory (iPCM), are a leading candidate for spin based memory device applications. Theoretically, the iPCM structure it has been predicted to form a 3D topological insulator or Dirac semimetal depending on the con

Wei-Can Syu, Da-Shin Lee, Kin-Wang Ng

We examine the quantum loop effects on the single-field inflationary models in a spatially flat Friedmann-Robertson-Walker (FRW) cosmological space-time with a general self-interacting scalar field potential, which is modeled in terms of the Hubble flow parameters in the effective field theory approach. In particular, we focus on the scenarios in both slow-r

Chenyun Luo, Junyan Zhang

We consider the three-dimensional incompressible free-boundary magnetohydrodynamics (MHD) equations in a bounded domain with surface tension on the boundary. We establish a priori estimate for solutions in the Lagrangian coordinates with $H^{3.5}$ regularity. To the best of our knowledge, this is the first result focusing on the incompressible ideal free-bou

Kush Bhatia, Yi-An Ma, Anca D. Dragan, Peter L. Bartlett

We study the problem of robustly estimating the posterior distribution for the setting where observed data can be contaminated with potentially adversarial outliers. We propose Rob-ULA, a robust variant of the Unadjusted Langevin Algorithm (ULA), and provide a finite-sample analysis of its sampling distribution. In particular, we show that after $T= \tilde{\

Viacheslav Dubeyko

Solid state drives have a number of interesting characteristics. However, there are numerous file system and storage design issues for SSDs that impact the performance and device endurance. Many flash-oriented and flash-friendly file systems introduce significant write amplification issue and GC overhead that results in shorter SSD lifetime and necessity to

Akshay Rangesh, Mohan M. Trivedi

This paper provides a simple solution for reliably solving image classification tasks tied to spatial locations of salient objects in the scene. Unlike conventional image classification approaches that are designed to be invariant to translations of objects in the scene, we focus on tasks where the output classes vary with respect to where an object of inter

Jiashan Zheng

This paper deals with the following quasilinear Keller-Segel-Navier-Stokes system modeling coral fertilization $(*)$: $$\left\{ \begin{array}{l} n_t+u\cdot\nabla n=\Delta n-\nabla\cdot(nS(x,n,c)\nabla c)-nm,\quad x\in \Omega, t>0, c_t+u\cdot\nabla c=\Delta c-c+m,\quad x\in \Omega, t>0, m_t+u\cdot\nabla m=\Delta m-nm,\quad x\in \Omega, t>0,\\ u_t+\kappa(u \cd

Eric J. Parish, Kevin T. Carlberg

This work proposes a machine-learning framework for modeling the error incurred by approximate solutions to parameterized dynamical systems. In particular, we extend the machine-learning error models (MLEM) framework proposed in Ref. 15 to dynamical systems. The proposed Time-Series Machine-Learning Error Modeling (T-MLEM) method constructs a regression mode

Grigoris Panoutsopoulos

We have become witnesses, as of late, to the development of an expansive and fertile debate regarding the implementation of future colliders, both within the confines of the scientific community, as well as outside of them. We consider a debate of this sort as imperative for such important research projects, but we also hold that they should develop upon sou

Kashyap Chitta, Jose M. Alvarez, Martial Hebert

Semantic segmentation with Convolutional Neural Networks is a memory-intensive task due to the high spatial resolution of feature maps and output predictions. In this paper, we present Quadtree Generating Networks (QGNs), a novel approach able to drastically reduce the memory footprint of modern semantic segmentation networks. The key idea is to use quadtree

Imke de Pater, R. J. Sault, Chris Moeckel, Arielle Moullet

We obtained the first maps of Jupiter at 1-3 mm wavelength with the Atacama Large Millimeter/Submillimeter Array (ALMA) on 3-5 January 2017, just days after an energetic eruption at 16.5S jovigraphic latitude had been reported by the amateur community, and about 2-3 months after the detection of similarly energetic eruptions in the northern hemisphere, at 22

Ziran Wang, Kyuntae Han, BaekGyu Kim, Guoyuan Wu

Connected and automated vehicle (CAV) technology is one of the promising solutions to addressing the safety, mobility and sustainability issues of our current transportation systems. Specifically, the control algorithm plays an important role in a CAV system, since it executes the commands generated by former steps, such as communication, perception, and pla

Thiago T. Santos, Leonardo L. de Souza, Andreza A. dos Santos, Sandra Avila

Agricultural applications such as yield prediction, precision agriculture and automated harvesting need systems able to infer the crop state from low-cost sensing devices. Proximal sensing using affordable cameras combined with computer vision has seen a promising alternative, strengthened after the advent of convolutional neural networks (CNNs) as an altern

Il Yong Chun, Zhengyu Huang, Hongki Lim, Jeffrey A. Fessler

Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision. INNs combine regression NNs and an iterative model-based image reconstruction (MBIR) algorithm, often leading to both good generalization capability and outperforming reconstruction quality over existing MBIR optimizat

F Alomari, M Harbi

Source code similarity are increasingly used in application development to identify clones, isolate bugs, and find copy-rights violations. Similar code fragments can be very problematic due to the fact that errors in the original code must be fixed in every copy. Other maintenance changes, such as extensions or patches, must be applied multiple times. Furthe

B. Paquelet Wuetz, P. L. Bavdaz, L. A. Yeoh, R. Schouten

Continuing advancements in quantum information processing have caused a paradigm shift from research mainly focused on testing the reality of quantum mechanics to engineering qubit devices with numbers required for practical quantum computation. One of the major challenges in scaling toward large-scale solid-state systems is the limited input/output (I/O) co

Matthew Middlehurst, William Vickers, Anthony Bagnall

Dictionary based classifiers are a family of algorithms for time series classification (TSC), that focus on capturing the frequency of pattern occurrences in a time series. The ensemble based Bag of Symbolic Fourier Approximation Symbols (BOSS) was found to be a top performing TSC algorithm in a recent evaluation, as well as the best performing dictionary ba

L. Zhang, X. Jiang, X. Xu, X. Hong

We report a magnetotransport study of spin relaxation in 1.4-21.2 nm epitaxial SrIrO$_3$ thin films coherently strained on SrTiO$_3$ substrates. Fully charge compensated semimetallic transport has been observed in SrIrO$_3$ films thicker than 1.6 nm, where the charge mobility at 10 K increases from 45 cm$^2$/Vs to 150 cm$^2$/Vs with decreasing film thickness

Ayush Raina, Christopher McComb, Jonathan Cagan

Humans as designers have quite versatile problem-solving strategies. Computer agents on the other hand can access large scale computational resources to solve certain design problems. Hence, if agents can learn from human behavior, a synergetic human-agent problem solving team can be created. This paper presents an approach to extract human design strategies

Hiram H. López, Gretchen Matthews, Ivan Soprunov

A monomial-Cartesian code is an evaluation code defined by evaluating a set of monomials over a Cartesian product. It is a generalization of some families of codes in the literature, for instance toric codes, affine Cartesian codes and $J$-affine variety codes. In this work we use the vanishing ideal of the Cartesian product to give a description of the dual

William O'Mullane, Niall Gaffney, Frossie Economou, Arfon M. Smith

Many astronomy data centres still work on filesystems. Industry has moved on; current practice in computing infrastructure is to achieve Big Data scalability using object stores rather than POSIX file systems. This presents us with opportunities for portability and reuse of software underlying processing and archive systems but it also causes problems for le

Armand Azonnahin

In this text, we recall some basics and results about complex geometry and currents in the complex scenario. Most of the results are classic and their evidence is not given here. On the other hand, we describe in detail some notions to help the reader who is unfamiliar with complex geometry or currents. The main references for abstract current theory are [Ch

Tao Hu, Chengjiang Long, Leheng Zhang, Chunxia Xiao

In this paper, we propose a novel way to interpret text information by extracting visual feature presentation from multiple high-resolution and photo-realistic synthetic images generated by Text-to-image Generative Adversarial Network (GAN) to improve the performance of image labeling. Firstly, we design a stacked Generative Multi-Adversarial Network (GMAN),

Mikolaj D. Uryszek, Elliot Christou, Akbar Jaefari, Frank Krüger

Two-dimensional semi-Dirac fermions are quasiparticles that disperse linearly in one direction and quadratically in the other. We investigate instabilities of semi-Dirac fermions towards charge, spin-density wave and superconducting orders, driven by short-range interactions. We analyze the critical behavior of the Yukawa theories for the different order par

Luiz F. Ferreira, Roldao da Rocha

The Kaluza-Klein tower of higher spin-$S$ tensor meson resonances is here scrutinized in the AdS/QCD hard wall model, encompassing the already established resonances $\rho(770)$, $f_2(1270)$, $\omega_3(1670)$, $f_4(2050)$, $\rho_5(2350)$, $f_6(2510)$ in PDG. A hybrid model employs both information theory and AdS/QCD, where configurational-entropic Regge traj

Peng Ru, Zhong-Bo Kang, Enke Wang, Hongxi Xing

Within the framework of the generalized QCD factorization formalism, we perform the first global analysis of the jet transport coefficient ($\hat q$) for cold nuclear matter. The analysis takes into account the world data on transverse momentum broadening in semi-inclusive electron-nucleus deep inelastic scattering, Drell-Yan dilepton and heavy quarkonium pr

Martin Scharlemann

In 1980 J. Powell proposed that five specific elements sufficed to generate the Goeritz group of any Heegaard splitting of $S^3$. This conjecture remains unresolved for genus $g \geq 4$. Here a short argument shows that one of his proposed generators is redundant, in fact a consequence of three of the other four.

Ross Berkowitz, Ashwin Sah, Mehtaab Sawhney

We examine the behavior of the number of $k$-term arithmetic progressions in a random subset of $\mathbb{Z}/n\mathbb{Z}$. We prove that if a set is chosen by including each element of $\mathbb{Z}/n\mathbb{Z}$ independently with constant probability $p$, then the resulting distribution of $k$-term arithmetic progressions in that set, while obeying a central l

Harish S. Bhat

We consider the problem of learning an interpretable potential energy function from a Hamiltonian system's trajectories. We address this problem for classical, separable Hamiltonian systems. Our approach first constructs a neural network model of the potential and then applies an equation discovery technique to extract from the neural potential a closed-form

Anna Karlsson

We show how the Bell correlations can be modelled locally by relaxing the joint probability relation for independent variables $P(a,b)=P(a)P(b)$ outside classical settings, with complex/quaternion generators for the measurement outcomes encoding a deviation from this (classical) requirement. The consideration is motivated by complementarity. We analyse the p

Kartikeya Bhardwaj, Chingyi Lin, Anderson Sartor, Radu Marculescu

Model compression has emerged as an important area of research for deploying deep learning models on Internet-of-Things (IoT). However, for extremely memory-constrained scenarios, even the compressed models cannot fit within the memory of a single device and, as a result, must be distributed across multiple devices. This leads to a distributed inference para

Omid Jafari, John Ossorgin, Parth Nagarkar

Similarity search queries in high-dimensional spaces are an important type of queries in many domains such as image processing, machine learning, etc. Since exact similarity search indexing techniques suffer from the well-known curse of dimensionality in high-dimensional spaces, approximate search techniques are often utilized instead. Locality Sensitive Has

Masato Kobayashi

We revisit $R$-polynomials with introducing the new idea ``shifted $R$-polynomials" (or Bruhat weight) for all Bruhat intervals in finite Coxeter groups. Then, we apply these polynomials to weighted counting of Bruhat paths. Further, we prove a new criterion of irregularity of lower intervals as analogy of Carrell-Peterson's and Dyer's results. Also, we pres

Stephanie van Willigenburg

By establishing relations between operators on compositions, we show that the posets of compositions arising from the right and left Pieri rules for noncommutative Schur functions can each be endowed with both the structure of dual graded graphs and dual filtered graphs when paired with the poset of compositions arising from the Pieri rules for quasisymmetri

Masato Kobayashi

We develop combinatorics of parabolic double cosets in finite Coxeter groups as a follow-up of recent articles by Billey-Konvalinka-Petersen-Slofstra-Tenner and Petersen. (1) We construct a double coset system as a generalization of a two-sided analogue of a Coxeter complex and present its order structure with its local dimension function on certain connecte

Nek Sharan, Josette Bellan

Combustion in automotive and aerospace applications employing diesel, gas turbine and liquid rocket engines is preceded by injection and mixing of fuel and oxidizer at high pressures, often exceeding mixture critical values. Experimental observations indicate that the jets injected at supercritical pressures exhibit significantly different dynamics than the

Ugur G. Abdulla, Amna Abu Weden

This paper presents a full classification of the short-time behavior of the interfaces in the Cauchy problem for the nonlinear second order degenerate parabolic PDE \[ u_t-\Delta u^m +b u^\beta=0, \ x\in \mathbb{R}^N, 0<t<T \] with nonnegative initial function $u_0$ such that \[ supp~u_0 = \{|x|<R\}, \ u_0 \sim C(R-|x|)^\alpha, \quad{as} \ |x|\to R-0, \] whe

Naoki Kitazawa

In the singularity and differential topological theory of Morse functions and higher dimensional versions or fold maps and application to algebraic and differential topology of manifolds, constructing explicit fold maps and investigating their source manifolds is fundamental, important and difficult. The author has introduced surgery operations (bubbling ope

Oluwatobi Olabiyi, Erik T. Mueller

Neural dialogue models, despite their successes, still suffer from lack of relevance, diversity, and in many cases coherence in their generated responses. These issues can attributed to reasons including (1) short-range model architectures that capture limited temporal dependencies, (2) limitations of the maximum likelihood training objective, (3) the concav

Hossein Rastgoftar, Jean-Baptiste Jeannin, Ella Atkins

Continuum deformation is a leader-follower multiagent cooperative control approach. Previous work showed a desired continuum deformation can be uniquely defined based on trajectories of d +1 leaders in a d-dimensional motion space and acquired by followers through local inter-agent communication. This paper formally specifies continuum deformation coordinati

Sarah Jenkins, Andrea Meo, Luke Elliott, Stephan K. Piotrowski

The magnetic stray field is an unavoidable consequence of ferromagnetic devices and sensors leading to a natural asymmetry in magnetic properties. Such asymmetry is particularly undesirable for magnetic random access memory applications where the free layer can exhibit bias. Using atomistic dipole-dipole calculations we numerically simulate the stray magneti

Rahmadi Trimananda, Janus Varmarken, Athina Markopoulou, Brian Demsky

Smart home devices are vulnerable to passive inference attacks based on network traffic, even in the presence of encryption. In this paper, we present PINGPONG, a tool that can automatically extract packet-level signatures for device events (e.g., light bulb turning ON/OFF) from network traffic. We evaluated PINGPONG on popular smart home devices ranging fro

Finn McGlade, Arun Ram, Yaping Yang

This is a survey on the combinatorics and geometry of integrable representations of quantum affine Lie algebras with a particular focus on level 0. Pictures and examples are included to illustrate the affine Weyl group orbits, crystal graphs and Macdonald polynomials that provide detailed understanding of the structure of the extremal weight modules and thei

Giovanni Paolini, Mario Salvetti

We prove the $K(\pi,1)$ conjecture for affine Artin groups: the complexified complement of an affine reflection arrangement is a classifying space. This is a long-standing problem, due to Arnol'd, Pham, and Thom. Our proof is based on recent advancements in the theory of dual Coxeter and Artin groups, as well as on several new results and constructions. In p

Mark D. Pepin, Erik Tryggestad, Hok Seum Wan Chan Tseung, Jiasen Ma

Purpose: Respiratory motion and the interplay effect cause the dose delivered to a patient with spot-scanned proton therapy to differ from the dose planned for during optimization. A new 4D robust optimization methodology was developed which incorporates patient breathing and the interplay effect, as well as their uncertainties, into the optimization process

Guangxiao Song, Yu Sun, Jiaming Liu, Zhijie Wang

Recent work has shown the effectiveness of the plug-and-play priors (PnP) framework for regularized image reconstruction. However, the performance of PnP depends on the quality of the denoisers used as priors. In this letter, we design a novel PnP denoising prior, called multiple self-similarity net (MSSN), based on the recurrent neural network (RNN) with se

Marcell Vazquez-Chanlatte, Sanjit A. Seshia

In many settings (e.g., robotics) demonstrations provide a natural way to specify tasks; however, most methods for learning from demonstrations either do not provide guarantees that the artifacts learned for the tasks, such as rewards or policies, can be safely composed and/or do not explicitly capture history dependencies. Motivated by this deficit, recent

Terry A. Loring

The Bott index is inherently global. The pseudospectal index is inherently local, and so now the preferred name is the localizer index. We look at these on a rather standard model for a Chern insulator, with an emphasis how to program these effectively. We also discuss how to tune the localizer index so it behaves like a global index.

Chon-Kit Pun, Sakib Matin, W. Klein, Harvey Gould

Prediction in complex systems at criticality is believed to be very difficult, if not impossible. Of particular interest is whether earthquakes, whose distribution follows a power law (Gutenberg-Richter) distribution, are in principle unpredictable. We study the predictability of event sizes in the Olmai-Feder-Christensen model at different proximities to cr

Saugata Ghose, Amirali Boroumand, Jeremie S. Kim, Juan Gómez-Luna

Many modern and emerging applications must process increasingly large volumes of data. Unfortunately, prevalent computing paradigms are not designed to efficiently handle such large-scale data: the energy and performance costs to move this data between the memory subsystem and the CPU now dominate the total costs of computation. This forces system architects

Nicolas Boullé, Vassilios Dallas, Yuji Nakatsukasa, D. Samaddar

We use standard deep neural networks to classify univariate time series generated by discrete and continuous dynamical systems based on their chaotic or non-chaotic behaviour. Our approach to circumvent the lack of precise models for some of the most challenging real-life applications is to train different neural networks on a data set from a dynamical syste

Nazanin Moradinasab, Hassan Jafarzadeh, M. R. Amin-Naseri, Cody H. Fleming

Petroleum industry is the world's biggest energy source, and its associated industries such as production, distribution, refining and retail are considered as the largest ones in the world. Having the increasing price and governments job creation and international environmental policies, the petroleum companies try to maximize the number of created job, and

Chao Gao, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor

How to best explore in domains with sparse, delayed, and deceptive rewards is an important open problem for reinforcement learning (RL). This paper considers one such domain, the recently-proposed multi-agent benchmark of Pommerman. This domain is very challenging for RL --- past work has shown that model-free RL algorithms fail to achieve significant learni

Alessandro Chiodo, Elana Kalashnikov

We show that there is an extra dimension to the mirror duality discovered in the early nineties by Greene-Plesser and Berglund-H\"ubsch. Their duality matches cohomology classes of two Calabi--Yau orbifolds. When both orbifolds are equipped with an automorphism $s$ of the same order, our mirror duality involves the weight of the action of $s^*$ on cohomology

Marco Celoria, Denis Comelli, Luigi Pilo, Rocco Rollo

We study the dynamics of inflation driven by an adiabatic self-gravitating medium, extending the previous works on fluid and solid inflation. Such a class of media comprises perfect fluids, zero and finite temperature solids. By using an effective field theory description, we compute the power spectrum for the scalar curvature perturbation of constant energy

Yipin Su, Bin Wu, Weiqiu Chen, Michel Destrade

We propose theoretical and numerical analyses of smart bending deformation of a dielectric-elastic bilayer in response to a voltage, based on the nonlinear theory of electro-elasticity and the associated linearized incremental field theory. We reveal that the mechanism allowing the bending angle of the bilayer can be tuned by adjusting the applied voltage. F

Myron A. Smith, Raimundo Lopes de Oliveira

We have examined soft (<2keV) "dips" in six archival XMM-Newton observations of gamma Cas (B0.5IV e) for "soft dips" (< 2keV) in X-ray light curves. We find that such events are sometimes accompanied by minor, near-simultaneous dips in the hard X-ray band. We investigate how these occurrences can be understood in the "magnetic star-disk interaction" X-ray ge

Bonan Zhang, Shen-Song Wan, Marco Ruggieri

We study the thermodynamic geometry of the Quark-Meson model, focusing on the curvature, $R$, around the chiral crossover at finite temperature and baryon chemical potential. We find a peculiar behavior of $R$ in the crossover region, in which the sign changes and a local maximum develops; in particular, the height of the peak of $R$ in the crossover region

Kaiwen Wu, Yaoliang Yu

Deep models, while being extremely versatile and accurate, are vulnerable to adversarial attacks: slight perturbations that are imperceptible to humans can completely flip the prediction of deep models. Many attack and defense mechanisms have been proposed, although a satisfying solution still largely remains elusive. In this work, we give strong evidence th

Matej Martinc, Senja Pollak, Marko Robnik-Šikonja

We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language models, whereas in the supervised setting, three different neural classification architectures are tested. We show that the proposed neural unsupervised approach is robust, transferable

Baihong Jin, Yingshui Tan, Alexander Nettekoven, Yuxin Chen

We present a novel unsupervised deep learning approach that utilizes the encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed not only to detect whether there exists an anomaly at a given time step, but also to predict what will happen next in the (sequential) proce

Abderrahim Boussaïri, Soufiane Lakhlifi, Imane Talbaoui

An $n$-tournament $T$ with vertex set $V$ is simple if there is no subset $M$ of $V$ such that $2\leq \left \vert M\right \vert \leq n-1$ and for every $x\in V\setminus M$, either $M\rightarrow x$ or $x \rightarrow M$. The simplicity index of an $n$-tournament $T$ is the minimum number $s(T)$ of arcs whose reversal yields a non-simple tournament. M\"{u}ller

Pritam Kumar Jana, Bortolo Matteo Mognetti

Much work has studied effective interactions between micron-sized particles carrying linkers forming reversible, inter-particle linkages. These studies allowed understanding the equilibrium properties of colloids interacting through ligand-receptor interactions. Nevertheless, understanding the kinetics of multivalent interactions remains an open problem. Her

Jesús Zavala, Carlos S. Frenk

The development of methods and algorithms to solve the $N$-body problem for classical, collisionless, non-relativistic particles has made it possible to follow the growth and evolution of cosmic dark matter structures over most of the Universe's history. In the best studied case $-$ the cold dark matter or CDM model $-$ the dark matter is assumed to consist

Jianhao Zhang, Vincent Pelgrin, Carlos Alonso-Ramos, Laurent Vivien

Dispersion engineering is among the most important steps towards a promising optical frequency comb. We propose a new and general approach to trim frequency combs using a self-adaptive boundary of the optical mode at different wavelengths in a sub-wavelength structured waveguide. The feasibility of ultra-wide bandwidth dispersion engineering comes from the f

Grzegorz Chlebus, Nasreddin Abolmaali, Andrea Schenk, Hans Meine

Explainability of decisions made by deep neural networks is of high value as it allows for validation and improvement of models. This work proposes an approach to explain semantic segmentation networks by means of layer-wise relevance propagation. As an exemplary application, we investigate which MRI sequences are most relevant for liver tumor segmentation.

Andrea Ceccarelli, Andrea Cioni, Maria Vittoria Garzelli, Piergiulio Lenzi

The accumulation of a large amount of new experimental data at an impressive rate at present and future collider experiments has led to important questions concerning data storage and organization, their public access and usability, as well as their efficient usage in order to discriminate between different theories. For the last fourty years, the HEPData da

Abe C. Ellison, Bengt Fornberg

Numerical solutions to wave-type PDEs utilizing method-of-lines require the ODE solver's stability domain to include a large stretch of the imaginary axis surrounding the origin. We show here that extrapolation based solvers of Gragg-Bulirsch-Stoer (GBS) type can meet this requirement. Extrapolation methods utilize several independent time stepping sequences

Noriyuki Kojima, Jia Deng

In this paper we compare learning-based methods and classical methods for navigation in virtual environments. We construct classical navigation agents and demonstrate that they outperform state-of-the-art learning-based agents on two standard benchmarks: MINOS and Stanford Large-Scale 3D Indoor Spaces. We perform detailed analysis to study the strengths and

F. Hueso-González, T. Bortfeld

Range uncertainties in proton therapy hamper treatment precision. Prompt gamma-rays were suggested 16 years ago for real-time range verification, and have already shown promising results in clinical studies with collimated cameras. Simultaneously, alternative imaging concepts without collimation are investigated to reduce the footprint and price of current p

ShengLi Tzeng, Jun Zhu, Amy Weisman, Tyler Bradshaw

Quantitative imaging biomarkers (QIB) are extracted from medical images in radiomics for a variety of purposes including noninvasive disease detection, cancer monitoring, and precision medicine. The existing methods for QIB extraction tend to be ad-hoc and not reproducible. In this paper, a general and flexible statistical approach is proposed for handling u

Adam M. Meier, Karl A. Burkhardt, Brian J. McMahon, Creston D. Herold

The Robust Phase Estimation (RPE) protocol was designed to be an efficient and robust way to calibrate quantum operations. The robustness of RPE refers to its ability to estimate a single parameter, usually gate amplitude, even when other parameters are poorly calibrated or when the gate experiences significant errors. Here we demonstrate the robustness of R

James Cummings, Charles Morgan

We prove that consistently there is a singular cardinal $\kappa$ of uncountable cofinality such that $2^\kappa$ is weakly inaccessible, and every regular cardinal strictly between $\kappa$ and $2^\kappa$ is the character of some uniform ultrafilter on $\kappa$.

Gerald A. Miller

{ {\bf Background:} Knowing the difference between the neutron and proton densities of nuclei is a significant topic because of its importance for understanding neutron star structures and cooling mechanisms. The coherent-nuclear photoproduction of pions, $({\g,\pi^0})$, combined with elastic electron scattering, has been suggested to be a very accurate prob

P. León, A. Sotomayor

The main objective of this work is to study in some detail the Randall-Sundrum gravity under the gravitational decoupling through the minimal geometric deformation approach (MGD-decoupling). We show a family of new black hole solutions as well as new exact interior solutions for self-gravitating stellar systems and we discuss the corresponding matching condi

Soumya Dutta, Ayan Biswas, James Ahrens

With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduce

D. V. Chubukov, L. V. Skripnikov, L. Bougas, L. N. Labzowsky

Present limit on the electron electric dipole moment ($e$EDM) is based on the electron spin precession measurement. We propose an alternative approach - observation of the $\mathcal{P}$,$\mathcal{T}$-odd Faraday effect in an external electric field on atoms and molecules using cavity-enhanced polarimetric scheme in combination with molecular (atomic) beam cr

E. A. Den Hartog, J. E. Lawler, C. Sneden, J. J. Cowan

We report new branching fractions for 121 UV lines from the low-lying odd-parity levels of Fe II belonging to the z6Do, z6Fo, z6Po, z4Fo, z4Do and z4Po terms of the 3d6(5D)4p configuration. These lines range in wavelength from 2250 - 3280 {\AA} and originate in levels ranging in energy from 38459 - 47626 cm-1. In addition, we report branching fractions for 1

F. Cannizzaro, B. Pantò, S. Caddemi, I. Caliò

The structural response of masonry arches is strongly dominated by the arch geometry, the stone block dimensions and the interaction with backfill material or surrounding walls. Due to their intrinsic discontinuous nature, the nonlinear structural response of these key historical structures can be efficiently modelled in the context of discrete element appro

Marco Abbadini

We provide a generalization of Mundici's equivalence between unital Abelian lattice-ordered groups and MV-algebras: the category of unital commutative lattice-ordered groups is equivalent to the category of MV-monoidal algebras. Roughly speaking, the structures we call unital commutative lattice-ordered groups are unital Abelian lattice-ordered groups withou

Avirup Ghosh, Rohit Mishra

We derive a general expression for obtaining Holographic subregion complexity for asymptotically $AdS$ spacetimes, pertubatively around pure $AdS$ using a variational technique. An essential step in finding subregion complexity is to identify the bulk minimal surface of the entangling subregion. Our method therefore heavily relies on solutions of an inhomoge

Jing Zhou

We describe an exponential Fermi accelerator in a two-dimensional billiard with a moving slit. We have found a mechanism of trapping regions which provides the exponential acceleration for almost all initial conditions with sufficiently high initial energy. Under an additional hyperbolicity assumption, we estimate the waiting time after which most high-energ

Florence Fauquant-Millet

In this paper, using Bourbaki's convention, we consider a simple Lie algebra $\mathfrak g\subset\mathfrak g\mathfrak l_m$ of type B, C or D and a parabolic subalgebra $\mathfrak p$ of $\mathfrak g$ associated with a Levi factor composed essentially, on each side of the second diagonal, by successive blocks of size two, except possibly for the first and the l