June 2020 arXiv papers — page 19

M. I. Belishev, S. A. Simonov

The work is carried out as part of the program to construct a new functio\-nal (so-called {\it wave}) model of symmetric operators. It is shown that an abstract evolutionary dynamic system of the first order (with respect to time) with boundary control, which is determined by a symmetric operator $L_0:{\mathscr H}\to{\mathscr H}$, is controllable if and only

F. H. Kamin, F. T. Tabesh, S. Salimi, F. Kheirandish

We define the quantum-incoherent relative entropy of coherence ($\mathcal{QI}$ REC) of quantum channels in the framework of the resource theory by using the Choi-Jamiolkowsky isomorphism. Coherence-breaking channels are introduced as free operations and their corresponding Choi states as free states. We also show the relationship between the coherence of cha

Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang

Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. However, training GNNs usually requires abundant task-specific labeled data, which is often arduously expensive to obtain. One effective way to reduce the labeling effort is to pre-train an expressive GNN model on unlabeled data with self-supervision and the

Beliz Gunel, Chenguang Zhu, Michael Zeng, Xuedong Huang

Neural models have become successful at producing abstractive summaries that are human-readable and fluent. However, these models have two critical shortcomings: they often don't respect the facts that are either included in the source article or are known to humans as commonsense knowledge, and they don't produce coherent summaries when the source a

F. Goryaev, A. Urnov

X-ray emission K-spectra of highly charged He- and Li-like argon ions recorded with high spectral, spatial, and temporal resolution at the TEXTOR tokamak were employed to develop a self-consistent approach (SCA) for deriving information on plasma parameters and the verification (estimation of accuracy) of both atomic data, needed for spectra interpretation,

Dong Wang, Fang Liu

Contact tracing in the COVID-19 pandemic is key to prevent the further spread of COVID-19. Countries and regions around the world have developed and deployed or are considering adopting contact-tracing software or mobile apps. While contact tracing apps and software play an important role in the pandemic, red flags have been raised regarding the privacy risk

Thiago Porcino, Esteban Clua, Daniela Trevisan, Érick Rodrigues

Virtual reality (VR) is an imminent trend in games, education, entertainment, military, and health applications, as the use of head-mounted displays is becoming accessible to the mass market. Virtual reality provides immersive experiences but still does not offer an entirely perfect situation, mainly due to Cybersickness (CS) issues. In this work, we first p

Archil Gulisashvili

We introduce stochastic volatility models, in which the volatility is described by a time-dependent nonnegative function of a reflecting diffusion. The idea to use reflecting diffusions as building blocks of the volatility came into being because of a certain volatility misspecification in the classical Stein and Stein model. A version of this model that use

T. R. Routray, S. P. Pattnaik, C. Gonzalez-Boquera, X. Viñas

We perform a systematic study of the dependence of the r-mode phenomenology in normal fluid pulsar neutron stars on the symmetry energy slope parameter $L$. An essential ingredient in this study is the bulk viscosity, which is evaluated consistently for several equations of state corresponding to different values of the slope parameter $L$. Direct Urca proce

Amith Khandakar, Annaufal Rizqullah, Anas Ashraf Abdou Berbar, Mohammad Rafi Ahmed

The adoption of electric vehicles (EVs) have proven to be a crucial factor to decreasing the emission of greenhouse gases (GHG) into the atmosphere. However, there are various hurdles that impede people from purchasing EVs. For example, long charging time, short driving range, cost and insufficient charging infrastructures available, etc. This article report

Miguel Angel Bautista, Walter Talbott, Shuangfei Zhai, Nitish Srivastava

State-of-the-art learning-based monocular 3D reconstruction methods learn priors over object categories on the training set, and as a result struggle to achieve reasonable generalization to object categories unseen during training. In this paper we study the inductive biases encoded in the model architecture that impact the generalization of learning-based 3

Martin Hayhurst Appel, Alexey Tiranov, Alisa Javadi, Matthias Christian Löbl

Solid-state quantum dots are promising candidates for efficient light-matter interfaces connecting internal spin degrees of freedom to the states of emitted photons. However, selection rules prevent the combination of efficient spin control and optical cyclicity in this platform. By utilizing a photonic crystal waveguide we here experimentally demonstrate op

Breno Raphaldini, Everton S. Medeiros, David Ciro, Daniel Ribeiro Franco

Geomagnetic field reversals remain as one of the most intriguing problems in geophysics and are regarded as chaotic processes resulting from a dynamo mechanism. In this article, we use the polarity scale data set for the last 170 Myr from the ocean's floor to provide robust evidence for an inverse relationship between the complexity of sequences of conse

Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu

This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, $\ell_q$ Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME). These methods exploit different nonsmooth loss functions to gain modeling flexibility, estimation robust

Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet

We present an approach for estimating the period with which an action is repeated in a video. The crux of the approach lies in constraining the period prediction module to use temporal self-similarity as an intermediate representation bottleneck that allows generalization to unseen repetitions in videos in the wild. We train this model, called Repnet, with a

Reyhaneh Naderi, Homa Saadati Mollaei, Arne Elofsson, Saman Hosseini Ashtiani

Background: Psoriasis is a multifactorial chronic inflammatory disorder of the skin with significant morbidity, characterized by hyper proliferation of the epidermis. Even though psoriasis etiology is not fully understood, it is believed to be multifactorial with numerous key components. Methods: In order to cast light on the complex molecular interactions i

A. Coletta, F. Fontani, V. M. Rivilla, C. Mininni

We have studied four complex organic molecules (COMs), methyl formate ($CH_3OCHO$), dimethyl ether ($CH_3OCH_3$), formamide ($NH_2CHO$), and ethyl cyanide ($C_2H_5CN$), towards a large sample of 39 high-mass star-forming regions representing different evolutionary stages, from early to evolved phases. We aim to identify potential correlations between the mol

David R. W. Sears

This chapter considers how string-based methods might be adapted to address music-analytic questions related to the discovery of musical organization, with particular attention devoted to the analysis of tonal harmony. I begin by applying the taxonomy of mental organization proposed by Mandler (1979) to the concept of musical organization. Using this taxonom

Caleb Belth, Xinyi Zheng, Danai Koutra

Frequent pattern mining is a key area of study that gives insights into the structure and dynamics of evolving networks, such as social or road networks. However, not only does a network evolve, but often the way that it evolves, itself evolves. Thus, knowing, in addition to patterns' frequencies, for how long and how regularly they have occurred---i.e.,

Jingwei Zhuo, Ziru Xu, Wei Dai, Han Zhu

Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems. Tree models, which formulate targets as leaves of a tree with trainable node-wise scorers, have attracted a lot of interests in tackling this challenge due to their logarithmic computational com

Duo Wang, Lei Liu, Houlong L. Zhuang

The Nitrogen-vacancy (NV) center in diamond plays important roles in emerging quantum technologies. Currently available methods to fabricate the NV center often involve complex processes such as N implantation. By contrast, in a diamond-like compound C3BN, creating a boron (B) vacancy immediately leads to an NV center analog. We use the strongly constrained

Kanghyun Kim, Pavan Chandra Konda, Colin L. Cooke, Ron Appel

Standard microscopes offer a variety of settings to help improve the visibility of different specimens to the end microscope user. Increasingly, however, digital microscopes are used to capture images for automated interpretation by computer algorithms (e.g., for feature classification, detection or segmentation), often without any human involvement. In this

Christopher Lloyd

The mean light curve of Betelgeuse is constructed from the visual data in BAA VSS and AAVSO archives. Period analysis reveals clusters of periods around 2000 and 400 days but these are swamped by the long-term trends. No identifiable periods emerge but the feature near 400 days is the most persistent and survives even when the range of variation is low. Hers

Stephan Humeniuk

For the inverse square long-range ferromagnetic Ising chain in a transverse field, the thermal phase boundary of the floating Kosterlitz-Thouless phase is obtained for several values of the transverse field down to the quantum critical point. The sharp domain walls in the classical model are increasingly smeared out by the transverse field, which is evidence

David R. W. Sears, Gerhard Widmer

Recurrent voice-leading patterns like the Mi-Re-Do compound cadence (MRDCC) rarely appear on the musical surface in complex polyphonic textures, so finding these patterns using computational methods remains a tremendous challenge. The present study extends the canonical n-gram approach by using skip-grams, which include sub-sequences in an n-gram list if the

Dominique Malicet

We consider products of a i.i.d. sequence in a set $\{f_1,\ldots,f_m\}$ of preserving orientation diffeomorphisms of the circle. we can naturally associate a Lyapunov exponent $λ$. Under few assumptions, it is known that $λ\leq 0$ and that the equality holds if and only if $f_1,\ldots,f_m$ are simultaneously conjugated to rotations. In this paper, we state a

Davor Horvatić, Dalibor Kekez, Dubravko Klabučar

Motivated by our earlier findings of sensitive quark-flavor dependence of QCD topological susceptibility on products of current quark masses and corresponding condensates, we allow the breaking of isospin symmetry. For the purpose of future investigations of U_A(1) symmetry breaking and restoration at T > 0, we perform (at T = 0) refitting of the quark-mass

Sven Führing

We prove that total spaces of $CP^2$-bundles generate the oriented cobordism ring $Ω_*$. Combined with the surgery lemma this yields a somewhat different proof of Gromov's and Lawson's theorem that all simply connected non-spin manifolds of dimension $\geq$ 5 carry a metric of positive scalar curvature.

Alberto Portone, Rocio Borrego-Varillas, Lucia Ganzer, Riccardo Di Corato

The miniaturization of diagnostic devices that exploit optical detection schemes requires the design of light-sources combining small size, high performance for effective excitation of chromophores, and mechanical flexibility for easy coupling to components with complex and non-planar shapes. Here, ZnO nanowire-in-fiber hybrids with internal architectural or

Yifan Song, Furkan Elibol, Mengkun She, David Nakath

Illuminating a scene with artificial light is a prerequisite for seeing in dark environments. However, nonuniform and dynamic illumination can deteriorate or even break computer vision approaches, for instance when operating a robot with headlights in the darkness. This paper presents a novel light calibration approach by taking multi-view and -distance imag

Cinzia Bisi, Joerg Winkelmann

The classical theorem of Picard states that a non-constant holomorphic function $f:\mathbb{C}\to\mathbb{C}$ can avoid at most one value. We investigate how many values a non-constant slice regular function of a quaternionic variable $f:\mathbb{H}\to\mathbb{H}$ may avoid.

Marco F. Eigenmann, Sach Mukherjee, Marloes H. Maathuis

Recent years have seen many advances in methods for causal structure learning from data. The empirical assessment of such methods, however, is much less developed. Motivated by this gap, we pose the following question: how can one assess, in a given problem setting, the practical efficacy of one or more causal structure learning methods? We formalize the pro

Chendi Ni, Yuying Li, Peter Forsyth, Ray Carroll

We propose a data-driven Neural Network (NN) optimization framework to determine the optimal multi-period dynamic asset allocation strategy for outperforming a general stochastic target. We formulate the problem as an optimal stochastic control with an asymmetric, distribution shaping, objective function. The proposed framework is illustrated with the asset

Bo Yang, Xuelin Cao, Xiangfang Li, Chau Yuen

This letter proposes an edge learning-based offloading framework for autonomous driving, where the deep learning tasks can be offloaded to the edge server to improve the inference accuracy while meeting the latency constraint. Since the delay and the inference accuracy are incurred by wireless communications and computing, an optimization problem is formulat

Sangram K. Jena, Ramesh K. Jallu, Gautam K. Das, Subhas C. Nandy

In this article, we study a generalized version of the maximum independent set and minimum dominating set problems, namely, the maximum $d$-distance independent set problem and the minimum $d$-distance dominating set problem on unit disk graphs for a positive integer $d>0$. We first show that the maximum $d$-distance independent set problem and the minimum $

Maurizio Consoli, Leonardo Cosmai

In the first version of the theory, with a classical scalar potential, the sector inducing SSB was distinct from the Higgs field interactions induced through its gauge and Yukawa couplings. We have adopted a similar perspective but, following most recent lattice simulations, described SSB in $λΦ^4$ theory as a weak first-order phase transition. In this case,

Kimball A. Milton, Hannah Day, Yang Li, Xin Guo

The classical electromagnetic self-force on an arbitrary time-dependent electric or magnetic dipole moving with constant velocity in vacuum, and in a medium, is considered. Of course, in vacuum there is no net force on such a particle. Rather, because of loss of mass by the particle due to radiation, the self-force precisely cancels this inertial effect, and

Gianlorenzo D'Angelo, Debashmita Poddar, Cosimo Vinci

In the influence maximization (IM) problem, we are given a social network and a budget $k$, and we look for a set of $k$ nodes in the network, called seeds, that maximize the expected number of nodes that are reached by an influence cascade generated by the seeds, according to some stochastic model for influence diffusion. In this paper, we study the adaptiv

Rafael Brandt, Nicola Strisciuglio, Nicolai Petkov

Efficient yet accurate extraction of depth from stereo image pairs is required by systems with low power resources, such as robotics and embedded systems. State-of-the-art stereo matching methods based on convolutional neural networks require intensive computations on GPUs and are difficult to deploy on embedded systems. In this paper, we propose a stereo ma

Baoquan Yuan, Yamin Xiao

In this paper, we study the Cauchy problem of the 2D incompressible magnetohydrodynamic equations in Lei-Lin space. The global well-posedness of a strong solution in the Lei-Lin space $χ^{-1}(\mathbb{R}^2)$ with any initial data in $χ^{-1}(\mathbb{R}^2)\cap L^2(\mathbb{R}^2)$ is established. Furthermore, the uniqueness of the strong solution in $χ^{-1}(\math

A. Di Piazza, M. A. Lopez-Lopez

We compute the general expression of the one-loop vertex correction in an arbitrary plane-wave background field for the case of two on-shell external electrons and an off-shell external photon. The properties of the vertex corrections under gauge transformations of the plane-wave background field and of the radiation field are studied. Concerning the diverge

Jaume Alonso, Sonja Hohloch

Semitoric systems are a special class of completely integrable systems with two degrees of freedom that have been symplectically classified by Pelayo and Vu Ngoc about a decade ago in terms of five symplectic invariants. If a semitoric system has several focus-focus singularities, then some of these invariants have multiple components, one for each focus-foc

Xiaoxu Li, Liyun Yu, Xiaochen Yang, Zhanyu Ma

Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the sample size is small. To ensure good generalizability of deep networks under small sample sizes, learning discriminative features is crucial. To this end, several loss functions have b

Mehmet Onur Fen

Unidirectionally coupled Lorenz systems in which the drive possesses a chaotic attractor and the response admits two stable equilibria in the absence of the driving is under investigation. It is found that double chaotic attractors coexist in the dynamics. The approach is applicable for chains of coupled Lorenz systems. The existence of four chaotic attracto

Dong Liu, Minh Thành Vu, Zuxing Li, Lars K. Rasmussen

Belief propagation (BP) algorithm is a widely used message-passing method for inference in graphical models. BP on loop-free graphs converges in linear time. But for graphs with loops, BP's performance is uncertain, and the understanding of its solution is limited. To gain a better understanding of BP in general graphs, we derive an interpretable belief

Mudit Jain, Alexander Vilenkin

Observational effects of cosmic string loops depend on how loops are distributed in space. Chernoff \cite{Chernoff} has argued that loops can be gravitationally captured in galaxies and that for sufficiently small values of $Gμ$ their distribution follows that of dark matter, independently of the loop's length. We re-analyze this issue using the spherica

Xianglin Zheng, Zehong Cao, Quan Bai

The new perspective in visual classification aims to decode the feature representation of visual objects from human brain activities. Recording electroencephalogram (EEG) from the brain cortex has been seen as a prevalent approach to understand the cognition process of an image classification task. In this study, we proposed a deep learning framework guided

J. C. Birget

We generalize the Brin-Higman-Thompson groups $n G_{k,1}$ to monoids $n M_{k,1}$, for $n \ge 1$ and $k \ge 2$, by replacing bijections by partial functions. The monoid $n M_{k,1}$ has $n G_{k,1}$ as its group of units, and is congruence-simple. Moreover, $n M_{k,1}$ is finitely generated, and for $n \ge 2$ its word problem is {\sf coNP}-complete. We also pre

Tomer Golany, Daniel Freedman, Kira Radinsky

Generating training examples for supervised tasks is a long sought after goal in AI. We study the problem of heart signal electrocardiogram (ECG) synthesis for improved heartbeat classification. ECG synthesis is challenging: the generation of training examples for such biological-physiological systems is not straightforward, due to their dynamic nature in wh

M. Ghayasuddin, M. Ali, R. B. Paris

In this paper, we introduce and investigate a new extension of the beta function by means of an integral operator involving a product of Bessel-Struve kernel functions. We also define a new extension of the well-known beta distribution, the Gauss hypergeometric function and the confluent hypergeometric function in terms of our extended beta function. In addi

Jun Liu, Qing Li, Rui Cao, Wenming Tang

Predicting depth from a single image is an attractive research topic since it provides one more dimension of information to enable machines to better perceive the world. Recently, deep learning has emerged as an effective approach to monocular depth estimation. As obtaining labeled data is costly, there is a recent trend to move from supervised learning to u

Marc Górriz, Saverio Blasi, Alan F. Smeaton, Noel E. O'Connor

Neural networks can be used in video coding to improve chroma intra-prediction. In particular, usage of fully-connected networks has enabled better cross-component prediction with respect to traditional linear models. Nonetheless, state-of-the-art architectures tend to disregard the location of individual reference samples in the prediction process. This pap

Daniel Sell

We investigate combinatorial properties of aperiodic simple Toeplitz subshifts, as well as spectral properties of Jacobi operators defined by them. More precisely, we derive explicit formulas for complexity, palindrome complexity and, for sufficiently large word length, repetitivity. In addition we give a complete description of the de Bruijn graphs. We char

Ao Cai, Xueyin Wang

For the quasi-periodic Schrödinger operators in the local perturbative regime where the frequency is Diophantine and the potential is $C^k$ sufficiently small depending on the Diophantine constants, we prove that the length of the corresponding spectral gap has a polynomial decay upper bound with respect to its label. This is based on a refined quantitative

Alaa Awad Abdellatif, Lutfi Samara, Amr Mohamed, Mohsen Guizani

Rapid evolution of wireless medical devices and network technologies has fostered the growth of remote monitoring systems. Such new technologies enable monitoring patients' medical records anytime and anywhere without limiting patients' activities. However, critical challenges have emerged with remote monitoring systems due to the enormous amount of

Mingzhe Yang, Yukino Baba

Iterative machine teaching is a method for selecting an optimal teaching example that enables a student to efficiently learn a target concept at each iteration. Existing studies on iterative machine teaching are based on supervised machine learning and assume that there are teachers who know the true answers of all teaching examples. In this study, we consid

Mark V Lawson

We revisit our construction of the Thompson groups from the polycyclic inverse monoids in the light of new research. Specifically, we prove that the Thompson group $G_{n,1}$ is the group of units of a Boolean inverse monoid $C_{n}$ called the Cuntz inverse monoid. This inverse monoid is proved to be the tight completion of the polycyclic inverse monoid $P_{n

Reza Gholami Mahmoodabadi, Richard W. Taylor, Martin Kaller, Susann Spindler

Interferometric scattering (iSCAT) microscopy is an emerging label-free technique optimized for the sensitive detection of nano-matter. Previous iSCAT studies have approximated the point spread function in iSCAT by a Gaussian intensity distribution. However, recent efforts to track the mobility of nanoparticles in challenging speckle environments and over ex

Roman Jakubicek, Tomas Vicar, Jiri Chmelik

Much of the recently available research and challenge data lack the meta-data containing any information about the patient position. This paper presents a tool for automatic rotation of CT data into a standardized (HFS) patient position. The proposed method is based on the prediction of rotation angle with CNN, and it achieved nearly perfect results with an

Erik Lindgren, Peter Lindqvist

We study the streamlines of $\infty$-harmonic functions in planar convex rings. We include convex polygons. The points where streamlines can meet are characterized: they lie on certain curves. The gradient has constant norm along streamlines outside the set of meeting points, the infinity-ridge.

Yongkang Gong, Liang Guo, Stephan Wong, Anthony J. Bennett

The realization of topological edge states (TESs) in photonic systems has provided unprecedented opportunities for manipulating light in novel manners. The Su-Schrieffer-Heeger (SSH) model has recently gained significant attention and has been exploited in a wide range of photonic platforms to create TESs. We develop a photonic topological insulator strategy

Jirong Mao, Liande Li, Jiancheng Wang

We statistically examine the gamma-ray burst (GRB) photon indices obtained by the Fermi-GBM and Fermi-LAT observations and compare the LAT GRB photon indices to the GBM GRB photon indices. We apply the jitter radiation to explain the GRB spectral diversities in the high-energy bands. In our model, the jitter radiative spectral index is determined by the spec

Pintu Bhunia, Debmalya Sain, Kallol Paul

We study the Davis-Wielandt shell and the Davis-Wielandt radius of an operator on a normed linear space $\mathcal{X}$. We show that after a suitable modification, the modified Davis-Wielandt radius defines a norm on $\mathcal{L}(\mathcal{X})$ which is equivalent to the usual operator norm on $\mathcal{L}(\mathcal{X})$. We introduce the Davis-Wielandt index o

Sergi Perez-Castanos, Javier Naranjo-Alcazar, Pedro Zuccarello, Maximo Cobos

Anomalous sound detection (ASD) is, nowadays, one of the topical subjects in machine listening discipline. Unsupervised detection is attracting a lot of interest due to its immediate applicability in many fields. For example, related to industrial processes, the early detection of malfunctions or damage in machines can mean great savings and an improvement i

Titinunt Kitrungrotsakul, Iwamoto Yutaro, Lanfen Lin, Ruofeng Tong

Deep learning techniques have successfully been employed in numerous computer vision tasks including image segmentation. The techniques have also been applied to medical image segmentation, one of the most critical tasks in computer-aided diagnosis. Compared with natural images, the medical image is a gray-scale image with low-contrast (even with some invisi

Hung Le, Steven C. H. Hoi

Pre-trained language models have shown remarkable success in improving various downstream NLP tasks due to their ability to capture dependencies in textual data and generate natural responses. In this paper, we leverage the power of pre-trained language models for improving video-grounded dialogue, which is very challenging and involves complex features of d

Arpita Mal, Kallol Paul, Subhrajit Dey

We characrterize extreme contractions defined between \ finite-dimensional polyhedral Banach spaces using $k$- smoothness of operators. We also explore weak L-P property, a recently introduced concept in the study of extreme contractions. We obtain a sufficient condition for a pair of finite-dimensional polyhedral Banach spaces to satisfy weak L-P property.

Subhabrata Mukherjee, Ahmed Hassan Awadallah

Recent success of large-scale pre-trained language models crucially hinge on fine-tuning them on large amounts of labeled data for the downstream task, that are typically expensive to acquire. In this work, we study self-training as one of the earliest semi-supervised learning approaches to reduce the annotation bottleneck by making use of large-scale unlabe

A. Alzubaidi, K. Mitra, P. Patel, E. Solaiman

Within cloud-based internet of things (IoT) applications, typically cloud providers employ Service Level Agreements (SLAs) to ensure the quality of their provisioned services. Similar to any other contractual method, an SLA is not immune to breaches. Ideally, an SLA stipulates consequences (e.g. penalties) imposed on cloud providers when they fail to conform

Swarup Chattopadhyay, Debasis Ganguly

Detecting communities or the modular structure of real-life networks (e.g. a social network or a product purchase network) is an important task because the way a network functions is often determined by its communities. Traditional approaches to community detection involve modularity-based algorithms, which generally speaking, construct partitions based on h

Rakshitha Godahewa, Trevor Yann, Christoph Bergmeir, Francois Petitjean

Stream classification methods classify a continuous stream of data as new labelled samples arrive. They often also have to deal with concept drift. This paper focuses on seasonal drift in stream classification, which can be found in many real-world application data sources. Traditional approaches of stream classification consider seasonal drift by including

Yuval Heller, Erik Mohlin

We study environments in which agents are randomly matched to play a Prisoner's Dilemma, and each player observes a few of the partner's past actions against previous opponents. We depart from the existing related literature by allowing a small fraction of the population to be commitment types. The presence of committed agents destabilizes previously

Yuval Heller, SharonPeleg-Lazar, Alon Raviv

Black and Cox (1976) claim that the value of junior debt is increasing in asset risk when the firm's value is low. We show, using closed-form solution, that the junior debt's value is hump-shaped. This has interesting implications for the market-discipline role of banks' junior debt.

Yuval Heller, Erik Mohlin

We develop a framework in which individuals' preferences coevolve with their abilities to deceive others about their preferences and intentions. Specifically, individuals are characterised by (i) a level of cognitive sophistication and (ii) a subjective utility function. Increased cognition is costly, but higher-level individuals have the advantage of be

K. Győry, L. Hajdu, A. Sárközy

In [10] the third author of this paper presented two conjectures on the additive decomposability of the sequence of ''smooth'' (or ''friable'') numbers. Elsholtz and Harper [4] proved (by using sieve methods) the second (less demanding) conjecture. The goal of this paper is to extend and sharpen their result in three direction

Yuval Heller, Eyal Winter

We investigate how distorted, yet structured, beliefs can persist in strategic situations. Specifically, we study two-player games in which each player is endowed with a biased-belief function that represents the discrepancy between a player's beliefs about the opponent's strategy and the actual strategy. Our equilibrium condition requires that (i) e

Y. Wang, C. -J. Kang, H. Miao, G. Kotliar

We study the normal state electronic structure of the recently discovered infinite-layer nickelate superconductor, Nd$_{1-x}$Sr$_x$NiO$_2$. Using SrNiO$_2$ as a reference, we find that Nd$_{1-x}$Sr$_x$NiO$_2$ is a multi-orbital electronic system with characteristic Hund's metal behaviors, such as metallicity, the importance of high-spin configurations, t

Wen He, Lu Wang, Ge Zhuang

The local intrinsic parallel current density driven by electron drift wave (DW) turbulence including electromagnetic (EM) effects is analytically studied. The scalings of the ratios of intrinsic current density driven by residual turbulent flux and by turbulent source to the bootstrap current density with electron density and temperature are predicted to be

Nora Müller, Wolfgang Bock

In this paper we apply the method of stochastic characteristics to a Lighthill-Whitham-Richards model. The stochastic perturbation can be seen as errors in measurement of the traffic density. For concrete examples we solve the equation perturbed by a standard Brownian motion and the geometric Brownian motion without drift.

Vasudevarao Allu, Himadri Halder

We say that a class $\mathcal{F}$ consisting of analytic functions $f(z)=\sum_{n=0}^{\infty} a_{n}z^{n}$ in the unit disk $\mathbb{D}:=\{z\in \mathbb{C}: |z|<1\}$ satisfies a Bohr phenomenon if there exists $r_{f} \in (0,1)$ such that $$ \sum_{n=1}^{\infty} |a_{n}z^{n}|\leq d(f(0),\partial f(\mathbb{D})) $$ for every function $f \in \mathcal{F}$ and $|z|=r\l

Hiroo Totsuji

Analysis has been made on a possibility of phase separation and critical point as a solution of fine particles in plasmas, extending the previous work which pointed out this as one of two such possibilities. Fine particles are treated as a solute in the solvent of weakly coupled plasmas of electrons and ions. Examples of phase diagram have been obtained and

Zhendong Zhang, Liangchao Chen, Kaixuan Yao, Cheng Chin

Preparation of molecular quantum gas promises novel applications including quantum control of chemical reactions, precision measurements, quantum simulation and quantum information processing. Experimental preparation of colder and denser molecular samples, however, is frequently hindered by fast inelastic collisions that heat and deplete the population. Her

Rakshitha Godahewa, Chang Deng, Arnaud Prouzeau, Christoph Bergmeir

In building management, usually static thermal setpoints are used to maintain the inside temperature of a building at a comfortable level irrespective of its occupancy. This strategy can cause a massive amount of energy wastage and therewith increase energy related expenses. This paper explores how to optimise the setpoints used in a particular room during i

Saeed Pourasad, Amir Hajibabaei, Chang Woo Myung, Kwang S. Kim

Liquid-liquid phase transition (LLPT) in supercooled water has been a long-standing controversial issue. We show simulation results of real stable first-order phase transitions between high and low density liquid (HDL and LDL)-like structures in confined supercooled water in both positive and negative pressures. These topological phase transitions originate

Wen He, Lu Wang, Shuitao Peng, Weixin Guo

The mean parallel current density evolution equation is presented using electromagnetic (EM) gyrokinetic equation. There exist two types of intrinsic current driving mechanisms resulted from EM electron temperature gradient (ETG) turbulence. The first type is the divergence of residual turbulent flux including a residual stress-like term and a kinetic stress

Akira Kanda, Mihai Prunescu, Renata Wong

We review the special theory of relativity kinematics and dynamics as well as the general theory of relativity under the aspect of logic.

Fan Guo, Yi-Hsin Liu, Xiaocan Li, Hui Li

Magnetic reconnection in strongly magnetized astrophysical plasma environments is believed to be the primary process for fast energy release and particle energization. Currently there is strong interest in relativistic magnetic reconnection, in that it may provide a new explanation for high-energy particle acceleration and radiation in strongly magnetized as

Yunlong Wang, Harald Reiterer

Unhealthy lifestyles largely contribute to many chronic diseases, which makes the research on health behavior change crucial for both individuals and the whole society. As an interdisciplinary research field, health behavior change research in the HCI community is still in the early stage. This research field is notably less developed in Chinese HCI communit

Qian Li, Qingyuan Hu, Yong Qi, Saiyu Qi

Data augmentation have been intensively used in training deep neural network to improve the generalization, whether in original space (e.g., image space) or representation space. Although being successful, the connection between the synthesized data and the original data is largely ignored in training, without considering the distribution information that th

Madan G. Kundu, Shoubhik Mondal

In a stratified clinical trial design with time to event end points, stratification factors are often accounted for the log-rank test and the Cox regression analyses. In this work, we have evaluated the impact of inclusion of stratification factors on the power of the stratified log-rank test and have compared the bias and standard error in HR estimate betwe

Madan Gopal Kundu, Samiran Ghosh

Survival analysis of right censored data arises often in many areas of research including medical research. Effect of covariates (and their interactions) on survival distribution can be studied through existing methods which requires to pre-specify the functional form of the covariates including their interactions. Survival trees offer relatively flexible ap

Peter J. Smith, Pawel A. Dmochowski, Ikram Singh, Richard Green

We present a flexible family of 3D mobility models suitable for unmanned aerial vehicles (UAV). Based on stochastic differential equations, the models offer a unique property of explicitly incorporating the mobility control mechanism and environmental perturbation, while enabling tractable steady state solutions for properties such as position and connectivi

Cristina L. Archer, Guido Cervone, Maryam Golbazi, Nicolas Al Fahel

The first goal of this study is to quantify the magnitude and spatial variability of air quality changes in the US during the COVID-19 pandemic. We focus on two federally regulated pollutants, nitrogen dioxide (NO2), and fine particulate matter (PM2.5). Observed concentrations at all available ground monitoring sites (240 and 480 for NO2 and PM2.5, respectiv

B. M. Dincau, E. Mai, Q. Magdelaine, J. A. Lee

A fiber withdrawn from a bath of a dilute particulate suspension exhibits different coating regimes depending on the physical properties of the fluid, the withdrawal speed, the particle sizes, and the radius of the fiber. Our experiments indicate that only the liquid without particles is entrained for thin coating films. Beyond a threshold capillary number,

Aminollah Khormali, Jeman Park, Hisham Alasmary, Afsah Anwar

The domain name system (DNS) is one of the most important components of today&#39;s Internet, and is the standard naming convention between human-readable domain names and machine-routable IP addresses of Internet resources. However, due to the vulnerability of DNS to various threats, its security and functionality have been continuously challenged over the

Philip Taylor, Chiaki Kobayashi, Lisa J. Kewley

We predict the mass fraction of oxygen lost from galaxies in a cosmological simulation as a function of stellar mass and environment at the present day. The distribution with stellar mass is bimodal, separating star-forming and quenched galaxies. The metallicity of gas and stars is self-consistently calculated using a chemical evolution model that includes s

Md. Shazibul Islam Shamim, Farzana Ahamed Bhuiyan, Akond Rahman

Kubernetes is an open-source software for automating management of computerized services. Organizations, such as IBM, Capital One and Adidas use Kubernetes to deploy and manage their containers, and have reported benefits related to deployment frequency. Despite reported benefits, Kubernetes deployments are susceptible to security vulnerabilities, such as th

Uday Tupakula, Vijay Varadharajan, Kallol Krishna Karmakar

Increasingly Industrial Control Systems (ICS) systems are being connected to the Internet to minimise the operational costs and provide additional flexibility. These control systems such as the ones used in power grids, manufacturing and utilities operate continually and have long lifespans measured in decades rather than years as in the case of IT systems.

Vijay Varadharajan, Uday Tupakula, Kallol Karmakar

The 5G network systems are evolving and have complex network infrastructures. There is a great deal of work in this area focused on meeting the stringent service requirements for the 5G networks. Within this context, security requirements play a critical role as 5G networks can support a range of services such as healthcare services, financial and critical i

Saulo B. Bastos, Marcelo M. Morato, Daniel O. Cajueiro anda Julio E Normey-Rico

The COVID-19 pandemic (SARS-CoV-2 virus) is the defying global health crisis of our time. The absence of mass testing and the relevant presence of asymptomatic individuals causes the available data of the COVID-19 pandemic in Brazil to be largely under-reported regarding the number of infected individuals and deaths. We propose an adapted Susceptible-Infecte