cssi/SciSciGPT-SciSciNet huggingface dataset:
Peripheral Convex Expansions of Resonance Graphs

A perfect matching of a graph is a set of independent edges that covers all vertices of the graph. A bipartite graph is elementary if and only if it is connected and each edge is contained in a perfect matching. The resonance graph of a plane bipartite graph G is a graph whose vertices are perfect matchings of G, and two perfect matchings are adjacent if edges contained in their union but not intersection form a cycle surrounding a finite face of G. It is well known that the resonance graph of a plane elementary bipartite graph is a median graph. Median graphs form an important subclass of partial cubes and have a wide range of applications. The most important structural characterization of a median graph is the Mulder’s convex expansion theorem. Fibonacci cubes used in network designs are median graphs and can be obtained from an edge by a sequence of peripheral convex expansions. Plane bipartite graphs whose resonance graphs are Fibonacci cubes were studied by Klavžar et al. first for their applications in chemistry and completely characterized by Zhang et al. later. Motivated from their work, we characterize all plane bipartite graphs whose resonance graphs can be constructed from an edge by a sequence of peripheral convex expansions.
Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography

In the presence of metal implants, metal artifacts are introduced to x-ray computed tomography CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the past decades, MAR is still one of the major problems in clinical x-ray CT. In this paper, we develop a convolutional neural network (CNN)-based open MAR framework, which fuses the information from the original and corrected images to suppress artifacts. The proposed approach consists of two phases. In the CNN training phase, we build a database consisting of metal-free, metal-inserted and pre-corrected CT images, and image patches are extracted and used for CNN training. In the MAR phase, the uncorrected and pre-corrected images are used as the input of the trained CNN to generate a CNN image with reduced artifacts. To further reduce the remaining artifacts, water equivalent tissues in a CNN image are set to a uniform value to yield a CNN prior, whose forward projections are used to replace the metal-affected projections, followed by the FBP reconstruction. The effectiveness of the proposed method is validated on both simulated and real data. Experimental results demonstrate the superior MAR capability of the proposed method to its competitors in terms of artifact suppression and preservation of anatomical structures in the vicinity of metal implants.
Large deviations for the current and tagged particle in 1D nearest-neighbor symmetric simple exclusion

Laws of large numbers, starting from certain nonequilibrium measures, have been shown for the integrated current across a bond, and a tagged particle in one-dimensional symmetric nearest-neighbor simple exclusion [Ann. Inst. Henri Poincare Probab. Stat. 42 (2006) 567–577]. In this article, we prove corresponding large deviation principles and evaluate the rate functions, showing different growth behaviors near and far from their zeroes which connect with results in [J. Stat. Phys. 136 (2009) 1–15].
Complexified path integrals and the phases of quantum field theory

Abstract The path integral by which quantum field theories are defined is a particular solution of a set of functional differential equations arising from the Schwinger action principle. In fact these equations have a multitude of additional solutions which are described by integrals over a complexified path. We discuss properties of the additional solutions which, although generally disregarded, may be physical with known examples including spontaneous symmetry breaking and theta vacua. We show that a consideration of the full set of solutions yields a description of phase transitions in quantum field theories which complements the usual description in terms of the accumulation of Lee–Yang zeroes. In particular we argue that non-analyticity due to the accumulation of Lee–Yang zeros is related to Stokes phenomena and the collapse of the solution set in various limits including but not restricted to, the thermodynamic limit. A precise demonstration of this relation is given in terms of a zero dimensional model. Finally, for zero dimensional polynomial actions, we prove that Borel resummation of perturbative expansions, with several choices of singularity avoiding contours in the complex Borel plane, yield inequivalent solutions of the action principle equations.
New Insights on Hadron Acceleration at Supernova Remnant Shocks

We outline the main features of nuclei acceleration at supernova remnant forward shocks, stressing the crucial role played by self-amplified magnetic fields in determining the energy spectrum observed in this class of sources. In particular, we show how the standard predictions of the non-linear theory of diffusive shock acceleration has to be completed with an additional ingredient, which we propose to be the enhanced velocity of the magnetic irregularities particles scatter against, to reconcile the theory of efficient particle acceleration with recent observations of gamma-ray bright supernova remnants.
Analysis of multilayer electro-active tubes under different constraints:

Dielectric elastomers are an emerging class of highly deformable electro-active materials employed for electromechanical transduction technology. For practical applications, the design of such tran...
Strengthening in multi-principal element alloys with local-chemical-order roughened dislocation pathways

High-entropy and medium-entropy alloys are presumed to have a configurational entropy as high as that of an ideally mixed solid solution (SS) of multiple elements in near-equal proportions. However, enthalpic interactions inevitably render such chemically disordered SSs rare and metastable, except at very high temperatures. Here we highlight the wide variety of local chemical ordering (LCO) that sets these concentrated SSs apart from traditional solvent-solute ones. Using atomistic simulations, we reveal that the LCO of the multi-principal-element NiCoCr SS changes with alloy processing conditions, producing a wide range of generalized planar fault energies. We show that the LCO heightens the ruggedness of the energy landscape and raises activation barriers governing dislocation activities. This influences the selection of dislocation pathways in slip, faulting, and twinning, and increases the lattice friction to dislocation motion via a nanoscale segment detrapping mechanism. In contrast, severe plastic deformation reduces the LCO towards random SS. Multi-principal-element alloys have been assumed to have the configurational entropy of an ideal solution. Here, the authors use atomistic simulations to show that instead NiCoCr exhibits local chemical order, raising the activation barriers of dislocation activities to elevate mechanical strength.
Constructing and constraining wave functions for identical quantum particles

I address the problem of explaining why wave functions for identical particles must be either symmetric or antisymmetric (the symmetry dichotomy) within two interpretations of quantum mechanics which include particles following definite trajectories in addition to, or in lieu of, the wave function: Bohmian mechanics and Newtonian quantum mechanics (a.k.a. many interacting worlds). In both cases I argue that, if the interpretation is formulated properly, the symmetry dichotomy can be derived and need not be postulated.
Characteristics of Ephemeral Coronal Holes

Small-scale ephemeral coronal holes may be a recurring feature on the solar disk but have received comparatively little attention. These events are characterized by compact structures and short total lifetimes, which are substantially less than a solar disk crossing. We present a systematic search for these events, using Atmospheric Imaging Assembly extreme ultraviolet image data from the Solar Dynamics Observatory, covering the time period of 2010–2015. Following strict criteria, this search yielded four clear examples of the ephemeral coronal hole phenomenon. The properties of each event are characterized, including their total lifetime, growth and decay rates, and areas. The magnetic properties of these events are also determined using Helioseismic and Magnetic Imager data. Based on these four events, ephemeral coronal holes experience rapid initial growth of up to ∼3000 Msq.m/hr, while the decay phases are typically more gradual. Like conventional coronal holes, the mean magnetic ﬁeld in each ephemeral coronal hole displays a consistent polarity, with mean magnetic ﬂux densities generally <10 G. No evidence of a corresponding signature is seen in solar wind data at 1 au. Further study is needed to determine whether ephemeral coronal holes are under reported events or are truly rare phenomena.
Interferometric Tests of Planckian Quantum Geometry Models

The effect of Planck scale quantum geometrical effects on measurements with interferometers is estimated with standard physics, and with a variety of proposed extensions. It is shown that effects are negligible in standard field theory with canonically quantized gravity. Statistical noise levels are estimated in a variety of proposals for nonstandard metric fluctuations, and these alternatives are constrained using upper bounds on stochastic metric fluctuations from LIGO. Idealized models of several interferometer system architectures are used to predict signal noise spectra in a quantum geometry that cannot be described by a fluctuating metric, in which position noise arises from holographic bounds on directional information. Lastly, predictions in this case are shown to be close to current and projected experimental bounds.
Is turing's thesis the consequence of a more general physical principle?

We discuss historical attempts to formulate a physical hypothesis from which Turing's thesis may be derived, and also discuss some related attempts to establish the computability of mathematical models in physics. We show that these attempts are all related to a single, unified hypothesis.
On the bridge number of knot diagrams with minimal crossings

Given a diagram $D$ of a knot $K$ , we consider the number $c(D)$ of crossings and the number $b(D)$ of overpasses of $D$ . We show that, if $D$ is a diagram of a nontrivial knot $K$ whose number $c(D)$ of crossings is minimal, then $1+\sqrt{1+c(D)} \leq b(D)\leq c(D)$ . These inequalities are sharp in the sense that the upper bound of $b(D)$ is achieved by alternating knots and the lower bound of $b(D)$ is achieved by torus knots. The second inequality becomes an equality only when the knot is an alternating knot. We prove that the first inequality becomes an equality only when the knot is a torus knot.
The Origin of Pluto's Orbit: Implications for the Solar System Beyond Neptune

The origin of the highly eccentric, inclined, and resonance-locked orbit of Pluto has long been a puzzle. A possible explanation has been proposed recently which suggests that these extraordinary orbital properties may be a natural consequence of the formation and early dynamical evolution of the outer solar system. A resonance capture mechanism is possible during the clearing of the residual planetesimal debris and the formation of the Oort Cloud of comets by planetesimal mass loss from the vicinity of the giant planets. If this mechanism were in operation during the early history of the planetary system, the entire region between the orbit of Neptune and approximately 50 AU would have been swept by first-order mean motion resonances. Thus, resonance capture could occur not only for Pluto, but quite generally for other trans-Neptunian small bodies. Some consequences of this evolution for the present-day dynamical structure of the trans-Neptunian region are (1) most of the objects in the region beyond Neptune and up to approximately 50 AU exist in very narrow zones located at orbital resonances with Neptune (particularly the 3:2 and the 2:1 resonances); and (2) these resonant objects would have significantly large eccentricities. The distribution of objects in the Kuiper Belt as predicted by this theory is presented here.
Topological Test Spaces

A test space is the set of outcome-sets associated with a collection of experiments. This notion provides a simple mathematical framework for the study of probabilistic theories—notably, quantum mechanics—in which one is faced with incommensurable random quantities. In the case of quantum mechanics, the relevant test space, the set of orthonormal bases of a Hilbert space, carries significant topological structure. This paper inaugurates a general study of topological test spaces. Among other things, we show that any topological test space with a compact space of outcomes is of finite rank. We also generalize results of Meyer and Clifton-Kent by showing that, under very weak assumptions, any second-countable topological test space contains a dense semi-classical test space.
Autofocused oracles for model-based design

Data-driven design is making headway into a number of application areas, including protein, small-molecule, and materials engineering. The design goal is to construct an object with desired properties, such as a protein that binds to a therapeutic target, or a superconducting material with a higher critical temperature than previously observed. To that end, costly experimental measurements are being replaced with calls to high-capacity regression models trained on labeled data, which can be leveraged in an in silico search for design candidates. However, the design goal necessitates moving into regions of the design space beyond where such models were trained. Therefore, one can ask: should the regression model be altered as the design algorithm explores the design space, in the absence of new data? Herein, we answer this question in the affirmative. In particular, we (i) formalize the data-driven design problem as a non-zero-sum game, (ii) develop a principled strategy for retraining the regression model as the design algorithm proceeds---what we refer to as autofocusing, and (iii) demonstrate the promise of autofocusing empirically.
Big bang nucleosynthesis: An update

The current status of big bang nucleosynthesis is reviewed with an emphasis on the comparison between the observational determination of the light element abundances of D, 3He, 4He and 7Li and the predictions from theory. In particular, we present new analyses for 4He and 7Li. Implications for physics beyond the standard model are also discussed. In addition, limits on the effective number of neutrino flavors are updated.
Gauge-Higgs unification in higher dimensions

Abstract The electroweak Higgs doublets are identified as components of a vector multiplet in a higher-dimensional supersymmetric field theory. We construct a minimal model in 6D where the electroweak SU(2)⊗U(1) gauge group is extended to SU(3), and unified 6D models with the unified SU(5) gauge symmetry extended to SU(6). In these realistic theories the extended gauge group is broken by orbifold boundary conditions, leaving Higgs doublet zero modes which have Yukawa couplings to quarks and leptons on the orbifold fixed points. In one SU(6) model the weak mixing angle receives power law corrections, while in another the fixed point structure forbids such corrections. A 5D model is also constructed in which the Higgs doublet contains the fifth component of the gauge field. In this case Yukawa couplings are introduced as nonlocal operators involving the Wilson line of this gauge field.
TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval

We introduce TV show Retrieval (TVR), a new multimodal retrieval dataset. TVR requires systems to understand both videos and their associated subtitle (dialogue) texts, making it more realistic. The dataset contains 109K queries collected on 21.8K videos from 6 TV shows of diverse genres, where each query is associated with a tight temporal window. The queries are also labeled with query types that indicate whether each of them is more related to video or subtitle or both, allowing for in-depth analysis of the dataset and the methods that built on top of it. Strict qualification and post-annotation verification tests are applied to ensure the quality of the collected data. Additionally, we present several baselines and a novel Cross-modal Moment Localization (XML) network for multimodal moment retrieval tasks. The proposed XML model uses a late fusion design with a novel Convolutional Start-End detector (ConvSE), surpassing baselines by a large margin and with better efficiency, providing a strong starting point for future work. (TVR dataset and code are publicly available: https://tvr.cs.unc.edu/. We also introduce TVC for multimodal captioning at https://tvr.cs.unc.edu/tvc.html).
On finite-density QCD at large Nc

Abstract Deryagin, Grigoriev, and Rubakov (DGR) have shown that in finite-density QCD at infinite Nc the Fermi surface is unstable with respect to the formation of chiral waves with wavenumber twice the Fermi momentum, while the BCS instability is suppressed. We show here that at large, but finite Nc, the DGR instability only occurs in a finite window of chemical potentials from above ΛQCD to μ crit ∼ exp (γ ln 2 N c +O( ln N c ln ln N c ))Λ QCD , where γ≈0.02173. Our analysis shows that, at least in the perturbative regime, the instability occurs only at extremely large Nc, Nc≳1000Nf, where Nf is the number of flavors. We conclude that the DGR instability is not likely to occur in QCD with three colors, where the ground state at finite density is expected to be a color superconductor. We speculate on the possible structure of the ground state of finite-density QCD with very large Nc.
Movement Coordination in Human–Robot Teams: A Dynamical Systems Approach

In order to be effective teammates, robots need to be able to understand high-level human behavior to recognize, anticipate, and adapt to human motion. We have designed a new approach to enable robots to perceive human group motion in real time to anticipate future actions and synthesize their own motion accordingly. We explore this within the context of joint action, in which humans and robots move together synchronously. In this paper we present an anticipation method, which takes high-level group behavior into account. We validate the method within a human–robot interaction scenario, in which an autonomous mobile robot observes a team of human dancers and then successfully and contingently coordinates its movements to “join the dance.” We compared the results of our anticipation method to move the robot with another method that did not rely on high-level group behavior and found that our method performed better both in terms of more closely synchronizing the robot's motion to the team and exhibiting more contingent and fluent motion. These findings suggest that the robot performs better when it has an understanding of high-level group behavior than when it does not. This study will help enable others in the robotics community to build more fluent and adaptable robots in the future.
SIGN OF GREEN'S FUNCTION OF PANEITZ OPERATORS AND THE Q CURVATURE

In a conformal class of metrics with positive Yamabe invariant, we derive a necessary and sufficient condition for the existence of metrics with positive Q curvature. The condition is conformally invariant. We also prove some inequalities between the Green's functions of the conformal Laplacian operator and the Paneitz operator.
A characterization on separable subgroups of 3-manifold groups

In this paper, we give a complete characterization on which finitely generated subgroups of finitely generated $3$-manifold groups are separable. Our characterization generalizes Liu's spirality character on $\pi_1$-injective immersed surface subgroups of closed $3$-manifold groups. A consequence of our characterization is that, for any compact, orientable, irreducible and $\partial$-irreducible $3$-manifold $M$ with nontrivial torus decomposition, $\pi_1(M)$ is LERF if and only if for any two adjacent pieces in the torus decomposition of $M$, at least one of them has a boundary component with genus at least $2$.
THE EFFECTIVE POTENTIAL AND ADDITIONAL LARGE RADIUS COMPACTIFIED SPACE–TIME DIMENSIONS

The consequences of large radius extra space–time compactified dimensions on the four-dimensional one-loop effective potential are investigated for a model which includes scalar self interactions and Yukawa coupling to fermions. The Kaluza–Klein tower of states associated with the extra compact dimensions shifts the location of the effective potential minimum and modifies its curvature. The dependence of these effects on the radius of the extra dimension is illustrated for various choices of coupling constants and masses. For large radii, the consequence of twisting the fermion boundary condition on the compactified dimensions is numerically found to produce but a negligible effect on the effective potential.
Viscous Marangoni propulsion

Marangoni propulsion is a form of locomotion wherein an asymmetric release of surfactant by a body located at the surface of a liquid leads to its directed motion. We present in this paper a mathematical model for Marangoni propulsion in the viscous regime. We consider the case of a thin rigid circular disk placed at the surface of a viscous fluid and whose perimeter has a prescribed concentration of an insoluble surfactant, to which the rest of its surface is impenetrable. Assuming a linearized equation of state between surface tension and surfactant concentration, we derive analytically the surfactant, velocity and pressure fields in the asymptotic limit of low capillary, Peclet and Reynolds numbers. We then exploit these results to calculate the Marangoni propulsion speed of the disk. Neglecting the stress contribution from Marangoni flows is seen to over-predict the propulsion speed by 50 %.
Expansion of Infinite Series Containing Modified Bessel Functions of the Second Kind

The aim of this work is to analyze general infinite sums containing modified Bessel functions of the second kind. In particular we present a method for the construction of a proper asymptotic expansion for such series valid when one of the parameters in the argument of the modified Bessel function of the second kind is small compared to the others. We apply the results obtained for the asymptotic expansion to specific problems that arise in the ambit of quantum field theory.
Patterns of primes in the Sato–Tate conjecture

Fix a non-CM elliptic curve $$E/\mathbb {Q}$$, and let $$a_E(p) = p + 1 - \#E(\mathbb {F}_p)$$ denote the trace of Frobenius at p. The Sato–Tate conjecture gives the limiting distribution $$\mu _{ST}$$ of $$a_E(p)/(2\sqrt{p})$$ within $$[-1, 1]$$. We establish bounded gaps for primes in the context of this distribution. More precisely, given an interval $$I\subseteq [-1, 1]$$, let $$p_{I,n}$$ denote the nth prime such that $$a_E(p)/(2\sqrt{p})\in I$$. We show $$\liminf _{n\rightarrow \infty }(p_{I,n+m}-p_{I,n}) < \infty $$ for all $$m\ge 1$$ for “most” intervals, and in particular, for all I with $$\mu _{ST}(I)\ge 0.36$$. Furthermore, we prove a common generalization of our bounded gap result with the Green–Tao theorem. To obtain these results, we demonstrate a Bombieri–Vinogradov type theorem for Sato–Tate primes.
Renormalization group flows on D3 branes at an orbifolded conifold

We consider D3-branes at an orbifolded conifold whose horizon X5 resolves into a smooth Einstein manifold which joins several copies of 1,1. We describe in details the resolution of the singular horizon X5 and describe different types of two-cycles appearing in the resolution. For a large number of D3 branes, the AdS/CFT conjecture becomes a duality between type IIB string theory on AdS5 × X5 and the = 1 field theory living on the D3 branes. We study the fractional branes as small perturbations of the string background and we reproduce the logarithmic flow of field theory couplings by studying fluxes of NS-NS and R-R two forms through different 2-cycles of the resolved horizon.
Characterizing the unit ball by its projective automorphism group

In this paper we study the projective automorphism group of domains in real, complex, and quaternionic projective space and present two new characterizations of the unit ball in terms of the size of the automorphism group and the regularity of the boundary.
Exact Correlators on the Wilson Loop in $\mathcal{N}=4$ SYM: Localization, Defect CFT, and Integrability

We compute a set of correlation functions of operator insertions on the 1/8 BPS Wilson loop in $$ \mathcal{N}=4 $$ SYM by employing supersymmetric localization, OPE and the Gram-Schmidt orthogonalization. These correlators exhibit a simple determinant structure, are position-independent and form a topological subsector, but depend nontrivially on the ’t Hooft coupling and the rank of the gauge group. When applied to the 1/2 BPS circular (or straight) Wilson loop, our results provide an infinite family of exact defect CFT data, including the structure constants of protected defect primaries of arbitrary length inserted on the loop. At strong coupling, we show precise agreement with a direct calculation using perturbation theory around the AdS2 string worldsheet. We also explain the connection of our results to the “generalized Bremsstrahlung functions” previously computed from integrability techniques, reproducing the known results in the planar limit as well as obtaining their finite N generalization. Furthermore, we show that the correlators at large N can be recast as simple integrals of products of polynomials (known as Q-functions) that appear in the Quantum Spectral Curve approach. This suggests an interesting interplay between localization, defect CFT and integrability.
Šapovalov elements and the Jantzen filtration for contragredient Lie superalgebras: a survey

This is a survey of some recent results on Sapovalov elements and the Jantzen filtration for contragredient Lie superalgebras. The topics covered include the existence and uniqueness of the Sapovalov elements, bounds on the degrees of their coefficients and the behavior of Sapovalov elements when the Borel subalgebra is changed. There is always a unique term whose coefficient has larger degree than any other term. This allows us to define some new highest weight modules. If X is a set of orthogonal isotropic roots and λ ∈ h* is such that λ + ρ is orthogonal to all roots in X, we construct highest weight modules with character eλpx. Here pX is a partition function that counts partitions not involving roots in X. When |X| = 1, these modules are used to give a Jantzen sum formula for Verma modules in which all terms are characters of modules in the category O with positive coefficients.
The Elemental Compositions of Extrasolar Planetesimals

Evidence is now compelling that most externally-polluted white dwarfs derive their heavy atoms by accretion from asteroids - the building blocks of rocky planets. Optical and ultraviolet spectroscopy of a small sample of suitable white dwarf stars shows that to zeroth order, the accreted extrasolar parent bodies compositionally resemble bulk Earth. (1) Extrasolar planetesimals are at least 85% by mass composed of O, Mg, Si and Fe. (2) Compared to the Sun, C is often deficient, usually by at least a factor of 10 and therefore comprises less than 1% of an extrasolar planetesimal's mass. At least to-date, C has never been found to be enhanced as would be expected if carbon-rich planetesimals have formed. (3) While there may be individual exceptions, considered as a whole, the population of extrasolar asteroids accreted onto a well-defined sample of local white dwarf stars is less than 1% water by mass.
Benchmarking data analysis and machine learning applications on the Intel KNL many-core processor

Knights Landing (KNL) is the code name for the second-generation Intel Xeon Phi product family. KNL has generated significant interest in the data analysis and machine learning communities because its new many-core architecture targets both of these workloads. The KNL many-core vector processor design enables it to exploit much higher levels of parallelism. At the Lincoln Laboratory Supercomputing Center (LLSC), the majority of users are running data analysis applications such as MATLAB and Octave. More recently, machine learning applications, such as the UC Berkeley Caffe deep learning framework, have become increasingly important to LLSC users. Thus, the performance of these applications on KNL systems is of high interest to LLSC users and the broader data analysis and machine learning communities. Our data analysis benchmarks of these application on the Intel KNL processor indicate that single-core double-precision generalized matrix multiply (DGEMM) performance on KNL systems has improved by ∼3.5× compared to prior Intel Xeon technologies. Our data analysis applications also achieved ∼60% of the theoretical peak performance. Also a performance comparison of a machine learning application, Caffe, between the two different Intel CPUs, Xeon E5 v3 and Xeon Phi 7210, demonstrated a 2.7× improvement on a KNL node.
Risk Prediction of Peer-to-Peer Lending Market by a LSTM Model with Macroeconomic Factor

In the peer to peer (P2P) lending platform, investors hope to maximize their return while minimizing the risk through a comprehensive understanding of the P2P market. A low and stable average default rate across all the borrowers denotes a healthy P2P market and provides investors more confidence in a promising investment. Therefore, having a powerful model to describe the trend of the default rate in the P2P market is crucial. Different from previous studies that focus on modeling the default rate at the individual level, in this paper, we are the first to comprehensively explore the monthly trend of the default rate at the aggregative level for the P2P data from October 2007 to January 2016 in the US. We use the long short term memory (LSTM) approach to sequentially predict the default risk of the borrowers in Lending Club, which is the largest P2P lending platform in the US. Although being first applied in modeling the P2P sequential data, the LSTM approach shows its great potential by outperforming traditionally utilized time series models in our experiments. Furthermore, incorporating the macroeconomic feature unemp_rate (i.e., unemployment rate) can improve the LSTM performance by decreasing RMSE on both the training and the testing datasets. Our study can broaden the applications of the LSTM algorithm by using it on the sequential P2P data and guide the investors in making investment strategies.
Invariant measures for hybrid stochastic systems

In this paper, we seek to understand the behavior of dynamical systems that are perturbed by a parameter that changes discretely in time. If we impose certain conditions, we can study certain embedded systems within a hybrid system as time-homogeneous Markov processes. In particular, we prove the existence of invariant measures for each embedded system and relate the invariant measures for the various systems through the flow. We calculate these invariant measures explicitly in several illustrative examples.
Blocks in the asymmetric simple exclusion process

In earlier work, the authors obtained formulas for the probability in the asymmetric simple exclusion process that the mth particle from the left is at site x at time t. They were expressed in general as sums of multiple integrals and, for the case of step initial condition, as an integral involving a Fredholm determinant. In the present work, these results are generalized to the case where the mth particle is the left-most one in a contiguous block of L particles. The earlier work depended in a crucial way on two combinatorial identities, and the present work begins with a generalization of these identities to general L.
Networked Model Predictive Control Using a Wavelet Neural Network

In this study, we use a wavelet neural network with a feedforward component and a model predictive controller for online nonlinear system identification over a communication network. The wavelet neural network (WNN) performs the online identification of the nonlinear system. The model predictive controller (MPC) uses the model to predict the future outputs of the system over an extended prediction horizon and calculates the optimal future inputs by minimizing a controller cost function. The Lyapunov theory is used to prove the stability of the MPC. We apply the methodology to the online identification and control of an unmanned autonomous vehicle. Simulation results show that the MPC with extended prediction horizon can effectively control the system in the presence of fixed or random network delay.
Iterative Training of Neural Networks for Intra Prediction

This paper presents an iterative training of neural networks for intra prediction in a block-based image and video codec. First, the neural networks are trained on blocks arising from the codec partitioning of images, each paired with its context. Then, iteratively, blocks are collected from the partitioning of images via the codec including the neural networks trained at the previous iteration, each paired with its context, and the neural networks are retrained on the new pairs. Thanks to this training, the neural networks can learn intra prediction functions that both stand out from those already in the initial codec and boost the codec in terms of rate-distortion. Moreover, the iterative process allows the design of training data cleansings essential for the neural network training. When the iteratively trained neural networks are put into H.265 (HM-16.15), −4.2% of mean BD-rate reduction is obtained, i.e. −1.8% above the state-of-the-art. By moving them into H.266 (VTM-5.0), the mean BD-rate reduction reaches −1.9%.
Distributed Resource Allocation Over Dynamic Networks With Uncertainty

Motivated by broad applications in various fields of engineering, we study a network resource allocation problem where the goal is to optimally allocate a fixed quantity of the resources over a network of nodes. We consider large scale networks with complex interconnection structures, thus any solution must be implemented in parallel and based only on local data resulting in a need for distributed algorithms. In this article, we study a distributed Lagrangian method for such problems. By utilizing the so-called distributed subgradient methods to solve the dual problem, our approach eliminates the need for central coordination in updating the dual variables, which is often required in classic Lagrangian methods. Our focus is to understand the performance of this distributed algorithm when the number of resources is unknown and may be time-varying. In particular, we obtain an upper bound on the convergence rate of the algorithm to the optimal value, in expectation, as a function of network topology. The effectiveness of the proposed method is demonstrated by its application to the economic dispatch problem in power systems, with simulations completed on the benchmark IEEE-14 and IEEE-118 bus test systems.
Complete positivity and self-adjointness

Abstract We specify the structure of completely positive operators and quantum Markov semigroup generators that are symmetric with respect to a family of inner products, also providing new information on the order structure and extreme points in some previously studied cases.
Excited state diabatization on the cheap using DFT: Photoinduced electron and hole transfer

Excited state electron and hole transfer underpin fundamental steps in processes such as exciton dissociation at photovoltaic heterojunctions, photoinduced charge transfer at electrodes, and electron transfer in photosynthetic reaction centers. Diabatic states corresponding to charge or excitation localized species, such as locally excited and charge transfer states, provide a physically intuitive framework to simulate and understand these processes. However, obtaining accurate diabatic states and their couplings from adiabatic electronic states generally leads to inaccurate results when combined with low-tier electronic structure methods, such as time-dependent density functional theory, and exorbitant computational cost when combined with high-level wavefunction-based methods. Here, we introduce a density functional theory (DFT)-based diabatization scheme that directly constructs the diabatic states using absolutely localized molecular orbitals (ALMOs), which we denote as Δ-ALMO(MSDFT2). We demonstrate that our method, which combines ALMO calculations with the ΔSCF technique to construct electronically excited diabatic states and obtains their couplings with charge-transfer states using our MSDFT2 scheme, gives accurate results for excited state electron and hole transfer in both charged and uncharged systems that underlie DNA repair, charge separation in donor–acceptor dyads, chromophore-to-solvent electron transfer, and singlet fission. This framework for the accurate and efficient construction of excited state diabats and evaluation of their couplings directly from DFT thus offers a route to simulate and elucidate photoinduced electron and hole transfer in large disordered systems, such as those encountered in the condensed phase.
New computations of the superbridge index

The knots 81, 82, 83, 85, 86, 87, 88, 810, 811, 812, 813, 814, 815, 97, 916, 920, 926, 928, 932, and 933 all have superbridge index equal to 4. This follows from new upper bounds on superbridge ind...
Homological Algebra for Persistence Modules

We develop some aspects of the homological algebra of persistence modules, in both the one-parameter and multi-parameter settings, considered as either sheaves or graded modules. The two theories are different. We consider the graded module and sheaf tensor product and Hom bifunctors as well as their derived functors, Tor and Ext, and give explicit computations for interval modules. We give a classification of injective, projective, and flat interval modules. We state Kunneth theorems and universal coefficient theorems for the homology and cohomology of chain complexes of persistence modules in both the sheaf and graded module settings and show how these theorems can be applied to persistence modules arising from filtered cell complexes. We also give a Gabriel–Popescu theorem for persistence modules. Finally, we examine categories enriched over persistence modules. We show that the graded module point of view produces a closed symmetric monoidal category that is enriched over itself.
Light quantum control of persisting Higgs modes in iron-based superconductors

The Higgs mechanism, i.e., spontaneous symmetry breaking of the quantum vacuum, is a cross-disciplinary principle, universal for understanding dark energy, antimatter and quantum materials, from superconductivity to magnetism. Unlike one-band superconductors (SCs), a conceptually distinct Higgs amplitude mode can arise in multi-band, unconventional superconductors via strong interband Coulomb interaction, but is yet to be accessed. Here we discover such hybrid Higgs mode and demonstrate its quantum control by light in iron-based high-temperature SCs. Using terahertz (THz) two-pulse coherent spectroscopy, we observe a tunable amplitude mode coherent oscillation of the complex order parameter from coupled lower and upper bands. The nonlinear dependence of the hybrid Higgs mode on the THz driving fields is distinct from any known SC results: we observe a large reversible modulation of resonance strength, yet with a persisting mode frequency. Together with quantum kinetic modeling of a hybrid Higgs mechanism, distinct from charge-density fluctuations and without invoking phonons or disorder, our result provides compelling evidence for a light-controlled coupling between the electron and hole amplitude modes assisted by strong interband quantum entanglement. Such light-control of Higgs hybridization can be extended to probe many-body entanglement and hidden symmetries in other complex systems. A collective excitation called Higgs mode may arise in multi-band superconductors via strong interband interaction, but it is yet to be accessed. Here, the authors observe a tunable coherent amplitude oscillation of the order parameter in Ba(Fe1−xCox)2As2, suggesting appearance and control of the Higgs mode by light tuning interband interaction.
Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins

We outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self-organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure-function relationships-however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.
On the Size of the Giant Component in Inhomogeneous Random K-out Graphs

Inhomogeneous random K-out graphs were recently introduced to model heterogeneous sensor networks secured by random pairwise key predistribution schemes. First, each of the n nodes is classified as type-1 (respectively, type-2) with probability 0 n arcs towards K n distinct nodes) selected uniformly at random, and then the orientation of the arcs is ignored. It was recently established that this graph, denoted by $\mathbb{H}\left( {n;\mu ,{K_n}} \right)$, is connected with high probability (whp) if and only if K n = ω(1). In other words, if K n = O(1), then $\mathbb{H}\left( {n;\mu ,{K_n}} \right)$ has a positive probability of being not connected as n gets large. Here, we study the size of the largest connected subgraph of $\mathbb{H}\left( {n;\mu ,{K_n}} \right)$ when K n = O(1). We show that the trivial condition of K n ≥ 2 for all n is sufficient to ensure that inhomogeneous K-out graph has a connected component of size n − O(1) whp. Put differently, even with K n = 2, all but finitely many nodes will form a connected sub-network in this model under any 0 n − 1)} + o(1). Numerical results are presented to demonstrate the size of the largest connected component when the number of nodes is finite.
Pareto efficient income taxation with stochastic abilities

Abstract This paper studies Pareto efficient income taxation in an economy with finitely-lived individuals whose income generating abilities evolve according to a two-state Markov process. The study yields three main results. First, when individuals are risk neutral, in any period the only individuals whose earnings are distorted are those who currently are and have always been low ability. In addition, the degree to which these perpetual low ability types have their earnings distorted decreases over time, converging to zero if the time horizon is long enough. Second, the earnings distortions are continuous with respect to the degree of risk aversion at the risk neutral solution. Third, Pareto efficient income tax systems can be time consistent even when the degree of correlation in ability types is large. The condition for time consistency suggests a novel theoretical reason why the classic equity–efficiency trade off may be steeper in a dynamic environment than previously thought.
Financial leverage and bargaining power with suppliers: Evidence from leveraged buyouts

Abstract This paper investigates whether leveraged buyouts (LBOs) increase the bargaining power of firms with their suppliers. We find that suppliers to LBO firms experience significantly negative abnormal returns at the announcements of downstream LBOs. We also find that suppliers who have likely made substantial relationship-specific investments are more negatively affected, both in terms of abnormal stock returns and reduced profit margins, than suppliers of commodity products or transitory suppliers. Interestingly, leveraged recapitalization announcements are not associated with negative returns to suppliers, suggesting that increased leverage without an accompanying change in organizational form does not, on average, lead to price concessions from suppliers.
Regulation fair disclosure and analysts' first-forecast horizon

We examine the impact of Regulation Fair Disclosure (RFD) on the horizon of analysts' first earnings forecasts, that is, the first-forecast horizon. The first-forecast horizon is computed as the nu...
An axiomatic approach to sustainable development

The paper proposes two axioms that capture the idea of sustainable development and derives the welfare criterion that they imply. The axioms require that neither the present nor the future should play a dictatorial role. Theorem 1 shows there exist sustainable preferences, which satisfy these axioms. They exhibit sensitivity to the present and to the long-run future, and specify trade-offs between them. It examines other welfare criteria which are generally utilized: discounted utility, lim inf. long run averages, overtaking and catching-up criteria, Ramsey's criterion, Rawlsian rules, and the criterion of satisfaction of basic needs, and finds that none satisfies the axioms for sustainability. Theorem 2 gives a characterization of all continuous independent sustainable preferences. Theorem 3 shows that in general sustainable growth paths cannot be approximated by paths which approximate discounted optima. Proposition 1 shows that paths which maximize the present value under a standard price system may fail to reach optimal sustainable welfare levels, and Example 4 that the two criteria can give rise to different value systems.
The Psychological Capital of Chinese Workers: Exploring the Relationship with Performance

abstract Everyone knows about China's huge population and the fast-growing economy. Although macro-level sociological and economic analyses have given some attention to the linkage between the two, at the micro level, the relationship between human resources and, more specifically, psychological capital of Chinese workers and their performance has been largely ignored. Within the context of three factories (two private and one state-owned) in the People's Republic of China, this exploratory study examined the relationship of a sample (n=422) of Chinese workers’ positive psychological capital states and their performance. Results indicated the workers’ positive states of hope, optimism, and resiliency, separately and when the three were combined into a core construct of psychological capital, significantly correlated with their performance, as rated by their supervisors. An analysis of workers in one of the factories (n=272) also found a significant relationship between the workers’ positive psychological capital and the performance outcome of relative merit-based salary. Limitations, future research, and practical implications conclude the article.