Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Blocks in High Energy Physics: Better Binning made easy!

The Bayesian Block algorithm, originally developed for applications in astronomy, can be used to improve the binning of histograms in high energy physics. The visual improvement can be dramatic, as shown here with two simple examples. More importantly, this algorithm and the histogram is produces is a non-parametric density estimate, providing a description of background distributions that does not suffer from the arbitrariness of ad hoc analytical functions. The statistical power of an hypothesis test based on Bayesian Blocks is nearly as good as that obtained by fitting analytical functions. Two examples are provided: a narrow peak on a smoothly-falling background, and an excess in the tail of a background that falls rapidly over several orders of magnitude. These examples show the usefulness of the binning provided by the Bayesian Blocks algorithm both for presentation of data and when searching for new physics.
AN APPROACH TO CONSTRUCTION AND ANALYSIS OF A CORPUS OF SHORT RUSSIAN TEXTS INTENDED TO TRAIN A SENTIMENT CLASSIFIER

The paper describes a method for construction and annotation of a corpus of short texts made up on the basis of Russian posts from Twitter. This corpus is intended to train a sentiment classifier that sorts the general topic texts into three classes: “positive”, “negative”, and “neutral”. The corpus is morphologically tagged in order to identify the characteristic features of each of the three classes of short texts. Parts of speech and unigrams and bigrams of terms were selected as the characteristic features. A vocabulary of emotional words was constructed based on the corpus; the weight of each term in the corpus was calculated.
APPLYING MACHINE INTELLIGENCE TECHNIQUE TO STUDY RADON EXHALATION FROM SOIL

A model has been prepared under KERAS, an artificial neural network software under PYTHON, to predict radon exhalation rate from soil due to its emission from radium, a radioactive material present in the soil which in turn originates from the decay of uranium, another radioactive material. The model has been built under machine intelligence technique using artificial neural network algorithm with the data set obtained from the radon study conducted in the Brahmaputra valley of Assam. The model not only predicts exhalation rate of radon of our own data set but also do the same for other region where similar study has been conducted by other workers.
UbiGDSS: a theoretical model to predict decision-makers’ satisfaction

This work is supported by “GECAD strategic project”, “EKRUCAMI – EuropeKorea Research on Ubiquitous Computing and Ambient Intelligence” and by National Funds through FCT “Fundacao para a Ciencia e a Tecnologia” under the projects: PEST-OE/EEI/UI0760/2014, 318878-FP7-PEOPLE and SFRH/BD/89697/2012 respectively.
Optimal Stealthy Innovation-Based Attacks With Historical Data in Cyber-Physical Systems

The problem of stealthy innovation-based attacks in cyber-physical systems is studied in this paper. Different from the existing results which only utilize the current data, a more general attack strategy is designed by combining the historical and the current innovations to deteriorate the estimation performance and keep stealthy to the detector simultaneously. Under the framework of the attacks, the remote state estimation error is analyzed, and the optimal attack policy is derived by solving a convex optimization problem to achieve the maximal estimation error. Moreover, it is proved that the optimal attack strategy is piecewise constant, such that the attack is designed with low calculation cost. Finally, simulation examples are provided to demonstrate the theoretical results.
The Emergence and Evolution of the Universe

The cosmological picture of the World is complemented by the inclusion of virtual objects, phenomenon of the mass defect, entropic interaction, and bifurcations. During the period before the emergence of our Universe, space is considered as an infinite homogeneous multifold of virtual point objects with a certain energy and are capable of transitioning into a real state of certain masses, electrical charges, and sizes once at irreversible local fluctuations of density and energy. The existence of such virtual point objects is explained on the basis of the mass defect phenomenon known in nuclear physics and the fact that resultant sum of all forces acting on the object is equal to zero at the homogeneous space. It is shown that the entropic changes caused by irreversible fluctuations should have led to bifurcation and produce the “Big Bang” event with the emergence and subsequent evolution of our Universe. The probability of the set of bifurcations and consequent “Big Bang” events, the origin of the corresponding Universes, the time of their occurring, and initial localization is discussed. The possibility of the transition of virtual objects into a real state is proved using the Principle of Least Action and Le Chatelier’s Principle. It is shown that this is a first-order phase transition which is accompanied by radiation of energy, violation of symmetry, the appearance of mass, electrical charge, motion, and other attributes of real state objects. The “negative” and “standard” gravity, the “negative pressure”, the expansion of our Universe and its “inflationary” period are explained as realizations of the entropic interaction between objects. The “Corpuscular-Wave and the Corpuscular-Field dualisms”, being a manifestation of the Complementarity and the Equivalence Principles, are used for the explanation of the evolution of the Universe. The Anthropic Principle is discussed.
The Influence of a Network's Spatial Symmetry, Topological Dimension, and Density on Its Percolation Threshold

Analyses of the processes of information transfer within network structures shows that the conductivity and percolation threshold of the network depend not only on its density (average number of links per node), but also on its spatial symmetry groups and topological dimension. The results presented in this paper regarding conductivity simulation in network structures show that, for regular and random 2D and 3D networks, an increase in the number of links (density) per node reduces their percolation threshold value. At the same network density, the percolation threshold value is less for 3D than for 2D networks, whatever their structure and symmetry may be. Regardless of the type of networks and their symmetry, transition from 2D to 3D structures engenders a change of percolation threshold by a value exp{−(d − 1)} that is invariant for transition between structures, for any kind of network (d being topological dimension). It is observed that in 2D or 3D networks, which can be mutually transformed by deformation without breaking and forming new links, symmetry of similarity is observed, and the networks have the same percolation threshold. The presence of symmetry axes and corresponding number of symmetry planes in which they lie affects the percolation threshold value. For transition between orders of symmetry axes, in the presence of the corresponding planes of symmetry, an invariant exists which contributes to the percolation threshold value. Inversion centers also influence the value of the percolation threshold. Moreover, the greater the number of pairs of elements of the structure which have inversion, the more they contribute to the fraction of the percolation threshold in the presence of such a center of symmetry. However, if the center of symmetry lies in the plane of mirror symmetry separating the layers of the 3D structure, the mutual presence of this group of symmetry elements do not affect the percolation threshold value. The scientific novelty of the obtained results is that for different network structures, it was shown that the percolation threshold for the blocking of nodes problem could be represented as an additive set of invariant values, that is, as an algebraic sum, the value of the members of which is stored in the transition from one structure to another. The invariant values are network density, topological dimension, and some of the elements of symmetry (axes of symmetry and the corresponding number of symmetry planes in which they lie, centers of inversion).
BACKSTEPPING CONTROL OF NONLINEAR ROLL MOTION FOR A TRAWLER WITH FIN STABILIZER

A backstepping control design procedure for nonlinear fin roll control of a trawler is presented in this paper. A roll equation consisting of linear and nonlinear damping and restoring moment on the roll response is expressed. Flow analyses are carried out for a scaled model of trawler type fishing vessel including fin stabilizers on both sides of the hull. The fin stabilizer geometry is chosen as NACA 0015 foil section which is widely used in the literature. The flow analyses are performed by using a commercial computational fluid dynamics (CFD) software based on finite volume method. The flow problem is modeled in a 3-dimensional manner while the flow is considered as steady, incompressible and fully turbulent. The numerical model consists of the ship wetted surface and the fin stabilizer in order to investigate the hull-fin interaction. Non-dimensional lift coefficients of the fin stabilizer for different angles of attack are gained. Both controlled and uncontrolled roll motions are examined and simulated in time domain for the maximum lift coefficient. Backstepping controller for roll motion has given a rapid and precise result.
An application of conjugate gradient method under strong Wolfe line search for solving unconstrained optimization

The conjugate gradient (CG) method is one of the most prominent methods for solving linear and nonlinear problems in optimization. In this paper, we propose a CG method with sufficient descent property under strong Wolfe line search. The proposed CG method is then applied to solve systems of linear equations. The numerical results obtained from the tests are evaluated based on number iteration and CPU time and then analyzed through performance profile. In order to examine its efficiency, the performance of our CG formula is compared to that of other CG methods. The results show that the proposed CG formula has better performance than the other tested CG methods.
A nonlinear saturation model of synchronous machines with account cross saturation

Article history: Received 20 January 2019 Received in revised form 25 June 2019 Accepted 27 June 2019 This paper developed an enhanced saturation model for synchronous machines based on simulation and experimental evaluation. The effect of taking account of cross-saturation is demonstrated. Local saturation factors are defined so as to adjust the flux-density distribution. The method of saturation modelling of dumper synchronous machine with and without cross-saturation is identified. Examples of numerical simulation are given to verify the model and its applications. Through experiments, it is shown that, by using the relationship between magnetizing current and flux as modelled in this paper, the nonlinear behavior of the synchronous machine is quite accurately estimated. Furthermore, the new model is compared with a classical model that neglects mutual saturation effects between a quadrature and direct axis windings.
Hadoop Performance Tuning based on Parameter Optimization

In order to better verify that Hadoop performance can be improved through optimization of parameters, we can use the following test methods: benchmarking, stability testing, high availability testing, scalability testing, and security testing. In this paper, the benchmark test method is used to verify the optimization of parameters and to optimize the performance of Hadoop. This article mainly focuses on the 17 parameters in Tab.1. The optimization results are shown in Tab.3. The optimization of the parameters was verified by the execution time of the TeraSort algorithm in the benchmark test. During the experiment, the CPU and memory utilization rate, disk IO and network IO throughput and other indicators were collected. Fig.1-3 fully illustrates the comparison between Hadoop and TeraSort algorithm after parameter default value and parameter adjustment. The experimental results show that after the Hadoop parameters are adjusted and optimized, the Hadoop performance tuning is achieved under certain conditions.
Investigation of Arc Characteristics of a DC Vacuum Circuit Breaker With Double-Break Under Asynchronous Interrupting

A 30-kV direct current vacuum circuit breaker with double-break (DB-DC VCB) in series was taken into consideration; the interrupting current and the transient recovery voltage (TRV) under both synchronous and asynchronous interrupting conditions were measured, and the arcing process during the interrupting was captured by a high-speed camera. The continuous transition model (CTM) was used to simulate the postarc dielectric recovery characteristic during the asynchronous interrupting. The experimental and simulation results show that when one of the breaks in the DB-DC VCB is delayed under 5-kA current interrupting due to the dispersion performance of the mechanism, the two breaks will exhibit a significant difference in the arc characteristic and the TRV. The delayed break has higher arc energy, higher concentration of vaporized metal particles (VMPs), and lower postarc dielectric recovery capacity. The different break gap widths during the arcing process led to the arc characteristic difference. When the delay exceeds a critical value (0.5 ms), the VMPs between the break for the delayed break are difficult to dissipate and easy to reignite at the breakdown weak point.
A Comparative Study of Machine Translation and Human Translation—A Case Study of Kingsoft Powerword

This paper intends to figure out the limitations of machine translation by conducting a comparative study of machine translation and human translation at lexical level and syntactic level. The outcome demonstrates that the most severe defect of machine translation is that it lacks human’s cognitive competence. With this defect, machine translation repeatedly makes errors in the target text, reducing the output quality. In order to address this issue, this paper states that human involvement should be supplemented to machine translation by pre-editing the source text and post-editing the target text. Finally, this paper expects that, with the improvement and advance in machine translation technology, machine translation could obtain a leap in its output quality, promoting the development of translation.
Multiring electrostatic guide for Rydberg positronium

We report the results of experiments in which positronium (Ps) atoms, optically excited to Rydberg-Stark states with principal quantum numbers ranging from n = 13 to 19, were transported along the axis of a multiring electrode structure. By applying alternate positive and negative potentials to the ring electrodes, inhomogeneous electric fields suitable for guiding low-field-seeking atoms along the guide axis were generated. The multiring configuration used has the advantage that once the atoms are confined within it appropriate time-varying fields can be generated for deceleration and trapping. However, in this type of structure the possibility of nonadiabatic transitions of the fast (100 km/s) Ps atoms to unconfined high-field-seeking states exists. We show that for typical guiding fields this is not a significant loss mechanism and that efficient Ps transport can be achieved. Our data are in accordance with a Landau-Zener analysis of adiabatic transport through the field minima and Monte Carlo simulations that take into account Ps velocity distributions, electric dipole moments, and lifetimes, as well as the electric-field distributions in the guide.
Structural Sentence Decomposition via Open Information Extraction

The field of construction engineering is governed by an important volume of legal texts. Each of these texts provide a set of requirements by means of sentences written in natural language. These texts support conformity checking process of objects in construction engineering. Automate or at-least semi-automate this conformity checking process is the target of our project. This automation imposes to be able to make legal requirements processable. Thus, we envisage to rephrase n atural language sentences into a set of atomic requirements (i.e a triple: ). Further, we must identify between the facts found in a given sentence, the exist relations (e.g sentence = f act1 AND f act2 or IF f act1 THEN f act2, etc.), expressible using logical operators. For facts extraction, we have used Open Information Extraction-systems. Since they face a drop of precision mainly due to multi-word expressions, provide a method to handle them before OIE itself. Moreover, we also tackle the problem of enumeration items to improve OIE performance. Using OIE for legal sentences semantics handling, handling of multi-word expressions and contextual phrases and finally computation of relations between facts constitute the originalit y of this work. Such decomposition is the prerequisite to our target: automatic conformity checking.
Finite-time synchronization of Markovian neural networks with proportional delays and discontinuous activations

In this paper, finite-time synchronization of neural networks (NNs) with discontinuous activation functions (DAFs), Markovian switching, and proportional delays is studied in the framework of Filippov solution. Since proportional delay is unbounded and different from infinite-time distributed delay and classical finite-time analytical techniques are not applicable anymore, new 1-norm analytical techniques are developed. Controllers with and without the sign function are designed to overcome the effects of the uncertainties induced by Filippov solutions and further synchronize the considered NNs in a finite time. By designing new Lyapunov functionals and using M-matrix method, sufficient conditions are derived to guarantee that the considered NNs realize synchronization in a settling time without introducing any free parameters. It is shown that, though the proportional delay can be unbounded, complete synchronization can still be realized, and the settling time can be explicitly estimated. Moreover, it is discovered that controllers with sign function can reduce the control gains, while controllers without the sign function can overcome chattering phenomenon. Finally, numerical simulations are given to show the effectiveness of theoretical results.
Health and the Marginal Utility of Consumption : Estimating Health State Dependence using Equivalence Scales

This paper estimates health state dependence in Europe. We develop a new approach that builds upon the approach by Finkelstein et al. (2013), and uses insights from the research domain of living standards and income adequacy. We estimate how much extra income is needed to keep financial wellbeing equal after a health shock, and we derive a simple relation between this estimate and the parameter for health state dependence. We find positive health state dependence, with a proportionality factor equal to 0.284 in the presence of ADL limitations. The positive health state dependence is not driven by medical expenditures, and the result is robust against alternative specifications and health measures such as mobility, IADL and the number of chronical diseases. Interestingly, for cognitive limitations we find negative health state dependence, presumably resulting from a decreased ability to plan, organize, and take initiatives. JEL: J14, I1 ∗Leiden University and Netspar, Steenschuur 25, 2311 ES Leiden, The Netherlands. Email: l.kools@law.leidenuniv. nl, m.g. knoef@law.leidenuniv. nl, The authors thank the Network for Studies on Pensions, Aging and Retirement (Netspar) for financial support. Furthermore we thank Rob Alessie, Clementine Garrouste, Arie Kapteyn, Luigi Pistaferri, Arthur van Soest, and participants of the Dutch Economist Day 2014, ESPE conference 2015, International Panel Data Conference 2015, Pension Workshop Paris 2015, Winter School on Inequality and Welfare 2016, Quantitative Society for Pensions and Saving Workshop 2016, EEA-ESEM 2016 and seminars at CPB and Netspar.
Evidence for 
B+→hcK+
 and observation of 
ηc(2S)→pp¯π+π−

A search for the decays $B^+ \rightarrow h_c K^+$ and $B^0 \rightarrow h_c K_S^0$ is performed. Evidence for the decay $B^+ \rightarrow h_c K^+$ is found; its significance is $4.8\sigma$. No evidence is found for $B^0 \rightarrow h_c K_S^0$. The branching fraction for $B^+ \rightarrow h_c K^+$ is measured to be $(3.7^{+1.0}_{-0.9}{}^{+0.8}_{-0.8}) \times 10^{-5}$; the upper limit for the $B^0 \rightarrow h_c K_S^0$ branching fraction is $1.4 \times 10^{-5}$ at $90\%$ C.L. In addition, a study of the $p \bar{p} \pi^+ \pi^-$ invariant mass distribution in the channel $B^+ \to (p \bar{p} \pi^+ \pi^-) K^+$ results in the first observation of the decay $\eta_c(2S) \to p \bar{p} \pi^+ \pi^-$ with $12.1\sigma$ significance. The analysis is based on the 711 $\mathrm{fb}^{-1}$ data sample collected by the Belle detector at the asymmetric-energy $e^+ e^-$ collider KEKB at the $\Upsilon(4S)$ resonance.
Increased Sample Entropy in EEGs During the Functional Rehabilitation of an Injured Brain

Complex nerve remodeling occurs in the injured brain area during functional rehabilitation after a brain injury; however, its mechanism has not been thoroughly elucidated. Neural remodeling can lead to changes in the electrophysiological activity, which can be detected in an electroencephalogram (EEG). In this paper, we used EEG band energy, approximate entropy (ApEn), sample entropy (SampEn), and Lempel–Ziv complexity (LZC) features to characterize the intrinsic rehabilitation dynamics of the injured brain area, thus providing a means of detecting and exploring the mechanism of neurological remodeling during the recovery process after brain injury. The rats in the injury group (n = 12) and sham group (n = 12) were used to record the bilateral symmetrical EEG on days 1, 4, and 7 after a unilateral brain injury in awake model rats. The open field test (OFT) experiments were performed in the following three groups: an injury group, a sham group, and a control group (n = 10). An analysis of the EEG data using the energy, ApEn, SampEn, and LZC features demonstrated that the increase in SampEn was associated with the functional recovery. After the brain injury, the energy values of the delta1 bands on day 4; the delta2 bands on days 4 and 7; the theta, alpha, and beta bands and the values of ApEn, SampEn, and LZC of the cortical EEG signal on days 1, 4 and 7 were significantly lower in the injured brain area than in the non-injured area. During the process of recovery for the injured brain area, the values of the beta bands, ApEn, and SampEn of the injury group increased significantly, and gradually became equal to the value of the sham group. The improvement in the motor function of the model rats significantly correlated with the increase in SampEn. This study provides a method based on EEG nonlinear features for measuring neural remodeling in injured brain areas during brain function recovery. The results may aid in the study of neural remodeling mechanisms.
Image contrast correction method in full-field optical coherence tomography

Abstract. An image contrast correction method is proposed for en face images obtained with full-field optical coherence tomography (FFOCT). First, the mechanism of image contrast decrease in FFOCT is considered and the theoretical models of main parameters that degrade image contrast are analyzed. Second, changes of contrast with depth are calculated under various conditions, from which the main parameters that affect contrast in tissue are identified. Then based on the analysis, the methods are proposed for correcting image contrast. Finally, the en face tomographic images of human liver tissue at different depths with and without contrast correction are presented to demonstrate the capability of the method. The results are very helpful for correctly interpreting FFOCT images for applications in disease diagnosis.