Miracle-dz/UniFAR-FTUs huggingface dataset:
A rejuvenation methodology of cluster recovery

While traditional security mechanisms rely on preventive controls and those are very limited in surviving malicious attacks, we propose a novel approach of the security issue to cluster recovery. In this paper, we present the cluster recovery model with a software rejuvenation methodology, which is applicable in security field. We propose two formal approaches, stochastic and Markov decision process. And we estimate the possibility of surviving under unknown attacks. The basic idea of rejuvenation is to investigate the consequences for the exact respond time in face of attacks and rejuvenating the running software/service, refresh its internal state, and resume or restart it. These actions deter the intruder's progress, prevent from more serious damages and provide time to perform more detailed analysis.
Under Review as a Conference Paper at Iclr 2017 Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks

We present an algorithm for policy search in stochastic dynamical systems using model-based reinforcement learning. The system dynamics are described with Bayesian neural networks (BNNs) that include stochastic input variables. These input variables allow us to capture complex statistical patterns in the transition dynamics (e.g. multi-modality and heteroskedasticity), which are usually missed by alternative modeling approaches. After learning the dynamics, our BNNs are then fed into an algorithm that performs random roll-outs and uses stochastic optimization for policy learning. We train our BNNs by minimizing α-divergences with α = 0.5, which usually produces better results than other techniques such as variational Bayes. We illustrate the performance of our method by solving a challenging problem where model-based approaches usually fail and by obtaining promising results in real-world scenarios including the control of a gas turbine and an industrial benchmark.
The HoloClean Framework Dataset to be cleaned Denial Constraints External Information t 1 t 4 t 2 t 3 Johnnyo ’ s

We introduce HoloClean, a framework for holistic data repairing driven by probabilistic inference. HoloClean unifies qualitative data repairing, which relies on integrity constraints or external data sources, with quantitative data repairing methods, which leverage statistical properties of the input data. Given an inconsistent dataset as input, HoloClean automatically generates a probabilistic program that performs data repairing. Inspired by recent theoretical advances in probabilistic inference, we introduce a series of optimizations which ensure that inference over HoloClean’s probabilistic model scales to instances with millions of tuples. We show that HoloClean finds data repairs with an average precision of ∼ 90% and an average recall of above ∼ 76% across a diverse array of datasets exhibiting different types of errors. This yields an average F1 improvement of more than 2× against state-of-the-art methods.
Overview of Microgrippers and Design of a Micromanipulation Station Based on a MMOC Microgripper

This paper deals with an overview of recent microgrippers. As the end-effectors of micromanipulation systems, microgrippers are crucial point of such systems for their efficiency and their reliability. The performances of current microgrippers are presented and offer a stroke extending from 50mum to approximately 2 mm and a maximum forces varying from 0.1 mN to 600 mN. Then, micromanipulation system based on a piezoelectric microgripper and a SCARA robot is presented
Microwave ac Zeeman force for ultracold atoms

We measure the AC Zeeman force on an ultracold gas of 87 Rb due to a microwave magnetic ﬁeld targeted to the 6.8 GHz hyperﬁne splitting of these atoms. An atom chip produces a microwave near-ﬁeld with a strong amplitude gradient, and we observe a force over three times the strength of gravity. Our measurements are consistent with a simple 2-level theory for the AC Zeeman eﬀect and demonstrate its resonant, bipolar, and spin-dependent nature. We observe that the dressed atom eigenstates gradually mix over time and have mapped out this behavior as a function of magnetic ﬁeld and detuning. We demonstrate the practical spin-selectivity of the force by pushing or pulling a speciﬁc spin state while leaving other spin states unmoved.
Models of internal jumps and the fronts of gravity currents: unifying two-layer theories and deriving new results

The steady speeds of the front of a gravity current and of an internal jump on a two-layer stratification are often sought in terms of the heights of the relatively dense fluid both up- and downstream from the front or jump, the height of the channel within which they flow, the densities of the two fluids and gravitational acceleration. In this study a unifying framework is presented for calculating the speeds by balancing mass and momentum fluxes across a control volume spanning the front or jump and by ensuring the assumed pressure field is single-valued, which is shown to be equivalent to forming a vorticity balance over the control volume. Previous models have assumed the velocity field is piecewise constant in each layer with a vortex sheet at their interface and invoked explicit or implicit closure assumptions about the dissipative effects to derive the speed. The new formulation yields all of the previously presented expressions and demonstrates that analysing the vorticity balance within the control volume is a useful means of constraining possible closure assumptions, which is arguably more effective than consideration of the flow energetics. However the new approach also reveals that a novel class of models may be developed in which there is shear in the velocity field in the wake downstream of the front or the jump, thus spreading the vorticity over a layer of non-vanishing thickness, rather than concentrating it into a vortex sheet. Mass, momentum and vorticity balances applied over the control volume allow the thickness of the wake and the speed of the front/jump to be evaluated. Results from this vortex-wake model are consistent with published numerical simulations and with data from laboratory experiments, and improve upon predictions from previous formulae. The results may be applied readily to Boussinesq and non-Boussinesq systems and because they arise as simple algebraic expressions, can be straightforwardly incorporated as jump conditions into spatially and temporally varying descriptions of the motion.
Simple oscillator model of frequency dependence of cochlear two-tone suppression

A simple oscillator model is proposed to investigate the frequency dependence of cochlear two-tone suppression (2TS), which is the cochlear response to a tone decrease when a second sound is simultaneously presented. The frequency dependence of 2TS exhibits two characteristics. First, the shapes of the input–output (IO) curves as a function of the input levels of the two tones depend on the frequency ratio of the two tones. Second, the temporal features of suppressed responses vary with the frequency ratio. To account for 2TS, the saturation function is widely used; however, it cannot explain the frequency dependence of cochlear 2TS. Transmission line models can reproduce the frequency dependence of cochlear 2TS. It has been suggested that complicated cochlear mechanics generate 2TS in the transmission line model. The model proposed in this study includes a one-degree-of-freedom oscillator and feedback via the saturation function, which produces basic cochlear properties such as amplification, frequency selectivity, and compressive nonlinearity in the model. Simulations show that the transmission line model and our proposed model can reproduce the frequency dependence of 2TS in the IO functions and temporal responses, indicating that the frequency dependence of 2TS requires basic cochlear properties such as amplification, frequency selectivity, and compressive nonlinearity, which are involved in saturating the feedback.
Determinants of domestic and cross-border bank acquisition in the European Union

This paper analyzes the determinants of bank acquisitions both within and across countries in the EU-25 over the period 1997-2004. The findings of this paper are broadly in line with those of the academic literature on the subject, which are mainly based on the US experience. Our results suggest poorly managed EU-25 banks (high cost to income) are more likely to be acquired by other EU-25 banks, in the same country. Nevertheless, this underperformance of target banks does hold for cross border bank acquisitions only if compared to the median of the market. Larger banks are more likely to be acquired by other banks in the same country. The probability of being acquired by another bank in the same market is larger for banks that are quoted in the stock market, which is consistent with the disciplinary character of listing in the stock markets. Finally, banks operating in more concentrated markets are less likely to be acquired by other banks operating within the same country but are more likely to be acquired by banks in other EU-25 countries.
Use of PCL library to obtain the Transformation Matrix. State of the Art

Map building and localization are two of the most important topics in autonomous mobile robots. In this sense, researchers have proposed many ways to solve these problems. One of the methods used for this purpose is through the point clouds obtained by 3D scanners. The Registration is used to know the movement of the robot into the map. Given two point clouds, this tool returns the Transformation Matrix. This work presents a state of the art about the tools implemented in the Point Cloud Library. Two big methods are used to make the Registration, the first one is based in alignment between keypoints from the point clouds, the second one consists in iterative registration algorithms. Both methods are presented in this work as well as several possible tools implemented for this purpose until now. Finally, a comparison between both methods is carried out. This work has been performed to gain access to TECNIT doctoral program at Universidad Miguel Hernndez de Elche and supported the Spanish government through the project DPI201341557-P.
Manipulator Differential Kinematics Acceleration and Advanced Applications

T his is the second and final article on the tutorial on manipulator differential kinematics. In Part I, we described a method of modelling kinematics using the elementary transform sequence (ETS), before formulating forward kinematics and the manipulator Jacobian. We then described some basic applications of the manipulator Jacobian including resolved-rate motion control (RRMC), inverse kinematics (IK), and some manipulator performance measures.
Under review as a conference paper at ICLR 2020 3 L INEAR INEQUALITY CONSTRAINTS FOR DEEP LEARNING MODELS We consider a generic L layer neural network

We propose a method to impose homogeneous linear inequality constraints of the form Ax ≤ 0 on neural network activations. The proposed method allows a datadriven training approach to be combined with modeling prior knowledge about the task. One way to achieve this task is by means of a projection step at test time after unconstrained training. However, this is an expensive operation. By directly incorporating the constraints into the architecture, we can significantly speed-up inference at test time; for instance, our experiments show a speed-up of up to two orders of magnitude over a projection method. Our algorithm computes a suitable parameterization of the feasible set at initialization and uses standard variants of stochastic gradient descent to find solutions to the constrained network. Thus, the modeling constraints are always satisfied during training. Crucially, our approach avoids to solve an optimization problem at each training step or to manually trade-off data and constraint fidelity with additional hyperparameters. We consider constrained generative modeling as an important application domain and experimentally demonstrate the proposed method by constraining a variational autoencoder.
Under review as a conference paper at ICLR 2017 S HIFT A GGREGATE E XTRACT N ETWORKS

The Shift Aggregate Extract Network (SAEN) is an architecture for learning representations on social network data. SAEN decomposes input graphs into hierarchies made of multiple strata of objects. Vector representations of each object are learnt by applying shift, aggregate and extract operations on the vector representations of its parts. We propose an algorithm for domain compression which takes advantage of symmetries in hierarchical decompositions to reduce the memory usage and obtain significant speedups. Our method is empirically evaluated on real world social network datasets, outperforming the current state of the art.
Evaluation of antidepressant activity of tramadol in albino mice using forced swim model

Background: The fact that tramadol can be used as an antidepressant, has been already proved by some animal studies. The objective of the present study was to evaluate antidepressant activity of tramadol in albino mice using forced swim model.Methods: Forced swimming test (FST) model was used to evaluate the antidepressant effect. Mice in the group "I" were given normal saline. Mice in the group II were given imipramine. Mice in the group III were given tramadol 10mg/kg. Mice in the group IV were given tramadol 20mg/kg. Mice in the group V were given tramadol 40mg/kg. All doses in all groups were given by intra peritoneum route.Results: The average values of immobility in group I were higher significantly compared to group III, IV and V. The values of group I and group II were found to be comparable. It was found that the baseline mean value was 196.33 which reduced to 5.16 with the effect of imipramine where imipramine was given to those mice. But in tramadol 10mg group, it was highest, and it came down to 40.66 and as the dose of tramadol was increased, the immobility time reduced from 40.66 at 10mg dose to 31.33 at 20mg dose and finally to 13.33mg at 40mg dose.Conclusions: Considering the results of two different animal models of depression it can be concluded that Tramadol has antidepressant activity at 10mg, 20mg, 40mg which was almost similar to Imipramine.
Adaptive mesh refinement method-based large eddy simulation for the flow over circular cylinder at ReD = 3900

Abstract With the development of computational power, large eddy simulation (LES) method is increasingly used in simulating complex flow. However, there still exist many factors affecting the LES quality and appropriate mesh resolution is among one of them. This work aims to develop an automatic procedure to refine the LES mesh by combining adaptive mesh refinement (AMR) and LES quality criteria. An LES refinement criterion is developed by estimating the proper grid length scale which meets the accuracy requirement of LES method. With this criterion, the baseline mesh is automatically refined with the AMR method. In this work, an efficient one-shot refinement strategy is also proposed to reduce the overall simulation time. Current AMR-based LES method is verified with the typical LES test case about the flow past circular cylinder at Re D = 3900. Results show that the automatically refined mesh provides systematically better agreement with experimental results and with current method the balance between accuracy and computational expense for LES can be obtained.
Evaluation of a new version of I2 with emphasis on diagnostic problems

Abstract This paper describes results stemming from an in-depth analysis of Higgins’ measure of heterogeneity for a meta-analysis applied in the context of meta-analysis for diagnostic problems. Higgins measure of heterogeneity I2 has been criticized for being confounded by the study-specific sample size, in the sense that different I2 –values can be achieved for the same value of across-study variance if only the study-specific variance is varying enough. In particular, I2 approaches one for any value of the heterogeneity variance (variance across studies) if the within-study variance becomes large. The paper proposes a measure which is unconfounded by sample size. It is essentially a philosophical question which heterogeneity measure is chosen. Nevertheless, a detailed simulation study has been launched and the results indicate that the newly suggest measure of heterogeneity has beneficial statistical properties. Both measures are also exemplified at hand of some meta-analytic case studies.
Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks

This thesis explore different approaches using Convolutional and Recurrent Neural Networks to classify and temporally localize activities on videos, furthermore an implementation to achieve it has been proposed. As the first step, features have been extracted from video frames using an state of the art 3D Convolutional Neural Network. This features are fed in a recurrent neural network that solves the activity classification and temporally location tasks in a simple and flexible way. Different architectures and configurations have been tested in order to achieve the best performance and learning of the video dataset provided. In addition it has been studied different kind of post processing over the trained network's output to achieve a better results on the temporally localization of activities on the videos. The results provided by the neural network developed in this thesis have been submitted to the ActivityNet Challenge 2016 of the CVPR, achieving competitive results using a simple and flexible architecture.
Connected Domination Polynomial of Graphs

Abstract Let G be a simple graph of order n. The connected domination polynomial of G is the polynomial Dc(G,x)=∑i=γc(G)|V(G)|dc(G,i)xi $D_c \left( {G,x} \right) = \sum\nolimits_{i = \gamma _c \left( G \right)}^{\left| {V\left( G \right)} \right|} {d_c \left( {G,i} \right)x^i }$ , where dc(G,i) is the number of connected dominating sets of G of size i and γc(G) is the connected domination number of G. In this paper we study Dc(G,x) of any graph. We classify many families of graphs by studying their connected domination polynomial.
AHRQ Quality Indicators Guide to Patient Safety Indicators

Preface In health care as in other arenas, that which cannot be measured is difficult to improve. Providers, consumers, policy makers, and others seeking to improve the quality of health care need accessible, reliable indicators of quality that they can use to flag potential problems or successes; follow trends over time; and identify disparities across regions, communities, and providers. As noted in a 2001 Institute of Medicine study, Envisioning the National Health Care Quality Report, it is important that such measures cover not just acute care but multiple dimensions of care: staying healthy, getting better, living with illness or disability, and coping with the end of life. The Agency for Healthcare Research and Quality (AHRQ) Quality Indicators (QIs) are one Agency response to this need for multidimensional, accessible quality indicators. They include a family of measures that providers, policy makers, and researchers can use with inpatient data to identify apparent variations in the quality of inpatient or outpatient care. refined these indicators based on the original Healthcare Cost and Utilization Project (HCUP) Quality Indicators developed in the early 1990s.
Molecular Cloud Structures and Massive Star Formation in N159

The N159 star-forming region is one of the most massive giant molecular clouds (GMCs) in the Large Magellanic Cloud (LMC). We show the 12CO, 13CO, CS molecular gas lines observed with ALMA in N159 west (N159W) and N159 east (N159E). We relate the structure of the gas clumps to the properties of 24 massive young stellar objects (YSOs) that include 10 newly identified YSOs based on our search. We use dendrogram analysis to identify properties of the molecular clumps, such as flux, mass, linewidth, size, and virial parameter. We relate the YSO properties to the molecular gas properties. We find that the CS gas clumps have a steeper size–linewidth relation than the 12CO or 13CO gas clumps. This larger slope could potentially occur if the CS gas is tracing shocks. The virial parameters of the 13CO gas clumps in N159W and N159E are low (<1). The threshold for massive star formation in N159W is 501 M⊙ pc−2, and the threshold for massive star formation in N159E is 794 M⊙ pc−2. We find that 13CO is more photodissociated in N159E than N159W. The most massive YSO in N159E has cleared out a molecular gas hole in its vicinity. All the massive YSO candidates in N159E have a more evolved spectral energy distribution type in comparison to the YSO candidates in N159W. These differences lead us to conclude that the giant molecular cloud complex in N159E is more evolved than the giant molecular cloud complex in N159W.
Employee Attrition Prediction using Nested Ensemble Learning Techniques

—In many industries, including the IT industry, rising employee attrition is a major concern. Hiring a candidate for an unsuitable job because of issues with the employment process can lead to employee attrition. Thus, enhancing the employment process would reduce the attrition rate. This paper aims to investigate the effect of ensemble learning techniques on enhancing the employment process by predicting employee attrition. This paper applied a two-layer nested ensemble model to the IBM HR Analytics Employee Attrition & Performance dataset. The performance of this model was compared to that of the random forest (RF) algorithm as a baseline for comparison. The results showed that the proposed model outperformed the baseline algorithm. The RF model achieved an accuracy of 94.2417%, an F1-score of 94.2%, and an AUC of 98.4%. However, the proposed model had the highest performance. It outperformed with an accuracy of 94.5255%, an F1-score of 94.5%, and an AUC of 98.5%. The performance of the proposed model was compared with that of the baseline comparison algorithm by using a paired t-test. According to the paired t-test, the performance of the proposed model was statistically better than that of the baseline comparison algorithm at the significance level of 0.05. Thus, the two-layer nested ensemble model improved the employee attrition prediction.
Dynamics of non-spherical dust in the coma of 67P/Churyumov– Gerasimenko constrained by GIADA and ROSINA data

Among the comet 67P/Churyumov–Gerasimenko (67P/C-G) in situ measurements, the closest that have ever been performed at a comet nucleus, are also those of speed, mass, and crosssection of cometary grains performed by the Grain Impact Analyser and Dust Accumulator (GIADA) instrument. To interpret GIADA data, we performed dust dynamical numerical simulations with both spherical and non-spherical (spheroids) shapes. This allowed us to analyse howthe grain non-sphericity affects the data interpretation.We find that some measured dust speeds are unlikely reproducible when a spherical shape is considered.We considered two GIADA observational periods, 2015 February 19–27 and 2015 March 13–28. Gas parameters calibrated with the Rosetta Orbiter Spectrometer for Ion and Neutral Analysis (ROSINA) measurements have been used to retrieve the gas conditions to set up the dust particle motion. The dust grains are assumed to be out of the near nucleus coma, i.e. where the gas velocity is radial and constant, therefore they are either aligned or have random but constant orientation with respect to the gas drag. We reproduced the GIADA dust speeds, using spheres and two different spheroidal shapes. We find that the particle shapes that reproduce best the GIADA dust speeds are consistent with the particle shape constrained by the GIADA data. We obtain different terminal velocities for spherical and non-spherical particles of the same mass. The shape, which reproduces the GIADA data, is oblate rather than prolate spheroid. We obtain rotational frequencies of the spheroidal particles that best fit the GIADA measurements in these periods.