Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Mining Heterogeneous Information Network ∗

The world we are living in is connected. In the other words, most of our real-world applications,like interconnected social media and social networks, scientific, engineering, and medical information systems, online e-commerce systems, and most database systems, can be structured into a expression of Network. Multi-type, and Multi-interactions make the network more like a semi-structured Heterogeneous Information Network. Realized the necessity to analyze such network, recently there are a lot of related papers about the Heterogeneous Information Network and leverage network analysis approach to reveal the rich information in the network. These work are mainly focused on Clustering, Ranking, and some Model Measurement. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions.
Towards Preparation of the Second BUCC Shared Task : Detecting Parallel Sentences in Comparable Corpora

In this paper we provide a summary of the rationale and the dataset contributing to the second shared task of the BUCC workshop. The shared task is aimed at detecting the best candidates for parallel sentences in a large text collection. The dataset for the shared task is based on a careful mix of parallel and non-parallel corpora. It contains 1.4 million French sentences and 1.9 million English sentences, in which 17 thousand sentence pairs are known to be parallel. The shared task itself is scheduled for the 2017 edition of the workshop.
On Some Issues of Information Resource Management in the 1990s

The basic objective of this paper is to provide an overview of key information resource management IRM concepts. This is done using a three-stage framework for its implementation. The discussion focuses on three areas: planning for the acquisition, organizing, and control of information resources in an organization. A concise strategy for the planning process is also presented here. While the underlying IRM concepts are relatively simple, we have to be aware of some hindrances that are likely to be encountered during planning and implementation. We offer some guidelines, on the basis of our own experience and existing literature, as to how these issues can be resolved. Finally, any new IRM initiative has to consider the changing role of computing technology and its practice. Therefore, a separate section has been researched into to provide details of the new technology of the 1990's and its influence over all aspects of IRM
Fundamental work cost of quantum processes

Information-theoretic approaches provide a promising avenue for extending the laws of thermodynamics to the nanoscale. Here, we provide a general fundamental lower limit, valid for systems with an arbitrary Hamiltonian and in contact with any thermodynamic bath, on the work cost for the implementation of any logical process. This limit is given by a new information measure---the coherent relative entropy---which accounts for the Gibbs weight of each microstate. The coherent relative entropy enjoys a collection of natural properties justifying its interpretation as a measure of information, and can be understood as a generalization of a quantum relative entropy difference. As an application, we show that the standard first and second laws of thermodynamics emerge from our microscopic picture in the macroscopic limit. Finally, our results have an impact on understanding the role of the observer in thermodynamics: Our approach may be applied at any level of knowledge---for instance at the microscopic, mesoscopic or macroscopic scales---thus providing a formulation of thermodynamics that is inherently relative to the observer. We obtain a precise criterion for when the laws of thermodynamics can be applied, thus making a step forward in determining the exact extent of the universality of thermodynamics and enabling a systematic treatment of Maxwell-demon-like situations.
Poverty, Parent Involvement, and Children’s Reading Skills: Testing the Compensatory Effect of the Amount of Classroom Reading Instruction

ABSTRACT Research Findings: Parent involvement is a critical way for children to learn about the importance of education and develop reading skills. Unfortunately, not all low-income parents are able to be involved in their children’s education, which can have negative implications for children’s reading development. The present study tested if the strength of the relation between low-income parents’ involvement and children’s reading skills in first grade varied by the amount of classroom reading instruction that children received at school. This study used data from the Early Childhood Longitudinal Study – Kindergarten: 2010–2011 (ECLS-K: 2011) to examine the relations between low-income parent involvement in education, amount of classroom reading instruction, and first-grade children’s reading skills. Parent involvement was significantly more impactful for children who received less than 2 hours of classroom reading instruction. Additionally, children from poor households scored lower, on average, on reading assessments than children from near-poor households. Practice or Policy: Parent involvement has a positive impact on children’s reading skills, but that impact can be contingent on what occurs within the classroom. This study underscores the need to consider both home and school influences on children’s reading skills. Implications for educational practice and policy are discussed.
Nanoscale Analysis of Materials using a Local-Electrode Atom Probe

A brief review of atom-probe tomography is provided. The rationale, implementation, and benefits of the latest instrumental developments in the field are described. Parameters such as specimen geometry, mass resolution, field of view, and data collection rate are discussed. The recent application of laser-based field evaporation to the atom probe has enabled analysis of materials with poor electrical conductivity. Examples from metallurgy and semiconductor applications are used to illustrate the capabilities of the technique.
Circumstellar interaction in Type IIn supernovae

Abstract Type IIn supernovae have bright optical emission and high bolometric luminosities. Due to their high mass loss, their are expected to have dense circumstellar interaction, thus produce bright radio and X-ray emission. We aim to carry out systematic study to understand their circumstellar interaction, mass loss properties. Here, I provide specific examples of two Type IIn supernovae, 2006jd and 2010jl.
Machine Learning Phases of Strongly Correlated Fermions

Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to distinguish finite-temperature phases of the strongly correlated fermions on cubic lattices. We show that a three dimensional convolutional network trained on auxiliary field configurations produced by quantum Monte Carlo simulations of the Hubbard model can correctly predict the magnetic phase diagram of the model at the average density of one (half filling). We then use the network, trained at half filling, to explore the trend in the transition temperature as the system is doped away from half filling. This transfer learning approach predicts that the instability to the magnetic phase extends to at least 5% doping in this region. Our results pave the way for other machine learning applications in correlated quantum many-body systems.
Effect of Peripheral Blood Mononuclear Cells on Induced Ischemia/Reperfusion in Skeletal Muscle of Adult Male Albino Rat: An Immunohistochemical Study

Peripheral vascular disease is a major health problem that is considered as one of the most frequent causes of debilitating diseases. Lower limb ischemia is a life-threatening clinical condition resulting from hypovolemia or vascular occlusion. Reperfusion injury occurs when re-introduction of blood is allowed to the ischemic limbs causing unfortunately ischemia/reperfusion injury (IRI) (Hynes et al., 2012; Paradis et al., 2016). The rat hind limb IRI model resembles skeletal muscle ischemia in human which might be sometimes complicated with multiple organ failure and death (Hiatt et al., 2015). Although the mechanisms of hind limb IRI are complicated, accumulating evidence has suggested that reactive oxygen species (ROS) and inflammation play a crucial role in the pathogenesis of hind limb IRI (Kılıç et al., 2017).
Spike neural models (part I): The Hodgkin-Huxley model

aUniversity of Ottawa Abstract Artificial neural networks, or ANNs, have grown a lot since their inception back in the 1940s. But no matter the changes, one of the most important components of neural networks is still the node, which represents the neuron. Within spiking neural networks, the node is especially important because it contains the functions and properties of neurons that are necessary for their network. One important aspect of neurons is the ionic flow which produces action potentials, or spikes. Forces of diffusion and electrostatic pressure work together with the physical properties of the cell to move ions around changing the cell membrane potential which ultimately produces the action potential. This tutorial reviews the Hodkgin-Huxley model and shows how it simulates the ionic flow of the giant squid axon via four differential equations. The model is implemented in Matlab using Euler’s Method to approximate the differential equations. By using Euler’s method, an extra parameter is created, the time step. This new parameter needs to be carefully considered or the results of the node may be impaired.
Anisotropy in the Distribution of Satellite Galaxy Orbits

Nearby clusters such as Virgo and Coma possess galaxy distributions that tend to be aligned with the principal axis of the cluster itself. This has also been confirmed by a recent statistical analysis of some 300 Abell clusters, where the effect has been linked to the dynamical state of the cluster. Moreover, the orbits of satellite galaxies in galactic systems like our own Milky Way also demonstrate a high degree of anisotropy—the so-called Holmberg effect, the origin of which has been the subject of debate for more than 30 years. This study presents the analysis of cosmological simulations focusing on the orbits of satellite galaxies within dark matter halos. The apocenters of the orbits of these satellites are preferentially found within a cone of opening angle ~40° around the major axis of the host halo, in accordance with the observed anisotropy found in galaxy clusters. We do, however, note that a link to the dynamical age of the cluster is not well established, as both of our oldest dark matter halos do show a clear anisotropy signal. Further analysis connects this distribution to the infall pattern of satellites along the filaments: the orbits are determined rather by the environment of the host halo than some "dynamical selection" during their life within the host's virial radius.
Chronic Dietary Zinc Deficiency Alters Gut Microbiota Composition and Function

: Zinc (Zn) deficiency is a prevalent micronutrient insufficiency. Although the gut is a vital organ for Zn utilization, and Zn deficiency is associated with impaired intestinal permeability and a global decrease in gastrointestinal health, alterations in the gut microbial ecology of the host under conditions of Zn deficiency have yet to be studied. By conducting a series of long-term in vivo ( Gallus gallus ) feeding trials, we aimed to characterize distinct cecal microbiota shifts induced by chronic dietary Zn depletion in the context of complete diets based on Zn-biofortified food crops that are relevant to target populations, and in geographical regions where dietary Zn deficiency is a major health concern. We demonstrate that Zn deficiency induces significant taxonomic alterations and decreases overall species richness and diversity, establishing a microbial profile resembling that of various other pathological states. Through metagenomic analysis, we show that the predicted Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways responsible for macro-and micronutrient uptake are significantly depleted under Zn deficiency; along with concomitant decreases in beneficial short-chain fatty acids, such depletions may further preclude optimal host Zn availability. We also identify several candidate microbes that may play a significant role in modulating the bioavailability and utilization of dietary Zn during prolonged deficiency. Our results are the first to characterize a unique and dysbiotic cecal microbiota during Zn deficiency, and they provide evidence for such microbial perturbations as potential effectors of the Zn-deficient phenotype.
Renewable Energy Based Shunt Compensator For Power Quality Improvement

This paper presents an optimal operation of Photo-voltaic (PV) system based Shunt Active Filter (PV-SAF) for harmonic mitigation and reactive power compensation. When the PV system generates excessive or equal power required to the load demand, then the coordinating logic disconnecting the service grid from the load and reduce of panel tariff and global warming gasses. The PV module is connected to the DC side of SAF through the DC-DC converter with fuzzy based Perturb & Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm to eliminate the drawback of the conventional PV system. The reference currents extract by the Fuzzy logic controller based PQ control strategy. This proposed PV-SAF, if connected at the terminals of a small industry or a home or a small enlightening institution can avoid the use of interruptible power supply and individual stabilizer. A MATLAB simulink is presented to validate the advantage of the proposed system
Exploring Capsules

To mend the flaws of the CNN, Hinton et al. brought forward the concept of capsules [2]. In a CNN, a neuron is a scalar that represents the activation of a specific feature. By contrast, a capsule is a group of neurons, i.e. a vector, whose length represents the probability that a specific type of entity exists and orientation represents the instantiation parameter of the entity. This allows capsules at one level to make predictions, via transformation matrices, for the instantiation parameters of higher-level capsules.
Research of numerical methods for solving ordinary differential equations in MS Excel

Methods of Euler and Runge-Kutta are the most famous ones among the numerical methods for solving ordinary differential equations. Euler’s method has slow convergence; therefore, methods of a higher order of accuracy are often used. There are various modifications of the Euler method to increase its accuracy. Euler methods are mostly common corrected and modified. The corrected and modified Euler methods are second-order methods; their accuracy is proportional to h2. Based on a comparative analysis of the obtained results of the study of numerical methods for solving ordinary differential equations in MS Excel, it was found that the modified Euler’s method provides the highest accuracy.
Quasi-Independent Bidirectional Communication Methods for Simultaneous Wireless Power and Information Transmission

Simultaneous wireless power and information transmission (SWPIT) is essential for power regulation, load detection, and maximum efficiency tracking in wireless power transfer (WPT) systems. To eliminate the interaction between power and information transmission, this paper proposes a novel bidirectional communication method based on differential phase shift keying (DPSK). First, the steady-state and transient models of the series–series compensated WPT system are established to analyze the characteristics of power transmission and the behavior of shared power/information channel. Second, regarding the stability of power transmission, principles of the forward and backward communication that integrated with power modulation through the full-bridge inverter and the semi-bridgeless active rectifier are elaborated. Then, the demodulator and half-duplex communication protocol are designed. Finally, the simulation is carried out and the experimental prototype is established, which shows that the proposed DPSK method is effective and the transmission of power and information is quasi-independent. Owing to the integration of power and information transmission, the complexity, cost, and volume of the SWPIT system are reduced significantly.
Probing the high-redshift universe with SPICA: Toward the epoch of reionisation and beyond

Abstract With the recent discovery of a dozen dusty star-forming galaxies and around 30 quasars at z > 5 that are hyper-luminous in the infrared (μ L IR > 1013 L⊙, where μ is a lensing magnification factor), the possibility has opened up for SPICA, the proposed ESA M5 mid-/far-infrared mission, to extend its spectroscopic studies toward the epoch of reionisation and beyond. In this paper, we examine the feasibility and scientific potential of such observations with SPICA’s far-infrared spectrometer SAFARI, which will probe a spectral range (35–230 μm) that will be unexplored by ALMA and JWST. Our simulations show that SAFARI is capable of delivering good-quality spectra for hyper-luminous infrared galaxies at z = 5 − 10, allowing us to sample spectral features in the rest-frame mid-infrared and to investigate a host of key scientific issues, such as the relative importance of star formation versus AGN, the hardness of the radiation field, the level of chemical enrichment, and the properties of the molecular gas. From a broader perspective, SAFARI offers the potential to open up a new frontier in the study of the early Universe, providing access to uniquely powerful spectral features for probing first-generation objects, such as the key cooling lines of low-metallicity or metal-free forming galaxies (fine-structure and H2 lines) and emission features of solid compounds freshly synthesised by Population III supernovae. Ultimately, SAFARI’s ability to explore the high-redshift Universe will be determined by the availability of sufficiently bright targets (whether intrinsically luminous or gravitationally lensed). With its launch expected around 2030, SPICA is ideally positioned to take full advantage of upcoming wide-field surveys such as LSST, SKA, Euclid, and WFIRST, which are likely to provide extraordinary targets for SAFARI.
An HRI Model for Remotely Instructed Robots ∗

Robotic operations within unstructured and hazardous environments, such as nuclear sites, require autonomy with complex decision making. As industry is conservative and safety is paramount autonomous operation is slow in uptake. In the nuclear industry it is still common to rely on teleoperated robots. Tele-operation however can be strenuous and demanding on operating personnel and productivity can be low without advanced HRI interfaces. To bridge the practical gap between autonomous and tele-operated robot, this paper introduces the concept and an architecture for Remotely Instructed Robots (RIRs), which are reliable but still rely on human intelligence. RIRs can accept high and low level commands from the operator and execute tasks based on operators’ descriptions and at a variety of complexity levels. The paper presents a theoretical and programming framework to develop novel human interaction for RIRs and discusses effective communications via novel VR solutions and learning based anticipatory behaviour for this family of robots.
TacTiles: Dual-Mode Low-Power Electromagnetic Actuators for Rendering Continuous Contact and Spatial Haptic Patterns in VR

We introduce TacTiles, light (1.8g), low-power (130 mW), and small form-factor (1 cm3) electromagnetic actuators that can form a flexible haptic array to provide localized tactile feedback. Our novel hardware design uses a custom 8-layer PCB, dampening materials, and asymmetric latching, enabling two distinct modes of actuation: contact and pulse mode. We leverage these modes in Virtual Reality (VR) to render continuous contact with objects and the exploration of object surfaces and volumes with spatial haptic patterns. Results from a series of experiments show that users are able to localize feedback, discriminate between modes with high accuracy, and differentiate objects from haptic surfaces and volumes even without looking at them.
Employee Engagement Level and Its Key Determinants: a Study in the Context of Nepalese Media Sector

This paper aims to measure the level of employee engagement and to propose employee engagement framework. Following two-step sampling method, a questionnaire survey was conducted among the employees of fifteen media houses. The results of the survey are analyzed with the help of SPSS 18 version. Majority of the employees of Nepalese media houses have been found being engaged to their job. The findings confirm that five HR practices: performance appraisal and recognition; participatory culture; working facility; communication; and job and resources are the determinants of employee engagement. Among those, performance appraisal and recognition, and participatory culture, should be the major focus of human resource managers to make the employees engaged. Additionally, the paper can serve as a reference and an analytical tool to researchers for expanding knowledge on the topic.
Financing the weak nation-state : Inadequate public finance in Latin America

This paper presents empirical evidence on the relationship between governmental effectiveness and the ability to collect sufficient tax revenues. All governments need sufficient revenue in order to properly function. Governments of developing nations need additional revenue in order to build needed infrastructure. The governments of Latin American countries are almost without exception chronically and materially underfunded. Significantly less tax revenue is collected by countries in the region compared to the rest of the world. This leads to a vicious circle in that an underfunded government is a weak government and a weak government has less ability to enforce its own laws including the collection of tax revenues. Less tax revenue in turn leads to greater weakness. The objective of this paper is to test the hypothesis that strength and capacity of a state impacts its ability to collect adequate tax revenue. In other words, do stronger states collect more tax revenue than do weaker states? Many possible causes of Latin America’s high level of low tax effort have been advanced by researchers and theorists. Utilizing cross country data drawn from the Inter-American Development Bank as well as other sources, a model is developed to test the theory that the principle cause of low tax effort in Latin America is the result of weakness in the governmental sector. The model finds that a country’s strength and capacity are correlated with its ability to collect tax revenue.
Identification of Plant Species by Deep Learning and Providing as A Mobile Application

Image processing techniques give highly successful results when used deep learning in classification studies. Applications benefit from this kind of work to make life easier. In this study, a mobile application is developed that takes photo of a plant and makes image processing on it to provide information about its name, the time to change the soil, the amount of sun light and nutrition it needs. The model is trained using the Convolutional Neural Networks, and dataset is successfully applied to the network. Currently, the application is capable to classify 43 different plants in mobile environment, and its classification capacity is planned to be expanded with new plant species as a future study. Up to 90% accuracy is reached in this study with the current version of the application.