Miracle-dz/UniFAR-FTUs huggingface dataset:
Right-to-Carry Laws and Violent Crime: A Comprehensive Assessment Using Panel Data and a State-Level Synthetic Controls Analysis

The 2005 report of the National Research Council (NRC) on Firearms and Violence recognized that violent crime was higher in the post-passage period (relative to national crime patterns) for states adopting right-to-carry (RTC) concealed handgun laws, but because of model dependence the panel was unable to identify the true causal effect of these laws from the then-existing panel data evidence. This study uses 14 additional years of panel data (through 2014) capturing an additional 11 RTC adoptions and new statistical techniques to see if more convincing and robust conclusions can emerge. Our preferred panel data regression specification (the “DAW model”) and the Brennan Center (BC) model, as well as other statistical models by Lott and Mustard (LM) and Moody and Marvell (MM) that had previously been offered as evidence of crime-reducing RTC laws, now consistently generate estimates showing RTC laws increase overall violent crime and/or murder when run on the most complete data. We then use the synthetic control approach of Alberto Abadie and Javier Gardeazabal (2003) to generate state-specific estimates of the impact of RTC laws on crime. Our major finding is that under all four specifications (DAW, BC, LM, and MM), RTC laws are associated with higher aggregate violent crime rates, and the size of the deleterious effects that are associated with the passage of RTC laws climbs over time. Ten years after the adoption of RTC laws, violent crime is estimated to be 13-15% percent higher than it would have been without the RTC law. Unlike the panel data setting, these results are not sensitive to the covariates included as predictors. The magnitude of the estimated increase in violent crime from RTC laws is substantial in that, using a consensus estimate for the elasticity of crime with respect to incarceration of . 15, the average RTC state would have to double its prison population to counteract the RTC-induced increase in violent crime.
Configuration of sensor network with uncertain location of nodes for parameter estimation in distributed parameter systems.* *This work was supported by the Ministry of Science and Higher Education under grant No. N N519 2971 33.

Abstract A sensor location problem for monitoring network with stationary nodes used for estimating unknown parameters of distributed-parameter system is addressed. In particular, the situation is considered, when the actual spatial positions of sensor nodes at the experimentation stage may be uncertain to some extent and randomly fluctuate around some locations specified at the configuration stage. In the presented approach, some results from experimental design theory for dynamic systems with random regressors are extended for the purpose of configuring a sensor network. Then, a simple algorithm based on the notion of approximate near minimum from statistical learning theory is adapted to select the most informative sensor locations. The delineated approach is illustrated by numerical example on a sensor network design for a two-dimensional convective diffusion process.
Degradation Characteristics of Soil-Quality-Related Physical and Chemical Properties Affected by Collapsing Gully: The Case of Subtropical Hilly Region, China

In the subtropical hilly areas of China, a collapsing gully, a particular type of permanent gully, poses a great threat to the productivity and sustainability of the local ecological and agricultural systems. However, few studies have been performed regarding the effects of collapsing gully erosion on soil degradation. The aim of this study was to evaluate the effects of collapsing gully erosion on soil-quality-related physical and chemical properties. The collapsing gullies that were severely affected by erosion processes were considered at three stages (initial, active and stable stages) and corresponding soil samples were collected to analyze the spatial variation of the soil physical and chemical quality at each stage. The changes in the properties were assumed to be considerable in the regions affected by the erosion process compared with those unaffected by this process. Soil physical properties were more susceptible than soil nutrients to collapsing gully erosion in different spatial locations. The soil quality index (SQI) system consists of total nitrogen (TN), total phosphorus (TP), pH, capillary porosity (CP), sand content (SA), soil cohesion (SC) and root density (RD). Collapsing gully erosion was found to affect the soil physical and chemical properties by progressively reducing the SQI. The mean SQI value was the lowest in the active stage of the collapsing gully, with a higher soil degradation. For the different spatial positions in the collapsing gullies, the scour channel showed the lowest SQI value. The limiting indicators varied in the different stages or spatial sites in the collapsing gullies.
Capacity-Building in South-East Asian Universities: International Challenges

The past two decades have seen a massive increase in the level of activity of universities based in developed countries in the south-east Asian region. I wish to locate my remarks in the context of two major developments in the global picture of education. One is the economic relocation of higher education and the transformation of knowledge into an economically grounded export commodity, significantly, under the aegis of the World Trade Organization. The other is the shift in the financial relationships existing between public universities and the State in many developed countries. Both of these themes speak to the economic relocation of the university and higher education in general. The implications flowing from these developments for developing countries are significant as they try to position themselves in an era of so-called free trade.
What Every Teacher Should Know About Media and Technology

From the Publisher: Find out how media can work for each of the student modalities! This slipcase collection of 10 compact volumes offers a complete training curriculum for teachers that covers all required essentials for teacher induction, mentoring, and support programs. The collection can be used for teacher certification and re-certification programs, as well as for fast-track alternative certification programs. Additional highlights embedded within each individual volume include assessment pre-tests and post-tests; resources; and content about urban learners, making this training program ideal for preparing highly qualified teachers for today's challenging classrooms, particularly in urban areas.
Mixed corpus design for researching the Eurolect: a genre-based comparable-parallel corpus in the PL EUROLECT project

Rozdzial z: Polskojezyczne korpusy rownolegle. Polish-language Parallel Corpora Redakcja / Edited by: Ewa Gruszczynska, Agnieszka Lenko-Szymanska http://rownolegle.blog. ils.uw. edu.pl/tom/
International Student Mobility: Growth and Dispersion

Recent years have seen an unprecedented growth and geographic dispersion in international student mobility. In this paper, we empirically test the predictions of two competing theoretical models underpinning the determinants of student mobility – the human capital model and the migration model – across traditional and emerging destinations. Our findings suggest that while the predictions of the migration model are generally valid in explaining student emigration to non-English speaking OECD destinations, student flows to English speaking countries and emerging economies are largely in line with the predictions of the human capital model. The growing dispersion of international students to emerging economies and continuing large flows to English speaking countries are therefore indicative of the rising demand to acquire tertiary skills and much less of the desire to migrate for permanent settlement.
OpenLISP Implementation Report

The RRG is working on the design of an alternate Internet Architecture in order solve issues of the current architecture related to scalability, mobility, multi-homing, and inter-domain routing. Among the various proposals, LISP (Locator/ID Separation Protocol) is one of the most advanced. UC Louvain is working on an implementation of this protocol on a FreeBSD platform. The present draft describes the overall architecture of this implementation and its main data structures.
Is Corporate Social Responsibility Used to Mask Corporate Speculation? Evidence from Emerging China

Why do rational, profit-orientated firms generously engage in corporate social responsibility (CSR)? Our study explores the real motives of speculative firms for CSR engagement and the hidden causality behind it. Using national survey data of privately owned firms in China, we find that corporate speculation positively influences firms’ engagement in CSR, revealing that CSR is instrumental and that firms use it as a tool to mask their speculative activities by building their reputations and buying ‘leniency insurance’ against potential penalties. Further, the relationship between speculation and CSR is less pronounced in firms with political involvement, revealing that the effect of political involvement as an informal institution somewhat protects speculators from potential sanctions without a CSR premium. We also discovered that the relationship between corporate speculation and CSR—as well as the moderating role of political involvement—is less pronounced among developed regions, revealing that the development of formal institutions can restrict the instrumentality of CSR and the effect of political involvement. Such findings have important implications for CSR in emerging economies.
Auto-focus algorithm based on image edge energy

High frequent energy represents differences among images in different defocus position.The edge energy sharpness evaluation function was presented by means of Sobel operator edge detection algorithm.The proposed evaluation function gains sharper performance than intensity variance method and gradient energy method,and it is qualified for high accurate auto-focus.On the basis of mountain climb servo,the improved random startup position mountain climb servo makes full use of the former mountain climbing information.The algorithm reduces mountain climbing steps,and enhances auto-focus speed.
Video Semantic Description Method for Chinese Sign Language Synthesis

To improve synthesis realistic of sign language videos,a method to describe sign language video semantics is proposed,and the sign language video database based on semantic description for sign language synthesis is constructed.Chinese sign language videos in specific research field are captured,then sign language video units and multi-dimensional transition units are cut from the captured sign language videos.By describing the semantic information of every frame in sign language videos,which include locations,hand shapes and rhythm information,their multi-dimensional semantic models are constructed.During sign language video synthesis,useful information can be used in real-time by parsing multi-dimensional semantic models.This method provides real-time and coherent semantic information for sign language video synthesis,and in the process of joining two sign language videos,different rhythm information can be parsed out from their semantic models,then interpolated locations of transition frames can be moderately adjusted to make the rhythm in transition frames gradually change.
The design of baseband system for digital interphone based on CMX618 and CMX7041

The baseband system of digital interphone based on CMX618 and CMX7041 chips is introduced in this paper.The CMX618 and CMX7041 researched by CML Microcircuits Inc are used for mobile communication in digital special network. In the paper, the hardware design and software design of baseband system are shown in detail, the problems and experience about the two chips are also contained.
Comparative study of balancing methods: case of imbalanced medical data

Imbalanced learning problems contain unequal distribution of data samples among different classes, where most of the samples belong to some classes and the rest to the other classes. Learning from the imbalanced data is of utmost important to the research community as it is present in many vital real-world classification problems, such as medical diagnosis. There have been many works dealing with classification of imbalanced data sets. In medical data classification, we often face the imbalanced number of data samples where at least one of the classes constitutes only a very small minority of the data. In this paper, we proposed a learning method based on a cost-sensitive extension of Least Mean Square (LMS) algorithm that penalises errors of different samples with different weights and some rules of thumb to determine those weights. After the balancing phase, we apply different classifiers (Support Vector Machine [SVM], k-Nearest Neighbour [k-NN] and Multilayer Perceptron [MLP]) for the new balanced data set. We have also compared the results obtained by the LMS algorithm with the results obtained by the sampling techniques (under-sampling, oversampling and Synthetic Minority Oversampling Technique (SMOTE)).
The Under Investment and Over Investment Hypotheses: An Analysis Using Panel Data

In this paper we study the relationship between firm value and investment in order to test the underinvestment and overinvestment hypotheses. The results obtained, using panel data methodology as the estimation method, indicate that the abovementioned relation is quadratic, which implies that there exists an optimal level of investment. As a consequence, firms that invest less than the optimal level suffer from an underinvestment problem, while those firms that have a level of investment higher than the optimum suffer from an overinvestment problem. The aforementioned quadratic relation is maintained when firms are classified depending on their investment opportunities. Moreover, in accordance with the theory, those firms with valuable investment opportunities maintain an optimal level of investment higher than that of those whose investment opportunities are of low quality.
Comparison of GA tuned fuzzy logic and NARMA-L2 controllers for motion control in 5-DOF robot

Abstract Robots are useful in industries in many ways. In today’s economy, the manufacturing industry needs to be efficient to cope with the competition. Installing robots in the industry is often a step to be more competitive because robots can do certain tasks more efficiently than humans. Some of the manufacturing tasks in which robots perform better are assembling products, polishing, and cutting. In order to accurately perform these industrial operations, an effective controller needs to be implemented instead of conventional Proportional plus Integral (PI) controller. When a fuzzy controller is implemented directly, there will be a problem of computational complexity. Therefore, soft computing-based approach namely, genetic-fuzzy systems are proposed in this paper. Fuzzy systems have been integrated with Genetic Algorithm (GA) to optimize the scaling factors that define the fuzzy systems. GAs inspired by the process of biological evolution, are adaptive search and optimization algorithms. A system to be optimized is represented by a binary string which encodes the parameters of the system. This methodology is highly robust and imprecision tolerant. If a unique optimum exists, the procedure approaches it through gradual improvement of the fitness and if the optimum is not unique, the method will approach one of the optimum solutions. Hence, GA tuned fuzzy system is proposed and compared with NARMA-L2 controller for controlling the motion of a 5DOF robotic manipulator. This analysis has been performed using SOLIDWORKS and MATLAB/SIMULINK environments.
Fast Adaptive Smoothing and Thresholding for Improved Activation Detection in Low-Signal fMRI

Functional magnetic resonance imaging is a noninvasive tool for studying cerebral function. Many factors challenge activation detection, especially in low-signal scenarios that arise in the performance of high-level cognitive tasks. We provide a fully automated fast adaptive smoothing and thresholding (FAST) algorithm that uses smoothing and extreme value theory on correlated statistical parametric maps for thresholding. Performance on experiments spanning a range of low-signal settings is very encouraging. The methodology also performs well in a study to identify the cerebral regions that perceive only-auditory-reliable or only-visual-reliable speech stimuli.
Content-Aware Convolutional Neural Network for Object Recognition Task

In existing Convolutional Neural Network (CNNs) for object recognition task, there are only few efforts known to reduce the noises from the images. Both convolution and pooling layers perform the features extraction without considering the noises of the input image, treating all pixels equally important. In computer vision field, there has been a study to weight a pixel importance. Seam carving resizes an image by sacrificing the least important pixels, leaving only the most important ones. We propose a new way to combine seam carving approach with current existing CNN model for object recognition task. We attempt to remove the noises or the “unimportant” pixels in the image before doing convolution and pooling, in order to get better feature representatives. Our model shows promising result with CIFAR-10 dataset.
Mutual fund trading around mergers and fund performance

Purpose The purpose of this paper is to investigate whether mutual funds (MFs) take positions in companies that subsequently engage in M&As and whether fund managers adjust portfolio holdings in the same direction as wealth creation from mergers. Further, the study is the first to examine the relation between active trading surrounding M&As and risk-adjusted performance in MFs. Design/methodology/approach The sample includes mergers conducted by publicly traded acquirers of public and private targets over 2003–2016. Several measures of MF managerial activeness in M&As are introduced: merger trading intensity (proportional change in fund’s holdings of M&A stocks), active merger weight (deviation of the fund’s actual weights in M&A stocks and value weights) and active merger trading (deviation of the fund’s actual weights in M&A stocks from the average weights in M&A stocks across the funds within the same Center for Research in Security Prices objective). Findings Fund managers who are more vested in the firms engaged in M&As and who are more active in their trades of M&A firms generate higher contemporaneous and subsequent risk-adjusted performance, indicative of managerial skill. Active M&A trading effect on performance is economically meaningful. Originality/value This is the first study to examine whether previously documented predictive power of active institutions regarding M&As’ profitability leads to higher risk-adjusted returns for MF investors. The study introduces several measures to gauge how actively fund managers trade companies engaged in M&As and contributes to the literature on MF managers’ ability to pick stocks.
Non-Curricular Strategies in the Implementation of Education for Sustainable Development in Three Prominent Green Schools in Indonesia

Non-curricular strategies have been qualitatively studied towards three leading green schools in Indonesia. These strategies have made those the three green schools reach an amazing success story in achieving national green school award from the government of Indonesia. This study follows a qualitative research paradigm. Content analysis was used to interpret the main pattern of the data resulted from the performed in depth interview upon three school principals. Evidences also collected to confirm the result of interview. According to the analysis, three patterns of strategies can be easily identified. Those the three strategies were related to character building. These non-curricular strategies were habituating Javanese culture to enhance positive ethical attitude, reinforcing student’s Islamic spiritual value to raise the respects towards nature and, strengthening student’s environmental literacy trough engaging student in research and project-based activities.
Distance, Formal and Informal Institutions in International Trade

This paper brings together three strands of literature on the determinants of international trade − distance, formal, and informal institutions − to explain differences in export performance across countries. Using an augmented gravity model, we find that the importance of formal institutions (rule of law) for bilateral trade increases with distance. Similarly, the pro-trade effect of informal institutions (migrant networks) is larger for distant countries. After confirming that informal institutions can substitute for weak formal institutions in promoting trade, we finally show that this substitution effect does not decrease with distance. Our findings contribute to explaining the persistent negative effect of distance on the export performance of many developing countries despite reductions in trade costs, and provide guidance to policy makers in terms of trade reform, regional trade liberalization and export promotions strategies.
Moca: An efficient Memory trace collection system

In modern High Performance Computing architectures, the memory subsystem is a common performance bottleneck. When optimizing an application, the developer has to study its memory access patterns and adapt accordingly the algorithms and data structures it uses. The objective is twofold: on one hand, it is necessary to avoid missuses of the memory hierarchy such as false sharing of cache lines or contention in a NUMA interconnect. On the other hand, it is essential to take advantage of the various cache levels and the memory hardware prefetcher. Still, most profiling tools focus on CPU metrics. The few of them able to provide an overview of the memory patterns involved by the execution rely on hardware instrumentation mechanisms and have two drawbacks. The first one is that they are based on sampling which precision is limited by hardware capabilities. The second one is that they trace a subset of all the memory accesses, usually the most frequent, without information about the other ones. In this study we present Moca an efficient tool for the collection of complete spatiotemporal memory traces. It is based on a Linux kernel module and provides a coarse grained trace of a superset of all the memory accesses performed by an application over its addressing space during the time of its execution. The overhead of Moca is reasonable when taking into account the fact that it is able to collect complete traces which are also more precise than the ones collected by comparable tools.
Design and Development of Efficient Standalone Photovoltaic System Using Matlab/Simulink

This work involves design, development and testing of a Buck/Stepdown Converterfor standalone application using solar photovoltaic array power with maximum power pointtracker to transfer maximum possible power at any insolation level.The proposed system is expected to be cost effective and of high efficient to increaseoverall SPV system cost per KW. The entire control algorithm is implemented in aMATLAB/SIMULINK 7.6a and results are presented.