Miracle-dz/UniFAR-FTUs huggingface dataset:
FARMERS FIELD SCHOOLS AND BITTER GOURD PRODUCTIVITY : AN EMPIRICAL ANALYSIS OF DISTRICT CHARSADDA , KHYBER PAKHTUNKHWA-PAKISTAN *

Farmer Field School (FFS) always aims to facilitate farmers to find out and study about field ecology and integrated crop management. The study was conducted to examine the change in productivity; to determine increase in per acre yield, to find out reduction in per acre input cost. A study for bitter gourd crop was conducted in ten villages namely Hafiz Jee Qila, Toor Kalay, Sarkai, Inzar Qila, Ghari Kaka Khel, Ghari Kanan Khel, Sher Khan Kalay, Afzalyano Korona, Yar Muhammad Khan Qila and Sultan Abad of district Charsadda. From each FFS, 15 farmers were selected randomly on the basis of identical allocation making sample size of 150. The research was based on primary as well as secondary data. Primary data were collected from farmers while secondary data were got from department of Agricultural Extension, District Charsadda. Extensive interview schedule was developed and each farmer was interviewed personally. The study was based on comparison of cost, yield and income of bitter gourd before and after FFS. Paired t-test was conducted using SPSS for analysis; result of the study show that Agricultural Extension Department has brought a positive change in the attitude of farmers of the study area through FFS approach. About 69% of the respondents growing bitter gourd were between the ages of 31-50 years, 23 % had no education and were illiterate, 25% had education of primary level, 15% middle, 27% matric, 6% intermediate and 5% at graduate level. Average land holding size was 9.89 acres, cost of fertilizer, crop protection from insect pest and crop protection from diseases was reduced by Rs. 2264, Rs. 3195.37 and Rs. 3090.20 respectively, the cost of seed and cost of farm yard manure was increased by Rs.1923. 07 and Rs. 1204.66 respectively while bitter gourd yield was increased by 988 Kg/acre from 17384.60 to 18372.60. The role of FFS initiated by department of Agriculture Extension for the development of agriculture was worth mentioning. It has increased the crop yield of bitter gourd and farmers’ income through FFS approach. On the basis of results of the research studies, Agriculture Extension Department should extend their developmental activities for utmost participation of the whole rural masses through participatory FFS approach.
Markups and Scale Elasticities for Differentiated Railroad Networks ∗

Railroads operate over expansive and differentiated networks and provide a multitude of different services. Each of the services differs in terms of demand and cost characteristics, and each location and shipment may face different sources and levels of competition. In this paper, we develop and estimate a model that provides both markups and scale elasticities that vary across railroads and through time for the traffic on their networks. Our model is based on a framework provided by Hall (1988) and Klette (1999) wherein markups and scale elasticities are estimated from production relations. In our model, we aggregate the shipments over each firm’s network, which provides a mapping from inputs and network and shipment characteristics to aggregate outputs over the network. Markups and scale elasticities are taken to follow a multivariate distribution. This allows for differences in markups and scale across firms and through time, but also for covariances across firms in markups and scale. We estimate the model with Bayesian methods to find markups that are generally well in excess of marginal costs and scale elasticities that generally point to increasing or constant returns in the industry. JEL codes: (L11, L52, L92)
Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features

The separation of crop types is essential for many agricultural applications, particularly when within-season information is required. Generally, remote sensing may provide timely information with varying accuracy over the growing season, but in small structured agricultural areas, a very high spatial resolution may be needed that exceeds current satellite capabilities. This paper presents an experiment using spectral and textural features of NIR-red-green-blue (NIR-RGB) bands data sets acquired with an unmanned aerial vehicle (UAV). The study area is located in the Swiss Plateau, which has highly fragmented and small structured agricultural fields. The observations took place between May 5 and September 29, 2015 over 11 days. The analyses are based on a random forest (RF) approach, predicting crop separation metrics of all analyzed crops. Three temporal windows of observations based on accumulated growing degree days (AGDD) were identified: an early temporal window (515–1232 AGDD, 5 May–17 June 2015) with an average accuracy (AA) of 70–75%; a mid-season window (1362–2016 AGDD, 25 June–22 July 2015) with an AA of around 80%; and a late window (2626–3238 AGDD, 21 August–29 September 2015) with an AA of <65%. Therefore, crop separation is most promising in the mid-season window, and an additional NIR band increases the accuracy significantly. However, discrimination of winter crops is most effective in the early window, adding further observational requirements to the first window.
Detecting Emotion in Text

Advancements in textual analysis have allowed the area of emotion detection to become a recent interest in computational linguistics. This paper presents a general overview of the many diverse approaches that recognize emotion in text. It goes over the components the approaches have in common and then goes on to describe the different types of algorithms and results that been developed to date. Additionally, the paper covers the challenges and concerns of the field and what will come of emotion detection in the future.
Regressing Controversy of Music Artists from Microblogs Mhd

Social media represents a valuable data source for researchers to analyze how people feel about a variety of topics, from politics to products to entertainment. This paper addresses the detection of controversies involving music artists, based on microblogs. In particular, we develop a new controversy detection dataset consisting of 53,441 tweets related to 95 music artists, and we devise and evaluate a comprehensive set of userand contentbased feature candidates to regress controversy. The evaluation results show a strong performance of the presented approach in the controversy detection task: F1 score of 0.811 in a classification task and RMSE of 0.688 in a regression task, using controversy scores in the range [1, 4]. In addition, the results obtained in applying the presented approach on a dataset from a different domain (CNN news controversy) demonstrate transferability of the developed feature set, with a significant improvement over prior approaches. A combination of the adopted Gradient Boosting based classifier and the developed feature set results in an F1 score of 0.775, which represents an improvement of 9.8% compared to the best prior result on this dataset.
Explicit non-Markovian SIS mean-field epidemic threshold for Weibull and Gamma infections but Poisson curings

Although non-Markovian processes are considerably more complicated to analyse, real-world epidemics are likely non-Markovian, because the infection time is not always exponentially distributed. Here, we present analytic expressions of the epidemic threshold in a Weibull and a Gamma SIS epidemic on any network, where the infection time is Weibull, respectively Gamma, but the recovery time is exponential. The theory is compared with precise simulations. The mean-field non-Markovian epidemic thresholds, both for a Weibull and Gamma infection time, are physically similar and interpreted via the occurrence time of an infection during a healthy period of each node in the graph. Our theory couples the type of a viral item, specified by a shape parameter of the Weibull or Gamma distribution, to its corresponding network-wide endemic spreading power, which is specified by the mean-field non-Markovian epidemic threshold in any network.
Specializing Distributional Vectors of All Words for Lexical Entailment

Semantic specialization methods fine-tune distributional word vectors using lexical knowledge from external resources (e.g. WordNet) to accentuate a particular relation between words. However, such post-processing methods suffer from limited coverage as they affect only vectors of words seen in the external resources. We present the first post-processing method that specializes vectors of all vocabulary words – including those unseen in the resources – for the asymmetric relation of lexical entailment (LE) (i.e., hyponymy-hypernymy relation). Leveraging a partially LE-specialized distributional space, our POSTLE (i.e., post-specialization for LE) model learns an explicit global specialization function, allowing for specialization of vectors of unseen words, as well as word vectors from other languages via cross-lingual transfer. We capture the function as a deep feed-forward neural network: its objective re-scales vector norms to reflect the concept hierarchy while simultaneously attracting hyponymy-hypernymy pairs to better reflect semantic similarity. An extended model variant augments the basic architecture with an adversarial discriminator. We demonstrate the usefulness and versatility of POSTLE models with different input distributional spaces in different scenarios (monolingual LE and zero-shot cross-lingual LE transfer) and tasks (binary and graded LE). We report consistent gains over state-of-the-art LE-specialization methods, and successfully LE-specialize word vectors for languages without any external lexical knowledge.
Future Trends in 5g Wireless Communication: A Survey

In modern era the exponential growth in smartphones with better applications need higher efficiency as compare with 4G devices. The data rate and spectrum efficiency is increasing day by day. Although 4G system provided much better services and better latency rate, there is still a gap between people’s requirements and what 4G system is providing. This article gives an overview of key enabling technologies which could be used in future 5G wireless systems. Some of these are Massive MIMO, Device-to-Device (D2D) communications, full duplex technique, millimeter wave communications, Internet of things (IoT) and Cloud RAN. By 2020 more than 9.2 billion devices will be there. And these key technologies will surely change the standards of future generation communication system.
Speed, Power Efficiency, and Noise Improvements for Switched Capacitor Voltage Converters

ix CHAPTER 1: INTRODUCTION 1 1.1 On-Chip Voltage Converters 3 1.1.1 LDO Regulators 4 1.1.2 Buck Converters 5 1.1.3 Switched Capacitor Voltage Converters 6 1.2 Objectives 7 1.3 Organization 7 CHAPTER 2: LEVERAGING ON-CHIP SC VOLTAGE REGULATORS TO IMPROVE HARDWARE SECURITY, TRANSIENT RESPONSE AND POWER SUPPLY INTEGRITY 9 2.
A Fast Method for Community Detection Based on the Contribution of Nodes

Communities exist in complex network of different areas, and there have been some methods to identify the communities in networks. However, the high computationally demanding limits their applications. In this paper, we propose a measurement of relationship between node and community, and then give a new comparative definition for community in complex networks. Based on these definitions we put forward the corresponding detecting algorithm. The effectiveness of our algorithm is demonstrated by extensive experiments on lots of computer generated graphs and public available real-world graphs. The result shows our algorithm is fast and accuracy in detecting communities.
Country : United Kingdom Final Report Summary-EUCBCC ( EUropean Cross Border Care CollaborationsShort Title : CrossEurope )

Final Report Summary EUCBCC (EUropean Cross Border Care CollaborationsShort Title: CrossEurope) Executive Summary: This project explored five aspects of cross-border health care: provisions with regard to the continuing quality of health professionals; treatment pathways; the content and scope of medical records; medical prescribing; and the reporting of quality in both health and long-term care. The study also looked at three areas where there are existing cross-border collaborations to identify practical issues that have arisen and how they have or have not been addressed. These areas of practice were collaborations between hospitals in border areas, telemedicine, and dentistry.
EFFECTS OF SUCROSE CONCENTRATIONS ON INCREASE IN BULB SIZE OF IN VITRO REGENERATED HYACINTH ( Hyacinthus orientalis L . ) BULBLETS

In vitro regenerated bulblets of Hyacinthus orientalis L. , improved their size using MS medium containing different sucrose concentrations. The sucrose concentrations showed higher growth and better development of the bulblets on MS medium stored at 24 °C. Sucrose showed the highest performance rate at 9 % concentration for both small and large bulblets. Sucrose treatments showed positive effects of induction of bulb diameter, number of shoots per bulb, shoot length and bulb weight in the culture medium.
Lawrence Berkeley National Laboratory Recent Work Title ScienceSearch : Enabling Search through Automatic Metadata Generation

Scientific facilities are increasingly generating and handling large amounts of data from experiments and simulations. Next-generation scientific discoveries rely on insights derived from data, especially across domain boundaries. Search capabilities are critical to enable scientists to discover datasets of interest. However, scientific datasets often lack the signals or metadata required for effective searches. Thus, we need formalized methods and systems to automatically annotate scientific datasets from the data and its surrounding context. Additionally, a search infrastructure needs to account for the scale and rate of application data volumes. In this paper, we present ScienceSearch, a system infrastructure that uses machine learning techniques to capture and learn the knowledge, context, and surrounding artifacts from data to generate metadata and enable search. Our current implementation is focused on a dataset from the National Center for Electron Microscopy (NCEM), an electron microscopy facility at Lawrence Berkeley National Laboratory sponsored by the Department of Energy which supports hundreds of users and stores millions of micrographs. In this paper, we describe a) our search infrastructure and model, b) methods for generating metadata using machine learning techniques, and c) optimizations to improve search latency, and deployment on an HPC system. We demonstrate that ScienceSearch is capable of producing valid metadata for NCEM’s dataset and providing low-latency good quality search results over a scientific dataset.
Molecular prevalence of Brucella abortus , Actinomyces pyogenes and Mycobacterium tuberculosis in reproductive organs of apparently healthy rams slaughtered in Iran

Iran, a country in the west Asia, is located within of the MiddleEast and links the Caspian Sea and Persian Gulf [28]. The sheep population in Iran is about 53 million head. The sheep are mostly kept by rural and peri-urban farmers. Sheep-keeping contributes greatly to the national economy and income of individual rural farmers through the production and sales of meat, milk and wool [25]. However, infectious diseases pose a threat to sheep industry due to reducing productivity of sheep and its products (meat, milk and wool). Zoonotic diseases that infect sheep is also a public concern because of the handling and consumption of sheep meat and milk [14]. Sheep can be infected by bacteria among other etiological agents. Mycobacterium tuberculosis complex such as Mycobacterium tuberculosis (M. tuberculosis) and Mycobacterium bovis are the causative agents of tuberculosis in several animals including but not limited to cattle and sheep and since it is a zoonotic [4], human can acquire the disease which is rare in sheep [20]. There are contrasting ideas on the degree of susceptibility of sheep to tuberculosis. The small number of cases of sheep infected by M. tuberculosis complex is not consistent with the perception that sheep are susceptible to the infection [2]. The information of infection in sheep by M. tuberculosis is scarce and once detected and estimated, the findings are regarded as isolated [20, 27]. According to Allan (1988), prevalence of tuberculosis in sheep is low, only 2 sheep with histological lesions typical SUMMARY
Learning-based Control for Autonomous Mobile Robots

Learning-based Control for Autonomous Mobile Robots Christopher John Ostafew Doctor of Philosophy Graduate Department of Aerospace Science and Engineering University of Toronto 2016 As mobile robots leave structured indoor environments to operate in challenging outdoor environments, their motion controllers require advanced techniques to mitigate the effects of unmodelled surface materials (e.g., snow, sand, grass), terrain topography (e.g., side-slopes, inclines), and complex robot dynamics. This thesis investigates learning-based control within the context of path-tracking autonomous mobile robots. Learning-based algorithms alleviate the need for significant engineering work in identifying and modelling all disturbances that a controller may be required to mitigate. Furthermore, they are capable of predicting and acting in anticipation of repeatable effects and disturbances not modelled prior to deployment. The learning-based algorithms reduce tracking errors, increase operational speed, and increase localization reliability. Specifically, the thesis presents four approaches: 1) Iterative Learning Control (ILC), 2) Learning-based Nonlinear Model Predictive Control (LB-NMPC), 3) Robust Min-Max LB-NMPC (MM-LB-NMPC), and 4) Robust Constrained LB-NMPC (RC-LB-NMPC). ILC generates an acausal feedforward signal that reduces the path-tracking errors using information from any previous trial. While the approach is computationally appealing, ILC typically assumes that the robot is initialized with identical initial conditions for each trial and tracking the same desired path (i.e., generalization is non-trivial). On the other hand, LB-NMPC is a technique that uses a learned process model directly, enabling interpolation and extrapolation from experiences. In this case, the current control action is obtained by solving a finite-horizon optimal control problem using the current
A BRIEF OVERVIEW OF HAND AND CHEMICAL THINNING OF APPLE FRUIT

A brief overview of the fruit thinning effect on apple fruit quality is presented in the current paper. Handy and chemical fruit thinning has been previously studied, existing many published reports in literature that examine various aspects of this technological procedure. The state of the art in the field of apple thinning reveal that this operation can improve fruit size, increase return bloom and reduce alternate bearing habit of apple trees.
Vertical Environment Adapts to Impacts System Power App Action Prompts

Many apps benefit from knowing their power consumption and adapting their behaviors on the fly. To offer apps power knowledge at run time, an OS often meters system power and divides it among apps. Since the impacts of concurrent apps on system power are entangled, this approach not only makes it difficult to reason about power but also results in power side channels, a serious vulnerability. To this end, we introduce a new OS principal called power sandbox, which enables one app to observe the fine-grained power consumption of itself running in its vertical slice of the hardware/software stack. The observed power is insulated from the impacts of other apps. Our contribution is a set of lightweight kernel extensions that simultaneously i) enforce the power sandbox boundaries and ii) confine entailed performance loss to the sandboxed apps. Our experiences on two embedded platforms show that power sandboxes simplify reasoning about power, maintain fairness among apps, and minimize power side channels, thus facilitating construction of power-aware apps.
Textual Data Partitioning with Relationship and Discriminative Analys

-Data partitioning methods are used to partition the data values with similarity. Similarity measures are used to estimate transaction relationships. Hierarchical clustering model produces tree structured results. Partitioned clustering produces results in grid format. Text documents are unstructured data values with high dimensional attributes. Document clustering group ups unlabeled text documents into meaningful clusters. Traditional clustering methods require cluster count (K) for the document grouping process. Clustering accuracy degrades drastically with reference to the unsuitable cluster count.
Independence of the Judiciary : Measuring the Political Bias of the Brazilian Courts

The degree of judicial independence has broad implications on economic development. For the executive’s non-expropriation commitment to be credible, it is necessary that the judiciary should be free to impartially mediate disputes between the State and its citizens. The prolific literature on de facto judicial independence misses a key-variable to explain political bias: the government’s discretion in appointing Supreme Court Justices. In this paper we explore a distinct feature of the Brazilian judiciary to assess political bias due to government appointment discretion. As there are two courts: the Supremo Tribunal Federal (STF) and the Superior Tribunal de Justiça (STJ), which deal with similar matters and have different restrictions on the appointment of its members, it is possible to compare the degree of political influence to which they are subject. Therefore, we test (1) if there are differences on the degree of political influence depending on the presidential discretion to nominate a justice, and (2) if the justices make strategic use of their positions, that is, actively benefit the party of the president that has appointed them. We find evidence of the first, but not of the second effect.
Observation of the effects of two-stage irrigation therapy , combined with pedicled flaps , for treatment of infected tissue defects of the ankles and feet

Objective: The aim of this study was to compare the clinical efficacy of two-stage irrigation combined with pedicled flaps and dressing-debridement-dressing circulation for treatment of infected tissue defects of the ankle and foot. Methods: Clinical data of 86 patients with infected tissue defects of the ankle and foot were analyzed, retrospectively. Results: Differences in ankle circumferences, preand post-operative, in group A at the 1st, 3rd, and 5th month were significantly smaller than those in group B (all P<0.001), as were visual analog scale (VAS) scores at the same time points (all P<0.001). Verbal rating scale (VRS) scores in group A at the 1st, 3rd, and 5th month were also lower than those in group B (P=0.001). The number of bacteria in group A was less than group B on the 10th day after the operation, as well as 2 weeks before and 2 weeks after drainage tube removal (all P<0.001). Wound healing rates in group A were significantly higher than those in group B at 12 months after the operation (P<0.001). Average hospitalization times and number of dressing changes in group A were respectively shorter and fewer, compared with group B (P<0.001 and P=0.002, respectively). Conclusion: Two-stage irrigation combined with pedicled flaps could reduce local inflammation, edema, pain, and bacterial colonization, as well as facilitate wound healing, shorten hospitalization times, and reduce the average number of dressing changes.