Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Context for Agriculture for Nutrition Research: Identifying Country Typologies of Child-Stunting Determinants

We use data from 52 countries on child stunting, poverty, determinants of food security, environmental health, and quality of maternal and child care to carry out a cluster analysis of country typologies. The purpose is to identify where agriculture-led interventions might address binding constraints to progress in improving nutrition outcomes and to identify how existing research on the links between agriculture and nutrition in particular country contexts may or may not be representative. We find that countries with average to poor nutrition outcomes within this sample set fall into groups where one supporting area tends to lag, such as environmental health or food security. Although integrated programs to address all aspects that support nutrition are needed in most of these 52 countries, we identify a group of 11 countries where interventions to improve diet diversity may be most relevant toward progress in alleviating child malnutrition. Agriculture for Nutrition and Health intervention research is underway in 6 of these 11 countries.
Self-organizing warehouse management based on communicating distributed memory tags

This paper is about a theoretic model for resource based communication using the example of an autonomous warehouse. Autonomous automata handle boxes in the warehouse just based on the information they can read in their direct environment. These automata act according to two concurring algorithms. One is for finding boxes and carrying them to an output. The other algorithm is used to store boxes inside the warehouse. There is no direct communication between different automata in the warehouse. Analogous to stationary pheromones their communication is based on information that can be read from and written to their direct environment. This environment is the common communication resource and is acting as collective memory used independently by all robot automata. By this simple communication model a structure emerges in the placement of the boxes. Although representable as a common cellular automaton this model is presented basing on robots moving on a grid and memory tags that can be read and written to.
Comment on David Neumark and William Wascher, “Employment Effects of Minimum and Subminimum Wages: Panel Data on State Minimum Wage Laws”

We re-examine the evidence presented by Neumark and Wascher (1992) on the employment effect of the minimum wage. We find three critical flaws in their analysis. First, the school enrollment variable that plays a pivotal role in their specifications is derived on the false assumption that teenagers either work or attend school. Measurement error biases contaminate all the empirical estimates that use this enrollment variable. Second, Neumark and Wascher measure the effect of the minimum wage by a coverage-weighted relative minimum wage index. This variable is negatively correlated with average teenage wages. Taken literally, their results show that a rise in the coverage-weighted relative minimum wage lowers teenage wages. Examining the direct effects of state-specific minimum wages, we find that increases in state minimum wages raise average teenage wages but have essentially no employment effects. Finally, a careful analysis of Neumark and Wascher's data shows that subminimum wage provisions are rarely used. This casts doubt on their claim that subminimum provisions blunt any disemployment effect of the minimum wage. Neumark and Wascher contend that other minimum wage studies are biased by failing to control for school enrollment, and by failing to consider the lagged effects of minimum wages. We re-analyze the experiences of individual states following the April 1990 increase in the Federal minimum wage, allowing for a full year lag in the effect of the law and controlling for changes in (properly measured) enrollment rates. Contrary to their claims, allowing for lagged effects and controlling for enrollment status actually strengthens the conclusion that the 1990 increase in the Federal minimum had no adverse employment effect.
Efficient interconnects for clustered microarchitectures

Clustering is an effective microarchitectural technique for reducing the impact of wire delays, the complexity, and the power requirements of microprocessors. In this work, we investigate the design of on-chip interconnection networks for clustered microarchitectures. This new class of interconnects has different demands and characteristics than traditional multiprocessor networks. In a clustered microarchitecture, a low inter-cluster communication latency is essential for high performance. We propose point-to-point interconnects together with an effective latency-aware instruction steering scheme and show that they achieve much better performance than bus-based interconnects. The results show that the connectivity of the network together with latency-aware steering schemes are key for high performance. We also show that these interconnects can be built with simple hardware and achieve a performance close to that of an idealized contention-free model.
Towards streamed services for co-located collaborative groups

From both technical and social viewpoints there is great value in services that require devices (and therefore people) to be co-located. The very act of co-location brings with it entirely new dynamics and collaboration; furthermore, devices in coalition can render services and provide experiences that a single device might not be able to. In this paper we describe the motivation, design, and uses of high experience coalition-based services and outline how such services could be architecture on both the server and client sides. Extensive use of video transcoding and region-of-interest techniques - to segment and stream only portions of video frames - makes delivering experiences like “social cinema” across several co-located devices feasible. On the client side smartphone-based interactive coalition setup and control is very viable. In this paper we explore and document our functional architecture and take a closer look at the similarities and differences between OnLive and our proposed architecture and services. The rising prominence of hi-resolution LED devices together with services such as OnLive make coalition services technically viable, desirable, and worthy of both industrial and academic investigation alike.
COMPETENCE ONTOLOGY FOR DOMAIN KNOWLEDGE DISSEMINATION AND RETRIEVAL

In this paper, we present a competence ontology for domain knowledge dissemination and retrieval services, which has been used in the MDKT project (Management and Dissemination of Knowledge in Telecommunication). The main objective of this project is to set up a computerized knowledge management system related to a specific domain in order to develop the human resources expertise for the needs of the enterprise. In the case of this project, the knowledge is about wireless networking and is expressed in digital documents. Among all the ontologies that implement the knowledge needed by the system, the competence ontology plays a key role. The competence ontology defines at a meta-level the concept of competence and its relationships with other concepts such as document or user. Its instantiation is used to characterize a user model and a document model. This knowledge organization makes it possible to infer which document, or more generally which domain knowledge information, is suitable for a given person or to whom specific domain knowledge information should be disseminated.
Workspace updates of visual models

In MDE, large models must be collaboratively developed in teams. Collaboration is usually supported by optimistic versioning based on workspaces and a repository. Workspace copies of a model are synchronize with the repository version by an update function. Most update functions currently available compromise the consistency of a model. We present a new approach which guarantees updated models to be consistent and processable by standard visual editors without post-processing. Our approach assumes changes in the repository to be represented as a consistency-preserving edit script. This edit script is executed in a controlled, interactive manner on the workspace copy. Unlike 3-way merging, our approach needs no conflict analysis. Conflicts are detected and resolved interactively. Our approach offers improved usability and requires less implementation effort than existing approaches.
Does fixed-term employment delay important partnership events? Comparing transitions into cohabitation, marriage, parenthood and home ownership among young adults in Germany

This article investigates whether fixed-term employment affects the realization of important partnership events. We are looking at four transitions: entering cohabitation, marriage, parenthood and home ownership. Data were obtained from a random sample of 1,083 young German adults aged 20 to 35 years based on the AGIPEB Survey. We use the Kaplan-Meier method and piecewise-constant exponential models to estimate survival curves and transition rates. Women and men who work under a fixed-term contract are similarly likely to enter cohabitation, marriage and parenthood as persons who work under a permanent contract. In contrast, fixed-term employment compared to working under a permanent contract prolongs the transition into home ownership. Zusammenfassung In diesem Beitrag wird untersucht, ob befristete Beschäftigungsverhältnisse die Realisierung zentraler Partnerschaftsereignisse beeinflussen. Hierfür untersuchen wir vier Übergänge: Eintritt in die Kohabitation, Eheschließung, Elternschaft und Erwerb von Immobilieneigentum. Die Daten wurden aus einer Zufallsstichprobe mit 1.083 jungen deutschen Erwachsenen auf Basis des AGIPEB-Survey gewonnen. Wir wenden die Kaplan-Meyer-Methode und piecewise-constant exponential models an, um Überlebenskurven und Übergangsraten zu schätzen. Frauen und Männer, die in einem befristeten Beschäftigungsverhältnis arbeiten, neigen in ähnlicher Weise wie Personen mit einem unbefristeten Arbeitsvertrag dazu, die Kohabitation zu beginnen, die Ehe zu schließen und Eltern zu werden. Im Gegensatz dazu verzögert das Arbeiten unter befristeten Verträgen ‒ im Vergleich zum Arbeiten unter unbefristeten Verträgen ‒ jedoch den Übergang zum Erwerb von Wohnungseigentum.
Verification of JML generic types with Yices

Despite some efforts to provide support to JML (Java Modelling Language) for Java 1.5 and 1.6, no full implementation for checking Java's new features has been carried out. In particular, there is still no full support to JML and Java generic types. This paper presents an approach to check satisfiability of JML generic types by mapping JML specifications into the input language of Yices and using Yices' SMT solver to do the checking. Our primary motivation for mapping JML generic types into Yices is to be able to use an efficient solver to verify a JML model for social networking we had obtained as translation from B. Our work focuses on the mapping of JML generic classes for sets and relations since B machines specifications are essentially predicate logic and set theory. We use the JML model for social networking as a running example to present our ideas throughout the paper.
Analysis and Prediction of Highly Effective Antiviral Peptides Based on Random Forests

The goal of this study was to examine and predict antiviral peptides. Although antiviral peptides hold great potential in antiviral drug discovery, little is done in antiviral peptide prediction. In this study, we demonstrate that a physicochemical model using random forests outperform in distinguishing antiviral peptides. On the experimental benchmark, our physicochemical model aided with aggregation and secondary structural features reaches 90% accuracy and 0.79 Matthew's correlation coefficient, which exceeds the previous models. The results suggest that aggregation could be an important feature for identifying antiviral peptides. In addition, our analysis reveals the characteristics of the antiviral peptides such as the importance of lysine and the abundance of α-helical secondary structures.
Impact of leadership on continued participation in online groups

Title of Dissertation: IMPACT OF LEADERSHIP ON CONTINUED PARTICIPATION IN ONLINE GROUPS Steven Lawrence Johnson, Doctor of Philosophy in Information System Directed by: Associate Professor Samer Faraj, McGill University; Professor Hank Lucas, Department of Decision, Operations & Information Technologies Online groups formed by volunteer members are increasingly recognized as sources of innovative ideas, as producers of information goods, and as a critical component for successful product marketing. Compared to formal organizations, online groups appear as anarchic collections of individuals largely devoid of formal authority. Yet online groups develop strong group norms, successfully generate information goods, and satisfy member needs—outcomes that seem impossible without some form of leadership by influential members. Research on open-membership voluntary online groups has consistently found that contribution to online groups is dominated by a small percentage of participants. The goal of this research is to better understand the role of leadership in online groups and to evaluate the impact of leadership in maintaining online groups by supporting continued participation intentions of existing members. I explored three related questions regarding leadership in online groups. First, does member interaction with group leaders contribute to continued participation intentions over and above a model based on past participation? Second, do shared context and direct communication with leaders impact continued participation intentions? And third, do group characteristics—group psychological safety, group size, and perceived number of leaders—moderate the relationship between group members and group leaders? I collected 535 survey responses from members of thirty-three different online groups (average of sixteen members per group) and also analyzed group communication history (a total of 135,477 messages). This cross-level analysis furthers our understanding of the relationship between interaction with group leadership, psychological safety, participation role intentions, and turnover intentions. I found that leadership in online groups is a determinant of online group outcomes. Online group leaders shape the group context, including psychological safety, which encourages or discourages participation. This study shows that leadership processes, group context, and differentiation among dimensions of participation intentions are all important considerations for further understanding of online groups. IMPACT OF LEADERSHIP ON CONTINUED PARTICIPATION IN ONLINE GROUPS
Learning context sensitive behavior models from observations for predicting traffic situations

Estimating and predicting traffic situations over time is an essential capability for sophisticated driver assistance systems or autonomous driving. When longer prediction horizons are needed, e.g., in decision making or motion planning, the uncertainty induced by incomplete environment perception and stochastic situation development over time cannot be neglected without sacrificing robustness and safety. Especially describing the unknown behavior of other traffic participants poses a complex problem. Building consistent probabilistic models of their manifold and changing interactions with the environment, the road network and other traffic participants by hand is error-prone. Further, the results could hardly cover the complete diversity of human behaviors. This paper presents an approach for learning continuous, non-linear, context dependent process models for the behavior of traffic participants from unlabeled observations. The resulting models are naturally embedded into a Dynamic Bayesian Network (DBN) that enables the prediction and estimation of traffic situations based on noisy and incomplete measurements. Using a hybrid state representation it combines discrete and continuous quantities in a mathematically sound way. Experiments show a significant improvement in estimation and prediction accuracy by the learned context dependent models over standard models, which only consider vehicle dynamics.
Centre-Based Child Care Quality in Urban Australia

THIS STUDY INVESTIGATES the quality of childcare centres in urban Australian communities designated according to different bands of Centre Location Demographics (CLD). Childcare centres were assessed using the Early Childhood Environment Rating Scale – Revised Edition (ECERS-R) and the Early Childhood Environment Rating Scale – Extension (ECERS-E). Statistically significant differences were seen in quality on total scores across the five CLD areas. On both scales, middle and partially disadvantaged areas were identified as having substantial disadvantage in terms of childcare centre quality. The provision, and potential impact, of quality regulation and accreditation for childcare centres is discussed.
Design of Robust Observer for the Switched Discrete-Time Linear System with Time Delays

In this paper, an observer applying for switched linear system with delays in state variables is proposed for the control of the system. The reduction of the conservatism is resolved by using of the switched Lyapunov function approach. Some results are also extended to the uncertain system. Several linear matrix inequalities (LMIs) are established to guarantee the asymptotical stability in the system. Numerical examples are described to show the effectiveness of the proposed methods.
The transferal of evidences derived from clinical research to single patient level: Automatic distinction of normal elderly vs. mild cognitive impairment subjects by resting EEG data processed by IFAST, a novel intelligent system

It has been shown that a new procedure (implicit function as squashing time, IFAST) based on artificial neural networks (ANNs) is able to compress eyes-closed resting electroencephalographic (EEG) data into spatial invariants of the instant voltage distributions for an automatic classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects with classification accuracy of individual subjects higher than 92%. Here we tested the hypothesis that this is the case also for the classification of individual normal elderly (Nold) vs. MCI subjects, an important issue for the screening of large populations at high risk of AD. Eyes-closed resting EEG data (10- 20 electrode montage) were recorded in 171 Nold and in 115 amnesic MCI subjects. The data inputs for the classification by IFAST were the weights of the connections within a non linear auto-associative ANN trained to generate the instant voltage distributions of 60-s artifact free EEG data. The most relevant features were selected and coincidently the dataset was split into two halves for the final binary classification (training and testing) performed by a supervised ANN. The classification of the individual Nold and MCI subjects reached 95.87% of sensitivity and 91.06% of specificity (93.46% of accuracy). These results indicate that IFAST can reliably distinguish eyes-closed resting EEG in individual Nold and MCI subjects, and may be used for large-scale periodic screening of large populations at risk of AD and personalized care.
CONVENIENCE, ACCESSIBILITY, AND THE DEMAND FOR FAST FOOD

This study explores the growth in demand for fast food. A distinguishing characteristic of fast food is its convenience; in today's pervasive marketplace, consumers need not travel far to find a fast food outlet. This greater availability translates into a decrease in the full price of obtaining a meal, which contributes to greater consumption. Market-level data are used to estimate demand equations in two time periods, incorporating changes in availability as well as prices, income, and various demographic characteristics. Our findings show that greater availability has led to increased consumption. Failure to account for these types of marketplace changes could lead to incorrect inferences regarding the factors responsible for the industry growth.
Neural mechanisms of binocular motion in depth perception

Motion in depth (MID) can be cued by two binocular sources of information. These are changes in retinal disparity over time (changing disparity, CD), and binocular opponent velocity vectors (inter-ocular velocity difference, IOVD). This thesis presents a series of psychophysical and fMRI experiments investigating the neural pathways supporting the perception of CD and IOVD. The first two experiments investigated how CD and IOVD mechanisms draw on information encoded in the magnocellular, parvocellular and koniocellular pathways. The chromaticity of CD and IOVD-isolating stimuli was manipulated to bias activity in these three pathways. Although all stimulus types and chromaticities supported a MID percept, fMRI revealed an especially dominant koniocellular contribution to the IOVD mechanism. Because IOVD depends on eye-specific velocity signals, experiment three sought to identify an area in the brain that encodes motion direction and eye of origin information. Classification and multivariate pattern analysis techniques were applied to fMRI data, but no area where both types of information were present simultaneously was identified. Results suggested that IOVD mechanisms inherit eye-specific information from V1. Finally, experiment four asked whether activity elicited by CD and IOVD stimuli could also be modulated by an attentional task where participants were asked to detect changes in MID or local contrast. fMRI activity was strongly modulated by attentional state, and activity in motion-selective areas was predictive of whether participants correctly identified the change in CD or IOVD MID. This suggests that these areas contain populations of neurons that are crucial for detecting, and behaviourally responding to, both types of MID. The work presented in this thesis detail a thorough investigation of the neural pathways that underlie the computation of CD and IOVD cues to MID.
Fast Seam Carving Using Gaussian Pyramid

Image retargeting means to change the aspect ratio and resolution of an image in order to display it on different devices. During retargeting, how to keep significant image areas from obvious distortion is the main consideration. The seam carving method handles the challenge by recursively removing pixel seam with minimal cost. Though it can generate convincing results, the computation needed to find a seam is very time consuming. In this paper, we propose a new framework to speed up seam carving using Gaussian pyramid. First, we build Gaussian pyramid of an image. Then we distribute the seams to be removed over the pyramid layers. Removing one seam in the second layer means two seams in the first, and so on. Finally, we map seams in different layers back to the original image and remove them. In this way, we can reach good retargeting results while simultaneously reducing the number of seams to be removed and computational time for finding each seam.