Miracle-dz/UniFAR-FTUs huggingface dataset:
Preschool Nutrition and Subsequent Schooling Attainment: Longitudinal Evidence from Tanzania

This study analyzes how childhood health determines future academic performance in the Kagera region in Tanzania. Academic outcomes considered are years of education and delay in enrollment, and the measure of childhood health is height (relative to the median). The repercussions of malnutrition in childhood on subsequent learning and school performance are analyzed by using a unique longitudinal data set. Results indicate the degree to which malnutrition leads to reduced lifetime earning capacity due to both delays in schooling and declines in total schooling.
Collision-free navigation of wheeled vehicles by gain switching

In this paper, a collision-free navigation method for multiple wheeled vehicles is investigated. For individual vehicle three navigation variables are transformed from the position and orientation of respective vehicle. These navigation variables are distance of the vehicle from the goal position, angle between the vehicle's orientation and the vehicle to goal (v-to-g) vector, and the angle between the goal orientation and vehicle to goal (v-to-g) vector. The path tracking control utilizes kinematic representation. Moreover, a switching algorithm is implemented to attack the problem of obstacle avoidance. As the center point of the vehicle is used to navigate the vehicle to the goal position, two more points are used for the obstacle avoidance. These points are on the left front, and right front of the vehicle. Depending upon the position of the obstacle with respect to these points, the closest point to the obstacle gets activated and control design takes the parameters according to that point. The main advantage of this approach is the obstacle avoidance by keeping the safe distance from the closest point of the vehicle to the obstacle. The effectiveness of the proposed control algorithm is demonstrated by simulations.
Congestion control for streaming video and audio applications

As the Internet continues to grow, new applications like streaming video and audio and Internet telephony are emerging. These applications must implement end-to-end congestion control as the Internet relies on end hosts implementing congestion control for its stability. However, these applications have different requirements from the applications that the Internet has supported traditionally, and therefore call for new congestion control algorithms to be developed to suit their needs. In particular, they prefer smoother transmission rates over maximum possible transmission rates due to their real-time interactive nature. This thesis introduces and analyzes a class of nonlinear congestion control algorithms called binomial algorithms, motivated in part by the needs of streaming audio and video applications for which a drastic reduction in transmission rate upon each congestion indication (e.g., packet loss) is problematic. Binomial algorithms generalize TCP-style additiveincrease by increasing inversely proportional to a power k of the current window (for TCP, k = 0) ; they generalize TCP-style multiplicative-decrease by decreasing proportional to a power 1 of the current window (for TCP, 1 = 1). We show that there are an infinite number of deployable TCP-friendly binomial algorithms, all of which satisfy k + I = 1, and that all binomial algorithms converge to fairness under a synchronized-feedback assumption provided k + I > 0; k, 1 > 0. Our simulation results show that binomial algorithms interact well with TCP across a RED gateway. We also study the behavior of various classes of slowly-responsive TCP-friendly congestion control algorithms and their co-existence with TCP. We find that such algorithms lead to loss in throughput and sometimes affect Internet stability, unless packet conservation is employed. However, the various TCP-friendly congestion controls with different speeds of response to the network conditions are unfair to each other over short time-scales, as well as under varying network conditions. Thesis Supervisor: Hari Balakrishnan Title: Assistant Professor
Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling

Techniques for systematically monitoring protein translation have lagged far behind methods for measuring messenger RNA (mRNA) levels. Here, we present a ribosome-profiling strategy that is based on the deep sequencing of ribosome-protected mRNA fragments and enables genome-wide investigation of translation with subcodon resolution. We used this technique to monitor translation in budding yeast under both rich and starvation conditions. These studies defined the protein sequences being translated and found extensive translational control in both determining absolute protein abundance and responding to environmental stress. We also observed distinct phases during translation that involve a large decrease in ribosome density going from early to late peptide elongation as well as widespread regulated initiation at non–adenine-uracil-guanine (AUG) codons. Ribosome profiling is readily adaptable to other organisms, making high-precision investigation of protein translation experimentally accessible.
A continuous time model for network evolution

The study of networks is in great focus of many branches of science. We suggest a novel approach to modeling network evolution, based on the dynamics of independent Markov chains. Evolution is measured in continuous time units, as opposed to other models, where it is measured by a discrete counter of iterations. We derive a closed solution for the expected time until a node has any specific degree. The model produces a highly skewed degree distribution and a small world’s criteria, in agreement with real world networks. Our study demonstrates that a network of complex topology can be composed of identical elements, that have independent behavior.
A SIP DoS flooding attack defense mechanism based on priority class queue

This paper focuses on the research of defense mechanism of DoS (denial of service) attacks in SIP network. An M/M/1/K queue analysis model based on queue theory is proposed to analyze the SIP DoS attack firstly. Then, a DoS attack defense mechanism is proposed in order to alleviate SIP server's influence by INVITE flooding attack. In our defense mechanism, the priority queue is introduced in SIP server's message processing. At last, simulation is taken to analyze the performance of the defense mechanism proposed in this paper. The simulation results proves that SIP DoS flooding attack defense mechanism not only prolongs the serving time of SIP server correctly, but also realizes the aim of differentiating legitimate SIP message from attack flow.
A Unified Framework for Grammar Error Correction

In this paper we describe the PKU system for the CoNLL-2014 grammar error correction shared task. We propose a unified framework for correcting all types of errors. We use unlabeled news texts instead of large amount of human annotated texts as training data. Based on these data, a tri-gram language model is used to correct the replacement errors while two extra classification models are trained to correct errors related to determiners and prepositions. Our system achieves 25.32% in f0.5 on the original test data and 29.10% on the revised test data.
Adaptability and survivability in spaceborne time- and space-partitioned systems

Future space missions call for innovative computing system architectures, meeting strict requisites of size, weight, power consumption (SWaP), cost, safety and timeliness. To answer the aerospace industry interests, especially the European Space Agency (ESA), the AIR (ARINC 653 in Space Real-Time Operating System) architecture has been defined, following the advanced notion of time and space partitioning (TSP). In order to achieve adaptability and survivability in the face of environmental changes or new mission requirements, it is of paramount importance that onboard computing systems are reconfigurable. In this paper we present recent and ongoing developments on AIR Technology to achieve adaptability and survivability of AIR-based systems, and a methodology for onboard software update in order to add new features to the mission plan.
Isolation and Characterization of Fatty Acid Content of Shrimp Shell Chitin

Biomarkers are biologically produced organic compounds that can be used to identify sources of organic matter in geologic materials (soils, sediments, rocks). This information allows scientists to track important environmental and ecological changes over time and space. Some of the most commonly used biomarkers are a class of lipids called fatty acids. These compounds resist degradation and are diverse in structure, making them valuable indicators of carbon sources. 1 Most fatty acids can be extracted from sediments using organic solvents. However, a significant fraction cannot be extracted in this way and must first be liberated by hydrolysis of ester or amide bonds. 2 The understanding of the sources of these " bound lipids " is limited, but multiple studies have speculated about a relationship between fatty acids and chitin, a biopolymer found in arthropods and fungi. Analyses of chitin from several sources (Red crab shells, 3 commercial chitin standard 4) have indicated that there are fatty acids associated with the material. However, it is not known how this association occurs, or whether it is natural or an artifact of the chitin preparation process. As a member of Neal Blair's organic geochemistry lab, this summer I am interested in investigating this property of chitin and gaining valuable research experience. Therefore, the purpose of this study is to attempt a new method for the isolation of chitin in order to determine if fatty acids are naturally associated with it. Chitin is the second most abundant biopolymer on earth. 5 It is a-1,4 linked polymer of N-acetylglucosamine, and is found mainly in arthropod exoskeletons and fungal cell walls (Fig. 1). 6 Chitin is an insoluble, high molecular weight polymer with an extensive hydrogen bonding network, and it is naturally strengthened and functionalized by associated minerals and proteins. This makes it a challenge to purify without compromising the polymer structure, and it is thus difficult to analyze natural fatty acid content. 7 Preliminary analyses in our lab indicated that a chitin standard from Sigma Aldrich chemical company contained bound fatty acids. The use of the methylating reagent tetramethyl ammonium hydroxide (TMAH) with Gas Chromatography/Mass Spectroscopy (GCMS) identified the presence of C16 and C18 fatty acids in the chitin (Fig. 2). 4 Even though this chitin was intended for laboratory use, the bound fatty acid content was most likely not considered in purity analyses. Due to this, it is uncertain as to whether the fatty …
Motor effort training with low exercise intensity improves muscle strength and descending command in aging

AbstractThis study explored the effect of high mental effort training (MET) and conventional strength training (CST) on increasing voluntary muscle strength and brain signal associated with producing maximal muscle force in healthy aging. Twenty-seven older adults (age: 75 ± 7.9 yr, 8 women) were assigned into 1 of 3 groups: MET group—trained with low-intensity (30% maximal voluntary contraction [MVC]) physical exercise combined with MET, CST group—trained with high-intensity muscle contractions, or control (CTRL) group—no training of any kind. MET and CST lasted for 12 weeks (5 sessions/week). The participants’ elbow flexion strength of the right arm, electromyography (EMG), and motor activity-related cortical potential (MRCP) directly related to the strength production were measured before and after training. The CST group had the highest strength gain (17.6%, P <0.001), the MET group also had significant strength gain (13.8%, P <0.001), which was not statistically different from that of the CST group even though the exercise intensity for the MET group was only at 30% MVC level. The CTRL group did not have significant strength changes. Surprisingly, only the MET group demonstrated a significant augmentation in the MRCP (29.3%, P <0.001); the MRCP increase in CST group was at boarder-line significance level (12.11%, P = 0.061) and that for CTRL group was only 4.9% (P = 0.539). These results suggest that high mental effort training combined with low-intensity physical exercise is an effective method for voluntary muscle strengthening and this approach is especially beneficial for those who are physically weak and have difficulty undergoing conventional strength training.
Search, Liquidity and the Dynamics of House Prices and Construction

The dynamics of house prices, sales, construction, and population growth in response to city-specific income shocks are characterized for 106 US cities. A dynamic model of search in the housing market in which construction, the entry of buyers, house prices, and sales are determined in equilibrium is then developed. The theory generates dynamics qualitatively consistent with the observations and a version calibrated to match key features of the US housing market offers a substantial quantitative improvement over models without search. In particular, variation in the time it takes to sell induces transaction prices to exhibit serially correlated growth.
Defect of characters of the symmetric group

Abstract Following the work of B. Külshammer, J. B. Olsson and G. R. Robinson on generalized blocks of the symmetric groups, we give a definition for the -defect of characters of the symmetric group 𝔖 n , where is an arbitrary integer. We prove that the -defect is given by an analogue of the hook-length formula, and use it to prove, when , an -version of the McKay conjecture in 𝔖 n .
Mobility Extensions for Knowledge Discovery Workflows in Data Mining Grids

In scientific and other domains, knowledge discovery has started to be widely supported by service oriented data mining grids. When access to such services is required anytime at anyplace, the integration of mobile devices and wireless networks into grids is useful. However, mobile technologies exhibit limited capabilities and movement further cause frequent changes of context, like location and, thus, network connectivity. In this paper, the integration of mobile devices as ubiquitous knowledge discovery clients is proposed. The major service classes in knowledge discovery workflow management are addressed, those are, the monitoring and controlling of executing services. The feasibility of the approach is demonstrated by means of a . NET-based prototypical implementation on PDAs for the knowledge discovery framework GridMiner
The Impacts of Internal Migration on Child Victimization in China: A Meta-Analysis

Objective: A 100 million children in China are affected by internal migration, a result of the growing demand for human labor due to economic growth. Whether moving to urban cities with their parents or being left behind in rural areas, these children are vulnerable to victimization. The meta-analysis presented in this study is the first to synthesize the rates of victimization among children affected by internal migration. Method: Studies providing data published before May 2016 on the prevalence of child victimization among 105,415 Chinese subjects aged 0–17 years were identified through eight English and Chinese databases. Two reviewers independently extracted data to generate summary effect sizes using a random effects meta-analytic model. A priori subgroup and sensitivity analyses were performed to evaluate heterogeneity and bias in these studies. Results: Our meta-analysis of 31 studies showed that children affected by internal migration, both migrant children and left-behind children, are at a higher risk of victimization in comparison to their unaffected counterparts (odds ratio [OR] = 1.492, p < .01), especially in their vulnerability to unintentional injuries (OR = 1.683, p < .01) and neglect (OR = 1.398, p < .01). These children are at a higher risk of being unintentionally injured from the ages of 6 to 11 years (OR = 1.644, p < .01). The highest victimization rate observed in this study was found in the central districts of China (OR = 1.599, p < .01). Conclusion: This study reveals the high prevalence of victimization among both migrant children and left-behind children. The increasing number of children affected by internal migration deserves continuous academic and policy attention.
Autonomía profesional y riesgos de seguridad de los periodistas en Colombia

This paper analyzes the relationship between professional autonomy and security risks of journalists in Colombia. A correlational-transversal research was conducted with a sample of 751 journalists who filled out the questionnaire “Worlds of Journalism Study”. The results show significant differences on the attacks received by the journalists depending on gender, news beat, region, news media, years of experience, capacity and educational level of journalists. Attacks on journalists correlates positively with the autonomy to publish and write news on governments, armed forces, criminal gangs and structural social problems (poverty, status of ethnic minorities, socioeconomic inequality, environmental damage) the latter being a predictor of high risk security.
Second-Depth Shadow Mapping

Depth-map algorithms for rendering antialiased shadows are computationally efficient and accommodate a wide variety of primitives. They have two drawbacks, however, that make them impractical for everyday use: They require the user to specify a bias that must be optimized for each particular scene and view; and they generally require the use of large shadow maps— frequently much larger than the final image that is to be rendered. This paper presents an improved depth-map shadow algorithm that does not require a bias for scenes composed of solids and produces accurate shadows with smaller depth maps. It consists of two novel changes to the basic depth-map algorithm. First, the depth map samples the depth values of surfaces that are second nearest the light source. Second, virtual samples are created on local tangent planes of surfaces that are visible from the camera's point of view to match real samples of the depth map. These two changes allow depth comparisons to be done accurately and without the need for a compensating bias value. They also allow the algorithm to produce superior results when using small depth maps. The new algorithm incorporates percentage-closer filtering to antialias shadow boundaries and can be accelerated using zbuffer hardware in the same manner as previous algorithms.
On the Cost of Mining Very Large Open Source Repositories

Open source bug tracking systems provide a rich information suite that is actively used by software engineering researchers to design solutions to triaging, duplicate classification and developer assignment problems. Today, open repositories often contain in excess of 100, 000 reports, and in cases of RedHat and Mozilla, over a million. Obtaining and analyzing the contents of such datasets are both time and resource consuming. By summarizing the related work we demonstrate that researchers often focused on smaller subsets of the data, and seldom embrace the “big-dataism”. With the emergence of cloud based computation systems such as Amazon EC2, one expects it to be easier to perform large scale analyses. However, our detailed time and cost analysis indicates that significant challenges still remain. Acquiring the open source data can be time intensive, and prone to being misinterpreted as Denial of Service attacks. Generating similarity scores for all prior reports, for example, is a polynomial time problem. In this paper, we present actual costs that we incurred when analyzing the complete repositories from Eclipse, Firefox and Open Office. In our approach, we relied on computing clusters to process the data in an attempt to reduce the cost of analyzing large datasets on the cloud. We present estimated costs for a researcher attempting to analyze complete datasets from Eclipse, Mozilla, Novell and RedHat using the best possible resources. In an ideal situation, with no bottlenecks, a researcher investing just over $40, 000 and 2 weeks of non stop computing time would be able to measure similarity of problem reports within all four datasets.
Ensemble of classifiers for handling biomedical problems

Machine learning and statistical techniques applied to gene expression data have been used to address the questions of distinguishing tumor morphology, predicting post treatment outcome, and finding molecular markers for disease. Today the classification of different morphologies, lineages and cell histologies can be performed successfully in many instances. The performance in predicting treatment outcome or drug response has been more limited but some of the results are quite promising. The scope of the research reported here is to investigate the efficiency of machine learning techniques in a number of different biomedical problems. To this end, a number of experiments have been conducted using representative learning algorithms. The decision of which particular method to choose is a complicated problem. A good alternative to choosing only one method is to create a hybrid forecasting system incorporating a number of possible solution methods as components (an ensemble of classifiers). For this purpose, we have implemented a hybrid decision support system that combines the representative algorithms using a stacking variant methodology and achieves better performance than any examined simple and ensemble method. In this work, we use a feature selection pre-process before the usage of the stacking. Feature subset selection is the process of identifying and removing as much irrelevant and redundant features as possible. This reduces the dimensionality of the data enabling the proposed ensemble to operate faster and more effectively.
3D Vehicle Location Based on Improved Hausdorff Distance and Distributed Estimation Algorithm

3D model is used for vision based vehicle location. An improved Hausdorff distance based on edge-strength is proposed to evaluate the similarity between 3D model projection and image feature, and to establish location optimization function In order to avoid local minimum during optimization, estimation of distribution algorithm concerning related multi-variables is used. The relationship between matching parameters are described with a probability model, and the distribution of parameter evolves towards the direction of dominant character through probability model learning and the corresponding operation, which is proposed to solve the problem of over many iteration and slow constringency velocity. The experiments show that the optimal matching parameters between 3D model and 2D image feature can be found accurately and efficiently, and the algorithm outperforms other approaches in both accuracy and rapidity.
A New Mutation Analysis Method for Testing Java Exception Handling Authors Affiliations Emails

Java exception mechanism can effectively free a program from abnormal exits and help developers locate faults with the exception tracing stacks. It is necessary to verify whether the exception handling constructs are arranged appropriately. There are some approaches have been developed to evaluate the test sets and improve the quality of them, so that they can raise more number of exceptions in programs. Mutation analysis is a practical method to evaluate the quality of test sets. This paper presents some new mutation operators for Java exception handling construct. Moreover, equivalent mutants can be identified with these mutation operators. A case study illustrates the effectiveness of these mutation operators and discusses their characteristic features.