Miracle-dz/UniFAR-FTUs huggingface dataset:
Organization of the Human Inferior Parietal Lobule Based on Receptor Architectonics

Human inferior parietal lobule (IPL) plays a key role in various cognitive functions. Its functional diversity, including attention, language, and action processing, is reflected by its structural segregation into 7 cytoarchitectonically distinct areas, each with characteristic connectivity patterns. We hypothesized that commonalities of the cytoarchitectonic, connectional, and functional diversity of the IPL should be reflected by a correlated transmitter receptor–based organization. Since the function of a cortical area requires a well-tuned receptor balance, the densities of 15 different receptors were measured in each IPL area. A hierarchical cluster analysis of the receptor balance revealed a tripartite segregation of the IPL into a rostral, middle, and caudal group. Comparison with other cortical areas showed strong similarities with Broca's region for all 3 groups, with the superior parietal cortex for the middle, and with extrastriate visual areas for the caudal group. Notably, caudal-most area PGp has a receptor fingerprint very similar to that of ventral extrastriate visual cortex. We therefore propose a new organizational model of the human IPL, consisting of 3 clusters, which corresponds to its known cytoarchitectonic, connectional, and functional diversity at the molecular level. This might reflect a general organizational principle of human IPL, beyond specific functional domains.
GRAPHiQL: A graph intuitive query language for relational databases

Graph analytics is becoming increasingly popular, driving many important business applications from social network analysis to machine learning. Since most graph data is collected in a relational database, it seems natural to attempt to perform graph analytics within the relational environment. However, SQL, the query language for relational databases, makes it difficult to express graph analytics operations. This is because SQL requires programmers to think in terms of tables and joins, rather than the more natural representation of graphs as collections of nodes and edges. As a result, even relatively simple graph operations can require very complex SQL queries. In this paper, we present GRAPHiQL, an intuitive query language for graph analytics, which allows developers to reason in terms of nodes and edges. GRAPHiQL provides key graph constructs such as looping, recursion, and neighborhood operations. At runtime, GRAPHiQL compiles graph programs into efficient SQL queries that can run on any relational database. We demonstrate the applicability of GRAPHiQL on several applications and compare the performance of GRAPHiQL queries with those of Apache Giraph (a popular `vertex centric' graph programming language).
Dietary Predictors of Overweight and Obesity in Iranian Adolescents

Background Considering both diet and energy expenditures possess some influence on weight status, research into dietary determinants of obesity is challenging but essential to rational planning of well-organized interventions to avoid obesity. Objectives This study aimed to determine whether dietary factors were predictive of overweight and obesity in adolescents in the Iranian population. Patients and Methods A total of 840 students, ages 15 - 17, from six schools were enrolled in this cross-sectional study. A diet-patterns approach often has been used to describe the eating patterns in adolescents. Height, weight, and waist circumference anthropometric indices, physical activity, waist hip ratio, and BMI measurements were determined. Daily dietary data and weighed food records were collected in 2010 and 2011. Abdominal obesity was defined according to world health organization guidelines, and the relationship between dietary predictor variables and the measures of adiposity were determined by using linear regression. Usual dietary intakes were assessed in an experimental study of Esfahani students. Results In total, 38.5% of girls and 32.2% of boys had a Western dietary pattern as the more prevalent pattern. The diet quality of adolescents with the lowest score on each dietary pattern was compared with those recording the highest scores. Those with the Western dietary pattern score were less likely to exercise and had a higher prevalence of general obesity. Adolescents in the greater quartile of the Mediterranean dietary patterns had the lowest odds of being overweight (OR 0.50, 95%; CI 0.27 - 0.73) and obese (OR 0.48, 95%; CI 0.15 - 0.80) than those in the lower quartile, whereas those in the greater quartile of the Western dietary pattern had the highest odds of being overweight (OR 1.69, 95%; CI 1.10 - 2.04) and obese (OR 1.44, 95% CI 1.05 - 1.84). Higher consumption of a Western dietary pattern and a salty dietary pattern were associated significantly with obesity (P < 0.05). Intake of a Western dietary pattern and a salty–sweet dietary pattern were associated positively with measures of adiposity, namely body mass index and waist circumference. Conclusions This study showed significant associations between the seven dietary patterns and overweight and obesity among adolescents. Using dietary patterns within adolescents can provide important information on dietary consumption, and this approach is clearer and much easier to follow.
Performance and Stability of the Chelonia Storage Cloud

In this paper we present the Chelonia storage cloud middleware. It was designed to fill the requirements gap between those of large, sophisticated scientific collaborations which have adopted the grid paradigm for their distributed storage needs, and of corporate business communities which are gravitating towards the cloud paradigm. The similarities to and differences between Chelonia and several well-known grid- and cloud-based storage solutions are commented. The design of Chelonia has been chosen to optimize high reliability and scalability of an integrated system of heterogeneous, geographically dispersed storage sites and the ability to easily expand the system dynamically. The architecture and implementation in term of web-services running inside the Advanced Resource Connector Hosting Environment Dameon (ARC HED) are described. We present results of tests in both local-area and wide-area networks that demonstrate the fault-tolerance, stability and scalability of Chelonia.
Digital processing of ECG and PPG signals for study of arterial parameters for cardiovascular risk assessment

One of the biggest single cause of mortality in the world is cardiovascular disease (CVD) and hence it is recognized that an early detection of its onset is vital for effective preventive therapies. Aortic stiffness has been shown to be an independent predictor of CVD, but its measurement is complex and time consuming. An alternative much simpler way of doing it is by looking for arterial properties such as arterial stiffness. This paper presents methods of estimating arterial parameters from measured ECG and PPG signals. PPG signal can be acquired easily by measuring the transmission of infra-red light through the finger tip. Two methods of obtaining arterial parameters have been used, one using simultaneously acquired ECG and PPG signals and another using PPG signal only. Pulse wave velocity, which is an indicator of arterial stiffness, has been measured using pulse wave transmit time derived from simultaneously recorded ECG and PPG signals. Arterial parameters like stiffness index, augmentation index etc have been derived from the PPG signals. These parameters have been obtained for subjects in two age groups and it is observed that the parameters show clear changes for the two age groups thus demonstrating their efficacy in detecting changes in the arteries with age. Power spectral analysis has been performed to show how frequency spectra are different for the two age groups. This study demonstrates the usefulness of ECG and PPG analysis in investigating the changes in the arterial characteristics of the vascular system with age; it has potential applications in healthcare.
A Loan Portfolio Model Subject to Random Liabilities and Systemic Jump Risk

We extend the Vasi\v{c}ek loan portfolio model to a setting where liabilities fluctuate randomly and asset values may be subject to systemic jump risk. We derive the probability distribution of the percentage loss of a uniform portfolio and analyze its properties. We find that the impact of liability risk is ambiguous and depends on the correlation between the continuous aggregate factor and the asset-liability ratio as well as on the default intensity. We also find that systemic jump risk has a significant impact on the upper percentiles of the loss distribution and, therefore, on both the VaR-measure as well as on the expected shortfall.
Research on interpretation of nominal compound

Nominal compounds which constituted of two nouns together are very common in reading materials or web pages. The interpretation of these compounds can help us know the meaning of a text or sentences. Traditional approaches utilized the method based on verbs and rules to obtain the interpretation of compounds with low recall. So we investigate an interpretation method based on similarity which makes use of the interpretation templates and similar words to achieve the automatic interpretation. Experimental results show that our method can interpret these nominal compounds with a relatively high precision (84.91%), give an increase of 10.48% in recall than the general method, which contributes to the overall nominal compound recall improvement significantly.
Action Recognition Using HOG Feature in Different Resolution Video Sequences

This paper summarizes the recent development of action recognition at first. Then based on Hierarchical Filtered Motion model and Nearest Neighbor classifier, we do action recognition using HOG feature in video sequences of different resolutions. Here we use KTH dataset for training and MSR action dataset II for testing. The experiment demonstrates that the new feature extraction process is effective and has better performance in the cross-dataset action recognition.
Fully Distributed Social Welfare Optimization With Line Flow Constraint Consideration

This paper proposes a fully distributed social welfare optimization solution that solves the economic dispatch and demand response problems in an integrated way. Compared with sequentially implementing these two operations one after another, the integrated solution can efficiently maximize the benefits of customers and minimize the generation cost of generators simultaneously. By adjusting both generations and dispatchable loads, line flow constraints and generation bounds can be satisfied easier. The proposed solution has two layers of operations for consensus-based information discovery and gradient-based generation or demand adjustment, respectively. It is fully distributed in the sense that there is no need for a specialized/central controller to coordinate the operations of the autonomous local controllers (agents). Compared with centralized solutions, the multiagent system-based distributed solution is more reliable against single-point failures and can better accommodate customer participation. The proposed solution has been tested with a 5-bus system and the IEEE 30-bus system under light- and heavy-load conditions. Both static optimization and dynamic simulation results are provided to demonstrate the performance of the proposed solution.
Poverty Measures in a Fuzzy Logic Framework

In the income (or consumption) poverty framework, a relevant open problem is connected with the distinction between poor and non-poor. This is usually done by considering a pre-specified income threshold called poverty line. If the income of an observation unit is lower (higher) than the poverty line, then such an observation unit is poor (non-poor or rich). Unfortunately, this procedure may often lead to questionable results because the concept of poverty is vague. In this connection, a promising line of research is the measurement of poverty according to a fuzzy logic approach. In this work, following such an approach, some new poverty measures based on the Gini and Bonferroni inequality indices are proposed.
Identifying and Comparing Academic and Student Perceptions of the Uses, Benefits, and Concerns of Using Facebook and Discussion Boards in Higher Education

Learning through a variety of online platforms, including Learning Management Systems (LMS) and functionalities within them, such as Discussion Boards, and Social Networking Sites (SNS), such as Facebook, is becoming an increasingly popular way of communicating with students and enhancing student learning (Caldwell, 2015). However, despite this increasing popularity amongst educators and students, there is limited research concerning SNS and LMS (i.e. Facebook and Discussion Boards) as functionalities for promoting effective learning and teaching in higher education. Therefore, this study has drawn upon the work of Bandura’s Self-efficacy model (1977) to critically examine academics’ and students’ perceptions of the perceived use, benefits and concerns of Facebook and Discussion Boards for learning and teaching. Following ethical clearance, data were collected using two surveys consisting of Likert-scale and open-ended questions investigating academic and student perceptions pertaining to Facebook and Discussion Board uses, benefits, concerns, frequency of use, General Self-efficacy, Facebook Self-efficacy and Discussion Board Self-efficacy. Surveys were administered to students and academic population consisting of a total of 339 participants (275 students; 64 academics), drawn from the Griffith Health Group, at Griffith University. The Griffith Health Group consists of eight schools, which are the School of Applied Psychology, School of Nursing and Midwifery, School of Allied Health Science, School of Medical Science, School of Medicine, School of Dentistry and Oral Health, and the School of Human Services and Social Work. Data were analysed using a mixed-methods approach, involving, where applicable, both correlational and thematic analysis.
Traffic manager: a flexible desktop simulation tool enabling future ATM research

In 1997, the National Aerospace Laboratory of the Netherlands (NLR) started working on an air traffic management (ATM) concept called "free flight. " Under this concept, aircraft are allowed to choose their own path, while maintaining separation from all other aircraft. The study originally focused on the human factors of airborne separation in a free flight environment. To provide flight crews with a challenging task that might provide insight into some interesting cockpit human factors issues of airborne separation, artificially high traffic densities and high conflict rates were required. This called for a simulation platform scalable to meet the traffic needs and flexible enough to model the interaction of aircraft in this new environment. The simulation tool developed was called the traffic manager (TMX). In this paper, we present a short history on the development of TMX, give a description of its major features, cover specific research capabilities such as the airborne separation assistance system, and give some examples of applications of TMX in research projects.
Vehicle Detection in Satellite Images by Parallel Deep Convolutional Neural Networks

Deep convolutional Neural Networks (DNN) is the state-of-the-art machine learning method. It has been used in many recognition tasks including handwritten digits, Chinese words and traffic signs, etc. However, training and test DNN are time-consuming tasks. In practical vehicle detection application, both speed and accuracy are required. So increasing the speeds of DNN while keeping its high accuracy has significant meaning for many recognition and detection applications. We introduce parallel branches into the DNN. The maps of the layers of DNN are divided into several parallel branches, each branch has the same number of maps. There are not direct connections between different branches. Our parallel DNN (PNN) keeps the same structure and dimensions of the DNN, reducing the total number of connections between maps. The more number of branches we divide, the more swift the speed of the PNN is, the conventional DNN becomes a special form of PNN which has only one branch. Experiments on large vehicle database showed that the detection accuracy of PNN dropped slightly with the speed increasing. Even the fastest PNN (10 times faster than DNN), whose branch has only two maps, fully outperformed the traditional methods based on features (such as HOG, LBP). In fact, PNN provides a good solution way for compromising the speed and accuracy requirements in many applications.
Transfer learning method using multi-prediction deep Boltzmann machines for a small scale dataset

In this article, we propose a transfer learning method using the multi-prediction deep Boltzmann machine (MPDBM). In recent years, deep learning has been widely used in many applications such as image classification and object detection. However, it is hard to apply a deep learning method to medical images because the deep learning method needs a large number of training data to train the deep neural network. Medical image datasets such as X-ray CT image datasets do not have enough training data because of privacy. In this article, we propose a method that re-uses the network trained on non-medical images (source domain) to improve performance even if we have a small number of medical images (target domain). Our proposed method firstly trains the deep neural network for solving the source task using the MPDBM. Secondly, we evaluate the relation between the source domain and the target domain. To evaluate the relation, we input the target domain into the deep neural network trained on the source domain. Then, we compute the histograms based on the response of the output layer. After computing the histograms, we select the variables of the output layer corresponding to the target domain. Then, we tune the parameters in such a way that the selected variables respond as the outputs of the target domain. In this article, we use the MNIST dataset as the source domain and the lung dataset of the X-ray CT images as the target domain. Experimental results show that our proposed method can improve classification performance.
Interference cancellation in massive MIMO base stations with certain precoding techniques in faded environment

Precoding design for MIMO wireless system has gained momentum currently as a new emerging research area and has been established in various wireless standards. In this paper, certain linear and nonlinear precoding methods are investigated to address the transmission problems of MU-MIMO systems in faded channels. It is studied how various precoding techniques at the transmitter side is improving the Bit error rate (BER), with full channel knowledge at the receiver end. Error performances prove that nonlinear DPC or TH technique achieves the capacity region of the multiple antennas, as compared to linear methods. DPC method seems to outperform all other techniques in terms of error performance and capacity limits.
Self-organization and a dynamical transition in traffic-flow models.

A simple model that describes traffic flow in two dimensions is studied. A sharp jamming transition is found that separates between the low-density dynamical phase in which all cars move at maximal speed and the high-density jammed phase in which they are all stopped. Self-organization effects in both phases are studied and discussed.
Analysis of blood samples for counting leukemia cells using Support vector machine and nearest neighbour

analysis of blood samples for counting leukemia cells is discussed in this paper. support vector machine and nearest neighbour concept is presented in this paper. The identification of blood disorders, it can lead to classification of certain diseases related to blood. Several classification techniques are investigated till today. One of the best methods for classification techniques nearest neighbour and SVM (Support Vector Machine). By identifying and counting blood cell within the blood smear using classification techniques it's quite possible to detect so many diseases. If one of the new classifier is used i.e. nearest neighbour and SVM it is quiet possible to detect the cancer cell from the blood cell counting. Keyword: Blood, Classification technique, Nearest Neighbour Network, SVM
Local and Non-local Graph Regularized Sparse Coding for Face Recognition

The recent emerging sparse coding (SC) algorithms do not take local manifold structure of samples into consideration, while graph regularized sparse coding (GraphSC) algorithm only constrains the locality consistency of samples. Furthermore, the graph construction approach based on k-nearest-neighbor usually pre-defines the number of neighbors for all the samples, which may fails to fit the intrinsic structure of each sample. To address these issues, we propose an local and nonlocal graph regularized sparse coding (LN-GraphSC) algorithm. LN-GraphSC incorporates both local and nonlocal information of samples at the same time. On the other hand, to alleviate the problem of neighbor parameter selection, we use average distance of each sample to wisely determine its own local and nonlocal samples. To verify the effectiveness of our proposed method, we evaluate our method on the task of face recognition. The experimental results on ORL and Yale face databases show our method has competitive performance when compared to SC and GraphSC.
Evaluating MT output with entailment technology

Constant evaluation is vital to the progress of machine translation. However, human evaluation is costly, time-consuming, and difficult to do reliably. On the other hand, automatic measures of machine evaluation performance (such as BLEU, NIST, TER, and METEOR), while cheap and objective, have increasingly come under suspicion as to whether they are satisfactory measuring instruments. Recent work (e.g., CallisonBurch et al. (2006)) has demonstrated that for current state-of-the-art MT systems, the correlation between BLEU scores and human adequacy and fluency ratings is often low; BLEU scores tend to favor statistical over rule-based systems; and BLEU-like measures tend to perform worse at the segment level than at the corpus level. The core of the problem is that BLEU (Papineni et al., 2001), and to a first approximation other automatic measures, work at the surface level, looking for shared word sequences between a system translation and one or more reference translations. This evaluation ignores many known facts about linguistic semantics, whereby the same meaning can be conveyed in many different ways, whether by the use of syntactic rearrangements or by exploiting lexical semantics (synonyms, etc.) and larger semantic paraphrases. Consider the two (real-world) example sentences in Figure 1, which are largely equivalent, but differ substantially on the surface. The equivalence of the two sentences hinges not only on the synonymy of individual words (practice/policy), but also on phrasal replacements (promote/make statements in favor of ) and lexical-semantic properties of words (such as the “built-in” negation of barring). In this paper, we present a study whose goal is to improve the prediction of adequacy judgments for MT system translations by accounting for such semantic phenomena. To do so, we model MT evaluation as an instance of the “recognition of textual entailment” (RTE) task (Dagan et al., 2005). RTE was introduced as a “practical” inference procedure that determines the existence of a causal relation between two short segments of text, the premise and the hypothesis: Is the hypothesis entailed by the premise, or not? Textual entailment has been found to be beneficial for a range of applications, for example in answer validation in Question Answering or in word sense disambiguation (Dagan et al., 2006; Harabagiu and Hickl, 2006). Our intuition is that the evaluation of MT output for adequacy can also be seen as an entailment task: A candidate translation (i.e., MT