Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of the social brain during adolescence

This article describes recent research which informs our understanding of changes in social cognition during adolescence. The focus will be on mentalising, the ability to attribute and manipulate mental states in the self and others. Mentalising is supported by the medial prefrontal cortex (MPFC) and both anterior and posterior regions of the temporal lobes. In the past decade, studies have demonstrated development during adolescence of white and grey matter brain structure, with most protracted changes observed in frontal and temporal lobes, including those regions supporting mentalising. This article presents evidence that certain aspects of social cognition continue to change during adolescence, highlighting results from recent research investigating the use of theory of mind information in a communicative context. The findings highlight how adolescence, and not only childhood, is a time of continued maturation of brain and behaviour, when education and the environment can have an impact on cognitive development. © 2015 Colegio Oficial de Psicólogos de Madrid. Published by Elsevier España, S.L. U. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Desarrollo del cerebro social durante la adolescencia
Compilation Techniques for Incremental Collection Processing

Many map-reduce frameworks as well as NoSQL systems rely on collection programming as their interface of choice due to its rich semantics along with an easily parallelizable set of primitives. Unfortunately, the potential of collection programming is not entirely fulfilled by current systems as they lack efficient incremental view maintenance (IVM) techniques for queries producing large nested results. This comes as a consequence of the fact that the nesting of collections does not enjoy the same algebraic properties underscoring the optimization potential of typical collection processing constructs. We propose the first solution for the efficient incrementalization of collection programming in terms of its core constructs as captured by the positive nested relational calculus (NRC+) on bags (with integer multiplicities). We take an approach based on delta query derivation, whose goal is to generate delta queries which, given a small change in the input, can update the materialized view more efficiently than via recomputation. More precisely, we model the cost of NRC+ operators and classify queries as efficiently incrementalizable if their delta has a strictly lower cost than full re-evaluation. Then, we identify IncNRC+, a large fragment of NRC+ that is efficiently incrementalizable and we provide a semantics-preserving translation that takes any NRC+ query to a collection of IncNRC+ queries. Furthermore, we prove that incrementalmaintenance for NRC+ is within the complexity class NC0 and we showcase how Recursive IVM, a technique that has provided significant speedups over traditional IVM in the case of flat queries, can also be applied to IncNRC+ . Existing systems are also limited wrt. the size of inner collections that they can effectively handle before running into severe performance bottlenecks. In particular, in the face of nested collections with skewed cardinalities developers typically have to undergo a painful process of manual query re-writes in order to ensure that the largest inner collections in their workloads are not impacted by these limitations. To address these issues we developed SLeNDer, a compilation framework that given a nested query generates a set of semantically equivalent (partially) shredded queries that can be efficiently evaluated and incrementalized using state of the art techniques for handling skew and applying delta changes, respectively. The derived queries expose nested collections to the same opportunities for distributing their processing and incrementally updating their contents as those enjoyed by top-level collections, leading on our benchmark to up to 16.8x and 21.9x speedups in terms of offline and online processing, respectively. In order to enable efficient IVM for the increasingly common case of collection programming with functional values as in Links, we also discuss the efficient incrementalization of simplytyped lambda calculi, under the constraint that their primitives are themselves efficiently incrementalizable.
Determinants of SME international competitiveness : a case of Ghanaian horticultural exporters

Ghana's Non-Traditional Export (NTE) sector is central to her export and economic growth. Dominated by SME exporters, the sector has historically underperformed (GEPC, 2005). Therefore, this paper aims to evaluate the determinants of SME international competitiveness by addressing the question: To what extent are horticultural SME exporters in Ghana, competitive in the international market? In the framework part of the study five hypotheses were tested on the key issues of the paper. The study was conducted using a quantitative approach which involved surveying data of 105 Ghanaian horticultural SMEs which were statistically analysed employing SPSS. The main findings suggest that Government support policies, Access to finance, Technological Innovation, Entrepreneurial Factors, and the number of exporting destinations of the SME had a positive impact on the competitiveness of SME horticultural exporters in international markets. The findings also contribute to the advancement of the empirical, theoretical and managerial understanding of SME international competitiveness in developing economies.
Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement

The analysis of data from user interactions with technologies is literally changing how organisations function, prioritise and compete in an international market. All industries have been influenced or impacted by the so called digital revolution and the associated analysis of user data. In the higher education (HE) sector this wave of data analytics has flowed through the concept of learning analytics (LA). This field of research has been touted as a game changer for education whereby the outcomes of LA implementations will address core education challenges. These include concerns regarding student retention and academic performance, demonstration of learning and teaching quality, and developing models of personalised and adaptive learning. While there is broad consensus across the sector as to the importance for LA there remain challenges in how such endeavours are effectively and efficiently rolled out across an organisation. The lack of institutional exemplars and resources that can guide implementation and build institutional capacity represents a significant barrier for systemic adoption. This report seeks to unpack these challenges to institutional adoption and provide new insights that can aid future implementations of LA and help advance the sophistication of such deployments. The study does so by interrogating the assumptions underpinning the adoption of LA in the Australian University sector and contrasting this with the perspectives of an international panel of LA experts. The findings and recommendations highlight the need for a greater understanding of the field of LA including the diversity of LA research and learning and teaching applications, alongside the promotion of capacity building initiatives and collaborations amongst universities, government bodies and industry. ; ;
Has Productivity Contributed to the Economic Growth of Yangtze River Delta ?

The principal target of my thesis is to identify the sources of economic growth in YRD zone. A review of YRD post-reform development is conducted. The empirical study examines total factor productivity growth, technical progress, efficiency change and the scale effect in 16 component cities in YRD. Panel data of 1985-2007 are analyzed by using the stochastic frontier model. The major issues consider as (1) analyzing the source of YRD growth in which productivity growth is decomposed into three components: technical progress, changes in technical efficiency and the scale effect, (2) comparing the productivity and efficiency performance among cities and finding the tendency between the results from different parts of TFP growth, and (3) providing the explanation for my estimation and policy implication. YRD’s growth has been mostly input-driven and TFP growth by average 9.5% each year. For sustaining the engine position in China, YRD should take effort to enhance the technology level and introduce in technical innovation from outsides. INDEX WORDS: Productivity growth, Sources of productivity growth, Efficiency change, Technical change, Scale effect, Stochastic frontier analysis.
PRACTICAL LABORATORY AGEING METHOD FOR POROUS ASPHALT

During the mix design phase of porous asphalt (PA) pavements, it is imperative to know the PA properties after field ageing. This will help to optimize pavement performance and to increase the confidence level of the pavement design. A study was initiated to develop a laboratory ageing procedure for PA mixtures. This procedure should be able to mimic 10 years of field ageing within 10 days in a laboratory using only standard lab equipment. Conventional empirical and sophisticated fundamental rheological tests (penetration, softening point ring and ball, DSR), chemical tests (FTIR) and adhesion tests (Wilhelmy plate) were conducted on binders recovered from field cores and on binders recovered from specimens that were subjected to laboratory ageing. Finally two ageing procedures were developed: one for the mortar in PA and one for the loose PA mix. To accomplish the same ageing of the bitumen as in 10 year period in the field, the mortar has to be heated in an oven for 2 hours at 165°C and then be placed in a PAV at 90°C for 7 days under air pressure of 2.1 MPa. The loose PA mix has to be heated for 44 hours at 135oC to accomplish the same degree of ageing. In this paper the research program is discussed and the findings are presented.
Antioksidant Effect of Medicinal Plants in Meat and Meat Products

Meat and meat products have a rich nutritional composition due to protein, lipid, water and small amounts of carbohydrates, regarding this their sensitivity to deterioration is high [1]. In despite of the flavor, food variety, convenience, and good nutritional value desired by the consumer, processed meats are often sensed to be less healthy than many other types of food. Especially, red meat consumption can be associated with chronic heart diseases and several types of cancer [2]. World Health Organization issued a monograph grouping processed meat as carcinogen (Group I) and red meat as probable carcinogen (Group 2A) [3]. Synthetic antioxidants such as butylated hydroxyanisole (BHA) and butylated hydroxytoluene (BHT) have been used in the food industry to prevent lipid oxidation [4]. In recent years, consumer concerns about the safety of synthetic additives in food have led to the use of natural antioxidants and antimicrobials in meat and meat products as alternative compounds to protect the food [5]. Many herbs, spices, fruits and beekeeping products contain natural antioxidants, and these natural antioxidants can provide protection against the undesirable change of oxidation [6]. Plants and plant products always have been important in many parts of human life [7]. Medicinal plants such as thyme, sage, rosemary, oregano are widely used in meat products. Antioxidant activity of medicinal and aromatic plants is related to phenolic compounds which are affected by oxidation in structures [8]. Flavonoids, phenolic acids and phenolic terpenes are the most common among these compounds [9]. The chemical composition of these plants varies depending on many factors and the antioxidant effects can vary [9][10].
LANTHANIDE EFFECT ON PHYSICOCHEMICAL PROPERTIES OF LIPID BILAYER AS REVEALED BY INDICATOR DYE BROMOTHYMOL BLUE

Development of new formulations of antineoplastic drugs currently represents extensively growing research area. Efficiency of existing anti-tumor drugs is frequently limited by their high general toxicity, metabolic instability in an organism and bad penetration into a cancer cell. Besides, insignificant direct influence on tumoral growth also limits the application of antineoplastic drugs in a free form. One efficient way of drug delivery is based on the use of lipid vesicles (liposomes). Liposomes are spherical, self-closed structures formed by one or several concentric lipid bilayers with an aqueous phase inside and between the lipid bilayers. The lipid bilayer favors solubilization of hydrophobic compounds, whereas internal aqueous phase of lipid vesicles is suitable for encapsulation of hydrophilic drugs. Design of liposomal carriers is heavily based on the evaluation of bilayer-modifying properties of the drug. This is important not only for achieving maximum payload without compromising liposome stability, but also for prediction of therapeutic and toxic effects of a certain compound, because membrane interactions may prove critical for drug absorption, distribution, metabolism and elimination in an organism. In the present work the effect of the two potential antineoplastic drugs – europium coordination complexes (LC) – on the physicochemical properties of phosphatidylcholine (PC) model membranes has been investigated using the environmentally-sensitive pH indicator dye bromothymol blue (BTB). This dye responds to the changes in environmental conditions by the shifts of its protolytic and partition equlibria. Incorporation of LC into the lipid vesicles was found to exert no influence on the effective electrostatic potential of model membranes, i.e. the mean potential at location of the dye prototropic moiety in the interfacial region. In contrast, BTB membrane partitioning markedly enhanced in the presence of drugs, indicating that europium coordination complexes can affect molecular organization of a lipid bilayer, presumably through generation of structural defects and altering the conformation of PC headgroups. High lipophilicity of Eu(III) coordination complexes together with their relatively weak membrane-modifying propensities create prerequisites for the development of liposomal formulations of these compounds.
Cross-lingual Transfer Learning and Multitask Learning for Capturing Multiword Expressions

Recent developments in deep learning have prompted a surge of interest in the application of multitask and transfer learning to NLP problems. In this study, we explore for the first time, the application of transfer learning (TRL) and multitask learning (MTL) to the identification of Multiword Expressions (MWEs). For MTL, we exploit the shared syntactic information between MWE and dependency parsing models to jointly train a single model on both tasks. We specifically predict two types of labels: MWE and dependency parse. Our neural MTL architecture utilises the supervision of dependency parsing in lower layers and predicts MWE tags in upper layers. In the TRL scenario, we overcome the scarcity of data by learning a model on a larger MWE dataset and transferring the knowledge to a resource-poor setting in another language. In both scenarios, the resulting models achieved higher performance compared to standard neural approaches.
Analysis of scientific production based on trending research topics . An Artificial Intelligence case study

Scientific documentation research leads to the computation of large amounts of information from published works of the scientific community. It is necessary to explain these processes and create application frameworks. This paper provides the following: a) An Information System designed to extract scientific information from published papers, b) Accurate explanations of the main processing stages including data mining, natural language processing, and machine learning, and c) Categorized and explained results coming from the Artificial Intelligence case study. The results in this paper include the following: a) Topics and research area rankings and b) Quantity versus quality comparisons of topics and research areas.
LINK BETWEEN EXTERNAL PRESSURE AND BANKS’ SOCIAL DISCLOSURE

This paper explored the relationship between external pressure and social disclosure in South African banks. The South African Kings III guideline on corporate governance highlighted the importance of inclusion of social disclosure in the integrated reports of banks; however, little prior research has focussed on external pressure and social disclosure within the South African banking sector. Hence, this paper adds a nuance to this branch of literature within the South African context. It used a sample of banks within the JSE SRI Index and applied the content analysis in data collection on external pressure and bank’s social disclosure. It then applied the panel data multiple regression statistics. Results showed that profit motive, government pressure and customer pressure proved positively and significantly related to banks’ social disclosure at a P value of 0.05. The paper offers practical and policy implication for sustainability advocacy groups and regulators and for academics for research and academic studies. It recommends further expanded research with many years and more financial institutions aside of banks to research on likely strategic reasons behind banks’ social disclosure.
PROTEIN AGGREGATION IN A MEMBRANE ENVIRONMENT By GALYNA GORBENKO AND VALERIYA TRUSOVA

Biological membranes are featured by a remarkable ability to modulate a wide range of physiological and pathological processes. Of these, protein aggregation is currently receiving the greatest attention, as one type of the ordered protein aggregates, amyloid fibrils, proved to be involved in molecular etiology of a number of fatal diseases. It has been hypothesized that nucleation of amyloid fibrils and toxic action of their precursors is mediated by lipid–protein interactions. Lipid bilayer provides a variety of environments in which aggregated state of polypeptide chain appears to be more thermodynamically favorable than its monomeric form. The major factors responsible for the enhanced self-association propensity of membrane-bound proteins include (i) structural transition of polypeptide chain into aggregation-prone conformation; (ii) protein crowding in a lipid phase; (iii) particular aggregation-favoring orientation and bilayer embedment of the protein molecules. All these factors are considered in the present review with an emphasis being put on the role of electrostatic,
Discovering Modal Polysemy: Lexical Aspect might Help

This paper investigates how children figure out that functional modal verbs like moeten 'must' in Dutch and have to in English (1) can be used to express both epistemic and 'root' (i.e., non epistemic) flavors: speakers can use (1) to mean that John is required to exercise (deontic necessity) or that he likely exercises (epistemic necessity). In this study, we focus on the input that children get.
Measuring Algorithms in Online Marketplaces

of the Dissertation Measuring Algorithms in Online Marketplaces by Le Chen Doctor of Philosophy in Computer Science Northeastern University, May 2017 Dr. Christo Wilson, Adviser The integration of Big Data and algorithms have revolutionized many aspects of modern life. One of the revolutions is happening in markets, where data-centric algorithms are gradually automating services that used to require manual operations. For example, Real-Time Bidding (RTB) algorithms are widely adopted in the online advertising network, and are expected to grow from generating 31% of the revenue of the entire digital ads market in 2015 to 48% in 2020. Mortgage and credit lenders in the loan markets use algorithms to make lending decisions based on Big Data rather than traditional credit reports. Outside of advertising and financial markets, ride sharing companies like Uber and Lyft use surge pricing algorithms to dynamically balance the gaps between car supply and user demand. Sellers in e-commerce markets use dynamic pricing tools to adjust product prices and manage inventories in real-time. Surveys show that as of 2013, 13% of retailers had deployed dynamic pricing algorithms. Algorithms are powerful tools that have the potential to improve the efficiency of markets, but come at a cost of possible harms. On one hand, algorithms can be beneficial: RTB maps billions of Internet users to customized advertisements based on their interests in real time, and thus increases the effectiveness for both advertisers and publishers in terms of advertising inventory sold. Similarly, ride sharing has redefined the transportation market by matching millions of drivers and riders around the globe in real-time. On the other hand, evidence shows that problems may be caused by algorithms, such as racial discrimination on Google’s advertisement services, or unpredictable prices shown to users in online marketplaces. Unfortunately, we currently lack measurement tools or methodologies to audit the behavior of these algorithms. As a result, we are unable to measure the impact of these algorithms on people. For example, the general public is unable to access the details about how credit scores and prices are calculated in online marketplaces, since algorithms are typically proprietary trade secrets. Similarly,
Pre-proof Exact and Heuristic Approaches to Detect Failures in Failed kout-of-n Systems

This paper considers a k-out-of-n system that has just failed. There is an associated cost of testing each component. In addition, we have apriori information regarding the probabilities that a certain set of components is the reason for the failure. The goal is to identify the subset of components that have caused the failure with the minimum expected cost. In this work, we provide exact and approximate policies that detects components’ states in a failed k-out-of-n system. We propose two integer programming (IP) formulations, two novel Markov decision process (MDP) based approaches, and two heuristic algorithms. We show the limitations of exact algorithms and effectiveness of proposed heuristic approaches on a set of randomly generated test instances. Despite longer CPU times, IP formulations are flexible in incorporating further restrictions such as test precedence relationships, if need be. Numerical results illustrate that dynamic programming for the proposed MDP model is the most effective exact method, solving up to 12 components within one hour. The heuristic algorithms’ performances are presented against exact approaches for small to medium sized instances and against a lower bound for larger instances.
Chapter 3 Lumbar Intervertebral Disc Endoscopy

A functional spinal unit (FSU) is the smallest physiological motion unit of the spine to ex‐ hibit biomechanical characteristics similar to those of the entire spine (Fig. 1). A FSU con‐ sists of two adjacent vertebrae, the intervertebral disc and all adjoining ligaments between them and excludes other connecting tissues such as muscles. The intervertebral ligaments are (anterior to posterior): anterior longitudinal ligament, posterior longitudinal ligament, facet capsular ligaments, interspinous ligament, ligamentum flavum (yellow ligament), and supraspinous ligament.