Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling Device to Device Broadcast for LTE Cellular Networks

In recent years, the public safety community has been aligning behind Long Term Evolution (LTE) technology as the basis for next generation public safety networks. Correspondingly, the 3rd Generation Partnership Project (3GPP) standards body has started multiple technical work initiatives on LTE enhancements to better support public safety use cases and requirements. Of particular importance to the public safety community is the addition of direct device-to-device (D2D) broadcast capability that can enable off-network push-to-talk (PTT) group communications in a manner equivalent to existing public safety communication systems. In this paper, we briefly explore the design objectives for, and challenges associated with, providing a D2D broadcast service. We then present and analyze a new distributed media access control (MAC) scheme for interference-aware coordination of both unicast and broadcast transmission. System-level simulation results are provided to validate the performance and the effectiveness of our approach, using a CSMA-based approach as a basis for comparison.
Distinguishing between cause and effect

We describe eight data sets that together formed the CauseEffectPairs task in the Causality Challenge #2: Pot-Luck competition. Each set consists of a sample of a pair of statistically dependent random variables. One variable is known to cause the other one, but this information was hidden from the participants; the task was to identify which of the two variables was the cause and which one the effect, based upon the observed sample. The data sets were chosen such that we expect common agreement on the ground truth. Even though part of the statistical dependences may also be due to hidden common causes, common sense tells us that there is a significant cause-effect relation between the two variables in each pair. We also present baseline results using three different causal inference methods.
Testing the Neoclassical Model of Family Labor Supply and Fertility

The McElroy-Horney Nash-bargaining model of family demand behavior relaxes the restriction that nonearned income of husband and wife had the identical effect on family labor supply and commodity demands. This restriction of the neoclassical model of family behavior is tested for the determination of husband and wife labor supply and fertility based on the 1981 Socioeconomic Survey of Thailand. The neoclassical restriction is rejected for female labor supply and fertility. Another unexplored limitation of family demand studies, due to the sample self selection of intact marriages, is empirically treated through alternative estimation strategies. In this case, a more sharply focused theory of marital behavior is needed to identify family demand models.
Effectiveness of similarity measures in classification of time series data with intrinsic and extrinsic variability

Time series are hard to analyse because of their intrinsic variability which arises from the stochastic nature of the underlying process. Analysis is harder still if the underlying process is non-stationary. Further extrinsic variation may be imposed by the variability of the sampling process, e.g. by sampling at different or non-uniform time intervals. We explore the efficacy of some common distance/similarity measures - Euclidean (EUC), Neighbourhood Counting Metric (NCM), Dynamic Time Warping (DTW), Longest Common Subsequence (LCS) and All Common Subsequences (ACS) - in a nearest neighbour classifier for classifying time series data with and without extrinsic variability. An artificial dataset containing trajectories of a 2-dimensional dynamical system and a real dataset, the Australian Sign Language Dataset (AUSLAN), are explored.
An inference routing tree topology algorithm for reducing overhead in packet probing

Network structure (Topology) and Node dynamics (Links) are essential components of a secure robust network monitoring and application design. Packet probing is an important network measurement technique, used to understand packet delays, paths, and loss. In this paper we propose an RNJ(Route Neighbor Joining) algorithm which is a grouping type algorithm that recovers the tree topology by recursively joining the neighbors on the tree by using the concept of additive metrics which is a general adaptive framework for analyzing and designing routing topology in an efficient scalable network structure. The framework can flexibly fuse information from multiple measurements to achieve better estimation accuracy besides Packet probing. Based on the framework we introduce and develop a polynomial-time based inference algorithms that effectively manages the number of probing packets. In particular, for applications where nodes may join or leave frequently and randomly such as overlay networks, applicationlayer multicast, and peer-to-peer file systems, we propose a novel sequential topology inference algorithm that significantly reduces the probing overhead and can efficiently handle node dynamics. We demonstrate the effectiveness of the proposed inference algorithms using network simulations. KeywordsNetwork Measurement, Network Monitoring, Packet
Market Evidence on the Opaqueness of Banking Firms' Assets

We assess the market microstructure properties of U.S. banking firms' equity, to determine whether they exhibit more or less evidence of asset opaqueness than similar-sized nonbanking firms. The evidence strongly indicates that large banks (traded on NASDAQ) trade much less frequently despite microstructure characteristics. Problem (noncurrent) loans tend to raise the frequency with which the bank's equity trades, as well as the equity's return volatility. The implications for regulatory policy and future market microstructure research are discussed.
Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization, II: Shrinking Procedures and Optimal Algorithms

In this paper we study new stochastic approximation (SA) type algorithms, namely, the accelerated SA (AC-SA), for solving strongly convex stochastic composite optimization (SCO) problems. Specifically, by introducing a domain shrinking procedure, we significantly improve the large-deviation results associated with the convergence rate of a nearly optimal AC-SA algorithm presented by Ghadimi and Lan in [SIAM J. Optim. , 22 (2012), pp 1469--1492]. Moreover, we introduce a multistage AC-SA algorithm, which possesses an optimal rate of convergence for solving strongly convex SCO problems in terms of the dependence on not only the target accuracy, but also a number of problem parameters and the selection of initial points. To the best of our knowledge, this is the first time that such an optimal method has been presented in the literature. From our computational results, these AC-SA algorithms can substantially outperform the classical SA and some other SA type algorithms for solving certain classes of strongly.
Accelerating CABAC encoding for multi-standard media with configurability

This paper presents the study of how to accelerate CABAC encoding for emerging heterogeneous multimedia applications. The latest image and video compression standards such as JPEG2000 and H.264 both have adopted context adaptive binary arithmetic coding to achieve performance enhancement. However, CABAC requires high computing power. After investigating computational complexity of CABAC coding, firstly, instruction level acceleration is elaborated. Secondly, a configurable accelerator for CABAC encoding in multiple standards is proposed. Benchmarking performance and implementation cost is also addressed.
A pH-sensitive histidine residue as control element for ligand release from HLA-DR molecules

Class II MHC molecules undergo conformational changes on shifts of the pH. As a consequence, low-affinity peptides tightly bound at pH 7.0 can be released at pH 5.0. The imidazole group of histidine is the only amino acid side chain affected within this range. At pH 5.0 the group is positively charged, polar, and hydrophilic, whereas at pH 7.4 it is neutral, apolar, and hydrophobic. In this study, we used soluble forms of HLA-DR and substituted conserved histidine residues with tyrosine, an isosteric analogue to the uncharged form of histidine. The goal of this substitution was to identify crucial His residues by an increase in pH stability of the ligand complex. HLA-DM-mediated release experiments revealed that substitution of His-33 in the α1 domain of the HLA-DR molecule almost doubled the half-life of HLA-DR1/class II-associated invariant-chain peptide complexes. The divergence in the off-rate of WT and H33Y mutated complex was strictly pH-dependent and correlated with the theoretical titration curve of the imidazole group. For both HLA-DR1 and HLA–DR4 molecules the mutation resulted in a shift of class II-associated invariant-chain peptide release curves by up to 0.5 pH units. His-33α1 is present in all HLA-DR and H-2E molecules. It connects the α1 and α2 domains in its noncharged form by hydrophobic interactions with residue Val-136α2. It is located in close proximity to the putative interface with HLA-DM and may function as a pH-sensitive “button,” which is closed at pH 7.0 but opens below pH 6.0 to allow conformational transitions necessary for ligand exchange.
Analyzing sentimental influence of posts on social networks

Lots of effort has been conducted to analyze information of social networks, such as sentiment trend analysis of social network users. Our aim is to analyze the sentimental influence of posts and compare the result on various topics and different social media platforms. Large amounts of posts are generated on social networks every day. People are curious in finding the influence among them. Most researchers measured the influence of a post through the number of replies it received. However, we are not sure if the influence is made positively or negatively on other posts if their sentimental information is not considered. In this paper, three research questions are raised and methodologies are proposed for the measure of sentimental influence of posts. Finally, a preliminary experiment is designed and carried out with some interesting results found.
GIS for remote sensing, analysis and visualisation of marine pollution and other marine ecosystem components

Pollution detection, environment sensing and appropriate response strategies are important due to the marine ecosystemspsila continuous absorption of pollutants of various origins. Several approaches and techniques of measurements are available for marine environment monitoring including direct sampling, airborne and satellite imagery and underwater acoustics. The huge development in the information technology has provided the possibilities for much faster and more efficient access to survey data, allowing their remote, nearly real-time management, processing and visualisation. The proposed GIS is able to integrate many different types of marine pollution survey data, especially those acquired by various acoustic sensors like Multibeam Sonar Systems (MBSS), echosounder and Side Scan Sonars (SSS). It also supports instantaneous 2D and 3D visualisation in the form of thematic layers that can be overlaid. The system utilises the ArcSDE application server that facilitates storing and managing spatial data (raster, vector, and survey) in a database and ArcGIS Engine GlobeControl and MapControl components for georeferenced presentation of various objects. The set of geoprocessing and spatial analysis methods can be used in order to retrieve relevant information and analysis results.
Extending Hidden Markov ( tree ) models for word representations

There is ample research in natural language processing (NLP) on obtaining word representations, including vector space modeling, clustering and techniques derived from language models. Good word representations are vital for overcoming the lexical sparseness inherent to many NLP problems. Much less studied are approaches capturing wider or global context (see e.g. Nepal and Yates (2014)). We are interested in using syntax for learning semantic classes, by which a wider and more relevant context can be incorporated (Šuster & Van Noord, 2014). It has been shown that dependency trees can extend a) Hidden Markov Models (HMM) in a way that resulting word representations increase performance in NLP classification tasks (Grave et al., 2013), but also b) Brown clusters, resulting in higher similarity scores in a wordnet-based experiment (Šuster & Van Noord, 2014). A drawback of the existing approaches is that trees are exploited only partially—dependency links set the structure (word context), but the identity of dependency links is not part of the model.
Characterization of large deletions in the DHCR7 gene

Pathogenic variants in the DHCR7 gene cause Smith–Lemli–Opitz syndrome (SLOS), a defect of cholesterol biosynthesis resulting in an autosomal recessive congenital metabolic malformation disorder. In approximately 4% of patients, the second mutation remains unidentified. In this study, 12 SLOS patients diagnosed clinically and/or by elevated 7‐dehydrocholesterol (7‐DHC) have been investigated by customized multiplex ligation‐dependent probe amplification (MLPA) analysis, because only one DHCR7 sequence variant has been detected. Two unrelated patients of this cohort carry different large deletions in the DHCR7 gene. One patient showed a deletion of exons 3–6. The second patient has a deletion of exons 1 and 2 (non‐coding) and lacks the major part of the promoter. These two patients show typical clinical and biochemical phenotypes of SLOS. Second disease‐causing mutations are p.(Arg352Trp) and p.(Thr93Met), respectively. Deletion breakpoints were characterized successfully in both cases. Such large deletions are rare in the DHCR7 gene but will resolve some of the patients in whom a second mutation has not been detected.
Behavioral Segmentation of Pinterest Users

Pinterest is a website and mobile application that allows users to discover, save, and share content ('pins') across a wide range of interest areas. As the user base grows more diverse both demographically and psychographically, we wish to understand emerging patterns of behavior that reflect underlying differences in users' intent and satisfaction with the service. In this paper, we propose a methodology for generating a meaningful segmentation of Pinterest users based on three types of behavior: (1) engagement with various categories of content, (2) frequencies of various types of actions, and (3) sequences of actions.
An inductor-coupling resonated CMOS low noise amplifier for 3.1–10.6GHz ultra-wideband system

In this paper, a low power low-noise amplifier (LNA) using inductor-coupling resonated technique is designed for ultra-wideband (UWB) wireless system. The design consists of a wideband input impedance matching network, one stage cascode amplifier with inductor-coupling resonated load, and an output buffer; it was fabricated in TSMC 0.18um standard RF CMOS process. The UWB LNA gives 10.8dB power gain and 9.4GHz 3dB bandwidth (1.2GHz – 10.6GHz) while consuming only 6.2mW through a 1.2V supply, including output buffer. Over the 3.1GHz – 10.6GHz frequency band, a minimum noise figure of 3.9dB and input return loss lower than −5.7dB have been achieved.
Operationalising Factors That Explain the Emergence of Infectious Diseases: A Case Study of the Human Campylobacteriosis Epidemic

A framework of general factors for infectious disease emergence was made operational for Campylobacter utilising explanatory variables including time series and risk factor data. These variables were generated using a combination of empirical epidemiology, case-case and case-control studies, time series analysis, and microbial sub-typing (source attribution, diversity, genetic distance) to unravel the changing/emerging aetiology of human campylobacteriosis. The study focused on Scotland between 1990–2012 where there was a 75% increase in reported cases that included >300% increase in the elderly and 50% decrease in young children. During this period there were three phases 1990–2000 a 75% rise and a 20% fall to 2006, followed by a 19% resurgence. The rise coincided with expansions in the poultry industry, consumption of chicken, and a shift from rural to urban cases. The post-2000 fall occurred across all groups apart from the elderly and coincided with a drop of the prevalence of Campylobacter in chicken and a higher proportion of rural cases. The increase in the elderly was associated with uptake of proton pump inhibitors. During the resurgence the increase was predominantly in adults and the elderly, again there was increasing use of PPIs and high prevalences in chicken and ruminants. Cases associated with foreign travel during the study also increased from 9% to a peak of 16% in 2006 before falling to an estimated 10% in 2011, predominantly in adults and older children. During all three periods source attribution, genetic distance, and diversity measurements placed human isolates most similar to those in chickens. A combination of emergence factors generic for infectious diseases were responsible for the Campylobacter epidemic. It was possible to use these to obtain a putative explanation for the changes in human disease and the potential to make an informed view of how incidence rates may change in the future.
The prediction of trust rating based on the quality of services using fuzzy linear regression

With the advent of service-oriented computing, the issue of trust and Quality of Service (QoS) have become increasingly important. In service-oriented environments, when there are a few service providers providing the same service, a service client would be keen to know the trustworthiness of each service provider in the forthcoming transaction. The trust rating of a delivered service from a service provider can be predicted according to a set of advertised QoS data collected by the trust management authority. Although trust and QoS are qualitative by nature, most data sets represent trust and QoS in the ordinal form for the sake of simplicity. This paper introduces a new approach based on Fuzzy Linear Regression Analysis (FLRA) to extract qualitative information from quantitative data and so use the obtained qualitative information for better modeling of the data. For verification purposes, the proposed approach can be applied for the trust prediction in the forthcoming transaction based on a set of advertised QoS in service-oriented environments.
The effects of eccentric versus concentric resistance training on muscle strength and mass in healthy adults: a systematic review with meta-analysis

The aim of this systematic review was to determine if eccentric exercise is superior to concentric exercise in stimulating gains in muscle strength and mass. Meta-analyses were performed for comparisons between eccentric and concentric training as means to improve muscle strength and mass. In order to determine the importance of different parameters of training, subgroup analyses of intensity of exercise, velocity of movement and mode of contraction were also performed. Twenty randomised controlled trials studies met the inclusion criteria. Meta-analyses showed that when eccentric exercise was performed at higher intensities compared with concentric training, total strength and eccentric strength increased more significantly. However, compared with concentric training, strength gains after eccentric training appeared more specific in terms of velocity and mode of contraction. Eccentric training performed at high intensities was shown to be more effective in promoting increases in muscle mass measured as muscle girth. In addition, eccentric training also showed a trend towards increased muscle cross-sectional area measured with magnetic resonance imaging or computerised tomography. Subgroup analyses suggest that the superiority of eccentric training to increase muscle strength and mass appears to be related to the higher loads developed during eccentric contractions. The specialised neural pattern of eccentric actions possibly explains the high specificity of strength gains after eccentric training. Further research is required to investigate the underlying mechanisms of this specificity and its functional significance in terms of transferability of strength gains to more complex human movements.
The Use of Dental Sealing in Cluj, Romania: Frequency, Materials and Techniques – A Questionnaire Study

Aims. Dental decay is a condition that affects both temporary and permanent dentitions. Prophylactic methods for dental decays consist of: dental sealing, use of fluorides, oral hygiene and healthy diet habits. This article focuses on the frequency, dental materials and techniques of dental sealing used for caries prophylaxis in the Cluj County. Materials and methods. The study used a set of questionnaires that was applied to 175 doctors/dentists from Cluj. The questionnaire contained 11 questions and was delivered to the doctors personally or by the internet Results. From the interviewed dentists 85.7% use dental sealing and 86.7% use materials based on composite resins in this respect. For diagnostic purposes,76% of the dentists use inspection and palpation, 68.66% use cotton rolls and aspiration for isolation, 74% use professional brushing, 74% call back the patient after 6 months for a check-up and 60% of the doctors redo the sealing if the sealant is partially or entirely lost or they perform a filling if a secondary decay appeared. Conclusions. Most of the interviewed dentists perform dental sealing and consider it as a good caries prophylaxis method. Also, most of them use a material based on composite resins as pit and fissure sealant. The techniques for this procedure are different among the interviewed dentists.
Understanding Dynamic Situations through Context Explanation

This article presents advantages of using context to set up a pedagogical assistance for recognition of collective situations in virtual environment for training (VET). We are focusing on generation of explanations to the learner. Two assistances types have been envisaged thanks to context using, the first one consists in guiding the learner before action and the second can be used during action. Those assistances have been set up thanks to contextual graph and consist of animations in the virtual environment
NGOs, THE INTERNET AND SUSTAINABLE RURAL DEVELOPMENT

Today sustainable rural development is of paramount importance in Indonesian development. Yet, different social actors have different perspectives on it. Non-government organizations (NGOs) in Indonesia have established themselves in pivotal positions in the social, economic and political landscape across the country, and a large amount of their work has been connected with development in the rural sector. But, there has been little attempt to understand how NGOs in Indonesia, particularly rural NGOs, engage with the issue of sustainable rural development itself. Since rural development is one of the oldest issues to be discussed among activists, since the early days of Indonesian NGOs, it is interesting to see how they understand the issue of sustainability in rural development and rural reform. An empirical study was conducted recently to see how some Indonesian NGOs, in their endeavour to respond to and broaden the discourse, utilize Internet technology. The study employs a combination of quantitative and qualitative approaches to build a detailed story about how different organizations working in rural development deploy strategies to deal with the issue. By doing so, it aspires to contribute to the advancement of theory relating to the efficacy of the Internet as a tool for social reform and sustainable development by taking Indonesia as a case study.