Miracle-dz/UniFAR-FTUs huggingface dataset:
A General Framework for Observation Driven Time-Varying Parameter Models

We propose a new class of observation driven time series models referred to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled score of the likelihood function. This approach provides a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The GAS model encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity, the autoregressive conditional duration, the autoregressive conditional intensity, and the single source of error models. In addition, the GAS specification provides a wide range of new observation driven models. Examples include non-linear regression models with time-varying parameters, observation driven analogues of unobserved components time series models, multivariate point process models with time-varying parameters and pooling restrictions, new models for time-varying copula functions, and models for time-varying higher order moments. We study the properties of GAS models and provide several non-trivial examples of their application.
Data stream query processing

Stream data are also generated naturally by (message-based) Web services, in which loosely coupled systems interact by exchanging high volumes of business data (e.g., purchase orders, retail transactions) tagged in XML (the lingua franca of Web services), forming continuous XML data streams. A central aspect of Web services is the ability to efficiently operate on these XML data streams executing queries (expressed in some XML query language) to continuously match, extract and transform parts of the XML data stream to drive legacy back-end business applications. The objective of this tutorial is to provide a comprehensive and cohesive overview of the key research results in the area of data stream query processing, both for SQL-like and XML query languages.
Involvement of p90rsk in Neurite Outgrowth Mediated by the Cell Adhesion Molecule L1*

L1 is a neural cell adhesion molecule that has been shown to help guide nascent axons to their targets. This guidance is based on specific interactions of L1 with its binding partners and is likely to involve signaling cascades that alter cytoskeletal elements in response to these binding events. We have examined the phosphorylation of L1 and the role it may have in L1-directed neurite outgrowth. Cytosolic extracts from nerve growth factor-stimulated PC12 cells were fractionated by anion-exchange chromatography, and an activity was found that phosphorylated the cytoplasmic domain of L1. This activity was then assayed using a battery of L1-derived synthetic peptides. Based on these peptide assays and sequencing of radiolabeled L1 proteolytic fragments, the phosphorylation site was determined to be Ser1152. Western blot analysis demonstrated that the L1 kinase activity from PC12 cells that phosphorylated this site was co-eluted with the S6 kinase, p90rsk. Moreover, S6 kinase activity and p90rsk immunoreactivity co-immunoprecipitate with L1 from brain, and metabolic labeling studies have demonstrated that Ser1152 is phosphorylated in vivo in the developing rat brain. The phosphorylation site is located in a region of high conservation between mammalian L1 sequences as well as L1-related molecules in vertebrates from fish to birds. We performed studies to investigate the functional significance of this phosphorylation. Neurons were loaded with peptides that encompass the phosphorylation site, as well as the flanking regions, and their effects on neurite outgrowth were observed. The peptides, which include Ser1152, inhibit neurite outgrowth on L1 but not on a control substrate, laminin. A nonphosphorylatable peptide carrying a Ser to Ala mutation did not affect neurite outgrowth on either substrate. These data demonstrate that the membrane-proximal 15 amino acids of the cytoplasmic domain of L1 are important for neurite outgrowth on L1, and the interactions it mediates may be regulated by phosphorylation of Ser1152.
A Framework for Evaluation and Use of Automated Scoring

A framework for evaluation and use of automated scoring of constructed-response tasks is provided that entails both evaluation of automated scoring as well as guidelines for implementation and maintenance in the context of constantly evolving technologies. Consideration of validity issues and challenges associated with automated scoring are discussed within the framework. The fit between the scoring capability and the assessment purpose, the agreement between human and automated scores, the consideration of associations with independent measures, the generalizability of automated scores as implemented in operational practice across different tasks and test forms, and the impact and consequences for the population and subgroups are proffered as integral evidence supporting use of automated scoring. Specific evaluation guidelines are provided for using automated scoring to complement human scoring for tests used for high-stakes purposes. These guidelines are intended to be generalizable to new automated scoring systems and as existing systems change over time.
Mutual information-based evolution of hypernetworks for brain data analysis

Cortical analysis becomes increasingly important for brain research and clinical diagnosis. This problem involves a combinatorial search to find the essential modules among a large number of brain regions. Despite several statistical approaches, cortical analysis remains a formidable challenge due to high-dimensionality and sparsity of data. Here we describe an evolutionary method for finding significant modules from cortical data. The method uses a hypernetwork which is encoded as a population of hyperedges, where hyperedges represent building blocks or potential modules. We develop an efficient method for evolving the hypernetwork using mutual information to generate essential hyperedges. We evaluate the method on predicting intelligence quotient (IQ) levels and finding potential significant modules on IQ from brain MRI data consisting of 62 healthy adults with over 80,000 measured points (variables). The experimental results show that our information-theoretic evolutionary hypernetworks improve the classification accuracy by 5∼15%. Moreover, it extracts significant cortical modules that distinguish high IQ from low IQ groups.
Contexts and Innovation: a case study of the use of CPVE with A‐level students

Summary Within the post‐16 scene in England, curriculum innovation is currently endemic. Such change is often viewed as taking place in two separate contexts, the practical context of the classroom, and the theoretical, debating context of staffroom and LEA. Both contexts are explored through one case study of a teacher‐led innovation, attempting to use part‐time CPVE with A‐level students. It is shown that the contexts are not separate, but closely linked, in an organic and dialectical way. In the instance reported, the dysfunctional nature of these inter‐relationships was a major point of weakness in the innovation. The paper concludes that previous notions in the literature about the separation of the two contexts are over‐simplified, and that both innovators and researchers should show greater awareness of these interrelationships in future.
An ensemble multi-label feature selection algorithm based on information entropy

In multilabel classification, feature selection is able to remove redundant and irrelevant features, which makes the classifiers faster and improves the prediction performance of the classifiers. Currently, most of feature selection algorithms in multilabel classification are dependent on the con crete classifier, which leads to high computation complexity. Hence this paper proposes an Ensemble Multilabel Feature Sele ction algorithm based on Information Entropy (EMFSIE), which is independent on any concrete classifiers. Its core idea consists of: Employing the information gain to evaluate the correlation between the feature and the label set, and filtering out useful features more effectively. We calculate the information gain in an ensemble framework and filter out useful features according to the threshold value determined by the effective factor. We validate EMFSIE on four datasets from two domains using four different multilabel classifiers. The ex perimental results and their analysis show preliminarily that EMFSIE can not only remove more than 70% of original features, which makes the classifiers faster, but also keep the prediction performance of the classifiers as good as before, even enhance the prediction performance on three datasets underthe twotailed pttests at 0.05 significance level.
An integrative framework for identifying consistent microRNA expression signatures associated with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common and invasive renal-originated malignancy. Altered microRNA expression has been observed in many human cancers including ccRCC. Microarray is routinely used in labs worldwide for detecting cancer specific microRNA expression profiles, but no consistent conclusion could be drawn so far. The function of microRNAs in carcinogenesis of this tumor type is thereof largely unknown. In this study, we describe an integrative framework to improve the comparability of differentially expressed microRNAs (DE-miRNAs) from different experiments, and apply it to 4 publicly available microRNA expression datasets in ccRCC. The approach uses a novel statistic method for cancer outlier detection. The identified DE-miRNAs are then screened by POMA, an in-house developed predictor, for microRNAs with real regulatory activity in the disease. The proposed framework not only achieves high reproducibility across different datasets but also identifies a consistent set of 12 DE-miRNAs which could be putative biomarkers and therapeutic targets. The targets of DE-miRNAs in each dataset were then mapped to functional databases for enrichment analysis. Both novel and previously characterized microRNA-regulated molecular pathways are identified that are likely to contribute to the pathogenesis of ccRCC. Overlapping comparison suggests that independent ccRCC expression profiles are more consistent at pathway level than that at gene/microRNA level.
Appendix C : Negative Binomial Regression Models and Estimation Methods

The Poisson-Gamma model has properties that are very similar to the Poisson model discussed in Appendix B, in which the dependent variable yi is modeled as a Poisson variable with a mean i where the model error is assumed to follow a Gamma distribution. As its name implies, the Poisson-Gamma is a mixture of two distributions and was first derived by Greenwood and Yule (1920). This mixture distribution was developed to account for overdispersion that is commonly observed in discrete or count data (Lord et al., 2005). It became very popular because the conjugate distribution (same family of functions) has a closed form and leads to the negative binomial distribution. As discussed by Cook (2009), “the name of this distribution comes from applying the binomial theorem with a negative exponent. ” There are two major parameterizations that have been proposed and they are known as the NB1 and NB2, the latter one being the most commonly known and utilized. NB2 is therefore described first. Other parameterizations exist, but are not discussed here (see Maher and Summersgill, 1996; Hilbe, 2007).
Differential Association of Receptor-Gβγ Complexes with β-Arrestin2 Determines Recycling Bias and Potential for Tolerance of Delta Opioid Receptor Agonists

Opioid tendency to generate analgesic tolerance has been previously linked to biased internalization. Here, we assessed an alternative possibility; whether tolerance of delta opioid receptor agonists (DORs) could be related to agonist-specific recycling. A first series of experiments revealed that DOR internalization by DPDPE and SNC-80 was similar, but only DPDPE induced recycling. We then established that the non-recycling agonist SNC-80 generated acute analgesic tolerance that was absent in mice treated with DPDPE. Furthermore, both agonists stabilized different conformations, whose distinct interaction with Gβγ subunits led to different modalities of β-arrestin2 (βarr2) recruitment. In particular, bioluminescence resonance energy transfer (BRET) assays revealed that sustained activation by SNC-80 drew the receptor C terminus in close proximity of the N-terminal domain of Gγ2, causing βarr2 to interact with receptors and Gβγ subunits. DPDPE moved the receptor C-tail away from the Gβγ dimer, resulting in βarr2 recruitment to the receptor but not in the vicinity of Gγ2. These differences were associated with stable DOR-βarr2 association, poor recycling, and marked desensitization following exposure to SNC-80, while DPDPE promoted transient receptor interaction with βarr2 and effective recycling, which conferred protection from desensitization. Together, these data indicate that DORs may adopt ligand-specific conformations whose distinct recycling properties determine the extent of desensitization and are predictive of analgesic tolerance. Based on these findings, we propose that the development of functionally selective DOR ligands that favor recycling could constitute a valid strategy for the production of longer acting opioid analgesics.
Predicting the ability of elderly diabetes patients to acquire the insulin self‐injection technique based on the number of animal names recalled

To our knowledge, no studies have reported that cognitive tests can be used to evaluate whether or not patients can acquire the insulin self‐injection technique. We investigated whether or not the number of animal names recalled in 1 min by elderly diabetes patients could be used as a predictor of the patients’ ability to acquire the insulin self‐injection technique within 1 week.
Uncertainty Model for Configurable Hardware/Software and Resource Partitioning

Automatic hardware/software partitioning relies on characterization, estimation and design space exploration of the system performance and cost metrics. In real world situations, such estimates are complicated and cannot be 100 percent accurate. Furthermore, hardware/software co-design is so complicated nowadays that simply considering the bipartitioning between hardware and software is not sufficient. It is important to consider some of the other key design parameters and resource sharing together with the hardware/software partitioning problem. Under variable requirements of smart systems, more flexibility on the resource usage should be incorporated in system modelling. This paper considers uncertainty modeling for system partitioning with an enhanced set of parameters for hardware/software resource sharing. We harness state-of-the-art uncertainty theory for linear uncertain distribution and normal uncertain distribution. Our derivations convert the uncertainty model back to a regular constraint optimization problem. Experimental results show the effectiveness of our approach.
Balancing security and privacy in eGovernment services

Advances in digital technology are increasing the volume of computer data and accelerating the massive integration of software into our daily lives. The widespread interconnection of networks and digital convergence accentuates this computerization process, making computing, telecommunications and audiovisual information increasingly compatible and interoperable. eGovernment services to citizens and businesses can take advantage of these developments. However, security and privacy concerns must be properly addressed if eGovernment services are to be both fully functional and enjoy significant take-up by end-users. These are not straightforward issues, though, and the resolution of the interplay between security and privacy cannot be achieved by a purely technical means. Instead, there is a balance required between the technological and societal elements; of which there has been significant exploration and reflection recently in the EU Trust and Security communities [1]. In the on-line world, the conflict between privacy and security often manifests itself in a debate between anonymity and accountability. This paper expounds on this apparent dispute by describing the properties of anonymity and accountability; presenting an instructive use case; and, extracting some conclusions with regard to the consequences for relevant stakeholders, should either of these two properties triumph over the other. Steps towards quantifying and measuring the various components that comprise security and privacy are also outlined.
Target-based team working: Groups, work and interdependence in the UK civil service

In terms of its implications for work and production, studies of teamworking have generally relied on concepts of autonomy and flexibility. An in-depth, qualitative case study of the UK Inland Revenue reveals a form of teamworking in which both autonomy and flexibility play little part. It is argued that teamworking in this case is best conceptualized by giving greater weight to interdependence. In particular, teamworking in the Inland Revenue works through the outcome interdependence engendered by the emphasis on the team’s work targets. This form of teamworking is given the name ‘target-based teamworking’. Certain aspects of the nature of production appear to encourage this form of teamworking. More generally, the case promotes interdependence as a means of understanding teamworking, and suggests that the key to this understanding is an analysis of the relationship between interdependence and autonomy.
Comparison of the nicotine content of tobacco used in bidis and conventional cigarettes

OBJECTIVE To compare the nicotine content of 12 unfiltered brands of bidi cigarettes (hand rolled cigarettes imported from India) with 8 popular brands of filtered and unfiltered US and conventional cigarettes from India. MAIN OUTCOME MEASURES Identical laboratory procedures were used to determine nicotine content (in duplicate) and physical characteristics. RESULTS The nicotine concentration in the tobacco of bidi cigarettes (21.2 mg/g) was significantly greater than the tobacco from the commercial filtered (16.3 mg/g) and unfiltered cigarettes (13.5 mg/g). CONCLUSIONS Bidi cigarettes contain higher concentrations of nicotine than conventional cigarettes. Therefore, it is logical to presume that bidi smokers are at risk of becoming nicotine dependent. These findings belie a popular belief among US teens that bidis are a safe alternative to commercial cigarettes.
Text Classification Using a Graph of Terms

It is well known that supervised text classification methods need to learn from many labeled examples to achieve a high accuracy. However, in a real context, sufficient labeled examples are not always available. For this reason, there has been recent interest in methods that are capable of obtaining a high accuracy even if the size of the training set is not big. The main purpose of text mining techniques is to identify common patterns through the observation of vectors of features and then to use such patterns to make predictions. Most existing methods usually make use of a vector of features made up of weighted words that unfortunately are insufficiently discriminative when the number of features is much higher than the number of labeled examples. In this paper we demonstrate that, to obtain a greater accuracy in the analysis and revelation of common patterns, we could employ more complex features than simple weighted words. The proposed vector of features considers a hierarchical structure, named a mixed Graph of Terms, composed of a directed and an undirected sub-graph of words, that can be automatically constructed from a set of documents through the probabilistic Topic Model. The method has been tested on the top 10 classes of the ModApte split from the Reuters-21578 dataset, learned on several subsets of the original training set and showing a better performance than a method using a list of weighted words as a vector of features and linear support vector machines.
Common Pitfalls in Training and Evaluating Recommender Systems

This paper formally presents four common pitfalls in training and evaluating recommendation algorithms for information systems. Specifically, we show that it could be problematic to separate the server logs into training and test data for model generation and model evaluation if the training and the test data are selected improperly. In addition, we show that click through rate { a common metric to measure and compare the performance of different recommendation algorithms -- may not be a good measurement of profitability { the income a recommendation module brings to a website. Moreover, we demonstrate that evaluating recommendation revenue may not be a straightforward task as it first looks. Unfortunately, these pitfalls appeared in many previous studies on recommender systems and information systems. We explicitly explain these problems and propose methods to address them. We conducted experiments to support our claims. Finally, we review previous papers and competitions that may suffer from these problems.
A Focus Theory of Normative Conduct: When Norms Do and Do not Affect Behavior

In three experiments, respondents’ behavior conformed to the dictates of a relevant norm (the norm against littering) only under conditions of normative focus. This relationship held true across three types of procedures for producing normative focus (physiological arousal, modeling, and self-directed attention), across two types of settings (public and private), and across two types of norms (social and personal). Moreover, factors that would be expected to affect normative action were influential only when the norm was focal. These factors included the degree to which the action violated the relevant norm (Study 2) and the degree to which an individual subscribed to that norm (Study 3). Implications are discussed for developing campaigns to encourage prosocial behavior.
Assertiveness and Sexual Satisfaction in Employed Professional Women.

This paper examines the relationship between assertive personality traits and sexual satisfaction in 45 employed professional women. Data were collected by use of an Allport Scale to measure assertiveness and a Likert Scale to measure sexual satisfaction. Data were computer analyzed using Kendall's tau, factor analysis and cross-tabulations. Results show: (1) a positive correlation between assertiveness and reported sexual satisfaction; (2) that as age and length of marriage increase so does reported sexual satisfaction; and (3) that as assertiveness increases so do reports of more diverse sexual activity.
A Review of Interventions for Substance Use Among Homeless Youth

This review summarizes existing evidence on interventions to address substance use problems among homeless youth. Fifteen studies met the inclusion criteria of the review. The quality of included studies was analyzed using explicit and well-validated criteria. Interventions evaluated ranged from individual-focused therapies such as brief motivational intervention, community reinforcement approach, and knowledge and skills training, to broader interventions such as family therapy, support groups, and shelter-based health care and housing programs. Participants reported improvements in substance use outcomes over time in most of the studies. However, the superiority of a specific intervention is difficult to determine because of the heterogeneity of the interventions and the few studies conducted on each intervention. Implications for practice and research are discussed.