Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic Review of Methods in Low-Consensus Fields: Supporting Commensuration through `Construct-Centered Methods Aggregation’ in the Case of Climate Change Vulnerability Research

There is increasing interest in using systematic review to synthesize evidence on the social and environmental effects of and adaptations to climate change. Use of systematic review for evidence in this field is complicated by the heterogeneity of methods used and by uneven reporting. In order to facilitate synthesis of results and design of subsequent research a method, construct-centered methods aggregation, was designed to 1) provide a transparent, valid and reliable description of research methods, 2) support comparability of primary studies and 3) contribute to a shared empirical basis for improving research practice. Rather than taking research reports at face value, research designs are reviewed through inductive analysis. This involves bottom-up identification of constructs, definitions and operationalizations; assessment of concepts’ commensurability through comparison of definitions; identification of theoretical frameworks through patterns of construct use; and integration of transparently reported and valid operationalizations into ideal-type research frameworks. Through the integration of reliable bottom-up inductive coding from operationalizations and top-down coding driven from stated theory with expert interpretation, construct-centered methods aggregation enabled both resolution of heterogeneity within identically named constructs and merging of differently labeled but identical constructs. These two processes allowed transparent, rigorous and contextually sensitive synthesis of the research presented in an uneven set of reports undertaken in a heterogenous field. If adopted more broadly, construct-centered methods aggregation may contribute to the emergence of a valid, empirically-grounded description of methods used in primary research. These descriptions may function as a set of expectations that improves the transparency of reporting and as an evolving comprehensive framework that supports both interpretation of existing and design of future research.
Phase Structure and Heating Efficiency of As-Synthesised Iron-Oxide Nanoparticles and after Long Time Storage in Glycerol

Phase Structure and Heating Efficiency of As-Synthesised Iron-Oxide Nanoparticles and after Long Time Storage in Glycerol M. Prubaa,∗, I. Smolkova, K. Pawlik, M. Zubko and P. Pawlik Institute of Physics, Częstochowa University of Technology, al. Armii Krajowej 19, 42-200 Częstochowa, Poland Polimer Centre, Faculty of Technology, Tomas Bata University in Zlin, Zlin, Czech Republic Institute of Materials Science, University of Silesia, Katowice, Poland
Blurred Persistence

Persistent memory provides data durability in main memory and enables memory-level storage systems. To ensure consistency of such storage systems, memory writes need to be transactional and are carefully moved across the boundary between the volatile CPU cache and the persistent main memory. Unfortunately, cache management in the CPU cache is hardware-controlled. Legacy transaction mechanisms, which are designed for disk-based storage systems, are inefficient in ordered data persistence of transactions in persistent memory. In this article, we propose the Blurred Persistence mechanism to reduce the transaction overhead of persistent memory by blurring the volatility-persistence boundary. Blurred Persistence consists of two techniques. First, Execution in Log executes a transaction in the log to eliminate duplicated data copies for execution. It allows persistence of the volatile uncommitted data, which are detectable with reorganized log structure. Second, Volatile Checkpoint with Bulk Persistence allows the committed data to aggressively stay volatile by leveraging the data durability in the log, as long as the commit order across threads is kept. By doing so, it reduces the frequency of forced persistence and improves cache efficiency. Evaluations show that our mechanism improves system performance by 56.3% to 143.7% for a variety of workloads.
Automating Relational Database Schema Design for Very Large Semantic Datasets

Many semantic datasets or RDF datasets are very large but have no pre-defined data structures. Triple stores are commonly used as RDF databases yet they cannot achieve good query performance for large datasets owing to excessive self-joins. Recent research work proposed to store RDF data in column-based databases. Yet, some study has shown that such an approach is not scalable to the number of predicates. The third common approach is to organize an RDF data set in different tables in a relational database. Multiple “correlated” predicates are maintained in the same table called property table so that table-joins are not needed for queries that involve only the predicates within the table. The main challenge for the property table approach is that it is infeasible to manually design good schemas for the property tables of a very large RDF dataset. We propose a novel data-mining technique called Attribute Clustering by Table Load (ACTL) that clusters a given set of attributes into correlated groups, so as to automatically generate the property table schemas. While ACTL is an NP-complete problem, we propose an agglomerative clustering algorithm with several effective pruning techniques to approximate the optimal solution. Experiments show that our algorithm can efficiently mine huge datasets (e.g., Wikipedia Infobox data) to generate good property table schemas, with which queries generally run faster than with triple stores and column-based databases.
Optimization methodology for synchronous reluctance motor by Finite Element Method

An easy optimization methodology for design of permanent magnet synchronous motors is presented. The analyzed structure is a permanent magnet assisted synchronous reluctance motor (PMA-SRM). For a motor design optimization the parameters are the geometric and electrical magnitudes. The proposed method of optimization is divided into two parts: global optimization (geometric parameters) and local optimization (electrical optimization). The main objective is to minimize the motor cost. The performance computations are made with Finite Element Method. Finally, the method is used to compare the motors for two types of permanent magnets (NdFeB and Ferrite).
Word Similarity Based on an Ensemble Model Using Ranking SVMs

A novel ensemble model is suggested to measure the similarity between two words. The authors apply ranking support vector machines to combine the results of existing similarity models. Both training and test data are extracted from the standard Miller & Charles dataset randomly. Evaluations by cross validation show that the ensemble model outperforms known similarity models for not only English words, but also Chinese words.
Dissociative tendencies and individual differences in high hypnotic suggestibility

Introduction. Inconsistencies in the relationship between dissociation and hypnosis may result from heterogeneity among highly suggestible individuals, in particular the existence of distinct highly suggestible subtypes that are of relevance to models of psychopathology and the consequences of trauma. This study contrasted highly suggestible subtypes high or low in dissociation on measures of hypnotic responding, cognitive functioning, and psychopathology. Methods. Twenty-one low suggestible (LS), 19 low dissociative highly suggestible (LDHS), and 11 high dissociative highly suggestible (HDHS) participants were administered hypnotic suggestibility scales and completed measures of free recall, working memory capacity, imagery, fantasy-proneness, psychopathology, and exposure to stressful life events. Results. HDHS participants were more responsive to positive and negative hallucination suggestions and experienced greater involuntariness during hypnotic responding. They also exhibited impaired working memory capacity, elevated pathological fantasy and dissociative symptomatology, and a greater incidence of exposure to stressful life events. In contrast, LDHS participants displayed superior object visual imagery. Conclusions. These results provide further evidence for two highly suggestible subtypes: a dissociative subtype characterised by deficits in executive functioning and a predisposition to psychopathology, and a subtype that exhibits superior imagery and no observable deficits in functioning.
2008 International Workshop on Earth Observation and Remote Sensing Applications United States National Land Cover Data Base Development 1992-2001 and beyond * * * * *

Information presented in this paper represents the views of the author only. It does not necessarily reflect the views of USGS or any other participating organizations and no official endorsement should be inferred. ABSTRACT: An accurate, up-to-date and spatially-explicate national land cover database is required for monitoring the status and trends of the nation's terrestrial ecosystem, and for managing and conserving land resources at the national scale. With all the challenges and resources required to develop such a database, an innovative and scientifically sound planning must be in place and a partnership be formed among users from government agencies, research institutes and private sectors. In this paper, we summarize major scientific and technical issues regarding the development of the NLCD 1992 and 2001. Experiences and lessons learned from the project are documented with regard to project design, technical approaches, accuracy assessment strategy, and project implementation. Future improvements in developing next generation NLCD beyond 2001 are suggested, including: 1) enhanced satellite data preprocessing in correction of atmospheric and adjacency effect and the topographic normalization; 2) improved classification accuracy through comprehensive and consistent training data and new algorithm development; 3) multi-resolution and multi-temporal database targeting major land cover changes and land cover database updates; 4) enriched database contents by including additional biophysical parameters and/or more detailed land cover classes through synergizing multi-sensor, multi-temporal, and multi-spectral satellite data and ancillary data, and 5) transform the NLCD project into a national land cover monitoring program. The late 1990s and early 2000s has witnessed several major and formative developments in global-and continental-scale land cover dataset using remotely sensed data (e.g. The endeavour is primarily driven by an increasing demand for up-to-date and accurate land cover dataset for study of global change at regional to global scale. The successful completion of those large-area land cover characterization activities are attributable primarily to the improved technological and algorithm development for land cover characterization, and the availability of the high quality remote sensing and geospatial datasets. Along with development of the continental and global land cover datasets, significant progress has also been made in designing and implementing accuracy assessment for those land cover datasets based on rigorous and statistically defensible design (e. A closer dialogue between land remote sensing communities and data users is emerging. The continued improvement and availability of remote sensing and geospatial data has provided an excellent opportunity to further improve large-area land cover database development. …
Securing user defined containers for scientific computing

Linux containers and Docker have gained immense popularity as a lightweight alternative to hypervisor based Virtual Machines (VMs). In the context of High Performance Computing and the scientific community, it is clear that containers can serve many useful purposes from system administration, to improved cluster resource management and as a format for sharing reproducible research. However, when compared to VMs, containers seem to trade isolation for performance and ease of use, which poses unique security challenges. In this paper we review how Docker is being used in science, highlight easy to perform exploits, and evaluate the impact of these on HPC deployments. We also summarise a number of strategies for hardening such a system to reduce the vulnerability of hosting User Defined Containers. Based on these, an original solution to enforce default options and container ownership for nonadministrative users in the HPC use case is presented, in addition to the experience of implementing such a system on a cluster at the University of Huddersfield.
Can Systematic Reviews Inform GMO Risk Assessment and Risk Management?

Systematic reviews represent powerful tools to identify, collect, synthesize, and evaluate primary research data on specific research questions in a highly standardized and reproducible manner. They enable the defensible synthesis of outcomes by increasing precision and minimizing bias whilst ensuring transparency of the methods used. This makes them especially valuable to inform evidence-based risk analysis and decision making in various topics and research disciplines. Although seen as a “gold standard” for synthesizing primary research data, systematic reviews are not without limitations as they are often cost, labor and time intensive and the utility of synthesis outcomes depends upon the availability of sufficient and robust primary research data. In this paper, we (1) consider the added value systematic reviews could provide when synthesizing primary research data on genetically modified organisms (GMO) and (2) critically assess the adequacy and feasibility of systematic review for collating and analyzing data on potential impacts of GMOs in order to better inform specific steps within GMO risk assessment and risk management. The regulatory framework of the EU is used as an example, although the issues we discuss are likely to be more widely applicable.
Willingness and Acceptance of Delusions Scale: early findings on a new instrument for psychological flexibility

ABSTRACT Acceptance and Commitment Therapy and related constructs (experiential avoidance, cognitive defusion and committed action) have recently been applied to psychosis. However, with a few exceptions, this application has not resulted in symptom-specific assessment instruments. The current work intends to develop a measure for assessing experiential acceptance regarding delusions (the Willingness and Acceptance for Delusions Scale) and to conduct a preliminary study of its psychometric properties in a sample of 91 patients with a psychotic disorder, mostly male (87%), single (86%), unemployed (44%), presenting with a schizophrenia diagnosis (71%), and currently with delusions (last week – 52%). Exploratory factor analysis yielded a three-factor structure (Acceptance and Action, Non-entanglement and Non-struggling), which adequately fitted the data and reflected the intended constructs within an Acceptance and Commitment Therapy framework. Scores from all factors achieved adequate reliability and were associated with mindfulness and satisfaction with life. These early findings point to the internal and construct validity and reliability of the scores of the WADS. Although further research into the scale’s psychometric properties, particularly construct validity, is needed, its use in research and clinical practice with psychosis populations seems substantiated.
EXPLORING SECOND LANGUAGE WRITERS’ PAUSING AND REVISION BEHAVIORS

Abstract This study investigated the cognitive processes underlying pauses at different textual locations (e.g., within/between words) and various levels of revision (e.g., below word/clause). We used stimulated recall, keystroke logging, and eye-tracking methodology in combination to examine pausing and revision behaviors. Thirty advanced Chinese L2 users of English performed a version of the IELTS Academic Writing Task 2. During the writing task, participants’ key strokes were logged, and their eye movements were recorded. Immediately after the writing task, 12 participants also took part in a stimulated recall interview. The results revealed that, when participants paused at larger textual units, they were more likely to look back in the text and engage in higher-order writing processes. In contrast, during pauses at lower textual units, they tended to view areas closer to the inscription point and engage in lower-order writing processes. Prior to making a revision, participants most frequently had viewed the text that they subsequently revised or their eye gazes had been off-screen. Revisions focused more on language- than content-related issues, but there was a smaller difference in the number of language- and content-focused stimulated recall comments when larger textual units were revised.
1 Distributed Estimation Under Bandwidth and Energy Constraints

In parameter estimation problems a sequence of observations {x(n)}n=1 is used to estimate a random or deterministic parameter of interest s. Optimal estimation exploits the statistical correlation betweenx(n) ands that is described either by the joint probability distribution function (pdf) p(x(n), s) whens is assumed random; or by a family of observation pdfs p(x(n); s) parameterized by s whens is assumed deterministic. The optimal estimator function producing an estimate ŝ for a given set of observations {x(n)}n=1 is different for random and deterministic parameters. It also depends on the joint pdf p(x(n), s) (or family of pdfsp(x(n); s)) and the degree of knowledge about them; i.e., whether they are known, dependent on some other (nuisance) parameters, or completely unknown (Kay 1993). The distributed nature of a wireless sensor network (WSN) implies that observations are collected at different sensors and consequently it dictates that between collection and estimation a communication is present. If bandwidth and power were unlimited, the x(n) observations could be conveyed with arbitrary accuracy and, intuitively, no major impact would be expected. However, bandwidth and power are limited, and the seemingly innocuous communication stage turns out to have a significant impact on the design of optimal estimators and their performance assessed by the estimator variance. On the one hand, if digital communications are to be employed individual observations have to be quantized, transforming the estimation problem into that of estimating s using a set of quantized observations – certainly different from estimating s using the original analogamplitude observations. On the other hand, since components of the (vector) observation x(n) are
The Structure of Speed of Processing Across Cultures

We examined whether a general processing factor emerges when using response times for cognitive processing tasks and whether such a factor is valid across three different cultural groups (Chinese, Canadian, and Greek). Three hundred twenty university students from Canada (n = 115), China (n = 110), and Cyprus (n = 95) were assessed on an adaptation of the Das-Naglieri Cognitive Assessment System (D-N CAS; Naglieri & Das, 1997). Three alternative models were contrasted: a distinct abilities processing speed model (Model 1) that is dictated by the latent four cognitive factors of planning, attention, simultaneous and successive (PASS) processing, a unitary ability processing speed model (Model 2) that is dictated by the response time nature of all measures, and a bifactor model (Model 3) which included the latent scores of Models 1 and 2 and served as the full model. Results of structural equation modeling showed that (a) the model representing processing speed as a collection of four cognitive processes rather than a unitary processing speed factor was the most parsimonious, and (b) the loadings of the obtained factors were invariant across the three cultural groups. These findings enhance our understanding of the nature of speed of processing across diverse cultures and suggest that even when cognitive processes (i.e., PASS) are operationalized with response time measures, the processing component dominates speed.
A new neural network soft sensing method based on the multi-resolution data fusion

For the sampling frequency is different between the quality indexes and the auxiliary variables in the soft sensing training patterns, a new neural network soft sensing method based on the multi-resolution data fusion is presented in the paper. In the presented method, the neural network is trained based on the quality indexes and auxiliary variables with low sampling frequency first and the high resolution quality indexes is estimated using the trained network. Then the high resolution quality indexes are multi-scale decomposed using the biorthogonal wavelets and are fused with the low resolution quality indexes. Then the fused high resolution quality indexes are got and are used for the next training of the neural network to improve the accuracy of the soft sensing model. The method is used in the soft sensing modelling for the dry point of the crude gasoline of the Fluidized Catalytic Cracking Unit (FCCU) fractionator, results show that the neural network soft sensing model has higher accuracy after several iterative fusion and training steps.
Sustained attention assessment of narcoleptic patients: two case reports

Narcolepsy is a sleep disorder characterized by uncontrollable REM sleep attacks which alter the patients wake state and can lead to difficulties in attention aspects, such as maintaining attention when performing activities or tasks. This study aimed to evaluate sustained attention performance of two narcoleptic patients on the d2 Test, Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI) and Hamilton Rating Scale for Depression (HAM-D). Results showed that the maintenance of attention was associated with a slowing of the target symbols processing function in visual scanning with accuracy in task performance. A high degree of excessive sleepiness was observed, along with mild and moderate degrees of depressive signs and symptoms. One subject also presented with a nocturnal sleep disorder which could represent an important factor affecting attentional and affective capacity.