Miracle-dz/UniFAR-FTUs huggingface dataset:
Videotape tic counts in the assessment of Tourette's syndrome: stability, reliability, and validity.

OBJECTIVE We examined the short- and long-term temporal stability of tic counts to estimate the minimum length of videotape needed for a reliable index of overall tic activity and determined the interrater reliability and validity of tic counts based on prolonged videotape segments (> 10 minutes). METHOD Motor and phonic tic counts and clinician ratings were performed on 43 patients with Tourette's syndrome (TS), aged 7 to 50 years. Short-term stability was estimated by determining the mean interval-to-interval correlation of sequential equal-length segments from 30-minute videotape recordings of 20 subjects. Long-term stability was determined by correlating tic counts at 1-week (N = 14) and 2-week intervals (N = 11). In addition, tic counts were correlated with the most widely used clinical ratings of TS. RESULTS The short-term stability data indicated that estimates of motor and phonic tic frequencies should be based on videotape counts of at least 5 minutes' duration. Tic counts also were highly reliable and were significantly correlated with clinical ratings with the Yale Global Tic Severity Scale and the Clinical Global Impression Scale for Tourette Syndrome. CONCLUSIONS Standardized videotape tic counts can provide highly reliable, stable measures of tic frequencies that are moderately correlated with selected global ratings of tic severity.
Dynamic risk factors: scientific kinds or predictive constructs

ABSTRACT There are a number of theoretical problems evident in the concept of dynamic risk factors that arise from their (increasing) importation into the explanatory and treatment domains of forensic and correctional practice. More specifically: (a) the concept of dynamic risk factors has not been well defined; (b) relatedly, there is a lack of clarity whether dynamic risk factors refer to causal processes or are predictive constructs; and finally (c) because of the above problems no one is sure how best to integrate them into clinical assessment and treatment. I will examine each of these three conceptual problems in this paper and make some suggestions about how to utilise dynamic risk factors in explanations of offending.
Asymptotic performance of the fastica algorithm for independent component analysis and its iprovements

The fixed point algorithm, known as FastICA, is one of the most successful algorithms for independent component analysis in terms of accuracy and low computational complexity. This paper derives analytic closed form expressions that characterize separating ability of both one-unit and symmetric version of the algorithm in a local sense. Based on the analysis it is possible to combine advantages of the two versions. Although the analysis assumes a "good" initialization of the algorithms and long data records, our computer simulations demonstrate validity of the theoretical expressions in the case of arbitrary initialization and moderate data lengths
Punishing an Error Improves Learning: The Influence of Punishment Magnitude on Error-Related Neural Activity and Subsequent Learning

Punishing an error to shape subsequent performance is a major tenet of individual and societal level behavioral interventions. Recent work examining error-related neural activity has identified that the magnitude of activity in the posterior medial frontal cortex (pMFC) is predictive of learning from an error, whereby greater activity in this region predicts adaptive changes in future cognitive performance. It remains unclear how punishment influences error-related neural mechanisms to effect behavior change, particularly in key regions such as pMFC, which previous work has demonstrated to be insensitive to punishment. Using an associative learning task that provided monetary reward and punishment for recall performance, we observed that when recall errors were categorized by subsequent performance—whether the failure to accurately recall a number–location association was corrected at the next presentation of the same trial—the magnitude of error-related pMFC activity predicted future correction. However, the pMFC region was insensitive to the magnitude of punishment an error received and it was the left insula cortex that predicted learning from the most aversive outcomes. These findings add further evidence to the hypothesis that error-related pMFC activity may reflect more than a prediction error in representing the value of an outcome. The novel role identified here for the insular cortex in learning from punishment appears particularly compelling for our understanding of psychiatric and neurologic conditions that feature both insular cortex dysfunction and a diminished capacity for learning from negative feedback or punishment.
Factors in teachers' ratings of schoolchildren: Heymans' data reconstructed by modern standards

In this article we re‐analyse the data that Heymans and Wiersma collected on pupils from secondary schools, about 80 years ago. The main interest is in the structure of the adolescence data set in terms of modern standards and understood against the background of recent findings. More specifically, one question is whether the Heymans data can be understood in terms of the framework of the Big Five structure of personality traits. To that end, principal components analysis and multiple group analysis were performed. The general conclusion from these analyses is that the adolescence data are best described by a two‐dimensional structure. Another more specific research question is to what extent Heymans' own three dimensions can be detected in the data set. With emphasis on a small number of criterion items, used by Heymans to identify the three dimensions, a three‐factor solution was inspected for this purpose. The conclusion from this analysis is that the three Heymans dimensions cannot unequivocally be identified in the adolescence data set.
Design Guidelines for Metallic-Carbon-Nanotube-Tolerant Digital Logic Circuits

Metallic carbon nanotubes (CNTs) create source-drain shorts in carbon nanotube field effect transistors (CNFETs), causing excessive leakage, degraded noise margin and delay variation. There is no known CNT growth technique that guarantees 0% metallic CNTs. Therefore, metallic CNT removal techniques are necessary. Unfortunately, such removal techniques alone are imperfect and insufficient. This paper demonstrates the necessity for co-optimization of processing techniques for metallic CNT removal together with CNFET-based circuit design. We present a probabilistic CNFET circuit model which forms the basis for such co-optimization, and use the model to derive design and processing guidelines that enable design of CNFET-based digital circuits with practical constraints on leakage, noise margin and delay variations. These guidelines are essential for designing robust metallic- carbon-nanotube-tolerant digital circuits.
Cell-type specific regulation of cortical excitability through the allatostatin receptor system

Recent technical advances enable the regulation of neuronal circuit activity with high spatial and temporal resolution through genetic delivery of molecular activation or inactivation systems.Among them, the allatostatin receptor (AlstR)/ligand system has been developed for selective and quickly reversible silencing of mammalian neurons. However, targeted AlstR-mediated inactivation of specific neuronal types, particularly diverse types of inhibitory interneurons, remains to be established. In the present study, we achieved Cre-directed expression of AlstRs to excitatory and inhibitory cell-types in the cortex, and found that the AlstR-mediated inactivation was specific and robust at single-cell and neuronal population levels. Bath application of the allatostatin peptide markedly reduced spiking activity of AlstR-expressing excitatory and inhibitory neurons in response to intrasomatic current injections and laser photostimulation via glutamate uncaging, but control neurons without AlstR expression were not affected. As for the cortical network activity, the peptide application constrained photostimulation-evoked excitatory activity propagation detected by fast voltage-sensitive dye (VSD) imaging of the slices expressing AlstRs selectively in excitatory neurons, while it augmented excitatory activity in those slices with inhibitory neurons expressing AlstRs. In addition, AlstR-mediated inactivation effectively suppressed pharmacologically induced seizure activity in the slices targeting AlstRs to excitatory neurons. Taken together, our work demonstrated that the genetic delivery of AlstRs can be used for regulation of cortical excitability in a cell-type specific manner, and suggested that the AlstR system can be potentially used for fast seizure control.
PaTre: A Method for Paralogy Trees Construction

Genomes can be described as a collection of clusters, the gene families, whose members are called paralogs. Paralogs are genes that most probably share duplication history and show a significant similarity in their sequences, even if they perform slightly different biological function. Among the different mechanisms that have led to an increase of the genomic information during biological evolution, gene duplication is probably the most important. To better understand duplication events, the first step is to investigate the history of the gene families in order to detect which duplication events have taken place, and in which relative (partial) order. Here we present a method, called PaTre, that, given a gene family, attempts to construct the paralogy tree of the family. We will work under the hypothesis that every family member derives from a duplication process of another member. By the term paralogy tree, we mean a directed tree in which the root represents the most ancient paralog of the family and each oriented arc (a, b) represents the existence of a duplication event from the template gene a to its copy b. Notice that gene a survives the event and can serve as a template of more than one duplication event; in fact, there can be more than one arc leaving a. PaTre uses new algorithmic techniques motivated by the specific application at hand. The reliability of the inferential process has been tested by means of a simulator that implements different hypotheses on the duplication-with-modification paradigm and on three examples of different biological gene families, belonging either to lower and higher organisms.
A metadata geoparsing system for place name recognition and resolution in metadata records

This paper describes an approach for performing recognition and resolution of place names mentioned over the descriptive metadata records of typical digital libraries. Our approach exploits evidence provided by the existing structured attributes within the metadata records to support the place name recognition and resolution, in order to achieve better results than by just using lexical evidence from the textual values of these attributes. In metadata records, lexical evidence is very often insufficient for this task, since short sentences and simple expressions are predominant. Our implementation uses a dictionary based technique for recognition of place names (with names provided by Geonames), and machine learning for reasoning on the evidences and choosing a possible resolution candidate. The evaluation of our approach was performed in data sets with a metadata schema rich in Dublin Core elements. Two evaluation methods were used. First, we used cross-validation, which showed that our solution is able to achieve a very high precision of 0,99 at 0,55 recall, or a recall of 0,79 at 0,86 precision. Second, we used a comparative evaluation with an existing commercial service, where our solution performed better on any confidence level (p<0,001).
Recommendation as a service (RaaS): New challenges for, and evaluation metrics of recommender systems

Advertisement turns into recommendation, web services become independent apps, and mobile devices enforce context awareness. Recommendation as a Service (RaaS) turns into an independent application-based business. By analyzing the trends, the paper introduces a multi-dimensional evaluation scheme to characterize recommender systems. The chart is applied to different research implementations, emphasizing their specifics. Requirements and trends for the future are derived.
Feller semigroups, Lp -sub-Markovian semigroups, and applications to pseudo-differential operators with negative definite symbols

Abstract The question of extending Lp -sub-Markovian semigroups to the spaces Lq , q > p, and the interpolation of Lp -sub-Markovian semigroups with Feller semigroups is investigated. The structure of generators of Lp -sub-Markovian semigroups is studied. Subordination in the sense of Bochner is used to discuss the construction of refinements of Lp -sub-Markovian semigroups. The rôle played by some function spaces which are domains of definition for Lp -generators is pointed out. The problem of regularising powers of generators as well as some perturbation results are discussed.
The Efficacy and Effectiveness of Continuous Glucose Monitoring During Pregnancy: A Systematic Review

Objective Diabetic pregnancies carry a high risk for both mother and child, especially when glycemic control is poor. A novel technique that aims to improve glycemic control is the continuous glucose monitor (CGM). This tool is already in use to improve pregnancy outcome. This review presents the available evidence on the efficacy of CGM use in pregnancy and the effectiveness on pregnancy outcome. Methods A systematic search was conducted using PubMed, EMBASE, and the Cochrane Libraries for articles on CGM in pregnancy. We evaluated the selected articles with particular attention for clinical and cost-effectiveness of CGM to improve pregnancy outcome. Results We retrieved 5032 articles, 11 of which remained as relevant after selection according to predefined criteria. Most studies were limited to the evaluation of the role of CGM on clinical decision making. Only 2 studies were randomized controlled trials (RCTs) evaluating the effect on pregnancy outcome. One small RCT on retrospective CGM showed a significant reduction in third-trimester HbA1c and a significant reduction in neonatal macrosomia. A second RCT on real-time CGM did not show any effect on either glycemic control or on pregnancy outcome. Conclusions Current evidence on the efficacy of CGM on improving glycemic control during pregnancy as well as on the effectiveness on pregnancy outcome is limited to 2 RCTs with contradicting results. Evidence on the cost-effectiveness is lacking. Further proper RCTs on the effectiveness and cost-effectiveness of CGM in pregnancy are required before wide implementation in practice. Target Audience: Obstetricians and gynecologists, family physicians and internists Learning Objectives: After completing this CME activity, physicians should be better able to compare the results of the 2 major trials on the efficacy and effectiveness of the continuous glucose monitoring system (CGM) in treating pregnant women with diabetes and evaluate the evidence on the cost-effectiveness of CGMS use in pregnant women with diabetes.
Finding acceptable models in nonlinear inverse problems using a neighbourhood algorithm

A recently proposed new class of direct search method is applied to the problem of mapping out the region of data-acceptable models (sets of unknowns) in a finite-dimensional nonlinear inverse problem. A model is defined to be data acceptable if its fit to the observed data is better than some prescribed level. The neighbourhood algorithm (NA) can be used to generate ensembles of models which preferentially sample the data-acceptable regions of parameter space. Simple transformations of a data misfit criterion are proposed to assist in this task. Some numerical experiments are presented which are motivated by highly nonlinear geophysical inverse problems. In these cases it is shown how the NA can be used to map out the main features of data-acceptable regions in both high- and low-dimensional problems. It is also shown how the NA can concentrate sampling in multiple acceptable regions simultaneously.
Establishing a CoPs-based innovation ecosystem to enhance competence - the case of CGN in China

This research investigates how complex product systems (CoPs)-based innovation ecosystem is created in terms of structure and interactions among players, as well as how technology, value and capability evolve at different stages of an innovation ecosystem. Based on an exploratory case study on the innovation ecosystem of a nuclear power giant-China general nuclear power group (CGN) for the period 1987-2014, this paper presents a framework to explicate the micro-foundation of the formation mechanism of an innovation ecosystem for CoPs. Three ecosystem stages are identified: ecosystem incubation, ecosystem figuration and ecosystem self-renewal. Through the three stages, CGN has been extending its ecosystem gradually from core business to extended network and ecosystem periphery. This study provides theoretical and managerial implications for building and managing innovation ecosystems in developing countries.
Design and Implementation of Parallel Make

make is the standard UNIX utility for maintaining programs. It has been used by UNIX programmers for almost l0 years and many UNIX programs nowadays are maintained by it. The strength of make is that it allows the user to specify how to compile program components, and that the system, after an update, is regenerated according to the speciflcation and with minimum number of recompilations. With the appearance of multiple processor systems, the time needed to "make" a program, or target, can be reduced effectively. Although the hardware provides parallelism, few tools are able to exploit this parallelism. The intro- duction of parallelism to make is the subject of this paper. We describe a parallel make and give an analysis of its performance.
An integer genetic algorithm for optimal clustering in phased array antenna

A subarraying technique based on a novel genetic algorithm (GA) whose operators have been suitably customized in order to deal with integer variables is presented in this paper. The method is aimed to prevent the generation of high-level quantization lobes in the scanned radiation pattern by properly combining a given number of clusters selected from an arbitrarily defined alphabet of tiles of various shapes. A representative numerical result is reported in order to show advantages and drawbacks of the proposed methodology.
Adimen-SUMO: Reengineering an Ontology for First-Order Reasoning

In this paper, the authors present Adimen-SUMO, an operational ontology to be used by first-order theorem provers in intelligent systems that require sophisticated reasoning capabilities e.g. Natural Language Processing, Knowledge Engineering, Semantic Web infrastructure, etc. Adimen-SUMO has been obtained by automatically translating around 88% of the original axioms of SUMO Suggested Upper Merged Ontology. Their main interest is to present in a practical way the advantages of using first-order theorem provers during the design and development of first-order ontologies. First-order theorem provers are applied as inference engines for reengineering a large and complex ontology in order to allow for formal reasoning. In particular, the authors' study focuses on providing first-order reasoning support to SUMO. During the process, they detect, explain and repair several important design flaws and problems of the SUMO axiomatization. As a by-product, they also provide general design decisions and good practices for creating operational first-order ontologies of any kind.
Channel-statistics-based analog downlink beamforming for millimeter-wave multi-user massive MIMO

In this paper, we consider the design of analogue beamformers for the downlink of multi-user massive multiple-input-multiple-output (MIMO) systems. We specifically investigate systems where both link ends are equipped with hybrid digital/analog (HDA) beamforming structures, where the analog beamformers are adapted based on second order channel statistics, reducing the training overhead as well as hardware effort. We present a framework for the optimization of such beamformers operating in mm-wave channels, exploiting the directional characteristics and sparse nature of such channels. We develop an approximate upper bound of the ergodic sum capacity, based on which efficient beamforming algorithms are devised. Simulation results show significant performance improvements of the proposed algorithms compared to state-of-the-art algorithms.
Dynamic Hand Gesture Tracking and Recognition for Real-Time Immersive Virtual Object Manipulation

Immersive visualisation is increasingly being used for comprehensive and rapid analysis of objects in 3D and object dynamic behaviour in 4D. Challenges are therefore presented to provide natural user interaction to enable effortless virtual object manipulation. Presented in this paper is the development and evaluation of a human-computer interaction system based on natural hand gestures. By employing a hybrid inertial and ultrasonic tracking system to provide the absolute positions and orientations of the user’s head and hands as well as a pair of high degrees-of-freedoms data gloves to provide the relative positions and orientations of finger joints and tips in both hands, the proposed system is shown to be able to automatically track and recognise a number of simple hand gestures. The effectiveness and potential of the proposed system is demonstrated through the five basic object manipulation tasks involving selection, release, translation, rotation and scaling of a 3D virtual cube.
A Predictive Modeling Approach to Analyze Data in EEG-fMRI Experiments

In this paper, a novel technique based on blind source extraction (BSE) using linear prediction is proposed to extract rolandic beta rhythm from electroencephalogram (EEG) recorded in a simultaneous EEG-fMRI experiment. We call this method CLP-BSE standing for constrained-linear-prediction BSE. Extracting event-related oscillations is a crucial task due to nonphase-locked nature and inter-trial variability of this event. The main objective of this work is to extract rolandic beta rhythm to measure event-related synchronization (ERS) with acceptable signal-to-noise ratio (SNR). The extracted rhythm is utilized for constructing a regressor to analyze functional magnetic resonance imaging (fMRI). The proposed method is a semi-blind technique which uses a spatio-temporal constraint for beta rhythm extraction. This constraint is derived from recorded EEG signals based on the prior knowledge about the frequency and location of the source of interest. The main reason of employing linear prediction as an effective algorithm to extract the EEG rhythm is the ability of extracting sources which have specific temporal structure. Performance of the proposed method is evaluated using both synthetic and real EEG data. The obtained results show that the proposed technique is able to extract ERS effectively. The maximum percentage of ERS obtained by filtering is 152% while the obtained ERS by CLP-BSE is 214%. In another experiment, the extracted event-related oscillations in beta band are used to make the necessary regressor for fMRI analysis. The results of EEG-fMRI coregistration confirm that there are correlation between the extracted rolandic beta rhythm and simultaneously recorded fMRI. This conclude that, the results of EEG-fMRI combination support the reliability of CLP-BSE output.