Miracle-dz/UniFAR-FTUs huggingface dataset:
The Prediction of Violent Recidivism Amongst Individuals with Mental Disorders: Situational vs. Dispositional Factors

THE PREDICTION OF VIOLENT RECIDIVISM AMONGST INDIVIDUALS WITH MENTAL DISORDERS: SITUATIONAL VS. DISPOSITIONAL FACTORS By Michelle Nicole Harris August 2016 Committee Chair: Dr. Brent Teasdale Major Department: Criminal Justice and Criminology The objective of this study is to evaluate why some individuals with mental illness are more inclined to violently recidivate. There appears to be two perspectives that may explain recidivism: one that emphasizes situational factors and one that emphasizes dispositional factors. Situational factors are those that are constantly changing within one’s life, whereas dispositional factors are those that remain relatively stable over time. Therefore, dispositional factors would theoretically put individuals with mental illness at stable risk for recidivism because these factors remain relatively stable over time. In fact, perhaps individuals with mental illness repeatedly engage in violence because they have a dispositional trait (like low self-control, for example) that puts them at stable risk for recidivism. Conversely, situational factors would theoretically explain why individuals do not engage in recidivism because they are transient and constantly changing. Therefore, perhaps one desists from violence because some situational factor changed in that individual’s life. Using data from the MacArthur Violence Risk Assessment study (i.e. MacRisk), a longitudinal study of people with serious mental illness, violent recidivism will be evaluated across waves. Specifically, the objective is to determine if situational or dispositional factors influence violent recidivism. THE PREDICTION OF VIOLENT RECIDIVISM AMONGST INDIVIDUALS WITH MENTAL DISORDERS: SITUATIONAL VS. DISPOSITIONAL FACTORS
The Influence of Group Training in the Incredible Years Teacher Classroom Management Program on Preschool Teachers’ Classroom Management Strategies

This study examined changes in preschool teachers’ perceptions of classroom management strategies following group training in the recently revised Incredible Years Teacher Classroom Management Program (C. Webster-Stratton, 2006). The authors used a pre/post follow-up design across 2 groups that each met for 8 sessions over an 8–10-week period for a total of 32 hr of training. Twenty-four preschool teachers from one of the lowest income and highest unemployment counties in the state of Michigan participated in the program. To examine short-term maintenance effects, the authors collected follow-up data 16 weeks after all teachers completed the training. The authors found improvements in teachers’ perceptions of positive classroom management strategies and their use. Transporting this evidence-based teacher training program to school-based mental health service delivery settings warrants additio- nal study.
Evidence-Based Practices DNA AND RNA EXTRACTION FROM FORMALIN-FIXED , PARAFFIN-EMBEDDED TISSUE BIOSPECIMENS

This evidence-based, expert-vetted best practice document is applicable for nucleic acid extraction from standard-size FFPE blocks containing human tissue, although such guidance may also be applicable to tissue from mammalian animal models. Specifically, recommended procedures are suitable for standard cassette-sized, non-decalcified tissue biospecimens that were fixed in 10% neutral buffered formalin (NBF) for less than 48 h before paraffinembedding. Biospecimens processed under these procedural guidelines are suitable for analysis by polymerase chain reaction (PCR), methylation analysis, Sanger sequencing and next-generation sequencing, and microarray hybridization. Much of the evidence presented in this document pertains to mRNA molecules and DNA, thus shorter RNA molecules such as microRNAs (miRNAs) may require additional consideration and optimization. This document is not applicable for analysis of biospecimens by in situ hybridization. Guidance relating to optimization of procedures for specific analytical platforms are located within Summaries of Literature Evidence (see 7.0) and Summaries of Expert Recommendations (see 8.3). Analytical platform specific guidelines for determining specimen suitability are listed in Tables 1 and 2. It is strongly advised that SOPs developed under this Biospecimen EvidenceBased Practice (BEBP) undergo experimental validation for each analytical platform anticipated (see 8.3.1). Details regarding the expert review of this document and resultant recommendations can be found within section 8.0 and the names and credentials of experts that participated on the review panel can be found within section 8.2.
Oral Reading Skills of Children with Oral Language (Word-Finding) Difficulties

We examined how children with and without oral language (word-finding) difficulties (WFD) perform on oral reading (OR) versus silent reading recognition (SRR) tasks when reading the same words and how lexical factors influenced OR accuracy, error patterns, and nature of miscues. Primary-grade students were administered an experimental reading measure. Words were controlled for lexical factors known to influence oral language, such as frequency, lexical neighborhood, familiarity, and phonotactic probability. For learners with WFD, SRR was superior to OR; lexical factors predicted OR success; WF error-patterns emerged in OR; and miscues were higher in frequency, more familiar, and from denser neighborhoods than targets.
University of Amsterdam and Renmin University at TRECVID 2016: Searching Video, Detecting Events and Describing Video

In this paper we summarize our TRECVID 2016 [1] video recognition experiments. We participated in three tasks: video search, event detection and video description. Here we describe the tasks on event detection and video description. For event detection we explore semantic representations based on VideoStory and an ImageNet Shuﬄe for both zero-shot and few-example regimes. For the showcase task on video description we experiment with a deep network that predicts a visual representation from a natural language description, and use this space for the sentence matching. For generative description we enhance a neural image captioning model with Early Embedding and Late Reranking. The 2016 edition of the TRECVID benchmark has been a fruitful participation for our joint-team, resulting in the best overall result for zero-and few-example event detection as well as video description by matching and in generative mode.
A Novel Charge-Protection Superjunction-Insulator VDMOS

A novel charge-protection SuperjunctionInsulator (SJ-I) vertical double diffused MOSFET (SJVDMOSs) has been proposed. The proposed analyticalmodel of SJ-I drift layer is verified with the numerical simulation and compared with conventional SJ drift layer model. In the proposed device, we have inserted very thin (100nm) insulator pillar between two consecutive SJ pillars for charge protection and prohibit impurity inter diffusion. This method is optimized Charge Termination (CT) and covers less device area than conventional CT method. In proposed SJ-I devices, the Breakdown Voltage (BV) has been improved by more than 10% with identical area specific ON-resistance (RonA) as compared to conventional SJ devices. Further, we have investigated transfer, output, maximum switching frequency and transient response of SJI device.
Modeling Financial Time Series Volatility with Markov Switching Models

An analysis of financial time series volatility is an important issue in making many economic decisions. The volatility of high frequency financial series changes over time and the periods of the high volatility are clustering. Many authors use GARCH models, introduced by Bollerslev (1986), to capture these dependences. GARCH models describe the conditional variance clustering effect but their forecasts are often overstated (Anderson and Bollerslev, 1998). An application of the Markov switching specification to GARCH models can outperform forecasts of the standard GARCH structure. The first Markov switching model was used by Hamilton (1989) in the analysis of the business cycle. The ARCH model with Markov switching (SWARCH) was the first specification in this class of models (Hamilton, Susmel, 1994). Next the SWARCH structure was extended to GARCH parameters, giving the MS-GARCH model. The Markov switching GARCH model was characterized by Davidson (1994), Klassen (2002) and Gray (1996), and each of them defined an equation of the conditional variance in a different way. The conditional variance equation is then exploited in the estimation of MS-GARCH model parameters. The main purpose of this article is to check whether, a better quality volatility predictions can be obtained from MS-AR-GARCH than from AR-GARCH models. At first the estimation of those types of models has been carried out for
A PRELIMINARY MOLECULAR ANALYSIS OF PHYLOGENETIC RELATIONSHIPS OF AUSTRALASIAN WOLF SPIDER GENERA (ARANEAE, LYCOSIDAE)

Abstract A data-set from the mitochondrial 12S rRNA gene subunit of 11 Australasian lycosid species (six New Zealand species and five Australian species) was generated. Three North American lycosid species, one European species and one New Zealand pisaurid (outgroup) were also sequenced. The sequence data for the 16 species were combined with the published sequences of 12 European lycosids, two Asian lycosids and one Asian pisaurid and were analyzed using parsimony and maximum likelihood analyses. The resulting phylogenetic trees reveal that Australasian species largely form clades distinct from Palearctic and Holarctic species providing further evidence against the placement of Australasian species in Northern Hemisphere genera. New Zealand wolf spiders appear to be related to a subset of Australian genera whereas the other Australian lycosid genera are related to Asian/Holarctic faunas. Gene sequences in the 12S region were useful when examining relationships between closely related genera, but were not as informative for deeper generic relationships.
Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models

The possible responses of ecosystem processes to rising atmospheric CO2 concentration and climate change are illustrated using six dynamic global vegetation models that explicitly represent the interactions of ecosystem carbon and water exchanges with vegetation dynamics. The models are driven by the IPCC IS92a scenario of rising CO2 ( Wigley et al. 1991 ), and by climate changes resulting from effective CO2 concentrations corresponding to IS92a, simulated by the coupled ocean atmosphere model HadCM2‐SUL. Simulations with changing CO2 alone show a widely distributed terrestrial carbon sink of 1.4–3.8 Pg C y−1 during the 1990s, rising to 3.7–8.6 Pg C y−1 a century later. Simulations including climate change show a reduced sink both today (0.6–3.0 Pg C y−1) and a century later (0.3–6.6 Pg C y−1) as a result of the impacts of climate change on NEP of tropical and southern hemisphere ecosystems. In all models, the rate of increase of NEP begins to level off around 2030 as a consequence of the ‘diminishing return’ of physiological CO2 effects at high CO2 concentrations. Four out of the six models show a further, climate‐induced decline in NEP resulting from increased heterotrophic respiration and declining tropical NPP after 2050. Changes in vegetation structure influence the magnitude and spatial pattern of the carbon sink and, in combination with changing climate, also freshwater availability (runoff). It is shown that these changes, once set in motion, would continue to evolve for at least a century even if atmospheric CO2 concentration and climate could be instantaneously stabilized. The results should be considered illustrative in the sense that the choice of CO2 concentration scenario was arbitrary and only one climate model scenario was used. However, the results serve to indicate a range of possible biospheric responses to CO2 and climate change. They reveal major uncertainties about the response of NEP to climate change resulting, primarily, from differences in the way that modelled global NPP responds to a changing climate. The simulations illustrate, however, that the magnitude of possible biospheric influences on the carbon balance requires that this factor is taken into account for future scenarios of atmospheric CO2 and climate change.
Face recognition via deep sparse graph neural networks.

In recent years, deep learning based approaches have substantially improved the performance of face recognition. Most existing deep learning techniques work well, but neglect effective utilization of face correlation information. The resulting performance loss is noteworthy for personal appearance variations caused by factors such as illumination, pose, occlusion, and misalignment. We believe that face correlation information should be introduced to solve this network performance problem originating from by intra-personal variations. Obviously, different poses of same person have similar feature structure, and even different people may have some similar facial sub-regions. We propose a graph representation based on the face correlation information that is embedded via the sparse reconstruction and deep learning within an irregular domain. The proposed method achieves high recognition rates of 99.58% on the benchmark LFW facial evaluation database.
Breast cancer diagnosis based on mammary thermography and extreme learning machines

Introduction: Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods: To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes. Results: Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion: ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness.
Light-absorption of dust and elemental carbon in snow in the Indian Himalayas and the Finnish Arctic

Abstract. Light-absorbing impurities (LAIs) deposited in snow have the potential to substantially affect the snow radiation budget, with subsequent implications for snow melt. To more accurately quantify the snow albedo, the contribution from different LAIs needs to be assessed. Here we estimate the main LAI components, elemental carbon (EC) (as a proxy for black carbon) and mineral dust in snow from the Indian Himalayas and paired the results with snow samples from Arctic Finland. The impurities are collected onto quartz filters and are analyzed thermal–optically for EC, as well as with an additional optical measurement to estimate the light-absorption of dust separately on the filters. Laboratory tests were conducted using substrates containing soot and mineral particles, especially prepared to test the experimental setup. Analyzed ambient snow samples show EC concentrations that are in the same range as presented by previous research, for each respective region. In terms of the mass absorption cross section (MAC) our ambient EC surprisingly had about half of the MAC value compared to our laboratory standard EC (chimney soot), suggesting a less light absorptive EC in the snow, which has consequences for the snow albedo reduction caused by EC. In the Himalayan samples, larger contributions by dust (in the range of 50 % or greater for the light absorption caused by the LAI) highlighted the importance of dust acting as a light absorber in the snow. Moreover, EC concentrations in the Indian samples, acquired from a 120 cm deep snow pit (possibly covering the last five years of snow fall), suggest an increase in both EC and dust deposition. This work emphasizes the complexity in determining the snow albedo, showing that LAI concentrations alone might not be sufficient, but additional transient effects on the light-absorbing properties of the EC need to be considered and studied in the snow. Equally as imperative is the confirmation of the spatial and temporal representativeness of these data by comparing data from several and deeper pits explored at the same time.
Assessment of Verbal and Nonverbal Memory and Learning in Abstinent Alcoholics

Neuropsychological performance was measured in chronic alcoholics who maintained abstinence for at least six months and with matched controls. Specifically, measures of verbal memory were assessed utilizing the Rey Auditory Verbal Learning Test (RAVLT) and measures of nonverbal memory with the Rey Osterreith Complex Figure Test (ROCF) and a new measure, the Poreh Nonverbal Memory Test (PNMT). In addition, both the RAVLT and the PNMT provide a measure of operationalized learning, as they are multi-trial tasks utilizing five trials to assess recall in each trial. Verbal memory includes the ability to encode, store and retrieve information for words, language and verbal stimuli. Nonverbal memory reflects the ability to encode, store and retrieve information that is visual and spatial in nature. It is devoid of verbal components and includes abstract designs or nonsense figures. Currently, there are questions as to the validity of many nonverbal memory measures because they allow for sub-vocalization of the tasks thereby utilizing verbal mediation (Wisniewski, Wendling, Manning & Steinhoff, 2012). The present study assessed for differences in verbal and nonverbal memory in abstinent alcoholics and predicted that they would perform more poorly on nonverbal measures while verbal memory would remain intact. Additionally, a comparison of learning curves was examined for each group. Finally, the PNMT was validated by correlating with a current neuropsychological assessment of memory and learning, the RAVLT, and a nonverbal neuropsychological assessment, the ROCF. v Results indicated that the abstinent alcoholics differed significantly in nonverbal measurements depending upon the complexity of the tasks. Concerning verbal tasks, there was no significant difference in results across the groups. However, the length of alcohol dependence did significantly predict performance on the RAVLT recognition task indicating possible frontal lobe deficits and disordered recall. Correlational analyses indicate that the utility and validity of the new visual-spatial memory test, the PNMT, is consistent with the ROCF for nonverbal memory and the RAVLT as a learning assessment for verbal memory and learning. Further, the PNMT is not affected by education as is the ROCF and RAVLT. Together, these results showed that nonverbal memory was impaired in abstinent alcoholics despite length of abstinence and verbal memory remained relatively intact. Additionally, the PNMT is a valid measure of nonverbal memory and learning.
Toxicity in Vigna mungo ( L ) Hepper by Vetiver Grass

Deepak Kumar Gupta, Alok Srivastava & V. P. Singh Department of Plant Science,Faculty of Applied Sciences, M.J.P. Rohilkhand University, Bareilly-243 006, India vijay singh <singhvp03@rediffmail.com> ABSTRACT Two inbreds of Black gram (Vigna mungo) were sown in the field to study the phytotoxicity of lead on growth parameters. In a separate experiment Black gram is intercropped with vetiver grass to study the phytoremediation through vetiver. In both the cases plants were treated with 9.0, 10.0 & 11.0 mg lit–1 concentration of Pb(NO3)2 & a control set of plants (without any treatment) was also sown in the field. In the first set of experiment, the growth and metabolism of Black gram were adversely affected with the increase in lead concentration and a concentration dependent decrease was noticed in all the growth parameters studied. The maximum reduction (% of control) was obtained with the higher dose of lead i.e. 11 mg lit–1 in both the varieties of Black gram. Variety T-9 was found to be more susceptible to lead as compared to PU-35. In the second set of experiment (intercropping with vetiver) a significant recovery of this phytotoxicity was noticed in both the varieties of Black gram. Lead accumulation in roots of vetiver grown in lead treated soil is higher than growing in non contaminated soil. No stress symptoms were observed in vetiver plants. Results of the present study reveals that vetiver can be regarded as a potential phytoremediator plant that can be grown in a site contaminated with lead.
Picrorhiza kurroa : An ethnopharmacologically important plant species of Himalayan region

Picrorhiza kurroa Royle ex Benth. commonly known as Kutki, belongs to family Scrophulariaceae. It is found in the Himalayan regions of China, Pakistan, India, Bhutan and Nepal. It is considered as an important medicinal plant which is mostly used in the traditional medicinal system for asthama, jaundice, fever, malaria, snake bite and liver disorders Different pharmacological activities of P. kurroa include anti-microbial, anti-oxidant, anti-bacterial, antimutagenic, cardio-protective, hepato-protective, anti-malarial, anti-diabetic, anti-inflammatory, anti-cancer, anti-ulcer and nephro-protective activities were recorded from this plant. So far, Iridoids (Picroside I and II), Cucurbitacins and Phenolic components are the different phytochemicals which are extracted from P. kurroa. The authentification of P. kurroa raw material for commercially available herbal/botanical products is essential and it is done by the DNA fingerprinting of P. kurroa. Because of the over-exploitation of P. kurroa for medicinal purposes, the conservational status of P. kurroa in different regions became endangered. It is the need of the hour to utilize different conservational strategies and save this medicinal wealth from extinction as it is widely used by the local people for curing different diseases and thus it cause immense pressure on the plant population. Keyword: Ethnopharmacology; Conservation; Extinction.
Performance of Monte Carlo Tree Search Algorithms when Playing the Game Ataxx

Monte Carlo Tree Search (MCTS) has recently emerged as a promising technique to play games with very large state spaces. Ataxx is a simple two-player board game with large and deep game tree. In this work, we apply different MCTS algorithms to play the game Ataxx and evaluate its performance against different adversaries (e.g., minimax2). Our analysis highlights one key aspect of MCTS, the trade-off between samples (and accuracy) and chances of winning the game which translates to a trade-off between the delay in making a move and chances of winning.