Do Triarchic Psychopathy Components of New Zealand High-Risk Parolees Predict Probation Officer Relationship Quality, Quality of Life on Parole, and Recidivism?

The Triarchic Psychopathy Measure (TriPM) is a self-report scale based on the Triarchic Model that has been little used in research in the criminal justice system. We sought to examine associations between pre-release TriPM components, probation officer relationships, and parolee quality of life, both measured after 2 months in the community, and reconviction 12 months after release. Using data from 234 New Zealand male high-risk prisoners, we tested four multivariate models each across three timepoints. Pre-release, we found Boldness was not predictive, but Meanness predicted poorer relationship quality after 2 months, both from probation officer and parolee perspectives, with the former in turn predicting reconviction within 12 months. Disinhibition predicted 12-month recidivism regardless of relationship quality or external life circumstances. This relationship to recidivism was partially explained in the final model which linked Disinhibition and poorer subjective wellbeing, with the latter in turn predicting recidivism

Correlation of Solid Dosage Porosity and Tensile Strength with Acoustically Extracted Mechanical Properties

Currently, the compressed tablet and its oral administration is the most popular drug delivery modality in medicine. The accurate porosity and tensile strength characterization of a tablet design is vital for predicting its performance such as disintegration, dissolution, and drug-release efficiency upon administration as well as ensuring its mechanical integrity. In current work, a non-destructive contact ultrasonic approach and an associated testing procedure are presented and employed to quantify and relate the acoustically extracted mechanical properties of pharmaceutical compacts to direct porosity and tensile strength measurements. Based on a comprehensive set of experimental data, it is demonstrated how strongly the acoustic wave propagation is modulated and correlated to the tablet porosity and tensile strength of a compact made using spray-dried lactose and microcrystalline cellulose with varying mixture ratios. The effect of mixing ratio on the porosity and tensile strength on the resulting compacts is quantified and, with the acoustic experimental data, mixing ratio is related to the compact ultrasonic characteristics. The ultrasonic techniques provide a rapid, non-destructive means for evaluating compacts in formulation development and manufacturing. The presented approach and data could find critical applications in continuous tablet manufacturing, its real-time quality monitoring, as well as minimizing batch-to-batch quality variations

Modelling of River Flows, Sediment and Contaminants Transport

This book presents five articles that are also part of a Special Issue titled: Modelling of River flows, Sediment and Contaminants Transport published in the Water Journal under the section: Water Erosion and Sediment Transport. It covers a wide range of topics, such as predicting the impacts of wildfires on sediment transport and water quality in a mountainous region and estimating the sediment erosion due to release of ice-jams in cold region rivers

Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.Comment: 13 pages, Including Supporting Information, 7 Figures, Download the dataset from: http://wwm.phy.bme.hu/SupplementaryDataS1.zi

HP-GAN: Probabilistic 3D human motion prediction via GAN

Predicting and understanding human motion dynamics has many applications, such as motion synthesis, augmented reality, security, and autonomous vehicles. Due to the recent success of generative adversarial networks (GAN), there has been much interest in probabilistic estimation and synthetic data generation using deep neural network architectures and learning algorithms. We propose a novel sequence-to-sequence model for probabilistic human motion prediction, trained with a modified version of improved Wasserstein generative adversarial networks (WGAN-GP), in which we use a custom loss function designed for human motion prediction. Our model, which we call HP-GAN, learns a probability density function of future human poses conditioned on previous poses. It predicts multiple sequences of possible future human poses, each from the same input sequence but a different vector z drawn from a random distribution. Furthermore, to quantify the quality of the non-deterministic predictions, we simultaneously train a motion-quality-assessment model that learns the probability that a given skeleton sequence is a real human motion. We test our algorithm on two of the largest skeleton datasets: NTURGB-D and Human3.6M. We train our model on both single and multiple action types. Its predictive power for long-term motion estimation is demonstrated by generating multiple plausible futures of more than 30 frames from just 10 frames of input. We show that most sequences generated from the same input have more than 50\% probabilities of being judged as a real human sequence. We will release all the code used in this paper to Github