Prisons and Drugs: A global review of incarceration, drug use and drug services. Report 12

Prisons play an important role in drug policy. They are used to punish people who break drug laws and they also hold a large number of people who have experience of drug use and drug problems. They therefore have an important part to play in attempts to reduce the harm caused by drugs. Imprisonment itself can be seen as one type of harm, as it causes problems for prisoners and their families and creates a large financial burden for taxpayers. Theseharms and costs are difficult to calculate, but there is little evidence that large scale imprisonment of drug offenders has had the desired results in deterring drug use or reducing drug problems (Bewley- Taylor, Trace, & Stevens, 2005). In this paper, we examine the international prevalence of drug users, drug use and related problems in prisons and we report on the problems that are related to the issue of drugs in prison. We go on to examine the international guidelines and effective responses that have been developed in this area in the last decade. The paper is a review of the literature, based on a search of bibliographic databases, including Medline, PubMed, ISI as well as EMBASE and contacts with researchers and practitioners in the field up to January 2007

Skylab missions SL-1/SL-2, SL-3, and SL-4 hydrogen, and helium

Cryogenic boiling heat transfer for oxygen, nitrogen, hydrogen, and helium fluids - free and forced convection boiling method

Pricing Strategy for Italian Wine

2openopenBrentari E.; Levaggi R.Brentari, Eugenio; Levaggi, Rosell

Formative vs Reflective constructs: a CTA-PLS approach on a goalkeepers’ performance model

Nowadays, PLS-SEM is a trend-topic, whereas football is moving towards a data-driven approach; by combining these two worlds, we aim to show a new way for measuring football goalkeepers’ performance, by using data provided from EA Sports experts and available on the Kaggle data science platform. Furthermore, another objective is to refine the model, supporting football experts from a statistical point of view. For this purpose, we adopt a confirmatory tetrad analysis (CTA-PLS) to validate and evaluate the nature (e.g. formative or reflective) of each latent variable. Then, a second-order PLS-SEM model is built. We validate and compare this new indicator with a benchmark (the EA overall). The final goal is to prove the CTA approach on a real case study and to refine a composite performance indicator for helping football policy makers taking strategic decisions

Unsupervised Learning of Prosodic Boundaries in ASL

In both spoken and sign languages, prosodic cues signal the ends of intonational phrases. Children must somehow learn to associate these cues with phrase boundaries without explicitly being told where those boundaries are. We present two unsupervised statistical models that learn to identify the ends of intonational phrases (I-phrases) in American Sign Language (ASL) based on prosodic cues: a mixture model, and a hidden Markov model. Although neither model is presented with labeled phrase boundaries, both models recover reasonable parameters and achieve performance comparable to models that are trained with labeled boundaries. However, the between-state dependence of the hidden Markov model does not improve the performance. The success of these models sheds light on how infants might learn the prosodic system without explicit instruction

Amodal Aspects of Linguistic Design

All spoken languages encode syllables and constrain their internal structure. But whether these restrictions concern the design of the language system, broadly, or speech, specifically, remains unknown. To address this question, here, we gauge the structure of signed syllables in American Sign Language (ASL). Like spoken languages, signed syllables must exhibit a single sonority/energy peak (i.e., movement). Four experiments examine whether this restriction is enforced by signers and nonsigners. We first show that Deaf ASL signers selectively apply sonority restrictions to syllables (but not morphemes) in novel ASL signs. We next examine whether this principle might further shape the representation of signed syllables by nonsigners. Absent any experience with ASL, nonsigners used movement to define syllable-like units. Moreover, the restriction on syllable structure constrained the capacity of nonsigners to learn from experience. Given brief practice that implicitly paired syllables with sonority peaks (i.e., movement)—a natural phonological constraint attested in every human language—nonsigners rapidly learned to selectively rely on movement to define syllables and they also learned to partly ignore it in the identification of morpheme-like units. Remarkably, nonsigners failed to learn an unnatural rule that defines syllables by handshape, suggesting they were unable to ignore movement in identifying syllables. These findings indicate that signed and spoken syllables are subject to a shared phonological restriction that constrains phonological learning in a new modality. These conclusions suggest the design of the phonological system is partly amodal.</p

Twitter alloy steel disambiguation and user relevance via one-class and two-class news titles classifiers

This paper addresses the nontrivial task of Twitter financial disam- biguation (TFD), which is relevant to filter financial domain tweets (e.g., alloy steel or coffee prices) when no unique identifiers (e.g., cashtags) are adopted. To automate TFD, we propose a transfer learning approach that uses freely labeled news titles to train diverse one-class and two-class classification methods. These include different text handling transforms, adaptations of statistical measures and modern machine learning methods, including support vector machines (SVM), deep autoencoders and multilayer perceptrons. As a case study, we analyzed the domain of alloy steel prices, collecting a recent Twitter dataset. Overall, the best results were achieved by a two-class SVM fed with TFD statistical measures and topic model features, obtaining an 80% and 71% discrimination level when tested with 11,081 and 3,000 manually labeled tweets. The best one-class performance (78% and 69% for the same test tweets) was obtained by a term frequency-inverse document frequency classifier (TF-IDFC). These models were further used to gen- erate a Financial User Relevance rank (FUR) score, aiming to filter relevant users. The SVM and TF-IDFC FUR models obtained a predictive user discrimination level of 80% and 75% when tested with a manually labeled test sample of 418 users. These results confirm the proposed joint TFD-FUR approach as a valuable tool for the selection of Twitter texts and users for financial social media analytics (e.g., sentiment analysis, detection of influential users).Research carried out with the support of resources of Big and Open Data Innovation Laboratory (BODaI-Lab), University of Brescia, granted by Fondazione Cariplo and Regione Lombardia

"P" come Piacere

Applicazione dei modelli CUB all'Analisi Sensoriale sul caff