Adolescents in stepfamilies: Developmental family interventions.

Discusses issues and interventions for working with adolescents who live in stepfamilies. A developmental perspective, using psychoeducation and brief strategic intervention approaches, is proposed for working with stepfamilies. Six major issues for adolescents in stepfamilies are discussed: developmental issues, sexuality issues, parent–child relationships, parenting in stepfamilies, nonresidential parent–child issues, and changes in visitation and custody. Case illustrations and suggested interventions are presented for each of these areas. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Palliative treatments of cancers of upper respiratory and digestive tracts

Palliative treatment of head and neck cancers needs an initial evaluation based upon clinical symptoms and tumoral behaviour. Local recurrences are the most frequent presentation of tumoral failures. The therapeutic management of these local recurrences requires a mixture of unspecific and specific oncologic procedures. Radiotherapy and chemotherapy are frequently used in those circumstances in order to reduce the tumoral volume and to facilitate the effectiveness of unspecific treatments. Management of metastatic disease is often impaired by the moderate efficiency of chemotherapy while this treatment leads to marked side effects. Basically, the best therapeutic choice depends on the expected positive balance between the improvement of symptoms and the level of treatment-related side effects. However, daily clinical approach is a better way to care these patients than predefined rigid protocols.     

Randomization in individual choice behavior.

There is ample evidence that people cannot generate random series when instructed to do so. Rather, they produce sequences with too few symmetries and long runs and too many alternations among events. The authors propose a psychological theory to account for these findings, which assumes that subjects generate nonrandom sequences that locally represent theoretical random series subject to a constraint on their short-term memory. Closed-form expressions are then derived for the major statistics that have been used to test for deviations from randomness. Results from 3 experiments with 2 and 3 equiprobable alternatives support the model on both the individual and group levels. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Fooling anti-virus systems

Anti-virus (AV) products and other fingerprint-based systems are typical of the applications used to enforce security. AV products are probably the first step that most organizations take to protect their systems and enhance security. Many small to medium organizations rely almost entirely on AV products.     

A Maximum Entropy Approach for Collaborative Filtering

Collaborative filtering (CF) involves predicting the preferences of a user for a set of items given partial knowledge of the user's preferences for other items, while leveraging a database of profiles for other users. CF has applications e.g. in predicting Web sites a person will visit and in recommending products. Fundamentally, CF is a pattern recognition task, but a formidable one, often involving a huge feature space, a large data set, and many missing features. Even more daunting is the fact that a CF inference engine must be capable of predicting any (user-selected) items, given any available set of partial knowledge on the user's other preferences. In other words, the model must be designed to solve any of a huge (combinatoric) set of possible inference tasks. CF techniques include memory-based, classification-based, and statistical modelling approaches. Among these, modelling approaches scale best with large data sets and are the most adept at handling missing features. The disadvantage of these methods lies in the statistical assumptions (e.g. feature independence), which may be unjustified. To address this shortcoming we propose a new model-based CF method, based on the maximum entropy principle. For the MS Web application, the new method is demonstrated to outperform a number of CF approaches, including naive Bayes and latent variable (cluster) models, support vector machines (SVMs), and the (Pearson) correlation method.     

The prospects of non-volatile phase-change RAM

Phase-change read-and-write memory (PRAM) is a promising memory that can solve the problems of conventional memory—scalability, read/write speed, and reliability. We will review the opportunities and technical challenges of PRAM. The most important challenge of PRAM is the reduction of the writing current. Various approaches to reduce the writing current will be reviewed and the prospects of PRAM are discussed.