Bar-Ilan University

About: Bar-Ilan University is a education organization based out in Ramat Gan, Israel. It is known for research contribution in the topics: Population & Poison control. The organization has 12835 authors who have published 34964 publications receiving 995648 citations. The organization is also known as: Bar Ilan University & BIU.

Peer interaction and loneliness in high-functioning children with autism.

Abstract: Social interaction with peers and the understanding and feelings of loneliness were examined in 18 high-functioning children with autism and 17 typically developing children matched for IQ, chronological age, gender, and maternal education. Observations were conducted on children's spontaneous social initiations and responses to their peers in natural settings such as recess and snack time, and children reported on their understanding and feelings of loneliness and social interaction. Overall, children with autism revealed a good understanding of both social interaction and loneliness, and they demonstrated a high level of social initiation. However, they spent only half the time in social interactions with peers compared with their matched counterparts, and they interacted more often with a typically developing child than with another special education child. Despite the intergroup differences in frequency of interaction, a similar distribution of interactions emerged for both groups, who presented mostly positive social behaviors, fewer low-level behaviors, and very infrequent negative behaviors. Children with autism reported higher degrees of loneliness than their typical age-mates, as well as a lower association between social interaction and loneliness, suggesting their poorer understanding of the relations between loneliness and social interaction. Research and practice implications of these findings are discussed.

Chapter 7 Patterns of structural change

Abstract: Publisher Summary This chapter presents a discussion on the patterns of structural transformation during the transition from a low income, agrarian rural economy to an industrial urban economy with substantially higher per capita income. The chapter presents a review on the basic concepts of the empirical research program into the economic structure of developing countries during the transition process, which originated with the monumental work of Simon Kuznets. The chapter discusses methodological issues of empirical research on structural transformation. A summary of the main stylized facts of development, with emphasis on growth, accumulation, and sector proportions is presented. Attempts to model and explain the transformation are also presented. The chapter also discusses relative prices and the role of the state in facilitating, fostering, or at times hampering, an efficient transformation. Development economics can be characterized as dealing with issues of structure and growth in less developed countries. Analysis of structure appears in two variants. The first, and more recent, is concerned with the functioning of economies, their markets, institutions, mechanisms for allocating resources, income generation and its distribution, and so on. In the second variant, economic development is seen as an interrelated set of long-run processes of structural transformation that accompany growth. The central features of this approach are economy-wide phenomena such as industrialization, urbanization, and agricultural transformation, regarded as elements of what Kuznets identified as “modern economic growth.”

Deep multi-task learning with low level tasks supervised at lower layers

Abstract: In all previous work on deep multi-task learning we are aware of, all task supervisions are on the same (outermost) layer. We present a multi-task learning architecture with deep bi-directional RNNs, where different tasks supervision can happen at different layers. We present experiments in syntactic chunking and CCG supertagging, coupled with the additional task of POS-tagging. We show that it is consistently better to have POS supervision at the innermost rather than the outermost layer. We argue that this is because “lowlevel” tasks are better kept at the lower layers, enabling the higher-level tasks to make use of the shared representation of the lower-level tasks. Finally, we also show how this architecture can be used for domain adaptation.

Investigation, Treatment, and Monitoring of Late-Onset Hypogonadism in Males: ISA, ISSAM, EAU, EAA, and ASA Recommendations

Abstract: Demographic data clearly demonstrate that the percentage of the population in the older age group is increasing. Androgen deficiency in the aging male has become a topic of increasing interest and debate throughout the world. Cross-sectional and longitudinal data indicate that the testosterone falls progressively with age and that a significant percentage of men over the age of 60 years have serum testosterone levels that are below the lower limits of young adult (age 20–30 years) men (1–4). The principal questions raised by these observations are whether older hypogonadal men will benefit from testosterone treatment and what will be the risks associated with such intervention. The past decade has brought evidence of benefit of androgen treatment of hypogonadal men on multiple target organs and the recent studies show short-term beneficial effects of testosterone in older men that are similar to those in younger men. This has been comprehensively reviewed and summarized by the Institute of Medicine in ‘Testosterone and Aging: Clinical Research Directions’ (5). Long-term data on the effects of testosterone treatment in the older population are limited mainly to effects on body composition and bone mass (6–11). Key questions of the effects of testosterone on patient reported outcomes and functional benefits that may retard physical or mental frailty of the elderly or improve the quality of life are not yet available. Specific risk data on the prostate and cardiovascular systems are needed.

Robust Adaptive Beamforming Based on Interference Covariance Matrix Reconstruction and Steering Vector Estimation

Abstract: Adaptive beamformers are sensitive to model mismatch, especially when the desired signal is present in training snapshots or when the training is done using data samples. In contrast to previous works, this correspondence attempts to reconstruct the interference-plus-noise covariance matrix instead of searching for the optimal diagonal loading factor for the sample covariance matrix. The estimator is based on the Capon spectral estimator integrated over a region separated from the desired signal direction. This is shown to be more robust than using the sample covariance matrix. Subsequently, the mismatch in the steering vector of the desired signal is estimated by maximizing the beamformer output power under a constraint that prevents the corrected steering vector from getting close to the interference steering vectors. The proposed adaptive beamforming algorithm does not impose a norm constraint. Therefore, it can be used even in applications where gain perturbations affect the steering vector. Simulation results demonstrate that the performance of the proposed adaptive beamformer is almost always close to the optimal value across a wide range of signal to noise and signal to interference ratios.