Springer New York

Current rates of autism diagnoses have been cause for concern and research as well as rumor and misinformation. Important questions surround the condition: how early can an accurate diagnosis be made? At what age should intervention start? How can parents recognize warning signs? And what causes autism in the first place?

There are no easy answers, but the Handbook of Early Intervention for Autism Spectrum Disorders gives researchers, practitioners, and academics the science and guidance to better understand and intervene. Background chapters survey the history of professional understanding of the disorders and the ongoing debate over autism as a single entity or a continuum. Chapters on best methods in screening, assessment, and diagnosis reflect the transition between the DSM-V and older diagnostic criteria. And at the heart of the book, the intervention section ranges from evidence-based strategies for developing core skills to ethical concerns, cultural considerations, and controversial treatments.

Included in the Handbook's broad-based coverage:

• Designing curriculum programs for children with autism spectrum disorders (ASD).
• Mainstream education for children with ASD.
• Teaching independent living skills to children with ASD.
• Social skills and play.
• Behavioral and mental health disorders in children with ASD.
• Training and supporting caregivers in evidence-based practices.
• Teaching cognitive skills to children with ASD.

The Handbook of Early Intervention for Autism Spectrum Disorders is a comprehensive reference for researchers, professors, and graduate students as well as clinicians and other scientist-practitioners in clinical childand school psychology, child and adolescent psychiatry, social work, rehabilitation, special education, and pediatric medicine.

¿This brief presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular.  By considering both space complexity in terms of signal quantization and time complexity with respect to data window sizes, this study provides a new perspective to understand the fundamental relationship between probabilistic errors and resources, which may represent data sizes in computer usage, computational complexity in algorithms, sample sizes in statistical analysis and channel bandwidths in communications.