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Brain Networks for Studying Healthy and Pathological Aging Mechanisms and Intervention Efficacy
Previous studies showed that both healthy and pathological aging are associated with changes in brain structure and function of the mature human brain. The most prominent anatomical alteration are changes in prefrontal cortex morphology, volume loss and reduced white-matter integrity and hippocampal atrophy. Cognitive decline affects mainly the performance of episodic memory, speed of sensory information processing, working memory, inhibitory function and long-term memory. It has been also proposed that due to the aforementioned changes the aging brain engages in compensatory brain mechanism such as a broader activation of cortical regions (mainly frontal) rather than specialized activation....
Recent Advances in Financial Engineering
This book consists of 11 papers based on research presented at the KIER-TMU International Workshop on Financial Engineering, held in Tokyo in 2009. The Workshop, organised by Kyoto University's Institute of Economic Research (KIER) and Tokyo Metropolitan University (TMU), is the successor to the Daiwa International Workshop on Financial Engineering held from 2004 to 2008 by Professor Kijima (the Chair of this Workshop) and his colleagues. Academic researchers and industry practitioners alike have presented the latest research on financial engineering at this international venue. These papers address state-of-the-art techniques in financial engineering, and have undergone a rigorous selection process to make this book a high-quality one. This volume will be of interest to academics, practitioners, and graduate students in the field of quantitative finance and financial engineering
Studies in Perception and Action IX
Since 1991, the edited book series Studies in Perception and Action has appeared in conjunction with the biennial International Conference of Perception and Action (ICPA), a conference that provides an opportunity for individuals who share interests in e
Understanding the Enrichment of Heavy Elements by the Chemodynamical Evolution Models of Dwarf Galaxies
This book addresses the mechanism of enrichment of heavy elements in galaxies, a long standing problem in astronomy. It mainly focuses on explaining the origin of heavy elements by performing state-of-the-art, high-resolution hydrodynamic simulations of dwarf galaxies. In this book, the author successfully develops a model of galactic chemodynamical evolution by means of which the neutron star mergers can be used to explain the observed abundance pattern of the heavy elements synthesized by the rapid neutron capture process, such as europium, gold, and uranium in the Local Group dwarf galaxies. The book argues that heavy elements are significant indicators of the evolutionary history of the early galaxies, and presents theoretical findings that open new avenues to understanding the formation and evolution of galaxies based on the abundance of heavy elements in metal-poor stars.
Computer Vision – ECCV 2022
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
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Visual Domain Adaptation in the Deep Learning Era
Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains. The aim of this book is to provide an overview of such DA/TL methods applied to computer vision, a field whose popularit...
