The non-agricultural rural sector in Central and Eastern Europe

During the central planning era, rural development in Central and Eastern Europe (CEE) was frequently associated with agricultural development. Recently, opinion has begun to move away from this position. Attention is now focusing on the role of the non-farm sector in the context of rural development because of this sector’s potential for absorbing excess labor from agriculture, alleviating problems caused by urban-rural migration, contributing to income growth, and promoting a more equitable distribution of income. At the beginning of the transformation process in transition countries, economic policies focused mainly on macroeconomic problems, and the increasing income disparity between rural and urban regions was ignored. We now know that the increasing inter-regional divergence in the transition economies is one of the major transformation problems. This is one of the reasons why the World Bank, OECD, and the EU have formulated special rural development strategies

A computer vision model for visual-object-based attention and eye movements

This is the post-print version of the final paper published in Computer Vision and Image Understanding. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2008 Elsevier B.V.This paper presents a new computational framework for modelling visual-object-based attention and attention-driven eye movements within an integrated system in a biologically inspired approach. Attention operates at multiple levels of visual selection by space, feature, object and group depending on the nature of targets and visual tasks. Attentional shifts and gaze shifts are constructed upon their common process circuits and control mechanisms but also separated from their different function roles, working together to fulfil flexible visual selection tasks in complicated visual environments. The framework integrates the important aspects of human visual attention and eye movements resulting in sophisticated performance in complicated natural scenes. The proposed approach aims at exploring a useful visual selection system for computer vision, especially for usage in cluttered natural visual environments.National Natural Science of Founda- tion of Chin

Going Back Home

A soldier struggles with his return from Vietnam, searching for someone to talk with, to understand. Articles, stories, and other compositions in this archive were written by participants in the Mighty Pen Project. The program, developed by author David L. Robbins, and in partnership with Virginia Commonwealth University and the Virginia War Memorial in Richmond, Virginia, offers veterans and their family members a customized twelve-week writing class, free of charge. The program encourages, supports, and assists participants in sharing their stories and experiences of military experience so both writer and audience may benefit

TBFormer: Two-Branch Transformer for Image Forgery Localization

Image forgery localization aims to identify forged regions by capturing subtle traces from high-quality discriminative features. In this paper, we propose a Transformer-style network with two feature extraction branches for image forgery localization, and it is named as Two-Branch Transformer (TBFormer). Firstly, two feature extraction branches are elaborately designed, taking advantage of the discriminative stacked Transformer layers, for both RGB and noise domain features. Secondly, an Attention-aware Hierarchical-feature Fusion Module (AHFM) is proposed to effectively fuse hierarchical features from two different domains. Although the two feature extraction branches have the same architecture, their features have significant differences since they are extracted from different domains. We adopt position attention to embed them into a unified feature domain for hierarchical feature investigation. Finally, a Transformer decoder is constructed for feature reconstruction to generate the predicted mask. Extensive experiments on publicly available datasets demonstrate the effectiveness of the proposed model.Comment: 5 pages, 3 figure

Position clamping of optically trapped microscopic non-spherical probes

We investigate the degree of control that can be exercised over an optically trapped microscopic non-spherical force probe. By position clamping translational and rotational modes in different ways, we are able to dramatically improve the position resolution of our probe with no reduction in sensitivity. We also demonstrate control over rotational-translational coupling, and exhibit a mechanism whereby the average centre of rotation of the probe can be displaced away from its centre