Generating Sequence of Eye Fixations Using Decision-theoretic Attention Model

Human eyes scan images with serial eye fixations. We proposed a novel attention selectivity model for the automatic generation of eye fixations on 2D static scenes. An activation map was first computed by extracting primary visual features and detecting meaningful objects from the scene. An adaptable retinal filter was applied on this map to generate Regions of Interest (ROIs), whose locations corresponded to those of activation peaks and whose sizes were estimated by an iterative adjustment algorithm. The focus of attention was moved serially over the detected ROIs by a decision-theoretic mechanism. The generated sequence of eye fixations was determined from the perceptual benefit function based on perceptual costs and rewards, while the time distribution of different ROIs was estimated by a memory learning and decaying model. Finally, to demonstrate the effectiveness of the proposed attention model, the gaze tracking results of different human subjects and the simulated eye fixation shifting were compared

Experimental study on microlaser fluorescence spectrometer

This paper presents a kind of miniature handheld laser fluorescence spectrometer, which integrates a laser emission system, a spectroscopic system, and a detection system into a volume of 100 × 50 × 20 mm3. A universal serial bus interface is connected to PC for data processing and spectrum display. The emitted laser wavelength is 405 nm. A spectral range is 400 to 760 nm and 2-nm optical resolution has been achieved. This spectrometer has the advantages of compact structure, small volume, high sensitivity, and low cost. 1.Introductio

MosaicShape: Stochastic Region Grouping with Shape Prior

A novel method that combines shape-based object recognition and image segmentation is proposed for shape retrieval from images. Given a shape prior represented in a multi-scale curvature form, the proposed method identifies the target objects in images by grouping oversegmented image regions. The problem is formulated in a unified probabilistic framework and solved by a stochastic Markov Chain Monte Carlo (MCMC) mechanism. By this means, object segmentation and recognition are accomplished simultaneously. Within each sampling move during the simulation process, probabilistic region grouping operations are influenced by both the image information and the shape similarity constraint. The latter constraint is measured by a partial shape matching process. A generalized parallel algorithm [1], combined with a large sampling jump and other implementation improvements, greatly speeds up the overall stochastic process. The proposed method supports the segmentation and recognition of multiple occluded objects in images. Experimental results are provided for both synthetic and real images

Research on Embodied Carbon Transfer Measurement and Carbon Compensation among Regions in China

The existence of interprovincial embodied carbon transfer not only makes it difficult to achieve carbon emission reductions but also exacerbates the inequity, inefficiency, and high costs of interprovincial carbon emission reduction rights and responsibilities. This paper uses multi-regional input–output analysis (MRIOA) to measure the interprovincial embodied carbon transfer in 2017, obtains the net carbon transfer between 30 provinces (municipalities and autonomous regions) and eight regions in 2017, and accounts for the interprovincial carbon compensation amount based on the carbon price in the national carbon market. This study finds that carbon transfer from economically developed provinces to less developed provinces still exists in China, and the overall distribution shows a spatial transfer pattern from south to north and from east to west, with the northwestern region bearing most of the carbon emission pressure for which it should receive corresponding financial compensation. As part of the process to achieve the “dual carbon” target, appropriate emission reduction policies should be formulated according to the characteristics of provincial carbon transfer and the principle of “who benefits, who compensates”, and economically developed regions should give corresponding financial or technical compensation to less developed regions based on net carbon transfer. Compensation and support should be given to less developed regions based on net carbon transfer to prevent further regional development imbalances

Abstract MosaicShape: Stochastic Region Grouping with Shape Prior

A method that combines shape-based object recognition and image segmentation is proposed for shape retrieval from images. Given a shape prior represented in a multiscale curvature form, the proposed method identifies the target objects in images by grouping oversegmented image regions. The problem is formulated in a unified probabilistic framework, and object segmentation and recognition are accomplished simultaneously by a stochastic Markov Chain Monte Carlo (MCMC) mechanism. Within each sampling move during the simulation process, probabilistic region grouping operations are influenced by both the image information and the shape similarity constraint. The latter constraint is measured by a partial shape matching process. A generalized cluster sampling algorithm [1], combined with a large sampling jump and other implementation improvements, greatly speeds up the overall stochastic process. The proposed method supports the segmentation and recognition of multiple occluded objects in images. Experimental results are provided for both synthetic and real images.

Accounting for China’s Net Carbon Emissions and Research on the Realization Path of Carbon Neutralization Based on Ecosystem Carbon Sinks

Carbon sinks are an important way to achieve carbon neutrality. In this study, carbon emissions in each year from 2019 to 2060 were predicted by constructing the LEAP (Long-range Energy Alternatives Planning System)-China model. The ecosystem carbon sinks in five representative years of 2012, 2017, 2019, 2030, and 2060 were predicted by reviewing related literature to calculate China’s net carbon emission accounts in these five key years and to quantitatively analyze the path to achieving carbon neutrality in China. The results show that China’s annual carbon emissions will peak in 2028, with a peak of 10.27 billion tons of carbon dioxide; that they will then decrease year by year to 7227 million tons of carbon dioxide in 2060; and that the ecosystem carbon sinks generated by land use are more stable, with a total of approximately 5.5 billion tons of carbon dioxide. To achieve carbon neutrality, a dependence only on ecosystem carbon sinks is insufficient. National energy conservation, voluntary emission reduction by enterprises, and a reliance on new energy and new technologies are needed to ensure the final implementation of China’s carbon neutrality strategy