How to Measure Carbon Equity: Carbon Gini Index Based on Historical Cumulative Emission Per Capita

This paper uses Lorenz Curve and Gini Index with adjustment to per capita historical cumulative emission and constructs Carbon Gini Index to measure inequality in climate change area. The analysis using Carbon Gini Index shows that 70% of carbon space in the atmosphere has been used for unequal distribution, which is almost the same as that of income in the country with the biggest gap between rich and poor in the world. The carbon equity should be an urgency and priority in the climate agenda. Carbon Gini Index established in this paper can be used to measure inequality in the distribution of carbon space and provide a quantified indicator for measurement of carbon equity among different proposals.Climate Change, Carbon Equity, Long-term Mitigation Goal, Cumulative Emission Per Capita, Carbon Gini Index

k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training

For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identifiable data is restricted and prohibited by law. Then, "how can we ensure the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification?" becomes a challenge issue. In this work, we propose an approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN, which leverages the k-Same-Anonymity mechanism, the Generative Adversarial Network, and the hyperparameter tuning methods. Moreover, to speed up model training and reduce memory consumption, the mixed precision training technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, to validate its applicability, the proposed work has been applied to actual datasets - RafD and CelebA for performance testing. Besides protecting privacy of high-resolution facial images, the proposed system is also justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters

Mechanism of Exact Transition between Cationic and Anionic Redox Activities in Cathode Material Li2FeSiO4.

The discovery of anion redox activity is promising for boosting the capacity of lithium ion battery (LIB) cathodes. However, fundamental understanding of the mechanisms that trigger the anionic redox is still lacking. Here, using hybrid density functional study combined with experimental soft X-ray absorption spectroscopy (sXAS) measurements, we unambiguously proved that Li(2- x)FeSiO4 performs sequent cationic and anionic redox activity through delithiation. Specifically, Fe2+ is oxidized to Fe3+ during the first Li ion extraction per formula unit (f.u.), while the second Li ion extraction triggered the oxygen redox exclusively. Cationic and anionic redox result in electron and hole polaron states, respectively, explaining the poor conductivity of Li(2- x)FeSiO4 noted by previous experiments. In contrast, other cathode materials in this family exhibit diversity of the redox process. Li2MnSiO4 shows double cationic redox (Mn2+-Mn4+) during the whole delithiation, while Li2CoSiO4 shows simultaneous cationic and anionic redox. The present finding not only provides new insights into the oxygen redox activity in polyanionic compounds for rechargeable batteries but also sheds light on the future design of high-capacity rechargeable batteries

Freeway Incident Likelihood Prediction and Response Decision-Making

This research project consisted of two parts. The first part developed a set of real-time incident likelihood prediction models. The second part developed a freeway incident response decision-making methodology based on sequential hypothesis testing methods. The freeway incident likelihoods predicted by the real-time prediction models act as prior probabilities for the freeway incident response decision-making system. The products of this research project will be incorporated in the Advanced Traffic Management System that is being implemented on the Borman Expressway, a 16-mile segment of I-80 in northwest Indiana. The decision-making system can be used by traffic management personnel to assist in responding to various freeway incidents in a near optimal manner to minimize traffic delays and reduce the number of secondary incidents

Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX

This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) signals. Through the assessment of 16 prevalent NN models and their variants across four brain-computer interface (BCI) paradigms, we gauged their information representation capability. Rooted in comprehensive literature review findings, we proposed EEGNeX, a novel, purely ConvNet-based architecture. We pitted it against both existing cutting-edge strategies and the Mother of All BCI Benchmarks (MOABB) involving 11 distinct EEG motor imagination (MI) classification tasks and revealed that EEGNeX surpasses other state-of-the-art methods. Notably, it shows up to 2.1%-8.5% improvement in the classification accuracy in different scenarios with statistical significance (p < 0.05) compared to its competitors. This study not only provides deeper insights into designing efficient NN models for EEG data but also lays groundwork for future explorations into the relationship between bioelectric brain signals and NN architectures. For the benefit of broader scientific collaboration, we have made all benchmark models, including EEGNeX, publicly available at (https://github.com/chenxiachan/EEGNeX).Comment: 19 pages, 6 figure

Increasing Interest in Inclusive Education in the Context of Action Plan for the Development and Enhancement of Special Education during the Fourteenth Five-Year Period in China: An Analysis of Baidu Index Data

Objective: Current evidence shows that public interest in inclusive education has been rising since the implementation of Action Plan for the development and enhancement of special education during the Fourteenth Five-Year period in China. The aim of this study was to quantify recent trends in public interest and related online search behavior for inclusive education in the context of this Action Plan. Methods: Baidu Index, a database of search engines with massive information, was employed. By searching for the keyword inclusive education, and using content analysis to understand the data information related to inclusive education. This study also extracted the search trend data of Chinese netizens on the related terms "Law on the Protection of Persons with Disabilities " and "Regulation on the Education of Persons with Disabilities" from January 1, 2022 to October 27, 2022. Finally, it compares the search trend of public search interests of inclusive education with related terms. Results: The public's interest in "inclusive education" and the related terms "Law on the Protection of Persons with Disabilities " and "Regulation on the Education of Persons with Disabilities" has been on the rise since the implementation of the Action Plan. The search trend reached its peak in February and May 2022, the valley in January 2022, and the search volume in other time periods tended to be stable. Conclusion: Baidu Index can understand the public's interest in inclusive education. The study shows that the rising search trend of inclusive education is closely related to the implementation of the Action Plan. The search volume of the "Law on the Protection of Persons with Disabilities " and "Regulation on the Education of Persons with Disabilities" is basically the same as that of "inclusive education", but the average search volume daily of "inclusive education" is slightly higher than that of "Regulation on the Education of Persons with Disabilities"

Dynamic Network Representation Learning Method Based on Improved GRU Network

As social networks have been rapidly growing, traditional network representation learning methods are struggling to accurately characterize their dynamic changes, and to output effective node classification and link prediction. To address this problem, this paper proposes IproGRU, a dynamic network representation learning method based on an improved Gated Recurrent Unit (GRU) network to improve the dynamic network representation. First, the method quickly generates embedding for an influenced node by sampling and aggregating features of its neighboring nodes when the network changes. Second, it updates the embedding of the influenced node on time series by the improved GRU network to fully adapt to the changes of the dynamic network. Experimental results on node classification and link prediction for three datasets of dynamic networks show that the proposed method improves the accuracy by 5–10 % on average from those of the traditional Node2vec and GraphSAGE methods and has a slight advantage over Graph Convolutional Networks (GCNs). The results demonstrate that our method is effective for dynamic network representation.

Optical Nondestructive Controlled-NOT Gate without Using Entangled Photons

We present and experimentally demonstrate a novel optical nondestructive controlled-NOT gate without using entangled ancilla. With much fewer measurements compared with quantum process tomography, we get a good estimation of the gate fidelity. The result shows a great improvement compared with previous experiments. Moreover, we also show that quantum parallelism is achieved in our gate and the performance of the gate can not be reproduced by local operations and classical communications.Comment: 5 pages, 3 figures, Slight changes have been made, Journal-ref adde

Collaborating on ESG consulting, reporting, and communicating education: Using partner maps for capability building design

Given the rising demand for environmental, social, and governance (ESG) talents, this study aims to provide a multidisciplinary outlook of specific capability requirements for ESG talents, focusing on the use of ESG and carbon information, thereby providing a roadmap for ESG education. Following design science framework conventions and running design workshops that integrate design thinking of “how might we” design questions, literature analysis, and expert interviews across disciplines, this study presents findings regarding three main activities—consulting, reporting, and communicating. Based on the iterations of design workshops that adopt a circular economy-based partner map design canvas for stakeholder analysis with procedures such as expert interviews and literature analysis, three partner/capability maps were generated to map stakeholders and explore the capabilities needed. ESG and carbon information digital and data skills emerged as the core capability to complete all the three tasks. A conceptual framework—a Smart System of ESG and Carbon Information—is proposed to summarize planning, operating, and communicating with ESG and carbon information, along with high-level organizational actions and talent capabilities. It identifies the building blocks of an ESG operating system within an enterprise to engage various stakeholders for value-creation collaboration. Despite the limitation of a lack of comprehensive review and limited geographic and disciplinary representation, this study provides a roadmap for enterprises and universities to explore and define talent requirements and create specific education and training programs