CO2 Emission Reduction Potential in China's Electricity Sector: Scenario Analysis Based on LMDI Decomposition

AbstractThe CO2 emission reduction from China's electricity sector will matter not only for China but impact the result of the global action on climate change. This paper firstly analyzed the main factors that affect the CO2 emission in accordance with the LMDI decomposition model. Then three scenarios were assumed based on the main factors to explore the CO2 reduction potential. Furthermore, LMDI method was used again to measure the contribution of each factor to CO2 emission reduction potential in the future. The results showed that the CO2 emission will continue to grow in the three scenarios from 2010 to 2020, with an annual growth rate of 10.7%, 6.5% and 4.5%, respectively. The active low carbon policies taken on the driving factors will contribute to 2701Mt - 3688Mt CO2 emission reduction. The share of low-carbon power generation and thermal power generation efficiency are most important factors for emission reduction. However, in the long run, low-carbon power generation will contribute more. Terminal electricity consumption is always the most important factor driving CO2 emission up. Finally, policies for low-carbon development of China's electricity sector are proposed based on the analysis results

Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene

Learning on 3D scene-based point cloud has received extensive attention as its promising application in many fields, and well-annotated and multisource datasets can catalyze the development of those data-driven approaches. To facilitate the research of this area, we present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks and also an effective learning framework for its hierarchical segmentation task. The dataset was generated via the photogrammetric processing on unmanned aerial vehicle (UAV) images of the National University of Singapore (NUS) campus, and has been point-wisely annotated with both hierarchical and instance-based labels. Based on it, we formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies. To solve this problem, a two-stage method including multi-task (MT) learning and hierarchical ensemble (HE) with consistency consideration is proposed. Experimental results demonstrate the superiority of the proposed method and potential advantages of our hierarchical annotations. In addition, we benchmark results of semantic and instance segmentation, which is accessible online at https://3d.dataset.site with the dataset and all source codes.Comment: Accepted by the 28th ACM International Conference on Multimedia (ACM MM 2020

Deep Learning for Natural Language Processing

With the constantly growing number of topical or sentiment-bearing texts and dialogs on the Web, the demand for automatic language or text analysis algorithms continues to expand. This chapter discusses about advanced deep learning techniques for classical and hot research directions in the field of natural language processing, including text classification, sentiment analysis, and task-oriented dialog systems. In text classification, we focus on tasks of multi-label text classification and extreme multi-label text classification, which allow for automatically annotates the texts with the most relevant labels. In sentiment analysis, we look into aspect-based sentiment analysis that makes automatic extraction of fine-grained sentiment information from texts, and multimodal sentiment analysis that classifies people’s opinions or attitudes from multimedia data through fusion techniques. In dialog system, we introduce how deep learning techniques work in pipeline mode and end-to-end mode for task-oriented dialog system. In this chapter, the rapidly evolving state of the research on the three topics is reviewed. Furthermore, trends in the research on deep learning for natural language processing are identified, and a discussion about future advances is provided

Chinese Open Instruction Generalist: A Preliminary Release

Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat.openai.com/}}. Despite impressive progress in English-oriented large-scale language models (LLMs), it is still under-explored whether English-based foundation LLMs can perform similarly on multilingual tasks compared to English tasks with well-designed instruction tuning and how we can construct the corpora needed for the tuning. To remedy this gap, we propose the project as an attempt to create a Chinese instruction dataset by various methods adapted to the intrinsic characteristics of 4 sub-tasks. We collect around 200k Chinese instruction tuning samples, which have been manually checked to guarantee high quality. We also summarize the existing English and Chinese instruction corpora and briefly describe some potential applications of the newly constructed Chinese instruction corpora. The resulting \textbf{C}hinese \textbf{O}pen \textbf{I}nstruction \textbf{G}eneralist (\textbf{COIG}) corpora are available in Huggingface\footnote{\url{https://huggingface.co/datasets/BAAI/COIG}} and Github\footnote{\url{https://github.com/FlagOpen/FlagInstruct}}, and will be continuously updated

A deep learning algorithm to identify carotid plaques and assess their stability

BackgroundCarotid plaques are major risk factors for stroke. Carotid ultrasound can help to assess the risk and incidence rate of stroke. However, large-scale carotid artery screening is time-consuming and laborious, the diagnostic results inevitably involve the subjectivity of the diagnostician to a certain extent. Deep learning demonstrates the ability to solve the aforementioned challenges. Thus, we attempted to develop an automated algorithm to provide a more consistent and objective diagnostic method and to identify the presence and stability of carotid plaques using deep learning.MethodsA total of 3,860 ultrasound images from 1,339 participants who underwent carotid plaque assessment between January 2021 and March 2023 at the Shanghai Eighth People’s Hospital were divided into a 4:1 ratio for training and internal testing. The external test included 1,564 ultrasound images from 674 participants who underwent carotid plaque assessment between January 2022 and May 2023 at Xinhua Hospital affiliated with Dalian University. Deep learning algorithms, based on the fusion of a bilinear convolutional neural network with a residual neural network (BCNN-ResNet), were used for modeling to detect carotid plaques and assess plaque stability. We chose AUC as the main evaluation index, along with accuracy, sensitivity, and specificity as auxiliary evaluation indices.ResultsModeling for detecting carotid plaques involved training and internal testing on 1,291 ultrasound images, with 617 images showing plaques and 674 without plaques. The external test comprised 470 ultrasound images, including 321 images with plaques and 149 without. Modeling for assessing plaque stability involved training and internal testing on 764 ultrasound images, consisting of 494 images with unstable plaques and 270 with stable plaques. The external test was composed of 279 ultrasound images, including 197 images with unstable plaques and 82 with stable plaques. For the task of identifying the presence of carotid plaques, our model achieved an AUC of 0.989 (95% CI: 0.840, 0.998) with a sensitivity of 93.2% and a specificity of 99.21% on the internal test. On the external test, the AUC was 0.951 (95% CI: 0.962, 0.939) with a sensitivity of 95.3% and a specificity of 82.24%. For the task of identifying the stability of carotid plaques, our model achieved an AUC of 0.896 (95% CI: 0.865, 0.922) on the internal test with a sensitivity of 81.63% and a specificity of 87.27%. On the external test, the AUC was 0.854 (95% CI: 0.889, 0.830) with a sensitivity of 68.52% and a specificity of 89.49%.ConclusionDeep learning using BCNN-ResNet algorithms based on routine ultrasound images could be useful for detecting carotid plaques and assessing plaque instability

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning

Recently, there have been significant advancements in large language models (LLMs), particularly focused on the English language. These advancements have enabled these LLMs to understand and execute complex instructions with unprecedented accuracy and fluency. However, despite these advancements, there remains a noticeable gap in the development of Chinese instruction tuning. The unique linguistic features and cultural depth of the Chinese language pose challenges for instruction tuning tasks. Existing datasets are either derived from English-centric LLMs or are ill-suited for aligning with the interaction patterns of real-world Chinese users. To bridge this gap, we introduce COIG-CQIA, a high-quality Chinese instruction tuning dataset. Our aim is to build a diverse, wide-ranging instruction-tuning dataset to better align model behavior with human interactions. To this end, we collect a high-quality human-written corpus from various sources on the Chinese Internet, including Q&A communities, Wikis, examinations, and existing NLP datasets. This corpus was rigorously filtered and carefully processed to form the COIG-CQIA dataset. Furthermore, we train models of various scales on different subsets of CQIA, following in-depth evaluation and analyses. The findings from our experiments offer valuable insights for selecting and developing Chinese instruction-tuning datasets. We also find that models trained on CQIA-Subset achieve competitive results in human assessment as well as knowledge and security benchmarks. Data are available at https://huggingface.co/datasets/m-a-p/COIG-CQI

A Pan-Cancer Analysis of Prognostic and Immunological Roles for Cell Death Genes

The dysregulation of cell death is closely associated with the development, progression, tumor microenvironment (TME), and prognosis of cancer. However, there is no study that comprehensively explores the prognostic and immunological role of cell death in human pan-cancer. We used published human pan-cancer RNA-sequencing and clinical data to explore the prognostic and immunological roles of programmed cell death, which included apoptosis, autophagy, ferroptosis, necroptosis, and pyroptosis. A total of 9925 patients were included for bioinformatic analysis, with 6949 and 2976 patients in the training cohort and validation cohort, respectively. Five-hundred and ninety-nine genes were defined as programmed-cell-death-related genes. In the training cohort, 75 genes were identified to define PAGscore by survival analysis. According to the median value of PAGscore, patients were divided into high- and low-risk groups, and subsequent analyses demonstrated that the high-risk group had a higher level of genomic mutation frequency, hypoxia score, immuneScore, expression of immune genes, activity of malignant signaling pathways, and cancer immunity cycle. Most anti-tumor and pro-tumor components of the TME showed greater activity in high-risk patients. Scores of malignant cell properties were also higher in high-risk patients. These findings were confirmed in the validation cohort and external cohort. Our study constructed a reliable gene signature to distinguish prognosis-favorable and prognosis-unfavorable patients and demonstrated that cell death was significantly associated with cancer prognosis and the TME

A Novel Filtering Method of 3D Reconstruction Point Cloud from Tomographic SAR

With the development of airborne synthetic aperture radar (SAR) technology, the 3D SAR point cloud reconstruction has emerged as a crucial development trend in the current SAR community. However, due to measurement errors, environmental interference, radar decoherence, and other noises associated with the SAR system, the reconstructed tomogram is often deteriorated by numerous noisy scatterers. As a result, it becomes challenging to obtain high-quality 3D point clouds of the observed object, making it difficult to further process the point cloud and realize target identification. To address these issues, we propose a K nearest neighbor comprehensive weighted filtering algorithm. The filtered point cloud is evaluated quantitatively using three-dimensional entropy. In this study, we adopted various filtering methods for simulated data, P-band data of Genhe, and Ku-band data of Yuncheng to refine the tomogram and compare their performances. Both qualitative and quantitative analyses demonstrate the superiority of the filtering algorithm proposed in this paper

A review of the design, properties, applications, and prospects of Ni-based composite powders

Ni-based composite powder (NCP) has attracted increasing attention due to its unique advantages. Various NCPs have been synthesized successfully by different raw powders, and synthesis strategies and applications of NCP have been designed. However, the mechanism of Ni coating is still in the early stage, and timely updates of recent research progress on new remarkable breakthroughs in NCP are highly desired. In light of the above, the novel synthesis designs and mechanism of NCP were discussed in this review. In addition, this review focused on the application of NCP in electrocatalysis, functional coating, and energy field. The future directions of various NCPs were summarized. Some innovative suggestions were discussed at the end of the article. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Study on High Temperature Pyrolysis Light Cycle Oil to Acetylene and Carbon Black

The reaction performance of producing acetylene by light cycle oil (LCO) high temperature pyrolysis was investigated with a self-made electromagnetic induction heating device. The results showed that the reaction temperature and residence time were the main factors restricting the production of acetylene during LCO high temperature cracking. When the reaction temperature was 1800 °C and the residence time was 8.24 ms, the yield of acetylene reached 7.90%. At the same time, the comparative study of different raw materials shows that Yangzhou heavy cycle oil (YZHCO) with a higher content of chain alkanes, cycloalkanes, and tetrahydro-naphthalene aromatics was beneficial to the formation of acetylene, and the highest yield of acetylene reached to 12.7%. The preliminary characterization of byproduct carbon black showed it had a good structure and could be used for lithium electron conductive agent