Approaching pre-modern China through the computer: the benefits and risks of using electronic resources in sinological research

Around the middle of the 1990s, Chinese Studies both in China and around the world irreversibly entered the digital era. Only a decade later, the means and resources for studying premodern Chinese civilization have dramatically changed. Online library catalogs, Internet-based bibliographies and indexes, electronic journals and books, and fulltext databases, many of which were beyond the wildest imagination of scholars of the previous generation, have now become indispensable tools for daily research. Especially noticeable is the largescale digitization of premodern Chinese texts which has, literally, revolutionized the informationseeking behavior and research process of today’s sinologists

Algorithm-Directed Crash Consistence in Non-Volatile Memory for HPC

Fault tolerance is one of the major design goals for HPC. The emergence of non-volatile memories (NVM) provides a solution to build fault tolerant HPC. Data in NVM-based main memory are not lost when the system crashes because of the non-volatility nature of NVM. However, because of volatile caches, data must be logged and explicitly flushed from caches into NVM to ensure consistence and correctness before crashes, which can cause large runtime overhead. In this paper, we introduce an algorithm-based method to establish crash consistence in NVM for HPC applications. We slightly extend application data structures or sparsely flush cache blocks, which introduce ignorable runtime overhead. Such extension or cache flushing allows us to use algorithm knowledge to \textit{reason} data consistence or correct inconsistent data when the application crashes. We demonstrate the effectiveness of our method for three algorithms, including an iterative solver, dense matrix multiplication, and Monte-Carlo simulation. Based on comprehensive performance evaluation on a variety of test environments, we demonstrate that our approach has very small runtime overhead (at most 8.2\% and less than 3\% in most cases), much smaller than that of traditional checkpoint, while having the same or less recomputation cost.Comment: 12 page

A Comparative Study of Insertion Loss of Traffic Noise Barriers in Georgia

In this paper, three types of traffic noise barriers, interlocking steel panels, precast concrete panels, and Paragon panel 23-T, were evaluated in terms of insertion loss. Field test was conducted for the noise barriers recently installed as part of the Northwest Express Project in Georgia. The noise insertion loss was measured as noise difference in A-weighted decibels (dBA) immediately before and after the barriers. The insertion loss was then evaluated by correlating with noise barrier types and other influential variables, including the separation distance of barriers from traffic, the level of traffic, wind speed, and pavement types. The results showed that under prevailing conditions represented by other influential variables, all three barrier types achieved an insertion loss in the range of 7.02 dBA to 13.58 dBA, exceeding the noise reduction design goal of 7 dBA as stated in the Georgia Department of Transportation’s noise abatement policy. Among the three, the Paragon panel 23-T barriers effected the highest insertion loss, followed by the precast concrete panel barriers and the interlocking steel panel barriers

Semi-supervised multiscale dual-encoding method for faulty traffic data detection

Inspired by the recent success of deep learning in multiscale information encoding, we introduce a variational autoencoder (VAE) based semi-supervised method for detection of faulty traffic data, which is cast as a classification problem. Continuous wavelet transform (CWT) is applied to the time series of traffic volume data to obtain rich features embodied in time-frequency representation, followed by a twin of VAE models to separately encode normal data and faulty data. The resulting multiscale dual encodings are concatenated and fed to an attention-based classifier, consisting of a self-attention module and a multilayer perceptron. For comparison, the proposed architecture is evaluated against five different encoding schemes, including (1) VAE with only normal data encoding, (2) VAE with only faulty data encoding, (3) VAE with both normal and faulty data encodings, but without attention module in the classifier, (4) siamese encoding, and (5) cross-vision transformer (CViT) encoding. The first four encoding schemes adopted the same convolutional neural network (CNN) architecture while the fifth encoding scheme follows the transformer architecture of CViT. Our experiments show that the proposed architecture with the dual encoding scheme, coupled with attention module, outperforms other encoding schemes and results in classification accuracy of 96.4%, precision of 95.5%, and recall of 97.7%.Comment: 16 pages, 8 figure

Optimal scheduling of industrial task-continuous load management for smart power utilization

In the context of climate change and energy crisis around the world, an increasing amount of attention has been paid to developing clean energy and improving energy efficiency. The penetration of distributed generation (DG) is increasing rapidly on the user’s side of an increasingly intelligent power system. This paper proposes an optimization method for industrial task-continuous load management in which distributed generation (including photovoltaic systems and wind generation) and energy storage devices are both considered. To begin with, a model of distributed generation and an energy storage device are built. Then, subject to various constraints, an operation optimization problem is formulated to maximize user profit, renewable energy efficiency, and the local consumption of distributed generation. Finally, the effectiveness of the method is verified by comparing user profit under different power modes

Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification

Classification using supervised learning requires annotating a large amount of classes-balanced data for model training and testing. This has practically limited the scope of applications with supervised learning, in particular deep learning. To address the issues associated with limited and imbalanced data, this paper introduces a sample-efficient co-supervised learning paradigm (SEC-CGAN), in which a conditional generative adversarial network (CGAN) is trained alongside the classifier and supplements semantics-conditioned, confidence-aware synthesized examples to the annotated data during the training process. In this setting, the CGAN not only serves as a co-supervisor but also provides complementary quality examples to aid the classifier training in an end-to-end fashion. Experiments demonstrate that the proposed SEC-CGAN outperforms the external classifier GAN (EC-GAN) and a baseline ResNet-18 classifier. For the comparison, all classifiers in above methods adopt the ResNet-18 architecture as the backbone. Particularly, for the Street View House Numbers dataset, using the 5% of training data, a test accuracy of 90.26% is achieved by SEC-CGAN as opposed to 88.59% by EC-GAN and 87.17% by the baseline classifier; for the highway image dataset, using the 10% of training data, a test accuracy of 98.27% is achieved by SEC-CGAN, compared to 97.84% by EC-GAN and 95.52% by the baseline classifier.Comment: 14 pages, 5 figure

Elucidating the susceptibility to breast cancer: an in-depth proteomic and transcriptomic investigation into novel potential plasma protein biomarkers

Objectives: This study aimed to identify plasma proteins that are associated with and causative of breast cancer through Proteome and Transcriptome-wide association studies combining Mendelian Randomization.Methods: Utilizing high-throughput datasets, we designed a two-phase analytical framework aimed at identifying novel plasma proteins that are both associated with and causative of breast cancer. Initially, we conducted Proteome/Transcriptome-wide association studies (P/TWAS) to identify plasma proteins with significant associations. Subsequently, Mendelian Randomization was employed to ascertain the causation. The validity and robustness of our findings were further reinforced through external validation and various sensitivity analyses, including Bayesian colocalization, Steiger filtering, heterogeneity and pleiotropy. Additionally, we performed functional enrichment analysis of the identified proteins to better understand their roles in breast cancer and to assess their potential as druggable targets.Results: We identified 5 plasma proteins demonstrating strong associations and causative links with breast cancer. Specifically, PEX14 (OR = 1.201, p = 0.016) and CTSF (OR = 1.114, p < 0.001) both displayed positive and causal association with breast cancer. In contrast, SNUPN (OR = 0.905, p < 0.001), CSK (OR = 0.962, p = 0.038), and PARK7 (OR = 0.954, p < 0.001) were negatively associated with the disease. For the ER-positive subtype, 3 plasma proteins were identified, with CSK and CTSF exhibiting consistent trends, while GDI2 (OR = 0.920, p < 0.001) was distinct to this subtype. In ER-negative subtype, PEX14 (OR = 1.645, p < 0.001) stood out as the sole protein, even showing a stronger causal effect compared to breast cancer. These associations were robustly supported by colocalization and sensitivity analyses.Conclusion: Integrating multiple data dimensions, our study successfully pinpointed plasma proteins significantly associated with and causative of breast cancer, offering valuable insights for future research and potential new biomarkers and therapeutic targets

Directory of English/Chinese Names of Scholars in Chinese Studies - 海外中国研究学者名录（英中对照）

The Directory of English/Chinese Names of Scholars in Chinese Studies was a by-product of the "Chinese Studies in North America - Research and Resources" project. It provides both the English and the Chinese names of scholars involved in Chinese Studies mainly in North America. The Chinese names for western scholars resulted from an extensive in the relevant literature and on the internet at appropriate sites to find and authenticate the Chinese names used by these scholars. Where we could not find the Chinese name adopted by a scholar, we have transliterated their name into Chinese characters using the standard reference book 英语姓名译名手册. It is hoped that this directory will be useful for people needing to search for the Chinese names used by western scholars, or for the standard transliterations of their names into Chinese characters. Corrections of inaccurate information and addition of new names of Chinese Studies scholars worldwide are welcome. For corrections, comments and updates, please send emails to Haihui Zhang (Librarian for Chinese studies at East Asian Library, University Library System at University of Pittsburgh) at [email protected]