A Critical Discourse Study of Chinese Professors' Image Construction in Microblogging Discourse

With the aim of elaborating what and how Chinese professors' identities are constructed in microblogging discourse, through high-frequency words, collocates and concordance lines, the present study analyzes the ideologies, the academic and moral image of professor behind the collected microblogging data posted on Sina Weibo with topic of "'Mistress Gate' in Fudan University". The results suggest that: (1) Under the new media, Weibo, network catchwords are growing popular and tucao (revealing the inside story) is full of violence. (2) Although most of the netizens hold decent ethics and values, they go emotional easily. (3) A series of negative cases in the media have already made "professor" who usually has a relatively decent social status have a negative connotation

SAFA : a semi-asynchronous protocol for fast federated learning with low overhead

Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the unreliable nature of end devices while the cost of device-server communication cannot be neglected. In this paper, we propose SAFA, a semi-asynchronous FL protocol, to address the problems in federated learning such as low round efficiency and poor convergence rate in extreme conditions (e.g., clients dropping offline frequently). We introduce novel designs in the steps of model distribution, client selection and global aggregation to mitigate the impacts of stragglers, crashes and model staleness in order to boost efficiency and improve the quality of the global model. We have conducted extensive experiments with typical machine learning tasks. The results demonstrate that the proposed protocol is effective in terms of shortening federated round duration, reducing local resource wastage, and improving the accuracy of the global model at an acceptable communication cost

Contact stiffness of bolted joint with different material combination in machine tools

Bolted joint is a commonly used complex flexible interface in machine tools. The stiffness influential factors-based dynamic model provides a high accuracy modeling method of bolted joints in machine tools. The key of wide application of this method is the database of the stiffness matrices of bolted joints under different conditions. This paper mainly concerns the contact stiffness of bolted joints with different material combination in machine tools and tries to establish the relationship of them. Using the stiffness influential factors-based dynamic modeling method, the contact stiffness of bolted joint is expressed as the stiffness matrix of the connection finite element. After impact modal tests were carried on the specimens, stiffness matrices of bolted joints with different material combinations are identified from the frequency response functions. The ratio of the stiffness matrices validates the effectiveness of the conclusion that the contact stiffness of bolted joints with different material combination is proportional to the corresponding equivalent elastic modulus deduced from Hertz contact theory. The reliable proportional relationship provides a great convenience to the wide application of the stiffness influential factors-based dynamic modeling method of bolted joint

FedProf: Selective Federated Learning with Representation Profiling

Federated Learning (FL) has shown great potential as a privacy-preserving solution to learning from decentralized data that are only accessible to end devices (i.e., clients). In many scenarios however, a large proportion of the clients are probably in possession of low-quality data that are biased, noisy or even irrelevant. As a result, they could significantly slow down the convergence of the global model we aim to build and also compromise its quality. In light of this, we propose FedProf, a novel algorithm for optimizing FL under such circumstances without breaching data privacy. The key of our approach is a data representation profiling and matching scheme that uses the global model to dynamically profile data representations and allows for low-cost, lightweight representation matching. Based on the scheme we adaptively score each client and adjust its participation probability so as to mitigate the impact of low-value clients on the training process. We have conducted extensive experiments on public datasets using various FL settings. The results show that FedProf effectively reduces the number of communication rounds and overall time (up to 4.5x speedup) for the global model to converge and provides accuracy gain.Comment: 23 pages (references and appendices included

Landau-Zener-St\"{u}ckelberg Interference of Microwave Dressed States of a Superconducting Phase Qubit

We present the first observation of Landau-Zener-St\"{u}ckelberg (LZS) interference of the dressed states arising from an artificial atom, a superconducting phase qubit, interacting with a microwave field. The dependence of LZS interference fringes on various external parameters and the initial state of the qubit agrees quantitatively very well with the theoretical prediction. Such LZS interferometry between the dressed states enables us to control the quantum states of a tetrapartite solid-state system with ease, demonstrating the feasibility of implementing efficient multipartite quantum logic gates with this unique approach.Comment: 6 pages, 3 figures To appear in Physical Review B(R

Emerging nanotechnology for treatment of Alzheimer\u27s and Parkinson\u27s disease

The prevalence of the two most common neurodegenerative diseases, Parkinson\u27s disease (PD) and Alzheimer\u27s Disease (AD), are expected to rise alongside the progressive aging of society. Both PD and AD are classified as proteinopathies with misfolded proteins α-synuclein, amyloid-β, and tau. Emerging evidence suggests that these misfolded aggregates are prion-like proteins that induce pathological cell-to-cell spreading, which is a major driver in pathogenesis. Additional factors that can further affect pathology spreading include oxidative stress, mitochondrial damage, inflammation, and cell death. Nanomaterials present advantages over traditional chemical or biological therapeutic approaches at targeting these specific mechanisms. They can have intrinsic properties that lead to a decrease in oxidative stress or an ability to bind and disaggregate fibrils. Additionally, nanomaterials enhance transportation across the blood-brain barrier, are easily functionalized, increase drug half-lives, protect cargo from immune detection, and provide a physical structure that can support cell growth. This review highlights emergent nanomaterials with these advantages that target oxidative stress, the fibrillization process, inflammation, and aid in regenerative medicine for both PD and AD