Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation

News recommendation is critical for personalized news access. Most existing news recommendation methods rely on centralized storage of users' historical news click behavior data, which may lead to privacy concerns and hazards. Federated Learning is a privacy-preserving framework for multiple clients to collaboratively train models without sharing their private data. However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients. In this paper, we propose an efficient federated learning framework for privacy-preserving news recommendation. Instead of training and communicating the whole model, we decompose the news recommendation model into a large news model maintained in the server and a light-weight user model shared on both server and clients, where news representations and user model are communicated between server and clients. More specifically, the clients request the user model and news representations from the server, and send their locally computed gradients to the server for aggregation. The server updates its global user model with the aggregated gradients, and further updates its news model to infer updated news representations. Since the local gradients may contain private information, we propose a secure aggregation method to aggregate gradients in a privacy-preserving way. Experiments on two real-world datasets show that our method can reduce the computation and communication cost on clients while keep promising model performance

PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation

Chinese word segmentation (CWS) is a fundamental step of Chinese natural language processing. In this paper, we build a new toolkit, named PKUSEG, for multi-domain word segmentation. Unlike existing single-model toolkits, PKUSEG targets multi-domain word segmentation and provides separate models for different domains, such as web, medicine, and tourism. Besides, due to the lack of labeled data in many domains, we propose a domain adaptation paradigm to introduce cross-domain semantic knowledge via a translation system. Through this method, we generate synthetic data using a large amount of unlabeled data in the target domain and then obtain a word segmentation model for the target domain. We also further refine the performance of the default model with the help of synthetic data. Experiments show that PKUSEG achieves high performance on multiple domains. The new toolkit also supports POS tagging and model training to adapt to various application scenarios. The toolkit is now freely and publicly available for the usage of research and industry

Broadband and continuous wave pumped second-harmonic generation from microfiber coated with layered GaSe crystal

The conversion-efficiency for second-harmonic (SH) in optical fibers is significantly limited by extremely weak second-order nonlinearity of fused silica, and pulse pump lasers with high peak power are widely employed. Here, we propose a simple strategy to efficiently realize the broadband and continuous wave (CW) pumped SH, by transferring a crystalline GaSe coating onto a microfiber with phase-matching diameter. In the experiment, high efficiency up to 0.08 %W-1mm-1 is reached for a C-band pump laser. The high enough efficiency not only guarantees SH at a single frequency pumped by a CW laser, but also multi-frequencies mixing supported by three CW light sources. Moreover, broadband SH spectrum is also achieved under the pump of a superluminescent light-emitting diode source with a 79.3 nm bandwidth. The proposed scheme provides a beneficial method to the enhancement of various nonlinear parameter processes, development of quasi-monochromatic or broadband CW light sources at new wavelength regions

Ad-Syn-Net: Systematic Identification of alzheimer\u27s Disease-Associated Mutation and Co-Mutation Vulnerabilities Via Deep Learning

Alzheimer\u27s disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. to address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework (\u27AD-Syn-Net\u27), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors

Human endeavor for anti-SARS-CoV-2 pharmacotherapy: A major strategy to fight the pandemic

The global spread of COVID-19 constitutes the most dangerous pandemic to emerge during the last one hundred years. About seventy-nine million infections and more than 1.7 million death have been reported to date, along with destruction of the global economy.With the uncertainty evolved by alarming level of genome mutations, coupled with likelihood of generating only a short lived immune response by the vaccine injections, the identification of antiviral drugs for direct therapy is the need of the hour. Strategies to inhibit virus infection and replication focus on targets such as the spike protein and non-structural proteins including the highly conserved RNA-dependent-RNA-polymerase, nucleotidyl-transferases, main protease and papain-like proteases. There is also an indirect option to target the host cell recognition systems such as angiotensin-converting enzyme 2 (ACE2), transmembrane protease, serine 2, host cell expressed CD147, and the host furin. A drug search strategy consensus in tandem with analysis of currently available information is extremely important for the rapid identification of anti-viral.An unprecedented display of cooperation among the scientific community regarding SARS-CoV-2 research has resulted in the accumulation of an enormous amount of literature that requires curation. Drug repurposing and drug combinations have drawn tremendous attention for rapid therapeutic application, while high throughput screening and virtual searches support de novo drug identification. Here, we examine how certain approved drugs targeting different viruses can play a role in combating this new virus and analyze how they demonstrate efficacy under clinical assessment. Suggestions on repurposing and de novo strategies are proposed to facilitate the fight against the COVID-19 pandemic

Probabilistic short-term power load forecasting based on B-SCN

Grid management and power dispatching rely on accurate short-term power load prediction. Different algorithms have been constantly developed and tested to improve forecast precision. However, these forecasts are constrained by a number of uncertain factors, which are caused by dynamic environment, the nonlinearity and stochasticity of power demand. To obtain more accurate load forecasting value and quantify the uncertainty effectively, this research proposes a boosting stochastic configuration network(B-SCN) based probabilistic forecasting method. First, correlation analysis is taken in multidimensional input parameters. Second, an adaptive B-SCN network architecture is proposed to construct the prediction model and improve the stability of model outputs significantly. The probabilistic forecasting is then used to actualize the model’s uncertainty evaluation by creating the confidence intervals using the Gaussian process. Consequently, experimental results reveal that the proposed boosting-SCN prediction model achieves superior forecasting accuracy than the single SCN model and other commonly used forecasting models. The probabilistic forecasting can efficiently obtain the uncertainties in power load data and provide support for system operation

Clonal analysis and virulent traits of pathogenic extraintestinal <it>Escherichia coli</it> isolates from swine in China

<p>Abstract</p> <p>Background</p> <p>Extraintestinal pathogenic <it>Escherichia coli</it> (ExPEC) can cause a variety of infections outside the gastrointestinal tract in humans and animals. Infections due to swine ExPECs have been occurring with increasing frequency in China. These ExPECs may now be considered a new food-borne pathogen that causes cross-infections between humans and pigs. Knowledge of the clonal structure and virulence genes is needed as a framework to improve the understanding of phylogenetic traits of porcine ExPECs.</p> <p>Results</p> <p>Multilocus sequence typing (MLST) data showed that the isolates investigated in this study could be placed into four main clonal complexes, designated as CC10, CC1687, CC88 and CC58. Strains within CC10 were classified as phylogroup A, and these accounted for most of our porcine ExPEC isolates. Isolates in the CC1687 clonal complex, formed by new sequence types (STs), was classified as phylogroup D, with CC88 isolates considered as B2 and CC58 isolates as B1. Porcine ExPECs in these four clonal complexes demonstrated significantly different virulence gene patterns. A few porcine ExPECs were indentified in phylogroup B2, the phylogroup in which human ExPECs mainly exist. However some STs in the four clonal groups of porcine ExPECs were reported to cause extraintestinal infections in human, based on data in the MLST database.</p> <p>Conclusion</p> <p>Porcine ExPECs have different virulence gene patterns for different clonal complexes. However, these strains are mostly fell in phylogenentic phylogroup A, B1 and D, which is different from human ExPECs that concentrate in phylogroup B2. Our findings provide a better understanding relating to the clonal structure of ExPECs in diseased pigs and indicate a need to re-evaluate their contribution to human ExPEC diseases.</p

Triboelectric–Electromagnetic Hybrid Generator for Harvesting Blue Energy

Abstract Progress has been developed in harvesting low-frequency and irregular blue energy using a triboelectric–electromagnetic hybrid generator in recent years. However, the design of the high-efficiency, mechanically durable hybrid structure is still challenging. In this study, we report a fully packaged triboelectric–electromagnetic hybrid generator (TEHG), in which magnets were utilized as the trigger to drive contact–separation-mode triboelectric nanogenerators (CS-TENGs) and coupled with copper coils to operate rotary freestanding-mode electromagnetic generators (RF-EMGs). The magnet pairs that produce attraction were used to transfer the external mechanical energy to the CS-TENGs, and packaging of the CS-TENG part was achieved to protect it from the ambient environment. Under a rotatory speed of 100 rpm, the CS-TENGs enabled the TEHG to deliver an output voltage, current, and average power of 315.8 V, 44.6 μA, and ~ 90.7 μW, and the output of the RF-EMGs was 0.59 V, 1.78 mA, and 79.6 μW, respectively. The cylinder-like structure made the TEHG more easily driven by water flow and demonstrated to work as a practical power source to charge commercial capacitors. It can charge a 33 μF capacitor from 0 to 2.1 V in 84 s, and the stored energy in the capacitor can drive an electronic thermometer and form a self-powered water-temperature sensing system

Efficient flexible perovskite solar cells and modules using a stable SnO&lt;sub&gt;2&lt;/sub&gt;-nanocrystal isopropanol dispersion

The outstanding advantages of lightweight and flexibility enable flexible perovskite solar cells (PSCs) to have great application potential in mobile energy devices. Due to the low cost, low-temperature processibility, and high electron mobility, SnO2 nanocrystals have been widely employed as the electron transport layer in flexible PSCs. To prepare high-quality SnO2 layers, a monodispersed nanocrystal solution is normally used. However, the SnO2 nanocrystals can easily aggregate, especially after long periods of storage. Herein, we develop a green and cost-effective strategy for the synthesis of high-quality SnO2 nanocrystals at low temperatures by introducing small molecules of glycerol, obtaining a stable and well-dispersed SnO2-nanocrystal isopropanol dispersion successfully. Due to the enhanced dispersity and super wettability of this alcohol-based SnO2-nanocrystal solution, large-area smooth and dense SnO2 films are easily deposited on the plastic conductive substrate. Furthermore, this contributes to effective charge transfer and suppressed non-radiative recombination at the interface between the SnO2 and perovskite layers. As a result, a greatly enhanced power conversion efficiency (PCE) of 21.8% from 19.2% is achieved for small-area flexible PSCs. A large-area 5 cm x 5 cm flexible perovskite solar mini-module with a champion PCE of 16.5% and good stability is also demonstrated via this glycerol-modified SnO2-nanocrystal isopropanol dispersion approach

Waveguide-Integrated MoTe2p- i- n Homojunction Photodetector

Funding Information: This project was primarily supported by the National Key R&D Program of China (Grant 2018YFA0307200), the National Natural Science Foundation of China (Grants 91950119, 61905196, and 62090033), Key Research and Development Program in Shaanxi Province of China (Grant 2020JZ-10), and the Fundamental Research Funds for the Central Universities (Grants 310201911cx032, 3102019JC008, and D5000210905). The authors also thank the Analytical & Testing Center of NPU for their assistance in device fabrication and characterizations. Publisher Copyright: © 2022 American Chemical Society.Two-dimensional (2D) materials, featuring distinctive electronic and optical properties and dangling-bond-free surfaces, are promising for developing high-performance on-chip photodetectors in photonic integrated circuits. However, most of the previously reported devices operating in the photoconductive mode suffer from a high dark current or a low responsivity. Here, we demonstrate a MoTe2p-i-n homojunction fabricated directly on a silicon photonic crystal (PC) waveguide, which enables on-chip photodetection with ultralow dark current, high responsivity, and fast response speed. The adopted silicon PC waveguide is electrically split into two individual back gates to selectively dope the top regions of the MoTe2 channel in p- or n-types. High-quality reconfigurable MoTe2 (p-i-n, n-i-p, n-i-n, p-i-p) homojunctions are realized successfully, presenting rectification behaviors with ideality factors approaching 1.0 and ultralow dark currents less than 90 pA. Waveguide-assisted MoTe2 absorption promises a sensitive photodetection in the telecommunication O-band from 1260 to 1340 nm, though it is close to MoTe2's absorption band-edge. A competitive photoresponsivity of 0.4 A/W is realized with a light on/off current ratio exceeding 104 and a record-high normalized photocurrent-to-dark-current ratio of 106 mW-1. The ultrasmall capacitance of p-i-n homojunction and high carrier mobility of MoTe2 promise a high dynamic response bandwidth close to 34.0 GHz. The proposed device geometry has the advantages of employing a silicon PC waveguide as the back gates to build a 2D material p-i-n homojunction directly and simultaneously to enhance light-2D material interaction. It provides a potential pathway to develop 2D material-based photodetectors, laser diodes, and electro-optic modulators on silicon photonic chips.Peer reviewe