DEPHN: Different Expression Parallel Heterogeneous Network using virtual gradient optimization for Multi-task Learning

Recommendation system algorithm based on multi-task learning (MTL) is the major method for Internet operators to understand users and predict their behaviors in the multi-behavior scenario of platform. Task correlation is an important consideration of MTL goals, traditional models use shared-bottom models and gating experts to realize shared representation learning and information differentiation. However, The relationship between real-world tasks is often more complex than existing methods do not handle properly sharing information. In this paper, we propose an Different Expression Parallel Heterogeneous Network (DEPHN) to model multiple tasks simultaneously. DEPHN constructs the experts at the bottom of the model by using different feature interaction methods to improve the generalization ability of the shared information flow. In view of the model's differentiating ability for different task information flows, DEPHN uses feature explicit mapping and virtual gradient coefficient for expert gating during the training process, and adaptively adjusts the learning intensity of the gated unit by considering the difference of gating values and task correlation. Extensive experiments on artificial and real-world datasets demonstrate that our proposed method can capture task correlation in complex situations and achieve better performance than baseline models\footnote{Accepted in IJCNN2023}

Landslide Surface Displacement Prediction Based on VSXC-LSTM Algorithm

Landslide is a natural disaster that can easily threaten local ecology, people's lives and property. In this paper, we conduct modelling research on real unidirectional surface displacement data of recent landslides in the research area and propose a time series prediction framework named VMD-SegSigmoid-XGBoost-ClusterLSTM (VSXC-LSTM) based on variational mode decomposition, which can predict the landslide surface displacement more accurately. The model performs well on the test set. Except for the random item subsequence that is hard to fit, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the trend item subsequence and the periodic item subsequence are both less than 0.1, and the RMSE is as low as 0.006 for the periodic item prediction module based on XGBoost\footnote{Accepted in ICANN2023}

FaFCNN: A General Disease Classification Framework Based on Feature Fusion Neural Networks

There are two fundamental problems in applying deep learning/machine learning methods to disease classification tasks, one is the insufficient number and poor quality of training samples; another one is how to effectively fuse multiple source features and thus train robust classification models. To address these problems, inspired by the process of human learning knowledge, we propose the Feature-aware Fusion Correlation Neural Network (FaFCNN), which introduces a feature-aware interaction module and a feature alignment module based on domain adversarial learning. This is a general framework for disease classification, and FaFCNN improves the way existing methods obtain sample correlation features. The experimental results show that training using augmented features obtained by pre-training gradient boosting decision tree yields more performance gains than random-forest based methods. On the low-quality dataset with a large amount of missing data in our setup, FaFCNN obtains a consistently optimal performance compared to competitive baselines. In addition, extensive experiments demonstrate the robustness of the proposed method and the effectiveness of each component of the model\footnote{Accepted in IEEE SMC2023}

Mobile Crowdsourcing in Smart Cities

Local administrations and governments aim at leveraging wireless communications and Internet of Things (IoT) technologies to manage the city infrastructures and enhance the public services in an efficient and sustainable manner. Furthermore, they strive to adopt smart and cost-effective mobile applications to deal with major urbanization problems, such as natural disasters, pollution, and traffic congestion. Mobile crowdsourcing (MCS) is known as a key emerging paradigm for enabling smart cities, which integrates the wisdom of dynamic crowds with mobile devices to provide decentralized ubiquitous services and applications. Using MCS solutions, residents (i.e., mobile carriers) play the role of active workers who generate a wealth of crowdsourced data to significantly promote the development of smart cities. In this paper, we present an overview of state-of-the-art technologies and applications of MCS in smart cities. First, we provide an overview of MCS in smart cities and highlight its major characteristics.Second, we introduce the general architecture of MCS and its enabling technologies. Third, we study novel applications of MCS in smart cities. Finally, we discuss several open problems and future research challenges in the context of MCS in smart cities.Peer reviewe

A shared bus profiling scheme for smart cities based on heterogeneous mobile crowdsourced data

Mobile crowdsourcing (MCS), as an effective and crucial technique of Industrial Internet of Things, is enabling smart city initiatives in the real world. It aims at incorporating the intelligence of dynamic crowds to collect and compute decentralized ubiquitous sensing data that can be used to solve major urbanization problems such as traffic congestion. The shared bus, as a neotype transportation mode, aims at improving the resource utilization rate and maintaining the advantages of convenience and economy. In this article, we provide a scheme to profile shared buses through heterogeneous mobile crowdsourced data (TRProfiling). First, we design an MCS-based shared bus data generation and collection solution to overcome the aforementioned data scarcity issue. Then, we propose a travel profiling to profile resident travel and design a method called multiconstraint evolution algorithm to optimize the routes. Experimental results demonstrate that TRProfiling has an excellent performance in satisfying passengers' travel requirements. © 2005-2012 IEEE

Fabrication and characterization of a piezoelectric energy harvester with clamped-clamped beams

This work presents a piezoelectric energy harvester with clamped-clamped beams, and it is fabricated with MEMS process. When excited by sinusoidal vibration, the energy harvester has a sharp jumping down phenomenon and the measured frequency responses of the clamped-clamped beams structure show a larger bandwidth which is about 56Hz, more efficient than that with cantilever beams. When the exciting acceleration ac is 12m/s2, the energy harvester achieves to a maximum open-circuit voltage of 94mV on one beam. The load voltage is proportional to the load resistance, and it increased with the increase of load resistance. Connected four beams in series, the output power reaches the maximum value of 730 nW and the optimal load is 15KΩ to one beam

Safety, Immunogenicity, and Protective Efficacy of an H5N1 Chimeric Cold-Adapted Attenuated Virus Vaccine in a Mouse Model

H5N1 influenza virus is a threat to public health worldwide. The virus can cause severe morbidity and mortality in humans. We constructed an H5N1 influenza candidate virus vaccine from the A/chicken/Guizhou/1153/2016 strain that was recommended by the World Health Organization. In this study, we designed an H5N1 chimeric influenza A/B vaccine based on a cold-adapted (ca) influenza B virus B/Vienna/1/99 backbone. We modified the ectodomain of H5N1 hemagglutinin (HA) protein, while retaining the packaging signals of influenza B virus, and then rescued a chimeric cold-adapted H5N1 candidate influenza vaccine through a reverse genetic system. The chimeric H5N1 vaccine replicated well in eggs and the Madin-Darby Canine Kidney cells. It maintained a temperature-sensitive and cold-adapted phenotype. The H5N1 vaccine was attenuated in mice. Hemagglutination inhibition (HAI) antibodies, micro-neutralizing (MN) antibodies, and IgG antibodies were induced in immunized mice, and the mucosal IgA antibody responses were detected in their lung lavage fluids. The IFN-γ-secretion and IL-4-secretion by the mouse splenocytes were induced after stimulation with the specific H5N1 HA protein. The chimeric H5N1 candidate vaccine protected mice against lethal challenge with a wild-type highly pathogenic avian H5N1 influenza virus. The chimeric H5 candidate vaccine is thus a potentially safe, attenuated, and reassortment-incompetent vaccine with circulating A viruses

High-throughput manufacturing of epitaxial membranes from a single wafer by 2D materials-based layer transfer process

Layer transfer techniques have been extensively explored for semiconductor device fabrication as a path to reduce costs and to form heterogeneously integrated devices. These techniques entail isolating epitaxial layers from an expensive donor wafer to form freestanding membranes. However, current layer transfer processes are still low-throughput and too expensive to be commercially suitable. Here we report a high-throughput layer transfer technique that can produce multiple compound semiconductor membranes from a single wafer. We directly grow two-dimensional (2D) materials on III–N and III–V substrates using epitaxy tools, which enables a scheme comprised of multiple alternating layers of 2D materials and epilayers that can be formed by a single growth run. Each epilayer in the multistack structure is then harvested by layer-by-layer mechanical exfoliation, producing multiple freestanding membranes from a single wafer without involving time-consuming processes such as sacrificial layer etching or wafer polishing. Moreover, atomic-precision exfoliation at the 2D interface allows for the recycling of the wafers for subsequent membrane production, with the potential for greatly reducing the manufacturing cost. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.11Nsciescopu