Zhengcheng Tao

Product reviews are discussions about the value of the product, for which customers will have different emotional expressions. It is necessary to analyze whether customers have negative, positive, or other emotions about the value of the product, to facilitate manufacturers and sales manufacturers to upgrade and improve the product and analyze customers’ demand for the product. A text sentiment analysis model is constructed through sentiment dictionary and machine learning, a benchmark model is set up, and a method combining Bi-LSTM (Bi-directional Long Short-Term Memory), and Max pooling is proposed to construct a deep learning model of text sentiment analysis. Through experiments, the accuracy of the model in the sentiment classification of product reviews reaches 0.9332, which is higher than the result of SVM (Support Vector Machine) (0.9214) in machine learning and the benchmark model (Gradient Boosting) (0.8972).

During the High Luminosity LHC, the CMS detector will need charged particle tracking at the hardware trigger level to maintain a manageable trigger rate and achieve its physics goals. The tracklet approach is a track-finding algorithm based on a road-search algorithm that has been implemented on commercially available FPGA technology. The tracklet algorithm has achieved high performance in track-finding and completes tracking within 3.4 μs on a Xilinx Virtex-7 FPGA. An overview of the algorithm and its implementation on an FPGA is given, results are shown from a demonstrator test stand and system performance studies are presented.

The upgrades of the Compact Muon Solenoid particle physics experiment at CERN's Large Hadron Collider provide a major challenge for the real-time collision data selection. This paper presents a novel approach to pattern recognition and charged particle trajectory reconstruction using an all-FPGA solution. The challenges include a large input data rate of about 20 to 40 Tbps, processing a new batch of input data every 25 ns, each consisting of about 10,000 precise position measurements of particles (`stubs'), perform the pattern recognition on these stubs to find the trajectories, and produce the list of parameters describing these trajectories within 4 μs. A proposed solution to this problem is described, in particular, the implementation of the pattern recognition and particle trajectory determination using an all-FPGA system. The results of an end-to-end demonstrator system based on Xilinx Virtex-7 FPGAs that meets timing and performance requirements are presented.

This paper focuses on the problem of adversarial attacks from Byzantine machines in a Federated Learning setting where non-Byzantine machines can be partitioned into disjoint clusters. In this setting, non-Byzantine machines in the same cluster have the same underlying data distribution, and different clusters of non-Byzantine machines have different learning tasks. Byzantine machines can adversarially attack any cluster and disturb the training process on clusters they attack. In the presence of Byzantine machines, the goal of our work is to identify cluster membership of non-Byzantine machines and optimize the models learned by each cluster. We adopt the Iterative Federated Clustering Algorithm (IFCA) framework of Ghosh et al. (2020) to alternatively estimate cluster membership and optimize models. In order to make this framework robust against adversarial attacks from Byzantine machines, we use coordinate-wise trimmed mean and coordinate-wise median aggregation methods used by Yin et al. (2018). Specifically, we propose a new Byzantine-Robust Iterative Federated Clustering Algorithm to improve on the results in Ghosh et al. (2019). We prove a convergence rate for this algorithm for strongly convex loss functions. We compare our convergence rate with the convergence rate of an existing algorithm, and we demonstrate the performance of our algorithm on simulated data.

Depression is a prevalent mental disorder, with young people being particularly vulnerable to it. Therefore, we propose a new intelligent and rapid screening method for depression risk in young people based on eye tracking technology. We hypothesized that the “emotional perception of eye movement” could characterize defects in emotional perception, recognition, processing, and regulation in young people at high risk for depression. Based on this hypothesis, we designed the “eye movement emotional perception evaluation paradigm” and extracted digital biomarkers that could objectively and accurately evaluate “facial feature perception” and “facial emotional perception” characteristics of young people at high risk of depression. Using stepwise regression analysis, we identified seven digital biomarkers that could characterize emotional perception, recognition, processing, and regulation deficiencies in young people at high risk for depression. The combined effectiveness of an early warning can reach 0.974. Our proposed technique for rapid screening has significant advantages, including high speed, high early warning efficiency, low cost, and high intelligence. This new method provides a new approach to help effectively screen high-risk individuals for depression.

With the High Luminosity LHC upgrades, incorporating tracking information into the CMS Level-1 trigger becomes necessary in order to maintain a manageable trigger rate and good trigger performance e.g. to retain thresholds for electroweak physics. The main challenges Level-1 track finding faces are the large data throughput from the detector at a collision rate of 40 MHz and a 4 microsecond latency budget to reconstruct charged particle tracks with sufficiently low transverse momentum to be used in the Level-1 trigger decision. Dedicated all-FPGA hardware systems with time-multiplexed architecture have been developed for track finding to address these challenges. The algorithm and performance of the pattern recognition and particle trajectory determination are discussed. The implementation on customized electronics with commercially available FPGAs is presented as well.