This paper presents a$240\times 180$event-based vision sensor (EVS) readout integrated circuit (ROIC) that incorporates global threshold voltage calibration technology. Conventional EVS suffer from error events and intra-array mismatch. This paper delves into four types of error events and presents effective solutions for them. This paper proposes a global threshold voltage calibration (GTVC) technique to minimize the impact of mismatch in the pixel array. The pixel size measures$10\times 10~\mu $m${}^{\mathbf {2}}$, using 40 nm CMOS technology. The analog pixel circuits work with supply voltages of 2.5 V and 1.1 V, while the power consumption of this chip is 12.16 mW. By using the global threshold voltage calibration technology, which incorporates eight reference pixels for determining the input voltage of event comparators, the error in the detection result is reduced to 2.84 mV. The maximum event rate reaches 360 Meps with a 40 MHz system clock, boasting a dynamic range of 97.3 dB. Further, the event power efficiency stands at 29.6 Ge/W.

In Vehicular Ad-hoc Networks (VANETs), federated learning (FL) enables collaborative training a global intelligent transportation model without sharing vehicles’ raw data. Achieving model convergence in VANETs requires multiple rounds of FL for aggregation and updates. To improve the efficiency of model convergence, researchers explore methods of multi-round aggregation. The latest work, Flamingo (S&P 2023), proposes a multi-round aggregation scheme based on a reusable key mechanism. Its application in VANETs with dynamic network structures enhances the efficiency of model training. However, this scheme has a defect: vehicles cannot verify the correctness of the aggregation result. Once a semi-trusted server returns incorrect aggregation results due to computational errors, it may reduce the model’s accuracy or even cause training failure. In this scheme, we propose a verifiable multi-round aggregation scheme for FL in VANETs (VMFL), enabling vehicles to verify the aggregation results. Firstly, a multi-round verification mechanism is designed to reduce the number of interactions for vehicles by using reusable keys. Additionally, we propose a lightweight proof scheme that allows vehicles to verify the results with minimal computation, reducing the computational overhead of the verification process. Finally, a security analysis of VMFL is performed to demonstrate its security in cases of vehicle dropout. We validated the efficiency of VMFL across different datasets through experiments, showing no significant increase in time overhead compared to Flamingo, and demonstrating that its verification time is reduced by about 90% compared to the state-of-the-art scheme, thereby demonstrating the scheme’s usability.

Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features while streamlining the redundant data in primary sequences and bypassing the time-consuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-of-the-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO.

Recently, the emerging bio-inspired event cameras have demonstrated potentials for a wide range of robotic applications in dynamic environments. In this paper, we propose a novel fast and asynchronous event-based corner detection method which is called FA-Harris. FA-Harris consists of several components, including an event filter, a Global Surface of Active Events (G-SAE) maintaining unit, a corner candidate selecting unit, and a corner candidate refining unit. The proposed G-SAE maintenance algorithm and corner candidate selection algorithm greatly enhance the real-time performance for corner detection, while the corner candidate refinement algorithm maintains the accuracy of performance by using an improved event-based Harris detector. Additionally, FA-Harris does not require artificially synthesized event-frames and can operate on asynchronous events directly. We implement the proposed method in C++ and evaluate it on public Event Camera Datasets. The results show that our method achieves approximately 8× speed-up when compared with previously reported event-based Harris detector, and with no compromise on the accuracy of performance.