Self-Selective Correlation Ship Tracking Method for Smart Ocean System

In recent years, with the development of the marine industry, navigation environment becomes more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count the sailing ships to ensure the maritime security and facilitates the management for Smart Ocean System. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly include: 1) A self-selective model with negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of classifier at the same time; 2) A bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were higher than Discriminative Scale Space Tracking (DSST) by over 8 percentage points on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 Frames Per Second (FPS)

On p.p.-rings which are reduced

Denote the 2×2 upper triangular matrix rings over ℤ and ℤp by UTM2(ℤ) and UTM2(ℤp), respectively. We prove that if a ring R is a p.p.-ring, then R is reduced if and only if R does not contain any subrings isomorphic to UTM2(ℤ) or UTM2(ℤp). Other conditions for a p.p.-ring to be reduced are also given. Our results strengthen and extend the results of Fraser and Nicholson on r.p.p.-rings

MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition

Cutting-edge research in facial expression recognition (FER) currently favors the utilization of convolutional neural networks (CNNs) backbone which is supervisedly pre-trained on face recognition datasets for feature extraction. However, due to the vast scale of face recognition datasets and the high cost associated with collecting facial labels, this pre-training paradigm incurs significant expenses. Towards this end, we propose to pre-train vision Transformers (ViTs) through a self-supervised approach on a mid-scale general image dataset. In addition, when compared with the domain disparity existing between face datasets and FER datasets, the divergence between general datasets and FER datasets is more pronounced. Therefore, we propose a contrastive fine-tuning approach to effectively mitigate this domain disparity. Specifically, we introduce a novel FER training paradigm named Mask Image pre-training with MIx Contrastive fine-tuning (MIMIC). In the initial phase, we pre-train the ViT via masked image reconstruction on general images. Subsequently, in the fine-tuning stage, we introduce a mix-supervised contrastive learning process, which enhances the model with a more extensive range of positive samples by the mixing strategy. Through extensive experiments conducted on three benchmark datasets, we demonstrate that our MIMIC outperforms the previous training paradigm, showing its capability to learn better representations. Remarkably, the results indicate that the vanilla ViT can achieve impressive performance without the need for intricate, auxiliary-designed modules. Moreover, when scaling up the model size, MIMIC exhibits no performance saturation and is superior to the current state-of-the-art methods

Baer semisimple modules and Baer rings

We consider Baer rings and Baer semisimple R-modules which are generalizations of semisimple modules. Several characterization theorems of Baer semisimple modules are obtained. In particular, we prove that a ring R is a Baer ring if and only if R itself, regarded as a regular R-module, is Baer semisimple

Compact Primitive Semigroups Having (CEP)

Abstract Compact completely simple semigroups having congruence extension property (in brevity (CEP)) were first studied by Dumesnil in 1997. In this paper, we study the compact primitive semigroups having (CEP) and characterize such semigroups, so that the result of Dumesnil on compact completely simple semigroups having (CEP) is extended to compact primitive semigroups

Expression of cellobiose dehydrogenase gene in Aspergillus niger C112 and its effect on lignocellulose degrading enzymes

Cellobiose dehydrogenase (CDH) is one of the cellulase auxiliary proteins, which is widely used in the field of biomass degradation. However, how to efficiently and cheaply apply it in industrial production still needs further research. Aspergillus niger C112 is a significant producer of cellulase and has a relatively complete lignocellulose degradation system, but its CDH activity was only 3.92 U. To obtain a recombinant strain of A. niger C112 with high cellulases activity, the CDH from the readily available white-rot fungus Grifola frondose had been heterologously expressed in A. niger C112, under the control of the gpdA promoter. After cultivation in the medium with alkali-pretreated poplar fiber as substrate, the enzyme activity of recombinant CDH reached 36.63 U/L. Compared with the original A. niger C112, the recombinant A. niger transformed with Grifola frondosa CDH showed stronger lignocellulase activity, the activities of cellulases, β-1, 4-glucosidase and manganese peroxidase increased by 28.57, 35.07 and 121.69%, respectively. The result showed that the expression of the gcdh gene in A. niger C112 could improve the activity of some lignocellulose degrading enzymes. This work provides a theoretical basis for the further application of gcdh gene in improving biomass conversion efficiency

A co-design-based reliable low-latency and energy-efficient transmission protocol for uwsns

Recently, underwater wireless sensor networks (UWSNs) have been considered as a powerful technique for many applications. However, acoustic communications in UWSNs bring in huge QoS issues for time-critical applications. Additionally, excessive control packets and multiple copies during the data transmission process exacerbate this challenge. Faced with these problems, we propose a reliable low-latency and energy-efficient transmission protocol for dense 3D underwater wireless sensor networks to improve the QoS of UWSNs. The proposed protocol exploits fewer control packets and reduces data-packet copies effectively through the co-design of routing and media access control (MAC) protocols. The co-design method is divided into two steps. First, the number of handshakes in the MAC process will be greatly reduced via our forwarding-set routing strategy under the guarantee of reliability. Second, with the help of information from the MAC process, network-update messages can be used to replace control packages through mobility prediction when choosing a route. Simulation results show that the proposed protocol has a considerably higher reliability, and lower latency and energy consumption in comparison with existing transmission protocols for a dense underwater wireless sensor network.This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. U19A2061, 61772228 and 61902143), National key research and development program of China (Grant No. 2017YFC1502306)