An Online Sparse Streaming Feature Selection Algorithm

Online streaming feature selection (OSFS), which conducts feature selection in an online manner, plays an important role in dealing with high-dimensional data. In many real applications such as intelligent healthcare platform, streaming feature always has some missing data, which raises a crucial challenge in conducting OSFS, i.e., how to establish the uncertain relationship between sparse streaming features and labels. Unfortunately, existing OSFS algorithms never consider such uncertain relationship. To fill this gap, we in this paper propose an online sparse streaming feature selection with uncertainty (OS2FSU) algorithm. OS2FSU consists of two main parts: 1) latent factor analysis is utilized to pre-estimate the missing data in sparse streaming features before con-ducting feature selection, and 2) fuzzy logic and neighborhood rough set are employed to alleviate the uncertainty between estimated streaming features and labels during conducting feature selection. In the experiments, OS2FSU is compared with five state-of-the-art OSFS algorithms on six real datasets. The results demonstrate that OS2FSU outperforms its competitors when missing data are encountered in OSFS

Iterative distributed minimum total-MSE approach for secure communications in MIMO interference channels

In this paper, we consider the problem of joint transmit precoding (TPC) matrix and receive filter matrix design subject to both secrecy and per-transmitter power constraints in the MIMO interference channel, where K legitimate transmitter-receiver pairs communicate in the presence of an external eavesdropper. Explicitly, we jointly design the TPC and receive filter matrices based on the minimum total mean-squared error (MT-MSE) criterion under a given and feasible information-theoretic degrees of freedom. More specifically, we formulate this problem by minimizing the total MSEs of the signals communicated between the legitimate transmitter-receiver pairs, whilst ensuring that the MSE of the signals decoded by the eavesdropper remains higher than a certain threshold. We demonstrate that the joint design of the TPC and receive filter matrices subject to both secrecy and transmit power constraints can be accomplished by an efficient iterative distributed algorithm. The convergence of the proposed iterative algorithm is characterized as well. Furthermore, the performance of the proposed algorithm, including both its secrecy rate and MSE, is characterized with the aid of numerical results. We demonstrate that the proposed algorithm outperforms the traditional interference alignment (IA) algorithm in terms of both the achievable secrecy rate and the MSE. As a benefit, secure communications can be guaranteed by the proposed algorithm for the MIMO interference channel even in the presence of a "sophisticated/strong" eavesdropper, whose number of antennas is much higher than that of each legitimate transmitter and receiver

Gap analysis for DNA-based biomonitoring of aquatic ecosystems in China

DNA-based taxon identification is improving the assessment and management of biodiversity in rivers. However, the lack of comprehensive DNA barcode reference libraries and globally highly unequal coverage are still hindering the application prospects of this method worldwide. Here, we analyzed the COI barcode gap in two reference libraries, Barcode of Life Data Systems (BOLD) and NCBI GenBank, with a focus on three aquatic animal groups (freshwater fish, aquatic insects and molluscs) in Chinese rivers. Our data show gaps in barcode coverage (e.g., organisms without barcodes) of ca. 40–70% of taxa in these groups in the BOLD or NCBI GenBank database, respectively. These gaps can rise even further if the barcode thresholds are set to contain at least five reference sequences per taxon. Furthermore, most barcodes are from non-local samples, and only 14.4% (BOLD) and 28.8% (NCBI GenBank) of reference sequences were from organisms sampled in China, respectively. The pairwise genetic distance of local barcodes is 3 to 5 times lower than non-local barcodes, indicating that the latter may not be a good substitute. When looking at individual catchments, ca. 60% of the potentially occurring aquatic species have one or more barcodes, yet the barcode coverage varies slightly across ten major river catchments, ranging from 54.3% (Liao River basin) to 68.2% (Huai River basin). The taxa Salmoniformes and Perciformes in freshwater fish, Odonata and Diptera in aquatic insects, and Bivalvia in molluscs have the best barcode coverage in most catchments (mean coverage >70%). This study gives the first overview and current status of barcode reference libraries of three major aquatic animal groups in Chinese rivers. Our results will help to better interpret current metabarcoding studies from China, and also provide a basis to develop a strategy of filling the gaps in the reference libraries of aquatic species in China

MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning

Medical image segmentation is a critical task for clinical diagnosis and research. However, dealing with highly imbalanced data remains a significant challenge in this domain, where the region of interest (ROI) may exhibit substantial variations across different slices. This presents a significant hurdle to medical image segmentation, as conventional segmentation methods may either overlook the minority class or overly emphasize the majority class, ultimately leading to a decrease in the overall generalization ability of the segmentation results. To overcome this, we propose a novel approach based on multi-step reinforcement learning, which integrates prior knowledge of medical images and pixel-wise segmentation difficulty into the reward function. Our method treats each pixel as an individual agent, utilizing diverse actions to evaluate its relevance for segmentation. To validate the effectiveness of our approach, we conduct experiments on four imbalanced medical datasets, and the results show that our approach surpasses other state-of-the-art methods in highly imbalanced scenarios. These findings hold substantial implications for clinical diagnosis and research