Efficient Near Maximum-Likelihood Efficient Near Maximum-Likelihood Reliability-Based Decoding for Short LDPC Codes

In this paper, we propose an efficient decoding algorithm for short low-density parity check (LDPC) codes by carefully combining the belief propagation (BP) decoding and order statistic decoding (OSD) algorithms. Specifically, a modified BP (mBP) algorithm is applied for a certain number of iterations prior to OSD to enhance the reliability of the received message, where an offset parameter is utilized in mBP to control the weight of the extrinsic information in message passing. By carefully selecting the offset parameter and the number of mBP iterations, the number of errors in the most reliable positions (MRPs) in OSD can be reduced, thereby significantly improving the overall decoding performance of error rate and complexity. Simulation results show that the proposed algorithm can approach the maximum-likelihood decoding (MLD) for short LDPC codes with only a slight increase in complexity compared to BP and a significant decrease compared to OSD. Specifically, the order-(m-1) decoding of the proposed algorithm can achieve the performance of the order-m OSD

When Distributed Consensus Meets Wireless Connected Autonomous Systems: A Review and A DAG-based Approach

The connected and autonomous systems (CAS) and auto-driving era is coming into our life. To support CAS applications such as AI-driven decision-making and blockchain-based smart data management platform, data and message exchange/dissemination is a fundamental element. The distributed message broadcast and forward protocols in CAS, such as vehicular ad hoc networks (VANET), can suffer from significant message loss and uncertain transmission delay, and faulty nodes might disseminate fake messages to confuse the network. Therefore, the consensus mechanism is essential in CAS with distributed structure to guaranteed correct nodes agree on the same parameter and reach consistency. However, due to the wireless nature of CAS, traditional consensus cannot be directly deployed. This article reviews several existing consensus mechanisms, including average/maximum/minimum estimation consensus mechanisms that apply on quantity, Byzantine fault tolerance consensus for request, state machine replication (SMR) and blockchain, as well as their implementations in CAS. To deploy wireless-adapted consensus, we propose a Directed Acyclic Graph (DAG)-based message structure to build a non-equivocation data dissemination protocol for CAS, which has resilience against message loss and unpredictable forwarding latency. Finally, we enhance this protocol by developing a two-dimension DAG-based strategy to achieve partial order for blockchain and total order for the distributed service model SMR

Transcriptome profiling reveals the crucial biological pathways involved in cold response in Moso bamboo (Phyllostachys edulis).

Most bamboo species including Moso bamboo (Phyllostachys edulis) are tropical or subtropical plants that greatly contribute to human well-being. Low temperature is one of the main environmental factors restricting bamboo growth and geographic distribution. Our knowledge of the molecular changes during bamboo adaption to cold stress remains limited. Here, we provided a general overview of the cold-responsive transcriptional profiles in Moso bamboo by systematically analyzing its transcriptomic response under cold stress. Our results showed that low temperature induced strong morphological and biochemical alternations in Moso bamboo. To examine the global gene expression changes in response to cold, 12 libraries (non-treated, cold-treated 0.5, 1 and 24 h at -2 °C) were sequenced using an Illumina sequencing platform. Only a few differentially expressed genes (DEGs) were identified at early stage, while a large number of DEGs were identified at late stage in this study, suggesting that the majority of cold response genes in bamboo are late-responsive genes. A total of 222 transcription factors from 24 different families were differentially expressed during 24-h cold treatment, and the expressions of several well-known C-repeat/dehydration responsive element-binding factor negative regulators were significantly upregulated in response to cold, indicating the existence of special cold response networks. Our data also revealed that the expression of genes related to cell wall and the biosynthesis of fatty acids were altered in response to cold stress, indicating their potential roles in the acquisition of bamboo cold tolerance. In summary, our studies showed that both plant kingdom-conserved and species-specific cold response pathways exist in Moso bamboo, which lays the foundation for studying the regulatory mechanisms underlying bamboo cold stress response and provides useful gene resources for the construction of cold-tolerant bamboo through genetic engineering in the future

Short Block-length Codes for Ultra-Reliable Low-Latency Communications

This paper reviews the state of the art channel coding techniques for ultra-reliable low latency communication (URLLC). The stringent requirements of URLLC services, such as ultra-high reliability and low latency, have made it the most challenging feature of the fifth generation (5G) mobile systems. The problem is even more challenging for the services beyond the 5G promise, such as tele-surgery and factory automation, which require latencies less than 1ms and failure rate as low as $10^{-9}$. The very low latency requirements of URLLC do not allow traditional approaches such as re-transmission to be used to increase the reliability. On the other hand, to guarantee the delay requirements, the block length needs to be small, so conventional channel codes, originally designed and optimised for moderate-to-long block-lengths, show notable deficiencies for short blocks. This paper provides an overview on channel coding techniques for short block lengths and compares them in terms of performance and complexity. Several important research directions are identified and discussed in more detail with several possible solutions.Comment: Accepted for publication in IEEE Communications Magazin

High-Frequency Space Diffusion Models for Accelerated MRI

Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation. It can serve as a deep generative prior to solving the inverse problem in magnetic resonance (MR) reconstruction. However, low-frequency regions of $k$-space data are typically fully sampled in fast MR imaging, while existing diffusion models are performed throughout the entire image or $k$-space, inevitably introducing uncertainty in the reconstruction of low-frequency regions. Additionally, existing diffusion models often demand substantial iterations to converge, resulting in time-consuming reconstructions. To address these challenges, we propose a novel SDE tailored specifically for MR reconstruction with the diffusion process in high-frequency space (referred to as HFS-SDE). This approach ensures determinism in the fully sampled low-frequency regions and accelerates the sampling procedure of reverse diffusion. Experiments conducted on the publicly available fastMRI dataset demonstrate that the proposed HFS-SDE method outperforms traditional parallel imaging methods, supervised deep learning, and existing diffusion models in terms of reconstruction accuracy and stability. The fast convergence properties are also confirmed through theoretical and experimental validation. Our code and weights are available at https://github.com/Aboriginer/HFS-SDE.Comment: accepted for IEEE TM

The prevalence of food allergy in cesarean-born children aged 0–3 years: A systematic review and meta-analysis of cohort studies

PurposePrevious studies reported a higher risk of food allergy for cesarean-born children than vaginal-born children. This study aims to systematically compare the prevalence of food allergy among cesarean-born and vaginal-born children aged 0–3 years.MethodsThree English and two Chinese databases were searched using terms related to food allergies and cesarean sections. Cohort studies that reported the prevalence of food allergy in cesarean-born and vaginal-born children aged 0–3 years were included. Two reviewers performed study selection, quality assessment, and data extraction. The pooled prevalence of food allergy in cesarean-born and vaginal-born children was compared by meta-analysis.ResultsNine eligible studies, with 9,650 cesarean-born children and 20,418 vaginal-born children aged 0–3 years, were included. Of them, 645 cesarean-born children and 991 vaginal-born children were identified as having food allergies. The pooled prevalence of food allergy was higher in cesarean-born children (7.8%) than in vaginal-born children (5.9%). Cesarean section was associated with an increased risk of food allergy [odds ratio (OR): 1.45; 95% confidence interval (CI): 1.03–2.05] and cow's milk allergy (OR: 3.31; 95% CI: 1.98–5.53). Additionally, cesarean-born children with a parental history of allergy had an increased risk of food allergy (OR: 2.60; 95% CI: 1.28–5.27).ConclusionThis study suggests that cesarean sections was associated with an increased risk of food and cow's milk allergies in children aged 0–3 years. Cesarean-born children with a parental history of allergy demonstrated a higher risk for food allergy than did vaginal-born children. These results indicate that caregivers should be aware of the risks of food allergies in cesarean-born children, reducing the risk of potentially fatal allergic events. Further research is needed to identify the specific factors affecting food allergies in young children.Systematic Review Registrationhttp://www.crd.york.ac.uk/prospero, identifier: International Prospective Register of Systematic Reviews (NO. CRD42019140748)

Decoding Techniques based on Ordered Statistics

Short code design and related decoding algorithms have gained a great deal of interest among industry and academia recently, triggered by the stringent requirements of the new ultra-reliable and low-latency communications (URLLC) service for mission-critical Internet of Things (IoT) services. URLLC services mandate the use of short block-length codes to achieve hundred-of-microsecond time-to-transmit latency and ultra-low block error rates. As a theoretical milestone, Polyanskiy et al. have given new capacity bounds tighter than Shannon's work at the finite block length regime. However, with most conventional channel codes such as LDPC, Polar, Turbo, and convolutional codes suffering from performance degradation when the code length is short, it is still an open research problem to seek potential coding schemes for URLLC. As a kind of maximum-likelihood decoding algorithm, ordered statistics decoding (OSD) can be applied with classical strong channel codes, e.g. BCH codes and Reed-Solomon codes, to potentially meet the requirements of URLLC. In this thesis, I am taking a step towards seeking practical decoders for URLLC by revisiting the OSD and significantly reducing its decoding complexity. I first provide a comprehensive analysis of the OSD algorithm by characterizing the statistical properties, evolution and the distribution of the Hamming distance, and the weighted Hamming distance (WHD) from codeword estimates to the received sequence in the OSD algorithm. I prove that the distance distributions in OSD can be characterized as mixture models capturing the decoding error probability and code weight distribution, reflecting the inherent relations between error rate performance, distance, and channel conditions. Based on the statistical properties of distances and with the aim to reduce the decoding complexity, several decoding techniques are proposed, and their decoding error performance and complexity are accordingly analyzed. Simulation results for decoding various eBCH codes demonstrate that the proposed techniques can be conveniently combined with the OSD algorithm and its variants to significantly reduce the decoding complexity with a negligible loss in decoding error performance. Finally, I proposed two complete decoding designs, namely segmentation-discarding decoding, and probability-based ordered statistics decoding, as potential solutions for URLLC scenarios. Simulation results for different codes show that our proposed decoding algorithm can significantly reduce the decoding complexity compared to the existing OSD algorithms in the literature

Multilayer Photonic Spiking Neural Networks: Generalized Supervised Learning Algorithm and Network Optimization

We propose a generalized supervised learning algorithm for multilayer photonic spiking neural networks (SNNs) by combining the spike-timing dependent plasticity (STDP) rule and the gradient descent mechanism. A vertical-cavity surface-emitting laser with an embedded saturable absorber (VCSEL-SA) is employed as a photonic leaky-integrate-and-fire (LIF) neuron. The temporal coding strategy is employed to transform information into the precise firing time. With the modified supervised learning algorithm, the trained multilayer photonic SNN successfully solves the XOR problem and performs well on the Iris and Wisconsin breast cancer datasets. This indicates that a generalized supervised learning algorithm is realized for multilayer photonic SNN. In addition, network optimization is performed by considering different network sizes