Anti-Ramsey numbers of loose paths and cycles in uniform hypergraphs

For a fixed family of $r$-uniform hypergraphs $\mathcal{F}$, the anti-Ramsey number of $\mathcal{F}$, denoted by $ar(n,r,\mathcal{F})$, is the minimum number $c$ of colors such that for any edge-coloring of the complete $r$-uniform hypergraph on $n$ vertices with at least $c$ colors, there is a rainbow copy of some hypergraph in $\mathcal{F}$. Here, a rainbow hypergraph is an edge-colored hypergraph with all edges colored differently. Let $\mathcal{P}_k$ and $\mathcal{C}_k$ be the families of loose paths and loose cycles with $k$ edges in an $r$-uniform hypergraph, respectively. In this paper, we determine the exact values of $ar(n,r,\mathcal{P}_k)$ and $ar(n,r,\mathcal{C}_k)$ for all $k\geq 4$ and $r\geq 3$

An Optimization Model for Offline Scheduling Policy of Low-density Parity-check Codes

In this study, an optimization model for offline scheduling policy of low-density parity-check (LDPC) codes is proposed to improve the decoding efficiency of the belief propagation (BP). The optimization model uses the number of messages passed (NMP) as a metric to evaluate complexity, and two metrics, average entropy (AE), and gap to maximum a posteriori (GAP), to evaluate BP decoding performance. Based on this model, an algorithm is proposed to optimize the scheduling sequence for reduced decoding complexity and superior performance compared to layered BP. Furthermore, this proposed algorithm does not add the extra complexity of determining the scheduling sequence to the decoding process

Effectiveness and safety of co-administration of moxifloxacin with netilmicin in drug-resistant tuberculosis patients, and its impact on inflammatory factors and immune function

Purpose: To study the effectiveness and safety of co-administration of moxifloxacin with netilmicin in drug-resistant tuberculosis (TB) patients, and its impact on levels of inflammatory factors and immune function. Methods: We enrolled 100 patients with drug-resistant TB admitted to People’s Hospital of Rizhao between May 2017 and October 2019. The patients were randomly allocated to control group and study group, with 50 patients per group. The control group received moxifloxacin at a dose of 0.2 g t.i.d. for 6 months and the study group received netilmicin at a dose of 0.1 g t.i.d. plus. The response, incidence of adverse reactions, expression levels of inflammatory factors, immune function, and sputum-negative status after 2, 4 and 6 months of TB treatment were compared. Results: The study group showed markedly higher response than the control group (p < 0.05). Moreover, there were lower incidence of adverse effects in the study group than in the control group (p < 0.05). The expression levels of inflammatory factors were significantly lower in the study group, while the concentrations of CD3+, CD4+, and CD8+ were markedly higher (p < 0.05). After 2, 4 and 6 months of TB treatment, cases of sputum-negative conversion were significantly higher in the study group than in the control group (p < 0.05). Conclusion: Co-administration of moxifloxacin with netilmicin produces much higher effectiveness and safety than moxifloxacin monotherapy, decreases inflammatory factor levels and improves immune function in patients with drug-resistant TB

Improving the Gilbert-Varshamov Bound by Graph Spectral Method

We improve Gilbert-Varshamov bound by graph spectral method. Gilbert graph $G_{q,n,d}$ is a graph with all vectors in $\mathbb{F}_q^n$ as vertices where two vertices are adjacent if their Hamming distance is less than $d$. In this paper, we calculate the eigenvalues and eigenvectors of $G_{q,n,d}$ using the properties of Cayley graph. The improved bound is associated with the minimum eigenvalue of the graph. Finally we give an algorithm to calculate the bound and linear codes which satisfy the bound

The Detection and Phylogenetic Analysis of Bovine Hepacivirus in China

Hepacivirus has been identified in cattle in Africa, Europe, and South America. In this survey of bovine hepacivirus (BovHepV) in 131 serum samples from Chinese cattle herds using RT-PCR, five of 131 sera were BovHepV positive, with the infection rate of 3.82%. Phylogenetic analysis based on the partial NS3 coding sequence showed that the BovHepV of the five positive samples clustered with other BovHepV but formed a separate branch. The results indicated that these new BovHepV represent emerging and novel strains. Further investigations are necessary to determine the epidemiology and viral pathogenesis of these BovHepV strains, as well as the potential threat to ruminant and livestock workers in China

On the Weight Distribution of Weights Less than 2wmin⁡2w_{\min} in Polar Codes

The number of low-weight codewords is critical to the performance of error-correcting codes. In 1970, Kasami and Tokura characterized the codewords of Reed-Muller (RM) codes whose weights are less than $2w_{\min}$, where $w_{\min}$ represents the minimum weight. In this paper, we extend their results to decreasing polar codes. We present the closed-form expressions for the number of codewords in decreasing polar codes with weights less than $2w_{\min}$. Moreover, the proposed enumeration algorithm runs in polynomial time with respect to the code length

On the Weight Spectrum Improvement of Pre-transformed Reed-Muller Codes and Polar Codes

Pre-transformation with an upper-triangular matrix (including cyclic redundancy check (CRC), parity-check (PC) and polarization-adjusted convolutional (PAC) codes) improves the weight spectrum of Reed-Muller (RM) codes and polar codes significantly. However, a theoretical analysis to quantify the improvement is missing. In this paper, we provide asymptotic analysis on the number of low-weight codewords of the original and pre-transformed RM codes respectively, and prove that pre-transformation significantly reduces low-weight codewords, even in the order sense. For polar codes, we prove that the average number of minimum-weight codewords does not increase after pre-transformation. Both results confirm the advantages of pre-transformation

On the Performance of Low-complexity Decoders of LDPC and Polar Codes

Efficient decoding is crucial to high-throughput and low-power wireless communication scenarios. A theoretical analysis of the performance-complexity tradeoff toward low-complexity decoding is required for a better understanding of the fundamental limits in the above-mentioned scenarios. This study aims to explore the performance of decoders with complexity constraints. Specifically, we investigate the performance of LDPC codes with different numbers of belief-propagation iterations and the performance of polar codes with an SSC decoder. We found that the asymptotic error rates of both polar codes and LDPC codes are functions of complexity $T$ and code length $N$, in the form of $2^{-a2^{b\frac{T}{N}}}$, where $a$ and $b$ are constants that depend on channel and coding schemes. Our analysis reveals the different performance-complexity tradeoffs for LDPC and polar codes. The results indicate that if one aims to further enhance the decoding efficiency for LDPC codes, the key lies in how to efficiently pass messages on the factor graph. In terms of decoding efficiency, polar codes asymptotically outperform $(J, K)$-regular LDPC codes with a code rate $R \le 1-\frac{J(J-1)}{2^J+(J-1)}$ in the low-complexity regime $(T \le O(NlogN))$.Comment: arXiv admin note: text overlap with arXiv:2012.13378 by other author

A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care

The COVID-19 pandemic highlighted the need for predictive deep-learning models in health care. However, practical prediction task design, fair comparison, and model selection for clinical applications remain a challenge. To address this, we introduce and evaluate two new prediction tasks?outcome-specific length-of-stay and early-mortality prediction for COVID-19 patients in intensive care?which better reflect clinical realities. We developed evaluation metrics, model adaptation designs, and open-source data preprocessing pipelines for these tasks while also evaluating 18 predictive models, including clinical scoring methods and traditional machine-learning, basic deep-learning, and advanced deep-learning models, tailored for electronic health record (EHR) data. Benchmarking results from two real-world COVID-19 EHR datasets are provided, and all results and trained models have been released on an online platform for use by clinicians and researchers. Our efforts contribute to the advancement of deep-learning and machine-learning research in pandemic predictive modeling