Incorporating red blanket protocol within code crimson:Streamlining definitive trauma care amid the chaos

The care of a patient involved in major trauma with exsanguinating haemorrhage is time-critical to achieve definitive haemorrhage control,and it requires coordinated multidisciplinary care.During initial resuscitation of a patient in the emergency department(ED),Code Crimson activation facilitates rapid decisionmaking by multi-disciplinary specialists for definitive haemorrhage control in operating theatre(OT)and/or interventional radiology(IR)suite.Once this decision has been made,there may still be various factors that lead to delay in transporting the patient from ED to OT/IR.Red Blanket protocol identifies and addresses these factors and processes which cause delay,and aims to facilitate rapid and safe transport of the haemodynamically unstable patient from ED to OT,while minimizing delay in resuscitation during the transfer.The two processes,Code Crimson and Red Blanket,complement each other.It would be ideal to merge the two processes into a single protocol rather than having two separate workflows.Introducing these quality improvement strategies and coor-dinated processes within the trauma framework of the hospitals/healthcare systems will help in further improving the multi-disciplinary care for the complex trauma patients requiring rapid and definitive haemorrhage control.

外泌体来源非编码RNA对骨关节炎微环境的作用及临床应用进展

背景:骨关节炎是一种常见的关节退行性疾病,其发生发展的病因学机制尚不清楚。早期骨关节炎的及时诊治十分重要,目前尚无确切有效的方法。外泌体来源广泛,其中包含非编码RNA(Non-coding RNAs,ncRNAs),如微小RNAs、环状RNAs和长链非编码RNAs。外泌体ncRNAs可直接从原始细胞递送至邻近或远程细胞,通过细胞间通讯调节细胞活性,在重塑骨关节微环境中具有重要的调控作用。目的:总结外泌体来源ncRNAs对骨关节炎关节内微环境的干预作用,以及在临床应用中取得的进展,阐明其在骨关节炎诊治方面的潜力。方法:以“exosomes,non-coding RNA,osteoarthritis,application,signal pathway,synovial fluid,cartilage cells,cartilage matrix,subchondral bone,mechanism”为检索词在PubMed数据库中进行检索,最终纳入66篇相关文献进行综述分析。结果与结论:①外泌体来源ncRNAs在骨关节炎的发病过程中对关节内微环境具有重要调控作用,主要体现在外泌体ncRNAs调控关节内炎症反应、软骨细胞及软骨基质的退化,软骨下骨重塑和细胞间通讯。②外泌体中的ncRNAs可以作为骨关节炎的生物标志物,帮助骨关节炎的早期诊断和监测疾病进展与预后。③外泌体ncRNAs可作为骨关节炎的治疗靶点,外泌体携带miRNAs递送到关节的软骨细胞及软骨基质,发挥调控作用。④外泌体ncRNAs可以通过调控基因表达、促进细胞间通讯,提高软骨组织工程的效果,修复或再生受损软骨。⑤未来研究者应继续发掘外泌体来源ncRNAs对骨关节炎的干预机制,并可结合最新软骨组织工程的研究成果应用到临床之中,这将有效帮助解决骨关节炎患者的病痛。

肾病综合征病人血清lncRNA ANRIL和BDNF水平变化与血栓栓塞的相关性分析

目的探究肾病综合征(NS)病人血清长链非编码RNA(lncRNA)细胞周期激酶抑制因子4基因座中反义非编码RNA(ANRIL)和脑源性神经营养因子(BDNF)水平变化与血栓栓塞(TE)的相关性。方法选取2019年3月至2021年12月在承德市中心医院住院治疗的220例NS病人为研究对象,根据随访结果将其分为NS组118例和TE组102例,另外选取同期体检健康者100例为对照组。采用实时荧光定量PCR(qRT-PCR)测定研究对象血清lncRNA ANRIL水平;采用酶联免疫吸附测定(ELISA)检测研究对象血清BDNF水平;使用全自动生化分析仪检测相关生化指标;采用Pearson相关性分析血清lncRNA ANRIL、BDNF水平与生化指标相关性;采用多因素logistic回归分析NS病人发生TE的影响因素;采用受试者操作特征曲线(ROC曲线)分析血清lncRNA ANRIL、BDNF在预测NS病人发生TE中的价值。结果与对照组相比较,NS组、TE组血清lncRNA ANRIL水平显著升高(2.54±0.43比5.82±1.34、8.35±2.17),BDNF水平显著降低[(32.77±8.25)μg/L比(24.49±4.58)μg/L、(18.63±3.62)μg/L](P<0.05);与NS组相比较,TE组血清lncRNA ANRIL水平显著升高,BDNF水平显著降低(P<0.05);对照组、NS组、TE组三组的尿酸、血肌酐、乳酸脱氢酶、血尿素氮、尿蛋白水平呈逐渐升高趋势,血清白蛋白、血红蛋白水平呈逐渐降低趋势(P<0.05)。Pearson相关性分析显示,NS合并TE病人血清lncRNA ANRIL水平与尿酸、血肌酐、乳酸脱氢酶、血尿素氮、尿蛋白水平呈正相关,与血清白蛋白、血红蛋白水平呈负相关(P<0.05);NS合并TE病人血清BDNF水平与尿酸、血肌酐、乳酸脱氢酶、血尿素氮、尿蛋白水平呈负相关,与血清白蛋白、血红蛋白水平呈正相关(P<0.05)。多因素logistic回归分析显示,lncRNA ANRIL、尿蛋白和BDNF是NS病人发生TE的影响因素(P<0.05);ROC曲线结果显示,血清lncRNA ANRIL、BDNF预测NS病人发生TE的曲线下面积(AUC)分别为0.84、0.85,两者联合预测的AUC为0.92,高于lncRNA ANRIL、BDNF单独检测,特异度为83.05%,灵敏度为88.24%。结论NS伴TE病人中血清lncRNA ANRIL水平升高,BDNF水平降低,二者对NS病人发生TE具有一定的预测价值。

Efficient unequal error protection for online fountain codes

In this paper,an efficient unequal error protection(UEP)scheme for online fountain codes is proposed.In the buildup phase,the traversing-selection strategy is proposed to select the most important symbols(MIS).Then,in the completion phase,the weighted-selection strategy is applied to provide low overhead.The performance of the proposed scheme is analyzed and compared with the existing UEP online fountain scheme.Simulation results show that in terms of MIS and the least important symbols(LIS),when the bit error ratio is 10-4,the proposed scheme can achieve 85%and 31.58%overhead reduction,respectively.

Design of Protograph LDPC-Coded MIMO-VLC Systems with Generalized Spatial Modulation

This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC systems.Moreover, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.

基于Radix-4 Booth编码的并行乘法器设计

速度和面积是评价乘法器单元性能优劣的两个基本指标。针对当前乘法器设计难以平衡版图面积和传输延时的问题,采用Radix-4 Booth算法,设计了一种新型的16位有符号定点乘法器。在部分积生成过程中,首先改进对乘数的取补码电路,然后优化基数为4的改进Booth编码器和解码器,此结构采用较少的逻辑门资源,并且易对输入比特进行并行化处理。在Wallace压缩电路中,对符号扩展位进行预处理并设计新的压缩器结构,优化整个Wallace压缩模块。在第二级压缩过程中提前对高位使用纹波进位加法器结构计算,减小了多bit伪和的求和位数。在求和电路中,使用两级超前进位加法器结构,在缩短关键路径传输延时的同时避免增大芯片面积,提高了乘法器的运行速度。新型定点乘法器与已有的乘法器结构相比,减少了12.0%的面积,降低了20.5%的延时。

Physical Layer Encryption of OFDM-PON Based on Quantum Noise Stream Cipher with Polar Code

Orthogonal frequency division multiplexing passive optical network(OFDM-PON) has superior anti-dispersion property to operate in the C-band of fiber for increased optical power budget. However,the downlink broadcast exposes the physical layer vulnerable to the threat of illegal eavesdropping. Quantum noise stream cipher(QNSC) is a classic physical layer encryption method and well compatible with the OFDM-PON. Meanwhile, it is indispensable to exploit forward error correction(FEC) to control errors in data transmission. However, when QNSC and FEC are jointly coded, the redundant information becomes heavier and thus the code rate of the transmitted signal will be largely reduced. In this work, we propose a physical layer encryption scheme based on polar-code-assisted QNSC. In order to improve the code rate and security of the transmitted signal, we exploit chaotic sequences to yield the redundant bits and utilize the redundant information of the polar code to generate the higher-order encrypted signal in the QNSC scheme with the operation of the interleaver.We experimentally demonstrate the encrypted 16/64-QAM, 16/256-QAM, 16/1024-QAM, 16/4096-QAM QNSC signals transmitted over 30-km standard single mode fiber. For the transmitted 16/4096-QAM QNSC signal, compared with the conventional QNSC method, the proposed method increases the code rate from 0.1 to 0.32 with enhanced security.

Improved Belief Propagation Decoder for LDPC-CRC-Polar Codes with Bit-Freezing

Though belief propagation bit-flip(BPBF)decoding improves the error correction performance of polar codes,it uses the exhaustive flips method to achieve the error correction performance of CA-SCL decoding,thus resulting in high decoding complexity and latency.To alleviate this issue,we incorporate the LDPC-CRC-Polar coding scheme with BPBF and propose an improved belief propagation decoder for LDPC-CRC-Polar codes with bit-freezing(LDPCCRC-Polar codes BPBFz).The proposed LDPCCRC-Polar codes BPBFz employs the LDPC code to ensure the reliability of the flipping set,i.e.,critical set(CS),and dynamically update it.The modified CS is further utilized for the identification of error-prone bits.The proposed LDPC-CRC-Polar codes BPBFz obtains remarkable error correction performance and is comparable to that of the CA-SCL(L=16)decoder under medium-to-high signal-to-noise ratio(SNR)regions.It gains up to 1.2dB and 0.9dB at a fixed BLER=10-4compared with BP and BPBF(CS-1),respectively.In addition,the proposed LDPC-CRC-Polar codes BPBFz has lower decoding latency compared with CA-SCL and BPBF,i.e.,it is 15 times faster than CA-SCL(L=16)at high SNR regions.

Doped low-density parity-check codes

In this paper,we propose a doping approach to lower the error floor of Low-Density Parity-Check(LDPC)codes.The doping component is a short block code in which the information bits are selected from the coded bits of the dominant trapping sets of the LDPC code.Accordingly,an algorithm for selecting the information bits of the short code is proposed,and a specific two-stage decoding algorithm is presented.Simulation results demonstrate that the proposed doped LDPC code achieves up to 2.0 dB gain compared with the original LDPC code at a frame error rate of 10^(-6)Furthermore,the proposed design can lower the error floor of original LDPC Codes.

A free monoid containing all prefix codes

In this paper,we exhibit a free monoid containing all prefix codes in connection with the sets of i-th powers of primitive words for all i≥2.This extends two results given by Shyr and Tsai in 1998 at the same time.

A GAN-EfficientNet-Based Traceability Method for Malicious Code Variant Families

Due to the diversity and unpredictability of changes in malicious code,studying the traceability of variant families remains challenging.In this paper,we propose a GAN-EfficientNetV2-based method for tracing families of malicious code variants.This method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code images.The method includes a lightweight classifier and a simulator.The classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile,embedded,and other devices.The simulator utilizes an enhanced generative adversarial network to simulate different variants of malicious code and generates datasets to validate the model’s performance.This process helps identify model vulnerabilities and security risks,facilitating model enhancement and development.The classifier achieves 98.61%and 97.59%accuracy on the MMCC dataset and Malevis dataset,respectively.The simulator’s generated image of malicious code variants has an FID value of 155.44 and an IS value of 1.72±0.42.The classifier’s accuracy for tracing the family of malicious code variants is as high as 90.29%,surpassing that of mainstream neural network models.This meets the current demand for high generalization and anti-obfuscation abilities in malicious code classification models due to the rapid evolution of malicious code.

Focus control of wide-angle metalens based on digitally encoded metasurface

Based on the principle of super-symmetric lens with quadratic phase gradient transformation, combined with the principle of digital coding of metasurface, we propose a wide-angle coded metalens for focusing control in two-dimensional space. This metalens achieves focus shift in the x-direction by changing the oblique incidence angle of the incident wave,and focus control in the y-direction by combining with the convolution principle of the digitally coded metasurface to achieve flexible control of light focusing in the two-dimensional plane. The metasurface unit is mainly composed of threelayer of metal structure and two layers of medium, and the transmission phase is obtained by changing the middle layer of metal structure, which in turn obtains the required phase distribution of the metalens. The design of the metalens realizes the function of the lens with a large viewing angle at the x-polarized incidence, and realizes two-dimensional focus control. Experimentally, we prepared the designed coding metalens and tested the focus control function of the wide-angle coding metalens. The experimental results are in good agreement with the design results.

Design and implementation of code acquisition using sparse Fourier transform

Code acquisition is the kernel operation for signal synchronization in the spread-spectrum receiver.To reduce the computational complexity and latency of code acquisition,this paper proposes an efficient scheme employing sparse Fourier transform(SFT)and the relevant hardware architecture for field programmable gate array(FPGA)and application-specific integrated circuit(ASIC)implementation.Efforts are made at both the algorithmic level and the implementation level to enable merged searching of code phase and Doppler frequency without incurring massive hardware expenditure.Compared with the existing code acquisition approaches,it is shown from theoretical analysis and experimental results that the proposed design can shorten processing latency and reduce hardware complexity without degrading the acquisition probability.

Parallel Implementation of the CCSDS Turbo Decoder on GPU

This paper presents a software turbo decoder on graphics processing units(GPU).Unlike previous works,the proposed decoding architecture for turbo codes mainly focuses on the Consultative Committee for Space Data Systems(CCSDS)standard.However,the information frame lengths of the CCSDS turbo codes are not suitable for flexible sub-frame parallelism design.To mitigate this issue,we propose a padding method that inserts several bits before the information frame header.To obtain low-latency performance and high resource utilization,two-level intra-frame parallelisms and an efficient data structure are considered.The presented Max-Log-Map decoder can be adopted to decode the Long Term Evolution(LTE)turbo codes with only small modifications.The proposed CCSDS turbo decoder at 10 iterations on NVIDIA RTX3070 achieves about 150 Mbps and 50Mbps throughputs for the code rates 1/6 and 1/2,respectively.

Gray code based gradient-free optimization algorithm for parameterized quantum circuit

A Gray code based gradient-free optimization(GCO)algorithm is proposed to update the parameters of parameterized quantum circuits(PQCs)in this work.Each parameter of PQCs is encoded as a binary string,named as a gene,and a genetic-based method is adopted to select the offsprings.The individuals in the offspring are decoded in Gray code way to keep Hamming distance,and then are evaluated to obtain the best one with the lowest cost value in each iteration.The algorithm is performed iteratively for all parameters one by one until the cost value satisfies the stop condition or the number of iterations is reached.The GCO algorithm is demonstrated for classification tasks in Iris and MNIST datasets,and their performance are compared by those with the Bayesian optimization algorithm and binary code based optimization algorithm.The simulation results show that the GCO algorithm can reach high accuracies steadily for quantum classification tasks.Importantly,the GCO algorithm has a robust performance in the noise environment.

An Encode-and CRT-Based Scalability Scheme for Optimizing Transmission in Blockchain

Blockchain technology has witnessed a burgeoning integration into diverse realms of economic and societal development.Nevertheless,scalability challenges,characterized by diminished broadcast efficiency,heightened communication overhead,and escalated storage costs,have significantly constrained the broad-scale application of blockchain.This paper introduces a novel Encode-and CRT-based Scalability Scheme(ECSS),meticulously refined to enhance both block broadcasting and storage.Primarily,ECSS categorizes nodes into distinct domains,thereby reducing the network diameter and augmenting transmission efficiency.Secondly,ECSS streamlines block transmission through a compact block protocol and robust RS coding,which not only reduces the size of broadcasted blocks but also ensures transmission reliability.Finally,ECSS utilizes the Chinese remainder theorem,designating the block body as the compression target and mapping it to multiple modules to achieve efficient storage,thereby alleviating the storage burdens on nodes.To evaluate ECSS’s performance,we established an experimental platformand conducted comprehensive assessments.Empirical results demonstrate that ECSS attains superior network scalability and stability,reducing communication overhead by an impressive 72% and total storage costs by a substantial 63.6%.

Blind Recognition of Non-Binary LDPC Codes Based on Ant Colony Optimization

This paper introduces a novel blind recognition of non-binary low-density parity-check(LDPC)codes without a candidate set,using ant colony optimization(ACO)algorithm over additive white Gaussian noise(AWGN)channels.Specifically,the scheme that effectively combines the ACO algorithm and the non-binary elements over finite fields is proposed.Furthermore,an improved,simplified elitist ACO algorithm based on soft decision reliability is introduced to recognize the parity-check matrix over noisy channels.Simulation results show that the recognition rate continuously increases with an increased signalto-noise ratio(SNR)over the AWGN channel.

How Does Naming Affect Language Models on Code Analysis Tasks?

The Large Language Models (LLMs), such as GPT and BERT, were proposed for natural language processing (NLP) and have shown promising results as general-purpose language models. An increasing number of industry professionals and researchers are adopting LLMs for program analysis tasks. However, one significant difference between programming languages and natural languages is that a programmer has the flexibility to assign any names to variables, methods, and functions in the program, whereas a natural language writer does not. Intuitively, the quality of naming in a program affects the performance of LLMs in program analysis tasks. This paper investigates how naming affects LLMs on code analysis tasks. Specifically, we create a set of datasets with code containing nonsense or misleading names for variables, methods, and functions, respectively. We then use well-trained models (CodeBERT) to perform code analysis tasks on these datasets. The experimental results show that naming has a significant impact on the performance of code analysis tasks based on LLMs, indicating that code representation learning based on LLMs heavily relies on well-defined names in code. Additionally, we conduct a case study on some special code analysis tasks using GPT, providing further insights.

Variational Learned Talking-Head Semantic Coded Transmission System

Video transmission requires considerable bandwidth,and current widely employed schemes prove inadequate when confronted with scenes featuring prominently.Motivated by the strides in talkinghead generative technology,the paper introduces a semantic transmission system tailored for talking-head videos.The system captures semantic information from talking-head video and faithfully reconstructs source video at the receiver,only one-shot reference frame and compact semantic features are required for the entire transmission.Specifically,we analyze video semantics in the pixel domain frame-by-frame and jointly process multi-frame semantic information to seamlessly incorporate spatial and temporal information.Variational modeling is utilized to evaluate the diversity of importance among group semantics,thereby guiding bandwidth resource allocation for semantics to enhance system efficiency.The whole endto-end system is modeled as an optimization problem and equivalent to acquiring optimal rate-distortion performance.We evaluate our system on both reference frame and video transmission,experimental results demonstrate that our system can improve the efficiency and robustness of communications.Compared to the classical approaches,our system can save over 90%of bandwidth when user perception is close.

C-CORE:Clustering by Code Representation to Prioritize Test Cases in Compiler Testing

Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-CORE(short for Clustering by Code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-CORE significantly enhances testing efficiency.Across four datasets,C-CORE increases the average of the percentage of faults detected(APFD)value by 0.16 to 0.31 and reduces test time by over 50% in 46% of cases.When compared to the best results from approaches using predicted code coverage,C-CORE improves the APFD value by 1.1% to 12.3% and achieves an overall time-saving of 159.1%.