A Hybrid BP-EP-VMP Approach to Joint Channel Estimation and Decoding for FTN Signaling over Frequency Selective Fading Channels

This paper deals with low-complexity joint channel estimation and decoding for faster-than-Nyquist (FTN) signaling over frequency selective fading channels. The inter-symbol interference (ISI) imposed by FTN signaling and the frequency selective channel are intentionally separated to fully exploit the known structure of the FTN-induced ISI. Colored noise due to the faster sampling rate than that of the Nyquist signaling system is approximated by autoregressive process. A Forney style factor graph representation of the FTN system is developed and Gaussian message passing is performed on the graph. Expectation propagation (EP) is employed to approximate the message from channel decoder to Gaussian distribution. Since the inner product between FTN symbols and channel coefficients is infeasible by belief propagation (BP), we propose to perform variational message passing (VMP) on an equivalent soft node in factor graph to tackle this problem. Simulation results demonstrate that the proposed low-complexity hybrid BP-EP-VMP algorithm outperforms the existing methods in FTN system. Compared with the Nyquist counterpart, FTN signaling with the proposed algorithm is able to increase the transmission rate by over 40%, with only negligible BER performance loss

PDHL-EDAS method for multiple attribute group decision making and its application to 3D printer selection

With the rapid development of 3D printing technology, 3D printers are manufactured based on the principle of 3D printing technology are more and more widely used in the manufacturing industry. Choosing high quality 3D printers for industrial production is of great significance to the economic growth of enterprises. In fact, it is difficult to select the most optimal 3D printers under a single and simple standard. Therefore, this paper establishes the probabilistic double hierarchy linguistic EDAS (PDHL-EDAS) method for the multiple attribute group decision making (MAGDM). Then the CRITIC model is introduced to derive objective weight and the cumulative prospect theory is leaded into obtain the cumulative weight of PDHLTS. In addition, what’s more, the PDHL-EDAS method is built and applied to the choice of high-quality 3D printer. Finally, compared with the available MAGDM methods under PDHLTS, the built method is proved to be scientific and effective. First published online 15 December 202

Acceleration for Timing-Aware Gate-Level Logic Simulation with One-Pass GPU Parallelism

Witnessing the advancing scale and complexity of chip design and benefiting from high-performance computation technologies, the simulation of Very Large Scale Integration (VLSI) Circuits imposes an increasing requirement for acceleration through parallel computing with GPU devices. However, the conventional parallel strategies do not fully align with modern GPU abilities, leading to new challenges in the parallelism of VLSI simulation when using GPU, despite some previous successful demonstrations of significant acceleration. In this paper, we propose a novel approach to accelerate 4-value logic timing-aware gate-level logic simulation using waveform-based GPU parallelism. Our approach utilizes a new strategy that can effectively handle the dependency between tasks during the parallelism, reducing the synchronization requirement between CPU and GPU when parallelizing the simulation on combinational circuits. This approach requires only one round of data transfer and hence achieves one-pass parallelism. Moreover, to overcome the difficulty within the adoption of our strategy in GPU devices, we design a series of data structures and tune them to dynamically allocate and store new-generated output with uncertain scale. Finally, experiments are carried out on industrial-scale open-source benchmarks to demonstrate the performance gain of our approach compared to several state-of-the-art baselines

Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval

Zero-shot entity retrieval, aiming to link mentions to candidate entities under the zero-shot setting, is vital for many tasks in Natural Language Processing. Most existing methods represent mentions/entities via the sentence embeddings of corresponding context from the Pre-trained Language Model. However, we argue that such coarse-grained sentence embeddings can not fully model the mentions/entities, especially when the attention scores towards mentions/entities are relatively low. In this work, we propose GER, a \textbf{G}raph enhanced \textbf{E}ntity \textbf{R}etrieval framework, to capture more fine-grained information as complementary to sentence embeddings. We extract the knowledge units from the corresponding context and then construct a mention/entity centralized graph. Hence, we can learn the fine-grained information about mention/entity by aggregating information from these knowledge units. To avoid the graph information bottleneck for the central mention/entity node, we construct a hierarchical graph and design a novel Hierarchical Graph Attention Network~(HGAN). Experimental results on popular benchmarks demonstrate that our proposed GER framework performs better than previous state-of-the-art models. The code has been available at https://github.com/wutaiqiang/GER-WSDM2023.Comment: 9 pages, 5 figure

Research on the Law of Garlic Price Based on Big Data

In view of the frequent fluctuation of garlic price under the market economy and the current situation of garlic price, the fluctuation of garlic price in the circulation link of garlic industry chain is analyzed, and the application mode of multidisciplinary in the agricultural industry is discussed. On the basis of the big data platform of garlic industry chain, this paper constructs a Garch model to analyze the fluctuation law of garlic price in the circulation link and provides the garlic industry service from the angle of price fluctuation combined with the economic analysis. The research shows that the average price rate of the price of garlic shows “agglomeration” and cyclical phenomenon, which has the characteristics of fragility, left and a non-normal distribution and the fitting value of the GARCH model is very close to the true value. Finally, it looks into the industrial service form from the perspective of garlic price fluctuation

DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging

As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. Recently, diffusion models have emerged as the new state-of-the-art generative model to generate high-quality samples and have demonstrated strong potential for various tasks in medical imaging. However, it is difficult to extend diffusion models for 3D image reconstructions due to the memory burden. Directly stacking 2D slices together to create 3D image volumes would results in severe inconsistencies between slices. Previous works tried to either apply a penalty term along the z-axis to remove inconsistencies or reconstruct the 3D image volumes with 2 pre-trained perpendicular 2D diffusion models. Nonetheless, these previous methods failed to produce satisfactory results in challenging cases for PET image denoising. In addition to administered dose, the noise levels in PET images are affected by several other factors in clinical settings, e.g. scan time, medical history, patient size, and weight, etc. Therefore, a method to simultaneously denoise PET images with different noise-levels is needed. Here, we proposed a Dose-aware Diffusion model for 3D low-dose PET imaging (DDPET-3D) to address these challenges. We extensively evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of 600 testing studies), and demonstrated superior performance over previous diffusion models for 3D imaging problems as well as previous noise-aware medical image denoising models. The code is available at: https://github.com/xxx/xxx.Comment: Paper under review. 16 pages, 11 figures, 4 table

Advances in phytoplankton population ecology in the Pearl river estuary

Phytoplankton is an important primary producer of the estuarine ecosystem, which is essential for the biogeochemical cycle of water elements and nutrient transfer. The Pearl River estuary (PRE) is a dynamically complex estuary, and the environment of PRE is significantly impacted by anthropogenic activities, variation of phytoplankton community structure in the PRE are complex. This review aims to compare phytoplankton species, species diversity, and abundance variation characteristics from the 1980s and 2020s, evaluate the overall trend of phytoplankton in the PRE, and discuss the main environmental factors affecting phytoplankton growth in the PRE. The data from the past 40 years in PRE showed that the number of phytoplankton species significantly decreased (p < 0.05). There was no significant difference in the abundance of phytoplankton at the 10-year scale, however, the fluctuation range of the abundance has increased. Under the conditions of a decreasing species number and no significant difference in abundance, the species diversity of phytoplankton showed a downward trend. In addition, the dominant phytoplankton species in the nearshore waters were relatively homogenous, and the abundance of phytoplankton in the nearshore waters was higher than that in the open waters, which suggested that human activities have a great influence. This review can form the basis for facilitating health management in the PRE ecosystem. Further, relevant guidelines can be developed and implemented for promoting the ecological health of the Guangdong-Hong Kong-Macao Greater Bay Area and ensuring its sustainable development

Feasibility and Efficacy of S-Adenosyl-L-methionine in Patients with HBV-Related HCC with Different BCLC Stages

Aims. To understand the feasibility and efficacy of treatment with SAMe in patients with hepatitis B-related HCC with different Barcelona Clinic Liver Cancer (BCLC) stages. Methods. We retrospectively enrolled 697 patients with BCLC early-stage (stages 0-A) and advanced-stage (stages B-C) HCC who underwent SAMe therapy (354 cases) or no SAMe therapy (343 cases). The baseline characteristics, postoperative recoveries, and 24-month overall survival rates of the patients in the 2 groups were compared. Cox regression model analysis was performed to confirm the independent variables influencing the survival rate. Results. For patients in the early-stage (BCLC stages A1–A4) group, little benefit of SAMe therapy was observed. For advanced-stage (BCLC B-C) patients, SAMe therapy reduced alanine aminotransferase (ALT) and aspartate transaminase (AST) levels and effectively delayed the recurrence time and enhanced the 24-month survival rate. Cox regression model analysis in the advanced-stage group revealed that treatment with SAMe, preoperative viral load, and Child-Pugh grade were independent variables influencing survival time. Conclusion. SAMe therapy exhibited protective and therapeutic efficacy for BCLC advanced-stage HBV-related HCC patients. And the efficacy of SAMe therapy should be further explored in randomized prospective clinical trials