Combined Tracking Strategy Based on Unscented Kalman Filter for Global Positioning System L2C CM/CL Signal

In a global positioning system receiver, the tracking algorithm plays a dominant role since the code delay and Doppler frequency shift need to be accurately estimated as well as their variation over time need to be continuously updated. Combine unscented Kalman filter (UKF) with CM/CL signal to improve the signal tracking precision is proposed. It allow weighting assignment between CM code and CL code incoming signal, masked by a mass of noise, and to describe a UKF tracking loop aiming at decreasing numerical errors. UKF here involves state and measuring equations which calculate absolute offsets to adjust initial code and carrier phase then dramatically decrease the tracking error. In particular, the algorithm is implemented in both open space and jammed environment to highlight the advantages of tracking approach, by comparing single code and combined code, UKF and EKF tracking loop. It proves that signal tracking based on UKF, with low energy dissipation as well as high precision, is particularly appealing for a software receiver implementation

Simple and Efficient Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) over homogeneous graphs, especially the attention mechanism and the multi-layer structure. These mechanisms bring excessive complexity, but seldom work studies whether they are really effective on heterogeneous graphs. This paper conducts an in-depth and detailed study of these mechanisms and proposes Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN). To easily capture structural information, SeHGNN pre-computes the neighbor aggregation using a light-weight mean aggregator, which reduces complexity by removing overused neighbor attention and avoiding repeated neighbor aggregation in every training epoch. To better utilize semantic information, SeHGNN adopts the single-layer structure with long metapaths to extend the receptive field, as well as a transformer-based semantic fusion module to fuse features from different metapaths. As a result, SeHGNN exhibits the characteristics of simple network structure, high prediction accuracy, and fast training speed. Extensive experiments on five real-world heterogeneous graphs demonstrate the superiority of SeHGNN over the state-of-the-arts on both accuracy and training speed.Comment: Accepted by AAAI 202

Seasonal fluxes and sources apportionment of dissolved inorganic nitrogen wet deposition at different land-use sites in the Three Gorges reservoir area.

To identify seasonal fluxes and sources of dissolved inorganic nitrogen (DIN) wet deposition, concentrations and δ15N signatures of nitrate (NO3−) and ammonium (NH4+) in wet precipitation were measured at four typical land-use types in the Three Gorges reservoir (TGR) area of southwest China for a one-year period. Higher DIN fluxes were recorded in spring and summer and their total fluxes (averaged 7.58 kg N ha−1) were similar to the critical loads in aquatic ecosystems. Significant differences of precipitation δ15N were observed for NH4+-N between town and wetland sites in spring and between urban and rural sites in summer. For NO3−-N, significant differences of precipitation δ15N were observed between town and rural sites in spring and between urban and town sites in autumn, respectively. Quantitative results of NO3−-N sources showed that both biomass burning and coal combustion had higher fluxes at the urban site especially in winter (0.18 ± 0.09 and 0.19 ± 0.08 kg N ha−1), which were about three times higher than those at the town site. A similar finding was observed for soil emission and vehicle exhausts in winter. On the whole, DIN wet deposition averaged at 12.13 kg N ha−1 yr−1 with the urban site as the hotspot (17.50 kg N ha−1 yr−1) and regional NO3−-N fluxes had a seasonal pattern with minimum values in winter. The contribution to NO3−-N wet deposition from biomass burning was 26.1 ± 14.1%, which is the second dominant factor lower than coal combustion (26.5 ± 12.6%) in the TGR area during spring and summer. Hence N emission reduction from biomass burning, coal combustion and vehicle exhausts should be strengthened especially in spring and summer to effectively manage DIN pollution for the sustainable development in TGR area

Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data.

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor T-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parametrize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit

HiHGNN: Accelerating HGNNs through Parallelism and Data Reusability Exploitation

Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both structural and semantic information in HetGs, HGNNs first aggregate the neighboring feature vectors for each vertex in each semantic graph and then fuse the aggregated results across all semantic graphs for each vertex. Unfortunately, existing graph neural network accelerators are ill-suited to accelerate HGNNs. This is because they fail to efficiently tackle the specific execution patterns and exploit the high-degree parallelism as well as data reusability inside and across the processing of semantic graphs in HGNNs. In this work, we first quantitatively characterize a set of representative HGNN models on GPU to disclose the execution bound of each stage, inter-semantic-graph parallelism, and inter-semantic-graph data reusability in HGNNs. Guided by our findings, we propose a high-performance HGNN accelerator, HiHGNN, to alleviate the execution bound and exploit the newfound parallelism and data reusability in HGNNs. Specifically, we first propose a bound-aware stage-fusion methodology that tailors to HGNN acceleration, to fuse and pipeline the execution stages being aware of their execution bounds. Second, we design an independency-aware parallel execution design to exploit the inter-semantic-graph parallelism. Finally, we present a similarity-aware execution scheduling to exploit the inter-semantic-graph data reusability. Compared to the state-of-the-art software framework running on NVIDIA GPU T4 and GPU A100, HiHGNN respectively achieves an average 41.5$\times$ and 8.6$\times$ speedup as well as 106$\times$ and 73$\times$ energy efficiency with quarter the memory bandwidth of GPU A100