Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction

Model Compression and Efficient Inference for Large Language Models: A Survey

Transformer based large language models have achieved tremendous success. However, the significant memory and computational costs incurred during the inference process make it challenging to deploy large models on resource-constrained devices. In this paper, we investigate compression and efficient inference methods for large language models from an algorithmic perspective. Regarding taxonomy, similar to smaller models, compression and acceleration algorithms for large language models can still be categorized into quantization, pruning, distillation, compact architecture design, dynamic networks. However, Large language models have two prominent characteristics compared to smaller models: (1) Most of compression algorithms require finetuning or even retraining the model after compression. The most notable aspect of large models is the very high cost associated with model finetuning or training. Therefore, many algorithms for large models, such as quantization and pruning, start to explore tuning-free algorithms. (2) Large models emphasize versatility and generalization rather than performance on a single task. Hence, many algorithms, such as knowledge distillation, focus on how to preserving their versatility and generalization after compression. Since these two characteristics were not very pronounced in early large models, we further distinguish large language models into medium models and ``real'' large models. Additionally, we also provide an introduction to some mature frameworks for efficient inference of large models, which can support basic compression or acceleration algorithms, greatly facilitating model deployment for users.Comment: 47 pages, review 380 papers. The work is ongoin

Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services

In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i.e., in different geographical locations and at different times, user click behavior will change significantly. Appropriate spatiotemporal enhancement modeling of user click behavior and large-scale sparse attributes is key to building an LBS model. Although most of existing methods have been proved to be effective, they are difficult to apply to takeaway scenarios due to insufficient modeling of spatiotemporal information. In this paper, we address this challenge by seeking to explicitly model the timing and locations of interactions and proposing a Spatiotemporal-Enhanced Network, namely StEN. In particular, StEN applies a Spatiotemporal Profile Activation module to capture common spatiotemporal preference through attribute features. A Spatiotemporal Preference Activation is further applied to model the personalized spatiotemporal preference embodied by behaviors in detail. Moreover, a Spatiotemporal-aware Target Attention mechanism is adopted to generate different parameters for target attention at different locations and times, thereby improving the personalized spatiotemporal awareness of the model.Comprehensive experiments are conducted on three large-scale industrial datasets, and the results demonstrate the state-of-the-art performance of our methods. In addition, we have also released an industrial dataset for takeaway industry to make up for the lack of public datasets in this community.Comment: accepted by CIKM workshop 202

Oxygenated Aromatic Compounds are Important Precursors of Secondary Organic Aerosol in Biomass Burning Emissions

Biomass burning is the largest combustion-related source of volatile organic compounds (VOCs) to the atmosphere. We describe the development of a state-of-the-science model to simulate the photochemical formation of secondary organic aerosol (SOA) from biomass-burning emissions observed in dry (RH <20%) environmental chamber experiments. The modeling is supported by (i) new oxidation chamber measurements, (ii) detailed concurrent measurements of SOA precursors in biomass-burning emissions, and (iii) development of SOA parameters for heterocyclic and oxygenated aromatic compounds based on historical chamber experiments. We find that oxygenated aromatic compounds, including phenols and methoxyphenols, account for slightly less than 60% of the SOA formed and help our model explain the variability in the organic aerosol mass (R² = 0.68) and O/C (R² = 0.69) enhancement ratios observed across 11 chamber experiments. Despite abundant emissions, heterocyclic compounds that included furans contribute to ∼20% of the total SOA. The use of pyrolysis-temperature-based or averaged emission profiles to represent SOA precursors, rather than those specific to each fire, provide similar results to within 20%. Our findings demonstrate the necessity of accounting for oxygenated aromatics from biomass-burning emissions and their SOA formation in chemical mechanisms