EquiDiff: A Conditional Equivariant Diffusion Model For Trajectory Prediction

Accurate trajectory prediction is crucial for the safe and efficient operation of autonomous vehicles. The growing popularity of deep learning has led to the development of numerous methods for trajectory prediction. While deterministic deep learning models have been widely used, deep generative models have gained popularity as they learn data distributions from training data and account for trajectory uncertainties. In this study, we propose EquiDiff, a deep generative model for predicting future vehicle trajectories. EquiDiff is based on the conditional diffusion model, which generates future trajectories by incorporating historical information and random Gaussian noise. The backbone model of EquiDiff is an SO(2)-equivariant transformer that fully utilizes the geometric properties of location coordinates. In addition, we employ Recurrent Neural Networks and Graph Attention Networks to extract social interactions from historical trajectories. To evaluate the performance of EquiDiff, we conduct extensive experiments on the NGSIM dataset. Our results demonstrate that EquiDiff outperforms other baseline models in short-term prediction, but has slightly higher errors for long-term prediction. Furthermore, we conduct an ablation study to investigate the contribution of each component of EquiDiff to the prediction accuracy. Additionally, we present a visualization of the generation process of our diffusion model, providing insights into the uncertainty of the prediction

Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning

Wastewater treatment plants are designed to eliminate pollutants and alleviate environmental pollution. However, the construction and operation of WWTPs consume resources, emit greenhouse gases (GHGs) and produce residual sludge, thus require further optimization. WWTPs are complex to control and optimize because of high nonlinearity and variation. This study used a novel technique, multi-agent deep reinforcement learning, to simultaneously optimize dissolved oxygen and chemical dosage in a WWTP. The reward function was specially designed from life cycle perspective to achieve sustainable optimization. Five scenarios were considered: baseline, three different effluent quality and cost-oriented scenarios. The result shows that optimization based on LCA has lower environmental impacts compared to baseline scenario, as cost, energy consumption and greenhouse gas emissions reduce to 0.890 CNY/m3-ww, 0.530 kWh/m3-ww, 2.491 kg CO2-eq/m3-ww respectively. The cost-oriented control strategy exhibits comparable overall performance to the LCA driven strategy since it sacrifices environmental bene ts but has lower cost as 0.873 CNY/m3-ww. It is worth mentioning that the retrofitting of WWTPs based on resources should be implemented with the consideration of impact transfer. Specifically, LCA SW scenario decreases 10 kg PO4-eq in eutrophication potential compared to the baseline within 10 days, while significantly increases other indicators. The major contributors of each indicator are identified for future study and improvement. Last, the author discussed that novel dynamic control strategies required advanced sensors or a large amount of data, so the selection of control strategies should also consider economic and ecological conditions

Improving Heterogeneous Model Reuse by Density Estimation

This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assuming that a local model has been trained for each party. Considering the potential sample selection bias among different parties, some heterogeneous model reuse approaches have been developed. However, although pre-trained local classifiers are utilized in these approaches, the characteristics of the local data are not well exploited. This motivates us to estimate the density of local data and design an auxiliary model together with the local classifiers for reuse. To address the scenarios where some local models are not well pre-trained, we further design a multiparty cross-entropy loss for calibration. Upon existing works, we address a challenging problem of heterogeneous model reuse from a decision theory perspective and take advantage of recent advances in density estimation. Experimental results on both synthetic and benchmark data demonstrate the superiority of the proposed method.Comment: 9 pages, 5 figues. Accepted by IJCAI 202

CCL21/CCR7 enhances the proliferation, migration, and invasion of human bladder cancer T24 cells

Objective To investigate the effects of CCL21/CCR7 on the proliferation, migration, and invasion of T24 cells and the possible associated mechanisms: expression of MMP-2 and MMP-9, and regulation of BCL-2 and BAX proteins. Methods T24 cells received corresponding treatments including vehicle control, antibody (20ng/mL CCR7 antibody and 50 ng/ml CCL21), and 50, 100. and 200 ng/ml CCL21. Proliferation was evaluated by MTT assay; cell migration and invasion were assayed using a transwell chamber. Cell apoptosis was induced by Adriamycin (ADM). The rate of cell apoptosis was examined by flow cytometry using annexin V-FITC/PI staining. Western-blot was used to analyze MMP-2 and MMP-9 and BCL-2 and BAX proteins. Results CCL21 promoted T24 cell proliferation in concentration-dependent manner with that 200 ng/mL induced the largest amount of proliferation. Significant differences of cell migration were found between CCL21treatment groups and the control group in both the migration and invasion studies (P \u3c 0.001 for all). The expressions of MMP-2 and MMP-9 proteins were significantly increased after CCL21 treatment (p \u3c 0.05 for all). Protein expression of Bcl-21 follows an ascending trend while the expression of Bax follows a descending trend as the concentration of CCL21 increases. No difference was found between the control group and antibody group for all assessments. Conclusion CCL21/CCR7 promoted T24 cell proliferation and enhanced its migration and invasion via the increased expression of MMP-2 and MMP-9. CCL21/CCR7 had antiapoptotic activities on T24 cells via regulation of Bcl-2 and Bax proteins. CCL21/CCR7 may promote bladder cancer development and metastasis

A Microservice-Based Big Data Analysis Platform for Online Educational Applications

The booming development of data science and big data technology stacks has inspired continuous iterative updates of data science research or working methods. At present, the granularity of the labor division between data science and big data is more refined. Traditional work methods, from work infrastructure environment construction to data modelling and analysis of working methods, will greatly delay work and research efficiency. In this paper, we focus on the purpose of the current friendly collaboration of the data science team to build data science and big data analysis application platform based on microservices architecture for education or nonprofessional research field. In the environment based on microservices that facilitates updating the components of each component, the platform has a personal code experiment environment that integrates JupyterHub based on Spark and HDFS for multiuser use and a visualized modelling tools which follow the modular design of data science engineering based on Greenplum in-database analysis. The entire web service system is developed based on spring boot

An indoor positioning technology based on GA-BP Neural Network

Conference Name:6th International Conference on Computer Science and Education, ICCSE 2011. Conference Address: Singapore, Singapore. Time:August 3, 2011 - August 5, 2011.With the development of wireless positioning technologies, indoor positioning technologies are getting more and more noticeable. RFID positioning technology reaches the goal of recognizing and positioning by using radio frequency to transmit data through non-contact two-way communication. This technology has the advantages of long transmission effective range, low-cost, non-contact, non-line-of-sight and so on. Using the technology, a GA-BP Neural Network based indoor positioning algorithm is proposed in this thesis, series of tests to it in practical setting are also carried out. The result shows that the algorithm can solve the problems caused by the complication and variation of indoor environment at a certain degree. ? 2011 IEEE

Loss of atm in Zebrafish as a Model of Ataxia–Telangiectasia Syndrome

Ataxia–telangiectasia mutated (ATM) is a key DNA damage signaling kinase that is mutated in humans with ataxia–telangiectasia (A-T) syndrome. This syndrome is characterized by neurodegeneration, immune abnormality, cancer predisposition, and premature aging. To better understand the function of ATM in vivo, we engineered a viable zebrafish model with a mutated atm gene. Zebrafish atm loss-of-function mutants show characteristic features of A-T-like motor disturbance, including coordination disorders, immunodeficiency, and tumorigenesis. The immunological disorder of atm homozygote fish is linked to the developmental blockade of hematopoiesis, which occurs at the adulthood stage and results in a decrease in infection defense but, with little effect on wound healing. Malignant neoplasms found in atm mutant fish were mainly nerve sheath tumors and myeloid leukemia, which rarely occur in A-T patients or Atm−/− mice. These results underscore the importance of atm during immune cell development. This zebrafish A-T model opens up a pathway to an improved understanding of the molecular basis of tumorigenesis in A-T and the cellular role of atm

The Phase Evolution and Physical Properties of Binary Copper Oxide Thin Films Prepared by Reactive Magnetron Sputtering

P-type binary copper oxide semiconductor films for various O2 flow rates and total pressures (Pt) were prepared using the reactive magnetron sputtering method. Their morphologies and structures were detected by X-ray diffraction, Raman spectrometry, and SEM. A phase diagram with Cu2O, Cu4O3, CuO, and their mixture was established. Moreover, based on Kelvin Probe Force Microscopy (KPFM) and conductive AFM (C-AFM), by measuring the contact potential difference (VCPD) and the field emission property, the work function and the carrier concentration were obtained, which can be used to distinguish the different types of copper oxide states. The band gaps of the Cu2O, Cu4O3, and CuO thin films were observed to be (2.51 ± 0.02) eV, (1.65 ± 0.1) eV, and (1.42 ± 0.01) eV, respectively. The resistivities of Cu2O, Cu4O3, and CuO thin films are (3.7 ± 0.3) × 103 Ω·cm, (1.1 ± 0.3) × 103 Ω·cm, and (1.6 ± 6) × 101 Ω·cm, respectively. All the measured results above are consistent

FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling

Abstract Car-following is a control process in which a following vehicle adjusts its acceleration to keep a safe distance from the lead vehicle. Recently, there has been a booming of data-driven models that enable more accurate modeling of car-following through real-world driving datasets. Although there are several public datasets available, their formats are not always consistent, making it challenging to determine the state-of-the-art models and how well a new model performs compared to existing ones. To address this gap and promote the development of microscopic traffic flow modeling, we establish the first public benchmark dataset for car-following behavior modeling. This benchmark consists of more than 80 K car-following events extracted from five public driving datasets under the same criteria. To give an overview of current progress in car-following modeling, we implemented and tested representative baseline models within the benchmark. The established benchmark provides researchers with consistent data formats and metrics for cross-comparing different car-following models, coming with open datasets and codes