Agile Autonomous Driving using End-to-End Deep Imitation Learning

We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost sensors. By imitating a model predictive controller equipped with advanced sensors, we train a deep neural network control policy to map raw, high-dimensional observations to continuous steering and throttle commands. Compared with recent approaches to similar tasks, our method requires neither state estimation nor on-the-fly planning to navigate the vehicle. Our approach relies on, and experimentally validates, recent imitation learning theory. Empirically, we show that policies trained with online imitation learning overcome well-known challenges related to covariate shift and generalize better than policies trained with batch imitation learning. Built on these insights, our autonomous driving system demonstrates successful high-speed off-road driving, matching the state-of-the-art performance.Comment: 13 pages, Robotics: Science and Systems (RSS) 201

Head3D: Complete 3D Head Generation via Tri-plane Feature Distillation

Head generation with diverse identities is an important task in computer vision and computer graphics, widely used in multimedia applications. However, current full head generation methods require a large number of 3D scans or multi-view images to train the model, resulting in expensive data acquisition cost. To address this issue, we propose Head3D, a method to generate full 3D heads with limited multi-view images. Specifically, our approach first extracts facial priors represented by tri-planes learned in EG3D, a 3D-aware generative model, and then proposes feature distillation to deliver the 3D frontal faces into complete heads without compromising head integrity. To mitigate the domain gap between the face and head models, we present dual-discriminators to guide the frontal and back head generation, respectively. Our model achieves cost-efficient and diverse complete head generation with photo-realistic renderings and high-quality geometry representations. Extensive experiments demonstrate the effectiveness of our proposed Head3D, both qualitatively and quantitatively

The influence on coagulant of patients with cancer by PICC placement

目的  研究PICC置管对肿瘤患者凝血功能的影响。方法  应用ELISA方法测定46例肿瘤患者在PICC前1天、置管后1天、置管后30天和置管后90天四次共147例次血浆D-二聚体（ D-D）、凝血酶原时间（PT）、部分凝血活酶时间（APTT）、凝血酶时间（TT）、抗凝血酶Ⅲ（ATⅢ）、纤维蛋白原（Fib）和血小板（PLT）计数，分析PICC对凝血功能的影响。结果  （1）置管前后四次的血浆D-D水平均明显高于正常参考值，但四次之间无明显差异（P>0.05） 。（2）血浆PT、APTT、TT、ATⅢ、Fib水平和PLT计数均在正常参考值范围，且四次比较均无统计学意义（均为 P＞0.05）。（3）化疗未置管的48例中无一例有血栓形成，置管88例中有3例发生血栓，两者比较无统计学意义（P=0.27）。结论  肿瘤患者PICC置管前后血浆D-D、PT、APTT、TT、ATⅢ、Fib水平和PLT计数等各项指标均无明显变化；血浆D-D水平虽均升高，但与PICC也无关。PICC对凝血功能影响不大，是安全可行的辅助治疗手段。Objective: To study the influence on coagulant of patients with cancer by PICC placement. Methods: To test the plasma levels of D-dimer(D-D),prothrombin(PT),activated partial thromplasmtin(APTT),thrombin time(TT),antithrombin(ATⅢ),fibrinogen(Fib) and platelet count by ELISA in 46 patients with cancer in one day before PICC placement, in one day,30 day, and 90 day after PICC placement, and analyses the change feature and clinical significance. Results: (1)The levels of plasma D-D were higher than control group in both pre-PICC placement and post-PICC placement, but there were no differences in both pre-PICC placement and post-PICC placement(P>0.05).(2)The levels of plasma PT, APTT,TT,ATⅢ,Fib and platelet were not different in both pre-PICC placement and post-PICC placement(all P>0.05),and there were no differences from control group.(3)There were no cases with thromboembolism in 48 cancer patients without PICC placement, and  3 cases of thromboembolism occurred among 88 cancer patients treated with PICC placement. But there were no significances(P=0.27). Conclusion: Therelevels of D-D,PT,APTT,TT,ATⅢ, Fib and platelet in patients with cancer did not change in both pre-PICC placement and post-PICC placement. There was no effect of coagulant on patients with cancer by PICC placement. The PICC placement was safe

Fraction Constraint in Partial Wave Analysis

To resolve the non-convex optimization problem in partial wave analysis, this paper introduces a novel approach that incorporates fraction constraints into the likelihood function. This method offers significant improvements in both the efficiency of pole searching and the reliability of resonance selection within partial wave analysis

Event Generation and Consistence Test for Physics with Sliced Wasserstein Distance

In the field of modern high-energy physics research, there is a growing emphasis on utilizing deep learning techniques to optimize event simulation, thereby expanding the statistical sample size for more accurate physical analysis. Traditional simulation methods often encounter challenges when dealing with complex physical processes and high-dimensional data distributions, resulting in slow performance. To overcome these limitations, we propose a solution based on deep learning with the sliced Wasserstein distance as the loss function. Our method shows its ability on high precision and large-scale simulations, and demonstrates its effectiveness in handling complex physical processes. By employing an advanced transformer learning architecture, we initiate the learning process from a Monte Carlo sample, and generate high-dimensional data while preserving all original distribution features. The generated data samples have passed the consistence test, that is developed to calculate the confidence of the high-dimentional distributions of the generated data samples through permutation tests. This fast simulation strategy, enabled by deep learning, holds significant potential not only for increasing sample sizes and reducing statistical uncertainties but also for applications in numerical integration, which is crucial in partial wave analysis, high-precision sample checks, and other related fields. It opens up new possibilities for improving event simulation in high-energy physics research