Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generate its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to find an optimal policy which is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmac

Elucidating the solution space of extended reverse-time SDE for diffusion models

Diffusion models (DMs) demonstrate potent image generation capabilities in various generative modeling tasks. Nevertheless, their primary limitation lies in slow sampling speed, requiring hundreds or thousands of sequential function evaluations through large neural networks to generate high-quality images. Sampling from DMs can be seen alternatively as solving corresponding stochastic differential equations (SDEs) or ordinary differential equations (ODEs). In this work, we formulate the sampling process as an extended reverse-time SDE (ER SDE), unifying prior explorations into ODEs and SDEs. Leveraging the semi-linear structure of ER SDE solutions, we offer exact solutions and arbitrarily high-order approximate solutions for VP SDE and VE SDE, respectively. Based on the solution space of the ER SDE, we yield mathematical insights elucidating the superior performance of ODE solvers over SDE solvers in terms of fast sampling. Additionally, we unveil that VP SDE solvers stand on par with their VE SDE counterparts. Finally, we devise fast and training-free samplers, ER-SDE-Solvers, achieving state-of-the-art performance across all stochastic samplers. Experimental results demonstrate achieving 3.45 FID in 20 function evaluations and 2.24 FID in 50 function evaluations on the ImageNet $64\times64$ dataset

ChanEstNet: A Deep Learning Based Channel Estimation for High-Speed Scenarios

Aiming at the problem that the downlink channel estimation performance is limited due to the fast time-varying and non-stationary characteristics in the high-speed mobile scenarios, we propose a channel estimation network based on deep learning, called ChanEstNet. ChanEstNet uses the convolutional neural network (CNN) to extract channel response feature vectors and recurrent neural network (RNN) for channel estimation. We use a large amount of high-speed channel data to conduct offline training for the learning network, fully exploit the channel information in the training sample, make it learn the characteristics of fast time-varying and non-stationary channels, and better track the features of channels changing in high-speed environments. The simulation results show that in the high-speed mobile scenarios, compared with the traditional methods, the proposed channel estimation method has low computational complexity and significant performance improvement

An NgAgo tool for genome editing: did CRISPR/Cas9 just find a competitor?

AbstractWhile CRISPR/Cas9-mediated genome editing technology has been experiencing a rapid transformation during the past few years, a recent report on NgAgo-mediated single-stranded DNA-guided genome editing may offer an attractive alternative for genome manipulation. While it's too early to predict whether NgAgo will be able to compete with or be superior to CRISPR/Cas9, the scientific community is anxiously waiting for further optimization and broader applications of the NgAgo genome editing technology

Comparison of immune cell profiles associated with heatstroke, sepsis, or cardiopulmonary bypass: Study protocol for an exploratory, case-control study trial

IntroductionHeatstroke is a life-threatening illness involving extreme hyperthermia and multi-organ failure, and it is associated with high mortality. The immune profiles of heatstroke have not been fully elucidated, and diagnostic and prognostic biomarkers of heatstroke are lacking. This study will analyze immune profiles in heatstroke patients as they differ from profiles in patients with sepsis or aseptic inflammation patients in order to identify diagnostic and prognostic biomarkers.MethodsThis exploratory, case–control study will recruit patients with heatstroke, patients with sepsis, patients undergoing cardiopulmonary bypass as well as healthy controls at West China Hospital of Sichuan University from 1 January 2023 to 31 October 2023. The four cohorts will be profiled at one time point in terms of lymphocytes, monocytes, natural killer cells, and granulocytes using flow cytometry, and cell populations will be visualized in two dimensions using t-SNE and UMAP, then clustered using PhenoGraph and FlowSOM. Gene expression in the specific immune cell populations will also be compared across the four cohorts, as will levels of plasma cytokines using enzyme-linked immunosorbent assays. Outcomes in the cohorts will be monitored during 30-day follow-up.DiscussionThis trial is, to our knowledge, the first attempt to improve the diagnosis of heatstroke and prediction of prognosis based on immune cell profiles. The study is also likely to generate new insights into immune responses during heatstroke, which may help clarify the disease process and lay the foundation for immunotherapies

The optimal dose of oxycodone in PCIA after laparoscopic surgery for gastrointestinal cancer in elderly patients: A randomized controlled trial

ObjectiveTo explore the optimal bolus dose of oxycodone for patient controlled intravenous analgesia (PCIA) without background dose in elderly patients after laparoscopic surgery for gastrointestinal cancer.MethodsIn this prospective, randomized, double-blind, parallel-controlled study, we recruited patients aged 65 years or older. They underwent laparoscopic resection for gastrointestinal cancer and received PCIA after surgery. Eligible patients were randomly divided into 0.01, 0.02, or 0.03 mg/kg group according to the bolus dose of oxycodone in PCIA. The primary outcome was VAS scores of pain on mobilization at 48 h after surgery. Secondary endpoints included the VAS scores of rest pain, the total and effective numbers of press in PCIA, cumulative dose of oxycodone used in PCIA, the incidence of nausea, vomiting and dizziness, as well as patients’ satisfaction at 48 h after surgery.ResultsA total of 166 patients were recruited and randomly assigned to receive a bolus dose of 0.01 mg/kg (n = 55), 0.02 mg/kg (n = 56) or 0.03 mg/kg (n = 55) of oxycodone in PCIA. The VAS scores of pain on mobilization, the total and effective numbers of press in PCIA in 0.02 mg/kg group and 0.03 mg/kg group were lower than those in 0.01 mg/kg group (P < 0.05). Cumulative dose of oxycodone used in PCIA and patients’ satisfaction in 0.02 and 0.03 mg/kg groups were more than those in 0.01 mg/kg group (P < 0.01). The incidence of dizziness in 0.01 and 0.02 mg/kg groups was lower than that in 0.03 mg/kg group (P < 0.01). There were no significant differences in VAS scores of rest pain, the incidence of nausea and vomiting among three groups (P > 0.05).ConclusionFor elderly patients undergoing laparoscopic surgery for gastrointestinal cancer, 0.02 mg/kg bolus dose of oxycodone in PCIA without background infusion may be a better choice

Grassland health assessment based on indicators monitored by UAVs: a case study at a household scale

Grassland health assessment (GHA) is a bridge of study and management of grassland ecosystem. However, there is no standardized quantitative indicators and long-term monitor methods for GHA at a large scale, which may hinder theoretical study and practical application of GHA. In this study, along with previous concept and practices (i.e., CVOR, the integrated indexes of condition, vigor, organization and resilience), we proposed an assessment system based on the indicators monitored by unmanned aerial vehicles (UAVs)-UAVCVOR, and tested the feasibility of UAVCVOR at typical household pastures on the Qinghai-Tibetan Plateau, China. Our findings show that: (1) the key indicators of GHA could be measured directly or represented by the relative counterpart indicators that monitored by UAVs, (2) there was a significantly linear relationship between CVOR estimated by field- and UAV-based data, and (3) the CVOR decreased along with the increasing grazing intensity nonlinearly, and there are similar tendencies of CVOR that estimated by the two methods. These findings suggest that UAVs is suitable for GHA efficiently and correctly, which will be useful for the protection and sustainable management of grasslands

Hybrid weakness and continuous flowering caused by compound expression of FTLs in Chrysanthemum morifolium × Leucanthemum paludosum intergeneric hybridization

Hybridization is an important evolutionary mechanism ubiquitous to plants. Previous studies have shown that hybrid polyploidization of cultivated chrysanthemum, ‘Zhongshanzigui’, and Leucanthemum paludosum exhibit spring-flowering traits. This study explores the function of the LpFTLs gene via the phenotype of A. thaliana after heterologous transformation of the LpFTLs gene, and analyzes the mechanism ofthe continuous flowering phenotype and heterosis of hybrid offspring. The results suggest that the flowering phenotype of hybrid offspring in spring may be related to the expression of the LpFTLs gene. Ectopic expression of Leucanthemum paludosumLpFTLs in Arabidopsis thaliana resulted in earlier flowering, indicating that the LpFTLs gene also affects the flowering time in L. paludosum. Compound expression of FTLs in C. morifolium × L. paludosum intergeneric hybridization directly leads to serious heterosis in the hybrid offspring. Moreover, continuous flowering appears to be accompanied by hybrid weakness under the balance of vegetative and reproductive growth. Therefore, in future studies on chrysanthemum breeding, a suitable balance point must be established to ensure the target flowering time under normal growth

Changes in microstates of first-episode untreated nonsuicidal self-injury adolescents exposed to negative emotional stimuli and after receiving rTMS intervention

BackgroundNonsuicidal self-injury (NSSI) is a common mental health threat in adolescents, peaking in adolescence with a lifetime prevalence of ~17%–60%, making it a high-risk risk factor for suicide. In this study, we compared changes in microstate parameters in depressed adolescents with NSSI, depressed adolescents, and healthy adolescents during exposure to negative emotional stimuli, and further explored the improvement of clinical symptoms and the effect of microstate parameters of repetitive transcranial magnetic stimulation (rTMS) in depressed adolescents with NSSI, and more evidence was provided for potential mechanisms and treatment optimization for the occurrence of NSSI behaviors in adolescents.MethodsSixty-six patients with major depressive disorder (MDD) exhibiting NSSI behavior (MDD + NSSI group), 52 patients with MDD (MDD group), and 20 healthy subjects (HC group) were recruited to perform neutral and negative emotional stimulation task. The age range of all subjects was 12–17 years. All participants completed the Hamilton Depression Scale, the Patient Health Questionnaire-9, the Ottawa Self-Injury Scale and a self-administered questionnaire to collect demographic information. We provided two different treatments to 66 MDD adolescents with NSSI; 31 patients received medication and completed post-treatment scale assessments and EEG acquisitions, and 21 patients received medication combined with rTMS and completed post-treatment scale assessments and EEG acquisitions. Multichannel EEG was recorded continuously from 64 scalp electrodes using the Curry 8 system. EEG signal preprocessing and analysis was performed offline, using the EEGLAB toolbox in MATLAB. Use the Microstate Analysis Toolbox in EEGLAB for segmentation and computation of microstates, and calculate a topographic map of the microstate segmentation of the EEG signal for a single subject in each dataset, and four parameters were obtained for each microstate classification: global explained variance (GEV), mean duration (Duration), average number of occurrences per second (Occurrence), and average percentage of total analysis time occupied (Coverage), which were then statistically analyzed.ResultsOur results indicate that MDD adolescents with NSSI exhibit abnormalities in MS 3, MS 4, and MS 6 parameters when exposed to negative emotional stimuli compared to MDD adolescents and healthy adolescents. The results also showed that medication combined with rTMS treatment improved depressive symptoms and NSSI performance more significantly in MDD adolescents with NSSI compared to medication treatment, and affected MS 1, MS 2, and MS 4 parameters in MDD adolescents with NSSI, providing microstate evidence for the moderating effect of rTMS.ConclusionMDD adolescents with NSSI showed abnormal changes in several microstate parameters when receiving negative emotional stimuli, and compared to those not receiving rTMS treatment, MDD adolescents with NSSI treated with rTMS showed more significant improvements in depressive symptoms and NSSI performance, as well as improvements in EEG microstate abnormalities