Multi-Robot Trajectory Planning with Feasibility Guarantee and Deadlock Resolution: An Obstacle-Dense Environment

This article presents a multi-robot trajectory planning method which guarantees optimization feasibility and resolves deadlocks in an obstacle-dense environment. The method is proposed via formulating an optimization problem, where the modified buffered Voronoi cell with warning band is utilized to avoid the inter-robot collision and the deadlock is resolved by an adaptive right-hand rule. Meanwhile, a novel safe corridor derived from historical planned trajectory is proposed to provide a proper space for obstacle avoidance in trajectory planning. Comparisons with state-of-the-art works are conducted to illustrate the safety and deadlock resolution in cluttered scenarios. Additionally, hardware experiments are carried out to verify the performance of the proposed method where eight nano-quadrotors fly through a 0.6m cubic framework

Multi-site Reaction Dynamics Through Multi-fragment Density Matrix Embedding

The practical description of disordered chemical reactions, where reactions involve multiple species at multiple sites, is presently challenge using correlated electronic structure methods. Here we describe the gradient theory of multi-fragment density matrix embedding theory, which potentially provides a minimal computational framework to model such processes at the correlated electron level. We present the derivation and implementation of the gradient theory, its validation on model systems and chemical reactions using density matrix embedding, and its application to a molecular dynamics simulation of proton transport in a small water cluster, a simple example of multi-site reaction dynamics

Trust Region Methods For Nonconvex Stochastic Optimization Beyond Lipschitz Smoothness

In many important machine learning applications, the standard assumption of having a globally Lipschitz continuous gradient may fail to hold. This paper delves into a more general $(L_0, L_1)$-smoothness setting, which gains particular significance within the realms of deep neural networks and distributionally robust optimization (DRO). We demonstrate the significant advantage of trust region methods for stochastic nonconvex optimization under such generalized smoothness assumption. We show that first-order trust region methods can recover the normalized and clipped stochastic gradient as special cases and then provide a unified analysis to show their convergence to first-order stationary conditions. Motivated by the important application of DRO, we propose a generalized high-order smoothness condition, under which second-order trust region methods can achieve a complexity of $\mathcal{O}(\epsilon^{-3.5})$ for convergence to second-order stationary points. By incorporating variance reduction, the second-order trust region method obtains an even better complexity of $\mathcal{O}(\epsilon^{-3})$, matching the optimal bound for standard smooth optimization. To our best knowledge, this is the first work to show convergence beyond the first-order stationary condition for generalized smooth optimization. Preliminary experiments show that our proposed algorithms perform favorably compared with existing methods

Geology, U-Pb geochronology and stable isotope geochemistry of the Heihaibei gold deposit in the southern part of the Eastern Kunlun Orogenic Belt, China : A granitic intrusion-related gold deposit?

The Heihaibei gold deposit is a newly discovered gold deposit in the southern part of the Eastern Kunlun Orogenic Belt. Its most distinctive features are that the gold mineralization is hosted in monzogranite, and that the presence of pre-ore (possibly syn-ore) monzogranite and post-ore gabbro allows to constrain the minerali-zation's formation age. Zircons from the monzogranites yield U-Pb ages of 454 +/- 3 Ma, while zircons separated from the gabbro dikes cutting the monzogranites and gold mineralized body yield U-Pb ages of 439 +/- 3 Ma, which is interpreted to be the minimum age of the Au mineralizing event. Combined with the regional geological background, we proposed that the Heihaibei Au mineralization occurred during the subduction stage of the Early Paleozoic Proto-Tethys ocean. The ore assemblage is dominated by pyrite, arsenopyrite and native gold. The hydrothermal alteration that has led to the peculiar enrichment of Au is not systematically distributed and displays no clear concentric zoning pattern. The main mineralization formed during three stages: the K-feldspar-quartz-pyrite (Py1)-arsenopyrite-sericite-epidote stage (I), the quartz-pyrite (Py2)-native gold-chlorite stage (II), and the quartz-carbonate stage (III). The main gold mineralization occurred during stage II. Fluid inclusion homogenization temperature and salinities decrease from stage I (Th., 268-412 C; W., 6.87-16.63 wt% NaCl equiv.) to stage II (Th., 183-288 C; W., 3.69-14.84 wt% NaCl equiv.). The 818O and 8D values (818OH2O = 4.9 to 9.7%o; 8DV-SMOW =-84.1%o to -81.1%o) of quartz samples from stage I and stage II are comparable to a magmatic-hydrothermal ore-forming fluid that possibly underwent fluid-rock interaction with the Nachitai Group metamorphic rocks during the early ore-forming stage. The relatively uniform 834S values (834SV-CDT = 7.7 to 8.5%o) are slightly elevated compared to magmatic 834S values, but could be derived from a magma if a significant crustal melt component is present. Moreover, the 834S values are within the S isotopic composition range of a granitic reservoir, suggesting that they are probably inherited from the Heihaibei monzogranites. The Pb and Hf isotope compositions imply a close genetic association between the gold mineralization and granitic magmatism, which are both the products of the mixing of crustal and mantle sources. The trace element compositions of pyrite provide additional evidence that the gold mineralization in the Heihaibei deposit was related to the magmatism. Compared with the typical characteristics of orogenic gold and intrusion-related gold systems (IRGS) deposits, the Heihaibei gold deposit may instead be classified as a granitic intrusion-related gold deposit.Peer reviewe

Non-syndromic enlarged vestibular aqueduct caused by novel compound mutations of the SLC26A4 gene: a case report and literature review

Enlarged vestibular aqueduct is an autosomal genetic disease mainly caused by mutations in the SLC26A4 gene and includes non-syndromic and syndromic types. This study aimed to identify genetic defects in a Chinese patient with non-syndromic enlarged vestibular aqueduct (NSEVA) and to investigate the impact of variants on the severity of non-syndromic enlarged vestibular aqueduct. A male patient with NSEVA, aged approximately 6 years, was recruited for this study. The clinical characteristics and results of auxiliary examinations, including laboratory and imaging examinations, were collected, and 127 common hereditary deafness genes were detected by chip capture high-throughput sequencing. Protein structure predictions, the potential impact of mutations, and multiple sequence alignments were analyzed in silico. Compound heterozygote mutations c.1523_1528delinsAC (p.Thr508Asnfs*3) and c.422T>C (p.Phe141Ser) in the SLC26A4 gene were identified. The novel frameshift mutation c.1523_1528delinsAC produces a severely truncated pendrin protein, and c.422T>C has been suggested to be a disease-causing mutation. Therefore, this study demonstrates that the novel mutation c.1523_1528delinsAC in compound heterozygosity with c.422T>C in the SLC26A4 gene is likely to be the cause of NSEVA. Cochlear implants are the preferred treatment modality for patients with NSEVA and severe-to-profound sensorineural hearing loss Genetic counseling and prenatal diagnosis are essential for early diagnosis. These findings expand the mutational spectrum of SLC26A4 and improve our understanding of the molecular mechanisms underlying NSEVA

The impact of immunoglobulin G N-glycosylation level on COVID-19 outcome: evidence from a Mendelian randomization study

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID‐19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.MethodsWe conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochran’s Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.ResultsWe found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92–0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.ConclusionsOur study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential implications for developing targeted interventions to improve COVID-19 outcomes by modulating IgG N-glycosylation levels

New Zealand Cellular Network Base Station Spatial Distribution Analysis by Using Alpha-stable Distribution Model

Studying the distribution model of cellular network base station not only can optimize the user's wireless communications and Internet experience, but also reveal the patterns of the regional development and human demands for information and communications technologies (ICT). Because the needs of users motivate the deployment of base stations, and the distribution of base stations could be different in various regions with different users’ needs. In addition, different speed of regional development could lead to different populations of ICT users. Therefore, the study of the distribution of base stations can direct the way for the future ICT development. Some previous analysis results of Europe and China have shown that alpha-stable model is suitable to analyze the base station distribution in the urban areas where the population are concentrated. However, in the sparsely populated rural areas, the Weibull model and the Log-normal model are more suitable for analyzing the distribution of base stations. This project is to study the network base stations distribution in New Zealand and explore what distribution models are best fit for New Zealand cases. It could help New Zealand improving its network environment and provide research assistance for future ICT networks and industries that rely on ICT networks. This project is inspired from the adoption of the alpha-stable distribution model in financial market to study the economical phenomena, thus the alpha-stable model might be applied to study the distribution of cellular network base stations. Firstly, it introduces the alpha-stable distribution model in detail, such as its concept and formulations, as well as how the various parameters in the alpha-stable model affect its performance and shapes. Secondly, we have analyzed the distribution of cellular network base stations in New Zealand. The experimental results show that the distribution of cellular network base stations in New Zealand is presenting the alpha-stable distribution model, Weibull model or Log-normal model according to different regions. It is concluded that the distribution of New Zealand's network base stations is relatively loosely with diverse patterns. Some major cities occupy most base stations, but other places have fewer base stations, which also causes the heavy-tail effect of the distribution model. Finally, this report discusses the impact that network base station distribution may affect other industries, such as autonomous and electric vehicles, unmanned factories and 5G technology because possible ICT infrastructure sharing in the future