Miracle-dz/UniFAR-FTUs huggingface dataset:
An optimal hierarchical framework of the trajectory following by convex optimisation for highly automated driving vehicles

ABSTRACT This paper presents a hierarchical trajectory following framework for SAE-level 2/3 automatic driving by convex optimisation, which aims for daily common driving manoeuvres with low traffics on the highway, such as free driving, adaptively following and overtaking. More specifically, it includes several combined functionalities of lane keeping, adaptive following, active obstacle avoidance and speed adaptation. The trajectory-planning module would generate the optimal path and speed profiles for the structure road (e.g. highway) with different geometric shapes, by using the natural cubic spline and convex optimisation approach. While modules of trajectory following or adaptive following are realised by a linearised time-varying model predictive control method. It is expected that the proposed framework could improve the driving safety, as well as the driving efficiency and driving comforts simultaneously. Thus, typical scenarios are designed to verify the effectiveness of the proposed trajectory following framework. Simulation results demonstrate that it could successfully deal with daily manoeuvres on a straight or curved highway without human driver's interventions.
VASAD: a Volume and Semantic dataset for Building Reconstruction from Point Clouds

3D scene reconstruction has important applications to help to produce digital twins of existing buildings. While the community has mostly focused on surface reconstruction or semantic segmentation as separate problems, the joint reconstruction of both volumes and semantics has little been discussed, mostly due to the lack of large scale volume datasets with semantic annotations. In this work, we introduce a new dataset called VASAD for Volume And Semantic Architectural Dataset. It is composed of 6 building models, with full volume description and semantic labels. It approximately represents 62,000 m2 of building floors, making it large enough for the development and evaluation of learning-based approaches. We propose several methods to jointly reconstruct both geometry and semantics and evaluate on the test set of the dataset. We show that the proposed dataset is challenging enough to stimulate research. The dataset is available at https://github.com/palanglois/vasad.
Learning Human Viewpoint Preferences from Sparsely Annotated Models

View quality measures compute scores for given views and are used to determine an optimal view in viewpoint selection tasks. Unfortunately, despite the wide adoption of these measures, they are rather based on computational quantities, such as entropy, than human preferences. To instead tailor viewpoint measures towards humans, view quality measures need to be able to capture human viewpoint preferences. Therefore, we introduce a large‐scale crowdsourced data set, which contains 58k annotated viewpoints for 3220 ModelNet40 models. Based on this data, we derive a neural view quality measure abiding to human preferences. We further demonstrate that this view quality measure not only generalizes to models unseen during training, but also to unseen model categories. We are thus able to predict view qualities for single images, and directly predict human preferred viewpoints for 3D models by exploiting point‐based learning technology, without requiring to generate intermediate images or sampling the view sphere. We will detail our data collection procedure, describe the data analysis and model training and will evaluate the predictive quality of our trained viewpoint measure on unseen models and categories. To our knowledge, this is the first deep learning approach to predict a view quality measure solely based on human preferences.
Therapeutic angiogenesis-based strategy for peripheral artery disease

Peripheral artery disease (PAD) poses a great challenge to society, with a growing prevalence in the upcoming years. Patients in the severe stages of PAD are prone to amputation and death, leading to poor quality of life and a great socioeconomic burden. Furthermore, PAD is one of the major complications of diabetic patients, who have higher risk to develop critical limb ischemia, the most severe manifestation of PAD, and thus have a poor prognosis. Hence, there is an urgent need to develop an effective therapeutic strategy to treat this disease. Therapeutic angiogenesis has raised concerns for more than two decades as a potential strategy for treating PAD, especially in patients without option for surgery-based therapies. Since the discovery of gene-based therapy for therapeutic angiogenesis, several approaches have been developed, including cell-, protein-, and small molecule drug-based therapeutic strategies, some of which have progressed into the clinical trial phase. Despite its promising potential, efforts are still needed to improve the efficacy of this strategy, reduce its cost, and promote its worldwide application. In this review, we highlight the current progress of therapeutic angiogenesis and the issues that need to be overcome prior to its clinical application.
A Novel Minimized Energy Routing Technique for IoT Assisted WSN (short paper)

The problem of routing in WSN (Wireless Sensor Network) is to minimize the energy consumption during data transmission, the IoT (Internet of Things) monitoring system use the horizontal clustering of WSN to achieve this goal. The goal of this work is to create multi clusters with multi cluster head to communicate with sink node, the sink node directly connects to IoT server. A set of clusters has been created by dividing the WSN area in to 5 clusters horizontally, in each cluster the CH (Cluster Head) collects the data from all sensor nodes and communicate with sink node. The energy consumption is calculated based on wireless radio model and proposed clustering algorithm. The total energy consumption, normalized average energy and residual energy of proposed protocol is better than the two existing protocols that compared, the two protocols are PEGASIS (Power-Efficient Gathering in Sensor) and IEEPB (Improved Energy-Efficient PEGASIS-Based protocol). The results show that the H-IEEPB (Horizontal Improved Energy-Efficient PEGASIS-Based protocol) has an improvement in energy consumption and minimize it more than 10% and 25% compared with PEGASIS and IEEPB respectively, the residual energy and the normalized average energy also get good results compared with the others.
The effect of metformin on low birth weight girls with precocious puberty: A protocol for systematic review and meta-analysis

Background: In recent years, the role of metformin in girls with precocious puberty (PP) has been increasingly frequently studied. The objective of this present study is to assess the effect of metformin on low birth weight girls with precocious puberty (LBW-PP girls). Methods: We search the confirmed studies about circulating metformin and PP from the databases of EMBASE, PubMed, and Web of Science. Data were reported as weighted mean difference (WMD) and associated 95% confidence intervals (CIs). Analysis was performed by Review Manager 5.3 and Stata version 12.0. Results: A total of 205 cases (metformin group n = 102, untreated group n = 103) were included in this study. The meta-analysis of randomized controlled trials (RCTs) suggested that metformin had statistically significant effects on testosterone (P = .001), androstenedione (P = .022), bone mineral density (BMD; P = .151), triglycerides (P ≤ .001), body mass index Z score (BMI Z score; P ≤ .001), dehydroepiandrosterone-sulfate (DHEAS; P = .053), sex hormone-binding globulin (SHBG; P = .049), high-density lipoprotein (HDL) cholesterol (P ≤ .001), low-density lipoprotein (LDL) cholesterol (P = .021), fat mass (P ≤ .001), lean mass (P = .025), and fasting insulin (P = .002). Conclusion: This meta-analysis provided evidence of the efficacy of metformin in girls with LBW-PP girls, which proved that metformin could improve metabolism and reduce weight. Metformin had a positive effect on preventing LBW-PP girls from developing into obesity and polycystic ovarian syndrome. In addition, this meta-analysis provided important reference opinions and directions for the treatment of LBW-PP girls.
A prospective study on sex differences in functional capacity, quality of life and prognosis in patients with heart failure

Background: Due to the lack of evidence and inconsistency of sex differences in Heart failure (HF) in the Chinese population, this study aimed to compare sex differences in functional capacity and quality of life (QoL) between women and men after standard HF medications therapies, and analyze whether sex differences were associated with the composite endpoints of all-cause mortality or HF-related hospitalization and cardiac event-free survival rate in Chinese patients with HF. Methods: This was a 1-year longitudinal study. Participants included patients with HF from March 2017 to December 2018. At baseline and followed up at 1, 6, and 12 months later, functional capacity was assessed by 6-minute walk testing (6MWT), QoL was measured with the Kansas City Cardiomyopathy Questionnaire (KCCQ) and EuroQoL five dimensions (EQ-5D). The Cox proportional hazards model and Kaplan-Meier curves were used to determine sex differences in subsequent outcomes. The Cox proportional hazards model was used to identify the risk factors for composite endpoints. Kaplan-Meier curves were used to compare survival. Results: All patients were assigned to either men group (n = 94) or women group (n = 60). Longitudinal follow-ups showed a continuously increasing in 6MWT, Kansas City Cardiomyopathy Questionnaire overall score, EQ-5D visual analogue scale, and EQ-5D Index score in both groups (all P < 0.001); however, women reported a lower level of all parameters at the 1, 6, and 12 months follow-ups (all P < 0.05). In addition, women had a higher risk of all-cause mortality or HF-related hospitalization and a lower cardiac event-free survival rate than men (log-rank test, P = 0.027). Conclusion: Women reported worse functional capacity, QoL, and prognosis than men in a sample of Chinese patients with HF. Our findings highlight the importance of paying attention to sex differences in HF.
Off-Path Network Traffic Manipulation via Revitalized ICMP Redirect Attacks

ICMP redirect is a mechanism that allows an end host to dynamically update its routing decisions for particular destinations. Previous studies show that ICMP redirect may be exploited by attackers to manipulate the routing of victim traffic. However, it is widely believed that ICMP redirect attacks are not a real-world threat since they can only occur under specific network topologies (e.g., LAN). In this paper, we conduct a systematic study on the legitimacy check mechanism of ICMP and uncover a fundamental gap between the check mechanism and stateless protocols, resulting in a wide range of vulnerabilities. In particular, we find that off-path attackers can utilize a suite of stateless protocols (e.g., UDP, ICMP, GRE, IPIP and SIT) to easily craft evasive ICMP error messages, thus revitalizing ICMP redirect attacks to cause serious damage in the real world, particularly, on the wide-area network. First, we show that off-path attackers can conduct a stealthy DoS attack by tricking various public servers on the Internet into mis-redirecting their traffic into black holes with a single forged ICMP redirect message. For example, we reveal that more than 43K popular websites on the Internet are vulnerable to this DoS attack. In addition, we identify 54.47K open DNS resolvers and 186 Tor nodes on the Internet are vulnerable as well. Second, we show that, by leveraging ICMP redirect attacks against NATed networks, off-path attackers in the same NATed network can perform a man-in-the-middle
Fractal-Like Density in Single Cell Data

Short Abstract — Waddington’s Landscape is an influential paradigm describing how single cells develop and differentiate into their mature forms. For over 60 years, the landscape has been quantitatively characterized as representing a dynamical system built on and on top of gene expression, with cell types corresponding to regions around attractors in the dynamics of the underlying gene regulatory network. With the advent of scRNA-seq, it was thought that these attractor regions would be seen in the high-dimensional space of mRNA levels. Our analysis shows, however, that this isn’t the case, recontextualizing expected heterogeneity amongst single cells during development
Uji Aktivitas Antibakteri Cuka Apel Terhadap Bakteri Salmonella Paratyphi A THE JOURNAL OF

Salmonella paratyphi A causes enteric fever, which is paratyphic fever which attacks the intestines and is acute. Apple cider vinegar contains acetic acid, apple cider vinegar contains organic acids, namely acetic acid and amino acids, flavonoids, polyphenols, pectins and it’s also rich of vitamins and minerals. The acetic acid content of apple cider vinegar acts as an antimicrobial. The objective of the research was to determine the concentration of apple cider vinegar which was effective in inhibiting the growth of Salmonella paratyphi A bacteria. The type of research used was true experiment using posttest only control group design. The results obtained are apple vinegar can inhibit the growth of Salmonella paratyphi A bacteria with the result that the inhibition zone diameter at a concentration of 15% is 8,8 mm, 20% is 11,22 mm, 25% is 11,92 mm, and 30% is 13,05 mm, whereas in the positive control group it was obtained 29,95 mm and the negative control group was 0 mm or no inhibition zone is formed. The different test using Kruskal Wallis resulted in a value of sig (p) < (0.05) so it indicated that there were differences in the inhibition zone for the growth of Salmonella paratyphi A bacteria against various concentrations of apple cider vinegar. It was concluded that there was an antibacterial activity of apple cider vinegar against the growth of Salmonella paratyphi A bacteria and the effective concentration that could inhibit the growth of Salmonella paratyphi A bacteria was at 30% concentration.
Optimal sizing of energy storage considering the spatial‐temporal correlation of wind power forecast errors

Energy storage is considered as an effective approach to deal with the power deviation that caused by the stochastic wind power forecast error. Because of the spatial-temporal correlation of forecast errors for wind farms, which locate close to each other and integrate into the same regional power grid, energy storage could be deployed effectively and economically. Therefore, this study proposes an optimisation method to size the capacity of energy storage system (ESS) considering the spatial-temporal correlation of forecast errors for multiple nearby wind farms. The copula theory based correlation model of high-dimensional forecast errors is built to capture the spatial-temporal characteristics of forecast error. Then, according to multiple scenarios technique, an optimal ESS sizing model is established to minimise the investment and operation costs of the ESS. Meanwhile, an operation strategy considering the trend of prediction and correlation of errors is used to implement rolling operation strategy of ESS. The case studies demonstrate the effectiveness of the new method and illustrate the significant impact of the correlation of forecast error on the capacity of ESS.
Rancang Bangun Mobile Robot Pengikut Objek berdasarkan Warna dan Bentuk Menggunakan Template Matching Berbasis Mini PC

Penerapan teknologi di bidang robotika telah banyak dimanfaatkan oleh kalangan industri di dunia kerja dalam rangka meningkatkan efisiensi dan efektivitas berbagai aktivitas kerja manusia. Sebuah mobile robot dibuat agar dapat mengikuti pergerakan objek berupa manusia berdasarkan warna menggunakan webcam. Mobile robot yang dibuat dapat melakukan proses pengolahan citra dari warna objek yang ditangkap oleh webcam, sehingga dapat mengenali warna objek yang diikuti. Proses pengolahan citra akan di proses di dalam Raspberry Pi sebagai pusat kontrol, yang akan menggerakkan motor pada mobile robot. Ada beberapa proses pengolahan citra untuk mengikuti pergerakan objek, yaitu: mencari nilai HSV masing-masing warna pada kondisi pencahayaan yang berbeda, menganalisa warna objek yang akan diikuti dengan membandingkan nilai HSV yang didapat dengan nilai HSV pada proses sebelumnya, dan mendeteksi warna lingkaran pada objek yang tujuan dengan menggunakan metode template matching. Mobile robot akan mengikuti pergerakan objek yang telah ditangkap oleh webcam, Dari penelitian yang dilakukan, didapatkan nilai HSV yang tepat untuk semua keadaan pencahayaan untuk masing-masing warna yaitu: nilai H (Hue) untuk warna, merah: 0-66, nilai S (Saturation): 64-169, dan nilai V (Value): 75-124. Dari 30 kali pengujian sistem yang dilakukan pada semua kondisi pencahayaan, diperoleh 7 kali kegagalan dengan persentase kegagalan sebesar 28,6% dan 29 kali keberhasilan dengan persentase keberhasilan sebesar 71,4 %. Kata kunci: HSV, Robot, Raspberry, Webcam, Template Matching
Research Progress of the Endocrine-Disrupting Effects of Disinfection Byproducts

Since 1974, more than 800 disinfection byproducts (DBPs) have been identified from disinfected drinking water, swimming pool water, wastewaters, etc. Some DBPs are recognized as contaminants of high environmental concern because they may induce many detrimental health (e.g., cancer, cytotoxicity, and genotoxicity) and/or ecological (e.g., acute toxicity and development toxicity on alga, crustacean, and fish) effects. However, the information on whether DBPs may elicit potential endocrine-disrupting effects in human and wildlife is scarce. It is the major objective of this paper to summarize the reported potential endocrine-disrupting effects of the identified DBPs in the view of adverse outcome pathways (AOPs). In this regard, we introduce the potential molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs) associated with exposure to specific DBPs. The present evidence indicates that the endocrine system of organism can be perturbed by certain DBPs through some MIEs, including hormone receptor-mediated mechanisms and non-receptor-mediated mechanisms (e.g., hormone transport protein). Lastly, the gaps in our knowledge of the endocrine-disrupting effects of DBPs are highlighted, and critical directions for future studies are proposed.
Mechanism Design and Action Strategy for Small Hopping Rover

小型の月,惑星探査ローバ,特に質量数 kg 程度のローバは, 小型の着陸機にも搭載可能であり,様々なミッションでローバを 展開することが可能になる [1]. 小型ローバはその搭載性と引き 換えに,移動性能に制約を受ける [2]. 車輪の不整地走破性は,基 本的にその直径に依存する. そのため,小型ローバでは段差,岩 石などの障害物上の走破が困難になり,移動に制約を受けてしま うこの問題の解決策の一つに,跳躍移動の利用が考えられる. 小 型であるが故のローバの軽量さを有効活用し,機体より大きな障 害物も飛び越えることで回避可能になる. 跳躍はまたスタックを 回避することにも有効である. レゴリス上では車輪だけでなく優 秀な懸架機構を有していないと車輪がスタックする可能性が高い. 小型ローバでは複雑な懸架機構の搭載が難しく,車輪移動のみで はスタックし立往生する危険をはらむ. 跳躍移動によりスタック 回避も可能になり,小型ローバの移動性向上に大きく寄与できる. 本論文では,小型跳躍ローバの要となる跳躍機構と,ローバ 全体の設計論,並びにこれらを用いた移動戦略について議論し, JAXAの次期月着陸探査機 SLIMへの搭載を目指す小型ローバ の設計に関する検討についてまとめる.
Anxiety and Its Influencing Factors in Patients With Drug-Induced Liver Injury

Objective This study aims to investigate anxiety and its influencing factors in patients with drug-induced liver injury (DILI). Materials and Methods Ninety-four patients with DILI were enrolled and evaluated with a self-rating anxiety scale (SAS). According to the anxiety score, they were divided into four groups: the non-anxiety, mild anxiety, moderate anxiety, or severe anxiety groups, and the scores were analyzed based on demographic and biochemical indicators. Results Of the 94 patients with DILI, 63 did not have anxiety and 31 had anxiety (32.9%), of which 27 had mild, 3 had moderate, and 1 had severe anxiety. There were no statistically significant differences in gender, age, occupation, and level of education between the groups (F = 1.42, H = 2.361, H = 6.751, H = 1.796, and P > 0.05); anxiety score and degree of anxiety between the types of drugs that led to the liver injury (H = 0.812, H = 1.712, and P > 0.05); anxiety score between the different degrees of liver injury (H = 2.836, H = 4.957, P > 0.05); or length of hospital stay or prognosis between the degrees of anxiety (F = 1.487, H = 0.761, P > 0.05). However, there were statistically significant differences in the degree of anxiety between different degree and types of liver injury (H = 7.981, H = 8.208, P < 0.05). Conclusion Patients with DILI may have anxiety, especially mild anxiety. The occurrence of anxiety in patients with DILI is not related to gender, age, occupation, or level of education but may be related to the degree and type of liver injury. Anxiety has no impact on the length of stay in hospital or the prognosis of the DILI. These findings may contribute to the development of management strategies for patients with DILI.
Mental Stress Assessment Using Ultra Short Term HRV Analysis Based on Non-Linear Method

Mental stress is on the rise as one of the major health problems in modern society. It is important to detect and manage mental stress to prevent various diseases caused by stress and to maintain a healthy life. The purpose of this paper is to present new heart rate variability (HRV) features based on empirical mode decomposition and to detect acute mental stress through short-term HRV (5 min) and ultra-short-term HRV (under 5 min) analysis. HRV signals were acquired from 74 young police officers using acute stressors, including the Trier Social Stress Test and horror movie viewing, and a total of 26 features, including the proposed IMF energy features and general HRV features, were extracted. A support vector machine (SVM) classification model is used to classify the stress and non-stress states through leave-one-subject-out cross-validation. The classification accuracies of short-term HRV and ultra-short-term HRV analysis are 86.5% and 90.5%, respectively. In the results of ultra-short-term HRV analysis using various time lengths, we suggest the optimal duration to detect mental stress, which can be applied to wearable devices or healthcare systems.
TR-Net: A Transformer-Based Neural Network for Point Cloud Processing

Point cloud is a versatile geometric representation that could be applied in computer vision tasks. On account of the disorder of point cloud, it is challenging to design a deep neural network used in point cloud analysis. Furthermore, most existing frameworks for point cloud processing either hardly consider the local neighboring information or ignore context-aware and spatially-aware features. To deal with the above problems, we propose a novel point cloud processing architecture named TR-Net, which is based on transformer. This architecture reformulates the point cloud processing task as a set-to-set translation problem. TR-Net directly operates on raw point clouds without any data transformation or annotation, which reduces the consumption of computing resources and memory usage. Firstly, a neighborhood embedding backbone is designed to effectively extract the local neighboring information from point cloud. Then, an attention-based sub-network is constructed to better learn a semantically abundant and discriminatory representation from embedded features. Finally, effective global features are yielded through feeding the features extracted by attention-based sub-network into a residual backbone. For different downstream tasks, we build different decoders. Extensive experiments on the public datasets illustrate that our approach outperforms other state-of-the-art methods. For example, our TR-Net performs 93.1% overall accuracy on the ModelNet40 dataset and the TR-Net archives a mIou of 85.3% on the ShapeNet dataset for part segmentation.