HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds

3D object detection in point clouds is important for autonomous driving systems. A primary challenge in 3D object detection stems from the sparse distribution of points within the 3D scene. Existing high-performance methods typically employ 3D sparse convolutional neural networks with small kernels to extract features. To reduce computational costs, these methods resort to submanifold sparse convolutions, which prevent the information exchange among spatially disconnected features. Some recent approaches have attempted to address this problem by introducing large-kernel convolutions or self-attention mechanisms, but they either achieve limited accuracy improvements or incur excessive computational costs. We propose HEDNet, a hierarchical encoder-decoder network for 3D object detection, which leverages encoder-decoder blocks to capture long-range dependencies among features in the spatial space, particularly for large and distant objects. We conducted extensive experiments on the Waymo Open and nuScenes datasets. HEDNet achieved superior detection accuracy on both datasets than previous state-of-the-art methods with competitive efficiency. The code is available at https://github.com/zhanggang001/HEDNet.Comment: Accepted by NeurIPS 202

Co-movement Pattern Mining from Videos

Co-movement pattern mining from GPS trajectories has been an intriguing subject in spatial-temporal data mining. In this paper, we extend this research line by migrating the data source from GPS sensors to surveillance cameras, and presenting the first investigation into co-movement pattern mining from videos. We formulate the new problem, re-define the spatial-temporal proximity constraints from cameras deployed in a road network, and theoretically prove its hardness. Due to the lack of readily applicable solutions, we adapt existing techniques and propose two competitive baselines using Apriori-based enumerator and CMC algorithm, respectively. As the principal technical contributions, we introduce a novel index called temporal-cluster suffix tree (TCS-tree), which performs two-level temporal clustering within each camera and constructs a suffix tree from the resulting clusters. Moreover, we present a sequence-ahead pruning framework based on TCS-tree, which allows for the simultaneous leverage of all pattern constraints to filter candidate paths. Finally, to reduce verification cost on the candidate paths, we propose a sliding-window based co-movement pattern enumeration strategy and a hashing-based dominance eliminator, both of which are effective in avoiding redundant operations. We conduct extensive experiments for scalability and effectiveness analysis. Our results validate the efficiency of the proposed index and mining algorithm, which runs remarkably faster than the two baseline methods. Additionally, we construct a video database with 1169 cameras and perform an end-to-end pipeline analysis to study the performance gap between GPS-driven and video-driven methods. Our results demonstrate that the derived patterns from the video-driven approach are similar to those derived from groundtruth trajectories, providing evidence of its effectiveness

Application of implementation science framework to develop and adopt regulatory science in different national regulatory authorities

IntroductionThe purpose of developing and adopting regulatory science (RS) for drug regulatory authorities (DRAs) is to enhance regulatory capacity by advancing the scientific approach for the evaluation of health-related products. While many DRAs around the world advocate the concept of RS, the implementation approaches of RS vary according to local needs and have not been systemically examined. This study aimed to systematically identify the evidence about how RS was developed, adopted, and advanced by the selected DRAs, and analyzed and compared the implementation experiences of RS development under the guidance of an implementation science framework.MethodsDocumentary analysis of government documents and a scoping literature review were conducted, and data analysis was performed under the guidance of the PRECEDE-PROCEED Model (PPM). DRAs in the United States, the European Union, Japan, and China had officially launched RS initiatives and were therefore selected as the target countries in this study.ResultsThere is no common consensus on the definition of RS among the DRAs. However, these DRAs shared the same goal of developing and adopting RS, which was used to develop new tools, standards, and guidelines that could improve the effectiveness and efficiency of the risk and benefit assessment of the regulated products. Each DRA had decided its own priority areas for RS development and thus set specific objectives that might be technology-based (e.g., toxicology and clinical evaluation), process-based (e.g., partnership with healthcare systems and high-quality review/consultation services), or product-based (e.g., drug-device combination products and innovative emerging technologies). To advance RS, considerable resources had been allocated for staff training, advancing information technology and laboratory infrastructure, and funding research projects. DRAs also took multifaceted approaches to expand scientific collaborations through public–private partnerships, research funding mechanisms, and innovation networks. Cross-DRA communications were also reinforced through horizon scanning systems and consortiums to better inform and assist the regulatory decision-making process. The output measurements might be scientific publications, funded projects, DRAs interactions, and evaluation methods and guidelines. Improved regulatory efficiency and transparency leading to benefits to public health, patient outcomes, and translation of drug research and development as the key primary outcomes of RS development were anticipated but not yet clearly defined.ConclusionThe application of the implementation science framework is useful for conceptualizing and planning the development and adoption of RS for evidence-based regulatory decision-making. Continuous commitment to the RS development and regular review of the RS goals by the decision-makers are important for DRAs to meet the ever-changing scientific challenges in their regulatory decision-making process

Differentiation potential of STRO-1+ dental pulp stem cells changes during cell passaging

<p>Abstract</p> <p>Background</p> <p>Dental pulp stem cells (DPSCs) can be driven into odontoblast, osteoblast, and chondrocyte lineages in different inductive media. However, the differentiation potential of naive DPSCs after serial passaging in the routine culture system has not been fully elucidated.</p> <p>Results</p> <p>DPSCs were isolated from human/rat dental pulps by the magnetic activated cell sorting based on STRO-1 expression, cultured and passaged in the conventional culture media. The biological features of STRO-1⁺DPSCs at the 1^stand 9^thpassages were investigated. During the long-term passage, the proliferation ability of human STRO-1⁺DPSCs was downregulated as indicated by the growth kinetics. When compared with STRO-1⁺DPSCs at the 1^stpassage (DPSC-P1), the expression of mature osteoblast-specific genes/proteins (alkaline phosphatase, bone sialoprotein, osterix, and osteopontin), odontoblast-specific gene/protein (dentin sialophosphoprotein and dentin sialoprotein), and chondrocyte-specific gene/protein (type II collagen) was significantly upregulated in human STRO-1⁺DPSCs at the 9^thpassage (DPSC-P9). Furthermore, human DPSC-P9 cells in the mineralization-inducing media presented higher levels of alkaline phosphatase at day 3 and day 7 respectively, and produced more mineralized matrix than DPSC-P9 cells at day 14. <it>In vivo </it>transplantation results showed that rat DPSC-P1 cell pellets developed into dentin, bone and cartilage structures respectively, while DPSC-P9 cells can only generate bone tissues.</p> <p>Conclusions</p> <p>These findings suggest that STRO-1⁺DPSCs consist of several interrelated subpopulations which can spontaneously differentiate into odontoblasts, osteoblasts, and chondrocytes. The differentiation capacity of these DPSCs changes during cell passaging, and DPSCs at the 9^thpassage restrict their differentiation potential to the osteoblast lineage <it>in vivo</it>.</p

Energy-Efficient Hybrid Flow-Shop Scheduling under Time-of-Use and Ladder Electricity Tariffs

With the increasing influence of climate change, green development has become an important trend. Since manufacturing represents approximately one-half of total energy consumption, reducing the amount of energy consumed within this industry is imperative. This study provides a hybrid flow shop scheduling issue under a time-of-use and ladder electricity price system to reduce total energy consumption without compromising maximum completion time. An improved non-dominated sorting genetic algorithm II with some optimization strategies is proposed to solve the problem. First, an enhanced constructive heuristic algorithm is used to improve the quantity of initial solution in the initialization. Besides, an adaptive genetic operation is introduced, aiming to avoid the emergence of locally optimal solutions. In addition, the right-shift approach is developed to reduce the total energy consumption without affecting completion time. By maintaining the production efficiency and reducing the energy consumption cost by 4.33%. A trade-off proposal is made between productivity and sustainability in view of the calculation results

Multi-Attention Module for Dynamic Facial Emotion Recognition

Video-based dynamic facial emotion recognition (FER) is a challenging task, as one must capture and distinguish tiny facial movements representing emotional changes while ignoring the facial differences of different objects. Recent state-of-the-art studies have usually adopted more complex methods to solve this task, such as large-scale deep learning models or multimodal analysis with reference to multiple sub-models. According to the characteristics of the FER task and the shortcomings of existing methods, in this paper we propose a lightweight method and design three attention modules that can be flexibly inserted into the backbone network. The key information for the three dimensions of space, channel, and time is extracted by means of convolution layer, pooling layer, multi-layer perception (MLP), and other approaches, and attention weights are generated. By sharing parameters at the same level, the three modules do not add too many network parameters while enhancing the focus on specific areas of the face, effective feature information of static images, and key frames. The experimental results on CK+ and eNTERFACE&rsquo;05 datasets show that this method can achieve higher accuracy

A Feature Fusion Method with Guided Training for Classification Tasks

In this paper, a feature fusion method with guiding training (FGT-Net) is constructed to fuse image data and numerical data for some specific recognition tasks which cannot be classified accurately only according to images. The proposed structure is divided into the shared weight network part, the feature fused layer part, and the classification layer part. First, the guided training method is proposed to optimize the training process, the representative images and training images are input into the shared weight network to learn the ability that extracts the image features better, and then the image features and numerical features are fused together in the feature fused layer to input into the classification layer for the classification task. Experiments are carried out to verify the effectiveness of the proposed model. Loss is calculated by the output of both the shared weight network and classification layer. The results of experiments show that the proposed FGT-Net achieves the accuracy of 87.8%, which is 15% higher than the CNN model of ShuffleNetv2 (which can process image data only) and 9.8% higher than the DNN method (which processes structured data only)

Systematic analysis of randomised controlled trials of Chinese herb medicine for non-alcoholic steatohepatitis (NASH): implications for future drug development and trial design

Abstract Background Non-alcoholic steatohepatitis (NASH) is a liver disease currently lacking an approved therapy, resulting in significant clinical demand. Traditional Chinese medicines (TCMs) have been commonly used to manage NASH. This study aimed to systematically analyse the randomised controlled trials (RCTs) using TCMs for NASH management. Methods A systematic literature review was performed by following PRISMA guidelines 2020 in six electronic databases: PubMed, Web of Science, Scopus, Embase, the Cochrane Library, and China National Knowledge Infrastructure, from inception until August 2022. RCTs using TCMs for NASH were included in the analysis, irrespective of language or blinding. Results 112 RCTs were included in this review, with 10,573 NASH participants. 108 RCTs were conducted in China, and 4 RCTs were in other countries. Herbal medicine decoction was the major dosage form used for treating NASH (82/112). 11 TCMs products have been approved for NASH treatment (8 in China, 2 in Iran, and 1 in Japan). Classic prescriptions, such as “Huang Lian Jie Du decoction”, “Yin Chen Hao decoction”, and “Yi Guan Jian” were used in some studies. The TCMs treatment of NASH involved the use of 199 different plants, with the top 5 herbs being Salviae Miltiorrhizae Radix Et Rhizoma, Alismatis Rhizoma, Bupleuri Radix, Poria, and Curcumae Radix. “Salviae Miltiorrhizae Radix Et Rhizoma + Bupleuri Radix/Alismatis Rhizoma” were the mostly common drug-pair in the herbs network analysis. Nowadays, “Bupleuri Radix/Alismatis Rhizoma + Atractylodis Macrocephalae Rhizoma” are increasingly applied in herbal formulas for NASH. Based on the PICOS principles, the included studies varied in terms of the population, intervention, comparator, outcomes, and study design. However, some studies reported unstandardised results and failed to report diagnostic standards, inclusion or exclusion criteria, or sufficient patient information. Conclusion Adopting Chinese classic prescriptions or drug-pair may provide a basis for developing new drugs of NASH management. Further research is needed to refine the clinical trial design and obtain more convincing evidence for using TCMs to treat NASH