Introducing Depth into Transformer-based 3D Object Detection

In this paper, we present DAT, a Depth-Aware Transformer framework designed for camera-based 3D detection. Our model is based on observing two major issues in existing methods: large depth translation errors and duplicate predictions along depth axes. To mitigate these issues, we propose two key solutions within DAT. To address the first issue, we introduce a Depth-Aware Spatial Cross-Attention (DA-SCA) module that incorporates depth information into spatial cross-attention when lifting image features to 3D space. To address the second issue, we introduce an auxiliary learning task called Depth-aware Negative Suppression loss. First, based on their reference points, we organize features as a Bird's-Eye-View (BEV) feature map. Then, we sample positive and negative features along each object ray that connects an object and a camera and train the model to distinguish between them. The proposed DA-SCA and DNS methods effectively alleviate these two problems. We show that DAT is a versatile method that enhances the performance of all three popular models, BEVFormer, DETR3D, and PETR. Our evaluation on BEVFormer demonstrates that DAT achieves a significant improvement of +2.8 NDS on nuScenes val under the same settings. Moreover, when using pre-trained VoVNet-99 as the backbone, DAT achieves strong results of 60.0 NDS and 51.5 mAP on nuScenes test. Our code will be soon.Comment: revisio

HumanTOMATO: Text-aligned Whole-body Motion Generation

This work targets a novel text-driven whole-body motion generation task, which takes a given textual description as input and aims at generating high-quality, diverse, and coherent facial expressions, hand gestures, and body motions simultaneously. Previous works on text-driven motion generation tasks mainly have two limitations: they ignore the key role of fine-grained hand and face controlling in vivid whole-body motion generation, and lack a good alignment between text and motion. To address such limitations, we propose a Text-aligned whOle-body Motion generATiOn framework, named HumanTOMATO, which is the first attempt to our knowledge towards applicable holistic motion generation in this research area. To tackle this challenging task, our solution includes two key designs: (1) a Holistic Hierarchical VQ-VAE (aka H$^2$VQ) and a Hierarchical-GPT for fine-grained body and hand motion reconstruction and generation with two structured codebooks; and (2) a pre-trained text-motion-alignment model to help generated motion align with the input textual description explicitly. Comprehensive experiments verify that our model has significant advantages in both the quality of generated motions and their alignment with text.Comment: 31 pages, 15 figures, 16 tables. Project page: https://lhchen.top/HumanTOMAT

Fault Diagnosis Based on Evidences Screening in Virtual Network

Abstract-Network virtualization has been regarded as a core attribute of Future Internet. To improve the quality of virtual network, it is important to diagnose the faulty components quickly and accurately. Recently more and more researches focus on end-user fault diagnosis, which can fit incomplete knowledge and dynamic challenges. In this paper, we present a fault diagnosis system called DiaEO in virtual network. It improves the present end-user fault diagnosis methods by screening evidences before analyzing to reduce the time-consuming. Besides that, DiaEO also improves the anti-noise ability of the system. The simulation results show that the proposed method can keep high accuracy and ameliorate time performance

The contribution of double-fed wind farms to transient voltage and damping of power grids

Kako bi se povećala mogućnost održavanja prolaznog napona i oscilacija sustava prigušenja, u radu se predstavlja dodatna prolazna upravljačka shema vjetroelektrane. Analiza pokazuje da u uvjetima jakih mreža, oslanjanje na vlastitu reaktivnu snagu turbina na vjetar u svrhu podrške pada prolaznog napona, dovodi do značajnog povećanja struje rotora; u uvjetima slabih mreža, prolazna reaktivna snaga vjetroelektrana ne samo da služi za prigušivanje pada prolaznog napona nego i čini prihvatljivom povećanje uzbudne struje rotora. Uz to, kad se dvostruko napajani indukcioni generatori pomiješaju s konvencionalnim energanama za prijenos snage, moguće je upravljati povećanjem prigušenja sustava dodajući prigušenje preko vjetroelektrana u nastojanju poboljšanja stabilnosti cjelokupnog sustava. U svrhu implementacije ove sheme, proizvodimo eksperimentalni prototip sastavljen od IPC, glavne sabirnice i optičke opreme i provodimo ispitivanje zatvorene petlje na digitalnom simulatoru u realnom vremenu (RTDS). Simulacije pokazuju da u uvjetima slabih mreža implementacija regulacije reaktivne snage vjetreoelektrana može donekle prigušiti pad napona.To improve the ability of transient voltage support and that of damping system oscillation, this paper presents the additional transient control scheme of wind farm. The analysis indicates that under the condition of strong grids, relying on wind turbines’ own reactive power to support the transient voltage drop leads to the significant increase of rotor current; under the condition of weak grids, transient reactive power released by wind farms not only serves to suppress transient voltage drop but also makes acceptable the increase of rotor excitation current. In addition, when double-fed induction generators are mixed with conventional power plants for power transmission, we can control the increase of system damping by adding damping through wind turbines in a bid to improve the stability of the overall system. In order to implement this scheme, we produce the experimental prototype composed of IPC, fieldbus and optical equipment and conduct the closed-loop test on the real-time digital simulator (RTDS). Simulations show that under the condition of weak grids, implementing the reactive power regulation of wind farms can suppress their voltage drop to a certain extent

Eugene: Towards deep intelligence as a service

National Research Foundation (NRF) Singapore under its International Research Centres in Singapore Funding Initiativ

Knowledge level and influencing factors of sugar-sweetened beverages among Chinese adults aged 18-64 years in 2021

BackgroundExcessive intake of sugar-sweetened beverages (SSBs) is harmful to health. In recent decades, the consumption of SSBs by Chinese residents has increased rapidly, increasing the risk of death and burden of disease. ObjectiveTo analyze the knowledge level and influencing factors of SSBs for Chinese residents aged 18-64 years in 2021. MethodsA multi-stage cluster random sampling approach was used to conduct a questionnaire survey among residents aged 18-64 years in 302 survey sites across the country in 2021, and 98567 valid questionnaires were obtained. Four questions are about SSBs among the questionnaire's 5 dimensions. Respondents who answered 3 or more questions correctly were considered to have a basic understanding of SSBs. Frequency and weighted proportion were used for description. With individual as level 1 and resident council (village council) as level 2, a two-level logistic regression model was applied to examine the influencing factors. A null model was used to determine whether the two-level logistic regression model was appropriate. ResultsThe knowledge awareness rate of SSBs was 57.0% among the Chinese residents aged 18-64 years in 2021. The knowledge point with the lowest correct rate was "It is best to consume no more than 25 grams of added sugar per day" (22.6%), while the one with the highest correct rate was "Excessive intake of SSBs can increase the risk of obesity and diabetes" (81.1%). The results of the null model showed that SSBs knowledge level had a clustering effect at resident council (village council) level (t=25.00, P<0.0001), so a two-level model fit better than a one-level model. The results of the two-level logistic model revealed that residents who were female (OR=1.14, 95%CI: 1.11, 1.18) or working in medical and health (OR=1.36, 95%CI: 1.27, 1.45) and education institutions (OR=1.16, 95%CI: 1.07, 1.24) had a higher knowledge level compared to males or residents of other occupations. The knowledge level was lower among residents in central (OR=0.87, 95%CI: 0.77, 0.97) and western (OR=0.85, 95%CI: 0.75, 0.94) areas than in eastern areas. Those with chronic diseases (OR=0.81, 95%CI: 0.78, 0.84) and who did not know if they had a chronic disease (OR=0.75, 95%CI: 0.72, 0.78) had a lower knowledge level than those without chronic diseases. Compared with 18-24 years, the knowledge level was higher in ages 35-44 years (OR=1.07, 95%CI: 1.02, 1.12) and lower in ages 55-64 years (OR=0.92, 95%CI: 0.86, 0.97), and not different from the ages 25-34 years and 45-54 years. The knowledge level increased with the level of education, the trend was statistically significant (P＜0.001). ConclusionOnly about half of Chinese adults aged 18-64 years had a basic understanding of SSBs in 2021. The awareness rate of added sugar intake was low in particular. The knowledge levels of male, central and western, or less educated populations were even lower. Awareness of the negative health outcomes of SSBs was high among the population

detrex: Benchmarking Detection Transformers

The DEtection TRansformer (DETR) algorithm has received considerable attention in the research community and is gradually emerging as a mainstream approach for object detection and other perception tasks. However, the current field lacks a unified and comprehensive benchmark specifically tailored for DETR-based models. To address this issue, we develop a unified, highly modular, and lightweight codebase called detrex, which supports a majority of the mainstream DETR-based instance recognition algorithms, covering various fundamental tasks, including object detection, segmentation, and pose estimation. We conduct extensive experiments under detrex and perform a comprehensive benchmark for DETR-based models. Moreover, we enhance the performance of detection transformers through the refinement of training hyper-parameters, providing strong baselines for supported algorithms.We hope that detrex could offer research communities a standardized and unified platform to evaluate and compare different DETR-based models while fostering a deeper understanding and driving advancements in DETR-based instance recognition. Our code is available at https://github.com/IDEA-Research/detrex. The project is currently being actively developed. We encourage the community to use detrex codebase for further development and contributions.Comment: project link: https://github.com/IDEA-Research/detre

The nutrition-based comprehensive intervention study on childhood obesity in China (NISCOC): a randomised cluster controlled trial

<p>Abstract</p> <p>Background</p> <p>Childhood obesity and its related metabolic and psychological abnormalities are becoming serious health problems in China. Effective, feasible and practical interventions should be developed in order to prevent the childhood obesity and its related early onset of clinical cardiovascular diseases. The objective of this paper is to describe the design of a multi-centred random controlled school-based clinical intervention for childhood obesity in China. The secondary objective is to compare the cost-effectiveness of the comprehensive intervention strategy with two other interventions, one only focuses on nutrition education, the other only focuses on physical activity.</p> <p>Methods/Design</p> <p>The study is designed as a multi-centred randomised controlled trial, which included 6 centres located in Beijing, Shanghai, Chongqing, Shandong province, Heilongjiang province and Guangdong province. Both nutrition education (special developed carton style nutrition education handbook) and physical activity intervention (Happy 10 program) will be applied in all intervention schools of 5 cities except Beijing. In Beijing, nutrition education intervention will be applied in 3 schools and physical activity intervention among another 3 schools. A total of 9750 primary students (grade 1 to grade 5, aged 7-13 years) will participate in baseline and intervention measurements, including weight, height, waist circumference, body composition (bioelectrical impendence device), physical fitness, 3 days dietary record, physical activity questionnaire, blood pressure, plasma glucose and plasma lipid profiles. Data concerning investments will be collected in our study, including costs in staff training, intervention materials, teachers and school input and supervising related expenditure.</p> <p>Discussion</p> <p>Present study is the first and biggest multi-center comprehensive childhood obesity intervention study in China. Should the study produce comprehensive results, the intervention strategies would justify a national school-based program to prevent childhood obesity in China.</p> <p>Trial Registration</p> <p>Chinese clinical trial registry (Primary registry in the WHO registry network) Identifier: ChiCTR-TRC-00000402</p

Evolution of Immune and Stromal Cell States and Ecotypes During Gastric Adenocarcinoma Progression

Understanding tumor microenvironment (TME) reprogramming in gastric adenocarcinoma (GAC) progression may uncover novel therapeutic targets. Here, we performed single-cell profiling of precancerous lesions, localized and metastatic GACs, identifying alterations in TME cell states and compositions as GAC progresses. Abundant IgA+ plasma cells exist in the premalignant microenvironment, whereas immunosuppressive myeloid and stromal subsets dominate late-stage GACs. We identified six TME ecotypes (EC1–6). EC1 is exclusive to blood, while EC4, EC5, and EC2 are highly enriched in uninvolved tissues, premalignant lesions, and metastases, respectively. EC3 and EC6, two distinct ecotypes in primary GACs, associate with histopathological and genomic characteristics, and prognosis. Extensive stromal remodeling occurs in GAC progression. High SDC2 expression in cancer-associated fibroblasts (CAFs) is linked to aggressive phenotypes and poor survival, and SDC2 overexpression in CAFs contributes to tumor growth. Our study provides a high-resolution GAC TME atlas and underscores potential targets for further investigation

Optimum Design and Performance of Porous Concrete for Heavy-Load Traffic Pavement in Cold and Heavy Rainfall Region of NE China

The aim of the study was to solve the problem of drainage stability of pavement base in cold and Cloudburst area. With porous concrete as the research object, an optimum design of porous concrete was determined using a step filling and orthogonal test method, and the relationship between the porosity and the connected porosity of the porous concrete was analyzed. Furthermore, drainage performance and frost resistance of the pavement, compressive strength of the porous concrete, bending strength, and compressive elastic modulus were studied. The results show that the effects of water-cement ratio on the strength of porous concrete based on the step filling method are the most significant. In addition, the connected porosity and goal porosity have a good linear relationship; that is, the drainage performance increases with the increase in connected porosity, whereas the frost resistance, compressive strength, flexural tensile strength, and compressive elastic modulus decrease with the increase in connected porosity. Based on an engineering project in Inner Mongolia (in China), it was shown that porous concrete with a goal porosity of 15% used as a pavement base could meet the requirements of cold weather, showers, and heavy traffic