EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association

Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, we propose an ensemble data associate strategy for integrating the parametric and nonparametric statistic tests. By exploiting the nature of different statistics, our method can effectively aggregate the information of different measurements, and thus significantly improve the robustness and accuracy of data association. We then present an accurate object pose estimation framework, in which an outliers-robust centroid and scale estimation algorithm and an object pose initialization algorithm are developed to help improve the optimality of pose estimation results. Furthermore, we build a SLAM system that can generate semi-dense or lightweight object-oriented maps with a monocular camera. Extensive experiments are conducted on three publicly available datasets and a real scenario. The results show that our approach significantly outperforms state-of-the-art techniques in accuracy and robustness. The source code is available on: https://github.com/yanmin-wu/EAO-SLAM.Comment: Accepted to IROS 2020. Project Page: https://yanmin-wu.github.io/project/eaoslam/; Code: https://github.com/yanmin-wu/EAO-SLA

An Object SLAM Framework for Association, Mapping, and High-Level Tasks

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this paper, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance.Comment: Accepted by IEEE Transactions on Robotics(T-RO

Corrigendum to “Metabolic Syndrome, Inflammation, and Cancer”

Corrigendum to the article titled “Metabolic Syndrome, Inflammation, and Cancer”

Lactate, a Neglected Factor for Diabetes and Cancer Interaction

Increasing body of evidence suggests that there exists a connection between diabetes and cancer. Nevertheless, to date, the potential reasons for this association are still poorly understood and currently there is no clinical evidence available to direct the proper management of patients presenting with these two diseases concomitantly. Both cancer and diabetes have been associated with abnormal lactate metabolism and high level of lactate production is the key biological property of these diseases. Conversely, high lactate contribute to a higher insulin resistant status and a more malignant phenotype of cancer cells, promoting diabetes and cancer development and progression. In view of associations between diabetes and cancers, the role of high lactate production in diabetes and cancer interaction should not be neglected. Here, we review the available evidence of lactate's role in different biological characteristics of diabetes and cancer and interactive relationship between them. Understanding the molecular mechanisms behind metabolic remodeling of diabetes- and cancer-related signaling would endow novel preventive and therapeutic approaches for diabetes and cancer treatment

Is knowledge retained by healthcare providers after training? A pragmatic evaluation of drug-resistant tuberculosis management in China.

OBJECTIVES: Considering the urgent need of training to improve standardised management of drug-resistant infectious disease and the lack of evidence on the impact of training, this study evaluates whether training participants' knowledge on multidrug-resistant tuberculosis (MDR-TB) is improved immediately and a year after training. SETTING AND PARTICIPANTS: The study involved 91 MDR-TB healthcare providers (HCPs), including clinical doctors, nurses and CDC staff, who attended a new MDR-TB HCP training programme in Liaoning and Jiangxi provinces, China. MAIN OUTCOME MEASURES: A phone-based assessment of participants' long-term retention of knowledge about MDR-TB management was conducted in July 2017, approximately 1 year after training. The proportion of correct responses in the long-term knowledge assessment was compared with a pretraining test and an immediate post-training test using a χ2 test. Factors influencing participants' performance in the long-term knowledge assessment were analysed using linear regression. RESULTS: Across both provinces, knowledge of definitions of drug-resistant TB, standardised MDR-TB case detection protocols and laboratory diagnosis was improved 1 year after the training by 14.5% (p=0.037), 32.4% (p<0.001) and 31% (p<0.001) relative to pretraining. However, compared with immediately after training, the knowledge of the three topics declined by 26.5% (p=0.003), 19.8% (p=0.018) and 52.7% (p<0.001) respectively in Jiangxi, while no significant decline was observed in Liaoning. Additionally, we found that obtaining a higher score in the long-term knowledge assessment was associated with longer years of clinical experience (coefficient=0.51; 95 CI% 0.02 to 0.99; p=0.041) and attending training in Liaoning (coefficient=0.50; 95% CI 0.14 to 0.85; p=0.007). CONCLUSION: Our study, the first to assess knowledge retention of MDR-TB HCPs 1 year after training, showed an overall positive long-term impact of lecture-style group training on participants' knowledge. Knowledge decline 1 year after training was observed in one province, Jiangxi, and this may be partly addressed by targeted support to HCPs with fewer years of clinical experience

Recombinant Newcastle disease virus (NDV/Anh-IL-2) expressing human IL-2 as a potential candidate for suppresses growth of hepatoma therapy

AbstractNewcastle disease virus (NDV) have shown oncolytic therapeutic efficacy in preclinical study and are currently approved for clinical trials. NDV Anhinga strain which is a mesogenic strain should be classified as lytic strain and has a therapeutic efficacy in hepatocellular cancer. In this study, we evaluated the capacity of NDV Anhinga strain to elicit immune reaction in vivo and the possibility for using as a vaccine vector for expressing tumor therapeutic factors. Interleukin-2 (IL-2) could boost the immune response against the tumor cells. Therefore, we use NDV Anhinga strain as backbone to construct a recombinant virus (NDV/Anh-IL-2) expressing IL-2. The virus growth curve showed that the production of recombinant NDV/Anh-IL-2 was slightly delayed compared to the wild type. The NDV/Anh-IL-2 strain could express soluble IL-2 and effectively inhibit the growth of hepatocellular carcinoma in vivo. 60 days post-treatment, mice which were completely cured by previous treatment were well protected when rechallenged with the same tumor cell. From the H&E-stained sections, intense infiltration of lymphocyte was observed in the NDV Anhinga strain treated group, especially in NDV/Anh-IL-2 group. The NDV Anhinga strain could not only kill the tumor directly, but could also elicit immune reaction and a potent immunological memory when killing tumor in vivo. In conclusion, the Anhinga strain could be an effective vector for tumor therapy; the recombinant NDV/Anh-IL-2 strain expressing soluble IL-2 is a promising candidate for hepatoma therapy

A survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network