Co-Salient Object Detection with Co-Representation Purification

Co-salient object detection (Co-SOD) aims at discovering the common objects in a group of relevant images. Mining a co-representation is essential for locating co-salient objects. Unfortunately, the current Co-SOD method does not pay enough attention that the information not related to the co-salient object is included in the co-representation. Such irrelevant information in the co-representation interferes with its locating of co-salient objects. In this paper, we propose a Co-Representation Purification (CoRP) method aiming at searching noise-free co-representation. We search a few pixel-wise embeddings probably belonging to co-salient regions. These embeddings constitute our co-representation and guide our prediction. For obtaining purer co-representation, we use the prediction to iteratively reduce irrelevant embeddings in our co-representation. Experiments on three datasets demonstrate that our CoRP achieves state-of-the-art performances on the benchmark datasets. Our source code is available at https://github.com/ZZY816/CoRP.Comment: Accepted by TPAMI 202

Curricular Object Manipulation in LiDAR-based Object Detection

This paper explores the potential of curriculum learning in LiDAR-based 3D object detection by proposing a curricular object manipulation (COM) framework. The framework embeds the curricular training strategy into both the loss design and the augmentation process. For the loss design, we propose the COMLoss to dynamically predict object-level difficulties and emphasize objects of different difficulties based on training stages. On top of the widely-used augmentation technique called GT-Aug in LiDAR detection tasks, we propose a novel COMAug strategy which first clusters objects in ground-truth database based on well-designed heuristics. Group-level difficulties rather than individual ones are then predicted and updated during training for stable results. Model performance and generalization capabilities can be improved by sampling and augmenting progressively more difficult objects into the training samples. Extensive experiments and ablation studies reveal the superior and generality of the proposed framework. The code is available at https://github.com/ZZY816/COM.Comment: Accepted by CVPR 2023. The code is available at https://github.com/ZZY816/CO

Distribution and Determinants of Correlation between PM2.5 and O3 in China Mainland: Dynamitic simil-Hu Lines

In recent years, China has made great efforts to control air pollution. During the governance process, it is found that fine particulate matter (PM2.5) and ozone (O3) change in the same trend among some areas and the opposite in others, which brings some difficulties to take measures in a planned way. Therefore, this study adopted multi-year and large-scale air quality data to explore the distribution of correlation between PM2.5 and O3, and proposed a concept called dynamic similar hu lines to replace the single fixed division in the previous research. Furthermore, this study discussed the causes of distribution patterns quantitatively with geographical detector and random forest. The causes included natural factors and anthropogenic factors. And these factors could be divided into three parts according to the characteristics of spatial distribution: broadly changing with longitude, changing with latitude, and having local characteristics. Overall, regions with relatively more densely population, higher GDP, lower altitude, higher humidity, higher atmospheric pressure, higher surface temperature, less sunshine hours and more accumulated precipitation often corresponds to positive correlation coefficient between PM2.5 and O3, no matter in which season. The parts with opposite conditions that mentioned above are essentially negative correlation coefficient. And what's more, humidity, global surface temperature, air temperature and accumulated precipitation are four decisive factors to form the distribution of correlation between PM2.5 and O3. In general, collaborative governance of atmospheric pollutants should consider particular time and space background and also be based on the local actual socio-economic situations, geography and geomorphology, climate and meteorology and other comprehensive factors.Comment: Our research group have decided to withdraw this preprin

The Hydrodynamic Characteristics Induced by Multiple Layouts of Typical Artificial M-Type Reefs with Sea Currents Typical of Liaodong Bay, Bohai Sea

Artificial reefs are effective measures to improve the marine ecological environment and increase fishery production. However, there are several geometries being investigated nowadays and their setup, including the spacing between groups of them, can provide dissimilar effects on hydrodynamics. To enhance the understanding of this topic, in this paper, the focus is mainly on M-Type artificial reefs that will be adopted in Juehua Island, Liaodong Bay, China. An experimental campaign was carried out in order to simulate the influence that M-Type unit reef groups may have on the local flow field and the Particle Image Velocimetry (PIV) technique has been implemented to provide velocity maps. The results showed that with the increase of velocity’s current approaching the artificial reef, the height, length and area of the upwelling and the back vortex rise with the increase of spacing between the artificial reefs. Furthermore, when comparing different geometrical configurations with similar currents approaching the artificial reef, the maximum values of both upwelling and back vortex were obtained when the spacing between unit reefs was 1.25 L. Finally, the entropy method was used to evaluate the effects on the flow field under four kinds of spacing based on the hydrodynamic characteristics and the economic cost. The comprehensive score obtained for all the configurations followed the order 1.25 L > 1.50 L > 0.75 L > 1.00 L. Therefore, it is suggested that the original design spacing should be increased by 25% when the M-type unit reef is put into practice. Additionally, after having completed a comparative analysis, it is recommended to further change the reef group into four reef monocases. By executing this adjustment, the unit reef cost was reduced by 10%, and the influence range on the flow field increased by 10%, and this result can consequently achieve greater ecological benefits with less economic input. The results of this study provide a preliminary reference for the construction of artificial reefs M-Type from the perspective of theory and practice

Overall survival benefits of cancer drugs approved in China from 2005 to 2020

Importance: Of approximately 9 million patients with cancer in China in 2020, more than half were diagnosed with late-stage cancers. Recent regulatory reforms in China have focused on improving the availability of new cancer drugs. However, evidence on the clinical benefits of new cancer therapies authorized in China is not available. Objective: To characterize the clinical benefits of cancer drugs approved in China, as defined by the availability and magnitude of statistically significant overall survival (OS) results. Design, Setting, and Participants: This mixed-methods study comprising a systematic review and cross-sectional analysis identified antineoplastic agents approved in China between January 1, 2005, and December 31, 2020, using publicly available data and regulatory review documents issued by the National Medical Products Administration. The literature published up to June 30, 2021, was reviewed to collect results on end points used in pivotal trials supporting cancer drug approvals. Main Outcomes and Measures: The primary outcome measure was a documented statistically significant positive OS difference between a new cancer therapy and a comparator treatment. Secondary outcome measures were the magnitude of OS benefit and other primary efficacy measures in pivotal trials. Results: Between 2005 and 2020, 78 cancer drugs corresponding to 141 indications were authorized in China, including 20 drugs (25.6%) (for 30 indications) approved in China only. Of all indications, 26 (18.4%) were evaluated in single-arm or dose-optimization trials, most of which were authorized after 2017. By June 30, 2021, 34 drug indications (24.1%) had a documented lack of OS gain. For 68 indications (48.2%) that had documented evidence of OS benefit, the median magnitude of OS improvement was 4.1 (range, 1.0-35.0) months. After a median follow-up of 1.9 (range, 1.0-11.1) years from approval, OS data for 13 indications (9.2%) were either not reported or were still not mature. Fewer than one-third of cancer drug indications approved in China only had documented evidence of OS benefits (9 of 30 [30.0%]), whereas more than one-half of the cancer drug indications also available in the US or Europe had OS benefits (59 of 111 [53.1%]). Conclusions and Relevance: In this study, almost half of cancer drug indications approved in China had demonstrated OS gain. With the increase of cancer drug approvals based on single-arm trials or immature survival data in recent years, these findings highlight the need to routinely monitor the clinical benefits of new cancer therapies in Chin

Ethanol oxidation activity and structure of carbon-supported Pt-modified PdSn-SnO2 influenced by different stabilizers

PdSn-SnO2 nanoparticles supported on Vulcan XC-72 carbon were synthesized by chemical reduction in the presence of three different stabilizing agents: ethylene diamine tetra-acetic acid (EDTA), sodium citrate (Nacitrate) and hexamethylenetetramine (HMTA). TEM analysis showed that PdSn-SnO2 /C catalyst made using the HMTA stabilizer produced the smallest particle size. XRD analysis detected the presence of PdSn alloy and the SnO2 phase in all three PdSn-SnO2 /C samples, and showed that PdSn-SnO2 (HMTA) had the smallest lattice parameter. After PdSn-SnO2 samples were modified by Pt, the particle size distribution and average size of nanoparticles of Pt-PdSn-SnO2 did not obviously change, and the fcc structure of PdSn in all three samples was retained. XPS measurement showed a higher upshift of Pt 4f binding energy occurred for Pt/PdSn-SnO2 /C (HMTA) compared to those of Pt/PdSn-SnO2 /C (EDTA) and Pt/PdSn-SnO2 /C (Nacitrate). Pt/PdSn-SnO2 /C (HMTA) was also found to have the highest CO and ethanol oxidation activity among the three catalysts.Web of Scienc

Synthesis of carbon-supported PdSn–SnO2 nanoparticles with different degrees of interfacial contact and enhanced catalytic activities for formic acid oxidation

The conjunction of the PdSn alloy and SnO2 is of interest for improving catalytic activity in formic acid oxidation (FAO). Here, we report the synthesis of PdSn–SnO2 nanoparticles and a study of their catalytic FAO activity. Different degrees of interfacial contact between SnO2 and PdSn were obtained using two different stabilizers (sodium citrate and EDTA) during the reduction process in catalyst preparation. Compared to the PdSn alloy, PdSn–SnO2 supported on carbon black showed enhanced FAO catalytic activity due to the presence of SnO2 species. It was also found that interfacial contact between the PdSn alloy and the SnO2 phase has an impact on the activity towards CO oxidation and FAO.Web of Scienc

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i