Rectify the Regression Bias in Long-Tailed Object Detection

Long-tailed object detection faces great challenges because of its extremely imbalanced class distribution. Recent methods mainly focus on the classification bias and its loss function design, while ignoring the subtle influence of the regression branch. This paper shows that the regression bias exists and does adversely and seriously impact the detection accuracy. While existing methods fail to handle the regression bias, the class-specific regression head for rare classes is hypothesized to be the main cause of it in this paper. As a result, three kinds of viable solutions to cater for the rare categories are proposed, including adding a class-agnostic branch, clustering heads and merging heads. The proposed methods brings in consistent and significant improvements over existing long-tailed detection methods, especially in rare and common classes. The proposed method achieves state-of-the-art performance in the large vocabulary LVIS dataset with different backbones and architectures. It generalizes well to more difficult evaluation metrics, relatively balanced datasets, and the mask branch. This is the first attempt to reveal and explore rectifying of the regression bias in long-tailed object detection

Optimization of multi-temporal generation scheduling in power system under elevated renewable penetrations: A review

The traditional power generation mix and the geographical distribution of units have faced structural reform with the increasing renewables. The existing scheduling schemes confront the optimization challenges of multi-source collaborative and multi-temporal coordination. This paper reviews the optimization of generation scheduling in power systems with renewables integration in different time scales, which are medium- and long-term, short-term and real-time, respectively. First, the scheduling model and method are summarized. The connections and differences of the multi-source mathematic model with uncertainty, as well as the market mechanism, including thermal power, hydroelectric power, wind power, solar energy, and energy storage, are also indicated. Second, the scheduling algorithm and approach are sorted out from the two dimensions of certainty and uncertainty. The innovation and difference in algorithm between the traditional scheduling and the scheduling problem with renewables are presented. Meanwhile, the interaction and coupling relationship among the different time scales are pointed out in each section. The challenges and shortcomings of current research and references future directions are also provided for dispatchers

Neutrophil Extracellular Traps Promote Inflammatory Responses in Psoriasis via Activating Epidermal TLR4/IL-36R Crosstalk

Epidermal infiltration of neutrophils is a hallmark of psoriasis, where their activation leads to release of neutrophil extracellular traps (NETs). The contribution of NETs to psoriasis pathogenesis has been unclear, but here we demonstrate that NETs drive inflammatory responses in skin through activation of epidermal TLR4/IL-36R crosstalk. This activation is dependent upon NETs formation and integrity, as targeting NETs with DNase I or CI-amidine in vivo improves disease in the imiquimod (IMQ)-induced psoriasis-like mouse model, decreasing IL-17A, lipocalin2 (LCN2), and IL-36G expression. Proinflammatory activity of NETs, and LCN2 induction, is dependent upon activation of TLR4/IL-36R crosstalk and MyD88/nuclear factor-kappa B (NF-κB) down-stream signaling, but independent of TLR7 or TLR9. Notably, both TLR4 inhibition and LCN2 neutralization alleviate psoriasis-like inflammation and NETs formation in both the IMQ model and K14-VEGF transgenic mice. In summary, these results outline the mechanisms for the proinflammatory activity of NETs in skin and identify NETs/TLR4 as novel therapeutic targets in psoriasis

Grey’s Anatomy: Gender Differences in Specialty Choice for Medical Students in China

Gender differences in sub-major choices within the science, technology, engineering, and mathematics (STEM) fields have scarcely been discussed. This study uses administrative records from a top medical school in China to examine gender differences in medical students’ specialty choices. Results showed that, although the gender gap in choosing a clinical track shrinks over time, female students in the clinical track are far less likely to choose highly paid surgical specialties, and this gap persists over time. However, female students outperformed male students in all of the courses. Thus, academic performance cannot explain the underrepresentation of female students in surgery. We further collected questions such as “Why don’t female students choose surgical specialties” and answers to them in “Chinese Quora”, Zhihu.com. A preliminary text analysis showed that ultra-physical load, discrimination in recruitment, women-unfriendly work climates, and difficulties in taking care of family are barriers that prevent women from choosing surgery

Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering

Federated learning is a distributed machine learning technology, which realizes the balance between data privacy protection and data sharing computing. To protect data privacy, feder-ated learning learns shared models by locally executing distributed training on participating devices and aggregating local models into global models. There is a problem in federated learning, that is, the negative impact caused by the non-independent and identical distribu-tion of data across different user terminals. In order to alleviate this problem, this paper pro-poses a strengthened federation aggregation method based on adaptive OPTICS clustering. Specifically, this method perceives the clustering environment as a Markov decision process, and models the adjustment process of parameter search direction, so as to find the best clus-tering parameters to achieve the best federated aggregation method. The core contribution of this paper is to propose an adaptive OPTICS clustering algorithm for federated learning. The algorithm combines OPTICS clustering and adaptive learning technology, and can effective-ly deal with the problem of non-independent and identically distributed data across different user terminals. By perceiving the clustering environment as a Markov decision process, the goal is to find the best parameters of the OPTICS cluster without artificial assistance, so as to obtain the best federated aggregation method and achieve better performance. The reliability and practicability of this method have been verified on the experimental data, and its effec-tiveness and superiority have been proved