A Note on Improving Variational Estimation for Multidimensional Item Response Theory

Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure become multifaceted, multidimensional item response theory (MIRT) provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. However, the computational challenge of estimating MIRT models prohibits its wide use because many of the extant methods can hardly provide results in a realistic time frame when the number of dimensions, sample size, and test length are large. Instead, variational estimation methods, such as Gaussian Variational Expectation Maximization (GVEM) algorithm, have been recently proposed to solve the estimation challenge by providing a fast and accurate solution. However, results have shown that variational estimation methods may produce some bias on discrimination parameters during confirmatory model estimation, and this note proposes an importance weighted version of GVEM (i.e., IW-GVEM) to correct for such bias under MIRT models. We also use the adaptive moment estimation method to update the learning rate for gradient descent automatically. Our simulations show that IW-GVEM can effectively correct bias with modest increase of computation time, compared with GVEM. The proposed method may also shed light on improving the variational estimation for other psychometrics models

Learning Conditional Attributes for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations. One of the challenges is to model attributes interacted with different objects, e.g., the attribute ``wet" in ``wet apple" and ``wet cat" is different. As a solution, we provide analysis and argue that attributes are conditioned on the recognized object and input image and explore learning conditional attribute embeddings by a proposed attribute learning framework containing an attribute hyper learner and an attribute base learner. By encoding conditional attributes, our model enables to generate flexible attribute embeddings for generalization from seen to unseen compositions. Experiments on CZSL benchmarks, including the more challenging C-GQA dataset, demonstrate better performances compared with other state-of-the-art approaches and validate the importance of learning conditional attributes. Code is available at https://github.com/wqshmzh/CANet-CZSLComment: 10 pages, 4 figures, accepted in CVPR202

Research progress on effects of PM2.5 exposure during pregnancy on maternal and infant thyroid function

Ambient air pollution has become a widespread global public health problem. As one of the main components of ambient air pollution, fine particulate matter (PM2.5), with its small diameter and large surface area, can carry a variety of toxic substances and enter the blood circulation directly through the blood-air barrier, damaging various tissues and organs of human body. Studies have shown that PM2.5 exposure during pregnancy can disrupt the mother's and child's thyroid function. Since the fetal thyroid gland does not begin to develop until around the sixth week of pregnancy, the fetal thyroid hormone is almost entirely dependent on the mother during early stages of pregnancy, and maternal thyroid hormone level play a crucial role in the growth and development of fetus. When a mother is exposed to PM2.5 during pregnancy, placenta, the "bridge" between mother and fetus, is also affected to some extent, including changes in placental iodine uptake and oxidative stress, inflammation, and DNA methylation in placental tissue. Exposure to PM2.5 during pregnancy also alters maternal thyroid hormone level and normal placental function, which can have a detrimental effect on pregnancy outcomes, such as preterm birth, low birth weight, and neurological abnormalities. This paper reviewed the effects of PM2.5 exposure during different trimesters on maternal and infant thyroid function, placental function, and pregnancy outcomes, aiming to provide more accurate protection of maternal and fetal health

Investigation on permanent deformation in steel-concrete composite beam bridge deck pavement under temperature-load coupling effect

Rutting is one of the common distresses observed in asphalt pavement, influenced by temperature and load conditions. To clarify the permanent deformation behavior of steel-concrete composite beam (SCCB) bridge deck pavement under temperature-load coupling effect and provide references for the distress cause analysis, five typical SCCB bridge deck pavements were selected. The temperature distribution and the temperature stress of the pavement structures were analyzed by numerical simulation under periodic temperature variations. In addition, considering the daily variation in traffic volume, the permanent deformation of the five pavement structures were calculated under temperature-load coupling effect. Finally, the influence of heavy load on the development of rutting distress was also investigated. The results show that the temperature field and temperature stresses within the SCCB bridge deck pavement exhibit periodic variations under periodic temperature variations. Additionally, after 500,000 times of standard axle load application, “EA + SMA” exhibits the smallest permanent deformation and the best resistance to rutting distress under temperature-load coupling effect. Finally, heavy load conditions have a great influence on the permanent deformation of SCCB bridge deck pavement. In areas with severe rutting distresses, it is recommended to use “EA + SMA” pavement structure in SCCB bridge

SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning

Current closed-set instance segmentation models rely on pre-defined class labels for each mask during training and evaluation, largely limiting their ability to detect novel objects. Open-world instance segmentation (OWIS) models address this challenge by detecting unknown objects in a class-agnostic manner. However, previous OWIS approaches completely erase category information during training to keep the model's ability to generalize to unknown objects. In this work, we propose a novel training mechanism termed SegPrompt that uses category information to improve the model's class-agnostic segmentation ability for both known and unknown categories. In addition, the previous OWIS training setting exposes the unknown classes to the training set and brings information leakage, which is unreasonable in the real world. Therefore, we provide a new open-world benchmark closer to a real-world scenario by dividing the dataset classes into known-seen-unseen parts. For the first time, we focus on the model's ability to discover objects that never appear in the training set images. Experiments show that SegPrompt can improve the overall and unseen detection performance by 5.6% and 6.1% in AR on our new benchmark without affecting the inference efficiency. We further demonstrate the effectiveness of our method on existing cross-dataset transfer and strongly supervised settings, leading to 5.5% and 12.3% relative improvement.Comment: Accepted to Proc. Int. Conf. Computer Vision (ICCV) 2023. Code is at: https://github.com/aim-uofa/SegPromp

Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions

In recent years, low-carbon transportation has become an indispensable part as sustainable development strategies of various countries, and plays a very important responsibility in promoting low-carbon cities. However, the security of low-carbon transportation has been threatened from various ways. For example, denial of service attacks pose a great threat to the electric vehicles and vehicle-to-grid networks. To minimize these threats, several methods have been proposed to defense against them. Yet, these methods are only for certain types of scenarios or attacks. Therefore, this review addresses security aspect from holistic view, provides the overview, challenges and future directions of cyber security technologies in low-carbon transportation. Firstly, based on the concept and importance of low-carbon transportation, this review positions the low-carbon transportation services. Then, with the perspective of network architecture and communication mode, this review classifies its typical attack risks. The corresponding defense technologies and relevant security suggestions are further reviewed from perspective of data security, network management security and network application security. Finally, in view of the long term development of low-carbon transportation, future research directions have been concerned.Comment: 34 pages, 6 figures, accepted by journal Renewable and Sustainable Energy Review

How Does Culture Shape Creativity? A Mini-Review

The purpose of this study was to examine how culture shapes creativity by reviewing empirical findings across diverse studies. The impact of culture on creativity is typically manifested in three ways: (1) people from different cultures or settings have distinct implicit and/or explicit conceptions of creativity; (2) individuals from different cultures, particularly those from individualist and collectivist cultures, show differences in preferred creative processes and creative processing modes (e.g., usefulness seems more important than novelty in the East, whereas novelty seems equally important as usefulness, if not more so, in the West) when they are engaged in creative endeavors; (3) creativity may be assessed using different measures based on culture-related contents or materials, and findings are accurate only when culturally appropriate or culturally fair measures are used. Potential implications and future directions are also proposed

Development of Polysorbate 80/Phospholipid mixed micellar formation for docetaxel and assessment of its in vivo distribution in animal models

Docetaxel (DTX) is a very important member of taxoid family. Despite several alternative delivery systems reported recently, DTX formulated by Polysorbate 80 and alcohol (Taxotere®) is still the most frequent administration in clinical practice. In this study, we incorporated DTX into Polysorbate 80/Phospholipid mixed micelles and compared its structural characteristics, pharmacokinetics, biodistribution, and blood compatibility with its conventional counterparts. Results showed that the mixed micelles loaded DTX possessed a mean size of approximately 13 nm with narrow size distribution and a rod-like micelle shape. In the pharmacokinetics assessment, there was no significant difference between the two preparations (P > 0.05), which demonstrated that the DTX in the two preparations may share a similar pharmacokinetic process. However, the Polysorbate 80/Phospholipid mixed micelles can increase the drug residence amount of DTX in kidney, spleen, ovary and uterus, heart, and liver. The blood compatibility assessment study revealed that the mixed micelles were safe for intravenous injection. In conclusion, Polysorbate 80/Phospholipid mixed micelle is safe, can improve the tumor therapeutic effects of DTX in the chosen organs, and may be a potential alternative dosage form for clinical intravenous administration of DTX