Laser powder bed fusion of Fe60(CoCrNiMn)40 medium-entropy alloy with excellent strength-ductility balance

In this study, Fe60(CoCrNiMn)40 medium-entropy alloy (MEA) was fabricated by laser powder bed fusion (LPBF) via mixing of pure Fe and FeCoCrNiMn powders, the processability, microstructure and mechanical properties were systematically investigated, and the mechanism of strengthening and toughening were revealed through combination of experiments and molecular dynamics (MD) simulations. Results show that fraction of BCC phase decreased gradually with increasing volume energy density (VED), and thus heterostructue with varying FCC and BCC phases were produced through regulating the VED. The Fe60(CoCrNiMn)40 MEA (with scanning speeds of 700 and 800 mm/s) showed excellent strength-plasticity balance (e.g. 476 MPa, 612 MPa and 63 %) compared to the equiatomic FeCoCrNiMn HEA, which is ascribed to the synergistic strengthening and toughening effects involving the twinning induced plasticity (TWIP) and the reinforcement caused by the BCC phase (act as reinforced particle) embedded in the FCC matrix

Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation

Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned. However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities. In this paper, we propose a cross-platform association model for cross-platform video recommendation, i.e., Disparity-preserved Deep Cross-platform Association (DCA), taking platform-specific disparity and granularity difference into consideration. The proposed DCA model employs a partially-connected multi-modal autoencoder, which is capable of explicitly capturing platform-specific information, as well as utilizing nonlinear mapping functions to handle granularity differences. We then present a cross-platform video recommendation approach based on the proposed DCA model. Extensive experiments for our cross-platform recommendation framework on real-world dataset demonstrate that the proposed DCA model significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics

My3DGen: A Scalable Personalized 3D Generative Model

In recent years, generative 3D face models (e.g., EG3D) have been developed to tackle the problem of synthesizing photo-realistic faces. However, these models are often unable to capture facial features unique to each individual, highlighting the importance of personalization. Some prior works have shown promise in personalizing generative face models, but these studies primarily focus on 2D settings. Also, these methods require both fine-tuning and storing a large number of parameters for each user, posing a hindrance to achieving scalable personalization. Another challenge of personalization is the limited number of training images available for each individual, which often leads to overfitting when using full fine-tuning methods. Our proposed approach, My3DGen, generates a personalized 3D prior of an individual using as few as 50 training images. My3DGen allows for novel view synthesis, semantic editing of a given face (e.g. adding a smile), and synthesizing novel appearances, all while preserving the original person's identity. We decouple the 3D facial features into global features and personalized features by freezing the pre-trained EG3D and training additional personalized weights through low-rank decomposition. As a result, My3DGen introduces only $\textbf{240K}$ personalized parameters per individual, leading to a $\textbf{127}\times$ reduction in trainable parameters compared to the $\textbf{30.6M}$ required for fine-tuning the entire parameter space. Despite this significant reduction in storage, our model preserves identity features without compromising the quality of downstream applications.Comment: Project page: https://luchaoqi.com/my3dgen

Bringing Telepresence to Every Desk

In this paper, we work to bring telepresence to every desktop. Unlike commercial systems, personal 3D video conferencing systems must render high-quality videos while remaining financially and computationally viable for the average consumer. To this end, we introduce a capturing and rendering system that only requires 4 consumer-grade RGBD cameras and synthesizes high-quality free-viewpoint videos of users as well as their environments. Experimental results show that our system renders high-quality free-viewpoint videos without using object templates or heavy pre-processing. While not real-time, our system is fast and does not require per-video optimizations. Moreover, our system is robust to complex hand gestures and clothing, and it can generalize to new users. This work provides a strong basis for further optimization, and it will help bring telepresence to every desk in the near future. The code and dataset will be made available on our website https://mcmvmc.github.io/PersonalTelepresence/

Study on the trend of congenital heart disease inpatient costs and its influencing factors in economically underdeveloped areas of China, 2015–2020: a case study of Gansu Province

BackgroundEconomic data on congenital heart disease are scarce in economically underdeveloped areas of China. Therefore, this study aimed to shed light on the level and changing trend of congenital heart disease inpatients' economic burden in underdeveloped areas.MethodThis study used a multi-stage stratified cluster sampling method to select 11,055 inpatients with congenital heart disease from 197 medical and health institutions in Gansu Province. Their medical records and expenses were obtained from the Hospital Information System. Univariate analysis was conducted using the rank sum test and Spearman rank correlation. Quantile regression and random forest were used to analyze the influencing factors.ResultsFrom 2015 to 2020, the average length of stay for congenital heart disease patients in Gansu Province was 10.09 days, with an average inpatient cost of USD 3,274.57. During this period, the average inpatient costs per time increased from USD 3,214.85 to USD 3,403.41, while the average daily inpatient costs increased from USD 330.05 to USD 376.56. The average out-of-pocket costs per time decreased from USD 2,305.96 to USD 754.77. The main factors that affected the inpatient costs included length of stay, cardiac procedure, proportion of medications, age, and hospital level.ConclusionCongenital heart disease causes a significant economic burden on both families and society. Therefore, to further reduce the patient's financial burden, the length of stay should be reasonably reduced, and the rational distribution of medical resources should be continuously promoted to ensure equitable access to healthcare services