PREF: Predictability Regularized Neural Motion Fields

Knowing the 3D motions in a dynamic scene is essential to many vision applications. Recent progress is mainly focused on estimating the activity of some specific elements like humans. In this paper, we leverage a neural motion field for estimating the motion of all points in a multiview setting. Modeling the motion from a dynamic scene with multiview data is challenging due to the ambiguities in points of similar color and points with time-varying color. We propose to regularize the estimated motion to be predictable. If the motion from previous frames is known, then the motion in the near future should be predictable. Therefore, we introduce a predictability regularization by first conditioning the estimated motion on latent embeddings, then by adopting a predictor network to enforce predictability on the embeddings. The proposed framework PREF (Predictability REgularized Fields) achieves on par or better results than state-of-the-art neural motion field-based dynamic scene representation methods, while requiring no prior knowledge of the scene.Comment: Accepted at ECCV 2022 (oral). Paper + supplementary materia

Progressive Multi-view Human Mesh Recovery with Self-Supervision

To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e.g., motion capture, sport analysis) and robustness to single-view ambiguities. Existing solutions typically suffer from poor generalization performance to new settings, largely due to the limited diversity of image-mesh pairs in multi-view training data. To address this shortcoming, people have explored the use of synthetic images. But besides the usual impact of visual gap between rendered and target data, synthetic-data-driven multi-view estimators also suffer from overfitting to the camera viewpoint distribution sampled during training which usually differs from real-world distributions. Tackling both challenges, we propose a novel simulation-based training pipeline for multi-view human mesh recovery, which (a) relies on intermediate 2D representations which are more robust to synthetic-to-real domain gap; (b) leverages learnable calibration and triangulation to adapt to more diversified camera setups; and (c) progressively aggregates multi-view information in a canonical 3D space to remove ambiguities in 2D representations. Through extensive benchmarking, we demonstrate the superiority of the proposed solution especially for unseen in-the-wild scenarios.Comment: Accepted by AAAI202

Correlation of IL-10 with CD4^+CD25^+ T Regulatory Cells Acting on Effector T Cells

研究報告Original PaperRegulatory T (Treg) cells can suppress effector T cells, but the mechanism underlying its suppression function is not comprehended. Here we examine the inhibiting co-stimulating molecule CTLA4 and cytokines secreted by Treg cells, and explore the immunology mechanism of T regulatory cells acting on effector T cells in co-cultured system (CCS) and separating-cultured system (SCS). Compared with effector T cells, Treg cells expressed higher level CTLA4 and secreted much more IL-10 and TGF-β_1 (P<0.01). The inhibitory capacity of Treg cells co-cultured with effector T cells is much stronger than that in separating cultured group (P<0.01). Moreover, the inhibiting rate of Treg cells exerting on effector T cells through secreting IL-10 was more powerful than that through secreting TGF-β_1 (P<0.01). Both cell-to-cell contact and cytokines secretion mechanisms are involved in CD4^+CD25^+ Treg cells operating function. However, the former is more important. Interestingly, we for the first time point found that IL-10 make more powerful role than TGF-β_1 in the cytokines secretion mechanism

Antigen-primed CD4^+CD25^+ Regulatory T Cells Prevent Skin Graft Rejection : in vitro and in vivo studies

原著論文Original PaperIn the present studies, we examined the role of regulatory T cells in developing strategies to achieve skin graft-specific tolerance and explored the immune characteristic of Treg cells and the main mechanisms through which inducing transplantation tolerance. The 5×10^4 Treg could inhibit the MLR obviously, and the effect of the Treg to the MLR is dose dependent. The suppression rate of Treg to response T cell from the donor was higher than control group that came from the non-donor, indicating that the suppression of Treg to response T cell was antigen specific. SR of Treg in co-culture was greater than that in separate culture, inferring that CD4^+CD25^+ Treg cells exerted their suppressive effects on effector T cells through cell to cell contact mechanism and cytokines secretion mechanism. In the group 1×10^5 Treg injected, the mean survive time of skin grafts from C57BL/6 mice was obviously longer than the control group. These data suggest that antigen-primed CD^4+CD25^+Treg are effective therapeutic tool to prevent skin allograft rejection

Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network

A healthy and sustainable higher education system plays an important role in social development. The evaluation and prediction of such a system are vital for higher education. Existing models are usually constructed based on fewer indicators and original data are incomplete; thus, evaluation may be inefficient. In addition, these models are generally suitable for specific countries, rather than the whole universe. To tackle these issues, we proceed as follows: Firstly, we select a series of evaluation indicators that cover most aspects of higher education to establish a basic evaluation system. Then, we choose several representative countries to illustrate the system. Next, we use the analytic hierarchy process (AHP) to calculate a weight matrix of the indicators according to their importance. Furthermore, we obtain authoritative data from these countries. Then, we apply the indicators to the technique for order preference by similarity to an ideal solution (TOPSIS) algorithm to ascertain their relative levels. Finally, we combine the weight matrix with the relative levels to achieve a comprehensive evaluation of higher education. So far, a theoretical establishment of a higher education evaluation model has been generally completed. For better practical application, we add a predictive function to our evaluation model. Starting with China, we predict the development of national higher education for the next 20 years. We adopt a long short-term memory (LSTM) neural network as a method of prediction. Considering the significant influences of national policies on higher education, we address the issues under two circumstances: with or without policy influences. At last, we compare our model with existing models. Experimental results show that our model better reflects national higher education levels and provides more reasonable and robust prediction results

One-stop measurement model for fast and accurate tensor display characterization

Many light field displays are fundamentally different from other displays in that they do not have quantized pixels, quantized angular outputs, or a physical screen position, which can make definitions and characterization problematic. We have determined that it is more appropriate to express the spatial resolution in terms of spatial cutoff frequency rather than a physical distance as in the case of a display with actual quantized pixels. This concept is then extended to also encompass angular resolution. The technique exploits the fact that when spatial resolution of a sinusoidal grating pattern is halved, its contrast ratio is reduced by a known proportion. An improved model, based on an earlier design concept, has been developed. It not only can be used to measure spatial and angular cutoff frequencies, but also can enable comprehensive characterization of the display. This model provides fast, simple measurement with good accuracy. It does not use special equipment or require time-consuming subjective evaluations. Using the model to characterize images in a rapid, accurate manner validates the effectiveness of this technique.NRF (Natl Research Foundation, S’pore)Accepted versio