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Psoriasis vulgaris is a chronic inflammatory skin disease under the joint influence of multiple genetic and environmental factors. Its condition is repeated and protracted, which seriously affects the quality of life of patients. Sliding-cupping combined with calcipotriol ointment therapy as a form of traditional Chinese medicine combined with modern medicine therapy which is proved to be effective in RCTs and very suitable for patients, has been used in psoriasis vulgaris in patients for a long time, therefore a systematic review is necessary to provide available evidence for further study.

Whether in China or Japan, clothing solid modeling is a professional core course, but also a technical course. It requires the combination of rationality and sensibility in the process of creation. However, at present, there are many problems in the clothing modeling class of universities in China, such as prominent management problems, backward teaching ideas and poor students'enthusiasm. My innovative teaching ideas are: in this course, the introduction part of the course, combined with the three-dimensional structure of knowledge, with the clothing and platform as the carrier, through the profile experiment, the purpose is to arouse the students'initial consciousness of the body surface. The operation method is to allow students to cut the same circular, triangle, rectangle, square, polygon and other common plane geometric patterns before teaching, and put them on the human platform for modeling test. The open experimental proposition can give full play to students'imagination and stimulate students' enthusiasm. The teacher's assessment and examination are combined with the knowledge points of the course -- acupuncture, fabric and modeling techniques. The whole teaching method, with the geometry and the human platform as the carrier, let the students peel off the prototype thinking, think more about the layout of the cloth, and look for more stereoscopic material. In this way, we can get rid of the limitations of plane thinking and modeling techniques and expand creative thinking., Art and Design Research for Sustainable Development ; September 22, 2018 Conference: Tsukuba Global Science Week 2018 Date: September 20-22, 2018 Venue: Tsukuba International Congress Center Sponsored: University of Tsukuba

Bayesian optimization is a sequential decision making framework for optimizing expensive-to-evaluate black-box functions. Computing a full lookahead policy amounts to solving a highly intractable stochastic dynamic program. Myopic approaches, such as expected improvement, are often adopted in practice, but they ignore the long-term impact of the immediate decision. Existing nonmyopic approaches are mostly heuristic and/or computationally expensive. In this paper, we provide the first efficient implementation of general multi-step lookahead Bayesian optimization, formulated as a sequence of nested optimization problems within a multi-step scenario tree. Instead of solving these problems in a nested way, we equivalently optimize all decision variables in the full tree jointly, in a ``one-shot'' fashion. Combining this with an efficient method for implementing multi-step Gaussian process ``fantasization,'' we demonstrate that multi-step expected improvement is computationally tractable and exhibits performance superior to existing methods on a wide range of benchmarks.

Finite-horizon sequential experimental design (SED) arises naturally in many contexts, including hyperparameter tuning in machine learning among more traditional settings. Computing the optimal policy for such problems requires solving Bellman equations, which are generally intractable. Most existing work resorts to severely myopic approximations by limiting the decision horizon to only a single time-step, which can underweight exploration in favor of exploitation. We present BINOCULARS: Batch-Informed NOnmyopic Choices, Using Long-horizons for Adaptive, Rapid SED, a general framework for deriving efficient, nonmyopic approximations to the optimal experimental policy. Our key idea is simple and surprisingly effective: we first compute a one-step optimal batch of experiments, then select a single point from this batch to evaluate. We realize BINOCULARS for Bayesian optimization and Bayesian quadrature -- two notable SED problems with radically different objectives -- and demonstrate that BINOCULARS significantly outperforms myopic alternatives in real-world scenarios., 13 pages, 4 figures, 6 tables, 1 algorithm

Graph structured data are abundant in the real world. Among different graph types, directed acyclic graphs (DAGs) are of particular interest to machine learning researchers, as many machine learning models are realized as computations on DAGs, including neural networks and Bayesian networks. In this paper, we study deep generative models for DAGs, and propose a novel DAG variational autoencoder (D-VAE). To encode DAGs into the latent space, we leverage graph neural networks. We propose an asynchronous message passing scheme that allows encoding the computations on DAGs, rather than using existing simultaneous message passing schemes to encode local graph structures. We demonstrate the effectiveness of our proposed DVAE through two tasks: neural architecture search and Bayesian network structure learning. Experiments show that our model not only generates novel and valid DAGs, but also produces a smooth latent space that facilitates searching for DAGs with better performance through Bayesian optimization., Comment: Accepted by 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)

Active search is a learning paradigm for actively identifying as many members of a given class as possible. A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery. In this paper, we approach this problem in Bayesian decision framework. We first derive the Bayesian optimal policy under a natural utility, and establish a theoretical hardness of active search, proving that the optimal policy can not be approximated for any constant ratio. We also study the batch setting for the first time, where a batch of $b>1$ points can be queried at each iteration. We give an asymptotic lower bound, linear in batch size, on the adaptivity gap: how much we could lose if we query $b$ points at a time for $t$ iterations, instead of one point at a time for $bt$ iterations. We then introduce a novel approach to nonmyopic approximations of the optimal policy that admits efficient computation. Our proposed policy can automatically trade off exploration and exploitation, without relying on any tuning parameters. We also generalize our policy to batch setting, and propose two approaches to tackle the combinatorial search challenge. We evaluate our proposed policies on a large database of drug discovery and materials science. Results demonstrate the superior performance of our proposed policy in both sequential and batch setting; the nonmyopic behavior is also illustrated in various aspects., Machine Learning for Molecules and Materials (NeurIPS 2018 Workshop)