Adaptive Virtual Patient (AVP)
Deep neural networks have achieved great success in multiple learning problems, and attracted increasing attention from the medicine community. In reality, however, the limited availability and high costs of medical data is a major challenge of applying deep neural networks to computer-aided diagnosis and treatment planning. We address this challenge with adaptive virtual patients (AVPs) and the associated physics-informed learning framework. Specifically, the original training dataset is fused with an additional dataset of AVPs, which are generated by a data-driven model and the associated supervision (e.g., labels) is obtained by a physics-based approach. A key novelty in the proposed framework is the bidirectional and uncoupled generative invertible networks (GIN), which can extract pathophysiological features from the training medical image and generate pathophysiologically meaningful virtual patients. In order to mitigate the possibly high labeling cost of physical experiments, a $\mu$-measure design is conducted: this allows the AVPs to not only further explore the uncertain regions, but also balance the label distribution. We then discuss the pathophysiological interpretability of GIN both theoretically and experimentally, and demonstrate the effectiveness of AVPs using a real medical image dataset, in which the proposed AVPs lower the labeling cost by 90% while achieving a 15% improvement in prediction accuracy. …

Deep Graph Bayesian Optimization (DGBO)
Attributed graphs, which contain rich contextual features beyond just network structure, are ubiquitous and have been observed to benefit various network analytics applications. Graph structure optimization, aiming to find the optimal graphs in terms of some specific measures, has become an effective computational tool in complex network analysis. However, traditional model-free methods suffer from the expensive computational cost of evaluating graphs; existing vectorial Bayesian optimization methods cannot be directly applied to attributed graphs and have the scalability issue due to the use of Gaussian processes (GPs). To bridge the gap, in this paper, we propose a novel scalable Deep Graph Bayesian Optimization (DGBO) method on attributed graphs. The proposed DGBO prevents the cubical complexity of the GPs by adopting a deep graph neural network to surrogate black-box functions, and can scale linearly with the number of observations. Intensive experiments are conducted on both artificial and real-world problems, including molecular discovery and urban road network design, and demonstrate the effectiveness of the DGBO compared with the state-of-the-art. …

Sequential Set Generation (SSG)
Consider a general machine learning setting where the output is a set of labels or sequences. This output set is unordered and its size varies with the input. Whereas multi-label classification methods seem a natural first resort, they are not readily applicable to set-valued outputs because of the growth rate of the output space; and because conventional sequence generation doesn’t reflect sets’ order-free nature. In this paper, we propose a unified framework–sequential set generation (SSG)–that can handle output sets of labels and sequences. SSG is a meta-algorithm that leverages any probabilistic learning method for label or sequence prediction, but employs a proper regularization such that a new label or sequence is generated repeatedly until the full set is produced. Though SSG is sequential in nature, it does not penalize the ordering of the appearance of the set elements and can be applied to a variety of set output problems, such as a set of classification labels or sequences. We perform experiments with both benchmark and synthetic data sets and demonstrate SSG’s strong performance over baseline methods. …

Scala
Scala is an object-functional programming language for general software applications. Scala has full support for functional programming and a very strong static type system. This allows programs written in Scala to be very concise and thus smaller in size than other general-purpose programming languages. Many of Scala’s design decisions were inspired by criticism of the shortcomings of Java. Scala source code is intended to be compiled to Java bytecode, so that the resulting executable code runs on a Java virtual machine. Java libraries may be used directly in Scala code and vice versa (language interoperability). Like Java, Scala is object-oriented, and uses a curly-brace syntax reminiscent of the C programming language. Unlike Java, Scala has many features of functional programming languages like Scheme, Standard ML and Haskell, including currying, type inference, immutability, lazy evaluation, and pattern matching. It also has an advanced type system supporting algebraic data types, covariance and contravariance, higher-order types, and anonymous types. Other features of Scala not present in Java include operator overloading, optional parameters, named parameters, raw strings, and no checked exceptions. The name Scala is a portmanteau of ‘scalable’ and ‘language’, signifying that it is designed to grow with the demands of its users. …

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