Many tangible and intangible objects are represented as itemsets; i.e., composition of individual items. In this paper, we address the problem of finding the embedding of such items so as to use those embeddings in tasks like missing item prediction. We approach this problem by means of determinantal point process (DPP) in order to reflect the diversity within each set. Doing so requires an optimization of a log determinant of a symmetric positive definite (SPD) matrix. The standard practice to achieve this is to perform a low-rank decomposition of the matrix and derive update rules for the low rank matrix. In this work, we propose to approach this problem by means of item embedding. That is, we will learn the SPD matrix by trying to find the right vector representations for the given data for a fixed kernel function. To this end, we propose a novel algorithm to accurately compute the gradients of the log determinant with respect to the embedding vectors. We also show that our approach outperforms Autodiff-based learning in terms of gradient direction and running time, and that other general log determinant optimization problems can be addressed.

Designing a fallback authentication that is both memorable and strong poses a challenging task due to the need for authentication secrets to remain secure and easily recallable without frequent reinforcement. This could be especially prevalent for cloud computing security and resiliency. Inspired by the robust visual-spatial memory and associative memory of individuals, we introduce AuthZit, a novel system. AuthZit encodes authentication secrets as paths implementing a fault-tolerant algorithm through a 3D map of real-life places, navigated in both first person and 2D bird’s-eye perspective, coupled with a loci-tag (textual secret) associated with the location. Two experiments were conducted to iteratively design and evaluate AuthZit. First, it was observed that visual-spatial secrets are most memorable when navigated through a combination of 3D first-person and 2D bird’s-eye view perspectives. Second, we evaluated AuthZit against security questions and Android’s 9-dot pattern lock across three dimensions: memorability, security, and speed. AuthZit’s complexity-controlled secrets were significantly more memorable after three months, more resilient to shoulder surfing, and close adversaries.

Recent progress in deep learning-based models has improved photo-realistic (or perceptual) single-image super-resolution significantly. However, despite their powerful performance, many methods are difficult to apply to real-world applications because of the heavy computational requirements. To facilitate the use of a deep model under such demands, we focus on keeping the network efficient while maintaining its performance. In detail, we design an architecture that implements a cascading mechanism on a residual network to boost the performance with limited resources via multi-level feature fusion. In addition, our proposed model adopts group convolution and recursive schemes in order to achieve extreme efficiency. We further improve the perceptual quality of the output by employing the adversarial learning paradigm and a multi-scale discriminator approach. The performance of our method is investigated through extensive internal experiments and benchmarks using various datasets. Our results show that our models outperform the recent methods with similar complexity, for both traditional pixel-based and perception-based tasks.

OBJECTIVE: Accessing medical data from multiple institutions is difficult owing to the interinstitutional diversity of vocabularies. Standardization schemes, such as the common data model, have been proposed as solutions to this problem, but such schemes require expensive human supervision. This study aims to construct a trainable system that can automate the process of semantic interinstitutional code mapping. MATERIALS AND METHODS: To automate mapping between source and target codes, we compute the embedding-based semantic similarity between corresponding descriptive sentences. We also implement a systematic approach for preparing training data for similarity computation. Experimental results are compared to traditional word-based mappings. RESULTS: The proposed model is compared against the state-of-the-art automated matching system, which is called Usagi, of the Observational Medical Outcomes Partnership common data model. By incorporating multiple negative training samples per positive sample, our semantic matching method significantly outperforms Usagi. Its matching accuracy is at least 10% greater than that of Usagi, and this trend is consistent across various top-k measurements. DISCUSSION: The proposed deep learning-based mapping approach outperforms previous simple word-level matching algorithms because it can account for contextual and semantic information. Additionally, we demonstrate that the manner in which negative training samples are selected significantly affects the overall performance of the system. CONCLUSION: Incorporating the semantics of code descriptions more significantly increases matching accuracy compared to traditional text co-occurrence-based approaches. The negative training sample collection methodology is also an important component of the proposed trainable system that can be adopted in both present and future related systems.

We propose a novel method for identifying and classifying motions that offers significantly reduced computational cost as compared to deep convolutional neural network systems with comparable performance. Our new approach is inspired by the information processing network architecture of biological visual processing systems, whereby spatial pyramid kernel features are efficiently extracted in real-time from temporally-differentiated image data. In this paper, we describe this new method and evaluate its performance with a hand motion gesture recognition task.

Determinantal Point Process (DPP) has gained much popularity for modeling sets of diverse items. The gist of DPP is that the probability of choosing a particular set of items is proportional to the determinant of a positive definite matrix that defines the similarity of those items. However, computing the determinant requires time cubic in the number of items, and is hence impractical for large sets. In this paper, we address this problem by constructing a rapidly mixing Markov chain, from which we can acquire a sample from the given DPP in sub-cubic time. In addition, we show that this framework can be extended to sampling from cardinality-constrained DPPs. As an application, we show how our sampling algorithm can be used to provide a fast heuristic for determining the number of clusters, resulting in better clustering.