Building Disease Detection Algorithms with Very Small Numbers of Positive Samples

Although deep learning can provide promising results in medical image analysis, the lack of very large annotated datasets confines its full potential. Furthermore, limited positive samples also create unbalanced datasets which limit the true positive rates of trained models. As unbalanced datasets are mostly unavoidable, it is greatly beneficial if we can extract useful knowledge from negative samples to improve classification accuracy on limited positive samples. To this end, we propose a new strategy for building medical image analysis pipelines that target disease detection. We train a discriminative segmentation model only on normal images to provide a source of knowledge to be transferred to a disease detection classifier. We show that using the feature maps of a trained segmentation network, deviations from normal anatomy can be learned by a two-class classification network on an extremely unbalanced training dataset with as little as one positive for 17 negative samples. We demonstrate that even though the segmentation network is only trained on normal cardiac computed tomography images, the resulting feature maps can be used to detect pericardial effusion and cardiac septal defects with two-class convolutional classification networks

Symplectic Geometry on Quantum Plane

A study of symplectic forms associated with two dimensional quantum planes and the quantum sphere in a three dimensional orthogonal quantum plane is provided. The associated Hamiltonian vector fields and Poissonian algebraic relations are made explicit.Comment: 12 pages, Late

Compound memory networks for few-shot video classification

© Springer Nature Switzerland AG 2018. In this paper, we propose a new memory network structure for few-shot video classification by making the following contributions. First, we propose a compound memory network (CMN) structure under the key-value memory network paradigm, in which each key memory involves multiple constituent keys. These constituent keys work collaboratively for training, which enables the CMN to obtain an optimal video representation in a larger space. Second, we introduce a multi-saliency embedding algorithm which encodes a variable-length video sequence into a fixed-size matrix representation by discovering multiple saliencies of interest. For example, given a video of car auction, some people are interested in the car, while others are interested in the auction activities. Third, we design an abstract memory on top of the constituent keys. The abstract memory and constituent keys form a layered structure, which makes the CMN more efficient and capable of being scaled, while also retaining the representation capability of the multiple keys. We compare CMN with several state-of-the-art baselines on a new few-shot video classification dataset and show the effectiveness of our approach

Sharing of Unlicensed Spectrum by Strategic Operators

Facing the challenge of meeting ever-increasing demand for wireless data, the industry is striving to exploit large swaths of spectrum which anyone can use for free without having to obtain a license. Major standards bodies are currently considering a proposal to retool and deploy Long Term Evolution (LTE) technologies in unlicensed bands below 6 GHz. This paper studies the fundamental questions of whether and how the unlicensed spectrum can be shared by intrinsically strategic operators without suffering from the tragedy of the commons. A class of general utility functions is considered. The spectrum sharing problem is formulated as a repeated game over a sequence of time slots. It is first shown that a simple static sharing scheme allows a given set of operators to reach a subgame perfect Nash equilibrium for mutually beneficial sharing. The question of how many operators will choose to enter the market is also addressed by studying an entry game. A sharing scheme which allows dynamic spectrum borrowing and lending between operators is then proposed to address time-varying traffic and proved to achieve perfect Bayesian equilibrium. Numerical results show that the proposed dynamic sharing scheme outperforms static sharing, which in turn achieves much higher revenue than uncoordinated full-spectrum sharing. Implications of the results to the standardization and deployment of LTE in unlicensed bands (LTE-U) are also discussed.Comment: To appear in the IEEE Journal on Selected Areas in Communications, Special Issue on Game Theory for Network

Learning object categories from Google's image search

Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. We present an approach that can learn an object category from just its name, by utilizing the raw output of image search engines available on the Internet. We develop a new model, TSI-pLSA, which extends pLSA (as applied to visual words) to include spatial information in a translation and scale invariant manner. Our approach can handle the high intra-class variability and large proportion of unrelated images returned by search engines. We evaluate the models on standard test sets, showing performance competitive with existing methods trained on hand prepared datasets

Consumer Willingness to Pay and Marketing Opportunities for "Quality Guaranteed Tree-Ripened Peaches" in New York State

This study identifies consumer characteristics associated with willingness to pay a higher price for quality guaranteed tree-ripened peaches, with a focus on evaluating factors important to consumers when making decisions to purchase tree-ripened peaches. Telephone interviews were conducted with consumers in New York State in summer, 2002. Seventy-eight percent of the 258 survey respondents reported that they were willing to pay a higher price. A logistical regression model of willingness to pay was estimated. The empirical results indicated that willingness to pay was positively affected by the existence of previous experiences in purchasing tree-ripened peaches and by consumer dissatisfaction with peaches consumed in the past. An analysis of consumer experiences and consumer dissatisfaction showed that consumers in the two identified segments had mutually exclusive characteristics that present marketing opportunities for high quality New York-grown peaches.Consumer/Household Economics,

Cascaded lattice Boltzmann method for incompressible thermal flows with heat sources and general thermal boundary conditions

Cascaded or central-moment-based lattice Boltzmann method (CLBM) is a relatively recent development in the LBM community, which has better numerical stability and naturally achieves better Galilean invariance for a specified lattice compared with the classical single-relation-time (SRT) LBM. Recently, CLBM has been extended to simulate thermal flows based on the double-distribution-function (DDF) approach [L. Fei et al., Int. J. Heat Mass Transfer 120, 624 (2018)]. In this work, CLBM is further extended to simulate thermal flows involving complex thermal boundary conditions and/or a heat source. Particularly, a discrete source term in the central-moment space is proposed to include a heat source, and a general bounce-back scheme is employed to implement thermal boundary conditions. The numerical results for several canonical problems are in good agreement with the analytical solutions and/or numerical results in the literature, which verifies the present CLBM implementation for thermal flows