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

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

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

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,

Shuttling heat across 1D homogenous nonlinear lattices with a Brownian heat motor

We investigate directed thermal heat flux across 1D homogenous nonlinear lattices when no net thermal bias is present on average. A nonlinear lattice of Fermi-Pasta-Ulam-type or Lennard-Jones-type system is connected at both ends to thermal baths which are held at the same temperature on temporal average. We study two different modulations of the heat bath temperatures, namely: (i) a symmetric, harmonic ac-driving of temperature of one heat bath only and (ii) a harmonic mixing drive of temperature acting on both heat baths. While for case (i) an adiabatic result for the net heat transport can be derived in terms of the temperature dependent heat conductivity of the nonlinear lattice a similar such transport approach fails for the harmonic mixing case (ii). Then, for case (ii), not even the sign of the resulting Brownian motion induced heat flux can be predicted a priori. A non-vanishing heat flux (including a non-adiabatic reversal of flux) is detected which is the result of an induced dynamical symmetry breaking mechanism in conjunction with the nonlinearity of the lattice dynamics. Computer simulations demonstrate that the heat flux is robust against an increase of lattice sizes. The observed ratchet effect for such directed heat currents is quite sizable for our studied class of homogenous nonlinear lattice structures, thereby making this setup accessible for experimental implementation and verification.Comment: 9 pages, 10 figures. Phys. Rev. E (in press

Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy

Fractional exclusion and braid statistics in one dimension: a study via dimensional reduction of Chern-Simons theory

The relation between braid and exclusion statistics is examined in one-dimensional systems, within the framework of Chern-Simons statistical transmutation in gauge invariant form with an appropriate dimensional reduction. If the matter action is anomalous, as for chiral fermions, a relation between braid and exclusion statistics can be established explicitly for both mutual and nonmutual cases. However, if it is not anomalous, the exclusion statistics of emergent low energy excitations is not necessarily connected to the braid statistics of the physical charged fields of the system. Finally, we also discuss the bosonization of one-dimensional anyonic systems through T-duality.Comment: 19 pages, fix typo

Market Opportunities for New Sauerkraut Products

Food Consumption/Nutrition/Food Safety, Marketing,

Single Molecule Michaelis-Menten Equation beyond Quasi-Static Disorder

The classic Michaelis-Menten equation describes the catalytic activities for ensembles of enzyme molecules very well. But recent single-molecule experiment showed that the waiting time distribution and other properties of single enzyme molecule are not consistent with the prediction based on the viewpoint of ensemble. It has been contributed to the slow inner conformational changes of single enzyme in the catalytic processes. In this work we study the general dynamics of single enzyme in the presence of dynamic disorder. We find that at two limiting cases, the slow reaction and nondiffusion limits, Michaelis-Menten equation exactly holds although the waiting time distribution has a multiexponential decay behaviors in the nondiffusion limit.Particularly, the classic Michaelis-Menten equation still is an excellent approximation other than the two limits.Comment: 10 pages, 1 figur