Supervised classification of remotely sensed data using artificial neural networks (ANNs) is largely limited by the number of nodes and architecture of the ANN, given the limited training samples. The best practice is to train a number of networks and choose the one which gives maximum classification accuracy over the data other than the training data set. Recently, consensus schemes using mult...

Since the introduction of the Semantic Web, querying Linked Data mostly utilizes two types of interfaces: Linked Data documents, or the sparql protocol. However, both do not cover the wide spectrum of possible use cases and their specific requirements. Not only is the amount of public sparql endpoints is limited, they also suffer from frequent downtime [6, 15]. Predicting the consumption of com...

A classification function maps a set of vectors into several classes. machine learning problem is treated as design for partially defined functions. To realize functions MNIST hand written digits, three different architectures are considered: Single-unit realization, 45-unit and ×r realization. The realization consists 45 ternary classifiers, 10 counters, max selector. Test accuracy these compa...

This letter is concerned with the problem of selecting the best or most informative dimension for dimension reduction and feature extraction in high-dimensional data. The dimension of the data is reduced by principal component analysis; subsequent application of independent component analysis to the principal component scores determines the most nongaussian directions in the lower-dimensional s...

In this project, we discuss high-dimensional regression, where the dimension of the multivariate distribution is larger than the sample size, i.e. d n. With the assumption of sparse structure of the underlying multivariate distribution, we take the advantage of the `1 regularized method for parameter estimation. There are two major problems that will be discussed in this project: (1) a family o...

1. Introduction. This is a fascinating paper on an important topic: the choice of predictor variables in large-scale linear models. A previous paper in these pages attacked the same problem using the " LARS " algorithm (Efron, Hastie, Johnstone and Tibshirani [3]); actually three algorithms including the Lasso as middle case. There are tantalizing similarities between the Dantzig Selector (DS) ...

BACKGROUND In Drosophila, segment-specific muscle pattern is thought to be determined by the autonomous function of homeotic selector genes in the mesoderm in combination with inductive cues from the developing epidermis and nervous system. Here, we have examined the roles of homeotic genes in the patterning of the somatic muscles of the thoracic segments of Drosophila. RESULTS We determined ...

Ab affinity maturation in vivo is always accompanied by negative selection to maintain Ag specificity. In contrast, in vitro affinity maturation can lead to epitope spread, resulting in loss of specificity. Anti-ganglioside-GD2 mAbs are clinically effective against neuroblastoma; pain and neuropathy are major side effects. We used structural relatives of GD2 to define epitope spread during in v...

We consider post-selection inference for high-dimensional (generalized) linear models. Data carving from Fithian, Sun and Taylor [10] is a promising technique to perform this task. However, it suffers the instability of model selector hence, may lead poor replicability, especially in settings. propose multicarve method inspired by multisplitting improve upon stability replicability. Furthermore...

We initiate a study of garbled circuits that contain both Boolean and arithmetic gatesin secure multiparty computation. In particular, we incorporate the garbling gadgets for arithmeticcircuits recently presented by Ball, Malkin, and Rosulek (ACM CCS 2016) into the multiparty garblingparadigm initially introduced by Beaver, Micali, and Rogaway (STOC ’90). This is the first work that...