Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

On robust estimation of the location parameter

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

We address the problem of pulse shape discrimination (PSD) for radiation sources characterization by leveraging a Gaussian mixture variational autoencoder (GMVAE). When using PSD to characterize radiation sources, the number of emission sources and types of pulses to be classified is usually known. Yet, the creation of labeled data can be challenging for some classes as it requires expensive expert annotation. In this context, GMVAE can learn the distinct features of pulses from only unlabeled data. We show that classification accuracy can be further enhanced by adopting a semi-supervised GMVAE with auxiliary loss functions when labeled data are available. The preliminary results on two datasets with different number of classes suggest superior performance of GMVAE compared to other classifiers such as Gaussian mixture model (GMM) for unsupervised and semi-supervised learning and random forest for supervised learning.

Semi-Supervised Gaussian Mixture Variational Autoencoder for Pulse Shape Discrimination

structed suction, the spade-like tip of this modified suction dissector insinuates in the crevices between the surface of the facial nerve branches and the overlying parotid tissue, which can be readily lifted up using this robust instrument for meticulous division of the numerous vascular connections between the superficial and deep lobes of the parotid gland. In case there is some bleeding, gently stroking the nerve with the blunt sucker will give a clear view of the nerve branches. While the instrument is particularly well suited to parotidectomy, it proves very useful too for other neck operations such as block dissection. It is presented as a single instrument that can be used both to clearly identify the tissue planes and for retraction simultaneously and safely, thereby reducing operating time. Notably, our last 35 cases of parotidectomy have not been complicated by facial nerve injuries, either temporary or permanent.

Mesenteric panniculitis: a diagnosis of exclusion.

We demonstrate 3D time-of-flight imaging from a scattering target illuminated with a heralded single photon source. Our image reconstruction algorithm achieves millimeter depth resolution with only 0.3 average detected photons per image pixel.

/pdf/time-of-flight-depth-resolved-imaging-with-heralded-photon-q68jorf0sy.pdf

Time-of-Flight Depth-Resolved Imaging with Heralded Photon Source Illumination

We consider a graphical procedure to calculate the coding threshold for regular low-density parity-check (LDPC) codes over binary-input partial response (PR) channels using Gaussian approximations. The calculated threshold, while serving as a target for design of LDPC codes over PR channels, can also be used as a upper bound on the capacity-achieving SNR of PR channels.

Threshold for regular LDPC codes over PR channels

Depth profile measurements are performed in highly scattering underwater environments at stand-off distances equivalent to nine attenuation lengths. The optical system used a scanning transceiver and single-photon detection to produce high-resolution images with milliwatt average powers.

Stephen McLaughlin

Papers

Semi-Supervised Gaussian Mixture Variational Autoencoder for Pulse Shape Discrimination

Mesenteric panniculitis: a diagnosis of exclusion.

Time-of-Flight Depth-Resolved Imaging with Heralded Photon Source Illumination

Threshold for regular LDPC codes over PR channels

Underwater three-dimensional imaging using single-photon detection