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

Zeolites are one of humanity’s most important synthetic products. These aluminosilicate-based materials represent a large segment of the global economy. Indeed, the value of zeolites used in petroleum refining as catalysts and in detergents as water softeners is estimated at $350 billion per year. A major current goal in zeolite chemistry is to create a structure in which metal ions and functionalizable organic units make up an integral part of the framework. Such a structure, by virtue of the flexibility with which metal ions and organic moieties can be varied, is viewed as a key to further improving zeolite properties and accessing new applications. Recently, it was recognized that the Si−O−Si preferred angle in zeolites (145°) is coincident with that of the bridging angle in the M−Im−M fragment (where M is Zn or Co and Im is imidazolate), and therefore it should be possible to make new zeolitic imidazolate frameworks (ZIFs) with topologies based on those of tetrahedral zeolites. This idea was successfu...

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Synthesis, Structure, and Carbon Dioxide Capture Properties of Zeolitic Imidazolate Frameworks

2.1.2. Low Topology/Framework Density 1003 2.1.3. Side Group Directed Superstructures 1003 2.2. Synthesis Considerations 1003 2.3. Special Properties 1004 3. Metal Imidazolate Frameworks 1004 3.1. Chains and Rings 1004 3.2. Zeolitic and Zeolite-like Frameworks 1006 3.2.1. SOD-Type Zinc(II) 2-Methylimidazolate 1007 3.3. Nonporous 4-Connected Networks 1010 3.4. Polyimidazolates 1011 4. Metal Pyrazolate Frameworks 1011 4.1. Clusters and Chains 1011 4.2. 3D Networks Based on Polypyrazolates 1012 5. Metal 1,2,4-Triazolate Frameworks 1014 5.1. Simple 3-Connected Networks 1015 5.2. Quasi-Imidazolates 1018 5.3. With Coordinative Substituents 1019 5.4. With Secondary Counterions and/or Ligands 1021 6. Metal 1,2,3-Triazolate Frameworks 1023 7. Metal Tetrazolate Frameworks 1025 7.1. Univalent Coinage-Metal Tetrazolate Frameworks 1025

Metal Azolate Frameworks: From Crystal Engineering to Functional Materials

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Digital Coding of Waveforms

Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image’s gradient vector flow field and the evolving contour’s normal. We evaluate the proposed method for segmentation of various regions in real MRI and CT slices, X-ray images, and ultra sound images. Evaluation results confirm the advantage of weighting energy forces using local edge features to reduce leakage. These results also show that the proposed method leads to more accurate boundary detection results than the state-of-the-art edge-based level set segmentation methods, particularly around weak edges.

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Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation

Over the past decade, video anomaly detection has been explored with remarkable results. However, research on methodologies suitable for online performance is still very limited. In this paper, we present an online framework for video anomaly detection. The key aspect of our framework is a compact set of highly descriptive features, which is extracted from a novel cell structure that helps to define support regions in a coarse-to-fine fashion. Based on the scene’s activity, only a limited number of support regions are processed, thus limiting the size of the feature set. Specifically, we use foreground occupancy and optical flow features. The framework uses an inference mechanism that evaluates the compact feature set via Gaussian Mixture Models, Markov Chains, and Bag-of-Words in order to detect abnormal events. Our framework also considers the joint response of the models in the local spatio-temporal neighborhood to increase detection accuracy. We test our framework on popular existing data sets and on a new data set comprising a wide variety of realistic videos captured by surveillance cameras. This particular data set includes surveillance videos depicting criminal activities, car accidents, and other dangerous situations. Evaluation results show that our framework outperforms other online methods and attains a very competitive detection performance compared with state-of-the-art non-online methods.

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Video Anomaly Detection With Compact Feature Sets for Online Performance

We present an automatic system for planar 3D modeling of building interiors from point cloud data generated by range scanners. This is motivated by the observation that most building interiors may be modeled as a collection of planes representing ceilings, floors, walls and staircases. Our proposed system, which employs model-fitting and RANSAC, is capable of detecting large-scale architectural structures, such as ceilings and floors, as well as small-scale architectural structures, such as staircases. We experimentally validate our system on a number of challenging point clouds of real architectural scenes.

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Planar 3D modeling of building interiors from point cloud data

Computer simulation has been employed to evaluate proposed changes in the delivery of health care. However, little is known about the utility of simulation approaches for analysis of changes in the delivery of surgical care. We searched eight bibliographic databases for this comprehensive review of the literature published over the past five decades, and found 34 publications that reported on simulation models for the flow of surgical patients. The majority of these publications presented a description of the simulation approach: 91% outlined the underlying assumptions for modeling, 88% presented the system requirements, and 91% described the input and output data. However, only half of the publications reported that models were constructed to address the needs of policy-makers, and only 26% reported some involvement of health system managers and policy-makers in the simulation study. In addition, we found a wide variation in the presentation of assumptions, system requirements, input and output data, and results of simulation-based policy analysis.

Systematic Review of the Use of Computer Simulation Modeling of Patient Flow in Surgical Care

We propose a novel symmetry-based technique for scalable lossless compression of 3D medical image data. The proposed method employs the 2D integer wavelet transform to decorrelate the data and an intraband prediction method to reduce the energy of the sub-bands by exploiting the anatomical symmetries typically present in structural medical images. A modified version of the embedded block coder with optimized truncation (EBCOT), tailored according to the characteristics of the data, encodes the residual data generated after prediction to provide resolution and quality scalability. Performance evaluations on a wide range of real 3D medical images show an average improvement of 15% in lossless compression ratios when compared to other state-of-the art lossless compression methods that also provide resolution and quality scalability including 3D-JPEG2000, JPEG2000, and H.264/AVC intra-coding.

Victor Sanchez

Papers

Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation

Video Anomaly Detection With Compact Feature Sets for Online Performance

Planar 3D modeling of building interiors from point cloud data

Systematic Review of the Use of Computer Simulation Modeling of Patient Flow in Surgical Care

Symmetry-Based Scalable Lossless Compression of 3D Medical Image Data