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

Preface 1. Introduction Overview A Brief History of LMIs in Control Theory Notes on the Style of the Book Origin of the Book 2. Some Standard Problems Involving LMIs. Linear Matrix Inequalities Some Standard Problems Ellipsoid Algorithm Interior-Point Methods Strict and Nonstrict LMIs Miscellaneous Results on Matrix Inequalities Some LMI Problems with Analytic Solutions 3. Some Matrix Problems. Minimizing Condition Number by Scaling Minimizing Condition Number of a Positive-Definite Matrix Minimizing Norm by Scaling Rescaling a Matrix Positive-Definite Matrix Completion Problems Quadratic Approximation of a Polytopic Norm Ellipsoidal Approximation 4. Linear Differential Inclusions. Differential Inclusions Some Specific LDIs Nonlinear System Analysis via LDIs 5. Analysis of LDIs: State Properties. Quadratic Stability Invariant Ellipsoids 6. Analysis of LDIs: Input/Output Properties. Input-to-State Properties State-to-Output Properties Input-to-Output Properties 7. State-Feedback Synthesis for LDIs. Static State-Feedback Controllers State Properties Input-to-State Properties State-to-Output Properties Input-to-Output Properties Observer-Based Controllers for Nonlinear Systems 8. Lure and Multiplier Methods. Analysis of Lure Systems Integral Quadratic Constraints Multipliers for Systems with Unknown Parameters 9. Systems with Multiplicative Noise. Analysis of Systems with Multiplicative Noise State-Feedback Synthesis 10. Miscellaneous Problems. Optimization over an Affine Family of Linear Systems Analysis of Systems with LTI Perturbations Positive Orthant Stabilizability Linear Systems with Delays Interpolation Problems The Inverse Problem of Optimal Control System Realization Problems Multi-Criterion LQG Nonconvex Multi-Criterion Quadratic Problems Notation List of Acronyms Bibliography Index.

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Linear Matrix Inequalities in System and Control Theory

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

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Convex Analysisの二,三の進展について

The Theory of Matrices. By F R. Gantmacher. Two volumes, pp. 374 and 276. 1959. (Translated from the Russian by K. A. Hirsch; Chelsea Publishing Company, New York)

Stabilization of impulsive quadratic systems over polytopic sets

This paper considers the following problem: given an asymptotically stable equilibrium point of a nonlinear quadratic system, determine whether an assigned polytope surrounding the equilibrium point belongs to its domain of attraction. The proposed algorithm requires the solution of a feasibility problem that can be casted in terms of linear matrix inequalities constraints. In view of the important role played by quadratic models in systems biology, the work focuses on the application of the proposed technique for a quantitative study of the development of tumor phenomena in human beings.

Estimation of the Domain of Attraction of Equilibrium Points for Quadratic Systems: Application to Tumor Stability Analysis

State Constrained Control of Impulsive Quadratic Systems in Integrated Pest Management

OBJECTIVES
To compare second-look US with second-look DBT in the detection of additional lesions (ALs) with presurgical CESM.


METHODS
We retrospectively included 121 women with 128 ALs from patients who underwent CESM for presurgical staging at our centre from September 2016 to December 2018. These ALs underwent SL-US and a retrospective review of DBT (SL-DBT) performed 1-3 weeks prior to CESM to evaluate the performance of each technique individually and in combination. ALs in CESM images were evaluated according to enhancement type (focus, mass, or non-mass), size (<10 mm or >10 mm) and level of suspicion (BI-RADS 2, 3, four or 5). Our gold standard was post-biopsy histology, post-surgical specimen or >24 month negative follow-up. McNemar's test was used for the statistical analysis.


RESULTS
Out of the 128 Als, an imaging correlate was found for 71 (55.5 %,) with US, 79 (61.7%) with DBT, 53 (41.4 %) with DBT and US, and 97 (75.8%) with US and/or DBT. SL-DBT demonstrated a higher detection rate vs SL-US in non-mass enhancement (NME) pattern (p:0.0325) and DCIS histological type (p:0.0081). Adding SL-DBT improved the performance vs SL-US alone in the overall sample (p:<0.0001) and in every subcategory identified; adding SL-US to SL-DBT improved the detectability of ALs in the overall sample and in every category except for NME (p:0.0833), foci (p:0.0833) and B3 lesions (p:0.3173).


CONCLUSIONS
Combined second-look imaging (SL-DBT +SL US) for CESM ALs is superior to SL-DBT alone and SL-US alone. In B3 lesions, NME, and foci, the analysis of a larger sample could determine whether adding SL-US to SL-DBT is necessary or not.


ADVANCES IN KNOWLEDGE
Thanks to its high sensitivity, CESM is a useful tool in presurgical staging to detect the extent of the disease burden and identify ALs not detected with conventional imaging. Since CESM-guided biopsy systems are still scarcely available in clinical practice, it is necessary to look for other approaches to histologically characterize ALs detected with CESM. In our study, combined second-look imaging (SL-DBT +SL US) showed better performance in terms of detectability of ALs, than either SL-DBT or SL-US alone, and allowed us to identify 91.2% of ALs that turned out to be malignant at final histology; for the remaining 8.8% it was still necessary to perform MRI or MRI-guided biopsy. However, this issue could be solved once CESM-guided biopsies spread in clinical practice. SL-DBT demonstrated a higher detection rate than SL-US in NME and DCIS histology.

Francesco Amato

Papers

Stabilization of impulsive quadratic systems over polytopic sets

Estimation of the Domain of Attraction of Equilibrium Points for Quadratic Systems: Application to Tumor Stability Analysis

State Constrained Control of Impulsive Quadratic Systems in Integrated Pest Management

Comparison between second-look ultrasound and second-look digital breast tomosynthesis in the detection of additional lesions with presurgical CESM.

Relating the small world coefficient to the entropy of 2D networks and applications in neuromorphic engineering