There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Emotion and Adaptation

Recent interest in aspects common to quantum information and condensed matter has prompted a flurry of activity at the border of these disciplines that were far distant until a few years ago. Numerous interesting questions have been addressed so far. Here an important part of this field, the properties of the entanglement in many-body systems, are reviewed. The zero and finite temperature properties of entanglement in interacting spin, fermion, and boson model systems are discussed. Both bipartite and multipartite entanglement will be considered. In equilibrium entanglement is shown tightly connected to the characteristics of the phase diagram. The behavior of entanglement can be related, via certain witnesses, to thermodynamic quantities thus offering interesting possibilities for an experimental test. Out of equilibrium entangled states are generated and manipulated by means of many-body Hamiltonians.

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Entanglement in many-body systems

The theory of affordances

Strength reduction factors which permit estimation of inelastic strength demands from elastic strength demands are evaluated. Results from various investigations of strength reduction factors carried out over the last 30 years are reviewed, and their results are presented in a common format which facilitates their comparison. The main parameters that affect the magnitude of strength reductions are discussed. The evaluation of the results indicates that strength reductions are primarily influenced by the maximum tolerable displacement ductility demand, the period of the system and the soil conditions at the site. Simplified expressions of strength reduction factors to estimate inelastic design spectra as functions of these primary‐influencing parameters are presented.

Evaluation of Strength Reduction Factors for Earthquake-Resistant Design:

New digital musical instruments : control and interaction beyond the keyboard

Significance of residual drifts in building earthquake loss estimation

An approximate method to estimate the maximum lateral deformation demands in multistory buildings responding primarily in the fundamental mode when subjected to earthquake ground motions is presented. This method permits a rapid estimation of the maximum roof displacement and of the maximum interstory drift for a given acceleration time history or for a given displacement response spectrum. A multistory building is modeled as an equivalent continuum structure consisting of a combination of a flexural cantilever beam and a shear cantilever beam. The simplified model is used to investigate the ratio of the spectral displacement to the roof displacement and the ratio of the maximum interstory drift ratio to the roof drift ratio. The effect of the distribution of lateral forces along the height of the building and of the ratio of overall flexural and shear deformations is examined. Lateral deformation demands of a 10-story steel building computed with the simplified method when subjected to various earthquake...

Approximate Seismic Lateral Deformation Demands in Multistory Buildings

An approximate method to estimate floor acceleration demands in multistory buildings responding elastically or practically elastic when subjected to earthquake ground motion is presented. The method can be used to estimate floor acceleration demands at any floor level for a given ground motion record. The dynamic characteristics of the building are approximated by using a simplified model based on equivalent continuum structure that consists of a combination of a flexural beam and a shear beam. Closed-form solutions for mode shapes, period ratios, and modal participation factors are presented. The effect of reduction of lateral stiffness along the height is investigated. It is shown that the effect of reduction in lateral stiffness on the dynamic characteristics of the structure is small in buildings that deflect laterally like flexural beams. For other buildings, approximate correction factors to the closed-form solutions of the uniform case are presented to take into account the effects of reduction of lateral stiffness. Approximate dynamic properties of the building are then used to estimate acceleration demands in the building using modal analysis.

Eduardo Miranda

Papers

Evaluation of Strength Reduction Factors for Earthquake-Resistant Design:

New digital musical instruments : control and interaction beyond the keyboard

Significance of residual drifts in building earthquake loss estimation

Approximate Seismic Lateral Deformation Demands in Multistory Buildings

Approximate Floor Acceleration Demands in Multistory Buildings. I: Formulation