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Using Multivariate Statistics

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

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Twitter mood predicts the stock market.

How quickly inventors of new words get others to use their newly created words is an excellent measure for the persuasive power of the inventor. More generally, the speed with which new concepts are picked up by others is an efficient metric for the influence that the inventor of the new concept has within a community. Using Natural Language Processing (NLP) this influence can easily be measured. 

Measuring influence through quoting novel words

Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past. In this work, we present a deep learning forecasting framework which is capable to predict tomorrow's news topics on Twitter and news feeds based on yesterday's content and topic-interaction features. The proposed framework starts by generating topics from words using word embeddings and K-means clustering. Then temporal topic-networks are constructed where two topics are linked if the same user has worked on both topics. Structural and dynamic metrics calculated from networks along with content features and past activity, are used as input of a long short-term memory (LSTM) model, which predicts the number of mentions of a specific topic on the subsequent day. Utilizing dependencies among topics, our experiments on two Twitter datasets and the HuffPost news dataset demonstrate that selecting a topic's historical local neighbors in the topic-network as extra features greatly improves the prediction accuracy and outperforms existing baselines.

/pdf/towards-re-inventing-psychohistory-predicting-the-popularity-8rsb67qr6m.pdf

“Towards Re-Inventing Psychohistory”: Predicting the Popularity of Tomorrow’s News from Yesterday’s Twitter and News Feeds

Facial expressions indicate personality characteristics and ethical and moral values. Facial emotion recognition combined with machine learning automates this process. Because emotional responses predict tribal affiliation, the emotion shown on one's face in response to an external event predicts personality characteristics and moral values. 

Measuring moral values from facial expressions

To measure the flow state in teams, an entanglement metric for email logs of an organization has been defined. It looks at how synchronized the email exchange between two people is. The more they exchange messages in a similar rhythm, the more entangled they are. This metric has been validated in different organizations, finding that selective, focused entanglement of employees is a strong predictor of team creativity, employee satisfaction, employee performance, and customer satisfaction. 

Measuring entanglement

The chapter describes the interplay between morality and emotions. It shows how our emotional response to external events is dependent on our morals and value system. It explains how controlling our emotions can reduce stress, and introduces the moral value frameworks of Schwartz and Haidt, and the DOSPERT risk taking measurement system. 

Peter A. Gloor

Papers

Measuring influence through quoting novel words

“Towards Re-Inventing Psychohistory”: Predicting the Popularity of Tomorrow’s News from Yesterday’s Twitter and News Feeds

Measuring moral values from facial expressions

Measuring entanglement

The influence of morality on emotions