Many of California's local law enforcement agencies have access to facial recognition software for identifying suspects who appear in crime scene footage, documents obtained through public records requests show. Three California counties also have the capability to run facial recognition searches on each others' mug shot databases, and others could join if they choose to opt into a network maintained by a private law enforcement software company. The network is called California Facial Recognition Interconnect, and it's a service offered by DataWorks Plus, a Greenville, South Carolina–based company with law enforcement contracts in Los Angeles, San Bernardino, San Diego, San Francisco, Sacramento, and Santa Barbara. Currently, the three adjacent counties of Los Angeles, Riverside, and San Bernardino are able to run facial recognition against mug shots in each other's databases. That means these police departments have access to about 11.7 million mug shots of people who have previously been arrested, a majority of which come from the Los Angeles system.
Palo Alto Research Center, Mail Stop: 3333 Coyote Hill Road, Palo Alto, CA 94034 USA Abstract Our research aims at developing intelligent systems to reduce the transportation-related energy expenditure of a large city by influencing individual behavior. We introduce Copter - an intelligent travel assistant that evaluates multi-modal travel alternatives to find a plan that is acceptable to a person given their context and preferences. We propose a formulation for acceptable planning that brings together ideas from AI, machine learning, and economics. This formulation has been incorporated in Copter that produces acceptable plans in real-time. We adopt a novel empirical evaluation framework that combines human decision data with a high fidelity multi-modal transportation simulation to demonstrate a 4% energy reduction and 20% delay reduction in a realistic deployment scenario in Los Angeles, California, USA. 1. Introduction Transportation is one of the largest consumers of energy in the ...
Background: Stress is a contributing factor to many major health problems in the United States, such as heart disease, depression, and autoimmune diseases. Relaxation is often recommended in mental health treatment as a frontline strategy to reduce stress, thereby improving health conditions. Objective: The objective of our study was to understand how people express their feelings of stress and relaxation through Twitter messages. Methods: We first performed a qualitative content analysis of 1326 and 781 tweets containing the keywords "stress" and "relax", respectively. We then investigated the use of machine learning algorithms to automatically classify tweets as stress versus non stress and relaxation versus non relaxation. Finally, we applied these classifiers to sample datasets drawn from 4 cities with the goal of evaluating the extent of any correlation between our automatic classification of tweets and results from public stress surveys. Results: Content analysis showed that the most frequent topic of stress tweets was education, followed by work and social relationships. The most frequent topic of relaxation tweets was rest and vacation, followed by nature and water. When we applied the classifiers to the cities dataset, the proportion of stress tweets in New York and San Diego was substantially higher than that in Los Angeles and San Francisco. Conclusions: This content analysis and infodemiology study revealed that Twitter, when used in conjunction with natural language processing techniques, is a useful data source for understanding stress and stress management strategies, and can potentially supplement infrequently collected survey-based stress data.
Last year, communities banded together to prove that they can--and will--defend their privacy rights. As part of ACLU-led campaigns, three California cities--San Francisco, Berkeley, and Oakland--as well as three Massachusetts municipalities--Somerville, Northhampton, and Brookline--banned the government's use of face recognition from their communities. Following another ACLU effort, the state of California blocked police body cam use of the technology, forcing San Diego's police department to shutter its massive face surveillance flop. And in New York City, tenants successfully fended off their landlord's efforts to install face surveillance. Even the private sector demonstrated it had a responsibility to act in the face of the growing threat of face surveillance.
Kai-Yeung Siu Dept. of Electrical & Computer Engineering University of California, Irvine Irvine, CA 92717 Jehoshua Bruck IBM Research Division Almaden Research Center San Jose, CA 95120-6099 Abstract An important issue in neural computation is the dynamic range of weights in the neural networks. Many experimental results on learning indicate that the weights in the networks can grow prohibitively large with the size of the inputs. We show that there is an efficient way of simulating a network of LTEs with large weights by a network of LTEs with small weights. To prove these results, we use tools from harmonic analysis of Boolean functions. Our technique is quite general, it provides insights to some other problems.