University of California

About: University of California is a education organization based out in Oakland, California, United States. It is known for research contribution in the topics: Population & Layer (electronics). The organization has 55175 authors who have published 52933 publications receiving 1491169 citations. The organization is also known as: UC & University of California System.

Rapid growth of the US wildland-urban interface raises wildfire risk

Abstract: The wildland-urban interface (WUI) is the area where houses and wildland vegetation meet or intermingle, and where wildfire problems are most pronounced. Here we report that the WUI in the United States grew rapidly from 1990 to 2010 in terms of both number of new houses (from 30.8 to 43.4 million; 41% growth) and land area (from 581,000 to 770,000 km2; 33% growth), making it the fastest-growing land use type in the conterminous United States. The vast majority of new WUI areas were the result of new housing (97%), not related to an increase in wildland vegetation. Within the perimeter of recent wildfires (1990–2015), there were 286,000 houses in 2010, compared with 177,000 in 1990. Furthermore, WUI growth often results in more wildfire ignitions, putting more lives and houses at risk. Wildfire problems will not abate if recent housing growth trends continue.

Astrocytes: a central element in neurological diseases

Abstract: The neurone-centred view of the past disregarded or downplayed the role of astroglia as a primary component in the pathogenesis of neurological diseases. As this concept is changing, so is also the perceived role of astrocytes in the healthy and diseased brain and spinal cord. We have started to unravel the different signalling mechanisms that trigger specific molecular, morphological and functional changes in reactive astrocytes that are critical for repairing tissue and maintaining function in CNS pathologies, such as neurotrauma, stroke, or neurodegenerative diseases. An increasing body of evidence shows that the effects of astrogliosis on the neural tissue and its functions are not uniform or stereotypic, but vary in a context-specific manner from astrogliosis being an adaptive beneficial response under some circumstances to a maladaptive and deleterious process in another context. There is a growing support for the concept of astrocytopathies in which the disruption of normal astrocyte functions, astrodegeneration or dysfunctional/maladaptive astrogliosis are the primary cause or the main factor in neurological dysfunction and disease. This review describes the multiple roles of astrocytes in the healthy CNS, discusses the diversity of astroglial responses in neurological disorders and argues that targeting astrocytes may represent an effective therapeutic strategy for Alexander disease, neurotrauma, stroke, epilepsy and Alzheimer's disease as well as other neurodegenerative diseases.

The Use of Maximum Likelihood Estimates in {\chi^2} Tests for Goodness of Fit

Abstract: The usual test that a sample comes from a distribution of given form is performed by counting the number of observations falling into specified cells and applying the χ2 test to these frequencies. In estimating the parameters for this test, one may use the maximum likelihood (or equivalent) estimate based (1) on the cell frequencies, or (2) on the original observations. This paper shows that in (2), unlike the well known result for (1), the test statistic does not have a limiting χ2-distribution, but that it is stochastically larger than would be expected under the χ2 theory. The limiting distribution is obtained and some examples are computed. These indicate that the error is not serious in the case of fitting a Poisson distribution, but may be so for the fitting of a normal.

DeepMimic: example-guided deep reinforcement learning of physics-based character skills

Abstract: A longstanding goal in character animation is to combine data-driven specification of behavior with a system that can execute a similar behavior in a physical simulation, thus enabling realistic responses to perturbations and environmental variation. We show that well-known reinforcement learning (RL) methods can be adapted to learn robust control policies capable of imitating a broad range of example motion clips, while also learning complex recoveries, adapting to changes in morphology, and accomplishing user-specified goals. Our method handles keyframed motions, highly-dynamic actions such as motion-captured flips and spins, and retargeted motions. By combining a motion-imitation objective with a task objective, we can train characters that react intelligently in interactive settings, e.g., by walking in a desired direction or throwing a ball at a user-specified target. This approach thus combines the convenience and motion quality of using motion clips to define the desired style and appearance, with the flexibility and generality afforded by RL methods and physics-based animation. We further explore a number of methods for integrating multiple clips into the learning process to develop multi-skilled agents capable of performing a rich repertoire of diverse skills. We demonstrate results using multiple characters (human, Atlas robot, bipedal dinosaur, dragon) and a large variety of skills, including locomotion, acrobatics, and martial arts.

Phase Transitions and Generalized Motion by Mean Curvature

Abstract: We study the limiting behavior of solutions to appropriately rescaled versions of the Allen-Cahn equation, a simplified model for dynamic phase transitions. We rigorously establish the existence in the limit of a phase-antiphase interface evolving according to mean curvature motion. This assertion is valid for all positive time, the motion interpreted in the generalized sense of Evans-Spruck and Chen-Giga-Goto after the onset of geometric singularities.