Meteorological signals in primary productivity at two mountain lakes

Fluctuations in primary productivity at two subalpine lakes reveal both meteorological and biological influences. At Castle Lake, California, large-scale climate events such as the El Niño/Southern Oscillation affect total annual production and, combined with human fishing activity, modify the seasonal pattern of productivity. At Lake Tahoe, California-Nevada, local spring weather conditions modulate annual production and its seasonality by determining the depth of mixing and resulting internal nutrient load. Climatic conditions also contribute to deviations from the long-term trend in productivity by increasing the incidence of forest fires and through anomalous external nutrient loads during precipitation extremes. A 3-year cycle in productivity of as yet unknown origin has also been detected at Lake Tahoe.

Anomalous atmospheric circulation and large winter floods in six subregions of the southwestern United States

EXTRACT (SEE PDF FOR FULL ABSTRACT): We examined atmospheric circulation conditions conducive to occurrence of winter floods that exceed the 10-year peak discharge on rivers in six hydroclimatic subregions in Arizona, southern Utah, Nevada, and California. ... This relationship between flooding and broad-scale atmospheric patterns in the modern record will aid in paleoclimatic interpretations of paleoflood records over the last few thousand years.

Random function priors for exchangeable arrays with applications to graphs and relational data

A fundamental problem in the analysis of structured relational data like graphs, networks, databases, and matrices is to extract a summary of the common structure underlying relations between individual entities. Relational data are typically encoded in the form of arrays; invariance to the ordering of rows and columns corresponds to exchangeable arrays. Results in probability theory due to Aldous, Hoover and Kallenberg show that exchangeable arrays can be represented in terms of a random measurable function which constitutes the natural model parameter in a Bayesian model. We obtain a flexible yet simple Bayesian nonparametric model by placing a Gaussian process prior on the parameter function. Efficient inference utilises elliptical slice sampling combined with a random sparse approximation to the Gaussian process. We demonstrate applications of the model to network data and clarify its relation to models in the literature, several of which emerge as special cases.

Curso de nivelación para los alumnos de nuevo ingreso en la Escuela Universitaria de Arquitectura Técnica de la Universidad Politécnica de Madrid

En la Escuela Universitaria de Arquitectura Técnica de Madrid, se ha desarrollado un curso que cabe denominar de Nivelación. Incluyó un conjunto de actividades destinadas a lograr que los alumnos accedieran con éxito al mundo Universitario. Era un curso dirigido a los alumnos que una vez concluido el bachillerato y superadas las Pruebas de Acceso a Estudios Universitarios habían sido admitidos para cursar el primer curso de los estudios conducentes al título universi-tario oficial de Arquitecto Técnico. El curso ha sido impartido por profesores de la Escuela Universitaria de Arquitectura Técnica de la Universidad Politécnica de Madrid adscritos, respectivamente, a los Departamentos "EXPRE-SIÓN GRAFICA APLICADA A LA EDIFICACIÓN", "MATEMÁTICAS APLICADAS A LA ARQUITECTURA TÉCNICA" y "TECNOLOGÍA DE LA EDIFICACIÓN". Cada uno de los tres Departamentos concretó inicialmente las asignaturas y los temarios a des-arrollar sí bien a lo largo del proceso las enseñanzas se produjeron algunas modificaciones para una mejor adaptación del curso a las necesidades reales de los alumnos. En la ponencia se analizarán los avances y descubrimientos de los profesores en sus respectivos campos y se presentarán las conclusiones obtenidas así como las posibles modificaciones para futuras ediciones de cursos similares.

Asesoría en talla de piedras semipreciosas : Suárez, departamento del Cauca.

[39] hojas : ilustraciones, fotografías.

Productos de representación de las actividades tradicionales, religiosas y festivas de Ráquira elaboradas en cerámica.

14 hojas : ilustraciones, fotografías a color

Informe final de asesoría en diseño para el mejoramiento del producto e Implementación de empaque.

52 hojas : ilustraciones, fotografías.

Early promoted : a memoir of the Rev. William Spiller Cox by Edward W Cox

http://www.archive.org/details/earlypromotedame00coxwuoft

SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-Oriented Navigation System

Both animals and mobile robots, or animats, need adaptive control systems to guide their movements through a novel environment. Such control systems need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once the environment is familiar. How reactive and planned behaviors interact together in real time, and arc released at the appropriate times, during autonomous navigation remains a major unsolved problern. This work presents an end-to-end model to address this problem, named SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation system. The model comprises several interacting subsystems, governed by systems of nonlinear differential equations. As the animat explores the environment, a vision module processes visual inputs using networks that arc sensitive to visual form and motion. Targets processed within the visual form system arc categorized by real-time incremental learning. Simultaneously, visual target position is computed with respect to the animat's body. Estimates of target position activate a motor system to initiate approach movements toward the target. Motion cues from animat locomotion can elicit orienting head or camera movements to bring a never target into view. Approach and orienting movements arc alternately performed during animat navigation. Cumulative estimates of each movement, based on both visual and proprioceptive cues, arc stored within a motor working memory. Sensory cues are stored in a parallel sensory working memory. These working memories trigger learning of sensory and motor sequence chunks, which together control planned movements. Effective chunk combinations arc selectively enhanced via reinforcement learning when the animat is rewarded. The planning chunks effect a gradual transition from reactive to planned behavior. The model can read-out different motor sequences under different motivational states and learns more efficient paths to rewarded goals as exploration proceeds. Several volitional signals automatically gate the interactions between model subsystems at appropriate times. A 3-D visual simulation environment reproduces the animat's sensory experiences as it moves through a simplified spatial environment. The SOVEREIGN model exhibits robust goal-oriented learning of sequential motor behaviors. Its biomimctic structure explicates a number of brain processes which are involved in spatial navigation.

SOVEREIGN: An Autonomous Neural System for Incrementally Learning Planned Action Sequence to Navigate Towards a Rewarded Goal

How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goaloriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and sizeinvariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.

Chamber investigations of atmospheric mercury oxidation chemistry

Mercury is a potent neurotoxin even at low concentrations. The unoxidised metal has a high vapour pressure and can circulate through the atmosphere, but when oxidised can deposit and be accumulated through the food chain. This work aims to investigate the oxidation processes of atmospheric Hg0(g). The first part describes efforts to make a portable Hg sensor based on Cavity Enhanced Absorption Spectroscopy (CEAS). The detection limit achieved was 66 ngm−3 for a 10 second averaging time. The second part of this work describes experiments carried out in a temperature controlled atmospheric simulation chamber in the Desert Research Institute, Reno, Nevada, USA. The chamber was built around an existing Hg CRDS system that could measure Hg concentrations in the chamber of<100 ngm−3 at 1 Hz enabling reactions to be followed. The main oxidant studied was bromine, which was quantified with a LED based CEAS system across the chamber. Hg oxidation in the chamber was found to be mostly too slow for current models to explain. A seven reaction model was developed and tested to find which parameters were capable of explaining the deviation. The model was overdetermined and no unique solution could be found. The most likely possibility was that the first oxidation step Hg + Br →HgBr was slower than the preferred literature value by a factor of two. However, if the more uncertain data at low [Br2] was included then the only parameter that could explain the experiments was a fast, temperature independent dissociation of HgBr some hundreds of times faster than predicted thermolysis or photolysis rates. Overall this work concluded that to quantitatively understand the reaction of Hg with Br2, the intermediates HgBr and Br must be measured. This conclusion will help to guide the planning of future studies of atmospheric Hg chemistry.

Agroclimas del cultivo de girasol en la Argentina

p.25-44