Influence of street setbacks on solar reflection and air cooling by reflective streets in urban canyons

The ability of a climate model to accurately simulate the urban cooling effect of raising street albedo may be hampered by unrealistic representations of street geometry in the urban canyon. Even if the climate model is coupled to an urban canyon model (UCM), it is hard to define detailed urban geometries in UCMs. In this study, we relate simulated surface air temperature change to canyon albedo change. Using this relationship, we calculate scaling factors to adjust previously obtained surface air temperature changes that were simulated using generic canyon geometries. The adjusted temperature changes are obtained using a proposed multi-reflection urban canyon albedo model (UCAM), avoiding the need to rerun computationally expensive climate models. The adjusted temperature changes represent those that would be obtained from simulating with city-specific (local) geometries. Local urban geometries are estimated from details of the city's building stock and the city's street design guidelines. As a case study, we calculated average citywide seasonal scaling factors for realistic canyon geometries in Sacramento, California based on street design guidelines and building stock. The average scaling factors are multipliers used to adjust air temperature changes previously simulated by a Weather Research and Forecasting model coupled to an urban canyon model in which streets extended from wall to wall (omitting setbacks, such as sidewalks and yards). Sacramento's scaling factors ranged from 2.70 (summer) to 3.89 (winter), demonstrating the need to consider the actual urban geometry in urban climate studies

Applying the Grinblatt-Titman and the Conditional (Ferson-Schadt) Performance Measures: The Case of Industry Rotation Via the Dynamic Investment Model.

Nearly any standard financial model concludes that two assets with identical cash flows must sell for the same price. Alas, closed-end mutual fund company share prices seem to violate this fundamental tenant. Even when one considers several standard frictions, such as taxes and agency costs, classical financial models cannot explain the large persistent discounts found within the data. While the standard financial markets model may not explain the existence of large closed-end fund discounts, this paper shows that a rather close version of it does. In an otherwise frictionless market, if asset supplies vary randomly over time and agents possess finite lives, a closed-end mutual fund's stock price may not track its net asset value. Furthermore, the analysis provides a number of conditions under which these discrepancies will lead to the existence of systematic discounts for the mutual fund's shares. In addition, the model provides predictions regarding the correlation between current closed-end fund discounts and current changes in stock prices and future changes in corporate productivity. As the analysis shows, the same parameter values that lead to systematic discounts also lead to other fund price characteristics that resemble many of the results found within empirical studies.

Image-based food classification and volume estimation for dietary assessment: a review.

A daily dietary assessment method named 24-hour dietary recall has commonly been used in nutritional epidemiology studies to capture detailed information of the food eaten by the participants to help understand their dietary behaviour. However, in this self-reporting technique, the food types and the portion size reported highly depends on users' subjective judgement which may lead to a biased and inaccurate dietary analysis result. As a result, a variety of visual-based dietary assessment approaches have been proposed recently. While these methods show promises in tackling issues in nutritional epidemiology studies, several challenges and forthcoming opportunities, as detailed in this study, still exist. This study provides an overview of computing algorithms, mathematical models and methodologies used in the field of image-based dietary assessment. It also provides a comprehensive comparison of the state of the art approaches in food recognition and volume/weight estimation in terms of their processing speed, model accuracy, efficiency and constraints. It will be followed by a discussion on deep learning method and its efficacy in dietary assessment. After a comprehensive exploration, we found that integrated dietary assessment systems combining with different approaches could be the potential solution to tackling the challenges in accurate dietary intake assessment

Point2Volume: A vision-based dietary assessment approach using view synthesis

Dietary assessment is an important tool for nutritional epidemiology studies. To assess the dietary intake, the common approach is to carry out 24-h dietary recall (24HR), a structured interview conducted by experienced dietitians. Due to the unconscious biases in such self-reporting methods, many research works have proposed the use of vision-based approaches to provide accurate and objective assessments. In this article, a novel vision-based method based on real-time three-dimensional (3-D) reconstruction and deep learning view synthesis is proposed to enable accurate portion size estimation of food items consumed. A point completion neural network is developed to complete partial point cloud of food items based on a single depth image or video captured from any convenient viewing position. Once 3-D models of food items are reconstructed, the food volume can be estimated through meshing. Compared to previous methods, our method has addressed several major challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity, and it outperforms previous approaches in accurate portion size estimation

How predictable is technological progress?

Recently it has become clear that many technologies follow a generalized version of Moore's law, i.e. costs tend to drop exponentially, at different rates that depend on the technology. Here we formulate Moore's law as a correlated geometric random walk with drift, and apply it to historical data on 53 technologies. We derive a closed form expression approximating the distribution of forecast errors as a function of time. Based on hind-casting experiments we show that this works well, making it possible to collapse the forecast errors for many different technologies at different time horizons onto the same universal distribution. This is valuable because it allows us to make forecasts for any given technology with a clear understanding of the quality of the forecasts. As a practical demonstration we make distributional forecasts at different time horizons for solar photovoltaic modules, and show how our method can be used to estimate the probability that a given technology will outperform another technology at a given point in the future

Automatic Food Intake Assessment Using Camera Phones

Obesity is becoming an epidemic phenomenon in most developed countries. The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. It is essential to monitor everyday food intake for obesity prevention and management. Existing dietary assessment methods usually require manually recording and recall of food types and portions. Accuracy of the results largely relies on many uncertain factors such as user\u27s memory, food knowledge, and portion estimations. As a result, the accuracy is often compromised. Accurate and convenient dietary assessment methods are still blank and needed in both population and research societies. In this thesis, an automatic food intake assessment method using cameras, inertial measurement units (IMUs) on smart phones was developed to help people foster a healthy life style. With this method, users use their smart phones before and after a meal to capture images or videos around the meal. The smart phone will recognize food items and calculate the volume of the food consumed and provide the results to users. The technical objective is to explore the feasibility of image based food recognition and image based volume estimation. This thesis comprises five publications that address four specific goals of this work: (1) to develop a prototype system with existing methods to review the literature methods, find their drawbacks and explore the feasibility to develop novel methods; (2) based on the prototype system, to investigate new food classification methods to improve the recognition accuracy to a field application level; (3) to design indexing methods for large-scale image database to facilitate the development of new food image recognition and retrieval algorithms; (4) to develop novel convenient and accurate food volume estimation methods using only smart phones with cameras and IMUs. A prototype system was implemented to review existing methods. Image feature detector and descriptor were developed and a nearest neighbor classifier were implemented to classify food items. A reedit card marker method was introduced for metric scale 3D reconstruction and volume calculation. To increase recognition accuracy, novel multi-view food recognition algorithms were developed to recognize regular shape food items. To further increase the accuracy and make the algorithm applicable to arbitrary food items, new food features, new classifiers were designed. The efficiency of the algorithm was increased by means of developing novel image indexing method in large-scale image database. Finally, the volume calculation was enhanced through reducing the marker and introducing IMUs. Sensor fusion technique to combine measurements from cameras and IMUs were explored to infer the metric scale of the 3D model as well as reduce noises from these sensors