Embracing the future: embedding digital repositories in the University of London

Digital repositories can help Higher Education Institutions (HEIs) to develop coherent and coordinated approaches to capture, identify, store and retrieve intellectual assets such as datasets, course material and research papers. With the advances of technology, an increasing number of Higher Education Institutions are implementing digital repositories. The leadership of these institutions, however, has been concerned about the awareness of and commitment to repositories, and their sustainability in the future. This study informs a consortium of thirteen London institutions with an assessment of current awareness and attitudes of stakeholders regarding digital repositories in three case study institutions. The report identifies drivers for, and barriers to, the embedding of digital repositories in institutional strategy. The findings therefore should be of use to decision-makers involved in the development of digital repositories. Our approach was entirely based on consultations with specific groups of stakeholders in three institutions through interviews with specific individuals. The research in this report was prepared for the SHERPA-LEAP Consortium and conducted by RAND Europe

Ultrafast spectroscopy of single molecules

We present a single-molecule study on femtosecond dynamics in multichromophoric systems, combining fs pump-probe, emission-spectra and fluorescence-lifetime analysis. At the single molecule level a wide range of exciton delocalisation lengths and energy redistribution times is revealed. Next, two color pump-probe experiments are presented as a step to addressing ultrafast energy transfer in individual complexes

Time series forecasting by principal covariate regression.

This paper is concerned with time series forecasting in the presence of a large numberof predictors. The results are of interest, for instance, in macroeconomic and financialforecasting where often many potential predictor variables are available. Most of thecurrent forecast methods with many predictors consist of two steps, where the largeset of predictors is first summarized by means of a limited number of factors -forinstance, principal components- and, in a second step, these factors and their lags areused for forecasting. A possible disadvantage of these methods is that the constructionof the components in the first step is not directly related to their use in forecasting inthe second step. This motivates an alternative method, principal covariate regression(PCovR), where the two steps are combined in a single criterion. This method hasbeen analyzed before within the framework of multivariate regression models. Moti-vated by the needs of macroeconomic time series forecasting, this paper discusses twoadjustments of standard PCovR that are necessary to allow for lagged factors and forpreferential predictors. The resulting nonlinear estimation problem is solved by meansof a method based on iterative majorization. The paper discusses some numericalaspects and analyzes the method by means of simulations. Further, the empirical per-formance of PCovR is compared with that of the two-step principal component methodby applying both methods to forecast four US macroeconomic time series from a set of132 predictors, using the data set of Stock and Watson (2005).distributed lags;dynamic factor models;economic forecasting;iterative majorization;principal components;principal covariate regression

Forecast comparison of principal component regression and principal covariate regression

Forecasting with many predictors is of interest, for instance, inmacroeconomics and finance. This paper compares two methods for dealing withmany predictors, that is, principal component regression (PCR) and principalcovariate regression (PCovR). Theforecast performance of these methods is compared by simulating data fromfactor models and from regression models. The simulations show that, in general, PCR performs better for the first type of data and PCovR performs better for the second type of data. The simulations also clarify the effect of the choice of the PCovR weight on the orecast quality.economic forecasting;principal components;factor model;principal covariates;regression model

Improved Construction of diffusion indexes for macroeconomic forecasting

This article proposes a modified method for the construction of diffusionindexes in macroeconomic forecasting using principal component regres-sion. The method aims to maximize the amount of variance of the origi-nal predictor variables retained by the diffusion indexes, by matching thedata windows used for constructing the principal components and for es-timating the diffusion index models. The method is applied to constructforecasts of eight monthly US macroeconomic time series, using the dataset of Stock and Watson (2002a). The results show that the proposedmethod leads, on average, to simpler models with smaller forecast errorsthan previously used methods.principal components;forecasting;factor construction

Low-carbohydrate diets affect energy balance and fuel homeostasis differentially in lean and obese rats

In parallel with increased prevalence of overweight people in affluent societies are individuals trying to lose weight, often using low-carbohydrate diets. Nevertheless, long-term metabolic consequences of those diets, usually high in (saturated) fat, remain unclear. Therefore, we investigated long-term effects of high-fat diets with different carbohydrate/protein ratios on energy balance and fuel homeostasis in obese (fa/fa) Zucker and lean Wistar rats. Animals were fed high-carbohydrate (HC), high-fat (HsF), or low-carbohydrate, high-fat, high-protein (LC-HsF-HP) diets for 60 days. Both lines fed the LC-HsF-HP diet displayed reduced energy intake compared with those fed the HsF diet (Zucker, -3.7%) or the HC diet (Wistar rats, -12.4%). This was not associated with lower weight gain relative to HC fed rats, because of increased food efficiencies in each line fed HsF and particularly LC-HsF-HP food. Zucker rats were less glucose tolerant than Wistar rats. Lowest glucose tolerances were found in HsF and particularly in LC-HsF-HP-fed animals irrespective of line, but this paralleled reduced plasma adiponectin levels, elevated plasma resistin levels, higher retroperitoneal fat masses, and reduced insulin sensitivity (indexed by insulin-induced hypoglycemia) only in Wistar rats. In Zucker rats, however, improved insulin responses during glucose tolerance testing and tendency toward increased insulin sensitivities were observed with HsF or LC-HsF-HP feeding relative to HC feeding. Thus, despite adverse consequences of LC-HsF diets on blood glucose homeostasis, principal differences exist in the underlying hormonal regulatory mechanisms, which could have benefits for B-cell functioning and insulin action in the obese state but not in the lean state.

Macroeconomic forecasting with real-time data: an empirical comparison

Macroeconomic forecasting is not an easy task, in particular if future growth rates are forecasted in real time. This paper compares various methods to predict the growth rate of US Industrial Production (IP) and of the Composite Coincident Index (CCI) of the Conference Board, over the coming month, quarter, and half year. It turns out that future IP growth rates can be forecasted in real time from ten leading indicators, by means of the Composite Leading Index (CLI) or, even somewhat better, by principal componentsregression. This amends earlier negative findings for IP by Diebold and Rudebusch. For CCI, on the other hand, simple autoregressive models do not provide significantly less accurate forecasts than single-equation and bivariate vector autoregressive models with the CLI. This amends some of the more positive results for CCI recently reported by the Conference Board. Not surprisingly, all forecast methods improve considerably if `ex post' data are used, after possible data updates and revisions.composite coincident index;forecast evaluation;industrial production;leading indicators;recessions;vintage date

How do neural networks see depth in single images?

Deep neural networks have lead to a breakthrough in depth estimation from single images. Recent work often focuses on the accuracy of the depth map, where an evaluation on a publicly available test set such as the KITTI vision benchmark is often the main result of the article. While such an evaluation shows how well neural networks can estimate depth, it does not show how they do this. To the best of our knowledge, no work currently exists that analyzes what these networks have learned. In this work we take the MonoDepth network by Godard et al. and investigate what visual cues it exploits for depth estimation. We find that the network ignores the apparent size of known obstacles in favor of their vertical position in the image. Using the vertical position requires the camera pose to be known; however we find that MonoDepth only partially corrects for changes in camera pitch and roll and that these influence the estimated depth towards obstacles. We further show that MonoDepth's use of the vertical image position allows it to estimate the distance towards arbitrary obstacles, even those not appearing in the training set, but that it requires a strong edge at the ground contact point of the object to do so. In future work we will investigate whether these observations also apply to other neural networks for monocular depth estimation.Comment: Submitte

The future is in our hands

A lot has been said and written about leadership theories and the effectiveness of the same. No other major contributor to organisational performance is focused on than leadership. But each individual is unique in her/his own ways. In the Kenyan context, the landscape has been turned upside down. From a male dominated sector where all vice chancellors of the public universities were men, now plethora of female vice chancellors can be counted from the public to the private universities almost in equal numbers. The performance of management of the various universities has ranged from mediocre to exceptional. Some management systems and leadership styles that could be replicated must be hidden somewhere. In this realm of knowledge, it therefore portends great danger if one was to prescribe a one-fits-all dose of the applicable leadership style in our higher education set up. But that is the very essence of science, to search and discover the discernible patterns that can be replicated across the line for posterity. Being the custodians of knowledge and disseminators of the same, the various complaints emanating from students, lecturers, staff and other stakeholders on how universities preach water and take gallons of wine in the field of management is a complaint that needs serious consideration. Further, having seen the problems that other learning institutions have had in management mostly traced to the fact that most administrators were plucked from class and given positions of leadership without orientation, then it behoves those in the scholarly world of management and leadership to synthesise some bitable bits that could assist those in positions of authority to appreciate the scientific approach to management. We conclude that time might have come when leadership in universities will not be reserved to academicians but to corporate executives capable of inspiring the whole institutions to great heights of performance excellence