A comparative study of benchmarking approaches for non-domestic buildings: Part 1 – Top-down approach

Benchmarking plays an important role in improving energy efficiency of non-domestic buildings. A review of energy benchmarks that underpin the UK’s Display Energy Certificate (DEC) scheme have prompted necessities to explore the benefits and limitations of using various methods to derive energy benchmarks. The existing methods were reviewed and grouped into top-down and bottom-up approaches based on the granularity of the data used. In the study, two top-down methods, descriptive statistics and artificial neural networks (ANN), were explored for the purpose of benchmarking energy performances of schools. The results were used to understand the benefits of using these benchmarks for assessing energy efficiency of buildings and the limitations that affect the robustness of the derived benchmarks. Compared to the bottom-up approach, top-down approaches were found to be beneficial in gaining insight into how peers perform. The relative rather than absolute feedback on energy efficiency meant that peer pressure was a motivator for improvement. On the other hand, there were limitations with regard to the extent to which the energy efficiency of a building could be accurately assessed using the top-down benchmarks. Moreover, difficulties in acquiring adequate data were identified as a key limitation to using the top-down approach for benchmarking non-domestic buildings. The study suggested that there are benefits in rolling out of DECs to private sector buildings and that there is a need to explore more complex methods to provide more accurate indication of energy efficiency in non-domestic buildings

Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario

Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well. This problem can be framed as follows: how to predict whether a user will like a manga that received few ratings from the community? Content-based techniques can alleviate this issue but require extra information, that is usually expensive to gather. In this paper, we use a deep learning technique, Illustration2Vec, to easily extract tag information from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE (Blended Alternate Least Squares with Explanation), a new model for collaborative filtering, that benefits from this extra information to recommend mangas. We show, using real data from an online manga recommender system called Mangaki, that our model improves substantially the quality of recommendations, especially for less-known manga, and is able to provide an interpretation of the taste of the users.Comment: 6 pages, 3 figures, 1 table, accepted at the MANPU 2017 workshop, co-located with ICDAR 2017 in Kyoto on November 10, 201

Shadow sovereign ratings for unrated developing countries

The authors attempt to predict sovereign ratings for developing countries that do not have risk ratings from agencies such as Fitch, Moody's, and Standard and Poor's. Ratings affect capital flows to developing countries through international bond, loan, and equity markets. Sovereign rating also acts as a ceiling for the foreign currency rating of sub-sovereign borrowers. As of the end of 2006, however, only 86 developing countries have been rated by the rating agencies. Of these, 15 countries have not been rated since 2004. Nearly 70 developing countries have never been rated. The results indicate that the unrated countries are not always at the bottom of the rating spectrum. Several unrated poor countries appear to have a"B"or higher rating, in a similar range as the emerging market economies with capital market access. Drawing on the literature, the analysis presents a stylized relationship between borrowing costs and the credit rating of sovereign bonds. The launch spread rises as the credit rating deteriorates, registering a sharp rise at the investment grade threshold. Based on these findings, a case can be made in favor of helping poor countries obtain credit ratings not only for sovereign borrowing, but for sub-sovereign entities'access to international debt and equity capital. The rating model, along with the stylized relationship between spreads and ratings can be useful for securitization and other financial structures, and for leveraging official aid for improving borrowing terms in poor countries.Economic Theory&Research,Country Strategy&Performance,Financial Intermediation,External Debt,Inequality

Mental Imagery and Chunks: Empirical and Computational Findings

To investigate experts’ imagery in chess, players were required to recall briefly-presented positions in which the pieces were placed on the intersections between squares (intersection positions). Position types ranged from game positions to positions where both the piece distribution and location were randomized. Simulations were run with the CHREST model (Gobet & Simon, 2000). The simulations assumed that pieces had to be centered back one by one to the middle of the squares in the mind’s eye before chunks could be recognized. Consistent with CHREST’s predictions, chess players (N = 36), ranging from weak amateurs to grandmasters, exhibited much poorer recall on intersection positions than on standard positions (pieces placed on centers of squares). On the intersection positions, the skill difference in recall was larger on game positions than on the randomized positions. Participants recalled bishops better than knights, suggesting that Stroop-like interference impairs recall of the latter. The data supported both the time parameter in CHREST for shifting pieces in the mind’s eye (125 ms per piece) and the seriality assumption. In general, the study reinforces the plausibility of CHREST as a model of cognition

Agent familiarity and emotional context influence the everyday empathic responding of young children with autism

Whereas research addressing empathy in ASD tends to employ pencil-and-paper and lab-based behavioural methods, the current study is novel in eliciting parent-report data regarding everyday empathy, sampling various emotional situations regularly encountered by children. Parents of typically-developing children and children diagnosed with ASD and DS completed the newly-developed Day-to-Day Child Empathy Questionnaire. Analysis of descriptions of their children’s responses to the various empathy-inducing situations supports the notion of an empathy deficit in ASD, confirming previous laboratory-based findings. However, important moderation effects were also demonstrated, for both control and clinical groups. In particular, parents reported children in all groups to be more likely to respond empathically to a familiar agent. The nature of children’s responses also according to the specific emotional context

Network Model Selection for Task-Focused Attributed Network Inference

Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection to evaluate network representations from data, focusing on fundamental predictive tasks on networks. We present a modular methodology using general, interpretable network models, task neighborhood functions found across domains, and several criteria for robust model selection. We demonstrate our methodology on three online user activity datasets and show that network model selection for the appropriate network task vs. an alternate task increases performance by an order of magnitude in our experiments

Quality, trade, and exchange rate pass-through

This paper investigates the heterogeneous response of exporters to real exchange rate fluctuations due to product quality. We model theoretically the effects of real exchange rate changes on the optimal price and quantity responses of firms that export multiple products with heterogeneous levels of quality. The model shows that the elasticity of demand perceived by exporters decreases with a real depreciation and with quality, leading to more pricing-to-market and to a smaller response of export volumes to a real depreciation for higher quality goods. We test empirically the predictions of the model by combining a unique data set of highly disaggregated Argentinean firm-level wine export values and volumes between 2002 and 2009 with experts wine ratings as a measure of quality. In response to a real depreciation, we find that firms significantly increase more their markups and less their export volumes for higher quality products, but only when exporting to high income destination countries. These findings remain robust to different measures of quality, samples, specifications, and to the potential endogeneity of quality