Adaptive Matrix Completion for the Users and the Items in Tail

Recommender systems are widely used to recommend the most appealing items to users. These recommendations can be generated by applying collaborative filtering methods. The low-rank matrix completion method is the state-of-the-art collaborative filtering method. In this work, we show that the skewed distribution of ratings in the user-item rating matrix of real-world datasets affects the accuracy of matrix-completion-based approaches. Also, we show that the number of ratings that an item or a user has positively correlates with the ability of low-rank matrix-completion-based approaches to predict the ratings for the item or the user accurately. Furthermore, we use these insights to develop four matrix completion-based approaches, i.e., Frequency Adaptive Rating Prediction (FARP), Truncated Matrix Factorization (TMF), Truncated Matrix Factorization with Dropout (TMF + Dropout) and Inverse Frequency Weighted Matrix Factorization (IFWMF), that outperforms traditional matrix-completion-based approaches for the users and the items with few ratings in the user-item rating matrix.Comment: 7 pages, 3 figures, ACM WWW'1

Food security, risk management and climate change

This report identifies major constraints to the adaptive capacity of food organisations operating in Australia. This report is about food security, climate change and risk management. Australia has enjoyed an unprecedented level of food security for more than half a century, but there are new uncertainties emerging and it would be unrealistic – if not complacent – to assume the same level of food security will persist simply because of recent history. The project collected data from more than 36 case study organisations (both foreign and local) operating in the Australian food-supply chain, and found that for many businesses,  risk management practices require substantial improvement to cope with and exploit the uncertainties that lie ahead. Three risks were identified as major constraints to adaptive capacity of food organisations operating in Australia:  risk management practices; an uncertain regulatory environment – itself a result of gaps in risk management; climate change uncertainty and projections about climate change impacts, also related to risk management

Scalable processing and autocovariance computation of big functional data

This is the peer reviewed version of the following article: Brisaboa NR, Cao R, Paramá JR, Silva-Coira F. Scalable processing and autocovariance computation of big functional data. Softw Pract Exper. 2018; 48: 123–140 which has been published in final form at https://doi.org/10.1002/spe.2524 . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.[Abstract]: This paper presents 2 main contributions. The first is a compact representation of huge sets of functional data or trajectories of continuous-time stochastic processes, which allows keeping the data always compressed even during the processing in main memory. It is oriented to facilitate the efficient computation of the sample autocovariance function without a previous decompression of the data set, by using only partial local decoding. The second contribution is a new memory-efficient algorithm to compute the sample autocovariance function. The combination of the compact representation and the new memory-efficient algorithm obtained in our experiments the following benefits. The compressed data occupy in the disk 75% of the space needed by the original data. The computation of the autocovariance function used up to 13 times less main memory, and run 65% faster than the classical method implemented, for example, in the R package.This work was supported by the Ministerio de Economía y Competitividad (PGE and FEDER) under grants [TIN2016-78011-C4-1-R; MTM2014-52876-R; TIN2013-46238-C4-3-R], Centro para el desarrollo Tecnológico e Industrial MINECO [IDI-20141259; ITC-20151247; ITC-20151305; ITC-20161074]; Xunta de Galicia (cofounded with FEDER) under Grupos de Referencia Competitiva grant ED431C-2016-015; Xunta de Galicia-Consellería de Cultura, Educación e Ordenación Universitaria (cofounded with FEDER) under Redes grants R2014/041, ED341D R2016/045; Xunta de Galicia-Consellería de Cultura, Educación e Ordenación Universitaria (cofounded with FEDER) under Centro Singular de Investigación de Galicia grant ED431G/01.Xunta de Galicia; D431C-2016-015Xunta de Galicia; R2014/041Xunta de Galicia; ED341D R2016/045Xunta de Galicia; ED431G/0

International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

Fish to 2020: supply and demand in changing global markets

Using a state-of-the art computer model of global supply and demand for food and feed commodities, this book projects the likely changes in the fisheries sector over the next two decades. As prices for most food commodities fall, fish prices are expected to rise, reflecting demand for fish that outpaces the ability of the world to supply it. The model shows that developing countries will consume and produce a much greater share of the world's fish in the future, and trade in fisheries commodities will also increase. The authors show the causes and implications of these and other changes, and argue for specific actions and policies that can improve outcomes for the poor and for the environment.Supply balance, Trade, Aquaculture, Fishery management, Economic analysis, Environmental factors, Developing countries

Identifying the drivers of sustainable rural growth and poverty reduction in Honduras

"The overall objective of this paper is to develop an appropriate conceptual and analytical framework to better understand how prospects for growth and poverty reduction can be stimulated in rural Honduras. We employ complementary quantitative and qualitative methods of analysis, driven by an asset-base approach. Emphasis on assets is appropriate given high inequalities in the distribution of productive assets among households and geographical areas in Honduras. Such inequalities are likely to constrain how the poor share in the benefits of growth, even under appropriate policy regimes. We focus on household assets (broadly defined to include natural, physical, human, financial, social and locational assets) and their combinations necessary to take advantage of economic opportunities. We examine the relative contributions of these assets, and identify the combinations of productive, social, and location-specific assets that matter most to raise incomes and take advantage of prospects for poverty-reducing growth. Factor and cluster analysis techniques are used to identify and group different livelihood strategies; and econometric analysis is used to investigate the determinants of different livelihood strategies and the major factors that impact on income. Spatial analysis, community livelihood studies and project stocktakings are brought in to complement some of the more quantitative household survey data used. Our conclusions and recommendations are mainly focused on hillsides and hillside areas since the majority of the available data is for these areas." Authors' AbstractPoverty alleviation Latin America ,Sustainability ,Livelihoods ,Spatial analysis (Statistics) ,Hillside areas ,