An Analysis of Potential Tax Incentives to Increase Charitable Giving in Puerto Rico

Compares options for improving tax incentives for charitable giving, including lifting the ceiling on deductions as a percentage of adjusted gross income, and estimated effects on nonprofits in Puerto Rico, where average giving is high relative to AGI

The stability of a graph partition: A dynamics-based framework for community detection

Recent years have seen a surge of interest in the analysis of complex networks, facilitated by the availability of relational data and the increasingly powerful computational resources that can be employed for their analysis. Naturally, the study of real-world systems leads to highly complex networks and a current challenge is to extract intelligible, simplified descriptions from the network in terms of relevant subgraphs, which can provide insight into the structure and function of the overall system. Sparked by seminal work by Newman and Girvan, an interesting line of research has been devoted to investigating modular community structure in networks, revitalising the classic problem of graph partitioning. However, modular or community structure in networks has notoriously evaded rigorous definition. The most accepted notion of community is perhaps that of a group of elements which exhibit a stronger level of interaction within themselves than with the elements outside the community. This concept has resulted in a plethora of computational methods and heuristics for community detection. Nevertheless a firm theoretical understanding of most of these methods, in terms of how they operate and what they are supposed to detect, is still lacking to date. Here, we will develop a dynamical perspective towards community detection enabling us to define a measure named the stability of a graph partition. It will be shown that a number of previously ad-hoc defined heuristics for community detection can be seen as particular cases of our method providing us with a dynamic reinterpretation of those measures. Our dynamics-based approach thus serves as a unifying framework to gain a deeper understanding of different aspects and problems associated with community detection and allows us to propose new dynamically-inspired criteria for community structure.Comment: 3 figures; published as book chapte

Emergence of slow-switching assemblies in structured neuronal networks

Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal networks is a key step to advance our understanding of neuronal information processing. Here we investigate how particular features of network connectivity underpin the propensity of neural networks to generate slow-switching assembly (SSA) dynamics, i.e., sustained epochs of increased firing within assemblies of neurons which transition slowly between different assemblies throughout the network. We show that the emergence of SSA activity is linked to spectral properties of the asymmetric synaptic weight matrix. In particular, the leading eigenvalues that dictate the slow dynamics exhibit a gap with respect to the bulk of the spectrum, and the associated Schur vectors exhibit a measure of block-localization on groups of neurons, thus resulting in coherent dynamical activity on those groups. Through simple rate models, we gain analytical understanding of the origin and importance of the spectral gap, and use these insights to develop new network topologies with alternative connectivity paradigms which also display SSA activity. Specifically, SSA dynamics involving excitatory and inhibitory neurons can be achieved by modifying the connectivity patterns between both types of neurons. We also show that SSA activity can occur at multiple timescales reflecting a hierarchy in the connectivity, and demonstrate the emergence of SSA in small-world like networks. Our work provides a step towards understanding how network structure (uncovered through advancements in neuroanatomy and connectomics) can impact on spatio-temporal neural activity and constrain the resulting dynamics.Comment: The first two authors contributed equally -- 18 pages, including supplementary material, 10 Figures + 2 SI Figure

Randomised Controlled Trials in Diabetes Research : A Pathway to Interpreting Published Results

Altres ajuts: Ferrer, GluSense, Zealand, Eli Lilly and Company, Novartis, Sanofi, Medtronic, MSD, Sandoz, Abbott Diabetes Care, AstraZeneca España, Boehringer Ingelheim España, GSK, Roche España, Novo NordiskKey Summary Points: Randomised controlled trials (RCTs) remain the gold standard for direct treatment comparisons. However, interpreting the results of RCTs and making judgements about the quality of evidence and how results may be applicable to diabetes management can be difficult for healthcare practitioners (HCPs). In this article, a checklist of the points that we consider most important when reading and interpreting RCT publications is summarised, and may serve as useful guidance for HCPs

Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance

We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC's ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013-2014 (retrospective) and 2014-2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method's predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT's real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizon

Hábitos de Lectura y Consumo de libros en Colombia.

"El propósito de este capítulo es evaluar la situación de la lectura y la compra de libros en Colombia en 2005, de acuerdo con los resultados del módulo especial sobre hábitos de lectura de la Encuesta Continua de Hogares (ECH) del Departamento Administrativo Nacional de Estadística (DANE) aplicado en ese año".Hábitos de lectura, Libros, Consumo de libros, Economía de la Cultura, Compra de libros, Lectura en Internet, Colombia

CO2 capture and sequestration in the cement industry

AbstractThe cement industry is coming under increased scrutiny for its CO2 emissions. The industry has reduced its CO2 footprint through energy efficiency measures, reduction of clinker factor, and the use of alternative fuels. However in a carbon-constrained world, more significant reductions are anticipated and thus CEMEX has been investigating the deployment of CO2 capture and sequestration (CCS) technologies for its own cement plants. The goal of this paper is to present the groundwork for the development and demonstration of a commercial-scale CCS project at one of CEMEX Inc.’s U.S. cement plants. The first part of this paper presents the criteria to determine the most suitable CO2 capture technology in an integrated CCS system for a cement plant. The second part of this paper summarizes how CO2 sequestration potential in proximity to one of CEMEX’s cement plants was a critical factor in determining the suitability to host a commercial CCS demonstration. Findings of this work showed that the development and demonstration of a commercial-scale CCS in the cement industry is still far from deployment. Retrofitting a very compatible CO2 capture technology for the cement industry is a limiting factor for early implementation of CCS. A pilot phase under actual cement plant flue gas conditions is a must to develop this technology to a commercial level. Uncertainties regarding the level of CO2 purity for transportation, geological sequestration, and enhanced oil recovery (EOR) warrant further investigation

Encoding dynamics for multiscale community detection: Markov time sweeping for the Map equation

The detection of community structure in networks is intimately related to finding a concise description of the network in terms of its modules. This notion has been recently exploited by the Map equation formalism (M. Rosvall and C.T. Bergstrom, PNAS, 105(4), pp.1118--1123, 2008) through an information-theoretic description of the process of coding inter- and intra-community transitions of a random walker in the network at stationarity. However, a thorough study of the relationship between the full Markov dynamics and the coding mechanism is still lacking. We show here that the original Map coding scheme, which is both block-averaged and one-step, neglects the internal structure of the communities and introduces an upper scale, the `field-of-view' limit, in the communities it can detect. As a consequence, Map is well tuned to detect clique-like communities but can lead to undesirable overpartitioning when communities are far from clique-like. We show that a signature of this behavior is a large compression gap: the Map description length is far from its ideal limit. To address this issue, we propose a simple dynamic approach that introduces time explicitly into the Map coding through the analysis of the weighted adjacency matrix of the time-dependent multistep transition matrix of the Markov process. The resulting Markov time sweeping induces a dynamical zooming across scales that can reveal (potentially multiscale) community structure above the field-of-view limit, with the relevant partitions indicated by a small compression gap.Comment: 10 pages, 6 figure