Assessing the Computational Complexity of Multi-Layer Subgraph Detection

Multi-layer graphs consist of several graphs (layers) over the same vertex set. They are motivated by real-world problems where entities (vertices) are associated via multiple types of relationships (edges in different layers). We chart the border of computational (in)tractability for the class of subgraph detection problems on multi-layer graphs, including fundamental problems such as maximum matching, finding certain clique relaxations (motivated by community detection), or path problems. Mostly encountering hardness results, sometimes even for two or three layers, we can also spot some islands of tractability

Wheatering tight economic times: the sales evolution of consumer durables over the business cycle.

Despite its obvious importance, not much marketing research focuses on how business-cycle fluctuations affect individual companies and/or industries. Often, one only has aggregate information on the state of the national economy, even though cyclical contractions and expansions need not have an equal impact on every industry, nor on all firms in that industry. Using recent time-series developments, we introduce various measures to quantify the extent and nature of business-cycle fluctuations in sales. Specifically, we discuss the notions of cyclical volatility and cyclical comovement, and consider two types of cyclical asymmetry related, respectively, to the relative size of the peaks and troughs and the rate of change in upward versus downward parts of the cycle. In so doing, we examine how consumers adjust their purchasing behavior across different phases of the business cycle. We apply these concepts to a broad set (24) of consumer durables, for which we analyze the cyclical sensitivity in their sales evolution. In that way, we (i) derive a novel set of empirical generalizations, and (ii) test different marketing theory-based hypotheses on the underlying drivers of cyclical sensitivity. Consumer durables are found to be more sensitive to business-cycle fluctuations than the general economic activity, as expressed in an average cyclical volatility of more than four times the one in GNP, and an average comovement elasticity in excess of 2. This observation calls for an explicit consideration of cyclical variation in durable sales. Moreover, even though no evidence is found for depth asymmetry, the combined evidence across all durables suggests that asymmetry is present in the speed of up- and downward movements, as durables' sales falls much quicker during contractions than recover during economic expansions. Finally, key variables related to the industry's pricing activities, the nature of the durable (convenience vs. leisure), and the stage in a product's life cycle tend to moderate the extent of cyclical sensitivity in durable sales patterns.Business cycles; Companies; Consumer durables; Econometrics; Economy; Firms; Hypotheses; Industry; Information; Market; Marketing; Pricing; Product; Purchasing; Sales; Sales evolution; Sensitivity; Size; Time; Time-series econometrics; Time series; Variables; Volatility;

Stakeholder engagement: Defining strategic advantage for sustainable construction

This is the accepted version of the following article: Rodriguez-Melo, A. and Mansouri, S. A. (2011), Stakeholder Engagement: Defining Strategic Advantage for Sustainable Construction. Bus. Strat. Env., 20: 539–552, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/bse.715/abstract.Although sustainable development is increasingly becoming a part of business plans, it is unclear what makes the economic, social and environmental dynamics strategically compatible. This research examines which of the following in sustainable development – government policy, managerial attitude and stakeholder engagement – is the most influential on the profitability of companies in the UK construction sector. Quantitative and qualitative analyses were rendered through a survey and semi-structured interviews. Patterns of ambiguity in legislation were discovered as an obstacle for changing the sector's mind-set. Stakeholder engagement was identified as the defining factor increasing managers' awareness, helping legislation to be effectively implemented and making sustainability highly appealing to clients. These findings indicate that to gain competitive advantage, companies should embark on long-term strategic alliances which adopt the proposals of environmental non-governmental organisations and closely follow public opinion. This, strengthens brand equity, allows for premium pricing, increases market share and maximizes profit

Blood Pressure and Cognitive Decline Over 8 Years in Middle-Aged and Older Black and White Americans

Although the association between high blood pressure (BP), particularly in midlife, and late-life dementia is known, less is known about variations by race and sex. In a prospective national study of 22 164 blacks and whites ≥45 years without baseline cognitive impairment or stroke from the REGARDS cohort study (Reasons for Geographic and Racial Differences in Stroke), enrolled 2003 to 2007 and followed through September 2015, we measured changes in cognition associated with baseline systolic and diastolic BP (SBP and DBP), as well as pulse pressure (PP) and mean arterial pressure, and we tested whether age, race, and sex modified the effects. Outcomes were global cognition (Six-Item Screener; primary outcome), new learning (Word List Learning), verbal memory (Word List Delayed Recall), and executive function (Animal Fluency Test). Median follow-up was 8.1 years. Significantly faster declines in global cognition were associated with higher SBP, lower DBP, and higher PP with increasing age ( P<0.001 for age×SBP×follow-up-time, age×DBP×follow-up-time, and age×PP×follow-up-time interaction). Declines in global cognition were not associated with mean arterial pressure after adjusting for PP. Blacks, compared with whites, had faster declines in global cognition associated with SBP ( P=0.02) and mean arterial pressure ( P=0.04). Men, compared with women, had faster declines in new learning associated with SBP ( P=0.04). BP was not associated with decline of verbal memory and executive function, after controlling for the effect of age on cognitive trajectories. Significantly faster declines in global cognition over 8 years were associated with higher SBP, lower DBP, and higher PP with increasing age. SBP-related cognitive declines were greater in blacks and men

Power series expansions of modular forms and their interpolation properties

Let x be a CM point on a modular or Shimura curve and p a prime of good reduction, split in the CM field K. We define an expansion of an holomorphic modular form f in the p-adic neighborhood of x and show that the expansion coefficients give information on the p-adic ring of definition of f. Also, we show that letting x vary in its Galois orbit, the expansions coefficients allow to construct a p-adic measure whose moments squared are essentially the values at the centre of symmetry of L-functions of the automorphic representation attached to f based-changed to K and twisted by a suitable family of Grossencharakters for K.Comment: 45 pages. In this new version of the paper the restriction on the weight in the expansion principle in the quaternionic case has been removed. Also, the formula linking the square of the moment to the special value of the L-function has been greatly simplified and made much more explici

Statistical methods for the study of etiologic heterogeneity

Traditionally, cancer epidemiologists have investigated the causes of disease under the premise that patients with a certain site of disease can be treated as a single entity. Then risk factors associated with the disease are identified through case-control or cohort studies for the disease as a whole. However, with the rise of molecular and genomic profiling, in recent years biologic subtypes have increasingly been identified. Once subtypes are known, it is natural to ask the question of whether they share a common etiology, or in fact arise from distinct sets of risk factors, a concept known as etiologic heterogeneity. This dissertation seeks to evaluate methods for the study of etiologic heterogeneity in the context of cancer research and with a focus on methods for case-control studies. First, a number of existing regression-based methods for the study of etiologic heterogeneity in the context of pre-defined subtypes are compared using a data example and simulation studies. This work found that a standard polytomous logistic regression approach performs at least as well as more complex methods, and is easy to implement in standard software. Next, simulation studies investigate the statistical properties of an approach that combines the search for the most etiologically distinct subtype solution from high dimensional tumor marker data with estimation of risk factor effects. The method performs well when appropriate up-front selection of tumor markers is performed, even when there is confounding structure or high-dimensional noise. And finally, an application to a breast cancer case-control study demonstrates the usefulness of the novel clustering approach to identify a more risk heterogeneous class solution in breast cancer based on a panel of gene expression data and known risk factors

A spatial covariance (123)I-5IA-85380 SPECT study of α4β2 nicotinic receptors in Alzheimer's disease

Alzheimer's disease (AD) is characterized by widespread degeneration of cholinergic neurons, particularly in the basal forebrain. However, the pattern of these deficits and relationship with known brain networks is unknown. In this study, we sought to clarify this and used 123I-5-iodo-3-[2(S)-2-azetidinylmethoxy] pyridine (1235IA-85380) single photon emission computed tomography to investigate spatial covariance of α4β2 nicotinic acetylcholine receptors in AD and healthy controls. Thirteen AD and 16 controls underwent 1235IA-85380 and regional cerebral blood flow (99mTc-exametazime) single photon emission computed tomography scanning. We applied voxel principal component (PC) analysis, generating series of principal component images representing common intercorrelated voxels across subjects. Linear regression generated specific α4β2 and regional cerebral blood flow covariance patterns that differentiated AD from controls. The α4β2 pattern showed relative decreased uptake in numerous brain regions implicating several networks including default mode, salience, and Papez hubs. Thus, as well as basal forebrain and brainstem cholinergic system dysfunction, cholinergic deficits mediated through nicotinic acetylcholine receptors could be evident within key networks in AD. These findings may be important for the pathophysiology of AD and its associated cognitive and behavioral phenotypes

DSL: Discriminative Subgraph Learning via Sparse Self-Representation

The goal in network state prediction (NSP) is to classify the global state (label) associated with features embedded in a graph. This graph structure encoding feature relationships is the key distinctive aspect of NSP compared to classical supervised learning. NSP arises in various applications: gene expression samples embedded in a protein-protein interaction (PPI) network, temporal snapshots of infrastructure or sensor networks, and fMRI coherence network samples from multiple subjects to name a few. Instances from these domains are typically ``wide'' (more features than samples), and thus, feature sub-selection is required for robust and generalizable prediction. How to best employ the network structure in order to learn succinct connected subgraphs encompassing the most discriminative features becomes a central challenge in NSP. Prior work employs connected subgraph sampling or graph smoothing within optimization frameworks, resulting in either large variance of quality or weak control over the connectivity of selected subgraphs. In this work we propose an optimization framework for discriminative subgraph learning (DSL) which simultaneously enforces (i) sparsity, (ii) connectivity and (iii) high discriminative power of the resulting subgraphs of features. Our optimization algorithm is a single-step solution for the NSP and the associated feature selection problem. It is rooted in the rich literature on maximal-margin optimization, spectral graph methods and sparse subspace self-representation. DSL simultaneously ensures solution interpretability and superior predictive power (up to 16% improvement in challenging instances compared to baselines), with execution times up to an hour for large instances.Comment: 9 page