Mining Top-K Frequent Itemsets Through Progressive Sampling

We study the use of sampling for efficiently mining the top-K frequent itemsets of cardinality at most w. To this purpose, we define an approximation to the top-K frequent itemsets to be a family of itemsets which includes (resp., excludes) all very frequent (resp., very infrequent) itemsets, together with an estimate of these itemsets' frequencies with a bounded error. Our first result is an upper bound on the sample size which guarantees that the top-K frequent itemsets mined from a random sample of that size approximate the actual top-K frequent itemsets, with probability larger than a specified value. We show that the upper bound is asymptotically tight when w is constant. Our main algorithmic contribution is a progressive sampling approach, combined with suitable stopping conditions, which on appropriate inputs is able to extract approximate top-K frequent itemsets from samples whose sizes are smaller than the general upper bound. In order to test the stopping conditions, this approach maintains the frequency of all itemsets encountered, which is practical only for small w. However, we show how this problem can be mitigated by using a variation of Bloom filters. A number of experiments conducted on both synthetic and real bench- mark datasets show that using samples substantially smaller than the original dataset (i.e., of size defined by the upper bound or reached through the progressive sampling approach) enable to approximate the actual top-K frequent itemsets with accuracy much higher than what analytically proved.Comment: 16 pages, 2 figures, accepted for presentation at ECML PKDD 2010 and publication in the ECML PKDD 2010 special issue of the Data Mining and Knowledge Discovery journa

Exploring the role of ICT on household behavioural energy efficiency to mitigate global warming

With the advent of ICT in the energy system, new possibilities to inform and influence residential electricity consumption become available. We explore the potential of ICT-based interventions in households to decrease electricity usage, improve energy efficiency and thus contribute to reducing GHG (greenhouse gas) emissions from this sector. Based on a literature review on the subject, we suggest that ICT can affect some of the main behaviour-influencing factors, and discuss the causal avenues by which these effects can take hold. Our review finds that ICT-based effects on consumer behaviour can reduce household final electricity consumption by 0–5%. These and other findings from the literature are used to define parameter values, which reflect the efficacy of ICT at changing household energy usage patterns, and ultimately decreasing GHG emissions from the electricity sector. A quantitative analysis of the potential for ICT to contribute to reaching the 1.5 °C target in the context of the European Union (EU) energy sector is performed. It is found that ICT-based interventions in household energy use could contribute between 0.23% and 3.3% of the EU CO2e reduction target from the energy sector that would keep warming under 1.5 °C, corresponding to 4.5–64.7 mio. tCO2e abated per year.The authors gratefully acknowledge funding from the European Union’s Horizon 2020 research and innovation programme under the PEAKapp project, grant agreement No. 695945 (http://www.peakapp.eu/)

Random walks and search in time-varying networks

The random walk process underlies the description of a large number of real world phenomena. Here we provide the study of random walk processes in time varying networks in the regime of time-scale mixing; i.e. when the network connectivity pattern and the random walk process dynamics are unfolding on the same time scale. We consider a model for time varying networks created from the activity potential of the nodes, and derive solutions of the asymptotic behavior of random walks and the mean first passage time in undirected and directed networks. Our findings show striking differences with respect to the well known results obtained in quenched and annealed networks, emphasizing the effects of dynamical connectivity patterns in the definition of proper strategies for search, retrieval and diffusion processes in time-varying network

Estimating the proportion of guilty suspects and posterior probability of guilt in lineups using signal-detection models

Background The majority of eyewitness lineup studies are laboratory-based. How well the conclusions of these studies, including the relationship between confidence and accuracy, generalize to real-world police lineups is an open question. Signal detection theory (SDT) has emerged as a powerful framework for analyzing lineups that allows comparison of witnesses’ memory accuracy under different types of identification procedures. Because the guilt or innocence of a real-world suspect is generally not known, however, it is further unknown precisely how the identification of a suspect should change our belief in their guilt. The probability of guilt after the suspect has been identified, the posterior probability of guilt (PPG), can only be meaningfully estimated if we know the proportion of lineups that include a guilty suspect, P(guilty). Recent work used SDT to estimate P(guilty) on a single empirical data set that shared an important property with real-world data; that is, no information about the guilt or innocence of the suspects was provided. Here we test the ability of the SDT model to recover P(guilty) on a wide range of pre-existing empirical data from more than 10,000 identification decisions. We then use simulations of the SDT model to determine the conditions under which the model succeeds and, where applicable, why it fails. Results For both empirical and simulated studies, the model was able to accurately estimate P(guilty) when the lineups were fair (the guilty and innocent suspects did not stand out) and identifications of both suspects and fillers occurred with a range of confidence levels. Simulations showed that the model can accurately recover P(guilty) given data that matches the model assumptions. The model failed to accurately estimate P(guilty) under conditions that violated its assumptions; for example, when the effective size of the lineup was reduced, either because the fillers were selected to be poor matches to the suspect or because the innocent suspect was more familiar than the guilty suspect. The model also underestimated P(guilty) when a weapon was shown. Conclusions Depending on lineup quality, estimation of P(guilty) and, relatedly, PPG, from the SDT model can range from poor to excellent. These results highlight the need to carefully consider how the similarity relations between fillers and suspects influence identifications

Contagion dynamics in time-varying metapopulation networks

The metapopulation framework is adopted in a wide array of disciplines to describe systems of well separated yet connected subpopulations. The subgroups or patches are often represented as nodes in a network whose links represent the migration routes among them. The connections have been so far mostly considered as static, but in general evolve in time. Here we address this case by investigating simple contagion processes on time-varying metapopulation networks. We focus on the SIR process and determine analytically the mobility threshold for the onset of an epidemic spreading in the framework of activity-driven network models. We find profound differences from the case of static networks. The threshold is entirely described by the dynamical parameters defining the average number of instantaneously migrating individuals and does not depend on the properties of the static network representation. Remarkably, the diffusion and contagion processes are slower in time-varying graphs than in their aggregated static counterparts, the mobility threshold being even two orders of magnitude larger in the first case. The presented results confirm the importance of considering the time-varying nature of complex networks

Impact of a Reduced Nicotine Standard on Young Adult Appeal for Menthol and Non-Menthol Cigarettes

Introduction The Food and Drug Administration (FDA) announced its intention to reduce the nicotine content in cigarettes as a strategy to promote cessation and reduce smoking-related harm. A low nicotine product standard will apply to all cigarettes on the market, including menthol cigarettes. In December 2021, the FDA approved a modified risk tobacco product application for menthol and non-menthol flavoured very low nicotine cigarettes (VLNC) from the 22nd Century Group. Notably, experimentation with menthol cigarettes is linked to smoking progression, as well as greater nicotine dependence relative to non-menthol cigarette use. If menthol VLNCs are perceived as more appealing than non-menthol VLNCs, this would indicate that some aspect of menthol may maintain smoking even in the absence of nicotine and FDA’s regulatory authority to ban or restrict the sale of menthol cigarettes should apply to reduced nicotine content of cigarettes. In April 2022, the FDA announced proposed rulemaking to prohibit menthol cigarettes, however it is unclear if a menthol prohibition would apply to VLNCs. Methods and analysis This study will recruit 172 young adult menthol smokers (with a specific subsample of n=40 sexual and gender minority young adults) and measure appeal for smoking experimental menthol and non-menthol VLNCs, and the impact of proposed product standards on tobacco product purchasing behaviour using an Experimental Tobacco Marketplace. Appeal across product standards will be assessed in a controlled laboratory and using ecological momentary assessment. Ethics and dissemination The protocol was approved by the University of Oklahoma Health Sciences Center Institutional Review Board (#11865). Findings will examine the effects of a reduced nicotine standard and a menthol ban on young adult smoking and will be disseminated through peer-reviewed journal articles and presentations at scientific conferences. Trial registration number NCT04340947

Ultrafast nonlocal control of spontaneous emission

Solid-state cavity quantum electrodynamics systems will form scalable nodes of future quantum networks, allowing the storage, processing and retrieval of quantum bits, where a real-time control of the radiative interaction in the cavity is required to achieve high efficiency. We demonstrate here the dynamic molding of the vacuum field in a coupled-cavity system to achieve the ultrafast nonlocal modulation of spontaneous emission of quantum dots in photonic crystal cavities, on a timescale of ~200 ps, much faster than their natural radiative lifetimes. This opens the way to the ultrafast control of semiconductor-based cavity quantum electrodynamics systems for application in quantum interfaces and to a new class of ultrafast lasers based on nano-photonic cavities.Comment: 15 pages, 4 figure

Characterizing the community structure of complex networks

Community structure is one of the key properties of complex networks and plays a crucial role in their topology and function. While an impressive amount of work has been done on the issue of community detection, very little attention has been so far devoted to the investigation of communities in real networks. We present a systematic empirical analysis of the statistical properties of communities in large information, communication, technological, biological, and social networks. We find that the mesoscopic organization of networks of the same category is remarkably similar. This is reflected in several characteristics of community structure, which can be used as ``fingerprints'' of specific network categories. While community size distributions are always broad, certain categories of networks consist mainly of tree-like communities, while others have denser modules. Average path lengths within communities initially grow logarithmically with community size, but the growth saturates or slows down for communities larger than a characteristic size. This behaviour is related to the presence of hubs within communities, whose roles differ across categories. Also the community embeddedness of nodes, measured in terms of the fraction of links within their communities, has a characteristic distribution for each category. Our findings are verified by the use of two fundamentally different community detection methods.Comment: 15 pages, 20 figures, 4 table