Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The fine-grained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of fine-grained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model.Comment: Appears in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019

Two spatially distinct kinesin-14 proteins, Pkl1 and Klp2, generate collaborative inward forces against kinesin-5 Cut7 in S. pombe

Kinesin motors play central roles in bipolar spindle assembly. In many eukaryotes, spindle pole separation is driven by kinesin-5, which generates outward force. This outward force is balanced by antagonistic inward force elicited by kinesin-14 and/or dynein. In fission yeast, two kinesin-14 proteins, Pkl1 and Klp2, play an opposing role against the kinesin-5 motor protein Cut7. However, how the two kinesin-14 proteins coordinate individual activities remains elusive. Here, we show that although deletion of either pkl1 or klp2 rescues temperature-sensitive cut7 mutants, deletion of only pkl1 can bypass the lethality caused by cut7 deletion. Pkl1 is tethered to the spindle pole body, whereas Klp2 is localized along the spindle microtubule. Forced targeting of Klp2 to the spindle pole body, however, compensates for Pkl1 functions, indicating that cellular localizations, rather than individual motor specificities, differentiate between the two kinesin-14 proteins. Interestingly, human kinesin-14 (KIFC1 or HSET) can replace either Pkl1 or Klp2. Moreover, overproduction of HSET induces monopolar spindles, reminiscent of the phenotype of Cut7 inactivation. Taken together, this study has uncovered the biological mechanism whereby two different Kinesin- 14 motor proteins exert their antagonistic roles against kinesin-5 in a spatially distinct manner.This work was supported by the Japan Society for the Promotion of Science (JSPS) [KAKENHI Scientific Research (A) 16H02503 to T.T., a Challenging Exploratory Research grant 16K14672 to T.T., Scientific Research (C) 16K07694 to M.Y.], the Naito Foundation (T.T.) and the Uehara Memorial Foundation (T.T)

Time-delayed collective flow diffusion models for inferring latent people flow from aggregated data at limited locations

The rapid adoption of wireless sensor devices has made it easier to record location information of people in a variety of spaces (e.g., exhibition halls). Location information is often aggregated due to privacy and/or cost concerns. The aggregated data we use as input consist of the numbers of incoming and outgoing people at each location and at each time step. Since the aggregated data lack tracking information of individuals, determining the flow of people between locations is not straightforward. In this article, we address the problem of inferring latent people flows, that is, transition populations between locations, from just aggregated population data gathered from observed locations. Existing models assume that everyone is always in one of the observed locations at every time step; this, however, is an unrealistic assumption, because we do not always have a large enough number of sensor devices to cover the large-scale spaces targeted. To overcome this drawback, we propose a probabilistic model with flow conservation constraints that incorporate travel duration distributions between observed locations. To handle noisy settings, we adopt noisy observation models for the numbers of incoming and outgoing people, where the noise is regarded as a factor that may disturb flow conservation, e.g., people may appear in or disappear from the predefined space of interest. We develop an approximate expectation-maximization (EM) algorithm that simultaneously estimates transition populations and model parameters. Our experiments demonstrate the effectiveness of the proposed model on real-world datasets of pedestrian data in exhibition halls, bike trip data and taxi trip data in New York City

Psychosocial twin cohort studies in Japan: the Keio Twin Research Center (KoTReC)

The Keio Twin Research Center (KoTReC) was established in 2009 at Keio University to combine two longitudinal cohort projects — the Keio Twin Study (KTS) for adolescence and adulthood and the Tokyo Twin Cohort Project (ToTCoP) for infancy and childhood. KoTReC also conducted a two-time panel study of self-control and psychopathology in twin adolescence in 2012 and 2013 and three independent anonymous cross-sectional twin surveys (ToTcross) before 2012 — the ToTCross, the Junior and Senior High School Survey and the High School Survey. This article introduces the recent research designs of KoTReC and its publications

Enhanced Merge Sort- A New Approach to the Merging Process

AbstractOne of the major fundamental issues of Computer Science is arrangement of elements in the database. The efficiency of the sorting algorithms is to optimize the importance of other sorting algorithms11. The optimality of these sorting algorithms is judged while calculating their time and space complexities12. The idea behind this paper is to modify the conventional Merge Sort Algorithm and to present a new method with reduced execution time. The newly proposed algorithm is faster than the conventional Merge Sort algorithm having a time complexity of O(n log2 n). The proposed algorithm has been tested, implemented, compared and the experimental results are promising

Prognostic value of non-alcoholic fatty liver disease for predicting cardiovascular events in patients with diabetes mellitus with suspected coronary artery disease: a prospective cohort study

Background Risk stratification of cardiovascular events in patients with type 2 diabetes mellitus (T2DM) has not been established. Coronary artery calcium score (CACS) and non-alcoholic fatty liver disease (NAFLD) are independently associated with cardiovascular events in T2DM patients. This study examined the incremental prognostic value of NAFLD assessed by non-enhanced computed tomography (CT) in addition to CACS and Framingham risk score (FRS) for cardiovascular events in T2DM patients. Methods This prospective pilot study included 529 T2DM outpatients with no history of cardiovascular disease who underwent CACS measurement because of suspected coronary artery disease. NAFLD was defined on CT images as a liver:spleen attenuation ratio  Results Among 529 patients (61% men, mean age 65 years), NAFLD was identified in 143 (27%). Forty-four cardiovascular events were documented during a median follow-up of 4.4 years. In multivariate Cox regression analysis, NAFLD, CACS, and FRS were associated with cardiovascular events (hazard ratios and 95% confidence intervals 5.43, 2.82–10.44, p  Conclusions NAFLD assessed by CT, in addition to CACS and FRS, could be useful for identifying T2DM patients at higher risk of cardiovascular events