Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction

We introduce 'mixed LICORS', an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation. Mixed LICORS extends the recent LICORS algorithm (Goerg and Shalizi, 2012) from hard clustering of predictive distributions to a non-parametric, EM-like soft clustering. This retains the asymptotic predictive optimality of LICORS, but, as we show in simulations, greatly improves out-of-sample forecasts with limited data. The new method is implemented in the publicly-available R package "LICORS" (http://cran.r-project.org/web/packages/LICORS/).Comment: 11 pages; AISTATS 201

Seasonal forecasts of Indian summer monsoon rainfall using local polynomial based non-parametric regression model

In this paper, details of new statistical models for forecasting southwest monsoon (June-September) rainfall over India (ISMR) and for northwest India summer monsoon rainfall (NWISMR) are discussed. These models are based on the local polynomial based non-parametric regression method. Two predictor sets (SET-I & SET-II consisting of 4 and 5 predictors respectively) were selected for developing two separate models for making predictions in April and late June respectively. Another predictor set (SET-III) was selected for developing model for monsoon rainfall over NW India (NWISMR). Principle Component Analysis (PCA) of predictor data set was done and the first two principal components were selected for model development. Data for the period 1977-2005 have been used for developing the model and the Jackknife method was used to assess the skill of the model. Both the models showed useful skill in predicting ISMR and showed better performance than the model based on pure climatology. The Hit scores for the three category forecasts during the verification period by April and June models are 0.65 and 0.66 respectively. Root Mean Square Error (RMSE) of these models during the verification period is 5.99 and 6.0 respectively from the Long Period Average (LPA) as against 10.0 from the LPA of the model based on climatology alone. RMSE of the Northwest India model during the independent period is 11.5 from LPA as against 18.5 from the LPA of the model based on the climatology alone. Hit score for the three category forecast for NW India during the verification period is 0.55

Hawkes process as a model of social interactions: a view on video dynamics

We study by computer simulation the "Hawkes process" that was proposed in a recent paper by Crane and Sornette (Proc. Nat. Acad. Sci. USA 105, 15649 (2008)) as a plausible model for the dynamics of YouTube video viewing numbers. We test the claims made there that robust identification is possible for classes of dynamic response following activity bursts. Our simulated timeseries for the Hawkes process indeed fall into the different categories predicted by Crane and Sornette. However the Hawkes process gives a much narrower spread of decay exponents than the YouTube data, suggesting limits to the universality of the Hawkes-based analysis.Comment: Added errors to parameter estimates and further description. IOP style, 13 pages, 5 figure

Power-law distributions in empirical data

Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.Comment: 43 pages, 11 figures, 7 tables, 4 appendices; code available at http://www.santafe.edu/~aaronc/powerlaws

Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction

Abstract We introduce mixed LICORS, an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation. Mixed LICORS extends the recent LICORS algorithm (Goerg and Shalizi, 2012) from hard clustering of predictive distributions to a non-parametric, EM-like soft clustering. This retains the asymptotic predictive optimality of LICORS, but, as we show in simulations, greatly improves out-of-sample forecasts with limited data. The new method is implemented in the publicly-available R package LICORS

Epineuston vortex recapture enhances thrust in tiny water skaters

Vortex recapture underpins the exceptional mobility of nature’s finest fliers and swimmers. Utilized by agile fruit flies and efficient jellyfish, this phenomenon is well-documented in bulk fluids. Despite extensive studies on the neustontextemdasha vital fluidic interface where diverse life forms interact between air and watertextemdashneuston vortical hydrodynamics remain unexplored. We investigate epineuston (on water) vortical hydrodynamics in Microvelia americana, one of the smallest and fastest water striders, skating at 50 BL/s (15 cm/s). Their middle legs shed counter-rotating vortices, re-energized by hind legs, demonstrating epineuston vortex recapture. High-speed imaging, particle imaging velocimetry, physical models, and CFD simulations show re-energization increases thrust by creating positive pressure at the hind tarsi, acting as a virtual wall. This vortex capture is facilitated by the tripod gait, leg morphology, and precise spatio-temporal placement of the hind tarsi during the power stroke. Our study extends vortex recapture principles from bulk fluids to the neuston, offering insights into efficient epineuston locomotion, where surface tension and capillary waves challenge movement. Understanding epineuston vortex hydrodynamics can guide the development of energy-efficient microrobots to explore the planet’s neuston niches, critical frontlines of climate change and pollution.Competing Interest StatementThe authors have declared no competing interest

Homophily and Contagion Are Generically Confounded in Observational Social Network Studies

We consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual's covariates on their behavior or other measurable responses. We show that, generically, all of these are confounded with each other. Distinguishing them from one another requires strong assumptions on the parametrization of the social process or on the adequacy of the covariates used (or both). In particular we demonstrate, with simple examples, that asymmetries in regression coefficients cannot identify causal effects, and that very simple models of imitation (a form of social contagion) can produce substantial correlations between an individual's enduring traits and their choices, even when there is no intrinsic affinity between them. We also suggest some possible constructive responses to these results.Comment: 27 pages, 9 figures. V2: Revised in response to referees. V3: Ditt

Spreading in Social Systems: Reflections

In this final chapter, we consider the state-of-the-art for spreading in social systems and discuss the future of the field. As part of this reflection, we identify a set of key challenges ahead. The challenges include the following questions: how can we improve the quality, quantity, extent, and accessibility of datasets? How can we extract more information from limited datasets? How can we take individual cognition and decision making processes into account? How can we incorporate other complexity of the real contagion processes? Finally, how can we translate research into positive real-world impact? In the following, we provide more context for each of these open questions.Comment: 7 pages, chapter to appear in "Spreading Dynamics in Social Systems"; Eds. Sune Lehmann and Yong-Yeol Ahn, Springer Natur

Diabetes conversation map - A novel tool for diabetes management self-efficacy among type 2 diabetes patients in Pakistan: A randomized controlled trial

Background: This study aimed to measure the effect of diabetes education using the novel method of "diabetes conversation map (DCM)"as compared to routine counselling (RC) on diabetes management self-efficacy (DMSE) among patients living with type 2 diabetes in Karachi, Pakistan. Methods: A parallel arm randomized controlled trial among patients with type 2 diabetes aged 30-60 years, with HbA1c > 7%, diagnosed for at least 5 yrs., was conducted at the national institute of diabetes and endocrinology in Karachi, Pakistan. A total 123 type 2 diabetes patients were randomized into DCM (n = 62) or RC (n = 61). Four weekly diabetes control sessions of 40 min each using the DCM or RC was provided. DMSE was measured using a validated Urdu language DMSE tool at baseline and after three months of the randomization. Change in DMSE and HbA1c levels within groups (pre-post) and between the groups after 3 months of enrollment was compared. Results: Baseline characteristics except HbA1c were similar between the two arms. After 3 months of enrollment, there was no change in the DMSE score in the RC arm however, significant increase in DMSE score was noted in the DCM arm (P = < 0.001). The average difference (95% confidence interval) in DMSE score between the DCM and RC arm was 33.7(27.3, 40.0; p = < 0.001) after 3 months of the enrollment. Difference in HbA1c within groups was not significant. Conclusions: DCM significantly improved DMSE among type 2 diabetes patients in a developing country setting like Pakistan. Healthcare workers caring for type 2 diabetes patients need to be trained on DCM to effectively utilize this novel tool for educating diabetes patients. Trial registration: This trial was prospectively registered. ClinicalTrials.gov Identifier: NCT03747471. Date of registration: Nov 20. 2018