Computational Evidence that Self-regulation of Creativity is Good for Society

Excess individual creativity can be detrimental to society because creators invest in unproven ideas at the expense of propagating proven ones. Moreover, a proportion of individuals can benefit from creativity without being creative themselves by copying creators. We hypothesized that (1) societies increase their rate of cultural evolution by tempering the novelty-generating effects of creativity with the novelty-preserving effects of imitation, and (2) this is carried out by selectively rewarding and punishing creativity according to the value of the individuals' creative outputs. We tested this using an agent-based model of cultural evolution in which each agent self-regulated its invention-to-imitation ratio as a function of the fitness of its cultural outputs. In self-regulating societies, agents segregated into creators and imitators. The mean fitness of cultural outputs was higher than in non-self-regulating societies, and changes in diversity were rapider and more pronounced. We discuss limitations and possible social implications of our findings.Comment: 6 pages. arXiv admin note: substantial text overlap with arXiv:1310.475

Automatic cell segmentation by adaptive thresholding (ACSAT) for large-scale calcium imaging datasets

Advances in calcium imaging have made it possible to record from an increasingly larger number of neurons simultaneously. Neuroscientists can now routinely image hundreds to thousands of individual neurons. An emerging technical challenge that parallels the advancement in imaging a large number of individual neurons is the processing of correspondingly large datasets. One important step is the identification of individual neurons. Traditional methods rely mainly on manual or semimanual inspection, which cannot be scaled for processing large datasets. To address this challenge, we focused on developing an automated segmentation method, which we refer to as automated cell segmentation by adaptive thresholding (ACSAT). ACSAT works with a time-collapsed image and includes an iterative procedure that automatically calculates global and local threshold values during successive iterations based on the distribution of image pixel intensities. Thus, the algorithm is capable of handling variations in morphological details and in fluorescence intensities in different calcium imaging datasets. In this paper, we demonstrate the utility of ACSAT by testing it on 500 simulated datasets, two wide-field hippocampus datasets, a wide-field striatum dataset, a wide-field cell culture dataset, and a two-photon hippocampus dataset. For the simulated datasets with truth, ACSAT achieved >80% recall and precision when the signal-to-noise ratio was no less than ∼24 dB.DP2 NS082126 - NINDS NIH HHSPublished versio

Using bio-based materials in the automotive industry

The objective of this research is to evaluate the environmental performance of polylactic acid (PLA) + flax fiber bio-composite against the current in production composite of polypropylene (PP) + wood dust via life cycle analysis (LCA). The system boundary is an extended gate-to-gate LCA that includes the materials production process. In order to complete the LCA set forth, a necessary iterative process of dataset matching was done to convert NatureWorks LLC\u27s Ingeo (PLA) dataset from the USLCI database to the GaBi database in order to model the LCA conducted. The bio-composite of PLA + flax produces less greenhouse gas emissions that contribute to global warming potentials (GWP) largely due to the carbon sequestration of corn production. The current in production composite of PP + wood dust contributes less to both acidification potential (AP) of seawater and photochemical ozone creation potential (POCP) largely due to less agricultural processes. The polymer resin production process is the primary parameter for energy consumption in both composites

Intensification of the Decadal Activity in Equatorial Rossby Waves and Linkage to Changing Tropical Circulation

Equatorial Rossby waves (ERWs) are manifest as westward-propagating, planetary-scale waves that feature a symmetric pair of pressure and zonal wind fields about the equator. ERWs can modulate tropical convective activity, especially in South Asia and the Maritime Continents, and represent an important mode of intraseasonal variability additional to the Madden-Julian Oscillation. Changes in the frequency and intensity of ERWs during the recent decades were investigated based on observations of tropospheric winds and tropical convection. Spectral analyses indicated that ERWs appear to have intensified especially in the upper troposphere; this is associated with increased convective activity located off the equator. The strengthening and westward shift of the Walker circulation observed in the recent decades acted to increase the tropical vertical westerly shear and, subsequently, may contribute to the increased ERW activity. Further investigation on the dynamical process of the vertical zonal shear enhancement will improve the understanding of the changing ERW characteristics

Climate Diagnostics of the Extreme Floods in Peru During Early 2017

From January through March 2017, a series of extreme precipitation events occurred in coastal Peru, causing severe floods with hundreds of human casualties and billions of dollars in economic losses. The extreme precipitation was a result of unusually strong recurrent patterns of atmospheric and oceanic conditions, including extremely warm coastal sea surface temperatures (SST) and weakened trade winds. These climatic features and their causal relationship with the Peruvian precipitation were examined. Diagnostic analysis and model experiments suggest that an atmospheric forcing in early 2017, which was moderately linked to the Trans-Niño Index (TNI), initiated the local SST warming along coastal Peru that later expanded to the equator. In January 2017, soil moisture was increased by an unusual expansion of Amazonian rainfall. By March, localized and robust SST warming provided positive feedback to the weakening of the trade winds, leading to increased onshore wind and a subsequent enhancement in rainfall. The analysis points to a tendency towards more frequent and stronger variations in the water vapor flux convergence along the equator, which is associated with the increased precipitation in coastal Peru

Unscented Kalman Filtering for Single Camera Based Motion and Shape Estimation

Accurate estimation of the motion and shape of a moving object is a challenging task due to great variety of noises present from sources such as electronic components and the influence of the external environment, etc. To alleviate the noise, the filtering/estimation approach can be used to reduce it in streaming video to obtain better estimation accuracy in feature points on the moving objects. To deal with the filtering problem in the appropriate nonlinear system, the extended Kalman filter (EKF), which neglects higher-order derivatives in the linearization process, has been very popular. The unscented Kalman filter (UKF), which uses a deterministic sampling approach to capture the mean and covariance estimates with a minimal set of sample points, is able to achieve at least the second order accuracy without Jacobians’ computation involved. In this paper, the UKF is applied to the rigid body motion and shape dynamics to estimate feature points on moving objects. The performance evaluation is carried out through the numerical study. The results show that UKF demonstrates substantial improvement in accuracy estimation for implementing the estimation of motion and planar surface parameters of a single camera

Deep learning for vegetation health forecasting: a case study in Kenya

East Africa has experienced a number of devastating droughts in recent decades, including the 2010/2011 drought. The National Drought Management Authority in Kenya relies on real-time information from MODIS satellites to monitor and respond to emerging drought conditions in the arid and semi-arid lands of Kenya. Providing accurate and timely information on vegetation conditions and health—and its probable near-term future evolution—is essential for minimising the risk of drought conditions evolving into disasters as the country’s herders directly rely on the conditions of grasslands. Methods from the field of machine learning are increasingly being used in hydrology, meteorology, and climatology. One particular method that has shown promise for rainfall-runoff modelling is the Long Short Term Memory (LSTM) network. In this study, we seek to test two LSTM architectures for vegetation health forecasting. We find that these models provide sufficiently accurate forecasts to be useful for drought monitoring and forecasting purposes, showing competitive performances with lower resolution ensemble methods and improved performances over a shallow neural network and a persistence baseline

Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors

Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not individual variables. The group lasso has been proposed as a way of extending the ideas of the lasso to the problem of group selection. Nonconvex penalties such as SCAD and MCP have been proposed and shown to have several advantages over the lasso; these penalties may also be extended to the group selection problem, giving rise to group SCAD and group MCP methods. Here, we describe algorithms for fitting these models stably and efficiently. In addition, we present simulation results and real data examples comparing and contrasting the statistical properties of these methods

Exploring Trading Strategies and Their Effects in the Foreign Exchange Market

One of the most critical issues that developers face in developing automatic systems for electronic markets is that of endowing the agents with appropriate trading strategies. In this article, we examine the problem in the foreign exchange (FX) market, and we use an agent‐based market simulation to examine which trading strategies lead to market states in which the stylized facts (statistical properties) of the simulation match those of the FX market transactions data. Our goal is to explore the emergence of the stylized facts, when the simulated market is populated with agents using different strategies: a variation of the zero intelligence with a constraint strategy, the zero‐intelligence directional‐change event strategy, and a genetic programming‐based strategy. A series of experiments were conducted, and the results were compared with those of a high‐frequency FX transaction data set. Our results show that the zero‐intelligence directional‐change event agents best reproduce and explain the properties observed in the FX market transactions data. Our study suggests that the observed stylized facts could be the result of introducing a threshold that triggers the agents to respond to periodic patterns in the price time series. The results can be used to develop decision support systems for the FX market