Familiarity expands space and contracts time

When humans draw maps, or make judgments about travel-time, their responses are rarely accurate and are often systematically distorted. Distortion effects on estimating time to arrival and the scale of sketch-maps reveal the nature of mental representation of time and space. Inspired by data from rodent entorhinal grid cells, we predicted that familiarity to an environment would distort representations of the space by expanding the size of it. We also hypothesized that travel-time estimation would be distorted in the same direction as space-size, if time and space rely on the same cognitive map. We asked international students, who had lived at a college in London for 9 months, to sketch a south-up map of their college district, estimate travel-time to destinations within the area, and mark their everyday walking routes. We found that while estimates for sketched space were expanded with familiarity, estimates of the time to travel through the space were contracted with familiarity. Thus, we found dissociable responses to familiarity in representations of time and space. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc

The results of an agricultural analysis of the ERTS-1 MSS data at the Johnson Space Center

The initial analysis of the ERTS-1 multispectral scanner (MSS) data at the Johnson Space Center (JSC), Houston, Texas is discussed. The primary data set utilized was the scene over Monterey Bay, California, on July 25, 1972, NASA ERTS ID No. 1002-18134. It was submitted to both computerized and image interpretative processing. An area in the San Joaquin Valley was submitted to an intensive evaluation of the ability of the data to (1) discriminate between crop types and (2) to provide a reasonably accurate area measurement of agricultural features of interest. The results indicate that the ERTS-1 MSS data is capable of providing the identifications and area extent of agricultural lands and field crop types

Multivariate Calibration Domain Adaptation with Unlabeled Data

Multivariate calibration is about modeling the relationship between a substance\u27s chemical profile and its spectrum (here, near-infrared) in order to predict the concentration of new samples with known spectra. However, these new samples are often measured under different conditions than the primary conditions; different instruments, instrument drift, and temperature all affect the measurement conditions. Domain adaptation (DA) methods force the model to ignore these differences in order to generate an accurate model for the new domain (secondary conditions). There are two fundamental DA processes that individual methods can be classified under. One augments a few samples from the secondary domain with chemical reference values (labels) to the primary data and the other augments only secondary spectra (unlabeled data). In this work, we compare two existing labeled DA methods and two existing unlabeled DA methods to two novel labeled methods and a novel unlabeled approach. Since DA methods require selection of hyperparameters, a model selection framework based on model diversity and prediction similarity (MDPS) is applied to the DA methods. Regardless of the DA method, the MDPS process is shown to select models more accurate than the first quartile of all models generated by the DA process in three near-infrared datasets

Harnessing Model Diversity and Prediction Similarity for Selecting Multivariate Calibration Tuning Parameters

Spectral multivariate calibration offers a cost-effective mechanism to obtain sample analyte values of a substance (e.g. protein level). However, calibration requires varying one or more tuning parameters in order to identify the most accurate model. Model selection is particularly difficult for model updating where spectral and reference information in both the original (primary) conditions and new (secondary) conditions are combined in order to better predict new spectra. Secondary situations can be new instruments, temperatures, or other condition affecting the shape and magnitude of the spectra relative to the primary conditions and analyte values. This poster uses model diversity while maintaining similar analyte prediction values to choose a set of acceptable models. The model selection technique is tested across the calibration method partial least squares and four model updating methods: two require a small set of secondary samples with analyte values and two do not require the secondary analyte values (unlabeled data). Results are presented across a variety of datasets and conditions showing that the cosine of the angle between models in combination with model vector 2-norms and prediction differences are key to selecting models

PROBABILISTIC MODELLING OF EARTHQUAKE OCCURRENCE: FIRST EXAMPLES OF DATA INTEGRATION WITHIN A BAYESIAN FRAMEWORK

PROBABILISTIC MODELLING OF EARTHQUAKE OCCURRENCE: FIRST EXAMPLES OF DATA INTEGRATION WITHIN A BAYESIAN FRAMEWOR

Проблеми становлення і розвитку інформаційного законодавства в контексті євроінтеграції України

Щодо розвитку права і правової науки в інформаційній сфері в Україні.О развитии права и правовой науки в информационной сфере в Украине.On the development of law and law science in the informative sphere of Ukraine

Апеляційний перегляд постанов місцевого суду, винесених за розглядом скарг на постанову про порушення кримінальної справи

Досліджуються основні проблеми апеляційної перевірки правомірності порушення кримінальної справи.Исследованы основные проблемы апелляционной проверки правомерности возбуж­дения уголовного дела.The article is dedicated to the main problems of the appellate review of instituting pros­ecution