Evidence for a general neural signature of face familiarity

We explored the neural signatures of face familiarity using cross-participant and cross-experiment decoding of event-related potentials, evoked by unknown and experimentally familiarized faces from a set of experiments with different participants, stimuli, and familiarization-types. Participants were either familiarized perceptually, via media exposure, or by personal interaction. We observed significant cross-experiment familiarity decoding involving all three experiments, predominantly over posterior and central regions of the right hemisphere in the 270 - 630 ms time window. This shared face familiarity effect was most prominent between the Media and Personal, as well as between the Perceptual and Personal experiments. Cross-experiment decodability makes this signal a strong candidate for a general neural indicator of face familiarity, independent of familiarization methods and stimuli. Furthermore, the sustained pattern of temporal generalization suggests that it reflects a single automatic processing cascade that is maintained over time.

A Simultaneous Multi-scale Data Assimilation using Scale-dependent Localization in GSI-based Hybrid 4DEnVar for NCEP FV3-based GFS

A scale-dependent localization (SDL) method was formulated and implemented in the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (4DEnVar) system for NCEP FV3-based Global Forecast System (GFS). SDL applies different localization to different scales of ensemble covariances, while performing a single-step simultaneous assimilation of all available observations. Two SDL variants with (SDL-Cross) and without (SDL-NoCross) considering cross-waveband covariances were examined. The performance of two- and three-waveband SDL experiments (W2 and W3, respectively) was evaluated through one-month cycled data assimilation experiments. SDL improves global forecasts to five days over scale-invariant localization including the operationally-tuned level-dependent scale-invariant localization (W1-Ope). The W3 SDL-Cross experiment shows more accurate tropical storm track forecasts at shorter lead times than W1-Ope. Compared to the W2 SDL experiments, the W3 SDL counterparts applying tighter horizontal localization at medium-scale waveband generally show improved global forecasts below 100 hPa, but degraded global forecasts above 50 hPa. While the outperformance of the W3 SDL-NoCross experiment versus the W2 SDL-NoCross experiment below 100 hPa lasts for five days, that of the W3 SDL-Cross experiment versus the W2 SDL-Cross experiment lasts for three days. Due to local spatial averaging of ensemble covariances that may alleviate sampling error, the SDL-NoCross experiments show slightly better forecasts than the SDL-Cross experiments at shorter lead times. However, the SDL-Cross experiments outperform the SDL-NoCross experiments at longer lead times, likely from retention of more heterogeneity of ensemble covariances and resultant analyses with improved balance. Relative performance of tropical storm track forecasts in the W2 and W3 SDL experiments are generally consistent with that of global forecasts.

Test-driven development with mutation testing – an experimental study

Test-driven development (TDD) is a popular design approach used by the developers with testing being the important software development driving factor. On the other hand, mutation testing is considered one of the most effective testing techniques. However, there is not so much research on combining these two techniques together. In this paper, we propose a novel, hybrid approach called TDD+M which combines test-driven development process together with the mutation approach. The aim was to check whether this modified approach allows the developers to write a better quality code. We verify our approach by conducting a controlled experiment and we show that it achieves better results than the sole TDD technique. The experiment involved 22 computer science students split into eight groups. Four groups (TDD+M) were using our approach, the other four (TDD) – a normal TDD process. We performed a cross-experiment by measuring the code coverage and mutation coverage for each combination (code of group X, tests from group Y). The TDD+M tests achieved better coverage on the code from TDD groups than the TDD tests on their own code (53.3% vs. 33.5% statement coverage and 64.9% vs. 37.5% mutation coverage). The TDD+M tests also found more post-release defects in the TDD code than TDD tests in the TDD+M code. The experiment showed that adding mutation into the TDD process allows the developers to provide better, stronger tests and to write a better quality code.

Establishing Standard Protocols for Bacterial Culture in Biological Research in Canisters (BRIC) Hardware

The NASA GeneLab Data System (GLDS) was recently developed to facilitate cross-experiment comparisons in order to understand the response of microorganisms to the human spaceflight environment. However, prior spaceflight experiments have been conducted using a wide variety of different hardware, media, culture conditions, and procedures. Such confounding factors could potentially mask true differences in gene expression between spaceflight and ground control samples. In an attempt to mitigate such confounding factors, we describe here the development of a standardized set of hardware, media, and protocols for liquid cultivation of microbes in Biological Research in Canisters (BRIC) spaceflight hardware, using the model bacteria Bacillus subtilis strain 168 and Staphylococcus aureus strain UAMS-1 as examples.