Microbial mats of the Tswaing impact crater: results of a South African exobiology expedition and implications for the search for biological molecules on Mars

We describe microbial mats from the Tswaing impact crater in South Africa. The mats provide insights into the unique biological characteristics of impact craters and can help strategies for the search for biomolecules on Mars

Early EEG correlates of word frequency and contextual predictability in reading

Previous research into written language comprehension has been equivocal as to whether word frequency and contextual predictability effects share an early time course of processing. Target word frequency (low, high) and its predictability from prior context (low, high) were manipulated across two-sentence passages. Context sentences were presented in full, followed by word-by-word presentation (300 ms SOA) of target sentences. ERPs were analysed across left-to-right and anterior-to-posterior regions of interest within intervals from 50 to 550 ms post-stimulus. The onset of significant predictability effects (50–80 ms) preceded that of frequency (P1, 80–120 ms), while both main effects were generally sustained through the N400 (350–550 ms). Critically, the frequency-predictability interaction became significant in the P1 and was sustained through the N400, although the specific configuration of effects differed across components. The pattern of findings supports an early, chronometric locus of contextual predictability in recognising words during reading

Testing the limits of contextual constraint: interactions with word frequency and parafoveal preview during fluent reading

Contextual constraint is a key factor affecting a word's fixation duration and its likelihood of being fixated during reading. Previous research has generally demonstrated additive effects of predictability and frequency in fixation times. Studies examining the role of parafoveal preview have shown that greater preview benefit is obtained from more predictable and higher frequency words versus less predictable and lower frequency words. In two experiments, we investigated effects of target word predictability, frequency, and parafoveal preview. A 3 (Predictability: low, medium, high) × 2 (Frequency: low, high) design was used with Preview (valid, invalid) manipulated between experiments. With valid previews, we found main effects of Predictability and Frequency in both fixation time and probability measures, including an interaction in early fixation measures. With invalid preview, we again found main effects of Predictability and Frequency in fixation times, but no evidence of an interaction. Fixation probability showed a weak Predictability effect and Predictability-Frequency interaction. Predictability interacted with Preview in early fixation time and probability measures. Our findings suggest that high levels of contextual constraint exert an early influence during lexical processing in reading. Results are discussed in terms of models of language processing and eye movement control

A tool for subjective and interactive visual data exploration

We present SIDE, a tool for Subjective and Interactive Visual Data Exploration, which lets users explore high dimensional data via subjectively informative 2D data visualizations. Many existing visual analytics tools are either restricted to specific problems and domains or they aim to find visualizations that align with user’s belief about the data. In contrast, our generic tool computes data visualizations that are surprising given a user’s current understanding of the data. The user’s belief state is represented as a set of projection tiles. Hence, this user-awareness offers users an efficient way to interactively explore yet-unknown features of complex high dimensional datasets

Does segmentation always improve model performance in credit scoring?

Credit scoring allows for the credit risk assessment of bank customers. A single scoring model (scorecard) can be developed for the entire customer population, e.g. using logistic regression. However, it is often expected that segmentation, i.e. dividing the population into several groups and building separate scorecards for them, will improve the model performance. The most common statistical methods for segmentation are the two-step approaches, where logistic regression follows Classification and Regression Trees (CART) or Chi-squared Automatic Interaction Detection (CHAID) trees etc. In this research, the two-step approaches are applied as well as a new, simultaneous method, in which both segmentation and scorecards are optimised at the same time: Logistic Trees with Unbiased Selection (LOTUS). For reference purposes, a single-scorecard model is used. The above-mentioned methods are applied to the data provided by two of the major UK banks and one of the European credit bureaus. The model performance measures are then compared to examine whether there is improvement due to the segmentation methods used. It is found that segmentation does not always improve model performance in credit scoring: for none of the analysed real-world datasets, the multi-scorecard models perform considerably better than the single-scorecard ones. Moreover, in this application, there is no difference in performance between the two-step and simultaneous approache

Flexible delivery of Er:YAG radiation at 2.94 µm with negative curvature silica glass fibers:a new solution for minimally invasive surgical procedures

We present the delivery of high energy microsecond pulses through a hollow-core negative-curvature fiber at 2.94 µm. The energy densities delivered far exceed those required for biological tissue manipulation and are of the order of 2300 J/cm(2). Tissue ablation was demonstrated on hard and soft tissue in dry and aqueous conditions with no detrimental effects to the fiber or catastrophic damage to the end facets. The energy is guided in a well confined single mode allowing for a small and controllable focused spot delivered flexibly to the point of operation. Hence, a mechanically and chemically robust alternative to the existing Er:YAG delivery systems is proposed which paves the way for new routes for minimally invasive surgical laser procedures

Polychlorinated biphenyls in air and water of the North Atlantic and Arctic Ocean.

Air and seawater samples were collected on board the R/V Polarstern during a scientific expedition from Germany to the Arctic Ocean during June–August 2004. The air data show a strong decline with latitude with the highest polychlorinated biphenyl (PCB) concentrations in Europe and the lowest in the Arctic. ΣICES PCBs in air range from 100 pg m−3 near Norway to 0.8 pg m−3 in the Arctic. A comparison with other data from previous and ongoing land-based air measurements in the Arctic region suggests no clear temporal decline of PCBs in the European Arctic since the mid-1990s. Dissolved concentrations of Σ6PCBs (28/31, 52, 101, 118, 138, 153) in surface seawater were <1 pg L−1. Dominant PCBs in seawater were 28/31 and 52 (0.1–0.44 pg L−1), with PCBs 101, 118, and 138 < 0.1 pg L−1. In seawater, PCB 52 displayed the highest concentrations in the northernmost samples, while PCBs 101, 118, and 138 showed slightly decreasing trends with increasing latitude. Fractionation was observed for PCBs in seawater with the relative abundance of PCBs 28 and 52 increasing and that of the heavier congeners decreasing with latitude. However, in air only 15–20% of the variability of atmospheric PCBs can be explained by temperature. Owing to large uncertainties in the Henry's Law constant (HLC) values, fugacity quotients for PCBs were estimated using different HLCs reported in the literature. These indicate that on average, deposition dominates over volatilization for PCBs in the Arctic region with a strong increase in the middle of the transect near the marginal ice zone (78–79°N). The increase in fugacity ratio is mainly caused by an increase in air concentration in this region (possibly indirectly caused by ice melting being a source of PCBs to the atmosphere)

Efficient estimation of AUC in a sliding window

In many applications, monitoring area under the ROC curve (AUC) in a sliding window over a data stream is a natural way of detecting changes in the system. The drawback is that computing AUC in a sliding window is expensive, especially if the window size is large and the data flow is significant. In this paper we propose a scheme for maintaining an approximate AUC in a sliding window of length $k$. More specifically, we propose an algorithm that, given $\epsilon$, estimates AUC within $\epsilon / 2$, and can maintain this estimate in $O((\log k) / \epsilon)$ time, per update, as the window slides. This provides a speed-up over the exact computation of AUC, which requires $O(k)$ time, per update. The speed-up becomes more significant as the size of the window increases. Our estimate is based on grouping the data points together, and using these groups to calculate AUC. The grouping is designed carefully such that ($i$) the groups are small enough, so that the error stays small, ($ii$) the number of groups is small, so that enumerating them is not expensive, and ($iii$) the definition is flexible enough so that we can maintain the groups efficiently. Our experimental evaluation demonstrates that the average approximation error in practice is much smaller than the approximation guarantee $\epsilon / 2$, and that we can achieve significant speed-ups with only a modest sacrifice in accuracy