Butterfly catastrophe for fronts in a three-component reaction–diffusion system

We study the dynamics of front solutions in a three-component reaction–diffusion system via a combination of geometric singular perturbation theory, Evans function analysis, and center manifold reduction. The reduced system exhibits a surprisingly complicated bifurcation structure including a butterfly catastrophe. Our results shed light on numerically observed accelerations and oscillations and pave the way for the analysis of front interactions in a parameter regime where the essential spectrum of a single front approaches the imaginary axis asymptotically.

Navigating emotion research during a paradigm shift in psychology

Human emotional responses are highly individual. A comprehensive analysis of emotion research in cognitive psychology and physiology, including laboratory-based experiments, showed that understanding human emotions requires a dynamic systems approach incorporating insights from scientific disciplines beyond psychology. Importantly, subjective and automatic evaluations of emotive information are context-sensitive and changeable, confirming the dynamic nature of emotion and role of individual differences. Furthermore, a comparison of different statistical approaches established that statistical estimation, rather than averages, best captures our highly individual emotional responses. Emotion research needs a cross-disciplinary approach.

Behavioral consistency, the homology assumption, and the problems of induction

The ultimate goal of profiling is to identify the major behavioral and personality characteristics to narrow the suspect pool. Inferences about offender characteristics can be accomplished deductively, based on the analysis of discrete offender behaviors established within a particular case. They can also be accomplished inductively, involving prediction based on abstract offender averages from group data (these methods and the logic on which they are based is detailed extensively in Chapters 2 and 4). As discussed, these two approaches are by no means equal.

Do instructional attributes pose multicollinearity problems? An empirical exploration

It is commonly perceived that variables ‘measuring’ different dimensions of teaching (construed as instructional attributes) used in student evaluation of teaching (SET) questionnaires are so highly correlated that they pose a serious multicollinearity problem for quantitative analysis including regression analysis. Using nearly 12000 individual student responses to SET questionnaires and ten key dimensions of teaching and 25 courses at various undergraduate and postgraduate levels for multiple years at a large Australian university, this paper investigates whether this is indeed the case and if so under what circumstances. This paper tests this proposition first by examining variance inflation factors (VIFs), across courses, levels and over time using individual responses; and secondly by using class averages. In the first instance, the paper finds no sustainable evidence of multicollinearity. While, there were one or two isolated cases of VIFs marginally exceeding the conservative threshold of 5, in no cases did the VIFs for any of the instructional attributes come anywhere close to the high threshold value of 10. In the second instance, however, the paper finds that the attributes are highly correlated as all the VIFs exceed 10. These findings have two implications: (a) given the ordinal nature of the data ordered probit analysis using individual student responses can be employed to quantify the impact of instructional attributes on TEVAL score; (b) Data based on class averages cannot be used for probit analysis. An illustrative exercise using level 2 undergraduate courses data suggests higher TEVAL scores depend first and foremost on improving explanation, presentation, and organization of lecture materials.

Genetic influences on sulcal patterns of the brain

We present a shape-space approach for analyzing genetic influences on the shapes of the sulcal folding patterns on the cortex. Sulci are represented as continuously parameterized functions in a shape space, and shape differences between sulci are obtained via geodesics between them. The resulting statistical shape analysis framework is used not only to construct populations averages, but also used to compute meaningful correlations within and across groups of sulcal shapes. More importantly, we present a new algorithm that extends the traditional Euclidean estimate of the intra-class correlation to the geometric shape space, thereby allowing us to study heritability of sulcal shape traits for a population of 193 twin pairs. This new methodology reveals strong genetic influences on the sulcal geometry of the cortex.

Evaluation of visual stress symptoms in Age-matched Dyslexic, Meares–Irlen syndrome (MIS) and normal adults

AIM To examine the prevalence of dyslexia and Meares–Irlen syndrome (MIS) among female students and determine their level of visual stress in comparison with normal subjects. METHODS A random sample of 450 female medical students of King Saud University Riyadh (age range, 18 - 30 years) responded to a wide range of questions designed to accomplish the aims of this study. The detailed questionnaire consisted of 54 questions with twelve questions enquiring on ocular history and demography of participants while 42 questions were on visual symptoms. Items were categorized into; critical and non-critical questions (CQ and NCQ) and were rated on four point Likert scale. Based on the responses obtained, the subjects were grouped into normal (control), dyslexic with or without MIS (Group 1) and subjects with MIS only (Group 2). Responses were analysed as averages and mean scores were calculated and compared between-groups using one way analysis of variance to evaluate total (TVSS = NCQ + CQ), critical and non-critical visual stress scores. The relationship between categorical variables such as age, handedness and condition were assessed with Chi- Square test. RESULTS The completion rate was 96.8% and majority of the respondents (92%) were normal readers, 2% dyslexic and 6% had MIS. They were age-matched. More than half of the participants had visited an eye care practitioner in the last 2yrs. About 13% were recommended eye exercises and one participant experienced pattern glare. Hand preference was not associated with any condition but Group 1 subjects (3/9, 33%) were significantly more likely to be diagnosed of lazy eye than Group 2 (2/27, 7%) and control (27/414, 5%) subjects. The mean ± SD of TVSS responses were 63 ± 14 but it was 44 ± 9 for CQ and 19 ± 5 for NCQ. Responses from all three variables were normally distributed but the CQ responses were on the average more positive (82%) in Group 2 and less positive (46%) in Group 1 than control. With NCQ, the responses were equally less positive in Group 1 and 2 than control. Group 2 subjects showed significantly higher TVSS (P = 0.002), NCQ (P = 0.006) and CQ (P = 0.008) visual stress scores than control but no difference between Group 1 and control subjects, was observed for all scores (P > 0.05, for all comparisons). CONCLUSION The prevalence of dyslexia and MIS among Saudi female students was 2 and 6%, respectively. Critical questions performed best for assessing visual stress symptoms in dyslexic and MIS subjects. Generally, students with MIS were more sensitive to visual stress than normal students but dyslexics were more likely to present with a lazy eye than MIS and normal readers.

Automated feature engineering for HTTP tunnel detection

Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.

Early and late electrophysiological effects of distractor frequency in picture naming: Reconciling input and output accounts

The “distractor-frequency effect” refers to the finding that high-frequency (HF) distractor words slow picture naming less than low-frequency distractors in the picture–word interference paradigm. Rival input and output accounts of this effect have been proposed. The former attributes the effect to attentional selection mechanisms operating during distractor recognition, whereas the latter attributes it to monitoring/decision mechanisms operating on distractor and target responses in an articulatory buffer. Using high-density (128-channel) EEG, we tested hypotheses from these rival accounts. In addition to conducting stimulus- and response-locked whole-brain corrected analyses, we investigated the correct-related negativity, an ERP observed on correct trials at fronto-central electrodes proposed to reflect the involvement of domain general monitoring. The wholebrain ERP analysis revealed a significant effect of distractor frequency at inferior right frontal and temporal sites between 100 and 300-msec post-stimulus onset, during which lexical access is thought to occur. Response-locked, region of interest (ROI) analyses of fronto-central electrodes revealed a correct-related negativity starting 121 msec before and peaking 125 msec after vocal onset on the grand averages. Slope analysis of this component revealed a significant difference between HF and lowfrequency distractor words, with the former associated with a steeper slope on the time windowspanning from100 msec before to 100 msec after vocal onset. The finding of ERP effects in time windows and components corresponding to both lexical processing and monitoring suggests the distractor frequency effect is most likely associated with more than one physiological mechanism.