Adversarial attacks hidden in plain sight

Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue. Adversarial examples are designed to deliberately fool neural networks into making any desired incorrect classification, potentially with very high certainty. Several defensive approaches increase robustness against adversarial attacks, demanding attacks of greater magnitude, which lead to visible artifacts. By considering human visual perception, we compose a technique that allows to hide such adversarial attacks in regions of high complexity, such that they are imperceptible even to an astute observer. We carry out a user study on classifying adversarially modified images to validate the perceptual quality of our approach and find significant evidence for its concealment with regards to human visual perception

Temporal variables and personal factors in glare sensation

Previous laboratory experiments have provided evidence of an effect of time of day on glare sensation. During the tests, temporal variables and personal factors were also measured to analyse their influence on levels of visual discomfort as the day progresses. The results revealed statistically significant and practically relevant tendencies towards greater tolerance to source luminance from artificial lighting at all times of day for earlier chronotypes and for participants not having ingested caffeine. No conclusive evidence was found for the effect of fatigue, sky condition and prior light exposure on glare sensation throughout the day. These findings suggest that temporal variables and personal factors should be measured in conjunction with visual discomfort levels to explore the causes of the wide individual differences commonly associated with the subjective evaluation of glare sensation

Nonparametric Competitors to the Two-Way ANOVA

↵LARRY E. TOOTHAKER is David Ross Boyd Professor of Psychology at the University of Oklahoma, Norman, OK 73019. He specializes in robustness of ANOVA, including repeated measures designs, multiple comparison procedures, and nonparametrics.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Chapter Near-Infrared Spectroscopy Measured Cerebral Blood Flow from Spontaneous Oxygenation Changes in Neonatal Brain Injury

Neonates with hypoxic-ischaemic (HI) brain injury were monitored using a broadband near-infrared spectroscopy (NIRS) system in the neonatal intensive care unit. The aim of this work is to use the NIRS cerebral oxygenation data (HbD = oxygenated-haemoglobin – deoxygenated-haemoglobin) combined with arterial saturation (SaO2) from pulse oximetry to calculate cerebral blood flow (CBF) based on the oxygen swing method, during spontaneous desaturation episodes. The method is based on Fick’s principle and uses HbD as a tracer; when a sudden change in SaO2 occurs, the change in HbD represents a change in tracer concentration, and thus it is possible to estimate CBF. CBF was successfully calculated with broadband NIRS in 11 HIE infants (3 with severe injury) for 70 oxygenation events on the day of birth. The average CBF was 18.0 ± 12.7 ml 100 g−1 min−1 with a range of 4 ml 100 g−1 min−1 to 60 ml 100 g−1 min−1. For infants with severe HIE (as determined by magnetic resonance spectroscopy) CBF was significantly lower (p = 0.038, d = 1.35) than those with moderate HIE on the day of birth

Near-Infrared Spectroscopy Measured Cerebral Blood Flow from Spontaneous Oxygenation Changes in Neonatal Brain Injury

Neonates with hypoxic-ischaemic (HI) brain injury were monitored using a broadband near-infrared spectroscopy (NIRS) system in the neonatal intensive care unit. The aim of this work is to use the NIRS cerebral oxygenation data (HbD = oxygenated-haemoglobin - deoxygenated-haemoglobin) combined with arterial saturation (SaO2) from pulse oximetry to calculate cerebral blood flow (CBF) based on the oxygen swing method, during spontaneous desaturation episodes. The method is based on Fick's principle and uses HbD as a tracer; when a sudden change in SaO2 occurs, the change in HbD represents a change in tracer concentration, and thus it is possible to estimate CBF. CBF was successfully calculated with broadband NIRS in 11 HIE infants (3 with severe injury) for 70 oxygenation events on the day of birth. The average CBF was 18.0 ± 12.7 ml 100 g-1 min-1 with a range of 4 ml 100 g-1 min-1 to 60 ml 100 g-1 min-1. For infants with severe HIE (as determined by magnetic resonance spectroscopy) CBF was significantly lower (p = 0.038, d = 1.35) than those with moderate HIE on the day of birth

A robustness study of parametric and non-parametric tests in model-based multifactor dimensionality reduction for epistasis detection

BACKGROUND: Applying a statistical method implies identifying underlying (model) assumptions and checking their validity in the particular context. One of these contexts is association modeling for epistasis detection. Here, depending on the technique used, violation of model assumptions may result in increased type I error, power loss, or biased parameter estimates. Remedial measures for violated underlying conditions or assumptions include data transformation or selecting a more relaxed modeling or testing strategy. Model-Based Multifactor Dimensionality Reduction (MB-MDR) for epistasis detection relies on association testing between a trait and a factor consisting of multilocus genotype information. For quantitative traits, the framework is essentially Analysis of Variance (ANOVA) that decomposes the variability in the trait amongst the different factors. In this study, we assess through simulations, the cumulative effect of deviations from normality and homoscedasticity on the overall performance of quantitative Model-Based Multifactor Dimensionality Reduction (MB-MDR) to detect 2-locus epistasis signals in the absence of main effects. METHODOLOGY: Our simulation study focuses on pure epistasis models with varying degrees of genetic influence on a quantitative trait. Conditional on a multilocus genotype, we consider quantitative trait distributions that are normal, chi-square or Student’s t with constant or non-constant phenotypic variances. All data are analyzed with MB-MDR using the built-in Student’s t-test for association, as well as a novel MB-MDR implementation based on Welch’s t-test. Traits are either left untransformed or are transformed into new traits via logarithmic, standardization or rank-based transformations, prior to MB-MDR modeling. RESULTS: Our simulation results show that MB-MDR controls type I error and false positive rates irrespective of the association test considered. Empirically-based MB-MDR power estimates for MB-MDR with Welch’s t-tests are generally lower than those for MB-MDR with Student’s t-tests. Trait transformations involving ranks tend to lead to increased power compared to the other considered data transformations. CONCLUSIONS: When performing MB-MDR screening for gene-gene interactions with quantitative traits, we recommend to first rank-transform traits to normality and then to apply MB-MDR modeling with Student’s t-tests as internal tests for association

Area asymmetry of heart rate variability signal

BACKGROUND: Heart rate fluctuates beat-by-beat asymmetrically which is known as heart rate asymmetry (HRA). It is challenging to assess HRA robustly based on short-term heartbeat interval series. METHOD: An area index (AI) was developed that combines the distance and phase angle information of points in the Poincaré plot. To test its performance, the AI was used to classify subjects with: (i) arrhythmia, and (ii) congestive heart failure, from the corresponding healthy controls. For comparison, the existing Porta\u27s index (PI), Guzik\u27s index (GI), and slope index (SI) were calculated. To test the effect of data length, we performed the analyses separately using long-term heartbeat interval series (derived from >3.6-h ECG) and short-term segments (with length of 500 intervals). A second short-term analysis was further carried out on series extracted from 5-min ECG. RESULTS: For long-term data, SI showed acceptable performance for both tasks, i.e., for task i p < 0.001, Cohen\u27s d = 0.93, AUC (area under the receiver-operating characteristic curve) = 0.86; for task ii p < 0.001, d = 0.88, AUC = 0.75. AI performed well for task ii (p < 0.001, d = 1.0, AUC = 0.78); for task i, though the difference was statistically significant (p < 0.001, AUC = 0.76), the effect size was small (d = 0.11). PI and GI failed in both tasks (p > 0.05, d < 0.4, AUC < 0.7 for all). However, for short-term segments, AI indicated better distinguishability for both tasks, i.e., for task i, p < 0.001, d = 0.71, AUC = 0.71; for task ii, p < 0.001, d = 0.93, AUC = 0.74. The rest three measures all failed with small effect sizes and AUC values (d < 0.5, AUC < 0.7 for all) although the difference in SI for task i was statistically significant (p < 0.001). Besides, AI displayed smaller variations across different short-term segments, indicating more robust performance. Results from the second short-term analysis were in keeping with those findings. CONCLUSION: The proposed AI indicated better performance especially for short-term heartbeat interval data, suggesting potential in the ambulatory application of cardiovascular monitoring