Multilevel versus Single-Level Regression for the Analysis of Multilevel Information:The Case of Quantitative Intersectional Analysis

Intersectional MAIHDA involves applying multilevel models in order to estimate intercategorical inequalities. The approach has been validated thus far using both simulations and empirical applications, and has numerous methodological and theoretical advantages over single-level approaches, including parsimony and reliability for analyzing high-dimensional interactions. In this issue of SSM, Lizotte, Mahendran, Churchill and Bauer (hereafter “LMCB”) assert that there has been insufficient clarity on the interpretation of fixed effects regression coefficients in intersectional MAIHDA, and that stratum-level residuals in intersectional MAIHDA are not interpretable as interaction effects. We disagree with their second assertion; however, the authors are right to call for greater clarity. For this purpose, in this response we have three main objectives. (1) In their commentary, LMCB incorrectly describe model predictions based on MAIHDA fixed effects as estimates of “grand means” (or the mean of means), when they are actually “precision-weighted grand means.” We clarify the differences between average predicted values obtained by different models, and argue that predictions obtained by MAIHDA are more suitable to serve as reference points for residual/interaction effects. This further enables us to clarify the interpretation of residual/interaction effects in MAIHDA and conventional models. Using simple simulations, we demonstrate conditions under which the precision-weighted grand mean resembles a grand mean, and when it resembles a population mean (or the mean of all individual observations) obtained using single-level regression, explaining the results obtained by LMCB and informing future research. (2) We construct a modification to MAIHDA that constrains the fixed effects so that the resulting model predictions provide estimates of population means, which we use to demonstrate the robustness of results reported by Evans et al. (2018). We find that stratum-specific residuals obtained using the two approaches are highly correlated (Pearson corr = 0.98, p < 0.0001) and no substantive conclusions would have been affected if the preference had been for estimating population means. However, we advise researchers to use the original, unconstrained MAIHDA. (3) Finally, we outline the extent to which single-level and MAIHDA approaches address the fundamental goals of quantitative intersectional analyses and conclude that intersectional MAIHDA remains a promising new approach for the examination of inequalities

Raised Shoreline Phenomena and Postglacial Emergence in South-Central Newfoundland

Two types of raised marine shoreline features occur in the Burin-Hermitage area of southern Newfoundland marine benches cut in bedrock, and terraces and beaches developed in unconsolidated materials. Most of the benches are older than Late Wisconsinan, and a horizontal rock shoreline at 4.5 ± 1.5 m, which occurs throughout the region, was probably formed in the last interglacial period. Raised deltas and coastal outwash deposits graded to former sea level positions, which define the Late Wisconsinan marine limit across the northern part of the study area, are correlated with terraces and raised beaches further south on the Burin Peninsula. The elevations of these features are used to define the regional pattern of postglacial emergence. More than 30 m of emergence has occurred in the northwest, but the extreme southern part of the region is undergoing submergence.Dans la région de Burin-Hermitage, au sud de Terre-Neuve, on retrouve deux types de lignes de rivage marines soulevées: des plates-formes marines entaillées dans la roche en place ainsi que des terrasses et des plages développées dans des matériaux meubles. La plupart des plates-formes datent d'avant le Wisconsinien inférieur. Une ligne de rivage rocheuse horizontale située à 4,5 ± 1,5 m, qu'on retrouve à travers la région, fut probablement formée au cours du dernier interglaciaire. Des deltas soulevés et des épandages fluvioglaciaires côtiers, associés à des plans d'eau marins qui marquent la limite marine du Wisconsinien inférieur dans la partie nord de la zone d'étude, sont mis en relation avec des terrasses et des plages soulevées existant plus au sud dans la péninsule de Burin. L'altitude de ces formes sert à établir le mode régional d'émersion post-glaciaire. Il s'est produit une emersion de plus de 30 m dans le nord-ouest, alors que l'extrême-sud de la région est en phase de submersion

Benthic Foraminiferal response to sea level change in the mixed siliciclastic-carbonate system of southern Ashmore Trough (Gulf of Papua)

Ashmore Trough in the western Gulf of Papua (GoP) represents an outstanding modern example of a tropical mixed siliciclastic-carbonate depositional system where significant masses of both river-borne silicates and bank-derived neritic carbonates accumulate. In this study, we examine how benthic foraminiferal populations within Ashmore Trough vary in response to sea level–driven paleoenvironmental changes, particularly organic matter and sediment supply. Two 11.3-m-long piston cores and a trigger core were collected from the slope of Ashmore Trough and dated using radiocarbon and oxygen isotope measurements of planktic foraminifera. Relative abundances, principal component analyses, and cluster analyses of benthic foraminiferal assemblages in sediment samples identify three distinct assemblages whose proportions changed over time. Assemblage 1, with high abundances of Uvigerina peregrina and Bolivina robusta, dominated between ∼83 and 70 ka (early regression); assemblage 2, with high abundances of Globocassidulina subglobosa, dominated between ∼70 and 11 ka (late regression through lowstand and early transgression); and assemblage 3, with high abundances of neritic benthic species such as Planorbulina mediterranensis, dominated from ∼11 ka to the present (late transgression through early highstand). Assemblage 1 represents heightened organic carbon flux or lowered bottom water oxygen concentration, and corresponds to a time of maximum siliciclastic fluxes to the slope with falling sea level. Assemblage 2 reflects lowered organic carbon flux or elevated bottom water oxygen concentration, and corresponds to an interval of lowered siliciclastic fluxes to the slope due to sediment bypass during sea level lowstand. Assemblage 3 signals increased off-shelf delivery of neritic carbonates, likely when carbonate productivity on the outer shelf (Great Barrier Reef) increased significantly when it was reflooded. Benthic foraminiferal assemblages in the sediment sink (slopes of Ashmore Trough) likely respond to the amount and type of sediment supplied from the proximal source (outer GoP shelf)

Benchmarking Adversarially Robust Quantum Machine Learning at Scale

Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry. Despite their accuracy and sophistication, neural networks can be easily fooled by carefully designed malicious inputs known as adversarial attacks. While such vulnerabilities remain a serious challenge for classical neural networks, the extent of their existence is not fully understood in the quantum ML setting. In this work, we benchmark the robustness of quantum ML networks, such as quantum variational classifiers (QVC), at scale by performing rigorous training for both simple and complex image datasets and through a variety of high-end adversarial attacks. Our results show that QVCs offer a notably enhanced robustness against classical adversarial attacks by learning features which are not detected by the classical neural networks, indicating a possible quantum advantage for ML tasks. Contrarily, and remarkably, the converse is not true, with attacks on quantum networks also capable of deceiving classical neural networks. By combining quantum and classical network outcomes, we propose a novel adversarial attack detection technology. Traditionally quantum advantage in ML systems has been sought through increased accuracy or algorithmic speed-up, but our work has revealed the potential for a new kind of quantum advantage through superior robustness of ML models, whose practical realisation will address serious security concerns and reliability issues of ML algorithms employed in a myriad of applications including autonomous vehicles, cybersecurity, and surveillance robotic systems.Comment: 10 pages, 5 Figure

Node re-ordering as a means of anomaly detection in time-evolving graphs

© Springer International Publishing AG 2016. Anomaly detection is a vital task for maintaining and improving any dynamic system. In this paper, we address the problem of anomaly detection in time-evolving graphs, where graphs are a natural representation for data in many types of applications. A key challenge in this context is how to process large volumes of streaming graphs. We propose a pre-processing step before running any further analysis on the data, where we permute the rows and columns of the adjacency matrix. This pre-processing step expedites graph mining techniques such as anomaly detection, PageRank, or graph coloring. In this paper, we focus on detecting anomalies in a sequence of graphs based on rank correlations of the reordered nodes. The merits of our approach lie in its simplicity and resilience to challenges such as unsupervised input, large volumes and high velocities of data. We evaluate the scalability and accuracy of our method on real graphs, where our method facilitates graph processing while producing more deterministic orderings. We show that the proposed approach is capable of revealing anomalies in a more efficient manner based on node rankings. Furthermore, our method can produce visual representations of graphs that are useful for graph compression

Pathotypic diversity of Hyaloperonospora brassicae collected from Brassica oleracea

Downy mildew caused by Hyaloperonospora brassicae is an economically destructive disease of brassica crops in many growing regions throughout the world. Specialised pathogenicity of downy mildews from different Brassica species and closely related ornamental or wild relatives has been described from host range studies. Pathotypic variation amongst Hyaloperonospora brassicae isolates from Brassica oleracea has also been described; however, a standard set of B. oleracea lines that could enable reproducible classification of H. brassicae pathotypes was poorly developed. For this purpose, we examined the use of eight genetically refined host lines derived from our previous collaborative work on downy mildew resistance as a differential set to characterise pathotypes in the European population of H. brassicae. Interaction phenotypes for each combination of isolate and host line were assessed following drop inoculation of cotyledons and a spectrum of seven phenotypes was observed based on the level of sporulation on cotyledons and visible host responses. Two host lines were resistant or moderately resistant to the entire collection of isolates, and another was universally susceptible. Five lines showed differential responses to the H. brassicae isolates. A minimum of six pathotypes and five major effect resistance genes are proposed to explain all of the observed interaction phenotypes. The B. oleracea lines from this study can be useful for monitoring pathotype frequencies in H. brassicae populations in the same or other vegetable growing regions, and to assess the potential durability of disease control from different combinations of the predicted downy mildew resistance genes