Can Leakage Models Be More Efficient? Non-Linear Models in Side Channel Attacks

In the last decade, many side channel attacks have been published in academic literature detailing how to efficiently extract secret keys by mounting various attacks, such as differential or correlation power analysis, on cryptosystems. Among the most efficient and widely utilized leakage models involved in these attacks are the Hamming weight and distance models which give a simple, yet effective, approximation of the power consumption for many real-world systems. These leakage models reflect the number of bits switching, which is assumed proportional to the power consumption. However, the actual power consumption changing in the circuits is unlikely to be directly of that form. We, therefore, propose a non-linear leakage model by mapping the existing leakage model via a transform function, by which the changing power consumption is depicted more precisely, hence the attack efficiency can be improved considerably. This has the advantage of utilising a non-linear power model while retaining the simplicity of the Hamming weight or distance models. A modified attack architecture is then suggested to yield the correct key efficiently in practice. Finally, an empirical comparison of the attack results is presented.

Fast and frugal models of clinical judgement in novice and expert physicians

Our objective was to study whether “compensatory” models provide better descriptions of clinical judgment than fast and frugal models, according to expertise and experience. Fifty practitioners appraised 60 vignettes describing a child with an exacerbation of asthma and rated their propensities to admit the child. Linear logistic (LL) models of their judgments were compared with a matching heuristic (MH) model that searched available cues in order of importance for a critical value indicating an admission decision. There was a small difference between the 2 models in the proportion of patients allocated correctly (admit or not-admit decisions), 91.2% and 87.8%, respectively. The proportion allocated correctly by the LL model was lower for consultants than juniors, whereas the MH model performed equally well for both. In this vignette study, neither model provided any better description of judgments made by consultants or by pediatricians compared to other grades and specialties.

Efficient Non-Linear Idealisations of Aircraft Fuselage Panels in Compression

Aircraft fuselages are complex assemblies of thousands of components and as a result simulation models are highly idealised. In the typical design process, a coarse FE model is used to determine loads within the structure. The size of the model and number of load cases necessitates that only linear static behaviour is considered. This paper reports on the development of a modelling approach to increase the accuracy of the global model, accounting for variations in stiffness due to non-linear structural behaviour. The strategy is based on representing a fuselage sub-section with a single non-linear element. Large portions of fuselage structure are represented by connecting these non-linear elements together to form a framework. The non-linear models are very efficient, reducing computational time significantly

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Explaining young mortality

Stochastic modeling of mortality rates focuses on fitting linear models to logarithmically adjusted mortality data from the middle or late ages. Whilst this modeling enables insurers to project mortality rates and hence price mortality products it does not provide good fit for younger aged mortality. Mortality rates below the early 20's are important to model as they give an insight into estimates of the cohort effect for more recent years of birth. It is also important given the cumulative nature of life expectancy to be able to forecast mortality improvements at all ages. When we attempt to fit existing models to a wider age range, 5-89, rather than 20-89 or 50-89, their weaknesses are revealed as the results are not satisfactory. The linear innovations in existing models are not flexible enough to capture the non-linear profile of mortality rates that we see at the lower ages. In this paper we modify an existing 4 factor model of mortality to enable better fitting to a wider age range, and using data from seven developed countries our empirical results show that the proposed model has a better fit to the actual data, is robust, and has good forecasting ability.

The impact of curricular change on medical students' knowledge of anatomy

Background: In recent years, following the publication of Tomorrow's Doctors, the undergraduate medical curriculum in most UK medical schools has undergone major revision. This has resulted in a significant reduction in the time allocated to the teaching of the basic medical sciences, including anatomy. However, it is not clear what impact these changes have had on medical students' knowledge of surface anatomy. Aim: This study aimed to assess the impact of these curricular changes on medical students' knowledge of surface anatomy. Setting: Medical student intakes for 1995-98 at the Queen's University of Belfast, UK. Methods: The students were invited to complete a simple examination paper testing their knowledge of surface anatomy. Results from the student intake of 1995, which undertook a traditional, 'old' curriculum, were compared with those from the student intakes of 1996-98, which undertook a new, 'systems-based' curriculum. To enhance linear response and enable the use of linear models for analysis, all data were adjusted using probit transformations of the proportion (percentage) of correct answers for each item and each year group. Results: The student intake of 1995 (old curriculum) were more likely to score higher than the students who undertook the new, systems-based curriculum. Conclusion: The introduction of the new, systems-based course has had a negative impact on medical students' knowledge of surface anatomy.

Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.

Neural network based control for synchronous generators

In this paper, a Radial Basis Function neural network based AVR is proposed. A control strategy which generates local linear models from a global neural model on-line is used to derive controller feedback gains based on the Generalised Minimum Variance technique. Testing is carried out on a micromachine system which enables evaluation of practical implementation of the scheme. Constraints imposed by gathering training data, computational load, and memory requirements for the training algorithm are addressed.

Accounting for the effects of lipids in stable isotope (δ13C and δ15N values) analysis of skin and blubber of balaenopterid whales

RATIONALE Stable isotope values (d¹³C and d¹⁵N) of darted skin and blubber biopsies can shed light on habitat use and diet of cetaceans, which are otherwise difficult to study. Non-dietary factors affect isotopic variability, chiefly the depletion of C due to the presence of C-rich lipids. The efficacy of post hoc lipid-correction models (normalization) must be tested. METHODS For tissues with high natural lipid content (e.g., whale skin and blubber), chemical lipid extraction or normalization is necessary. C:N ratios, d¹³C values and d¹⁵N values were determined for duplicate control and lipid-extracted skin and blubber of fin (Balaenoptera physalus), humpback (Megaptera novaeangliae) and minke whales (B. acutorostrata) by continuous-flow elemental analysis isotope ratio mass spectrometry (CF-EA-IRMS). Six different normalization models were tested to correct d¹³C values for the presence of lipids. RESULTS Following lipid extraction, significant increases in d¹³C values were observed for both tissues in the three species. Significant increases were also found for d¹⁵N values in minke whale skin and fin whale blubber. In fin whale skin, the d¹⁵N values decreased, with no change observed in humpback whale skin. Non-linear models generally out-performed linear models and the suitability of models varied by species and tissue, indicating the need for high model specificity, even among these closely related taxa. CONCLUSIONS Given the poor predictive power of the models to estimate lipid-free d13C values, and the unpredictable changes in d N values due to lipid-extraction, we recommend against arithmetical normalization in accounting for lipid effects on d13C values for balaenopterid skin or blubber samples. Rather, we recommend that duplicate analysis of lipid-extracted (d13C values) and non-treated tissues (d15N values) be used. Copyright © 2012 John Wiley & Sons, Ltd.

Organizational culture predicts job satisfaction and perceived clinical effectiveness in pediatric primary care practices

Background: In recent years, there has been a growing understanding that organizational culture is related to an organization's performance. However, fewstudies have examined organizational culture in medical group practices. Objectives: The purpose of this study was to examine the relationship of organizational culture on provider job satisfaction and perceived clinical effectiveness in primary care pediatric practices. Research Design: This cross-sectional study included 36 primary care pediatric practices located in Connecticut. Participants: There were 374 participants in this study, which included 127 clinicians and 247 nonclinicians. Measures: Office managers completed a questionnaire that recorded staff and practice characteristics; all participants completed the Organizational Culture Scale, a questionnaire that assessed the practice on four cultural domains (i.e., group, developmental, rational, and hierarchical), and the Primary Care Organizational Questionnaire that evaluated perceived effectiveness and job satisfaction. Results: Hierarchical linear models using a restricted maximum likelihood estimation method were used to evaluate whether the practice culture types predicted job satisfaction and perceived effectiveness. Group culture was positively associated with both satisfaction and perceived effectiveness. In contrast, hierarchical and rational culture were negatively associated with both job satisfaction and perceived effectiveness. These relationships were true for clinicians, nonclinicians, and the practice as a whole. Conclusions: Our study demonstrates that practice culture is associated with job satisfaction and perceived clinical effectiveness and that a group culture was associated with high job satisfaction and perceived effectiveness. Copyright © 2010 Wolters Kluwer Health | Lippincott Williams & Wilkins.

A shared care model pilot for palliative home care in a rural area:Impact on symptoms, distress, and place of death

Context: Shared care models integrating family physician services with interdisciplinary palliative care specialist teams are critical to improve access to quality palliative home care and address multiple domains of end-of-life issues and needs. Objectives: To examine the impact of a shared care pilot program on the primary outcomes of symptom severity and emotional distress (patient and family separately) over time and, secondarily, the concordance between patient preferences and place of death. Methods: An inception cohort of patients (n = 95) with advanced, progressive disease, expected to die within six months, were recruited from three rural family physician group practices (21 physicians) and followed prospectively until death or pilot end. Serial measurement of symptoms, emotional distress (patient and family), and preferences for place of death was performed, with analysis of changes in distress outcomes assessed using t-tests and general linear models. Results: Symptoms trended toward improvement, with a significant reduction in anxiety from baseline to 14 days noted. Symptom and emotional distress were maintained below high severity (7-10), and a high rate of home death compared with population norms was observed. Conclusion: Future controlled studies are needed to examine outcomes for shared care models with comparison groups. Shared care models build on family physician capacity and as such are promising in the development of palliative home care programs to improve access to quality palliative home care and foster health system integration. © 2011 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved.

Phase I and II biotransformation enzymes and polycyclic aromatic hydrocarbons in the Mediterranean mussel (Mytilus galloprovincialis, Lamarck, 1819) collected in front of an oil refinery

The aim of the present study was to investigate the responses of phase I and II biotransformation enzymes and levels of PAHs in the Mediterranean mussel (Mytilus galloprovincialis, Lamarck, 1819) collected from three sites at different distance from an oil refinery. Phase I enzyme activities as NAD(P)H-cyt c red, NADH ferry red, B(a)PMO and phase II as UDPGT. GST were measured in digestive gland while 16 PAHs (US-EPA) in whole soft tissue. An added value to the data obtained in the present study rely on the RDA analysis which showed close correlations between PAHs levels and phase I enzyme activities in mussels collected in front of the refinery. And again a significant spatial correlation between B(a)P levels and NADPH-cyt c red activities was observed using linear models. No differences among sites for B(a) PMO and phase II GST activities were observed, while the application of UDPGT as biomarkers requires further investigation. (C) 2012 Elsevier Ltd. All rights reserved.

Role of Predalors, Habitat Attributes, and Spatial Autocorrelation on the Distribution of Eggs in the Northern Spectacled Salamander (Salamandrina perspicillata)

We predicted that the probability of egg occurrence of salamander Salamandrina perspicillata depended on stream features and predation by native crayfish Austropotamobius fulcisianus and the introduced trout Salmo trutta. We assessed the presence of S. perspicillata at 54 sites within a natural reserve of southern Tuscany, Italy. Generalized linear models with binomial errors were constructed using egg presence/absence and altitude, stream mean size and slope, electrical conductivity, water pH and temperature, and a predation factor, defined according to the presence/absence of crayfish and trout. Some competing models also included an autocovariate term, which estimated how much the response variable at any one sampling point reflected response values at surrounding points. The resulting models were compared using Akaike's information criterion. Model selection led to a subset of 14 models with Delta AIC(c) <7 (i.e., models ranging from substantial support to considerably less support), and all but one of these included an effect of predation. Models with the autocovariate term had considerably more support than those without the term. According to multimodel inference, the presence of trout and crayfish reduced the probability of egg occurrence from a mean level of 0.90 (SE limits: 0.98-0.55) to 0.12 (SE limits: 0.34-0.04). The presence of crayfish alone had no detectable effects (SE limits: 0.86-0.39). The results suggest that introduced trout have a detrimental effect on the reproductive output of S. perspicillata and confirm the fundamental importance of distinguishing the roles of endogenous and exogenous forces that act on population distribution.

Testing metabolic scaling theory using intraspecific allometries in Antarctic microarthropods

Quantitative scaling relationships among body mass, temperature and metabolic rate of organisms are still controversial, while resolution may be further complicated through the use of different and possibly inappropriate approaches to statistical analysis. We propose the application of a modelling strategy based on the theoretical approach of Akaike's information criteria and non-linear model fitting (nlm). Accordingly, we collated and modelled available data at intraspecific level on the individual standard metabolic rate of Antarctic microarthropods as a function of body mass (M), temperature (T), species identity (S) and high rank taxa to which species belong (G) and tested predictions from metabolic scaling theory (mass-metabolism allometric exponent b = 0.75, activation energy range 0.2-1.2 eV). We also performed allometric analysis based on logarithmic transformations (lm). Conclusions from lm and nlm approaches were different. Best-supported models from lm incorporated T, M and S. The estimates of the allometric scaling exponent linking body mass and metabolic rate resulted in a value of 0.696 +/- 0.105 (mean +/- 95% CI). In contrast, the four best-supported nlm models suggested that both the scaling exponent and activation energy significantly vary across the high rank taxa (Collembola, Cryptostigmata, Mesostigmata and Prostigmata) to which species belong, with mean values of b ranging from about 0.6 to 0.8. We therefore reached two conclusions: 1, published analyses of arthropod metabolism based on logarithmic data may be biased by data transformation; 2, non-linear models applied to Antarctic microarthropod metabolic rate suggest that intraspecific scaling of standard metabolic rate in Antarctic microarthropods is highly variable and can be characterised by scaling exponents that greatly vary within taxa, which may have biased previous interspecific comparisons that neglected intraspecific variability.

Large-scale spatial patterns in the distribution of Collembola (Hexapoda) species in Antarctic terrestrial ecosystems

We tested whether the distribution of three common springtail species (Gressittacantha terranova, Gomphiocephalus hodgsoni and Friesea grisea) in Victoria Land (Antarctica) could be modelled as a function of latitude, longitude, altitude and distance from the sea.

Victoria Land, Ross Dependency, Antarctica.

Generalized linear models were constructed using species presence/absence data relative to geographical features (latitude, longitude, altitude, distance from sea) across the species' entire ranges. Model results were then integrated with the known phylogeography of each species and hypotheses were generated on the role of climate as a major driver of Antarctic springtail distribution.

Based on model selection using Akaike's information criterion, the species' distributions were: hump-shaped relative to longitude and monotonic with altitude for Gressittacantha terranova; hump-shaped relative to latitude and monotonic with altitude for Gomphiocephalus hodgsoni; and hump-shaped relative to longitude and monotonic with latitude, altitude and distance from the sea for Friesea grisea.

No single distributional pattern was shared by the three species. While distributions were partially a response to climatic spatial clines, the patterns observed strongly suggest that past geological events have influenced the observed distributions. Accordingly, present-day spatial patterns are likely to have arisen from the interaction of historical and environmental drivers. Future studies will need to integrate a range of spatial and temporal scales to further quantify their respective roles.