Robustness of Machine Learning Models Beyond Adversarial Attacks

Correctly quantifying the robustness of machine learning models is a central aspect in judging their suitability for specific tasks, and thus, ultimately, for generating trust in the models. We show that the widely used concept of adversarial robustness and closely related metrics based on counterfactuals are not necessarily valid metrics for determining the robustness of ML models against perturbations that occur "naturally", outside specific adversarial attack scenarios. Additionally, we argue that generic robustness metrics in principle are insufficient for determining real-world-robustness. Instead we propose a flexible approach that models possible perturbations in input data individually for each application. This is then combined with a probabilistic approach that computes the likelihood that a real-world perturbation will change a prediction, thus giving quantitative information of the robustness of the trained machine learning model. The method does not require access to the internals of the classifier and thus in principle works for any black-box model. It is, however, based on Monte-Carlo sampling and thus only suited for input spaces with small dimensions. We illustrate our approach on two dataset, as well as on analytically solvable cases. Finally, we discuss ideas on how real-world robustness could be computed or estimated in high-dimensional input spaces.Comment: 25 pages, 7 figure

Datenschutz in der Informationstechnik: Eine Umfrage zum Datenschutzsiegel in Mecklenburg-Vorpommern

Der Arbeitskreis IT-Security der IT-Initiative Mecklenburg-Vorpommern hat mit Unterstützung des Landesbeauftragten für den Datenschutz und der PLANET IC GmbH vom 10.-31. Mai 2006 eine Umfrage zum Datenschutz in der Informationstechnik durchgeführt. Ziel dieser Erhebung ist es, den Kenntnisstand sowie den Bedarf an Normen, Standards und Zertifikaten zum Datenschutz in Mecklenburg-Vorpommern zu ermitteln und daraus Empfehlungen für das Land und die hier ansässigen Unternehmen, die auf dem Gebiet der Informationstechnologie ihr Geschäftsfeld haben, abzuleiten. Ein besonderer Schwerpunkt der Befragung ist die Ermittlung der Haltung der Unternehmen zu einem eigenen Datenschutzsiegel des Landes Mecklenburg-Vorpommern. --

Quenched QCD with fixed-point and chirally improved fermion

In this contribution we present results from quenched QCD simulations with the parameterized fixed-point (FP) and the chirally improved (CI) Dirac operator. Both these operators are approximate solutions of the Ginsparg-Wilson equation and have good chiral properties. We focus our discussion on observables sensitive to chirality. In particular we explore pion masses down to 210 MeV in light hadron spectroscopy, quenched chiral logs, the pion decay constant and the pion scattering length. We discuss finite volume effects, scaling properties of the FP and CI operators and performance issues in their numerical implementation.Comment: Lattice2002(chiral), 17 pages, 21 figures, (LaTeX style file espcrc2.sty and AMS style files

Dispelling urban myths about default uncertainty factors in chemical risk assessment - Sufficient protection against mixture effects?

© 2013 Martin et al.; licensee BioMed Central LtdThis article has been made available through the Brunel Open Access Publishing Fund.Assessing the detrimental health effects of chemicals requires the extrapolation of experimental data in animals to human populations. This is achieved by applying a default uncertainty factor of 100 to doses not found to be associated with observable effects in laboratory animals. It is commonly assumed that the toxicokinetic and toxicodynamic sub-components of this default uncertainty factor represent worst-case scenarios and that the multiplication of those components yields conservative estimates of safe levels for humans. It is sometimes claimed that this conservatism also offers adequate protection from mixture effects. By analysing the evolution of uncertainty factors from a historical perspective, we expose that the default factor and its sub-components are intended to represent adequate rather than worst-case scenarios. The intention of using assessment factors for mixture effects was abandoned thirty years ago. It is also often ignored that the conservatism (or otherwise) of uncertainty factors can only be considered in relation to a defined level of protection. A protection equivalent to an effect magnitude of 0.001-0.0001% over background incidence is generally considered acceptable. However, it is impossible to say whether this level of protection is in fact realised with the tolerable doses that are derived by employing uncertainty factors. Accordingly, it is difficult to assess whether uncertainty factors overestimate or underestimate the sensitivity differences in human populations. It is also often not appreciated that the outcome of probabilistic approaches to the multiplication of sub-factors is dependent on the choice of probability distributions. Therefore, the idea that default uncertainty factors are overly conservative worst-case scenarios which can account both for the lack of statistical power in animal experiments and protect against potential mixture effects is ill-founded. We contend that precautionary regulation should provide an incentive to generate better data and recommend adopting a pragmatic, but scientifically better founded approach to mixture risk assessment. © 2013 Martin et al.; licensee BioMed Central Ltd.Oak Foundatio

Biogeography of the Relationship between the Child Gut Microbiome and Innate Immune System.

The gut microbiome is a well-recognized modulator of host immunity, and its compositions differ between geographically separated human populations. Systemic innate immune responses to microbial derivatives also differ between geographically distinct human populations. However, the potential role of the microbiome in mediating geographically varied immune responses is unexplored. We here applied 16S amplicon sequencing to profile the stool microbiome and, in parallel, measured whole-blood innate immune cytokine responses to several pattern recognition receptor (PRR) agonists among 2-year-old children across biogeographically diverse settings. Microbiomes differed mainly between high- and low-resource environments and were not strongly associated with other demographic factors. We found strong correlations between responses to Toll-like receptor 2 (TLR2) and relative abundances of Bacteroides and Prevotella populations, shared among Canadian and Ecuadorean children. Additional correlations between responses to TLR2 and bacterial populations were specific to individual geographic cohorts. As a proof of concept, we gavaged germfree mice with human donor stools and found murine splenocyte responses to TLR stimulation were consistent with responses of the corresponding human donor populations. This study identified differences in immune responses correlating to gut microbiomes across biogeographically diverse settings and evaluated biological plausibility using a mouse model. This insight paves the way to guide optimization of population-specific interventions aimed to improve child health outcomes.IMPORTANCE Both the gut microbiome and innate immunity are known to differ across biogeographically diverse human populations. The gut microbiome has been shown to directly influence systemic immunity in animal models. With this, modulation of the gut microbiome represents an attractive avenue to improve child health outcomes associated with altered immunity using population-specific approaches. However, there are very scarce data available to determine which members of the gut microbiome are associated with specific immune responses and how these differ around the world, creating a substantial barrier to rationally designing such interventions. This study addressed this knowledge gap by identifying relationships between distinct bacterial taxa and cytokine responses to specific microbial agonists across highly diverse settings. Furthermore, we provide evidence that immunomodulatory effects of region-specific stool microbiomes can be partially recapitulated in germfree mice. This is an important contribution toward improving global child health by targeting the gut microbiome

Self-medication of migraine and tension-type headache: summary of the evidence-based recommendations of the Deutsche Migräne und Kopfschmerzgesellschaft (DMKG), the Deutsche Gesellschaft für Neurologie (DGN), the Österreichische Kopfschmerzgesellschaft (ÖKSG) and the Schweizerische Kopfwehgesellschaft (SKG)

The current evidence-based guideline on self-medication in migraine and tension-type headache of the German, Austrian and Swiss headache societies and the German Society of Neurology is addressed to physicians engaged in primary care as well as pharmacists and patients. The guideline is especially concerned with the description of the methodology used, the selection process of the literature used and which evidence the recommendations are based upon. The following recommendations about self-medication in migraine attacks can be made: The efficacy of the fixed-dose combination of acetaminophen, acetylsalicylic acid and caffeine and the monotherapies with ibuprofen or naratriptan or acetaminophen or phenazone are scientifically proven and recommended as first-line therapy. None of the substances used in self-medication in migraine prophylaxis can be seen as effective. Concerning the self-medication in tension-type headache, the following therapies can be recommended as first-line therapy: the fixed-dose combination of acetaminophen, acetylsalicylic acid and caffeine as well as the fixed combination of acetaminophen and caffeine as well as the monotherapies with ibuprofen or acetylsalicylic acid or diclofenac. The four scientific societies hope that this guideline will help to improve the treatment of headaches which largely is initiated by the patients themselves without any consultation with their physicians

Combined analgesics in (headache) pain therapy: shotgun approach or precise multi-target therapeutics?

<p>Abstract</p> <p>Background</p> <p>Pain in general and headache in particular are characterized by a change in activity in brain areas involved in pain processing. The therapeutic challenge is to identify drugs with molecular targets that restore the healthy state, resulting in meaningful pain relief or even freedom from pain. Different aspects of pain perception, i.e. sensory and affective components, also explain why there is not just one single target structure for therapeutic approaches to pain. A network of brain areas ("pain matrix") are involved in pain perception and pain control. This diversification of the pain system explains why a wide range of molecularly different substances can be used in the treatment of different pain states and why in recent years more and more studies have described a superior efficacy of a precise multi-target combination therapy compared to therapy with monotherapeutics.</p> <p>Discussion</p> <p>In this article, we discuss the available literature on the effects of several fixed-dose combinations in the treatment of headaches and discuss the evidence in support of the role of combination therapy in the pharmacotherapy of pain, particularly of headaches. The scientific rationale behind multi-target combinations is the therapeutic benefit that could not be achieved by the individual constituents and that the single substances of the combinations act together additively or even multiplicatively and cooperate to achieve a completeness of the desired therapeutic effect.</p> <p>As an example the fixesd-dose combination of acetylsalicylic acid (ASA), paracetamol (acetaminophen) and caffeine is reviewed in detail. The major advantage of using such a fixed combination is that the active ingredients act on different but distinct molecular targets and thus are able to act on more signalling cascades involved in pain than most single analgesics without adding more side effects to the therapy.</p> <p>Summary</p> <p>Multitarget therapeutics like combined analgesics broaden the array of therapeutic options, enable the completeness of the therapeutic effect, and allow doctors (and, in self-medication with OTC medications, the patients themselves) to customize treatment to the patient's specific needs. There is substantial clinical evidence that such a multi-component therapy is more effective than mono-component therapies.</p

Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery

International audienceThe relationships that control seed production in trees are fundamental to understanding the evolution of forest species and their capacity to recover from increasing losses to drought, fire, and harvest. A synthesis of fecundity data from 714 species worldwide allowed us to examine hypotheses that are central to quantifying reproduction, a foundation for assessing fitness in forest trees. Four major findings emerged. First, seed production is not constrained by a strict trade-off between seed size and numbers. Instead, seed numbers vary over ten orders of magnitude, with species that invest in large seeds producing more seeds than expected from the 1:1 trade-off. Second, gymnosperms have lower seed production than angiosperms, potentially due to their extra investments in protective woody cones. Third, nutrient-demanding species, indicated by high foliar phosphorus concentrations, have low seed production. Finally, sensitivity of individual species to soil fertility varies widely, limiting the response of community seed production to fertility gradients. In combination, these findings can inform models of forest response that need to incorporate reproductive potential

Modelling the long-term fairness dynamics of data-driven targeted help on job seekers

Abstract The use of data-driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This raises ethical concerns, as it has adversely affected minorities and historically discriminated groups. In this paper, we use an approach that combines statistics and data-driven approaches with dynamical modeling to assess long-term fairness effects of labor market interventions. Specifically, we develop and use a model to investigate the impact of decisions caused by a public employment authority that selectively supports job-seekers through targeted help. The selection of who receives what help is based on a data-driven intervention model that estimates an individual’s chances of finding a job in a timely manner and rests upon data that describes a population in which skills relevant to the labor market are unevenly distributed between two groups (e.g., males and females). The intervention model has incomplete access to the individual’s actual skills and can augment this with knowledge of the individual’s group affiliation, thus using a protected attribute to increase predictive accuracy. We assess this intervention model’s dynamics—especially fairness-related issues and trade-offs between different fairness goals- over time and compare it to an intervention model that does not use group affiliation as a predictive feature. We conclude that in order to quantify the trade-off correctly and to assess the long-term fairness effects of such a system in the real-world, careful modeling of the surrounding labor market is indispensable