Linear Regression from Strategic Data Sources

Linear regression is a fundamental building block of statistical data analysis. It amounts to estimating the parameters of a linear model that maps input features to corresponding outputs. In the classical setting where the precision of each data point is fixed, the famous Aitken/Gauss-Markov theorem in statistics states that generalized least squares (GLS) is a so-called "Best Linear Unbiased Estimator" (BLUE). In modern data science, however, one often faces strategic data sources, namely, individuals who incur a cost for providing high-precision data. In this paper, we study a setting in which features are public but individuals choose the precision of the outputs they reveal to an analyst. We assume that the analyst performs linear regression on this dataset, and individuals benefit from the outcome of this estimation. We model this scenario as a game where individuals minimize a cost comprising two components: (a) an (agent-specific) disclosure cost for providing high-precision data; and (b) a (global) estimation cost representing the inaccuracy in the linear model estimate. In this game, the linear model estimate is a public good that benefits all individuals. We establish that this game has a unique non-trivial Nash equilibrium. We study the efficiency of this equilibrium and we prove tight bounds on the price of stability for a large class of disclosure and estimation costs. Finally, we study the estimator accuracy achieved at equilibrium. We show that, in general, Aitken's theorem does not hold under strategic data sources, though it does hold if individuals have identical disclosure costs (up to a multiplicative factor). When individuals have non-identical costs, we derive a bound on the improvement of the equilibrium estimation cost that can be achieved by deviating from GLS, under mild assumptions on the disclosure cost functions.Comment: This version (v3) extends the results on the sub-optimality of GLS (Section 6) and improves writing in multiple places compared to v2. Compared to the initial version v1, it also fixes an error in Theorem 6 (now Theorem 5), and extended many of the result

Opportunities for a Truffle-based Golo Interpreter

Golo is a simple dynamically-typed language for the Java Virtual Machine. Initially implemented as a ahead-of-time compiler to JVM bytecode, it leverages invokedy-namic and JSR 292 method handles to implement a reasonably efficient runtime. Truffle is emerging as a framework for building interpreters for JVM languages with self-specializing AST nodes. Combined with the Graal compiler, Truffle offers a simple path towards writing efficient interpreters while keeping the engineering efforts balanced. The Golo project is interested in experimenting with a Truffle interpreter in the future, as it would provides interesting comparison elements between invokedynamic versus Truffle for building a language runtime

Исследование сезонной изменчивости циркуляции вод Южной Атлантики по данным спутниковой альтиметрии

Исследован сезонный цикл течений на поверхности Южной Атлантики по данным спутниковой альтиметрии за период с 1992 по 2002 гг. Показано, что западные и восточные течения усиливаются с фазовой разницей в несколько месяцев, тогда как их широтные смещения квазисинхронны. Для течений тропической зоны наблюдается запаздывание сезонного сигнала с запада на восток в среднем на 2 – 3 месяца, в полярных широтах оно увеличивается до 6 месяцев.Seasonal cycle of the currents on the South Atlantic surface is investigated using the satellite altimetry data from 1992 to 2002. It is shown that the western and the eastern currents increase with phase difference in several months whereas their latitudinal displacements are quasi-synchronous. For the currents of the tropical zone the seasonal signal delay from the west to the east on average for 2 – 3 months can be observed; at polar latitudes it increases up to 6 months

Fairness in Selection Problems with Strategic Candidates

To better understand discriminations and the effect of affirmative actions in selection problems (e.g., college admission or hiring), a recent line of research proposed a model based on differential variance. This model assumes that the decision-maker has a noisy estimate of each candidate's quality and puts forward the difference in the noise variances between different demographic groups as a key factor to explain discrimination. The literature on differential variance, however, does not consider the strategic behavior of candidates who can react to the selection procedure to improve their outcome, which is well-known to happen in many domains. In this paper, we study how the strategic aspect affects fairness in selection problems. We propose to model selection problems with strategic candidates as a contest game: A population of rational candidates compete by choosing an effort level to increase their quality. They incur a cost-of-effort but get a (random) quality whose expectation equals the chosen effort. A Bayesian decision-maker observes a noisy estimate of the quality of each candidate (with differential variance) and selects the fraction $\alpha$ of best candidates based on their posterior expected quality; each selected candidate receives a reward $S$. We characterize the (unique) equilibrium of this game in the different parameters' regimes, both when the decision-maker is unconstrained and when they are constrained to respect the fairness notion of demographic parity. Our results reveal important impacts of the strategic behavior on the discrimination observed at equilibrium and allow us to understand the effect of imposing demographic parity in this context. In particular, we find that, in many cases, the results contrast with the non-strategic setting.Comment: Accepted for publication in the proceedings of the Twenty-Third ACM Conference on Economics and Computation (EC'22

Towards a Decoupled Context-Oriented Programming Language for the Internet of Things

Easily programming behaviors is one major issue of a large and reconfigurable deployment in the Internet of Things. Such kind of devices often requires to externalize part of their behavior such as the sensing, the data aggregation or the code offloading. Most existing context-oriented programming languages integrate in the same class or close layers the whole behavior. We propose to abstract and separate the context tracking from the decision process, and to use event-based handlers to interconnect them. We keep a very easy declarative and non-layered programming model. We illustrate by defining an extension to Golo-a JVM-based dynamic language

Trading-off price for data quality to achieve fair online allocation

We consider the problem of online allocation subject to a long-term fairness penalty. Contrary to existing works, however, we do not assume that the decision-maker observes the protected attributes -- which is often unrealistic in practice. Instead they can purchase data that help estimate them from sources of different quality; and hence reduce the fairness penalty at some cost. We model this problem as a multi-armed bandit problem where each arm corresponds to the choice of a data source, coupled with the online allocation problem. We propose an algorithm that jointly solves both problems and show that it has a regret bounded by $\mathcal{O}(\sqrt{T})$. A key difficulty is that the rewards received by selecting a source are correlated by the fairness penalty, which leads to a need for randomization (despite a stochastic setting). Our algorithm takes into account contextual information available before the source selection, and can adapt to many different fairness notions. We also show that in some instances, the estimates used can be learned on the fly

T regulatory cells disrupt the CCL20-CCR6 axis driving Th17 homing to the gut

Background: During HIV-1 infection, the integrity of the intestinal immune barrier is disrupted due to a deep depletion of CD4 + T cells in the gut. The translocation of microbial products from the gut lumen into the bloodstream has been linked with systemic inflammation. Despite long-term effective cART, CD4 + T cells in the lamina propria are incompletely restored in most individuals. Aims: Among the chemotactic axes involved in CD4 + T cell homing to the gut, we focused on the CCR6-CCL20 axis as it governs Th17 cells homing, a T cell subset exerting a major role in antimicrobial immunity. We aimed to assess the factors regulating the expression of CCL20 by the enterocytes, and notably the role of the cytokines produced by Treg and Th17 cells. Methods: Small bowel biopsies were obtained by endoscopy in 20 HIV-1 + and 10 HIV-1-individuals. Intestinal lymphocytes phenotype was analyzed by flow cytometry. CCL20 mRNA was quantified by qRT-PCR. The effect of PRR ligands and cytokines on CCL20 expression was explored using an ex-vivosystem of human primary enterocytes. A coculture was done between the enterocytes and Th17/Treg cells. The expression of CCL20 by the enterocytes was evaluated by qRT-PCR and ELISA

Toxicology of mycotoxins, hazards and risks in human and animal food

Mycotoxins are secondary metabolites produced on plants either in the field or during storage. These toxins are found as natural contaminants on numerous foods and feeds of plant origin, such as cereals, fruits, nuts, almonds, grains, fodder, as well as processed foods and feeds using these ingredients. The toxicity of mycotoxins varies, ranging from hepatotoxic or even carcinogenic (aflatoxins) effects, to estrogenic (zearalenone), immunotoxic (patulin, trichothecenes, fumonisins), nephrotoxic (ochratoxin A) and neurotoxic (tremorgens) effects. Their toxicity can also be caused by the presence of mycotoxin residues in products deriving from animals fed with contaminated feedstuffs. The mycotoxic risk is difficult to evaluate, as mycotoxin are natural contaminants impossible to eliminate, fungal contaminations are difficult to control, and one mould may produce several toxins. Consequently, further research is needed to improve current knowledge on the toxicity of these products, particularly when various mycotoxins are combined, either together or with other toxins or pathogens.Les mycotoxines sont des produits du métabolisme secondaire de moisissures pouvant se développer sur la plante au champ ou en cours de stockage. Ces toxines se retrouvent à l'état de contaminants naturels de nombreuses denrées d'origine végétale : céréales, fruits, noix, amandes, grains, fourrages ainsi que d'aliments composés et manufacturés issus de ces filières. La toxicité des mycotoxines se révèle lors des mycotoxicoses des animaux d'élevage. Elle est variable, certaines exerçant un pouvoir hépa-totoxique voire cancérogène (aflatoxines), d'autres se révélant oestrogèniques (zéaralénone), immunotoxiques (patuline, trichothécènes, fumonisines), néphrotoxiques (ochratoxine A) ou neurotoxiques (trémorgènes). Un autre aspect de leur toxicité est la prise en compte des résidus présents dans les productions issues d'animaux ayant consommé une alimentation contaminée. L'évaluation du risque mycotoxique demeure délicate car ce risque est d'essence naturelle, l'homme n'en maîtrisant pas la survenue ; il est pernicieux car la contamination fongique est difficilement contrôlable et enfin il peut être multiple en raison de la possible association d'effets de toxines produites par une même moisissure. Devant ce constat, il convient de poursuivre une activité de recherche soutenue afin d'améliorer encore nos connaissances sur la toxicité de ces dérivés et notamment dans les cas d'associations entre mycotoxines ou entre toxines et agents pathogènes infectieux

Opportunities for a Truffle-based Golo Interpreter

Golo is a simple dynamically-typed language for the Java Virtual Machine. Initially implemented as a ahead-of-time compiler to JVM bytecode, it leverages invokedy-namic and JSR 292 method handles to implement a reasonably efficient runtime. Truffle is emerging as a framework for building interpreters for JVM languages with self-specializing AST nodes. Combined with the Graal compiler, Truffle offers a simple path towards writing efficient interpreters while keeping the engineering efforts balanced. The Golo project is interested in experimenting with a Truffle interpreter in the future, as it would provides interesting comparison elements between invokedynamic versus Truffle for building a language runtime