A novel KIF11 mutation in a Turkish patient with microcephaly, lymphedema, and chorioretinal dysplasia from a consanguineous family.

Microcephaly–lymphedema–chorioretinal dysplasia (MLCRD) syndrome is a rare syndrome that was first described in 1992. Characteristic craniofacial features include severe microcephaly, upslanting palpebral fissures, prominent ears, a broad nose, and a long philtrum with a pointed chin. Recently, mutations in KIF11 have been demonstrated to cause dominantly inherited MLCRD syndrome. Herein, we present a patient with MLCRD syndrome whose parents were first cousins. The parents are unaffected, and thus a recessive mode of inheritance for the disorder was considered likely. However, the propositus carries a novel, de novo nonsense mutationinexon2 of KIF11. The patient also had midline cleft tongue which has not previously been described in this syndrome

Abstract Interpretation of Supermodular Games

Supermodular games find significant applications in a variety of models, especially in operations research and economic applications of noncooperative game theory, and feature pure strategy Nash equilibria characterized as fixed points of multivalued functions on complete lattices. Pure strategy Nash equilibria of supermodular games are here approximated by resorting to the theory of abstract interpretation, a well established and known framework used for designing static analyses of programming languages. This is obtained by extending the theory of abstract interpretation in order to handle approximations of multivalued functions and by providing some methods for abstracting supermodular games, in order to obtain approximate Nash equilibria which are shown to be correct within the abstract interpretation framework

Accelerated Convergence for Counterfactual Learning to Rank

Counterfactual Learning to Rank (LTR) algorithms learn a ranking model from logged user interactions, often collected using a production system. Employing such an offline learning approach has many benefits compared to an online one, but it is challenging as user feedback often contains high levels of bias. Unbiased LTR uses Inverse Propensity Scoring (IPS) to enable unbiased learning from logged user interactions. One of the major difficulties in applying Stochastic Gradient Descent (SGD) approaches to counterfactual learning problems is the large variance introduced by the propensity weights. In this paper we show that the convergence rate of SGD approaches with IPS-weighted gradients suffers from the large variance introduced by the IPS weights: convergence is slow, especially when there are large IPS weights. To overcome this limitation, we propose a novel learning algorithm, called CounterSample, that has provably better convergence than standard IPS-weighted gradient descent methods. We prove that CounterSample converges faster and complement our theoretical findings with empirical results by performing extensive experimentation in a number of biased LTR scenarios -- across optimizers, batch sizes, and different degrees of position bias.Comment: SIGIR 2020 full conference pape

Homogeneisation du comportement polycristallin considerer la plasticite a l'echelle des grains ou des bandes de glissement ?

International audienc

Direct Exoplanet Detection Using L1 Norm Low-Rank Approximation

We propose to use low-rank matrix approximation using the component-wise L1-norm for direct imaging of exoplanets. Exoplanet detection by direct imaging is a challenging task for three main reasons: (1) the host star is several orders of magnitude brighter than exoplanets, (2) the angular distance between exoplanets and star is usually very small, and (3) the images are affected by the noises called speckles that are very similar to the exoplanet signal both in shape and intensity. We first empirically examine the statistical noise assumptions of the L1 and L2 models, and then we evaluate the performance of the proposed L1 low-rank approximation (L1-LRA) algorithm based on visual comparisons and receiver operating characteristic (ROC) curves. We compare the results of the L1-LRA with the widely used truncated singular value decomposition (SVD) based on the L2 norm in two different annuli, one close to the star and one far away.Comment: 13 pages, 4 figures, BNAIC/BeNeLearn 202

Inapproximability of maximal strip recovery

In comparative genomic, the first step of sequence analysis is usually to decompose two or more genomes into syntenic blocks that are segments of homologous chromosomes. For the reliable recovery of syntenic blocks, noise and ambiguities in the genomic maps need to be removed first. Maximal Strip Recovery (MSR) is an optimization problem proposed by Zheng, Zhu, and Sankoff for reliably recovering syntenic blocks from genomic maps in the midst of noise and ambiguities. Given $d$ genomic maps as sequences of gene markers, the objective of \msr{d} is to find $d$ subsequences, one subsequence of each genomic map, such that the total length of syntenic blocks in these subsequences is maximized. For any constant $d \ge 2$, a polynomial-time 2d-approximation for \msr{d} was previously known. In this paper, we show that for any $d \ge 2$, \msr{d} is APX-hard, even for the most basic version of the problem in which all gene markers are distinct and appear in positive orientation in each genomic map. Moreover, we provide the first explicit lower bounds on approximating \msr{d} for all $d \ge 2$. In particular, we show that \msr{d} is NP-hard to approximate within $\Omega(d/\log d)$. From the other direction, we show that the previous 2d-approximation for \msr{d} can be optimized into a polynomial-time algorithm even if $d$ is not a constant but is part of the input. We then extend our inapproximability results to several related problems including \cmsr{d}, \gapmsr{\delta}{d}, and \gapcmsr{\delta}{d}.Comment: A preliminary version of this paper appeared in two parts in the Proceedings of the 20th International Symposium on Algorithms and Computation (ISAAC 2009) and the Proceedings of the 4th International Frontiers of Algorithmics Workshop (FAW 2010

On k-Column Sparse Packing Programs

We consider the class of packing integer programs (PIPs) that are column sparse, i.e. there is a specified upper bound k on the number of constraints that each variable appears in. We give an (ek+o(k))-approximation algorithm for k-column sparse PIPs, improving on recent results of $k^2\cdot 2^k$ and $O(k^2)$. We also show that the integrality gap of our linear programming relaxation is at least 2k-1; it is known that k-column sparse PIPs are $\Omega(k/ \log k)$-hard to approximate. We also extend our result (at the loss of a small constant factor) to the more general case of maximizing a submodular objective over k-column sparse packing constraints.Comment: 19 pages, v3: additional detail

Attachment Styles Within the Coach-Athlete Dyad: Preliminary Investigation and Assessment Development

The present preliminary study aimed to develop and examine the psychometric properties of a new sport-specific self-report instrument designed to assess athletes’ and coaches’ attachment styles. The development and initial validation comprised three main phases. In Phase 1, a pool of items was generated based on pre-existing self-report attachment instruments, modified to reflect a coach and an athlete’s style of attachment. In Phase 2, the content validity of the items was assessed by a panel of experts. A final scale was developed and administered to 405 coaches and 298 athletes (N = 703 participants). In Phase 3, confirmatory factor analysis of the obtained data was conducted to determine the final items of the Coach-Athlete Attachment Scale (CAAS). Confirmatory factor analysis revealed acceptable goodness of fit indexes for a 3-first order factor model as well as a 2-first order factor model for both the athlete and the coach data, respectively. A secure attachment style positively predicted relationship satisfaction, while an insecure attachment style was a negative predictor of relationship satisfaction. The CAAS revealed initial psychometric properties of content, factorial, and predictive validity, as well as reliability

Dynamics of Transformation from Segregation to Mixed Wealth Cities

We model the dynamics of the Schelling model for agents described simply by a continuously distributed variable - wealth. Agents move to neighborhoods where their wealth is not lesser than that of some proportion of their neighbors, the threshold level. As in the case of the classic Schelling model where segregation obtains between two races, we find here that wealth-based segregation occurs and persists. However, introducing uncertainty into the decision to move - that is, with some probability, if agents are allowed to move even though the threshold level condition is contravened - we find that even for small proportions of such disallowed moves, the dynamics no longer yield segregation but instead sharply transition into a persistent mixed wealth distribution. We investigate the nature of this sharp transformation between segregated and mixed states, and find that it is because of a non-linear relationship between allowed moves and disallowed moves. For small increases in disallowed moves, there is a rapid corresponding increase in allowed moves, but this tapers off as the fraction of disallowed moves increase further and finally settles at a stable value, remaining invariant to any further increase in disallowed moves. It is the overall effect of the dynamics in the initial region (with small numbers of disallowed moves) that shifts the system away from a state of segregation rapidly to a mixed wealth state. The contravention of the tolerance condition could be interpreted as public policy interventions like minimal levels of social housing or housing benefit transfers to poorer households. Our finding therefore suggests that it might require only very limited levels of such public intervention - just sufficient to enable a small fraction of disallowed moves, because the dynamics generated by such moves could spur the transformation from a segregated to mixed equilibrium.Comment: 12 pages, 7 figure

Formant transitions in fricative identification: The role of native fricative inventory

The distribution of energy across the noise spectrum provides the primary cues for the identification of a fricative. Formant transitions have been reported to play a role in identification of some fricatives, but the combined results so far are conflicting. We report five experiments testing the hypothesis that listeners differ in their use of formant transitions as a function of the presence of spectrally similar fricatives in their native language. Dutch, English, German, Polish, and Spanish native listeners performed phoneme monitoring experiments with pseudowords containing either coherent or misleading formant transitions for the fricatives / s / and / f /. Listeners of German and Dutch, both languages without spectrally similar fricatives, were not affected by the misleading formant transitions. Listeners of the remaining languages were misled by incorrect formant transitions. In an untimed labeling experiment both Dutch and Spanish listeners provided goodness ratings that revealed sensitivity to the acoustic manipulation. We conclude that all listeners may be sensitive to mismatching information at a low auditory level, but that they do not necessarily take full advantage of all available systematic acoustic variation when identifying phonemes. Formant transitions may be most useful for listeners of languages with spectrally similar fricatives