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

Electromyography‐related pain: Muscle selection is the key modifiable study characteristic

Introduction : The aim of this study was to estimate the effects of patient, provider, and study characteristics on electromyography (EMG)‐related pain. Methods : Patients undergoing EMG rated their EMG‐related pain after each muscle was studied on a 100‐point visual analog scale (VAS). Investigators recorded the order in which the muscles were sampled, the total time spent with the needle in each muscle, and whether electrical endplate noise was noted. Results : A total of 1781 muscles were studied in 304 patients. Eleven muscles were associated with significantly more or less pain than the others. Endplate noise was associated with more pain (5.4 mm, 95% CI 2.8–7.0). There was a small, but significant effect from needling time (0.02 mm, 95% CI 0.00–0.04). Conclusions : Among factors that electromyographers can control, muscle selection has the greatest impact on pain. Our data include an extensive list of muscle‐specific EMG‐related pain scores. Provider and other study characteristics have little or no impact on EMG‐related pain. Muscle Nerve 49:570–574, 2014Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106736/1/mus23974.pd

Control flow in active inference systems Part I: Classical and quantum formulations of active inference

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. In this Part I, we introduce the free-energy principle (FEP) and the idea of active inference as Bayesian prediction-error minimization, and show how the control problem arises in active inference systems. We then review classical and quantum formulations of the FEP, with the former being the classical limit of the latter. In the accompanying Part II, we show that when systems are described as executing active inference driven by the FEP, their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implemented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales

Control flow in active inference systems Part II: Tensor networks as general models of control flow

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. In Part I, we introduced the free-energy principle (FEP) and the idea of active inference as Bayesian prediction-error minimization, and show how the control problem arises in active inference systems. We then review classical and quantum formulations of the FEP, with the former being the classical limit of the latter. In this accompanying Part II, we show that when systems are described as executing active inference driven by the FEP, their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implemented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales

Competitive portfolio selection using stochastic predictions

We study a portfolio selection problem where a player attempts to maximise a utility function that represents the growth rate of wealth. We show that, given some stochastic predictions of the asset prices in the next time step, a sublinear expected regret is attainable against an optimal greedy algorithm, subject to tradeoff against the \accuracy" of such predictions that learn (or improve) over time. We also study the effects of introducing transaction costs into the model

Social welfare and profit maximization from revealed preferences

Consider the seller's problem of finding optimal prices for her $n$ (divisible) goods when faced with a set of $m$ consumers, given that she can only observe their purchased bundles at posted prices, i.e., revealed preferences. We study both social welfare and profit maximization with revealed preferences. Although social welfare maximization is a seemingly non-convex optimization problem in prices, we show that (i) it can be reduced to a dual convex optimization problem in prices, and (ii) the revealed preferences can be interpreted as supergradients of the concave conjugate of valuation, with which subgradients of the dual function can be computed. We thereby obtain a simple subgradient-based algorithm for strongly concave valuations and convex cost, with query complexity $O(m^2/\epsilon^2)$, where $\epsilon$ is the additive difference between the social welfare induced by our algorithm and the optimum social welfare. We also study social welfare maximization under the online setting, specifically the random permutation model, where consumers arrive one-by-one in a random order. For the case where consumer valuations can be arbitrary continuous functions, we propose a price posting mechanism that achieves an expected social welfare up to an additive factor of $O(\sqrt{mn})$ from the maximum social welfare. Finally, for profit maximization (which may be non-convex in simple cases), we give nearly matching upper and lower bounds on the query complexity for separable valuations and cost (i.e., each good can be treated independently)

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

Integrating Learning and Reasoning with Deep Logic Models

Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns. Probabilistic logic reasoning, on the other hand, is capable to take consistent and robust decisions in complex environments. The integration of deep learning and logic reasoning is still an open-research problem and it is considered to be the key for the development of real intelligent agents. This paper presents Deep Logic Models, which are deep graphical models integrating deep learning and logic reasoning both for learning and inference. Deep Logic Models create an end-to-end differentiable architecture, where deep learners are embedded into a network implementing a continuous relaxation of the logic knowledge. The learning process allows to jointly learn the weights of the deep learners and the meta-parameters controlling the high-level reasoning. The experimental results show that the proposed methodology overtakes the limitations of the other approaches that have been proposed to bridge deep learning and reasoning

On-chip beam rotators, polarizers and adiabatic mode converters through low-loss waveguides with variable cross-sections

Photonics integrated circuitry would benefit considerably from the ability to arbitrarily control waveguide cross-sections with high precision and low loss, in order to provide more degrees of freedom in manipulating propagating light. Here, we report on a new optical-fibres-compatible glass waveguide by femtosecond laser writing, namely spherical phase induced multi-core waveguide (SPIM-WG), which addresses this challenging task with three dimensional on-chip light control. Precise deformation of cross-sections is achievable along the waveguide, with shapes and sizes finely controllable of high resolution in both horizontal and vertical transversal directions. We observed that these waveguides have high refractive index contrast of 0.017, low propagation loss of 0.14 dB/cm, and very low coupling loss of 0.19 dB coupled from a single mode fibre. SPIM-WG devices were easily fabricated that were able to perform on-chip beam rotation through varying angles, or manipulate polarization state of propagating light for target wavelengths. We also demonstrated SPIM-WG mode converters that provide arbitrary adiabatic mode conversion with high efficiency between symmetric and asymmetric non-uniform modes; examples include circular, elliptical modes and asymmetric modes from ppKTP waveguides which are generally applied in frequency conversion and quantum light sources. Created inside optical glass, these waveguides and devices have the capability to operate across ultra-broad bands from visible to infrared wavelengths. The compatibility with optical fibre also paves the way toward packaged photonic integrated circuitry, which usually needs input and output fibre connections