No relationship between lean mass and functional asymmetry in high-level female tennis players

The relationship between lean mass and functional asymmetry in terms of their magnitude and direction was examined in 22 high-level female tennis players (20.9 ± 3.6 years). Lean mass of both upper and lower extremities was examined using Dual X-ray Absorptiometry. Functional asymmetry was assessed using a battery of field tests (handgrip strength, seated shot-put throw, plate tapping, single leg countermovement jump, single leg forward hop test, 6 m single leg hop test, and 505 change of direction (time and deficit)). Paired sample t-tests compared the dominant (overall highest/best (performance) value) against the non-dominant value (highest/best (performance) value of the opposing extremity). Linear regressions were used to explore the relationship between lean mass and functional asymmetry magnitudes. Kappa coefficients were used to examine the consistency in direction between the extremity displaying the highest lean mass value and the extremity performing dominantly across tests. Significant asymmetry magnitudes (p 0.05) were found for all upper and lower extremity lean mass and functional values. No relationship was apparent between lean mass and functional asymmetry magnitudes (p-value range = 0.131–0.889). Despite finding perfect consistency in asymmetry direction (k-value = 1.00) for the upper extremity, poor to fair consistency (k-value range = −0.00–0.21) was found for the lower extremity. In conclusion, lean mass and functional asymmetries should be examined independently

Inheritance of the Sex-Determining Factor in the Absence of a Complete Y Chromosome in 46,XX Human Males

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71879/1/j.1749-6632.1987.tb25088.x.pd

E. Velz. Démocratiser pour réussir. De l’enseignement planifié à l’organisation de l’apprentissage.

Se situant hors des pesanteurs unidimensionnelles auxquelles sacrifient toujours les lobbies et les corporations, ce « texte passionné et passionnant » (Professeur A. Roosen, préface propose une reconstruction radicale, concrète et réaliste, du vieux paradigme enseignement-apprentissage : substitution de l'organisation dès l'apprentissage à la planification de l'enseignement, unification de la « culture générale » et de la « formation professionnelle », structuration modulaire du curriculum s..

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

Simulation of free surface and molten metal behavior during induction melting of an aluminium alloy

International audienceElectromagnetic forces are widely used for processing metal alloys in particular in the aluminium casting industry. Induction is used in melting technologies (both crucible and channel induction furnaces). Magnetic stirrers are also used in melting or casting furnaces. However these technologies applied to opaque melts require modelling to be done to understand the resultant impact on the fluid and improve the process control. This is especially the case of crucible induction furnaces. A 2D axially symmetric numerical model describing the coupled magnetohydrodynamic and free surface phenomena taking place in an induction metal bath has been developed. The model uses the Ansys Fluent software, supplemented with additional User Defined Functions for the calculation of the Lorentz forces acting on the metal. The calculation of the shape of the free surface is based on the Volume Of Fluid method and a RANS k-ω Shear Stress Transport (SST) approach is used to describe the turbulent stirring of the metal. An original feature of our model is the consideration of an oxide skin covering the metal free surface. It was considered that the oxide film behaves similarly to a deforming wall and that friction effects between the oxide film and the metal result in the development of a shear stress at the top surface of the melt. Two examples of application of model are reported, for lab scale and industrial scale induction furnaces. The lab scale results are compared with measurements of the free surface shape obtained using a fringe projection technique

Evolution of the Cathode Spot Distribution in an Axial Magnetic Field Controlled Vacuum Arc at Long Contact Gap

The distribution of cathode spots in a CuCr25 vacuum arc controlled by an axial magnetic field and ignited on the lateral surface of the cathode is investigated for long gap distances, from the processing of high-speed video images. The processing method includes also estimating the current carried by a single spot and reconstructing the distribution of the current density at the cathode. Various distributions depending partly on the arc current are described

Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data