3-D KINETIC CHARACTERISTICS OF OBESE CHILDREN AND NORMAL WEIGHT CHILDREN DURING NORMAL WALKING

Previous researches on obese children walking gait have been mainly focused on the kinematic characteristics, and little focused on the kinetic characteristics. The aim of this study is to identify and compare the kinetic characteristics of the walking gait of obese children with those of normal weight children

The Reactivity and Stability of Polyoxometalate Water Oxidation Electrocatalysts

This review describes major advances in the use of functionalized molecular metal oxides (polyoxometalates, POMs) as water oxidation catalysts under electrochemical conditions. The fundamentals of POM-based water oxidation are described, together with a brief overview of general approaches to designing POM water oxidation catalysts. Next, the use of POMs for homogeneous, solution-phasewater oxidation is described togetherwith a summary of theoretical studies shedding light on the POM-WOC mechanism. This is followed by a discussion of heterogenization of POMs on electrically conductive substrates for technologically more relevant application studies. The stability of POM water oxidation catalysts is discussed, using select examples where detailed data is already available. The review finishes with an outlook on future perspectives and emerging themes in electrocatalytic polyoxometalate-based water oxidation research

Hi-Fi: Hierarchical Feature Integration for Skeleton Detection

In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts, making object skeleton detection a challenging problem. We present a new convolutional neural network (CNN) architecture by introducing a novel hierarchical feature integration mechanism, named Hi-Fi, to address the skeleton detection problem. The proposed CNN-based approach has a powerful multi-scale feature integration ability that intrinsically captures high-level semantics from deeper layers as well as low-level details from shallower layers. % By hierarchically integrating different CNN feature levels with bidirectional guidance, our approach (1) enables mutual refinement across features of different levels, and (2) possesses the strong ability to capture both rich object context and high-resolution details. Experimental results show that our method significantly outperforms the state-of-the-art methods in terms of effectively fusing features from very different scales, as evidenced by a considerable performance improvement on several benchmarks.Comment: IJCAI201

Modeling of tip kinetics of undercooled Ti dendrites with consideration of forced flow and oxygen impurity effects

Tip velocities of undercooled dendrites in electromagnetically levitated melt droplets of pure Ti were modeled using a theory on three-dimensional dendritic growth with fluid flow and a dilute solute. The modeling shows that a forced flow due to electromagnetic stirring depresses tip velocities of the dendrites at low undercoolings and that its effect becomes negligible at high undercoolings. In contrast, an oxygen impurity dissolved in liquid Ti depresses tip velocities of the dendrites over a broad range of undercooling while it coarsens tip radii. Such modeling results allowed for reconciliation of discrepancies in literature data by considering an oxygen impurity effect. The modeling also predicts that the effect of the oxygen impurity becomes insignificant when its concentration is reduced below 50 ppm in atomic fraction

A Quantum Federated Learning Framework for Classical Clients

Quantum Federated Learning (QFL) enables collaborative training of a Quantum Machine Learning (QML) model among multiple clients possessing quantum computing capabilities, without the need to share their respective local data. However, the limited availability of quantum computing resources poses a challenge for each client to acquire quantum computing capabilities. This raises a natural question: Can quantum computing capabilities be deployed on the server instead? In this paper, we propose a QFL framework specifically designed for classical clients, referred to as CC-QFL, in response to this question. In each iteration, the collaborative training of the QML model is assisted by the shadow tomography technique, eliminating the need for quantum computing capabilities of clients. Specifically, the server constructs a classical representation of the QML model and transmits it to the clients. The clients encode their local data onto observables and use this classical representation to calculate local gradients. These local gradients are then utilized to update the parameters of the QML model. We evaluate the effectiveness of our framework through extensive numerical simulations using handwritten digit images from the MNIST dataset. Our framework provides valuable insights into QFL, particularly in scenarios where quantum computing resources are scarce

POM@ZIF Derived Mixed Metal Oxide Catalysts for Sustained Electrocatalytic Oxygen Evolution

The design of efficient and stable oxygen evolution reaction (OER) catalysts based on noble-metal-free materials is crucial for energy conversion and storage. In this work, it was demonstrated how polyoxometalate (POM)-doped ZIF-67 can be converted into a stable oxygen evolution electrocatalyst by chemical etching, cation exchange, and thermal annealing steps. Characterization by X-ray photoelectron spectroscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy and Raman spectroscopy indicate that POM-doped ZIF-67 derived carbon-supported metal oxides were synthesized. The resulting composite shows structural and compositional advantages which lead to low overpotential (306 mV at j=10 mA ⋅ cm−2) and long-term stability under harsh OER conditions (1.0 M aqueous KOH)