Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels

We present a Gaussian kernel loss function and training algorithm for convolutional neural networks that can be directly applied to both distance metric learning and image classification problems. Our method treats all training features from a deep neural network as Gaussian kernel centres and computes loss by summing the influence of a feature's nearby centres in the feature embedding space. Our approach is made scalable by treating it as an approximate nearest neighbour search problem. We show how to make end-to-end learning feasible, resulting in a well formed embedding space, in which semantically related instances are likely to be located near one another, regardless of whether or not the network was trained on those classes. Our approach outperforms state-of-the-art deep metric learning approaches on embedding learning challenges, as well as conventional softmax classification on several datasets.Comment: Accepted in the International Conference on Image Processing (ICIP) 2018. Formerly titled Nearest Neighbour Radial Basis Function Solvers for Deep Neural Network

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

OH hyperfine ground state: from precision measurement to molecular qubits

We perform precision microwave spectroscopy--aided by Stark deceleration--to reveal the low magnetic field behavior of OH in its ^2\Pi_{3/2} ro-vibronic ground state, identifying two field-insensitive hyperfine transitions suitable as qubits and determining a differential Lande g-factor of 1.267(5)\times10^{-3} between opposite parity components of the \Lambda-doublet. The data are successfully modeled with an effective hyperfine Zeeman Hamiltonian, which we use to make a tenfold improvement of the magnetically sensitive, astrophysically important \Delta F=\pm1 satellite-line frequencies, yielding 1720529887(10) Hz and 1612230825(15) Hz.Comment: 4+ pages, 3 figure

Quantum Parrondo's Games

Parrondo's Paradox arises when two losing games are combined to produce a winning one. A history dependent quantum Parrondo game is studied where the rotation operators that represent the toss of a classical biased coin are replaced by general SU(2) operators to transform the game into the quantum domain. In the initial state, a superposition of qubits can be used to couple the games and produce interference leading to quite different payoffs to those in the classical case.Comment: LateX, 10 pages, 2 figures, submitted to Physica A special issue (Gene Stanley Conference, Sicily, 2001), v2 minor correction to equations, v3 corrections to results section and table, acknowledgement adde

Entanglement Dynamics in 1D Quantum Cellular Automata

Several proposed schemes for the physical realization of a quantum computer consist of qubits arranged in a cellular array. In the quantum circuit model of quantum computation, an often complex series of two-qubit gate operations is required between arbitrarily distant pairs of lattice qubits. An alternative model of quantum computation based on quantum cellular automata (QCA) requires only homogeneous local interactions that can be implemented in parallel. This would be a huge simplification in an actual experiment. We find some minimal physical requirements for the construction of unitary QCA in a 1 dimensional Ising spin chain and demonstrate optimal pulse sequences for information transport and entanglement distribution. We also introduce the theory of non-unitary QCA and show by example that non-unitary rules can generate environment assisted entanglement.Comment: 12 pages, 8 figures, submitted to Physical Review

Martial Arts in the Pandemic

This study examines the impact of the Covid-19 pandemic on martial arts training worldwide. A mixed-method online questionnaire consisting of 28 items was used as a survey instrument. 306 martial artists responded. These were mainly from the United Kingdom, the USA, Germany, Italy and Japan. The questionnaire focused on pragmatic adaptations of training volume, training rhythm, training location, training mode (individual or group) and training methods. The survey sought to gain insights into modifications that martial artists made as a result of the Covid-19 pandemic in relation to their training, curriculum, alternative fitness, strength and health activities, as well as training goals. The results suggest that the training restrictions implemented by governments in order to try to combat the pandemic transformed the practice of martial arts on a massive and fundamental scale. Specifically, they led to two seemingly opposing developments: increasing digitisation and an increased focus on the importance of embodiment. The article concludes with a suggestion that these lines of development will mould the post-pandemic landscape of martial arts

Multimorbidity Content-Based Medical Image Retrieval Using Proxies

Content-based medical image retrieval is an important diagnostic tool that improves the explainability of computer-aided diagnosis systems and provides decision making support to healthcare professionals. Medical imaging data, such as radiology images, are often multimorbidity; a single sample may have more than one pathology present. As such, image retrieval systems for the medical domain must be designed for the multi-label scenario. In this paper, we propose a novel multi-label metric learning method that can be used for both classification and content-based image retrieval. In this way, our model is able to support diagnosis by predicting the presence of diseases and provide evidence for these predictions by returning samples with similar pathological content to the user. In practice, the retrieved images may also be accompanied by pathology reports, further assisting in the diagnostic process. Our method leverages proxy feature vectors, enabling the efficient learning of a robust feature space in which the distance between feature vectors can be used as a measure of the similarity of those samples. Unlike existing proxy-based methods, training samples are able to assign to multiple proxies that span multiple class labels. This multi-label proxy assignment results in a feature space that encodes the complex relationships between diseases present in medical imaging data. Our method outperforms state-of-the-art image retrieval systems and a set of baseline approaches. We demonstrate the efficacy of our approach to both classification and content-based image retrieval on two multimorbidity radiology datasets

A High-Speed X-Ray Detector System for Noninvasive Fluid Flow Measurements

The opaque nature of many multiphase flows has long posed a significant challenge to the visualization and measurement of desired characteristics. To overcome this difficulty, X-ray imaging, both in the form of radiography and computed tomography, has been used successfully to quantify various multiphase flow phenomena. However, the relatively low temporal resolution of typical X-ray systems limit their use to moderately slow flows and time-average values. This paper discusses the development of an X-ray detection system capable of high-speed radiographic imaging that can be used to visualize multiphase flows. Details of the hardware will be given and then applied to sample multiphase flows in which X-ray radiographic images of up to 1,000 frames per second were realized. The sample flows address two different multiphase flow arrangements. The first is a gas-liquid system representative of a small bubble column. The second is a gas-solid system typically found in a fluidized bed operation. Sample images are presented and potential challenges and solutions are discussed

Quantum games of asymmetric information

We investigate quantum games in which the information is asymmetrically distributed among the players, and find the possibility of the quantum game outperforming its classical counterpart depends strongly on not only the entanglement, but also the informational asymmetry. What is more interesting, when the information distribution is asymmetric, the contradictive impact of the quantum entanglement on the profits is observed, which is not reported in quantum games of symmetric information.Comment: 5 pages, 3 figure