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Transoesophageal echocardiography TEE is a valuable diagnostic and monitoring imaging modality. Proper image acquisition is essential for diagnosis, yet current assessment techniques are solely based on manual expert review. This paper presents a supervised deep learn ing framework for automatically evaluating and grading the quality of TEE images. To obtain the necessary dataset, 38 participants of varied experience performed TEE exams with a high-fidelity virtual reality VR platform.
Two different scoring strategies, a criteria-based percentage and an following squares testing report bitcoins price nears $8000 general impression, were used. Proposed strategies for automated TEE assessment can have a significant impact on the training process of new TEE operators, providing direct feedback and facilitating the development of the necessary dexterous skills.
Submitted 13 June, ; originally announced June Communication-efficient Learning via Atomic Sparsification. Distributed model training suffers from communication overheads due to frequent gradient updates transmitted between compute nodes. To mitigate these overheads, several studies propose the use of sparsified stochastic gradients. We argue that these are facets of a general sparsification method that can operate on any possible atomic decomposition.
Following squares testing report bitcoins price nears $8000 examples include element-wise, singular value, and Fourier decompositions. Given a gradient, an atomic decomposition, and a sparsity budget, ATOMO gives a random unbiased sparsification of the atoms minimizing variance.
Submitted 23 June, ; v1 submitted 11 June, ; originally announced June Techniques for reducing the variance of gradient estimates used in stochastic programming algorithms for convex finite-sum problems have received a great deal of attention in recent years. By leveraging dissipativity theory from control, we provide a new perspective on two important variance-reduction algorithms: SVRG and its direct accelerated variant Katyusha.
Our perspective provides a physically intuitive understanding of the behavior of SVRG-like methods via a principle of energy conservation. The tools discussed here allow us to automate the convergence analysis of SVRG-like methods by capturing their essential properties in small semidefinite programs amenable to standard analysis and computational techniques.
Our approach recovers existing convergence results for SVRG and Katyusha and generalizes the theory to alternative parameter choices. We also discuss how our approach complements the linear coupling technique.
Our combination of perspectives leads to a better understanding of accelerated variance-reduced stochastic methods for finite-sum problems.
Submitted 10 June, ; originally announced June The algorithm decreases measures of optimality primal and dual gaps rapidly, both in the number of iterations and in wall-clock time, outperforming even the efficient "lazified" conditional gradient algorithms of [arXiv: Nota bene the algorithm is lazified itself. Submitted 18 May, ; originally announced May Wen ZhaoBill S. WrightBaojiu Li. The gravitational-wave GW standard sirens, caused by the following squares testing report bitcoins price nears $8000 of binary neutron stars, provide a model-independent way to measure the distance of GW events, which can be used to determine the luminosity distances of SNe Ia by interpolation, provided the GW and SN Ia samples have similar redshift ranges.
Submitted 9 April, ; originally announced April In the last years, Bitcoins and Blockchain technologies are gathering a wide attention from different scientific communities. Notably, thanks to widespread industrial applications and to the continuous introduction of cryptocurrencies, even the public opinion is increasing its attention towards this field. The underlying structure of these technologies constitutes one of their core concepts. In particular, they are based on peer-to-peer networks.
Accordingly, all nodes lay at the same level, so that there is no place for privileged actors as, for instance, banking institutions in classical financial networks. In this work, we perform a preliminary investigation on two networks, i. Notably, we aim to analyze their global structure and to evaluate if they are provided with a small-world behavior.
Results suggest that the principle known as 'fittest-gets-richer', combined with a continuous increasing of connections, might constitute the mechanism leading these networks to reach their current structure. In addition, further observations open the way to new investigations into this direction.
Submitted 6 April, ; originally announced April Quantum heat engine operating between thermal and spin reservoirs. CarvalhoSalil BedkihalJoan A. Landauer's erasure principle is a cornerstone of thermodynamics and information theory. According to this principle, erasing information incurs a minimum energy cost.
Recently, Vaccaro and Barnett [Proc. As Landauer's erasure principle plays a fundamental role in heat engines, their result considerably widens the possible configurations that heat engines can have. Motivated by this, we propose here a following squares testing report bitcoins price nears $8000 optical heat engine that operates under a single thermal reservoir and a spin angular momentum reservoir coupled to a three level system with an energy-degenerate ground state.
The proposed heat engine operates without producing waste heat and goes beyond the traditional Carnot engine where the working fluid is subjected to two thermal baths at different temperatures.
Submitted 3 April, ; originally announced April Randomness and Permutations in Coordinate Descent Methods. We consider coordinate descent CD methods with exact line search on convex quadratic problems. Our main focus is to study the performance of the CD method that use random permutations in each epoch and compare it to the performance of the CD methods that use deterministic orders and random sampling with replacement.
We focus on a class of convex quadratic problems with a diagonally dominant Hessian matrix, for which we show that using random permutations instead of random with-replacement sampling improves the performance of the CD method in the worst-case.
Furthermore, we prove following squares testing report bitcoins price nears $8000 as the Hessian matrix becomes more diagonally dominant, the performance improvement attained by using random permutations increases.
We also show that for this problem class, using any fixed deterministic order yields a superior performance than using random permutations. We present detailed theoretical analyses with respect to three different convergence criteria that are used in the literature and support our theoretical results with numerical experiments.
Submitted 21 March, ; originally announced March We consider minimization of a smooth nonconvex objective function using an iterative algorithm based on Newton's method and linear conjugate gradient, with explicit detection and use of negative curvature directions for the Hessian of the objective function. The algorithm tracks Newton-conjugate following squares testing report bitcoins price nears $8000 procedures developed in the s closely, but includes enhancements that allow worst-case complexity results to be proved for convergence to points that satisfy approximate first-order and second-order optimality conditions.
The complexity results match the best known results in the literature for second-order methods. Submitted 9 March, ; v1 submitted 7 March, ; originally announced March We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving ERM problems with a nonsmooth regularization term. Current second-order and quasi-Newton methods for this problem either do not work well in the distributed setting or work only for specific regularizers.
Our algorithm uses successive quadratic approximations, and we describe how to maintain an approximation of the Hessian and solve subproblems efficiently in a distributed manner. The proposed method enjoys global linear convergence for a broad range of non-strongly convex problems that includes the most commonly used ERMs, thus requiring lower communication complexity.
It also converges on non-convex problems, so has the potential to be used on applications such as deep learning. Initial computational results on convex problems demonstrate that our method significantly improves on communication cost and running time over the current state-of-the-art methods.
Submitted 26 May, ; v1 submitted 4 March, ; originally announced March Ching-pei LeeStephen J. Successive quadratic approximations, or second-order proximal methods, are useful for minimizing functions that are a sum of a smooth part and a convex, possibly nonsmooth part that promotes a regularized solution.
Most analyses of iteration complexity focus on the special case of proximal gradient method, or accelerated variants thereof. There have been only following squares testing report bitcoins price nears $8000 few studies of methods that use a second-order approximation to the smooth part, due in part to the difficulty of obtaining closed-form solutions to the subproblems at each iteration.
In practice, iterative algorithms need to following squares testing report bitcoins price nears $8000 used to find inexact solutions to the subproblems. In this work, we present global analysis of the iteration complexity of inexact following squares testing report bitcoins price nears $8000 quadratic approximation methods, showing that it is sufficient to obtain an inexact solution of the subproblem to fixed multiplicative precision in order to guarantee the same order of convergence rate as the exact version, with complexity related proportionally to the degree of inexactness.
Our result allows flexible choices of the second-order terms, including Newton and quasi-Newton choices, and does not necessarily require more time to be spent on the subproblem solves on later iterations. For problems exhibiting a property related to strong convexity, the algorithm con- verges at a global linear rate.
Submitted 27 March, ; v1 submitted 3 March, ; originally announced March Training set bugs are flaws in the data that adversely affect machine learning. The training set is usually too large for man- ual inspection, but one may have the resources to verify a few trusted items.
The set of trusted items may not by itself be adequate for learning, so we propose an algorithm that uses these items to identify bugs in the training set and thus im- proves learning.
Specifically, our approach seeks the smallest set of changes to the training set labels such that the model learned from this corrected training set predicts labels of the trusted items correctly. Following squares testing report bitcoins price nears $8000 flag the items whose labels are changed as potential bugs, whose labels can be checked for veracity by human experts.
To find the bugs in this way is a challenging combinatorial following squares testing report bitcoins price nears $8000 optimization problem, but it can be relaxed into a continuous optimization problem. Ex- periments on toy and real data demonstrate that our approach can identify training set bugs effectively and suggest appro- priate changes to the labels.
Our algorithm is a step toward trustworthy machine learning. Submitted 24 January, ; originally announced January Randomized sampling for basis functions construction in generalized finite element methods. This work explores several random sampling strategies for construction of basis functions, and proposes a quantitative criterion to analyze and compare these sampling strategies.
Submitted 21 January, ; originally announced January The Galactic center offers us a unique opportunity to test General Relativity GR with the orbits of stars around a supermassive black hole. Observations of these stars have been one of the great successes of adaptive optics on m telescopes, driving the need for the highest angular resolution and astrometric precision.
New tests of gravitational physics in the strong gravity regime with stellar orbits will be made possible through the leap in angular resolution and sensitivity from the next generation of extremely large ground-based telescopes.
We present new simulations of specific science cases such as the detection following squares testing report bitcoins price nears $8000 the GR precession of stars, the measurement of extended dark mass, and the distance to following squares testing report bitcoins price nears $8000 Galactic center.
In additions, the simulations include observational issues such as the impact of source confusion on astrometry and radial velocities in the dense environment of the Galactic center. We qualitatively show how improvements in sensitivity, astrometric and spectroscopic precision, and increasing the number of stars affect the science with orbits at the Galactic center.
We developed a tool to determine the constraints on physical models using a joint fit of over stars that are expected to be observable with TMT.
These following squares testing report bitcoins price nears $8000 cases require very high astrometric precision and stability, thus they provide some of the most stringent constraints on the planned instruments and adaptive optics systems. Submitted 16 November, ; originally announced November A key challenge in online learning is that classical algorithms can be slow to adapt to changing environments.
