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Research, Technological change, Government Policy

Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation

We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation module. The detection module enables a region of interest to come to attention and produces a set of object region candidates which are further used as an attention model. Rather than dealing with the entire volume, the segmentation module distills the information from the potential region. This scheme is an efficient solution for volumetric data as it reduces the influence of the surrounding noise which is especially important for medical data with low signal-to-noise ratio. Experimental results on 3D ultrasound data of the femoral head shows superiority of the proposed method when compared with a standard fully convolutional network like the U-Net

Production of Jet Pairs at Large Relative Rapidity in Hadron-Hadron Collisions as a Probe of the Perturbative Pomeron

The production of jet pairs with small transverse momentum and large relative rapidity in high energy hadron-hadron collisions is studied. The rise of the parton-level cross section with increasing rapidity gap is a fundamental prediction of the BFKL `perturbative pomeron' equation of Quantum Chromodynamics. However, at fixed collider energy it is difficult to disentangle this effect from variations in the cross section due to the parton distributions. It is proposed to study instead the distribution in the azimuthal angle difference of the jets as a function of the rapidity gap. The flattening of this distribution with increasing dijet rapidity gap is shown to be a characteristic feature of the BFKL behaviour. Predictions for the Fermilab proton-antiproton collider are presented.Comment: 17 pages, 11 figures, preprint DTP/94/0

End-to-end detection-segmentation network with ROI convolution

We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object localization first, which is then used as a cue to guide the training of the segmentation network. We test the proposed method on a segmentation task of small objects on a clinical dataset of ultrasound images. We show that by jointly learning for detection and segmentation, the proposed network is able to improve the segmentation accuracy compared to only learning for segmentation. Code is publicly available at https://github.com/vincentzhang/roi-fcn.Comment: ISBI 201

The intercept of the BFKL pomeron from Forward Jets at HERA

Recently the H1 and ZEUS collaborations have presented cross sections for DIS events with a forward jet. The BFKL formalism is able to produce an excellent fit to these data. The extracted intercept of the hard pomeron suggests that when all higher order corrections are taken into account the cross section will still rise very rapidly as expected for low $x$ dynamics.Comment: 10 pages, one figure, accepted for publication in PL

Empirical Bayes inference in sparse high-dimensional generalized linear models

High-dimensional linear models have been extensively studied in the recent literature, but the developments in high-dimensional generalized linear models, or GLMs, have been much slower. In this paper, we propose the use an empirical or data-driven prior specification leading to an empirical Bayes posterior distribution which can be used for estimation of and inference on the coefficient vector in a high-dimensional GLM, as well as for variable selection. For our proposed method, we prove that the posterior distribution concentrates around the true/sparse coefficient vector at the optimal rate and, furthermore, provide conditions under which the posterior can achieve variable selection consistency. Computation of the proposed empirical Bayes posterior is simple and efficient, and, in terms of variable selection in logistic and Poisson regression, is shown to perform well in simulations compared to existing Bayesian and non-Bayesian methods.Comment: 30 pages, 2 table

Characterization of subglacial landscapes by a two-parameter roughness index

Peer reviewedPublisher PD

Lasing on a narrow transition in a cold thermal strontium ensemble

Highly stable laser sources based on narrow atomic transitions provide a promising platform for direct generation of stable and accurate optical frequencies. Here we investigate a simple system operating in the high-temperature regime of cold atoms. The interaction between a thermal ensemble of $^{88}$Sr at mK temperatures and a medium-finesse cavity produces strong collective coupling and facilitates high atomic coherence which causes lasing on the dipole forbidden $^1$S$_0 \leftrightarrow ^3$P$_1$ transition. We experimentally and theoretically characterize the lasing threshold and evolution of such a system, and investigate decoherence effects in an unconfined ensemble. We model the system using a Tavis-Cummings model, and characterize velocity-dependent dynamics of the atoms as well as the dependency on the cavity-detuning.Comment: 9 pages, 7 figure