Perturbative analysis of gauged matrix models

We analyze perturbative aspects of gauged matrix models, including those where classically the gauge symmetry is partially broken. Ghost fields play a crucial role in the Feynman rules for these vacua. We use this formalism to elucidate the fact that nonperturbative aspects of [script N] = 1 gauge theories can be computed systematically using perturbative techniques of matrix models, even if we do not possess an exact solution for the matrix model. As examples we show how the Seiberg-Witten solution for [script N] = 2 gauge theory, the Montonen-Olive modular invariance for [script N] = 1*, and the superpotential for the Leigh-Strassler deformation of [script N] = 4 can be systematically computed in perturbation theory of the matrix model or gauge theory (even though in some of these cases an exact answer can also be obtained by summing up planar diagrams of matrix models)

A new astrophysical solution to the Too Big To Fail problem - Insights from the MoRIA simulations

We test whether advanced galaxy models and analysis techniques of simulations can alleviate the Too Big To Fail problem (TBTF) for late-type galaxies, which states that isolated dwarf galaxy kinematics imply that dwarfs live in lower-mass halos than is expected in a {\Lambda}CDM universe. Furthermore, we want to explain this apparent tension between theory and observations. To do this, we use the MoRIA suite of dwarf galaxy simulations to investigate whether observational effects are involved in TBTF for late-type field dwarf galaxies. To this end, we create synthetic radio data cubes of the simulated MoRIA galaxies and analyse their HI kinematics as if they were real, observed galaxies. We find that for low-mass galaxies, the circular velocity profile inferred from the HI kinematics often underestimates the true circular velocity profile, as derived directly from the enclosed mass. Fitting the HI kinematics of MoRIA dwarfs with a theoretical halo profile results in a systematic underestimate of the mass of their host halos. We attribute this effect to the fact that the interstellar medium of a low-mass late-type dwarf is continuously stirred by supernova explosions into a vertically puffed-up, turbulent state to the extent that the rotation velocity of the gas is simply no longer a good tracer of the underlying gravitational force field. If this holds true for real dwarf galaxies as well, it implies that they inhabit more massive dark matter halos than would be inferred from their kinematics, solving TBTF for late-type field dwarf galaxies.Comment: 21 pages, 21 figures. Accepted for publication in A&A. Corrected certain values in Table

Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier

Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We propose an algorithm that extracts coronary artery centerlines in CCTA using a convolutional neural network (CNN). A 3D dilated CNN is trained to predict the most likely direction and radius of an artery at any given point in a CCTA image based on a local image patch. Starting from a single seed point placed manually or automatically anywhere in a coronary artery, a tracker follows the vessel centerline in two directions using the predictions of the CNN. Tracking is terminated when no direction can be identified with high certainty. The CNN was trained using 32 manually annotated centerlines in a training set consisting of 8 CCTA images provided in the MICCAI 2008 Coronary Artery Tracking Challenge (CAT08). Evaluation using 24 test images of the CAT08 challenge showed that extracted centerlines had an average overlap of 93.7% with 96 manually annotated reference centerlines. Extracted centerline points were highly accurate, with an average distance of 0.21 mm to reference centerline points. In a second test set consisting of 50 CCTA scans, 5,448 markers in the coronary arteries were used as seed points to extract single centerlines. This showed strong correspondence between extracted centerlines and manually placed markers. In a third test set containing 36 CCTA scans, fully automatic seeding and centerline extraction led to extraction of on average 92% of clinically relevant coronary artery segments. The proposed method is able to accurately and efficiently determine the direction and radius of coronary arteries. The method can be trained with limited training data, and once trained allows fast automatic or interactive extraction of coronary artery trees from CCTA images.Comment: Accepted in Medical Image Analysi

Efficient and Practical Transfer Hydrogenation of Ketones Catalyzed by a Simple Bidentate Mn−NHC Complex

Catalytic reductions of carbonyl‐containing compounds are highly important for the safe, sustainable, and economical production of alcohols. Herein, we report on the efficient transfer hydrogenation of ketones catalyzed by a highly potent Mn(I)−NHC complex. Mn−NHC 1 is practical at metal concentrations as low as 75 ppm, thus approaching loadings more conventionally reserved for noble metal based systems. With these low Mn concentrations, catalyst deactivation is found to be highly temperature dependent and becomes especially prominent at increased reaction temperature. Ultimately, understanding of deactivation pathways could help close the activity/stability‐gap with Ru and Ir catalysts towards the practical implementation of sustainable earth‐abundant Mn‐complexes

Automatic Segmentation of the Left Ventricle in Cardiac CT Angiography Using Convolutional Neural Network

Accurate delineation of the left ventricle (LV) is an important step in evaluation of cardiac function. In this paper, we present an automatic method for segmentation of the LV in cardiac CT angiography (CCTA) scans. Segmentation is performed in two stages. First, a bounding box around the LV is detected using a combination of three convolutional neural networks (CNNs). Subsequently, to obtain the segmentation of the LV, voxel classification is performed within the defined bounding box using a CNN. The study included CCTA scans of sixty patients, fifty scans were used to train the CNNs for the LV localization, five scans were used to train LV segmentation and the remaining five scans were used for testing the method. Automatic segmentation resulted in the average Dice coefficient of 0.85 and mean absolute surface distance of 1.1 mm. The results demonstrate that automatic segmentation of the LV in CCTA scans using voxel classification with convolutional neural networks is feasible.Comment: This work has been published as: Zreik, M., Leiner, T., de Vos, B. D., van Hamersvelt, R. W., Viergever, M. A., I\v{s}gum, I. (2016, April). Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 40-43). IEE

Baby Universes in String Theory

We argue that the holographic description of four-dimensional BPS black holes naturally includes multi-center solutions. This suggests that the holographic dual to the gauge theory is not a single AdS_2 times S^2 but a coherent ensemble of them. We verify this in a particular class of examples, where the two-dimensional Yang-Mills theory gives a holographic description of the black holes obtained by branes wrapping Calabi-Yau cycles. Using the free fermionic formulation, we show that O(e^{-N}) non-perturbative effects entangle the two Fermi surfaces. In an Euclidean description, the wave-function of the multi-center black holes gets mapped to the Hartle-Hawking wave-function of baby universes. This provides a concrete realization, within string theory, of effects that can be interpreted as the creation of baby universes. We find that, at least in the case we study, the baby universes do not lead to a loss of quantum coherence, in accord with general arguments.Comment: 39 pages, 7 figure

Carving out an empire? How China strategically used aid to facilitate Chinese business expansion in Africa

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Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients with FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted and clustered encodings. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 +- 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis.Comment: This paper was submitted in April 2017 and accepted in November 2017 for publication in Medical Image Analysis. Please cite as: Zreik et al., Medical Image Analysis, 2018, vol. 44, pp. 72-8

Dimensionality of charge transport in organic field-effect transistors

Application of a gate bias to an organic field-effect transistor leads to accumulation of charges in the organic semiconductor within a thin region near the gate dielectric. An important question is whether the charge transport in this region can be considered two-dimensional, or whether the possibility of charge motion in the third dimension, perpendicular to the accumulation layer, plays a crucial role. In order to answer this question we have performed Monte Carlo simulations of charge transport in organic field-effect transistor structures with varying thickness of the organic layer, taking into account all effects of energetic disorder and Coulomb interactions. We show that with increasing thickness of the semiconductor layer the source-drain current monotonically increases for weak disorder, whereas for strong disorder the current first increases and then decreases. Similarly, for a fixed layer thickness the mobility may either increase or decrease with increasing gate bias. We explain these results by the enhanced effect of state filling on the current for strong disorder, which competes with the effects of Coulomb interactions and charge motion in the third dimension. Our conclusion is that apart from the situation of a single monolayer, charge transport in an organic semiconductor layer should be considered three-dimensional, even at high gate bias