Improved Bounds on Quantum Learning Algorithms

In this article we give several new results on the complexity of algorithms that learn Boolean functions from quantum queries and quantum examples. Hunziker et al. conjectured that for any class C of Boolean functions, the number of quantum black-box queries which are required to exactly identify an unknown function from C is $O(\frac{\log |C|}{\sqrt{{\hat{\gamma}}^{C}}})$, where $\hat{\gamma}^{C}$ is a combinatorial parameter of the class C. We essentially resolve this conjecture in the affirmative by giving a quantum algorithm that, for any class C, identifies any unknown function from C using $O(\frac{\log |C| \log \log |C|}{\sqrt{{\hat{\gamma}}^{C}}})$ quantum black-box queries. We consider a range of natural problems intermediate between the exact learning problem (in which the learner must obtain all bits of information about the black-box function) and the usual problem of computing a predicate (in which the learner must obtain only one bit of information about the black-box function). We give positive and negative results on when the quantum and classical query complexities of these intermediate problems are polynomially related to each other. Finally, we improve the known lower bounds on the number of quantum examples (as opposed to quantum black-box queries) required for $(\epsilon,\delta)$-PAC learning any concept class of Vapnik-Chervonenkis dimension d over the domain $\{0,1\}^n$ from $\Omega(\frac{d}{n})$ to $\Omega(\frac{1}{\epsilon}\log \frac{1}{\delta}+d+\frac{\sqrt{d}}{\epsilon})$. This new lower bound comes closer to matching known upper bounds for classical PAC learning.Comment: Minor corrections. 18 pages. To appear in Quantum Information Processing. Requires: algorithm.sty, algorithmic.sty to buil

A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance Images: A Preliminary Machine Learning Study.

The increasing number of magnetic resonance imaging (MRI) studies could lead to delayed or missed diagnosis of significant brain pathologies like high-grade gliomas (HGG). Artificial intelligence methods could be applied in analyzing large amounts of data such as; brain MRI studies. In this study we aimed to propose a convolutional neural network (CNN) for the automatic detection of HGGs on T2-weighted MRI images

A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance I mages: A Preliminary Machine Learning Study

AIM: To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans. MATERIAL and METHODS: A total of 3580 images obtained from 179 individuals were used for training and validation. After random rotation and vertical flip, training data was augmented by factor of 10 in each iteration. In order to increase data processing time, every single image converted into a Jpeg image which has a resolution of 320x320. Accuracy, precision and recall rates were calculated after training of the algorithm. RESULTS: Following training, CNN achieved acceptable performance ratios of 0.854 to 0.944 for accuracy, 0.812 to 0.980 for precision and 0.738 to 0.907 for recall. Also, CNN was able to detect HGG cases even though there is no apparent mass lesion in the given image. CONCLUSION: Our preliminary findings demonstrate; currently proposed CNN model achieves acceptable performance results for the automatic detection of HGGs on T2-weighted images

Reducing Aortic Barotrauma and Vascular Extracellular Matrix Degradation by Pacemaker-Mediated QRS Widening

BACKGROUND: The extent of pressure-related damage might be related to acceleration rate of the applied pressure (peak dP/dt) in the vascular system. In this study, we sought to determine whether dP/dt applied to the aortic wall (aortic dP/dt) and in turn vascular extracellular matrix degradation can be mitigated via modulation of left ventricular (LV) contractility (LV dP/dt) by pacemaker-mediated desynchronization

Gradual Versus Abrupt Reperfusion During Primary Percutaneous Coronary Interventions in ST-Segment-Elevation Myocardial Infarction (GUARD)

BACKGROUND: Intramyocardial edema and hemorrhage are key pathological mechanisms in the development of reperfusion-related microvascular damage in ST-segment-elevation myocardial infarction. These processes may be facilitated by abrupt restoration of intracoronary pressure and flow triggered by primary percutaneous coronary intervention. We investigated whether pressure-controlled reperfusion via gradual reopening of the infarct-related artery may limit microvascular injury in patients undergoing primary percutaneous coronary intervention

Coronary microcirculation in nonculprit vessel territory in reperfused acute myocardial infarction

Background: There is an ongoing debate on the extension of reperfusion-related microvascular damage (MVD) throughout the remote noninfarcted myocardial regions in patients with ST-elevation myocardial infarction (STEMI) that undergo primary percutaneous intervention (pPCI). The aim of this study was to elucidate the impact of reperfusion on remote microcirculatory territory by analyzing hemodynamic alterations in the nonculprit-vessel in relation to reperfusion. Methods: A total of 20 patients with STEMI undergoing pPCI were included. Peri-reperfusion temporal changes in hemodynamic parameters were obtained in angiographically normal nonculprit vessels before and 1-h after reopening of the culprit vessel. Intracoronary pressure and flow velocity data were compared using pairwise analyses (before and 1-h after reperfusion). Results: In the non-culprit vessel, compared to the pre-reperfusion state, mean resting average peak velocity (33.4 ± 9.4 to 25.0 ± 4.9 cm/s, P < 0.001) and mean hyperemic average peak velocity (53.5 ± 14.4 to 42.1 ± 10.66 cm/s, P = 0.001) significantly decreased; whereas baseline (3.2 ± 1.0 to 4.0 ± 1.0 mmHg.cm −1.s, P < 0.001) and hyperemic microvascular resistance (HMR) (1.9 ± 0.6 to 2.4 ± 0.7 mmHg.cm −1.s, P < 0.001) and mean zero flow pressure (Pzf) values (32.5 ± 6.9 to 37.6 ± 8.3 mmHg, P = 0.003) significantly increased 1-h after reperfusion. In particular, the magnitude of changes in HMR and Pzf values following reperfusion were more prominent in patients with larger infarct size and with higher extent of MVD in the culprit vessel territory. Conclusion: Reperfusion-related microvascular injury extends to involve remote myocardial territory in relation to the magnitude of the adjacent infarction and infarct-zone MVD. (GUARD Clinical Trials NCT02732080)