Voltage-independent SK-channel dysfunction causes neuronal hyperexcitability in the hippocampus of Fmr1 knock-out mice

Neuronal hyperexcitability is one of the major characteristics of fragile X syndrome (FXS), yet the molecular mechanisms of this critical dysfunction remain poorly understood. Here we report a major role of voltage-independent potassium (

Error-voltage-based open-switch fault diagnosis strategy for matrix converters with model predictive control method

This paper proposes an error-voltage based open-switch fault diagnosis strategy for matrix converter (MC). A finite control set model predictive control (FCS-MPC) method is used to operate the MC. The MC system performances under normal operation and under a single open-switch fault operation are analyzed. A fault diagnosis strategy has also been implemented in two steps. First, the faulty phase is detected and identified based on a comparison of the reference and estimated output line-to-line voltages. Then, the faulty switch is located by considering the switching states of the faulty phase. The proposed fault diagnosis method is able to locate the faulty switch accurately and quickly without additional voltage sensors. Simulation and experimental results are presented to demonstrate the feasibility and effectiveness of the proposed strateg

PixelLink: Detecting Scene Text via Instance Segmentation

Most state-of-the-art scene text detection algorithms are deep learning based methods that depend on bounding box regression and perform at least two kinds of predictions: text/non-text classification and location regression. Regression plays a key role in the acquisition of bounding boxes in these methods, but it is not indispensable because text/non-text prediction can also be considered as a kind of semantic segmentation that contains full location information in itself. However, text instances in scene images often lie very close to each other, making them very difficult to separate via semantic segmentation. Therefore, instance segmentation is needed to address this problem. In this paper, PixelLink, a novel scene text detection algorithm based on instance segmentation, is proposed. Text instances are first segmented out by linking pixels within the same instance together. Text bounding boxes are then extracted directly from the segmentation result without location regression. Experiments show that, compared with regression-based methods, PixelLink can achieve better or comparable performance on several benchmarks, while requiring many fewer training iterations and less training data.Comment: AAAI-201

PixelLink: Detecting Scene Text via Instance Segmentation

Most state-of-the-art scene text detection algorithms are deep learning based methods that depend on bounding box regression and perform at least two kinds of predictions: text/non-text classification and location regression. Regression plays a key role in the acquisition of bounding boxes in these methods, but it is not indispensable because text/non-text prediction can also be considered as a kind of semantic segmentation that contains full location information in itself. However, text instances in scene images often lie very close to each other, making them very difficult to separate via semantic segmentation. Therefore, instance segmentation is needed to address this problem. In this paper, PixelLink, a novel scene text detection algorithm based on instance segmentation, is proposed. Text instances are first segmented out by linking pixels within the same instance together. Text bounding boxes are then extracted directly from the segmentation result without location regression. Experiments show that, compared with regression-based methods, PixelLink can achieve better or comparable performance on several benchmarks, while requiring many fewer training iterations and less training data.Comment: AAAI-201

Galaxy Zoo: Passive Red Spirals

We study the spectroscopic properties and environments of red spiral galaxies found by the Galaxy Zoo project. By carefully selecting face-on, disk dominated spirals we construct a sample of truly passive disks (not dust reddened, nor dominated by old stellar populations in a bulge). As such, our red spirals represent an interesting set of possible transition objects between normal blue spirals and red early types. We use SDSS data to investigate the physical processes which could have turned these objects red without disturbing their morphology. Red spirals prefer intermediate density regimes, however there are no obvious correlations between red spiral properties and environment - environment alone is not sufficient to determine if a spiral will become red. Red spirals are a small fraction of spirals at low masses, but are a significant fraction at large stellar masses - massive galaxies are red independent of morphology. We confirm that red spirals have older stellar popns and less recent star formation than the main spiral population. While the presence of spiral arms suggests that major star formation cannot have ceased long ago, we show that these are not recent post-starbursts, so star formation must have ceased gradually. Intriguingly, red spirals are ~4 times more likely than normal spirals to host optically identified Seyfert or LINER, with most of the difference coming from LINERs. We find a curiously large bar fraction in the red spirals suggesting that the cessation of star formation and bar instabilities are strongly correlated. We conclude by discussing the possible origins. We suggest they may represent the very oldest spiral galaxies which have already used up their reserves of gas - probably aided by strangulation, and perhaps bar instabilities moving material around in the disk.Comment: MNRAS in press, 20 pages, 15 figures (v3