Dynamic Spot App Download LINK

For an app still in early access, dynamicSpot is already quite capable. It supports notifications for all installed apps, complete with granular controls for each one, and quick action buttons for some of them.

dynamicSpot authorizes you to experience the efficient multitasking features that Apple solely provides to their iOS. This includes the unobtrusive dynamic spot or pop-up button wherein a simple tap will enable you to open the displayed application or notifications. If you wish to view its further details, you only have to long press the pop-up bar. It utilizes the Android notification system, boosting its compatibility power.

Dynamic Spot App Download

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There are multiple ways to accomplish this dynamic scaling. As an example, a script can be scheduled (e.g. via cron) to get the value of the ApproximateNumberOfMessagesVisible SQS metric periodically and then scale the Spot fleet according to defined thresholds. The current size of the Spot fleet can be obtained using the DescribeSpotFleetRequests API and the scaling can be carried out by using the new ModifySpotFleetRequest API. A sample script written for NodeJS is available here, and following is a sample IAM policy for an IAM role that could be used on an EC2 instance for running the script:

You could also leverage AWS Lambda for dynamically scaling your Spot fleet. As depicted in the diagram below, an AWS Lambda function can be scheduled (e.g using AWS datapipeline, cron or any form of scheduling) to get the ApproximateNumberOfMessagesVisible SQS metric for the SQS queue in a batch processing application. This Lambda function will check the current size of a Spot fleet using the DescribeSpotFleetRequests API, and then scale the Spot fleet using the ModifySpotFleetRequest API after also checking certain constraints such as the state or size of the Spot fleet similar to the script discussed above.

You could also use the sample IAM policy provided above to create an IAM role for the AWS Lambda function. A sample Lambda deployment package for dynamically scaling a Spot fleet based on the value of the ApproximateNumberOfMessagesVisible SQS metric can be found here. However, you could modify it to use any CloudWatch metric based on your use case. The sample script and Lambda function provided are only for reference and should be tested before using in a production environment.

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As for the Roi-based spot exclusion, you are right, I use the polygon, which cannot handle properly arbitrary shapes. Do you have of a workaround that would harness all IJ rois? We would change TrackMate to implement it.

The dCTA acquisition protocol has been previously described [5]. Briefly, we obtained non-contrast CT images followed by dCTA acquisition using 320-row volume CT scanner (Toshiba Aquilion ONETM). We acquired whole-brain angiographic images over a 60-second period: once at 7 seconds (used as a mask of subtraction) from the start of injection of contrast; then every 2 seconds from 10 to 35 seconds, followed by every 5 seconds to 60 seconds. An expert neuroradiologist (S.C.) blinded to follow-up scans and patient outcome determined the spot sign status [6]. We measured spot sign density using Quantomo Pro (Cybertrial, Calgary, AB). We estimated the rate of contrast extravasation as a measure of dCTA spot sign growth by calculating the slope of a time-density plot from the earliest point of spot sign appearance to the maximal contrast volume of the dCTA spot sign [3]. To classify the phase of spot sign appearance, we measured the maximum Hounsfield units (HU) of an arterial and venous structure in the plane of the dynamic spot sign in the hemisphere contralateral to the ICH at time of spot sign appearance [4]. We then categorized the phase of spot sign appearance into five acquisition phases: early arterial, late arterial, equilibrium, peak venous, and late venous phases, as previously published [4]. We pooled the groups into two phases, arterial and venous, for data analyses.

Phases of spot sign appearance are presented in Table 2. Spot signs appeared more frequently in the early arterial and peak arterial phases (62.9%, 14.3%) than in the peak venous and late venous phases (11.4%, 11.4%). Given that there were no spot signs appearing in the equilibrium phase, we pooled the early and peak arterial phases into an arterial cohort, and the peak and late venous phases into a venous cohort, respectively. Higher proportions of significant HE were observed in the arterial phase of spot sign appearance, but these results were not statistically significant (P = 0.67).

Reviewer #1: The authors examined 'spot sign' on dynamic CTA's (arterial vs. venous phase) ability to predict significant hematoma expansion (defined as 6mL or 33% relative volume growth). This is an important topic as clinical predictors will likely be of value when deciding on trial enrollment for potential therapeutics for ICH. The manuscript is well-written, direct, and easy to follow. The intent of the study is clearly stated and the methods for which the authors attempt to answer their hypothesis is rationale and scientifically sound.

The major limitation of the study is the sample size in each group, in particular the venous phase (n=8), which the authors explicitly mention in the discussion. With this low of a sample size it is challenging to draw a firm conclusion, however, the authors appropriately conclude that the study is 'exploratory' and 'predictability of significant HE and clinical outcome MAY differ across the timing of spot sign appearance on dCTA' and 'larger sample sizes are needed to confirm the findings of our study.' The authors collected subjects prospectively but could consider including retrospective cases if dCTA was being used at their center before they started the study to increase the number of subjects.

Reviewer #2: This article demonstrating a trend towards poor outcome in ICH patients with arterial phase spot sign on dCTA when compared to venous cohorts is well received. The small sample size is mentioned, and publication of this article will highlight the need for further study.

This work discusses the use of matched filtering Generalized Phase Contrast (mGPC) as an efficient and cost-effective beam shaper for applications such as in biophotonics, optical micromanipulation, microscopy and two-photon polymerization. The theoretical foundation of mGPC is described as a combination of Generalized Phase Contrast and phase-only correlation. Such an analysis makes it convenient to optimize an mGPC system for different setup conditions. Results showing binary-only phase generation of dynamic spot arrays and line patterns are presented.

Spot is designed for remote user operation and autonomous sensing. Using a series of integrated sensors, the robot can understand dynamic environments and avoid obstacles or people. It is through these twin capabilities that the Trek-Spot union excels as the hybrid device seamlessly enables data-rich reality capture via an automated experience. The result is a vast improvement in customers' time to decision while delivering more data, automated. Future applications will allow the Trek-Spot integration to autonomously capture 3D laser scans in hazardous, confined spaces and low-oxygen environments, otherwise dangerous for humans.

Space Shuttle photograph of the Hawaiian Islands, the southernmostpart of the long volcanic trail of the "Hawaiian hotspot" (seetext). Kauai is in the lower right corner (edge) and the Big Island of Hawaiiin the upper left corner. Note the curvature of the Earth (top edge). (Photographcourtesy of NASA.)

In 1963, J. Tuzo Wilson, the Canadian geophysicist who discovered transformfaults, came up with an ingenious idea that became known as the "hotspot"theory. Wilson noted that in certain locations around the world, such asHawaii, volcanism has been active for very long periods of time. This couldonly happen, he reasoned, if relatively small, long-lasting, and exceptionallyhot regions -- called hotspots -- existed below the plates that wouldprovide localized sources of high heat energy (thermal plumes) tosustain volcanism. Specifically, Wilson hypothesized that the distinctivelinear shape of the Hawaiian Island-Emperor Seamounts chain resulted fromthe Pacific Plate moving over a deep, stationary hotspot in the mantle,located beneath the present-day position of the Island of Hawaii. Heat fromthis hotspot produced a persistent source of magma by partly melting theoverriding Pacific Plate. The magma, which is lighter than the surroundingsolid rock, then rises through the mantle and crust to erupt onto the seafloor,forming an active seamount. Over time, countless eruptions cause the seamountto grow until it finally emerges above sea level to form an island volcano.Wilson suggested that continuing plate movement eventually carries the islandbeyond the hotspot, cutting it off from the magma source, and volcanismceases. As one island volcano becomes extinct, another develops over thehotspot, and the cycle is repeated. This process of volcano growth and death,over many millions of years, has left a long trail of volcanic islands andseamounts across the Pacific Ocean floor. 2351a5e196