Contacting the sun and creating burning plasma conditions inside a small target, compressed to a pressure even higher than the Sun's core, capture world-wide attention. However, progress and opportunity are found in all areas of applied and fundamental plasma physics.
Perhaps more interesting is Graphics Core 1, which offers a range of 3D curved surface and other hardware 3D functionality. How useful the more advanced functions in this core will be to development remains to be seen - Sony is likely to make a lot of noise about the NURBS and curved surface abilities of the PSP, but in the real world, developing games to use these tools is extremely difficult and most developers are likely to fall back on good old polygons. However, there's no doubt that other functions of Core 1 - such as compressed texture handling and proper hardware clipping - will speed up graphics on the PSP significantly, and developers who do decide to use the curved surface abilities may well turn out some very impressive games.
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It has been accepted that the 660-km discontinuity (D660) is caused by the post-spinel (Psp) transition, which is decomposition of ringwoodite (Rw) to bridgmanite (Brg) + ferropericlase. Nevertheless, all of in-situ X-ray diffraction studies with multi-anvil presses (MAP) gave distinctively lower transition pressures than that of the D660 (23.4 GPa). Although Fei et al. (2004) claimed that their Psp transition pressure explains the D660, it is still 0.5 GPa lower by considering the geotherm. If these results were accepted, the Psp would not account for the D660. In this study, we re-investigated the Psp transition pressure in Mg2SiO4 by in-situ X-ray diffraction using a MAP. A fine-grained mixture of forsterite, enstatite and periclase (Pc) and an MgO pressure marker were placed at the center of a furnace. The sample was compressed to 6-7 MN and heated to 1100 K to synthesize a mixture of Rw, akimotoite and Pc. After that, more press load was applied to obtain sample pressures of ca. 23 GPa, and the sample was then heated to 1700 K, keeping this temperature for 1-2 hours. During keeping the temperature, the press load was first rapidly, and then gradually increased to prevent pressure drop. Phase identification and pressure determination were conducted with alternatively accumulated diffraction patterns of the sample and pressure maker. We bracketed the transition pressures by 23.7 and 24.0 GPa at 1700 K based on the third-order Birch-Murnaghan and Vinet EOSs of MgO given by Tange et al. (2009), respectively. The transition pressure at 2000 K is estimated to be 23.2-23.5 GPa by applying the Psp transition slope based on Fei et al. (2004). Thus, the present transition pressure completely agrees with the D660 depth. The reason for the lower transition pressures by the previous studies is pressure drop during heating. Although the transition completes at the beginning of target temperature, pressure significantly drops during or even before accumulating a diffraction pattern for 3-5 minutes. We obtained the correct transition pressure by preventing the pressure drop by pumping. This problem should be omnipresent in high P-T in-situ X-ray diffraction experiments to determine a phase boundary.
The WISPR fits files are available as data products categorized in levels (L1...L3) describing the following processing and calibration steps: L1 Level-1: L1 data are de-compressed and oriented so that solar north is (approximately) up. No further calibrations or corrections are applied. L2 Level-2: L2 fits files are calibrated in units of Mean-Solar-Brightness (MSB). Furthermore, bias-, stray-light- and vignetting corrections are applied. L3 Level-3: Additional to the L2 calibration and correction, a model of the F-corona signal is subtracted to improve the visibility of the K-corona variations. L2b Level-2b: Background models for the full-field WISPR images are generated individually for every encounter. CGAUSS data-products: mp4 Encounter overview: Videos of different encounters built on the WISPR inner telescope (WIT) L3 images (left panel), running difference images (middle panel). In the right panel of each video is shown the variation of the position of the PSP together with the Sun and the solar system planets. mp4 Encounter overview: Videos of different encounters built on the WISPR outer telescope (WOT) L3 images (left panel), running difference images (middle panel). In the right panel of each video is shown the variation of the position of the PSP together with the Sun and the solar system planets. png Single images: Png of the video files. Additional to the full-field images, WISPR has a high cadence sub-field mode to track more detailed special regions of interest. WISPR data are provided by the U.S. Naval Research Laboratory and can be accessed on the WISPR Homepage.
WISPR data files are provided by the U.S. Naval Research Laboratory and can be accessed on the WISPR Homepage.
This leads us to the last and final data type that is critical to the advancements of AI. As humans we use visual cues to analyze, interpret, react, and navigate environments in our daily lives. Machines do this through images and videos. Often referred to as computer vision, machines use sensors to capture data from the scene and reconstruct that data into a visual format. Many people would only focus on the camera, its lens, and the image sensor, but for machines, they can visualize a scene in more ways than a human can. Visual sensors are not the only data that can re-construct a scene. Earlier we mentioned sonar sensors using audio to reconstruct a scene. There are also thermal sensors, radar sensors, lidar sensors, IR (infrared) sensors, and wireless radio sensors to name a few that can collect data on a scene and be reconstructed to represent a physical object in the environment. The pretty picture or videos we see from our cameras are part of an encoding process that is done to the raw data to present it as information that we as humans can interpret. The encoding process of raw data is actually destroying the data to form a new data set, resulting in the pretty image that we can see to understand the scene. This is the reason raw data files are larger in size than a compressed JPEG image or an H.264 video.
Now that the basic data types have been explored and an understanding of how valuable each data type is to the AI framework, edge devices are going to be the catalyst in the acceleration of AI because they are the collectors of these data types in their purest form before the data is altered, modified, destroyed and compressed. With many devices transitioning over to the digital world from analog, as a society, the number of smarter devices is growing in the billions. The days of dumb sensors are fading away. In the next decade from 2020-2030, we will be seeing more connected devices than we can imagine. There is a reason why there is a shift from IPV4 to IPV6, and that is due to an increase in edge devices being connected to the network. From smart locks, lights, switches, tankless water heaters, gas meters, electric meters, to any electronic device that consists of a processing unit, memory, and network connectivity. The growth of edge devices will be a leading factor in the acceleration and adoption of AI.
For many edge-based device manufacturers, the balance of cost versus what specifically can be done to achieve greater performance with AI from their devices can result in having a product now that meets the business case or delaying a product to ensure better performance and benefits when the hardware can support the software or application. With many options to choose from in the AI framework, compatibility and efficiency are key components that a device manufacturer will evaluate during the decision-making process. Many common ASIC architectures can provide some sort of AI implementation, but usually, the more efficient ones are the ones using an ASIC specifically designed to target various AI, machine learning, and deep learning frameworks. AI requires a lot of computational power and is the reason why companies like Google, Microsoft, and Amazon to name a few, have invested in resources around AI as part of their cloud services offering. Their server-based cloud solution for AI can process more data in bulk and because of their processing power, are highly efficient and offer a platform that can handle a diverse set of workloads for training machine and deep learning models. Nvidia is not only improving server-based cloud performance in the enterprise space, but they are also bridging the cloud offering to what can be done with an on-premise HPC (High Processing Computer). With commercially available GPU cards that include tensor cores, Nvidia is providing organizations a more cost-effective way to incorporate AI as a means to improve business operations and efficiencies. Investment from Intel into several FPGA (Field Programmable Gate Arrays) platforms such as their AI-optimized Stratix 10 NX FPGA is bringing back a platform that got outperformed by GPUs and ASIC architectures in AI computation.
The Intel FPGA AI platform provides flexibility for developers to quickly customize, reconfigure, and adapt to changes in AI acceleration based on application demand but also allows for a highly differentiated device. Low latency improvements and real-time processing of images and videos are additional performance boost in the design of this AI-optimized platform. Intel has also invested in bringing AI optimized hardware platforms to edge devices in their Movidius Vision Processing Units or VPU.
The creative chaos in edge-based devices will result in access to more data because of the exponential growth of these devices in the market, but we will also need to navigate through the irrelevant devices that offer no value to the data being collected due to performance and quality issues. As much as edge devices will be the toll gate to all the data being collected from various sensors, a bigger problem persists in the AI community. That problem is the standardization of the data structure and how to best orchestrate the data sets for everyone to follow and use. It is a free open reign that many manufactures have developed their own tools or APIs to access and process the raw or compressed data collected. With groups like NIST that created frameworks around cybersecurity for organizations to reference, or for the WiFi consortium that set standards for wireless use and security, the AI community needs to develop a working group of public and private sector leadership to develop a global standard around AI and how to ethically use this technology. be457b7860
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