Lovely Cat stocks something useful!

Fiction.

Today, I didn't think i could take a class, because i drank too much yesterday. 

Therefore, i asked to my friend ditching the class and they agreed. 

Then, one of them suggested to go to an amusement park.

At first, it attracts me so hard that i accepted, but i felt regret about my decision as my condition is getting worse. 

So, i told to my friend that i don't think i'll be able to tag along. 

  Reference: http://dcssrv1.oit.uci.edu/~wiedeman/cspace/me/infoluv.html

  The CIE, which stands for Commision Internationale de I'Eclairage (or International Commission on Illumination), in 1931, defined three standard primaries (X, Y, and Z) to replace red, green, and blue. It is inevitable since all visible colors could not be specified with only positive of red, green , and blue components. With this newly created X,Y, and Z primaries, all visible colors can be specified with only positive values of the primaries. Following picture shows the cone of visible colors, as well as the plane X+Y+Z=1. 

  Lowercase x,y, and z refer to the normalized X,Y and Z such that x = X/(X+Y+Z), y = Y/(X+Y+Z), and z = Z/(X+Y+Z). These lowercase letters are called chromaticity value. and a projection onto the (X,Y) plane of the (X+Y+Z = 1) plane of the above figure is called chromaticity diagram which is shown to following picture. The numbers along the boundary correspond to wavelength in nanometers.  

 The CIE LUV color space is a perceptually uniform derivation of this standard CIE XYZ space. Perceptually uniform means that two colors that are equally distant in the color space are equally distant perceptually). In this color space, the distance between two colors tells how different the colors are in luminance, chroma, and hue. 

Reference: http://www.futuretimeline.net/blog/computers-internet-blog.htm#.VWKCsU-8PGc

  Researchers at the University of Texas at Austin have developed a centimetre-accurate GPS-based positioning system that could revolutionise geolocation on virtual reality headsets, cellphones and other technologies – making global positioning and orientation far more precise than what is currently available on a mobile device.

  The researchers' new system could allow unmanned aerial vehicles to deliver packages to a specific spot on a consumer's back porch, improve collision avoidance technologies on cars and allow virtual reality (VR) headsets to be used outdoors. This ultra-accurate GPS, coupled with a smartphone camera, could be used to quickly build a globally referenced 3-D map of one's surroundings that would greatly expand the radius of a VR game. Currently, VR does not use GPS, which limits its use to indoors and usually a two- to three-foot radius.

  "Imagine games where, rather than sit in front of a monitor and play, you are in your backyard actually running around with other players," said Todd Humphreys, lead researcher and assistant professor in the Department of Aerospace Engineering and Engineering Mechanics. "To be able to do this type of outdoor, multiplayer virtual reality game, you need highly accurate position and orientation that is tied to a global reference frame."

  Humphreys and his team in the Radionavigation Lab have designed a low-cost system that reduces location errors from the size of a large car to the size of a nickel – a more than 100 times increase in accuracy. Humphreys collaborated on the new technology with Professor Robert W. Heath from the Department of Electrical and Computer Engineering, along with graduate students.

  Centimetre-accurate positioning systems are already used in geology, surveying and mapping – but the survey-grade antennas these systems employ are too large and costly for use in mobile devices. This breakthrough by Humphreys and his team is a powerful and sensitive software-defined GPS receiver that can extract centimetre accuracies from the inexpensive antennas found in mobile devices. Such precise measurements were not previously possible. The researchers anticipate that their software's ability to leverage low-cost antennas will reduce the overall cost of centimetre accuracy and make it economically feasible for mobile devices.

Climactic describes the high point, the most intense part of a movie, play, song, or, well, anything. Climatic refers to the climate, like the climatic changes that turned Santa's workshop into a sauna for elves.

Climactic comes from climax, with the x changed to a ct. A climax is the top point of something, so something climactic describes that intense moment. Climactic isoften used in the negative, anticlimactic, like when there's a build-up to something that falls short. But let's be positive:

But I have seldom seen this climactic moment staged to such haunting effect. (New York Times)

Still, a viewer would have to be made of stone not to get worked up during the stirring climactic section. (Reuters)

The skies were shattered with a final climactic crash of thunder. (Virginia Brooks)

Take out the extra "c," and climatic refers to weather conditions. The word climatic gets thrown around a lot these days, with all the talk about global warming, like the climaticinsanity that made it snow on Halloween. FYI: There's no such word as  "anticlimatic." Also, climatic changes come from the climate, not the soil:

Warmer and wetter have become cooler and drier, a wholesale change ofclimatic fortunes. (Bakersfield Now)

This will trigger unpredictable climatic disasters in many parts of the world. (Huffington Post)

Lee eased Louisiana's worst drought since 1902, according to the National Climatic Data Center. (Business Week)

To keep climatic and climactic straight, remember that a climactic moment could be the most intense act yet. Something climactic gives you a kick! But climatic is just air.

Reference: http://www.futuretimeline.net/blog/2015/03/28.htm#.VWHehU_tlBc

An innovative new process architecture can extend Moore's Law for flash storage - bringing significant improvements in density while lowering the cost of NAND flash.

  Intel Corporation – in partnership with Micron – have announced the availability of 3D NAND, the world's highest-density flash memory. Flash is the storage technology used inside the lightest laptops, fastest data centres, and nearly every cellphone, tablet and mobile device.

  3D NAND works by stacking the components in vertical layers with extraordinary precision to create devices with three times higher data capacity than competing NAND technologies. This enables more storage in a smaller space, bringing significant cost savings, low power usage and higher performance to a range of mobile consumer devices, as well as the most demanding enterprise deployments.

  As data cells begin to approach the size of individual atoms, traditional "planar" NAND is nearing its practical scaling limits. This poses a major challenge for the memory industry. 3D NAND is poised to make a dramatic impact by keeping flash storage aligned with Moore's Law, the exponential trend of performance gains and cost savings, driving more widespread use of flash storage in the future.

  "3D NAND technology has the potential to create fundamental market shifts," said Brian Shirley, vice president of Memory Technology and Solutions at Micron Technology. "The depth of the impact that flash has had to date – from smartphones to flash-optimised supercomputing – is really just scratching the surface of what's possible."

  One of the most significant aspects of this breakthrough is in the foundational memory cell itself. Intel and Micron used a floating gate cell, a universally utilised design refined through years of high-volume planar flash manufacturing. This is the first use of a floating gate cell in 3D NAND, which was a key design choice to enable greater performance, quality and reliability.

  The data cells are stacked vertically in 32 layers to achieve 256Gb multilevel cell (MLC) and 384Gb triple-level cell (TLC) dies within a standard package. This can enable gum stick-sized SSDs with 3.5TB of storage and standard 2.5-inch SSDs with greater than 10TB. Because capacity is achieved by stacking cells vertically, individual cell dimensions can be considerably larger. This is expected to increase both performance and endurance and make even the TLC designs well-suited for data centre storage.

 Key product features of this 3D NAND design include:

• Large Capacities – Triple the capacity of existing technology, up to 48GB of NAND per die, enabling 750GB to fit in a single fingertip-sized package.

• Reduced Cost per GB – First-generation 3D NAND is architected to achieve better cost efficiencies than planar NAND.

• Fast – High read/write bandwidth, I/O speeds and random read performance.

• Green – New sleep modes enable low-power use by cutting power to inactive NAND die (even when other dies in the same package are active), dropping power consumption significantly in standby mode.

• Smart – Innovative new features improve latency and increase endurance over previous generations, and also make system integration easier.

  The 256Gb MLC version of 3D NAND is sampling with select partners today, and the 384Gb TLC design will be sampling later this spring. The fab production line has already begun initial runs, and both devices will be in full production by the fourth quarter of this year. Both companies are also developing individual lines of SSD solutions based on 3D NAND technology and expect those products to be available within the next year.

-----------------------------------------------------------------------------------------------------------------------------------------------

What is NAND flash memory?

  NAND flash memory is a type of non-volatile storage technology that does not require power to retain data. An important goal of NAND flash development has been to reduce the cost per bit and increase maximum chip capacity so that flash memory can compete with magnetic storage devices like hard disks. NAND flash has found a market in devices to which large files are frequently uploaded and replaced. MP3 players, digital cameras and USB drives use NAND flash.

  NAND has a finite number of write cycles. NAND failure is usually gradual as individual cells fail and overall performance degrades, a concept known as wear-out. To help compensate, some vendors overprovision their systems by including more memory than is actually claimed. 

  When a NAND card wears out, the user simply buys a new one, and the device continues to function. By passing the expense of additional storage on to the consumer, manufacturers have been able to lower the price of consumer electronic devices significantly. New developments in NAND flash memory technology are making the chips smaller, increasing the maximum read-write cycles and lowering voltage demands. 

Reference: http://en.wikipedia.org/wiki/K-means_clustering 

  k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. It partitions n observations into k clusters in which each observation belongs to the cluster with the nearest mean. In terms of finding nearest mean, it uses squared Euclidean distance. Mathematically, this means partitioning the observations according to the Voronoi diagram generated by the means. 

 k-means clustering is NP-hard problem; however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. It is common that uses an iterative refinement. Following figure shows a brief description of the standard k clustering algorithm. 

As we mentioned above, The most common algorithm uses an iterative refinement technique. Given an initial set of k means m[1], ... , m[k], the algorithm proceeds by  alternating between two steps: assignment step and update step. 

Assignment step: Assign each observation to the cluster whose mean yields the least within-cluster sum of squares. Since the sum of squares is the squared Euclidean distance, this is intuitively the "nearest" mean. 

Update step: Calculate the new means to be the centroids of the observations in the new cluster. 

Initialization

  Forgy and Random Parition method usually uses for initialization. The Forgy method randomly choose k observations from the data set and uses these as the initial means. The Random Partition method first randomly assigns a cluster to each observation and then proceeds to calculate the new means to be the centroids of the observations in the new clusters. 

  The Forgy method tends to spread the initial means out, while Random Partition places all of them close to the center of the data set. In the standard k-means algorithms, the Forgy method is preferable as a initialization technique. 

Convergence

 As it is a heuristic algorithm, there is no guarantee that it will converge to the global optimum, and the result may depend on the initial cluster. As the algorithm is usually very fast, it is common to run it multiple times with different starting conditions. However, in the worst case, k-means can be very slow to converge: in particular it has been shown that there exist certain point sets, even in 2 dimensions, on which k-means takes exponential time to converge. 

1. Summary

This paper proposes a method for on-the-fly non-periodic infinite texturing of surfaces based on a single image. 

- Pattern repetition is avoided by defining patches within each texture whose content can be changed at run-time.

- Multi-scale is managed by using one input image per represented scale.  

- Undersampling artifacts are avoided by accounting for fine-scale features while colors are transferred between scales. 

- It allows for relief-enhanced rendering and provides a tool for intuitive creation of height maps. This is done using an adhoc local descriptor that measures feature self-similarity in order to propagate height values provided by the use for a few selected texels only. 

It is easy to implement on GPU because it is patch-based system.

2. Features

- Real-time non-periodic infinite surface texturing is achieved by modifying texture tiles at runtime, yet neither introducing visual artifacts nor requiring heavy computations. The memory consumption is also reduced compared to state-of-the-art tiling. 

- Multi-scale texturing consistently blends colors between multiple layers of texture: the color transfer mechanism avoids popping and ghosting artifacts by respecting the features of each scale.

- Relief enhancement is integrated as a height map texture, which is kept consistent with the color texture. 

3. Overview

 In this section, The process for mono-scale texturing will be described. 

3.1 Tile clustering and content selection

 As an input, it is given a set of tiles which are processed separately before the rendering stage. To break the repetition effect, the content of input will be randomly modified on-the-fly. Therefore, A tile partitioning is  pre-computed composed of patches with a set of interchangeable contents. All contents for a given patch have the same shape, but correspond to different locations in the tile.  At rendering, each time a tile is repeated the content of its patches is randomly selected. 

3.2 Fragmentation for multi-scale extension

 For rendering, the colors of two consecutive layers are blended according to the viewing distance. It requires colors to be transferred between scales. To minimize visual inconsistency such as popping, ghosting and blocky artifacts, the color is transferred on pre-computed fragments not on individual texels. Fragments are merely small pieces of textures, whose colors can be modified without introducing seams.  

3.3 Color conversion

 Fragments capture small features with a homogeneous color. which is decisive for multi-scale color transfer. In contrast, patches must encompass several texture features in order to break the repetition while keeping visual feature arrangements. Though fragment and patches have different properties, they have to be correlated each other, because content exchange which is performed per patch and color transfer which is performed per fragment will be combined at rendering. In addition, they share a common objective: hiding seams. It is achieved by exploiting fact that seams are less perceivable in regions of high contrast: fragments are computed such that their borders match high contrasts. 

A dedicated color space, that we call principal variation color space, is designed to meet several objective: to facilitate the building of fragments, and the measure of contrasts and seams. It allows to represent colors compactly and improve multi-scale anti-aliasing. It separates a few dominant colors from local variations for each texture tile. It is used throughout the process, and colors are converted back only at rendering. 

3.4 Relief enhancement

 If a height map is provided for the relief enhancement, it must be considered for the design of patches because content exchange may introduce geometric seams. Since patches are based on fragments which follow dominant colors, we introduce relief at the very beginning, as an additional coordinate to color channels.