This study investigated the perceptual adjustments that occur when listeners recognize highly compressed speech. In Experiment 1, adjustment was examined as a function of the amount of exposure to compressed speech by use of 2 different speakers and compression rates. The results demonstrated that adjustment takes place over a number of sentences, depending on the compression rate. Lower compression rates required less experience before full adjustment occurred. In Experiment 2, the impact of an abrupt change in talker characteristics was investigated; in Experiment 3, the impact of an abrupt change in compression rate was studied. The results of these 2 experiments indicated that sudden changes in talker characteristics or compression rate had little impact on the adjustment process. The findings are discussed with respect to the level of speech processing at which such adjustment might occur.

GML is a good example of a format that supports this kind of relational data model, though being a verbose format file sizes will be large. You can compress GML using gzip compression and can potentially get a 20:1 ratio but then you are relying on the software being able to support compressed GML.

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You are also confusing data storage with data representation. Your 4th point mentions being able to view the data at different scales, but this is a function of your renderer, not the format per se. Again, a hypothetical lossily compressed file could store data at various resolutions in a sort of LoD structure, but that is likely to increase data size if anything.

Results:  pymzML performs at par with established C programs when it comes to processing times. However, it offers the versatility of a scripting language, while adding unprecedented fast random access to compressed files. Additionally, we designed our compression scheme in such a general way that it can be applied to any field where fast random access to large data blocks in compressed files is desired.

Compressed sensing (CS) is a recent mathematical technique that leverages the sparsity in certain sets of data to solve an underdetermined system and recover a full set of data from a sub-Nyquist set of measurements of the data. Given the size and sparsity of the data, radar has been a natural choice to apply compressed sensing to, typically in the fast-time and slow-time domains. Polarimetric synthetic aperture radar (PolSAR) generates a particularly large amount of data for a given scene; however, the data tends to be sparse. Recently a technique was developed to recover a dropped PolSAR channel by leveraging antenna crosstalk information and using compressed sensing. In this dissertation, we build upon the initial concept of the dropped-channel PolSAR CS in three ways. First, we determine a metric which relates the measurement matrix to the l2 recovery error. The new metric is necessary given the deterministic nature of the measurement matrix. We then determine a range of antenna crosstalk required to recover a dropped PolSAR channel. Second, we propose a new antenna design that incorporates the relatively high levels of crosstalk required by a dropped-channel PolSAR system. Finally, we integrate fast- and slow-time compression schemes into the dropped-channel model in order to leverage sparsity in additional PolSAR domains and overall increase the compression ratio. The completion of these research tasks has allowed a more accurate description of a PolSAR system that compresses in fast-time, slow-time, and polarization; termed herein as highly compressed PolSAR. The description of a highly compressed PolSAR system is a big step towards the development of prototype hardware in the future.

The single-pixel imaging technique uses multiple patterns to modulate the entire scene and then reconstructs a two-dimensional (2-D) image from the single-pixel measurements. Inspired by the statistical redundancy of natural images that distinct regions of an image contain similar information, we report a highly compressed single-pixel imaging technique with a decreased sampling ratio. This technique superimposes an occluded mask onto modulation patterns, realizing that only the unmasked region of the scene is modulated and acquired. In this way, we can effectively decrease 75% modulation patterns experimentally. To reconstruct the entire image, we designed a highly sparse input and extrapolation network consisting of two modules: the first module reconstructs the unmasked region from one-dimensional (1-D) measurements, and the second module recovers the entire scene image by extrapolation from the neighboring unmasked region. Simulation and experimental results validate that sampling 25% of the region is enough to reconstruct the whole scene. Our technique exhibits significant improvements in peak signal-to-noise ratio (PSNR) of 1.5 dB and structural similarity index measure (SSIM) of 0.2 when compared with conventional methods at the same sampling ratios. The proposed technique can be widely applied in various resource-limited platforms and occluded scene imaging.

This columnar compression engine is based on hypertables, which automatically partition your PostgreSQL tables by time. At the user level, you would simply indicate which partitions (chunks in Timescale terminology) are ready to be compressed by defining a compression policy.

In TimescaleDB 2.3, we started to improve the flexibility of this high-performing columnar compression engine by allowing INSERTS directly into compressed data. The way we did this at first was by doing the following:

With this approach, when new rows were inserted into a previously compressed chunk, they were immediately compressed row-by-row and stored in the internal chunk. The new data compressed as individual rows was periodically merged with existing compressed data and recompressed. This batched, asynchronous recompression was handled automatically within TimescaleDB's job scheduling framework, ensuring that the compression policy continued to run efficiently.

The newly introduced ability to make changes to data that is compressed breaks the traditional trade-off of having to plan your compression strategy around your data lifecycle. You can now change already-compressed data without largely impacting data ingestion, database designers no longer need to consider updates and deletes when creating a data model, and the data is now directly accessible to application developers without post-processing.

However, with the advanced capabilities of TimescaleDB 2.11, backfilling becomes a straightforward process. The company can simulate or estimate the data for the new parameters for the preceding months and seamlessly insert this data into the already compressed historical dataset.

The evolution of the DOS of compressed As in the strongly stable bcc phase at 300, 400, 800, 1000, 1400, 1600, and 2000 GPa with the same MT sphere of 1.77 bohrs. Panels show the pressure dependence of the (a) total DOS, (b) s states, (c) p states, (d) d states, (e) eg states, and (f) t2g states.

$\mathbf{Q:}$ While teaching "Real Gases", my professor remarked last day that "Liquid phase is a highly compressed gaseous phase." But he did not explain the reason behind it and left it as food for our thought.

Now I can see from the graph that a certain finite amount of pressure needs to be applied in order that we can change the gaseous state from vapor to liquid. Ideal gases have considerable or high compressibility while ideal liquids are almost incompressible. But still can I call this "highly compressed"? So how do I prove the statement made by my professor?

When I create PNG files with very small disk size, I tend to wonder if the file size becomes less important than the time viewers would need to decompress the image. Technically that would be trivial too, but I've wondered about it for a long time. We all know that more-compressed PNG images take longer to compress, but do they take longer to decompress?

I then wrote a script to convert the image from png to tif (on the assumption that TIF is a relatively uncompressed file format so quite fast) 200 times and timed the output.In each case I ran the script quickly and aborted it after a few seconds so any system caching could come into effect before running the full test, thus reducing the impact of disk io (and my computer happens to use SSD which also minimizes that impact. The results were as follows:

But, this does not take into account the time taken to download the file. This will, of-course, depend on the speed of your connection, the distance to the server and the size of the file. If it takes more then about 0.5 seconds more to transmit the large file then the small file, then (on my system - which is an older ultrabook, so quite slow thus giving a conservative scenatio), it is better to send the more highly compressed file. In this case - this means sending 5.8 megabytes a second, which equates to - very roughly, 60 megabits per second - excluding latency issues.

Conclusion for large files - if you are on a lightly used LAN it is probably quicker to use the less compressed image, but once you hit the wider Internet using the more highly compressed file is better.

We processed reported R(T, Bappl) datasets for several annealed highly compressed hydrides by using Eq. (12) to extract Bc2(T) datasets. The obtained datasets were fitted to Eq. (11), and the deduced values are given in Table I. These materials are as follows:Sulfur superhydride H3S (P = 155 and 160 GPa), for which the raw data were reported by Mozaffari et al.31

Computed tomographic angiography showing aortic dissection at the descending aorta with a highly compressed true lumen (white arrow) and false lumen (FL) resembling a normal aorta. Findings have been detected at the same level as the left parasternal long-axis view (A) and subcostal view (B) by point-of-care ultrasonography.

Computed tomographic angiography showing aortic dissection at the abdominal aorta below the renal artery branch points (A), and the external and internal iliac arteries (B). White arrows show highly compressed true lumen. FL, false lumen. be457b7860

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