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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.

Ammonia is an important compound with many uses, such as in the manufacture of fertilizers, explosives and pharmaceuticals. As an archetypal hydrogen-bonded system, the properties of ammonia under pressure are of fundamental interest, and compressed ammonia has a significant role in planetary physics. We predict new high-pressure crystalline phases of ammonia (NH(3)) through a computational search based on first-principles density-functional-theory calculations. Ammonia is known to form hydrogen-bonded solids, but we predict that at higher pressures it will form ammonium amide ionic solids consisting of alternate layers of NH(4)(+) and NH(2)(-) ions. These ionic phases are predicted to be stable over a wide range of pressures readily obtainable in laboratory experiments. The occurrence of ionic phases is rationalized in terms of the relative ease of forming ammonium and amide ions from ammonia molecules, and the volume reduction on doing so. We also predict that the ionic bonding cannot be sustained under extreme compression and that, at pressures beyond the reach of current static-loading experiments, ammonia will return to hydrogen-bonded structures consisting of neutral NH(3) molecules.

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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.

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.

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.

The development of equations-of-state and transport models in areas such as shock compression and fusion energy science is critical to DOE programs. Notable shortcomings in these activities are phase transitions in highly compressed metals. Fully characterizing high energy density phenomena using pulsed power facilities is possible only with complementary numerical modeling for design, diagnostics, and data interpretation.

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

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.

Phosphorus is another element which can be converted into a superconductor by applying high pressure [34]. Wittig and Matthias reported Tc = 4.7 K [34] for this element when subjected to P ~ 10 GPa. Since the superconducting state of this element in a high-pressure condition is still under intensive theoretical and experimental investigation [35,36,37], we here report results of the revealed charge carrier interaction in this highly compressed superconducting element through the analysis of R(T) data.

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.

In 1999, in response to its escalating number of wells requiring abandonment, ChevronTexaco initiated research into the use of compressed sodium bentonite as an alternative to cement for permanent well plugging. The objective of this research was to identify a process to reduce plugging costs by at least 30% to encourage the expeditious abandonment of the growing back-log of wells. A subsidiary company, Benterra Corporation, was established to manage ChevronTexaco's research and subsequent implementation of any proposed new processes. Following pilot studies in California, Benterra has so far abandoned over 500 wells across the USA using highly compressed sodium bentonite, marketed as "ZoniteTM".

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? 006ab0faaa