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Whole-program analysis is an essential technique that enables advanced compiler

optimizations. An important example of such a method is points-to analysis used

by ahead-of-time (AOT) compilers to discover program elements (classes, methods,

fields) that may be used on at least one program path during the run of the

program and hence need to be compiled. GraalVM Native Image uses a points-to

analysis to optimize Java applications, which is a time-consuming step of the

build. We explore how much the analysis time can be improved by replacing the

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This is a dataset from an investigation of the geometrical magnetoresistance effect as a tool for carrier mobility extraction in InGaAs MOSFETs. 

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This dataset includes all experimental data and codes to produce figures of the paper, B. Kodak and Y. Vardar, FeelPen: A Haptic Stylus Displaying Multimodal Texture Feels on Touchscreens. IEEE/ASME Transactions on Mechatronics. 2023.

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Extra-Vehicular Activity and Intra-Vehicular Activity spacesuits block external sound. This induces sensory deprivation, a side effect is lower cognitive performance. This can increase the risk of an accident. This undesirable effect can be mitigated by designing suits with sound transparency. If the atmosphere is available, as on Mars, sound transparency can be realized by augmenting and processing external sounds. If no atmosphere is available, such as on the Moon, then an Earth-like sound can be re-created via generative AR techniques.

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These files are the data from the oscilloscope, RF spectrum analyzer, and optical spectrum analyzer for the manuscript of "Linearly-Polarized Mode-locked Yb3+-doped Phosphate Fiber Ring-Cavity Laser at 976 nm"

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The dataset contain reviews and rating of four domain i.e. electronics appliance , movie reviews , tripAdvisor reviews , and hindi electronic reviews dataset.

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Data for the alluvial charts and clustering

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This dataset contains the measurement data associated with the manuscript "Optoelectronic Oscillator Based on a Silicon Microring Modulator". 

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AHT2D dataset is composed of Handwritten Arabic letters with diacritics. In this dataset, we have 28 letter classes according to the number of Arabic letters. Each class contains a multiple letter form. We have different letter images from different sources such as the internet, our writers, etc. The AHT2D dataset includes only isolated letters. In addition, this dataset contains different writing styles, orientations, colors, thicknesses, sizes, and backgrounds, which makes it a very large and rich dataset.

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