You may have seen many helmets, spaceships, Scifi, robot, ship, and hand models. A very high-quality 3D Printable model for corpus Jesus is presented in the last minutes is presented. The concept of Christ in Christianity originated from the concept of messiah in Judaism. The model is created and rendered in ZBrush. This model is fully ripped to be used in any 3d Printer to get a perfect result. The following formats are included in the packages:
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corpus 3D.rar
Cerebral cortical regions of interest (ROI) and thalamus were manually defined according to Paxinos50 and the Allen Mouse Brain Atlas51 ( -map.org/static/atlas) by D.A. and K.P. without prior knowledge of the experimental groups. Image preprocessing was done with Advanced Normalization Tools (ANTs, v.2.2.0.0.dev297-gf23cb). The reconstruction of axonal pathways was performed with MRtrix361 software (v.3.0.0-65-g91788533) using constrained spherical deconvolution62 and probabilistic tracking (iFOD2) with a FOD amplitude cut-off of 0.1. The thalamus was used as a seeding point and each cortical ROI was used as a termination mask. To evaluate the integrity of the major white matter tracts between the groups, both internal capsules, anterior commissure and corpus callosum were manually delineated according to Paxinos50 and the Allen Mouse Brain Atlas51 ( -map.org/static/atlas) by D.A. and K.P. without prior knowledge of the experimental groups. Values of the fractional anisotropy (FA), apparent diffusion coefficient (ADC), radial (RD) and axial (AD) diffusivity were calculated using underlying scalar maps derived by MRtrix3.
Abstract:Real-word errors are characterized by being actual terms in the dictionary. By providing context, real-word errors are detected. Traditional methods to detect and correct such errors are mostly based on counting the frequency of short word sequences in a corpus. Then, the probability of a word being a real-word error is computed. On the other hand, state-of-the-art approaches make use of deep learning models to learn context by extracting semantic features from text. In this work, a deep learning model were implemented for correcting real-word errors in clinical text. Specifically, a Seq2seq Neural Machine Translation Model mapped erroneous sentences to correct them. For that, different types of error were generated in correct sentences by using rules. Different Seq2seq models were trained and evaluated on two corpora: the Wikicorpus and a collection of three clinical datasets. The medicine corpus was much smaller than the Wikicorpus due to privacy issues when dealing with patient information. Moreover, GloVe and Word2Vec pretrained word embeddings were used to study their performance. Despite the medicine corpus being much smaller than the Wikicorpus, Seq2seq models trained on the medicine corpus performed better than those models trained on the Wikicorpus. Nevertheless, a larger amount of clinical text is required to improve the results.Keywords: error correction; real-word error; seq2seq neural machine translation model; clinical texts; word embeddings; natural language processing
The Calgary corpus isthe oldest compression benchmark still in use. It was created in 1987 and describedin a survey of text compression models in 1989 (Bell, Witten and Cleary, 1989).It consists of 14 files with a total size of 3,141,622 bytes as follows:
The struture of the corpus is shown in the diagram below.Each pixel represents a match between consecutive occurrences of a string.The color of the pixel represents the length ofthe match: black for 1 byte, red for 2, green for 4 and blue for 8. The horizontalaxis represents the position of the second occurrence of the string. The verticalaxis represents the distance back to the match on a logarithmic scale. (The imagewas generated by the fv program with labels added by hand).
Early tests sometimes used an 18 file version of the corpus that included 4 additionalpapers (PAPER3 through PAPER6). Programs were often ranked by measuring bits percharacter (bpc) on each file separately and reporting them individually or takingthe average. Simply adding the compressed sizes is called a "weighted average" sinceit is weighted toward the larger files.
The Calgary corpus is no longer widely used due to its small size. However, it hasbeen used since 1996 in an ongoing compressionchallenge run by Leonid A. Broukhis with small cash prizes. The best compressionratios established as of Feb. 2010 are as follows.
The rules of the Calgary challenge specify that the compressed size include thesize of the decompression program, either as a Windows or Linux executable fileor as source code. This is to avoid programs that cheat by hiding information fromthe corpus in the decompression program. Furthermore, the program and compressedfiles must either be packed in an archive (in one of several specified formats),or else 4 bytes plus the length of each file name is added. This is to prevent cheatingby hiding information in the file names and sizes. Without such precautions, programslike barf could claim to compress to zero bytes.
Fixed order models compress better using longer contexts up to a point (order 3for the Calgary corpus). Beyond that, compression gets worse because many higherorder contexts are being seen for the first time and no prediction can be made.One solution is to collect statistics for different orders at the same time andthen use the longest matching context for which we know something. DMC does thisfor bit level predictions, and PPM for byte level predictions.
Shown below are compressed sizes of the Calgary corpus as a tar file and separatefiles. Compression and decompression times are the same. Option -o16 means use maximumorder 16. -m256 says use 256 MB memory. -r1 says to prune the context tree ratherthan discard it.
CTW is best suited for stationary sources. The published CTW implementation compressesthe Calgary corpus to 767,594 bytes as 14 separate files in 62 seconds with options-n16M -b16M -d12 set for maximum compression. With the same options, it compressescalgary.tar to 965,855 bytes. -d12 sets the maximum context order to 12. -b16M setsthe file buffer to 16 MB. -n16M limits the tree to 16 million nodes. When the treeis full, no new contexts are added but the counts and weights continue to be updated.These settings require 144 MB memory, the maximum that the published implementationcan use.
As of Feb. 2010, development remains active on the PAQ8 series. There have beenhundreds of versions with improvements and additional models. The latest is PAQ8PX_V67.Most of the improvements have been for file types not included in the Calgary corpussuch as x86, JPEG, BMP, TIFF, and WAV.
A benchmark for the Calgary corpus is given below for versions of PAQ from 2000to Jan. 2010 showing major code changes. About 150 intermediate versions with minorimprovements have been omitted. Older programs marked with * were benchmarked onslower machines such as a 750 MHz Duron and have been adjusted to show projectedtimes on a 2.0 GHz T3200, assumed to be 5.21 times faster. Sizes marked with a Duse an external English dictionary that must be present during decompression. Thesize shown does not include the dictionary, so it is artificially low. However,including it would give a size artificially high because the dictionary is not extractedfrom the test data. All versions of PAQ are archivers that compress in solid mode,exploiting similarities between files. Decompression time is about the same as compressiontime.
Component 14 is a model for CCITT binary fax images (PIC in the Calgary corpus).The image width is 1728 pixels or 216 bytes, mapped one bit per pixel in MSB toLSB order (0=white, 1=black). The context is the 8 bits from the previous scan lineand 2 additional bits from the second to last scan line.
Windows indicates that a compressed folder containing the Calgary corpus occupies1,916,928 bytes. On the large text benchmark, the 1 GB text file enwik9 compressesto 636 MB, slightly larger than an order 0 coder and about twice the size of zip.Copying enwik9 between 2 uncompressed folders takes 41 seconds on the test machine(a laptop with a 2.0 GHz T3200). Copying from a compressed folder to an uncompressedfolder takes 35 seconds, i.e. decompression is faster than copying. Copying froman uncompressed folder to a compressed folder takes 51 seconds. This is equivalentto compressing the Calgary corpus in 0.03 seconds over the time to copy it. 2ff7e9595c