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%PDF-1.7 4 0 obj /BitsPerComponent 8 /ColorSpace /DeviceRGB /Filter /DCTDecode /Height 100 /Length 3067 /Subtype /Image /Type /XObject /Width 1275 stream

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But one thing that’s new in Version 12.0 is that we’re now able to use self-normalizing networks automatically in Classify and Predict, so they can easily take advantage of neural nets when it makes sense.Computing with ImagesWe introduced ImageIdentify, for identifying what an image is of, back in Version 10.1. In Version 12.0 we’ve managed to generalize this, to figure out not only what an image is of, but also what’s in an image. So, for example, ImageCases will show us cases of known kinds of objects in an image: &#10005ImageCases[CloudGet[" more details, ImageContents gives a dataset about what’s in an image: ✕ImageContents[CloudGet[" can tell ImageCases to look for a particular kind of thing: &#10005ImageCases[CloudGet[" "zebra"]And you can also just test to see whether an image contains a particular kind of thing: &#10005ImageContainsQ[CloudGet[" "zebra"]In a sense, ImageCases is like a generalized version of FindFaces, for finding human faces in an image. Something new in Version 12.0 is that FindFaces and FacialFeatures have become more efficient and robust—with FindFaces now based on neural networks rather than classical image processing, and the network for FacialFeatures now being 10 MB rather than 500 MB: &#10005FacialFeatures[CloudGet[" // DatasetFunctions like ImageCases represent “new-style” image processing, of a type that didn’t seem conceivable only a few years ago. But while such functions let one do all sorts of new things, there’s still lots of value in more classical techniques. We’ve had fairly complete classical image processing in the Wolfram Language for a long time, but we continue to make incremental enhancements.An example in Version 12.0 is the ImagePyramid framework, for doing multiscale image processing: ✕ImagePyramid[CloudGet[" are several new functions in Version 12.0 concerned with color computation. A key idea is ColorsNear, which represents a neighborhood in perceptual color space, here around the color Pink: &#10005ChromaticityPlot3D[ColorsNear[Pink,.2]]The notion of color neighborhoods can be used, for example, in the new ImageRecolor function: ✕ImageRecolor[CloudGet[" ColorsNear[RGBColor[ Rational[1186, 1275], Rational[871, 1275], Rational[1016, 1275]], .02] -> Orange]Speech Recognition & More with AudioAs I sit at my computer writing this, I’ll say something to my computer, and capture it:Here’s a spectrogram of the audio I captured: So far we could do this in Version 11.3 (though Spectrogram got 10 times faster in 12.0). But now here’s something new: &#10005SpeechRecognize[%%]We’re doing speech-to-text! We’re using state-of-the-art %PDF-1.7 4 0 obj /BitsPerComponent 8 /ColorSpace /DeviceRGB /Filter /DCTDecode /Height 100 /Length 3067 /Subtype /Image /Type /XObject /Width 1275 stream 1)aadA10 (0)0 (0)0 (0)0 (0)0 (0)0 (0)0 (0)aadA99.3 (8)27.0 (7)0 (0)0 (0)12.5 (1)0 (0)0 (0)aadA118.1 (7)0 (0)33.3 (7)0 (0)0 (0)0 (0)0 (0)aacC0 (0)0 (0)0 (0)0 (0)0 (0)0 (0)0 (0)strA-strB1.2 (1)3.8 (1)0 (0)0 (0)0 (0)0 (0)0 (0)aph(3’)-IIIa5.8 (5)0 (0)19.0 (4)0 (0)12.5 (1)0 (0)0 (0)aac(6’)-aph(2’’)0 (0)0 (0)0 (0)0 (0)0 (0)0 (0)0 (0) Table 4. Primers used in this study. Table 4. Primers used in this study. Amplification TargetPrimers Sequence (5’-3’)Annealing Temperature (°C)PCR Product size (bp)ReferenceClass 1 intIF: CCTCCCGCACGATGATC57280[7]R: TCCACGCATCGTCAGGCClass 2 intIF: TTATTGCTGGGATTAGGC50233[7]R: ACGGCTACCCTCTGTTATCClass 1 gene cassetteF: GGCATCCAAGCAGCAAG58unpredictable[15]R: AAGCAGACTTGACCTGAaadA1F: CGGTGACCGTAAGGCTTGAT52193This studyR: ATGTCATTGCGCTGCCATTCaadA9F: ACGCCGACCTTGCAATTCT52373This studyR: TAGCCAATGAACGCCGAAGTaadA11F: CGTGCATTTGTACGGCTCTG53352This studyR: ACCTGCCAATGCAAGGCTATaacCF: TTGCTGCCTTCGACCAAGAA53256This studyR: TCCCGTATGCCCAACTTTGTstrA-strBF: TATCTGCGATTGGACCCTCTG60538[46]R: CATTGCTCATCATTTGATCGGCTaph(3’)-IIIaF: GGCTAAAATGAGAATATCACCGG55523[59]R: CTTTAAAAAATCATACAGCTCGCGaac(6’)-aph(2’’)F: CCAAGAGCAATAAGGGCATA48220[60]R: CACTATCATAACCACTACCG © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( Share and Cite MDPI and ACS Style Kwiecień, E.; Stefańska, I.; Chrobak-Chmiel, D.; Sałamaszyńska-Guz, A.; Rzewuska, M. New Determinants of Aminoglycoside Resistance and Their Association with the Class 1 Integron Gene Cassettes in Trueperella pyogenes. Int. J. Mol. Sci. 2020, 21, 4230. AMA Style Kwiecień E, Stefańska I, Chrobak-Chmiel D, Sałamaszyńska-Guz A, Rzewuska M. New Determinants of Aminoglycoside Resistance and Their Association with the Class 1 Integron Gene Cassettes in Trueperella pyogenes. International Journal of Molecular Sciences. 2020; 21(12):4230. Chicago/Turabian Style Kwiecień, Ewelina, Ilona Stefańska, Dorota Chrobak-Chmiel, Agnieszka Sałamaszyńska-Guz, and Magdalena Rzewuska. 2020. "New Determinants of Aminoglycoside Resistance and Their Association with the Class 1 Integron Gene Cassettes in Trueperella pyogenes" International Journal of Molecular Sciences 21, no. 12: 4230. APA Style Kwiecień, E., Stefańska, I., Chrobak-Chmiel, D., Sałamaszyńska-Guz, A., & Rzewuska, M. (2020). New Determinants of Aminoglycoside Resistance and Their Association with the Class 1 Integron Gene Cassettes in Trueperella pyogenes. International Journal of Molecular Sciences, 21(12), 4230. Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here. Article Metrics

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User1637

{28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\) -> 5, \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/6cx+OTNyMjIkIdN6n0UEwjwP8UiF8gEAcYvMeUEoHJMN7HK8WmKAeVSMeVShXvW/z8uxMTkgCn36xOIlMcqBwZLuHDLNQPt68cutYsXuzuB4Ls7UIr3LrrwgubmSf/TgVJcO9BkrjVwMjGxybMC5WaiSd1XhYUJk+FjNLlOuBSTxg1UqZVcCDkmlYMHD9ZvhsvNYkIHFhhyVnlKGHLrQT5myrz08v/9Nk0gi8XiAMLCKUCB8J9g5uM5c+bMxxowAwoAzGhtzQ=="], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\) -> 2, \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x8u4FBT01YEb11TJQPLIRiviYmJxaoTAorYWZiYmDj+wOR+bmNnQgGO25FMfVuS5YqQ4tqHZum3Z2DgzsQk+QK7sxZyM9luwOFkDyamJTiklvExeb/HLnWKn4n/MHapN15M/CtwmBjGxDQdh9RqASad19ilDvMz8S3CLvXBl4kpHIeJAcBgfIVdagsfE9Np7FIHeJiYrHBom8bEZITDjSC5KBxS/88ElbzBJYcNAAB0/LWr"], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\) -> 7}, "Perplexity"]Neural nets aren’t the only—or even always the best—way to do machine learning. But one thing that’s new in Version 12.0 is that we’re now able to use self-normalizing networks automatically in Classify and Predict, so they can easily take advantage of neural nets when it makes sense.Computing with ImagesWe introduced ImageIdentify, for identifying what an image is of, back in Version 10.1. In Version 12.0 we’ve managed to generalize this, to figure out not only what an image is of, but also what’s in an image. So, for example, ImageCases will show us cases of known kinds of objects in an image: &#10005ImageCases[CloudGet[" more details, ImageContents gives a dataset about what’s in an image: ✕ImageContents[CloudGet[" can tell ImageCases to look for a particular kind of thing: &#10005ImageCases[CloudGet[" "zebra"]And you can also just test to see whether an image contains a particular kind of thing: &#10005ImageContainsQ[CloudGet[" "zebra"]In a sense, ImageCases is like a generalized version of FindFaces, for finding human faces in an image. Something new in Version 12.0 is that FindFaces and FacialFeatures have become more efficient and robust—with FindFaces now based on neural networks rather than classical image processing, and the network for FacialFeatures now being 10 MB rather than 500 MB: &#10005FacialFeatures[CloudGet[" // DatasetFunctions like ImageCases represent “new-style” image processing, of a type that didn’t seem conceivable only a few years ago. But while such functions let one do all sorts of new things, there’s still lots of value in more classical techniques. We’ve had fairly complete classical image processing in the Wolfram Language for a long time, but we continue to make incremental enhancements.An example in Version 12.0 is the ImagePyramid framework, for doing multiscale image processing: ✕ImagePyramid[CloudGet[" are several new functions in Version 12.0 concerned with color computation. A key idea is ColorsNear, which represents a neighborhood in perceptual color space, here around the color Pink: &#10005ChromaticityPlot3D[ColorsNear[Pink,.2]]The notion of color neighborhoods can be used, for example, in the new ImageRecolor function: ✕ImageRecolor[CloudGet[" ColorsNear[RGBColor[ Rational[1186, 1275], Rational[871, 1275], Rational[1016, 1275]], .02] -> Orange]Speech Recognition & More with AudioAs I sit at my computer writing this, I’ll say something to my computer, and capture it:Here’s a spectrogram of the audio I captured: So far we could do this in Version 11.3 (though Spectrogram got 10 times faster in 12.0). But now here’s something new: &#10005SpeechRecognize[%%]We’re doing speech-to-text! We’re using state-of-the-art

2025-04-08
User8081

1)aadA10 (0)0 (0)0 (0)0 (0)0 (0)0 (0)0 (0)aadA99.3 (8)27.0 (7)0 (0)0 (0)12.5 (1)0 (0)0 (0)aadA118.1 (7)0 (0)33.3 (7)0 (0)0 (0)0 (0)0 (0)aacC0 (0)0 (0)0 (0)0 (0)0 (0)0 (0)0 (0)strA-strB1.2 (1)3.8 (1)0 (0)0 (0)0 (0)0 (0)0 (0)aph(3’)-IIIa5.8 (5)0 (0)19.0 (4)0 (0)12.5 (1)0 (0)0 (0)aac(6’)-aph(2’’)0 (0)0 (0)0 (0)0 (0)0 (0)0 (0)0 (0) Table 4. Primers used in this study. Table 4. Primers used in this study. Amplification TargetPrimers Sequence (5’-3’)Annealing Temperature (°C)PCR Product size (bp)ReferenceClass 1 intIF: CCTCCCGCACGATGATC57280[7]R: TCCACGCATCGTCAGGCClass 2 intIF: TTATTGCTGGGATTAGGC50233[7]R: ACGGCTACCCTCTGTTATCClass 1 gene cassetteF: GGCATCCAAGCAGCAAG58unpredictable[15]R: AAGCAGACTTGACCTGAaadA1F: CGGTGACCGTAAGGCTTGAT52193This studyR: ATGTCATTGCGCTGCCATTCaadA9F: ACGCCGACCTTGCAATTCT52373This studyR: TAGCCAATGAACGCCGAAGTaadA11F: CGTGCATTTGTACGGCTCTG53352This studyR: ACCTGCCAATGCAAGGCTATaacCF: TTGCTGCCTTCGACCAAGAA53256This studyR: TCCCGTATGCCCAACTTTGTstrA-strBF: TATCTGCGATTGGACCCTCTG60538[46]R: CATTGCTCATCATTTGATCGGCTaph(3’)-IIIaF: GGCTAAAATGAGAATATCACCGG55523[59]R: CTTTAAAAAATCATACAGCTCGCGaac(6’)-aph(2’’)F: CCAAGAGCAATAAGGGCATA48220[60]R: CACTATCATAACCACTACCG © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( Share and Cite MDPI and ACS Style Kwiecień, E.; Stefańska, I.; Chrobak-Chmiel, D.; Sałamaszyńska-Guz, A.; Rzewuska, M. New Determinants of Aminoglycoside Resistance and Their Association with the Class 1 Integron Gene Cassettes in Trueperella pyogenes. Int. J. Mol. Sci. 2020, 21, 4230. AMA Style Kwiecień E, Stefańska I, Chrobak-Chmiel D, Sałamaszyńska-Guz A, Rzewuska M. New Determinants of Aminoglycoside Resistance and Their Association with the Class 1 Integron Gene Cassettes in Trueperella pyogenes. International Journal of Molecular Sciences. 2020; 21(12):4230. Chicago/Turabian Style Kwiecień, Ewelina, Ilona Stefańska, Dorota Chrobak-Chmiel, Agnieszka Sałamaszyńska-Guz, and Magdalena Rzewuska. 2020. "New Determinants of Aminoglycoside Resistance and Their Association with the Class 1 Integron Gene Cassettes in Trueperella pyogenes" International Journal of Molecular Sciences 21, no. 12: 4230. APA Style Kwiecień, E., Stefańska, I., Chrobak-Chmiel, D., Sałamaszyńska-Guz, A., & Rzewuska, M. (2020). New Determinants of Aminoglycoside Resistance and Their Association with the Class 1 Integron Gene Cassettes in Trueperella pyogenes. International Journal of Molecular Sciences, 21(12), 4230. Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here. Article Metrics

2025-03-30
User8104

Again.You are really satisfied with the result of driverversion 1.09? What do you all record? NTSC?I continued testing. Also used the official Pinnacle Dazzle software. I tested the MPEG and DV function. I tested S-Video Input and Composite (Yellow) Input.I tested a Windows 10 Version 20H2 and Version 1809.All I can say, the result is not okay. Exactly like every 5 seconds, there are Inserted Frames (as VirtualDub2 noticed).I connected my Gamecube, to have a stable videosource with smooth 25i content.Every frame should be unique and smooth, but you weill notice Inserted Frames at:Frame (Time):507 (0:00:10.140) - 509 (0:00:10.180)763 (0:00:15.260) - 765 (0:00:15.300)1019 (0:00:20.380) - 1021 (0:00:20.420)1275 (0:00:25.500) - 1277 (0:00:25.540)1531 (0:00:30.620) - 1533 (0:00:30:660)1787 (0:00:35.740) - 1789 (0:00:35.780)I deinterlaced the source to have 50 fps progressive. Thats why the Inserted Frame is repeated 3 Frames.So be careful, if you want archive footage with that driverversion...With 1.08 and Windows Version I'm glad you noticed this, cause I didn't until I watched the playback of my video tape very carefully. This is kinda a bummer when it comes to the video quality, but hopefully they can get it fixed soon. I wonder, does the old video driver still work to go along with the update audio driver? Not sure I want to test that theory..... Member Just found this thread - thanks for starting itI had a Dazzle DV100 for importing video from tapes into computer via import and Pinn Studio 19Found that didn't work any good with Win 10, so I spent £50 on Dazzle DVD Recorder HD input device (looks similar to DV100) which comes with a (cut down) version of Studio 23? Also spent money on these smaller usb converters - same results - choppy audio and videoSpent ages thinking it was my VHS player - even took

2025-03-25
User7975

509 (0:00:10.180)763 (0:00:15.260) - 765 (0:00:15.300)1019 (0:00:20.380) - 1021 (0:00:20.420)1275 (0:00:25.500) - 1277 (0:00:25.540)1531 (0:00:30.620) - 1533 (0:00:30:660)1787 (0:00:35.740) - 1789 (0:00:35.780)I deinterlaced the source to have 50 fps progressive. Thats why the Inserted Frame is repeated 3 Frames.So be careful, if you want archive footage with that driverversion...With 1.08 and Windows Version --> Last edited by Morku; 13th Dec 2020 at 15:18. Member Here's my test.Windows 10 20H2 Build 19042.685Source: PAL VHSDMR-ES10 as TBCS-Video inputYadif(2x) deinterlace appliedThe divers seem to have fixed all problems plus some other glitches I experienced before like big input lag and very low audio.I don't see those "inserted frames" for now.EDITI looked more carefully at it and I can see inserted frames every 5/6 seconds, but it's very hard to notice, look at the text crawl . --> Last edited by kernelfree; 14th Dec 2020 at 09:11. Member Yes, you have the same distortion, exactly after every 5 seconds. 3 frame in a row showing the same.Nothing to downplay even if it is hard to notice. Myself, I directly notice. So the result is for trash I also hate when people play 23.976fps content on 59/60Hz.So thank you for confirmation. I still wait for comment of Corel, but now I can tell, that I am not alone. I hope they still take my feedback serious.EDIT:New feedback. So it's ongoing Thank you for contacting Corel Technical Support. I have reported the issue to the engineering team. The bug is with our developers and investigation is in progress. You have provided excellent detail that helped us investigate. As soon as I hear from engineering, I will advise.Please do not hesitate to contact us with any further questions. --> Last edited by Morku; 16th Dec 2020 at 14:41. Member Originally Posted by Morku Guys, let me ask

2025-03-30
User2009

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2025-03-27

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