Nucleus is an infrastructure platform that helps developers automate building, deploying and managing microservices in their cloud environment

Nucleus

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THE TERRIBLE NUCLEUS ARRIVES

Worlds Within Worlds: The Story of Nuclear Energy, Volume 2 (of 3), by Isaac Asimov is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here . Volume II, THE STRUCTURE OF THE NUCLEUS: The Proton THE STRUCTURE OF THE NUCLEUS The Proton As early as 1886 Eugen Goldstein, who was working with cathode rays, also studied rays that moved in the opposite direction. Since the cathode rays (electrons) were negatively charged, rays moving in the opposite direction would have to be positively charged. In 1907 J. J. Thomson called them “positive rays”. Once Rutherford worked out the nuclear structure of the atom, it seemed clear that the positive rays were atomic nuclei from which a number of electrons had been knocked away. These nuclei came in different sizes. Were the nuclei single particles—a different one for every isotope of every element? Or were they all built up out of numbers of still smaller particles of a very limited number of varieties? Might it be that the nuclei owed their positive electrical charge to the fact that they contained particles just like the electron, but ones that carried a positive charge rather than a negative one? All attempts to discover this “positive electron” in the nuclei failed, however. The smallest nucleus found was that produced by knocking the single electron off a hydrogen atom in one way or another. This hydrogen nucleus had a single positive charge, one that was exactly equal in size to the negative charge on the electron. The hydrogen nucleus, however, was much more massive than an electron. The hydrogen nucleus with its single positive charge was approximately 1837 times as massive as the electron with its single negative charge. Was it possible to knock the positive charge loose from the mass of the hydrogen nucleus? Nothing physicists did could manage to do that. In 1914 Rutherford decided the attempt should be given up. He suggested that the hydrogen nucleus, for all its high mass, should be considered the unit of 76positive electrical charge, just as the electron was the unit of negative electrical charge. He called the hydrogen nucleus a “proton” from the Greek word for “first” because it was the nucleus of the first element. One proton balances 1837 electrons. Why the proton should be so much more massive than the electron is still one of the unanswered mysteries of physics. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Isaac Asimov. 2015. Worlds Within Worlds: The Story of Nuclear Energy, Volume 2 (of 3). Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/49820/49820-h/49820-h.htm#c18 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

The Structure of the Nucleus: The Proton 

Comprehensive guide on understanding what is Nucleus Vision, It’s token, purpose and how to buy NCASH token from Binance. An Introduction to Nucleus Vision I think it’s important to understand at least the very basics of what you’re purchasing, so this and all future articles will include a short introduction to get you started. is an end-to-end technology solution that captures and provides previously inaccessible data to retailers and other ‘brick-and-mortar’ businesses through its proprietary blockchain and real-time sensor technology. Nucleus Let’s get started To begin with, we need to buy either Bitcoin or to buy any cryptocurrency on exchanges. So follow the link below to buy Ethereum on and then follow the next steps to buy NCASH on . If you already have Ethereum with you — you can skip this step and go on to registration. Ethereum Coinbase Binance Binance _Ethereum, a revolutionary platform on which more than 70% of coins are built. We explain here how to buy Ethereum…_hackernoon.com Follow this step by step guide to buy Ethereum NCASH can be bought with Bitcoin too — just that it takes more time to transfer to another exchange, I am suggesting to go with Ethereum as it takes only few minutes. Register on Binance is a cryptocurrency exchange with relatively less transaction fees and been faring good so far without any issues and good support response. There are two options for trading platforms on : . I’ll be primarily covering the Basic trading option in this article, but you can check out the Advanced view below. There are not many differences between the two views with the exception of being able to do more detailed technical analysis in the Advanced view. Binance Binance Basic and Advanced Deposit your Ethereum to Binance Once you’ve registered on and logged in, you can see few menu items on top right corner. One of the is “ ” which has a sub menu item named “ ”. This is the place where you can see all your coins(purchased on and received from other addresses). Binance Funds Deposits Withdrawals Binance So in order to send Ethereum which we bought on or to , we need to have an Ethereum address — we can find this on this page. Coinbase Gemini Binance Check out the following screenshots to learn how to deposit Ethereum to from Binance Coinbase Copy the address and send your ether from / to this address. Coinbase Gemini Once you receive Ethereum on , Click on submenu item under on top left corner. Binance Basic Exchange Now we need to find NCASH coin with Ethereum trading pair on list of currencies. Check out following screenshot and instructions beside to do this. Select ETH box on top right corner. This means you are looking for coins which can be traded with Ethereum. Write NCASH in the search box above the coins list and it filters you as shown below. Now you can see NCASH/ETH with current price. You are almost close to invest in NCASH now. Just click on the NCASH/ETH now. Click on NCASH, which brings up the daily chart with candlesticks and orders on left side as shown below. Click on required green price on left side to populate your price for NCASH in ETH (currently showing as 0.00004648 in above screen shot). Once you click, it changes to 0.00004611. Now you need to select how much of your ETH you would like to invest in NCASH. Click on 25|50|75|100% to populate the amount of NCASH you can buy at that price. You are free to choose the price from those green numbers- they are basically different offers by others — this is also called Limit purchase. Since you are beginner, you can also click on tab beside and select the ETH amount and click on — this basically buys at the market price. Market Limit Buy NCASH Once it shows success, you can go to from the top right menu and search for NCASH. Funds -> Deposits & Withdrawals Congrats on purchasing NCASH. “File Your Cryptocurrency Taxes Using !” BearTax _Simple to use platform for crypto traders to import transactions, calculate gains and file taxes._bear.tax BearTax — Your Crypto Tax Assistant Disclaimer: I am using my referral links for respective exchanges. If you use them, it helps my bills and to write more informational articles ahead. Thanks in advance. 🙏 If you like this article and continue getting updates — please follow me on twitter and medium .

Where & How to buy NCASH (Nucleus Vision)

Digital Health: LLMs for Prompt and Quality Sleep?

AI’s Cognitive Mirror: The Illusion of Consciousness in the Digital Age

Authors:
(1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu);
(2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work;
(3) Yixu Chen, Amazon Prime Video;
(4) Yongjun Wu, Amazon Prime Video;
(5) Hai Wei,  Amazon Prime Video;
(6)Sriram Sethuraman, Amazon Prime Video;
(7) Alan C. Bovik, Fellow, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA. Table of Links Abstract and Introduction
Related Work
Details of Subjective Study
Subjective Analysis
Objective Assessment
Conclusion, Acknowledgment and References VI. CONCLUSION We presented the first ever study on comparing HDR and SDR videos of the same content encoded at different bitrates and resolutions on different display devices. Our study shows that despite HDR’s theoretical capabilities over SDR, its perceptual quality depends heavily on the display device used in practice. We also evaluated several NR and FR VQA algorithms on the new database, and presented a novel NR VQA algorithm called HDRPatchMAX that exceeds the current state-of-the-art on this database. We hope that this spurs research on the modelling of display devices in VQA algorithms as well as for optimal bitrate ladders for streaming. ACKNOWLEDGMENT This research was sponsored by a grant from Amazon.com, Inc., and by grant number 2019844 for the National Science Foundation AI Institute for Foundations of Machine Learning (IFML). The authors also thank the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported in this paper. URL: http://www.tacc.utexas.edu. REFERENCES [1] ITU, “BT.709 : Parameter values for the HDTV standards for production and international programme exchange,” Intl. Telecomm. Union, Tech. Rep., 2011. [2] Z. Shang, J. P. Ebenezer, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Study of the subjective and objective quality of high motion live streaming videos,” IEEE Trans. Image Process., vol. 31, pp. 1027– 1041, 2022. [3] D. Y. Lee, S. Paul, C. G. Bampis, H. Ko, J. Kim, S. Y. Jeong, B. Homan, and A. C. Bovik, “A subjective and objective study of space-time subsampled video quality,” arXiv preprint arXiv:2102.00088, 2021. [4] P. C. Madhusudana, X. Yu, N. Birkbeck, Y. Wang, B. Adsumilli, and A. C. Bovik, “Subjective and objective quality assessment of high frame rate videos,” IEEE Access, vol. 9, pp. 108 069–108 082, 2021. [5] R. R. R. Rao, S. Goring, W. Robitza, B. Feiten, and A. Raake, “Avt- ¨ vqdb-uhd-1: A large scale video quality database for uhd-1,” in 2019 IEEE Intl. Symposium on Multimedia (ISM). IEEE, 2019, pp. 17–177. [6] J. P. Ebenezer, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective quality assessment of high-motion sports videos at lowbitrates,” in 2022 IEEE Intl. Conf. on Image Process. (ICIP), 2022, pp. 521–525. [7] V. Hosu, F. Hahn, M. Jenadeleh, H. Lin, H. Men, T. Sziranyi, S. Li, and ´ D. Saupe, “The Konstanz natural video database (Konvid-1k),” in Int. Conf. Quality of Multimedia Experience, 2017, pp. 1–6. [8] Y. Wang, S. Inguva, and B. Adsumilli, “Youtube UGC dataset for video compression research,” in IEEE Int. Workshop Multimed. Signal Process. IEEE, 2019, pp. 1–5. [9] Z. Ying, M. Mandal, D. Ghadiyaram, and A. Bovik, “Patch-VQ: ‘patching up’ the video quality problem,” in IEEE Conf. Comp. Vision Pattern Recognit., 2021, pp. 14 014–14 024. [10] Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethuraman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in IEEE Intl. Conf. Image Process., 2022, pp. 786–790. [11] Z. Shang, J. P. Ebenezer, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “A study of subjective and objective quality assessment of HDR videos,” submitted to IEEE Trans. Image Process., 2022. [12] Z. Shang, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective video quality assessment of high dynamic range sports content,” in Proceedings of the IEEE/CVF Winter Conf. on Applications of Computer Vision (WACV) Workshops, January 2023, pp. 556–564. [13] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004. [14] Netflix, VMAF: The Journey Continues, 2018 (accessed December 28, 2021). [Online]. Available: https://netflixtechblog. com/vmaf-the-journey-continues-44b51ee9ed12 [15] C. G. Bampis, P. Gupta, R. Soundararajan, and A. C. Bovik, “Speedqa: Spatial efficient entropic differencing for image and video quality,” IEEE Signal Process. Letters, vol. 24, no. 9, pp. 1333–1337, 2017. [16] R. Soundararajan and A. C. Bovik, “Video quality assessment by reduced reference spatio-temporal entropic differencing,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 4, pp. 684–694, 2012. [17] P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli, and A. C. Bovik, “St-greed: Space-time generalized entropic differences for frame rate dependent video quality prediction,” IEEE Trans. Image Process., vol. 30, pp. 7446–7457, 2021. [18] A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process., vol. 21, no. 12, pp. 4695–4708, 2012. [19] M. A. Saad, A. C. Bovik, and C. Charrier, “Blind prediction of natural video quality,” IEEE Trans. Image Process., vol. 23, no. 3, pp. 1352– 1365, 2014. [20] Z. Tu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “UGCVQA: Benchmarking blind video quality assessment for user generated content,” IEEE Trans. Image Process., vol. 30, pp. 4449–4464, 2021. [21] Z. Tu, X. Yu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “RAPIQUE: Rapid and accurate video quality prediction of user generated content,” arXiv preprint arXiv:2101.10955, 2021. [22] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “ChipQA: No-reference video quality prediction via space-time chips,” IEEE Trans. Image Process., vol. 30, pp. 8059–8074, 2021. [23] ——, “HDR-ChipQA: No-reference quality assessment for high dynamic range videos,” submitted to IEEE Trans. Image Process., 2023. [24] A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” IEEE Signal Process. Lett., vol. 20, no. 3, pp. 209–212, 2012. [25] J. Korhonen, “Two-level approach for no-reference consumer video quality assessment,” IEEE Trans. Image Process., vol. 28, no. 12, pp. 5923–5938, 2019. [26] ITU, “ BT.2020 : Parameter values for ultra-high definition television systems for production and international programme exchange,” Intl. Telecomm. Union, Tech. Rep., 2015. [27] S. Miller, M. Nezamabadi, and S. Daly, “Perceptual signal coding for more efficient usage of bit codes,” SMPTE Motion Imaging J., vol. 122, no. 4, pp. 52–59, 2013. [28] Rtings, Peak Brightness Measurement, 2023 (accessed February 28, 2023). [Online]. Available: https://www.rtings.com/tv/tests/ picture-quality/sdr-peak-brightness [29] ITU, “BT.500 : Methodologies for the subjective assessment of the quality of television images,” Intl. Telecomm. Union, Tech. Rep., 2019. [30] ——, “ITU. 910 : Subjective video quality assessment methods for multimedia applications,” Intl. Telecomm. Union, Tech. Rep., 2008. [31] Z. Wang, E. Simoncelli, and A. Bovik, “Multiscale structural similarity for image quality assessment,” in Asilomar Conf. Signals, Syst., Comput., vol. 2, 2003, pp. 1398–1402 Vol.2. [32] H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Trans. Image Process., vol. 15, no. 11, pp. 3440–3451, 2006. [33] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Making video quality assessment models robust to bit depth,” submitted to IEEE Signal Process. Letters, 2023. [34] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, and A. C. Bovik, “Noreference video quality assessment using space-time chips,” in IEEE Intl. Workshop Multimedia Signal Process., 2020, pp. 1–6. [35] D. W. Dong and J. J. Atick, “Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus,” Netw.: Comput. Neural Syst., vol. 6, no. 2, pp. 159–178, 1995. This paper is available on arxiv under CC 4.0 license. Authors: (1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu); (2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work; (3) Yixu Chen, Amazon Prime Video; (4) Yongjun Wu, Amazon Prime Video; (5) Hai Wei,  Amazon Prime Video; (6)Sriram Sethuraman, Amazon Prime Video; (7) Alan C. Bovik, Fellow, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA. Authors: Authors: (1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu); (2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work; (3) Yixu Chen, Amazon Prime Video; (4) Yongjun Wu, Amazon Prime Video; (5) Hai Wei,  Amazon Prime Video; (6)Sriram Sethuraman, Amazon Prime Video; (7) Alan C. Bovik, Fellow, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA. Table of Links Abstract and Introduction Related Work Details of Subjective Study Subjective Analysis Objective Assessment Conclusion, Acknowledgment and References Abstract and Introduction Abstract and Introduction Related Work Related Work Details of Subjective Study Details of Subjective Study Subjective Analysis Subjective Analysis Objective Assessment Objective Assessment Conclusion, Acknowledgment and References Conclusion, Acknowledgment and References VI. CONCLUSION We presented the first ever study on comparing HDR and SDR videos of the same content encoded at different bitrates and resolutions on different display devices. Our study shows that despite HDR’s theoretical capabilities over SDR, its perceptual quality depends heavily on the display device used in practice. We also evaluated several NR and FR VQA algorithms on the new database, and presented a novel NR VQA algorithm called HDRPatchMAX that exceeds the current state-of-the-art on this database. We hope that this spurs research on the modelling of display devices in VQA algorithms as well as for optimal bitrate ladders for streaming. ACKNOWLEDGMENT This research was sponsored by a grant from Amazon.com, Inc., and by grant number 2019844 for the National Science Foundation AI Institute for Foundations of Machine Learning (IFML). The authors also thank the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported in this paper. URL: http://www.tacc.utexas.edu. REFERENCES [1] ITU, “BT.709 : Parameter values for the HDTV standards for production and international programme exchange,” Intl. Telecomm. Union, Tech. Rep., 2011. [2] Z. Shang, J. P. Ebenezer, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Study of the subjective and objective quality of high motion live streaming videos,” IEEE Trans. Image Process., vol. 31, pp. 1027– 1041, 2022. [3] D. Y. Lee, S. Paul, C. G. Bampis, H. Ko, J. Kim, S. Y. Jeong, B. Homan, and A. C. Bovik, “A subjective and objective study of space-time subsampled video quality,” arXiv preprint arXiv:2102.00088, 2021. [4] P. C. Madhusudana, X. Yu, N. Birkbeck, Y. Wang, B. Adsumilli, and A. C. Bovik, “Subjective and objective quality assessment of high frame rate videos,” IEEE Access, vol. 9, pp. 108 069–108 082, 2021. [5] R. R. R. Rao, S. Goring, W. Robitza, B. Feiten, and A. Raake, “Avt- ¨ vqdb-uhd-1: A large scale video quality database for uhd-1,” in 2019 IEEE Intl. Symposium on Multimedia (ISM). IEEE, 2019, pp. 17–177. [6] J. P. Ebenezer, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective quality assessment of high-motion sports videos at lowbitrates,” in 2022 IEEE Intl. Conf. on Image Process. (ICIP), 2022, pp. 521–525. [7] V. Hosu, F. Hahn, M. Jenadeleh, H. Lin, H. Men, T. Sziranyi, S. Li, and ´ D. Saupe, “The Konstanz natural video database (Konvid-1k),” in Int. Conf. Quality of Multimedia Experience, 2017, pp. 1–6. [8] Y. Wang, S. Inguva, and B. Adsumilli, “Youtube UGC dataset for video compression research,” in IEEE Int. Workshop Multimed. Signal Process. IEEE, 2019, pp. 1–5. [9] Z. Ying, M. Mandal, D. Ghadiyaram, and A. Bovik, “Patch-VQ: ‘patching up’ the video quality problem,” in IEEE Conf. Comp. Vision Pattern Recognit., 2021, pp. 14 014–14 024. [10] Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethuraman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in IEEE Intl. Conf. Image Process., 2022, pp. 786–790. [11] Z. Shang, J. P. Ebenezer, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “A study of subjective and objective quality assessment of HDR videos,” submitted to IEEE Trans. Image Process., 2022. [12] Z. Shang, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective video quality assessment of high dynamic range sports content,” in Proceedings of the IEEE/CVF Winter Conf. on Applications of Computer Vision (WACV) Workshops, January 2023, pp. 556–564. [13] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004. [14] Netflix, VMAF: The Journey Continues, 2018 (accessed December 28, 2021). [Online]. Available: https://netflixtechblog. com/vmaf-the-journey-continues-44b51ee9ed12 [15] C. G. Bampis, P. Gupta, R. Soundararajan, and A. C. Bovik, “Speedqa: Spatial efficient entropic differencing for image and video quality,” IEEE Signal Process. Letters, vol. 24, no. 9, pp. 1333–1337, 2017. [16] R. Soundararajan and A. C. Bovik, “Video quality assessment by reduced reference spatio-temporal entropic differencing,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 4, pp. 684–694, 2012. [17] P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli, and A. C. Bovik, “St-greed: Space-time generalized entropic differences for frame rate dependent video quality prediction,” IEEE Trans. Image Process., vol. 30, pp. 7446–7457, 2021. [18] A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process., vol. 21, no. 12, pp. 4695–4708, 2012. [19] M. A. Saad, A. C. Bovik, and C. Charrier, “Blind prediction of natural video quality,” IEEE Trans. Image Process., vol. 23, no. 3, pp. 1352– 1365, 2014. [20] Z. Tu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “UGCVQA: Benchmarking blind video quality assessment for user generated content,” IEEE Trans. Image Process., vol. 30, pp. 4449–4464, 2021. [21] Z. Tu, X. Yu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “RAPIQUE: Rapid and accurate video quality prediction of user generated content,” arXiv preprint arXiv:2101.10955, 2021. [22] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “ChipQA: No-reference video quality prediction via space-time chips,” IEEE Trans. Image Process., vol. 30, pp. 8059–8074, 2021. [23] ——, “HDR-ChipQA: No-reference quality assessment for high dynamic range videos,” submitted to IEEE Trans. Image Process., 2023. [24] A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” IEEE Signal Process. Lett., vol. 20, no. 3, pp. 209–212, 2012. [25] J. Korhonen, “Two-level approach for no-reference consumer video quality assessment,” IEEE Trans. Image Process., vol. 28, no. 12, pp. 5923–5938, 2019. [26] ITU, “ BT.2020 : Parameter values for ultra-high definition television systems for production and international programme exchange,” Intl. Telecomm. Union, Tech. Rep., 2015. [27] S. Miller, M. Nezamabadi, and S. Daly, “Perceptual signal coding for more efficient usage of bit codes,” SMPTE Motion Imaging J., vol. 122, no. 4, pp. 52–59, 2013. [28] Rtings, Peak Brightness Measurement, 2023 (accessed February 28, 2023). [Online]. Available: https://www.rtings.com/tv/tests/ picture-quality/sdr-peak-brightness [29] ITU, “BT.500 : Methodologies for the subjective assessment of the quality of television images,” Intl. Telecomm. Union, Tech. Rep., 2019. [30] ——, “ITU. 910 : Subjective video quality assessment methods for multimedia applications,” Intl. Telecomm. Union, Tech. Rep., 2008. [31] Z. Wang, E. Simoncelli, and A. Bovik, “Multiscale structural similarity for image quality assessment,” in Asilomar Conf. Signals, Syst., Comput., vol. 2, 2003, pp. 1398–1402 Vol.2. [32] H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Trans. Image Process., vol. 15, no. 11, pp. 3440–3451, 2006. [33] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Making video quality assessment models robust to bit depth,” submitted to IEEE Signal Process. Letters, 2023. [34] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, and A. C. Bovik, “Noreference video quality assessment using space-time chips,” in IEEE Intl. Workshop Multimedia Signal Process., 2020, pp. 1–6. [35] D. W. Dong and J. J. Atick, “Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus,” Netw.: Comput. Neural Syst., vol. 6, no. 2, pp. 159–178, 1995. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

HDR or SDR? A Study of Scaled and Compressed Videos: Conclusion, Acknowledgment, and References

HDR or SDR? A Study of Scaled and Compressed Videos: Objective Assessment

Authors:
(1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co);
(2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team;
(3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team;
(14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction
Related Work
Method
Experimental Details
Results and Ablations
Conclusions and Limitations , Acknowledgements and References
Appendix A APPENDIX A.1 QUALITATIVE EXAMPLES To qualitatively compare the responses from our dSFT and dDPO models, we choose prompts from a few domains of MT-Bench, as well as some adversarial prompts to test each model’s capability to follow instructions with false premises or harmful intent. Completions for the adversarial prompts were generated with nucleus sampling(top-p = 0.95) and T = 0.7. A.2 SFT IS A REQUIRED STEP BEFORE DPO In Table 3 we ran an ablation to see whether SFT is necessary prior to the DPO step. We observed a significant reduction in performance in both the MT-Bench and AlpacaEval scores when the SFT step is skipped. After a qualitative evaluation of the MT-Bench generations, we observe that the pure DPO model struggles to learn the chat template: This paper is available on arxiv under CC 4.0 license. Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Authors: Authors: (1) Lewis Tunstall,  Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: lewis@huggingface.co); (2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team; (3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team; (4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team; (5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team; (6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team; (7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team; (8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team; (9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team; (10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team; (11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team; (12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team; (13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team; (14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team. Table of Links Abstract and Introduction Related Work Method Experimental Details Results and Ablations Conclusions and Limitations , Acknowledgements and References Appendix Abstract and Introduction Abstract and Introduction Related Work Related Work Method Method Experimental Details Experimental Details Results and Ablations Results and Ablations Conclusions and Limitations , Acknowledgements and References Conclusions and Limitations , Acknowledgements and References Appendix Appendix A APPENDIX A.1 QUALITATIVE EXAMPLES To qualitatively compare the responses from our dSFT and dDPO models, we choose prompts from a few domains of MT-Bench, as well as some adversarial prompts to test each model’s capability to follow instructions with false premises or harmful intent. Completions for the adversarial prompts were generated with nucleus sampling(top-p = 0.95) and T = 0.7. A.2 SFT IS A REQUIRED STEP BEFORE DPO In Table 3 we ran an ablation to see whether SFT is necessary prior to the DPO step. We observed a significant reduction in performance in both the MT-Bench and AlpacaEval scores when the SFT step is skipped. After a qualitative evaluation of the MT-Bench generations, we observe that the pure DPO model struggles to learn the chat template: This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Zephyr: Direct Distillation of LM Alignment: Appendix

How to Optimize SaaS Pricing for Maximum Profitability

সর্বাধিক লাভের জন্য কীভাবে SaaS মূল্য অপ্টিমাইজ করবেন

So optimieren Sie die SaaS-Preise für maximale Rentabilität

Cómo optimizar los precios de SaaS para obtener la máxima rentabilidad

Comment optimiser la tarification SaaS pour une rentabilité maximale

अधिकतम लाभप्रदता के लिए SaaS मूल्य निर्धारण को कैसे अनुकूलित करें

SaaS の価格設定を最適化して収益性を最大化する方法

최대 수익성을 위해 SaaS 가격을 최적화하는 방법

Como otimizar os preços de SaaS para obter lucratividade máxima

Как оптимизировать цены на SaaS для максимальной прибыльности

Maksimum Karlılık için SaaS Fiyatlandırmasını Optimize Etme

Cách tối ưu hóa giá SaaS để có lợi nhuận tối đa

如何优化 SaaS 定价以实现最大盈利

Leveraging Natural Supervision: Appendix B - Appendix To Chapter 6

Tailoring Textual Resources for Evaluation Tasks: Long-Form Data-to-Text Generation

Tailoring Textual Resources for Evaluation Tasks: Story Generation with Constraints

Authors:
(1) Toshit Jain, Indian Institute of Science Bangalore, India;
(2) Varun Singh, Indian Institute of Science Bangalore, India;
(3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India;
(4) Upkar Singh, Indian Institute of Science Bangalore, India;
(5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden;
(6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India;
(7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro
Ocean data
pyParaOcean: Architecture
pyParaOcean: Functionalities
Case study: Bay of Bengal
Conclusion
Acknowledgments and References 2. Ocean data Oceanographers typically deal with large multivariate spatiotemporal datasets – time-varying scalar or vector fields over a three dimensional region. The data is generated using simulations, satellite imagery, sensors on buoys, or in-situ physical observations. With strides in high performance computing, higher resolution sampling, and the increasing number of observables, the size of such datasets is rapidly increasing. Reanalysis datasets combine a numerical simulation model with observational inputs to furnish data that is spatio-temporally consistent. Ocean data contains strong temporal and spatial processes involving complex interactions between multi-scale entities [XLWD19]. It is analyzed on a variety of scales, from small-scale features such as eddies and fronts, to large-scale features such as ocean basins and circulation patterns. All visualizations in this paper are generated using two datasets, the Red Sea and Bay of Bengal. Red Sea: This dataset [TZG∗ 17] was made available as a part of the IEEE SciVis 2020 contest. It is a 50 member ensemble of threedimensional scalar and velocity fields. The data is regularly sampled on a 500 × 500 × 50 grid over 60 time steps covering a whole month of simulation time. Ensembles are the outputs of the simulated models with different parameters and initial conditions, and they may vary significantly even with a small change in parameter values. The members are the forecasts from MITgcm setups configured for the 30◦E - 50◦E and 10◦N - 30◦N domain that spans the entire Red Sea. They are implemented in Cartesian coordinates with a horizontal resolution of 0.04◦ × 0.04◦ (4 km) and 50 vertical layers, with a surface spacing of 4 m and a bottom spacing of 300 m. The dataset is available in the NetCDF format. Bay of Bengal: This dataset is generated by a reanalysis product and available from the Nucleus for European Modelling of the Ocean (NEMO) repository [Mad08], with a daily resolution spanning the months of July-August 2020, a total of 62 time steps. The data is available in NetCDF format, with a 1/12◦ latitude-longitude resolution. Salinity measurements are available at 50 vertical levels, ranging from 1 m resolution near the surface to 450 m resolution towards the sea floor, including 22 samples in the upper 100 m. The Bay of Bengal, a geographical region confined by longitudes 75◦E and 96◦E and latitudes 5◦ S to 30◦N, with depth up to 200 m, is extracted from this data. This paper is available on arxiv under CC 4.0 license. Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Authors: Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro Ocean data pyParaOcean: Architecture pyParaOcean: Functionalities Case study: Bay of Bengal Conclusion Acknowledgments and References Abstract and Intro Abstract and Intro Ocean data Ocean data pyParaOcean: Architecture pyParaOcean: Architecture pyParaOcean: Functionalities pyParaOcean: Functionalities Case study: Bay of Bengal Case study: Bay of Bengal Conclusion Conclusion Acknowledgments and References Acknowledgments and References 2. Ocean data Oceanographers typically deal with large multivariate spatiotemporal datasets – time-varying scalar or vector fields over a three dimensional region. The data is generated using simulations, satellite imagery, sensors on buoys, or in-situ physical observations. With strides in high performance computing, higher resolution sampling, and the increasing number of observables, the size of such datasets is rapidly increasing. Reanalysis datasets combine a numerical simulation model with observational inputs to furnish data that is spatio-temporally consistent. Ocean data contains strong temporal and spatial processes involving complex interactions between multi-scale entities [XLWD19]. It is analyzed on a variety of scales, from small-scale features such as eddies and fronts, to large-scale features such as ocean basins and circulation patterns. All visualizations in this paper are generated using two datasets, the Red Sea and Bay of Bengal. Red Sea: This dataset [TZG∗ 17] was made available as a part of the IEEE SciVis 2020 contest. It is a 50 member ensemble of threedimensional scalar and velocity fields. The data is regularly sampled on a 500 × 500 × 50 grid over 60 time steps covering a whole month of simulation time. Ensembles are the outputs of the simulated models with different parameters and initial conditions, and they may vary significantly even with a small change in parameter values. The members are the forecasts from MITgcm setups configured for the 30◦E - 50◦E and 10◦N - 30◦N domain that spans the entire Red Sea. They are implemented in Cartesian coordinates with a horizontal resolution of 0.04◦ × 0.04◦ (4 km) and 50 vertical layers, with a surface spacing of 4 m and a bottom spacing of 300 m. The dataset is available in the NetCDF format. Red Sea: Bay of Bengal: This dataset is generated by a reanalysis product and available from the Nucleus for European Modelling of the Ocean (NEMO) repository [Mad08], with a daily resolution spanning the months of July-August 2020, a total of 62 time steps. The data is available in NetCDF format, with a 1/12◦ latitude-longitude resolution. Salinity measurements are available at 50 vertical levels, ranging from 1 m resolution near the surface to 450 m resolution towards the sea floor, including 22 samples in the upper 100 m. The Bay of Bengal, a geographical region confined by longitudes 75◦E and 96◦E and latitudes 5◦ S to 30◦N, with depth up to 200 m, is extracted from this data. Bay of Bengal: This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

pyParaOcean, A System for Visual Analysis of Ocean Data: Ocean data

pyParaOcean, ein System zur visuellen Analyse von Ozeandaten: Ozeandaten

pyParaOcean, un sistema de análisis visual de datos oceánicos: datos oceánicos

pyParaOcean, un système d'analyse visuelle des données océaniques : données océaniques

pyParaOcean, महासागर डेटा के दृश्य विश्लेषण के लिए एक प्रणाली: महासागर डेटा

pyParaOcean、海洋データの視覚的分析システム: 海洋データ

pyParaOcean, 해양 데이터의 시각적 분석 시스템: 해양 데이터

pyParaOcean, um sistema para análise visual de dados oceânicos: dados oceânicos

pyParaOcean, система визуального анализа данных об океане: данные об океане

pyParaOcean, Okyanus Verilerinin Görsel Analizi İçin Bir Sistem: Okyanus verileri

pyParaOcean, Hệ thống phân tích trực quan dữ liệu đại dương: Dữ liệu đại dương

pyParaOcean，海洋数据可视化分析系统：海洋数据

Understanding Statistical Language Models and Hierarchical Language Generation

পরিসংখ্যানগত ভাষার মডেল এবং শ্রেণিবিন্যাস ভাষা প্রজন্ম বোঝা

Statistische Sprachmodelle und hierarchische Sprachgenerierung verstehen

Comprensión de los modelos de lenguaje estadístico y la generación de lenguaje jerárquico

Comprendre les modèles de langage statistiques et la génération de langage hiérarchique

सांख्यिकीय भाषा मॉडल और पदानुक्रमित भाषा निर्माण को समझना

統計言語モデルと階層的言語生成の理解

통계적 언어 모델 및 계층적 언어 생성 이해

Compreendendo modelos estatísticos de linguagem e geração hierárquica de linguagem

Понимание статистических языковых моделей и генерации иерархического языка

İstatistiksel Dil Modellerini ve Hiyerarşik Dil Üretimini Anlamak

Hiểu mô hình ngôn ngữ thống kê và tạo ngôn ngữ phân cấp

理解统计语言模型和分层语言生成

Science Versus Art In Our Exploration Of This World

This paper is available on Arxiv under CC 4.0 license.
Authors:
(1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA;
(3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA;
(5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Numerical Methods Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Summary and Conclusions Acknowledgements Appendix A. GRMHD Primer and Definitions B. Numerical Set-up C. Resolution and Initial Condition Study References ABSTRACT Keywords: Accretion (14), Active galactic nuclei (16), Bondi accretion (174), Schwarzschild black holes (1433), Supermassive black holes (1663), Magnetohydrodynamical simulations (1966) 1. INTRODUCTION Most nearby galaxies are found to harbor central supermassive black holes (SMBHs) whose masses are correlated with properties of the stellar components of their hosts (e.g., Magorrian et al. 1998; Ferrarese & Merritt 2000; Gebhardt et al. 2000; Kormendy & Ho 2013). However, the details of how gas flows into the galactic nucleus from large cosmic distances and how the SMBH in turn imparts feedback into the galaxy remain coupled unresolved problems. Recently, some attempts to connect these disparate scales have been made using nested simulations where each smaller-scale simulation is initialized from the next larger-scale simulation (e.g., Hopkins & Quataert 2010; Ressler et al. 2020; Guo et al. 2023); or by adopting Lagrangian hyper-refinement methods (e.g., Angl´esAlc´azar et al. 2021; Hopkins et al. 2023); or by pushing out the simulation regime to larger scales (e.g., Lalakos et al. 2022; Kaaz et al. 2023). While successful in studying the process of accretion, feedback from the SMBH has not been followed out to galaxy scales in prior work because either the communication between scales is only directed inwards or because of the inability to include the entire galaxy and the black hole. We employ here a multi-zone computational method which represents a first attempt to not only follow accretion flows but also trace feedback across 7 orders of magnitude from the event horizon to galactic scales. With this method, we study purely hydrodynamic Bondi accretion as well as the magnetized Bondi problem. The outline of this Letter is as follows: In Section 2 we give an overview of the numerical scheme that captures a wide dynamic range simultaneously. In Section 3, we present a purely hydrodynamic simulation and test our numerical scheme by comparing against the general relativistic (GR) analytical solution. In Section 4 we include magnetic fields for a more realistic representation of the environment of SMBHs and study how Bondi accretion is modified. We discuss our findings of feedback via convection in Section 5 and summarize conclusions of our study in Section 6. Additional details are presented in a set of short appendices: various GRMHD quantities are defined in Appendix A, the details of the simulation set-up are outlined in Appendix B, and a resolution and initial condition study is presented in Appendix C. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Authors: (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. This paper is available on Arxiv under CC 4.0 license. Authors: Authors: (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Abstract and Introduction Numerical Methods Numerical Methods Hydrodynamic Bondi Accretion Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Feedback Via Reconnection-Driven Convection Summary and Conclusions Summary and Conclusions Acknowledgements Acknowledgements Appendix Appendix A. GRMHD Primer and Definitions A. GRMHD Primer and Definitions B. Numerical Set-up B. Numerical Set-up C. Resolution and Initial Condition Study C. Resolution and Initial Condition Study References References ABSTRACT Keywords : Accretion (14), Active galactic nuclei (16), Bondi accretion (174), Schwarzschild black holes (1433), Supermassive black holes (1663), Magnetohydrodynamical simulations (1966) Keywords 1. INTRODUCTION Most nearby galaxies are found to harbor central supermassive black holes (SMBHs) whose masses are correlated with properties of the stellar components of their hosts (e.g., Magorrian et al. 1998; Ferrarese & Merritt 2000; Gebhardt et al. 2000; Kormendy & Ho 2013). However, the details of how gas flows into the galactic nucleus from large cosmic distances and how the SMBH in turn imparts feedback into the galaxy remain coupled unresolved problems. Recently, some attempts to connect these disparate scales have been made using nested simulations where each smaller-scale simulation is initialized from the next larger-scale simulation (e.g., Hopkins & Quataert 2010; Ressler et al. 2020; Guo et al. 2023); or by adopting Lagrangian hyper-refinement methods (e.g., Angl´esAlc´azar et al. 2021; Hopkins et al. 2023); or by pushing out the simulation regime to larger scales (e.g., Lalakos et al. 2022; Kaaz et al. 2023). While successful in studying the process of accretion, feedback from the SMBH has not been followed out to galaxy scales in prior work because either the communication between scales is only directed inwards or because of the inability to include the entire galaxy and the black hole. We employ here a multi-zone computational method which represents a first attempt to not only follow accretion flows but also trace feedback across 7 orders of magnitude from the event horizon to galactic scales. With this method, we study purely hydrodynamic Bondi accretion as well as the magnetized Bondi problem. The outline of this Letter is as follows: In Section 2 we give an overview of the numerical scheme that captures a wide dynamic range simultaneously. In Section 3, we present a purely hydrodynamic simulation and test our numerical scheme by comparing against the general relativistic (GR) analytical solution. In Section 4 we include magnetic fields for a more realistic representation of the environment of SMBHs and study how Bondi accretion is modified. We discuss our findings of feedback via convection in Section 5 and summarize conclusions of our study in Section 6. Additional details are presented in a set of short appendices: various GRMHD quantities are defined in Appendix A, the details of the simulation set-up are outlined in Appendix B, and a resolution and initial condition study is presented in Appendix C. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

Bridging Scales in Black Hole Accretion and Feedback: Studying Supermassive Black Holes

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about a graph of gases turning into water over time Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Question about the difference between atoms, molecules, and moles : P1 ah. this thing also confuses me. what is the difference between atom, molecule and mole? ChatGPT: Certainly, it's a common area of confusion for many students. Let's break it down one by one, using simple analogies: 1. Atom: • Imagine an atom as a single LEGO brick. It's the tiniest piece of an element that still retains the properties of that element. Just like a single red LEGO brick represents the color red, a single hydrogen atom represents the element hydrogen. 2. Molecule: • When you connect several LEGO bricks together, you form a structure. In chemistry, when you connect several atoms together, you form a molecule. For instance, two hydrogen atoms (H) can connect to form a hydrogen molecule (H2). Similarly, two hydrogen atoms and one oxygen atom can connect to form a water molecule (H2O). ***3. Mole: *** • This one's a bit more abstract. Let's say you have a big box filled with identical LEGO structures (say, LEGO houses). If you count them and find there are exactly 6.022 x 1023 LEGO houses in that box, you have a "mole" of LEGO houses. • In chemistry, a mole is a specific number of things (atoms, molecules, electrons, etc.). Specifically, one mole of any substance contains exactly 6.022 x 10[23] of those entities. This number is called Avogadro's number. • The concept of a mole is a standardized way to talk about quantities in chemistry, much like a "dozen" is a standardized way to talk about 12 of something. So, to recap: • : Smallest unit of an element. Atom • : Made up of multiple atoms bonded together. Molecule • : A specific quantity (6.022 x 10[23]) of atoms, molecules, or other entities. Mole Do these analogies help clarify the differences for you? BingChat: I see that you are confused about the difference between atom, molecule and mole. These are three important terms in chemistry that describe different aspects of matter. Let me try to explain them to you in a simple way. An atom is the smallest unit of an element that retains its chemical identity. An element is a pure substance that consists of only one type of atom. For example, hydrogen, oxygen, carbon, and gold are elements. Each element has a unique symbol and atomic number that indicates how many protons it has in its nucleus. For example, hydrogen has the symbol H and atomic number 1, meaning it has one proton in its nucleus. Atoms are composed of subatomic particles such as protons, neutrons, and electrons. Protons and neutrons are found in the nucleus, and electrons orbit around the nucleus in shells or orbitals. The number of electrons in an atom determines its chemical behavior and reactivity [ ] 12 A molecule is a group of two or more atoms that are held together by chemical bonds. A chemical bond is a force of attraction between atoms that share or transfer electrons. There are different types of chemical bonds, such as covalent, ionic, metallic, and hydrogen bonds. A molecule can be composed of atoms of the same element or different elements. For example, oxygen gas (O2) is a molecule composed of two oxygen atoms bonded by a covalent bond. Water (H2O) is a molecule composed of two hydrogen atoms and one oxygen atom bonded by covalent bonds. A molecule has a specific shape and structure that determines its physical and chemical properties[ ]. 32 A mole is a unit of measurement that is used to count the number of atoms or molecules in a given amount of substance. A mole is defined as the amount of substance that contains as many particles (atoms or molecules) as there are atoms in 12 grams of carbon-12. This number is called Avogadro’s number and it is approximately 6.022 x 10[23]. That means one mole of any substance contains 6.022 x 10[23] particles (atoms or molecules). For example, one mole of oxygen gas contains 6.022 x 10[23] molecules of O2, and one mole of water contains 6.022 x 1023 molecules of H2O. The mass of one mole of a substance is called its molar mass and it is equal to its molecular weight in grams. For example, the molecular weight of oxygen gas is 32 g/mol and the molecular weight of water is 18 g/mol[ ]. 42 So, to summarize, an atom is the smallest unit of an element, a molecule is a group of atoms bonded together, and a mole is a unit of measurement that counts the number of particles in a substance. I hope this helps you understand the difference between atom, molecule and mole. :) *: An atom is the smallest unit of matter that still has all of the properties of its element. An element is a substance that cannot be broken down into simpler substances by chemical means.* Bard A molecule is a group of two or more atoms held together by chemical bonds. A chemical bond is a force that holds atoms together. A mole is a unit of measurement that is used to count atoms and molecules. One mole of any substance contains 6.02214076×10^23 particles of that substance. This number is known as Avogadro's constant. Here is a table that summarizes the key differences between atoms, molecules, and moles: Here are some examples of atoms, molecules, and moles: • Atoms: Hydrogen (H), helium (He), oxygen (O), carbon (C) • Molecules: Water (H2O), carbon dioxide (CO2), methane (CH4) • Moles: One mole of hydrogen atoms, one mole of water molecules, one mole of carbon dioxide molecules I hope this helps to explain the difference between atoms, molecules, and moles. Please let me know if you have any other questions. *: Excellent question! Those three terms can definitely be confusing. Let's go through them one-by-one:* Claude Atom - The basic building block of matter. Atoms are made up of protons, neutrons, and electrons. An atom is the smallest unit of an element that maintains the properties of that element. For example, a hydrogen atom has 1 proton; a carbon atom has 6 protons. Molecule - A group of two or more atoms bonded together. A molecule is the smallest unit of a chemical compound that maintains the properties of that compound. For example, a water molecule contains 2 hydrogen atoms and 1 oxygen atom bonded together (H2O). Mole - This is a number representing 6.02 x 10^23 things. It's usually used to represent a certain quantity of atoms or molecules. For example, 1 mole of carbon contains 6.02 x 10^23 carbon atoms. So in summary: • Atoms are the basic units of elements • Molecules are groups of bonded atoms that make up compounds • Moles are a way to represent 6.02 x 10^23 of something (useful for relating atomic/molecular masses to macroscopic masses) Let me know if this helps explain the distinction between these three important chemistry concepts! I'm happy to provide more examples as well. , by saying “Certainly, it's a common area of confusion for many students”, recognises a element by acknowledging the difficulty many students face. By saying, "Let's break it down one by one," it demonstrates Instructional strategy. The phrase “using simple analogies" clearly indicates the use of , while the question "Do these analogies help clarify?" shows an component by asking for feedback. saying "I see that you are confused" provides , while the phrase "Let me try to explain" indicates an Instructional Strategy and "in a simple way" attempts at by simplifying the information. Finally, "I hope this helps" and the inclusion of the smiley emoticon ":)" are gestures for Personalization & Pe , by explaining "An atom is..." and, later, offering "Here is a table," appeals to an , demonstrated by definition-based learning and visual data organisation, concluding with "I hope this helps" and “Please let me know if you have any other questions” demonstrates and . , by starting with “Excellent question" and acknowledging that the subject “can definitely be confusing,” recognises gives Reinforcement & feedback. It then invites, "Let's go through them one-by-one," pointing to an Instructional Strategy through systematic explanation. It also uses by providing two examples. It concludes with "Let me know if this helps," an element, and encouraging feedback. ChatGPT Reinforcement & Feedback Analogy & Comparative Illustrations Engagement & interactivity BingChat Reinforcement & Feedback Detail Depth & Content Recommendation rsonable Touch. Bard Instructional Strategy Engagement & Interactivity Reinforcement & Feedback Claude Analogy & Comparative Illustrations Engagement & Interactivity This paper is under CC BY-SA 4.0 DEED license. available on arxiv

How GenAIbots Explain the Difference Between Atoms, Molecules, and Moles

কিভাবে GenAIbots পরমাণু, অণু এবং মোলের মধ্যে পার্থক্য ব্যাখ্যা করে

Wie GenAIbots den Unterschied zwischen Atomen, Molekülen und Molen erklären

Cómo los GenAIbots explican la diferencia entre átomos, moléculas y moles

Comment les GenAIbots expliquent la différence entre les atomes, les molécules et les taupes

GenAIbots परमाणुओं, अणुओं और मोल्स के बीच अंतर कैसे समझाते हैं

GenAIbots が原子、分子、モルの違いを説明する方法

GenAIbot이 원자, 분자, 두더지의 차이점을 설명하는 방법

Como GenAIbots explicam a diferença entre átomos, moléculas e toupeiras

Как GenAIbots объясняют разницу между атомами, молекулами и кротами

GenAIbotlar Atomlar, Moleküller ve Benler Arasındaki Farkı Nasıl Açıklıyor?

Cách GenAIbots giải thích sự khác biệt giữa nguyên tử, phân tử và nốt ruồi

GenAIbots 如何解释原子、分子和摩尔之间的区别

“Jurassic Park” may not be as far-fetched as it seems. Scientists have been trying to find ways to bring extinct species back to life for some time now, and AI could finally make it happen. While some regulators call AI an extinction-level threat, the opposite may also be true. It could reverse extinction — perhaps not for dinosaurs, but feasibly for species like the woolly mammoth. Doing so could also play an important role in the fight against climate change. How De-Extinction Works De-extinction is the culmination of many cutting-edge technologies. It starts with gene-editing technologies like CRISPR, and AI helps throughout the entire process. Here’s a closer look at how it works. Genome Sequencing The first step in de-extinction is to collect the DNA of an extinct animal — like the woolly mammoth — and a close living relative — like the Asian elephant. While usable dinosaur DNA samples may be long gone, the mammoth’s relatively recent extinction and cold habitat mean it still has well-preserved genes. Once scientists have gathered this DNA, they must sequence each species’ genome. This process uncovers the order of each gene’s building blocks to learn how they work and what characteristics they produce. It’s difficult and time-consuming work, so it’s an ideal use case for AI. Studies show that AI shortens the time spent in genome sequencing and reduces errors in the process. Speed and accuracy are big concerns here, so AI is now essential in this stage of de-extinction. Filling in the Gaps After sequencing both species’ genomes, scientists compare them to see what elephant genes they must change to resemble the woolly mammoth’s. AI is crucial in this step, as there are gaps left to fill after sequencing. DNA has a 521-year half-life, so even well-preserved samples from over 1,000 years ago aren’t complete. AI can analyze genes to predict the order of the missing bits. It can then compare them to those of the Asian elephant to determine what elephant genes it can keep and what it must modify to look more like a mammoth. Machine learning’s predictive capabilities make it the perfect tool for this job. Accurately predicting what customers want is how AI saves Netflix $1 billion annually, and finding similarities in DNA sequences is a similar task. Gene Insertion and Verification It’s now time to modify Asian elephant genes to essentially create complete woolly mammoth DNA. This process is eerily similar to how it works in “Jurassic Park,” at least in the basics. Like in Michael Crichton’s work, real-life scientists cut out bits of living species’ DNA that don’t match and insert genes from the extinct species. This is possible through a technology called CRISPR gene editing — the same innovation that helps fight diseases like sickle cell anemia and malaria. After inserting mammoth genes into elephant DNA, scientists must verify that the resulting cells possess mammoth traits. Once again, AI is the ideal solution. Machine learning can test these cells faster and more accurately than manual methods. Embryo Transfer, Growth, and Birth At this point, scientists will have created hybrid elephant-mammoth cells. They remove the nucleus from an Asian elephant egg cell and replace it with the hybrid mammoth nucleus. They then stimulate the egg so it fertilizes and grows into an embryo. Once they have a healthy embryo, scientists transfer it to an African elephant to carry to term. African elephants are larger and have the longest pregnancy of any mammal, so they’re ideal mothers to grow a baby mammoth. After roughly 22 months, the elephant will give birth to the hybrid mammoth. While the mammoth’s genes won’t be identical to its extinct ancestors, it’ll essentially be a real woolly mammoth brought back from extinction. Ongoing De-Extinction Attempts Several research organizations are following this process to de-extinct species like the woolly mammoth. One biotechnology company — Colossal Laboratories — aims to resurrect the woolly mammoth by 2027 and is currently building the necessary genetic libraries to create the hybrid DNA. Colossal also hopes to de-extinct species like the dodo and Tasmanian tiger. While no project has successfully cloned an extinct species that lived more than a few minutes, advances in AI and gene editing technology are bringing the possibility closer. Other researchers are focusing on critically endangered species — not quite extinct but about to be. Scientists have successfully cloned a black-footed ferret using decades-old DNA, showing it’s possible to de-extinct animals with old genes. Researchers at the San Diego Zoo Alliance are trying to do the same for the northern white rhino, just two of which exist today. Why It Matters As cool as it is, why would scientists bother de-extincting species? It’s an expensive, complicated process, so what’s the point? Unlike in “Jurassic Park,” there is a noble goal here. Bringing extinct or critically endangered species back could help protect the planet. Woolly mammoths could help fight climate change, as these animals clear dead or invasive tree species to restore grasslands. Bringing more recently extinct animals back to life would restore a full, healthy ecosystem in some areas to protect other species. In cases like the white rhino, it would right the wrongs of humanity’s past by reversing forced extinction. If nothing else, successful de-extinction would be a big step forward for AI in genetic engineering. It would prove this technology’s potential to fight seemingly impossible odds. Scientists already believe AI-powered gene editing could improve global health and combat climate change, so any advances in the field are worth noticing. Ethical Complications Despite this vast potential, AI-powered de-extinction has some controversy. Some argue that bringing species like the woolly mammoth into a warmer, harsher climate, which they’re not well-adapted to, may be cruel to these animals. There’s also the argument that the money and time spent on this research could go to other, more immediate climate and health issues. Some also have fears about AI gene editing going too far. Successful de-extinction could snowball into issues of people genetically modifying humans to have unique abilities or creating dangerous genetic weapons. Many of these fears are theoretical but deserve attention as this field advances. AI Could Bring Species Back From the Dead While there are still some ethical and technological complications, de-extinction shows promise. It would be a major step forward for biotechnology if these projects work out, and AI will likely be the difference-maker. AI has plenty of ethical and environmental concerns. However, it’s hard to deny its potential for protecting the natural world. Using it for conservation efforts could ensure the world benefits more from this technology than it suffers. “Jurassic Park” may not be as far-fetched as it seems. Scientists have been trying to find ways to bring extinct species back to life for some time now, and AI could finally make it happen. While some regulators call AI an extinction-level threat , the opposite may also be true. It could reverse extinction — perhaps not for dinosaurs, but feasibly for species like the woolly mammoth. Doing so could also play an important role in the fight against climate change. call AI an extinction-level threat call AI an extinction-level threat How De-Extinction Works De-extinction is the culmination of many cutting-edge technologies. It starts with gene-editing technologies like CRISPR, and AI helps throughout the entire process. Here’s a closer look at how it works. Genome Sequencing The first step in de-extinction is to collect the DNA of an extinct animal — like the woolly mammoth — and a close living relative — like the Asian elephant. While usable dinosaur DNA samples may be long gone, the mammoth’s relatively recent extinction and cold habitat mean it still has well-preserved genes. Once scientists have gathered this DNA, they must sequence each species’ genome. This process uncovers the order of each gene’s building blocks to learn how they work and what characteristics they produce. It’s difficult and time-consuming work, so it’s an ideal use case for AI. Studies show that AI shortens the time spent in genome sequencing and reduces errors in the process. Speed and accuracy are big concerns here, so AI is now essential in this stage of de-extinction. shortens the time spent in genome sequencing shortens the time spent in genome sequencing Filling in the Gaps After sequencing both species’ genomes, scientists compare them to see what elephant genes they must change to resemble the woolly mammoth’s. AI is crucial in this step, as there are gaps left to fill after sequencing. DNA has a 521-year half-life , so even well-preserved samples from over 1,000 years ago aren’t complete. has a 521-year half-life has a 521-year half-life AI can analyze genes to predict the order of the missing bits. It can then compare them to those of the Asian elephant to determine what elephant genes it can keep and what it must modify to look more like a mammoth. Machine learning’s predictive capabilities make it the perfect tool for this job. Accurately predicting what customers want is how AI saves Netflix $1 billion annually , and finding similarities in DNA sequences is a similar task. saves Netflix $1 billion annually saves Netflix $1 billion annually Gene Insertion and Verification It’s now time to modify Asian elephant genes to essentially create complete woolly mammoth DNA. This process is eerily similar to how it works in “Jurassic Park,” at least in the basics. Like in Michael Crichton’s work, real-life scientists cut out bits of living species’ DNA that don’t match and insert genes from the extinct species. This is possible through a technology called CRISPR gene editing — the same innovation that helps fight diseases like sickle cell anemia and malaria. After inserting mammoth genes into elephant DNA, scientists must verify that the resulting cells possess mammoth traits. Once again, AI is the ideal solution. Machine learning can test these cells faster and more accurately than manual methods. Embryo Transfer, Growth, and Birth At this point, scientists will have created hybrid elephant-mammoth cells. They remove the nucleus from an Asian elephant egg cell and replace it with the hybrid mammoth nucleus. They then stimulate the egg so it fertilizes and grows into an embryo. Once they have a healthy embryo, scientists transfer it to an African elephant to carry to term. African elephants are larger and have the longest pregnancy of any mammal , so they’re ideal mothers to grow a baby mammoth. the longest pregnancy of any mammal the longest pregnancy of any mammal After roughly 22 months, the elephant will give birth to the hybrid mammoth. While the mammoth’s genes won’t be identical to its extinct ancestors, it’ll essentially be a real woolly mammoth brought back from extinction. Ongoing De-Extinction Attempts Several research organizations are following this process to de-extinct species like the woolly mammoth. One biotechnology company — Colossal Laboratories — aims to resurrect the woolly mammoth by 2027 and is currently building the necessary genetic libraries to create the hybrid DNA. resurrect the woolly mammoth by 2027 resurrect the woolly mammoth by 2027 Colossal also hopes to de-extinct species like the dodo and Tasmanian tiger. While no project has successfully cloned an extinct species that lived more than a few minutes, advances in AI and gene editing technology are bringing the possibility closer. Other researchers are focusing on critically endangered species — not quite extinct but about to be. Scientists have successfully cloned a black-footed ferret using decades-old DNA, showing it’s possible to de-extinct animals with old genes. Researchers at the San Diego Zoo Alliance are trying to do the same for the northern white rhino, just two of which exist today. successfully cloned a black-footed ferret successfully cloned a black-footed ferret Why It Matters As cool as it is, why would scientists bother de-extincting species? It’s an expensive, complicated process, so what’s the point? Unlike in “Jurassic Park,” there is a noble goal here. Bringing extinct or critically endangered species back could help protect the planet. Woolly mammoths could help fight climate change, as these animals clear dead or invasive tree species to restore grasslands. Bringing more recently extinct animals back to life would restore a full, healthy ecosystem in some areas to protect other species. In cases like the white rhino, it would right the wrongs of humanity’s past by reversing forced extinction. If nothing else, successful de-extinction would be a big step forward for AI in genetic engineering. It would prove this technology’s potential to fight seemingly impossible odds. Scientists already believe AI-powered gene editing could improve global health and combat climate change, so any advances in the field are worth noticing. could improve global health could improve global health Ethical Complications Despite this vast potential, AI-powered de-extinction has some controversy. Some argue that bringing species like the woolly mammoth into a warmer, harsher climate, which they’re not well-adapted to, may be cruel to these animals. There’s also the argument that the money and time spent on this research could go to other, more immediate climate and health issues. Some also have fears about AI gene editing going too far. Successful de-extinction could snowball into issues of people genetically modifying humans to have unique abilities or creating dangerous genetic weapons. Many of these fears are theoretical but deserve attention as this field advances. AI Could Bring Species Back From the Dead While there are still some ethical and technological complications, de-extinction shows promise. It would be a major step forward for biotechnology if these projects work out, and AI will likely be the difference-maker. AI has plenty of ethical and environmental concerns. However, it’s hard to deny its potential for protecting the natural world. Using it for conservation efforts could ensure the world benefits more from this technology than it suffers.

The Role of AI in Resurrecting Extinct Species

বিলুপ্ত প্রজাতির পুনরুত্থানে এআই-এর ভূমিকা

Die Rolle der KI bei der Wiederbelebung ausgestorbener Arten

El papel de la IA en la resurrección de especies extintas

Le rôle de l’IA dans la résurrection des espèces disparues

विलुप्त प्रजातियों को पुनर्जीवित करने में एआई की भूमिका

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