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- HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “ ” award, featuring the most promising and innovative companies in the Seattle startup ecosystem. Seattle, WA - May 23,2023 Startups of the Year The list, compiled by the HackerNoon team, showcases that are making significant contributions to the Seattle economy and driving innovation in their respective industries. 200+ startups https://www.youtube.com/watch?v=ZqIW54yMsNc&t=2s&embedable=true Nominations are open. You can nominate your company as the . startup of the year in Seattle here The for Startups of the Year 2023 include: nominated Startups in Seattle Xealth Diagrid Twelve Labs XetData Coda Derisk pyq Logixboard Otto Robotics Protect AI Augment AI Proxi XetData Bungee Tech InsightStudios Pipe17 Coffee Meets Bagel Symbl.ai DefenseStorm Ozette Karat Kevala Moment Veeve Observa Cakework Forum3 Hal9 MotherDuck Read SBL Spiral “We are thrilled to recognize the top startups in Seattle and celebrate their achievements,” said . “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” David Smooke, HackerNoon Founder & CEO is a community-driven voting platform that recognizes startups that are changing technology and the world for the better. Starting May 8th, 2023, the internet can nominate and vote for the best ventures in their city. The voting period will remain open until December 31, 2023, and winners will be announced in January 2024 after a comprehensive evaluation by the HackerNoon team. Startups of the Year 2023 In its , a total of 44,427 startups were nominated in 16 different regions, and the community cast 234,344 votes during a 6-month long campaign in favor of budding companies that were growing and trending across every major city in the world. The received perks from sponsors and a free from HackerNoon. previous iteration 1,382 winners tech company news page As a proponent of the open data movement, HackerNoon releases the unique social data gathered from Startups of the Year on GitHub as “ ” for valuable insights. /where-startups-trend Apart from validation from the world's greenest tech publication, all nominees relevant to their domain, region, and funding. They will also receive an and a free version of their very own on HackerNoon. These pages include business overviews (such as head count, founding year, bio, social links, and domain ranking) coupled with dynamic updates every time the company is mentioned in HackerNoon stories or relevant headlines around the web. get free interviews amazing discount package designed for their startup Tech Company News Page People can , and vote once per day per award for who they think should be Startup of the Year all the way up to December 31st. until June 8th nominate their favorite startups In addition to the award itself, winners receive exclusive interviews, an exclusive NFT from HackerNoon, and a .Tech Domain for life. About HackerNoon HackerNoon is a technology publication that aims to empower developers, entrepreneurs, and tech enthusiasts to share their knowledge and expertise with a global audience. With a readership of over 4M+ monthly visitors, HackerNoon provides a platform for startups and innovators to showcase their work and connect with a vibrant community of like-minded individuals. Did you know that companies that actively engage in blogging experience a whopping 67% increase in leads? Take advantage of HackerNoon's ' ' to kickstart your own company's blogging journey. Startups Package Startups Of The Year 2023 is sponsored by . .Tech Domains For more information about the “Startups of the Year 2023” list and the nominated companies, please visit our . FAQ page Nominations are open. You can nominate your company as the . startup of the year in Seattle here

Startups of The Year 2023: 200+ Startups Nominated in Seattle

Hello everyone... I am here to briefly talk about the experiences and journeys the year 2017 had to offer me, and how some of you could achieve that too. Let me remind you that this is not some kind of inspirational story. I am here to share some of the experiences and opportunities that I got, and how you could easily get that too.. At the end of 2016 As the year 2016 ended, I was a regular 5th Semester Computer Science and Engineering student, studying at NSS College of Engineering, Palakkad, Kerala, India. I was being an active participant in the Entrepreneurship Development Cell (EDC) of our college, but nothing more. The year 2016 had passed by without much excitements.  The only news was our team of 5 getting selected for a startup training program by by selecting our self android product of an android game and then an interview roundA regular B.Tech kid... SV.CO Begining of 2017 At the start of 2017 itself, 5 of us registered our own private software startup, named " ". We signed legal agreements, and registered ourselves at Thiruvanthapuram. I co-founded the startup and was the . Our startup was digitally incubated under . Thus started our startup training and sessions. Karela Chief Technical Officer SV.CO Karela working in Men’s Hostel Alpha Launch at FreshWorks Office , Chennai On March 3rd, we had our first Alpha Product Launch at the office of FreshDesk (now FreshWorks) at Chennai. This was the 1st time that I see for live how a vibrant startup software company works. The entire office was fully vibrant and colorful. The entire setup and atmosphere made me wish I could join the company that day itself. On the Alpha Launch day, we presented our product in front of a jury of experts and received many good feedback. It was also an opportunity for me to meet up and network with many helpful and skillful persons. Product Pitching @ Freshworks This was actually the first time that I visited Chennai city. Smart India Hackathon 2017, Chandigarh I think this was one of the first unexpected and gifted journey of the year. Me and my team of 6, our team named “ ” got selected for solving a problem statement stated in (SIH) 2017 . SIH was an initiative undertaken by the and _AICTE t_o solve the problems faced by different ministries and government sectors by the students themselves. Techsols Smart India Hackathon Government of India Team Techsols at SIH 2017 @ CGC,Chandigarh The selection was an unexpected surprise to us. Moreover, our ministry’s program was scheduled at . All the expenses of the travel was covered and reimbursed by the government. This became an opportunity for us, and we undertook it. Even though the entire Hackathon was for just 2 days, our entire journey took 1 week. We had 4 entire days in train journey, 2 days in Delhi, and 1 days in Chandigarh apart from the event. . Chandigarh Group of Colleges (CGC), Chandigarh This was also the first time that I visit Delhi or Chandigarh You can still apply for SIH 2018, the deadline is Dec 31, 2017. Please do apply . here Customer insights Day at Bangalore Another 1 day trip was to Bangalore for the Customer Insights Day set up by SV.CO at the big office of [24]7 . We got to talk with many experts and get the feedbacks on our product. Customer Insights Day at [24]7 Bangalore This was not however my first visit to Bangalore :-p Visit to Silicon Valley 16–28 June This is one of the major explorations I had in 2017. As part of our training in we were sent to a one week visit to Silicon Valley funded by the Kerala Government and Kerala Startup Mission. This visit had been a life changing event for me.During our trip we visited headquarters of Facebook, Google and Intel . We visited many Investors, Google Launchpad, academic institutions like 42 Coding USA, and North Eastern University. SV.CO At Google HQ, Silicon Valley I also had the opportunity to visit Steve Jobs house, all thanks to Iqbal and Aravind, two random Indians we met on road and gave us a very good time.One entire day we walked through the city of San Fransisco, visited Golden Gate Bridge, etc. etc., the list goes on. The entire experience is more than one blog to explain, so I will shorten it up here. Golden Gate Bridge, San Fransisco IEDC Summit 2017 and NVIDIA GAMER CONNECT These two may not seem like a grand event, but both these events had given some great memories for me. Meeting many talented guys, seeing their good works , all of this came true at IEDC Summit. I also got to re-energise my old habit of gaming at the Gamer connect too... Nvidia Gamer Connect, Kochi Winning Digital Masala Challenge 2017, Kolkata When December had arrived, I thought maybe all the fun in this year was about to end. And somehow God laughed at me , and gave this surprise. Me and my team of 6 got selected for participating in the Digital Masala Challenge competition organised by and Facebook at Kolkata. The selection was a surprise to all of us in the team. Youth Ki Awaaz The entire travel was sponsored by Facebook and Youth Ki Awaaz. They booked us flight tickets, arranged our stay in a 5 star Hotel (The Lalit Great Eastern), and even booked the cabs for us. I personally was shocked by all these hospitality. Even though I have attended many hackathons and competitions, I had never received such a luxurious treatment. The competition was stretched for 2 days ( 17-18 Dec) with Idea Pitching at 18th evening. Among all the participating teams, ours was on the most youngest. But somehow when the results came, we won the prize money of $5000, which came as a shocker for all of us.Due to lack of timing , we could not roam much around the city, but we did visit the Tomb of Mother Theresa, Quest Mall, and Eden Gardens Stadium.This again was another first time trip of mine to Kolkata. My team for DMC, Kolkate Conclusion If you thought that all of these trips and explorations came because I was such a nerd, then let me tell you something, all of this can easily be achieved by you too. All you have to do is take up some necessary actions. All the opportunities that I got are still present. If you just look up your eyes and search for it, you can easily spot them. It seems there are many more present now. All the voyages that I undertook where not on my own. I had a team of friends besides me in all the journey that I had this year. Always have a team that you can depend upon, a team that you can call your own. I am not some super kid or genius. I am just an ordinary final year engineering student, who lived his year doing the work he loved, and do it wholeheartedly. Do the work you love, and enjoy doing it. Quoting Amir Khan from the film 3 idiots, “ Don’t run behind success, let the success run behind you” All these opportunities, does not mean that I have become some successful person. I am still a struggling engineering developer. But all I can say is that the explorations that I had last year, gave some amazing memories and experiences, which makes me look forward to another year. Happy New Year to all..

Exploration of an Engineering student from Kerala in the year 2017

Authors:
(1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author;
(2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India;
(3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction 2 Free fermionic model 3 Many body quantum Otto cycle 4 Universal scalings in work output 5 Transverse Ising model as working medium 6 Conclusion and References 6 Conclusion We construct a many body quantum Otto cycle with a WM that undergoes a quantum phase transition. The non-unitary strokes of the cycle are powered by finite temperature baths, while the unitary strokes involve driving the WM close to the critical point. This driving leads to nonadiabatic excitations which can be quantified using relative excess energy that follows universal scalings with the rate of driving as well as the temperature of the cold bath. The excess energy can be linked to the output work of the engine which thus manifests the universal scalings shown by the excess energy. Notably, we show that higher values of the cold bath temperature TC allows one to operate the engine close to the adiabatic limit for lower values of τ2 ≈ τmin, which further follows universal scaling relations. This raises interesting questions regarding the importance of control methods such as shortcuts to adiabaticity [41], or bath engineering [23], for finite temperature quantum heat engines. Furthermore, our results for one-dimensional transverse Ising model WM suggest the existence of an optimal value of the cold bath temperature TC > 0, for operating the QHE with high work output at high power. These counterintuitive results stem from the dominance of thermal fluctuations over quantum fluctuations in finite-temperature quantum critical heat engines, for higher bath temperatures. Acknowledgements. R.B.S. and U.D. acknowledge the use of HPC facility Chandra at IIT Palakkad. U.D. acknowledges support from SERB (SPG/2022/000708). V.M. acknowledges support from SERB through MATRICS (Project No. MTR/2021/000055) and a Seed Grant from IISER Berhampur. Data Availability Statement. Any data that support the findings of this study are included within the article. References [1] Jochen Gemmer, Mathias Michel, and G¨unter Mahler. Quantum thermodynamics: Emergence of thermodynamic behavior within composite quantum systems, volume 784. Springer, 2009. [2] Robert Alicki and Ronnie Kosloff. Introduction to Quantum Thermodynamics: History and Prospects. Springer International Publishing, 2018. [3] Sai Vinjanampathy and Janet Anders. Quantum thermodynamics. Contemporary Physics, 57:545, 2016. [4] Johannes Roßnagel, Samuel T. Dawkins, Karl N. Tolazzi, Obinna Abah, Eric Lutz, Ferdinand Schmidt-Kaler, and Kilian Singer. A single-atom heat engine. Science, 352(6283):325–329, 2016. [5] D. von Lindenfels, O. Grab, C. T. Schmiegelow, V. Kaushal, J. Schulz, Mark T. Mitchison, John Goold, F. Schmidt-Kaler, and U. G. Poschinger. Spin heat engine coupled to a harmonic-oscillator flywheel. Phys. Rev. Lett., 123:080602, Aug 2019. [6] John P. S. Peterson, Tiago B. Batalh˜ao, Marcela Herrera, Alexandre M. Souza, Roberto S. Sarthour, Ivan S. Oliveira, and Roberto M. Serra. Experimental characterization of a spin quantum heat engine. Phys. Rev. Lett., 123:240601, Dec 2019. [7] James Klatzow, Jonas N. Becker, Patrick M. Ledingham, Christian Weinzetl, Krzysztof T. Kaczmarek, Dylan J. Saunders, Joshua Nunn, Ian A. Walmsley, Raam Uzdin, and Eilon Poem. Experimental demonstration of quantum effects in the operation of microscopic heat engines. Phys. Rev. Lett., 122:110601, Mar 2019. [8] Obinna Abah and Eric Lutz. Optimal performance of a quantum otto refrigerator. Europhysics Letters, 113(6):60002, apr 2016. [9] Andreas Hartmann, Victor Mukherjee, Glen Bigan Mbeng, Wolfgang Niedenzu, and Wolfgang Lechner. Multi-spin counterdiabatic driving in many-body quantum otto refrigerators. Quantum, 4:377, 2020. [10] Thao P. Le, Jesper Levinsen, Kavan Modi, Meera M. Parish, and Felix A. Pollock. Spinchain model of a many-body quantum battery. Phys. Rev. A, 97:022106, Feb 2018. [11] Francesco Campaioli, Felix A. Pollock, and Sai Vinjanampathy. Quantum batteries - review chapter. arXiv: Quantum Physics, 2018. [12] Victor Montenegro, Marco G. Genoni, Abolfazl Bayat, and Matteo G. A. Paris. Quantum metrology with boundary time crystals. Communications Physics, 6(1):304, Oct 2023. [13] Karl Joulain, Jeremie Drevillon, Younes Ezzahri, and Jose Ordonez-Miranda. Quantum thermal transistor. Phys. Rev. Lett., 116:200601, May 2016. [14] Nikhil Gupt, Srijan Bhattacharyya, Bikash Das, Subhadeep Datta, Victor Mukherjee, and Arnab Ghosh. Floquet quantum thermal transistor. Phys. Rev. E, 106:024110, Aug 2022. [15] H. T. Quan, Yu-xi Liu, C. P. Sun, and Franco Nori. Quantum thermodynamic cycles and quantum heat engines. Phys. Rev. E, 76:031105, Sep 2007. [16] Ronnie Kosloff and Yair Rezek. The quantum harmonic otto cycle. Entropy, 19(4), 2017. [17] M. Campisi and R. Fazio. The power of a critical heat engine. Nat. Commun., 7:11895, 2016. [18] Thomas Fogarty and Thomas Busch. A many-body heat engine at criticality. Quantum Science and Technology, 6(1):015003, nov 2020. [19] Giulia Piccitto, Michele Campisi, and Davide Rossini. The ising critical quantum otto engine. New Journal of Physics, 2022. [20] Yu-Han Ma, Shan-He Su, and Chang-Pu Sun. Quantum thermodynamic cycle with quantum phase transition. Phys. Rev. E, 96:022143, Aug 2017. [21] Watanabe G. Yu YC Guan XW Chen, YY. and del Campo. An interaction-driven many particles quantum heat engine and its universal behavior. npj Quantum Inf, 5, 2019. [22] Revathy B.S, Victor Mukherjee, Uma Divakaran, and Adolfo del Campo. Universal finite-time thermodynamics of many-body quantum machines from kibble-zurek scaling. Phys. Rev. Res., 2:043247, 2020. [23] Revathy B.S, Victor Mukherjee, and Uma Divakaran. Bath engineering enhanced quantum critical engines. Entropy, 24(10), 2022. [24] Revathy B.S, Harsh Sharma, and Uma Divakaran. Improving performance of quantum heat engines by free evolution. arXiv preprint arXiv:2302.07003, 2023. [25] Jacek Dziarmaga. Dynamics of a quantum phase transition: Exact solution of the quantum ising model. Phys. Rev. Lett., 95:245701, Dec 2005. [26] Victor Mukherjee, Uma Divakaran, Amit Dutta, and Diptiman Sen. Quenching dynamics of a quantum xy spin- 1 2 chain in a transverse field. Phys. Rev. B, 76:174303, Nov 2007. [27] K. Sengupta, Diptiman Sen, and Shreyoshi Mondal. Exact results for quench dynamics and defect production in a two-dimensional model. Phys. Rev. Lett., 100:077204, Feb 2008. [28] Maximilian Keck, Simone Montangero, Giuseppe E Santoro, Rosario Fazio, and Davide Rossini. Dissipation in adiabatic quantum computers: lessons from an exactly solvable model. New Journal of Physics, 19(11):113029, nov 2017. [29] Souvik Bandyopadhyay, Sudarshana Laha, Utso Bhattacharya, and Amit Dutta. Exploring the possibilities of dynamical quantum phase transitions in the presence of a markovian bath. Scientific Reports, 8(1):11921, 2018. [30] Bogdan Damski and Wojciech H. Zurek. Adiabatic-impulse approximation for avoided level crossings: From phase-transition dynamics to landau-zener evolutions and back again. Phys. Rev. A, 73:063405, Jun 2006. [31] Anatoli Polkovnikov, Krishnendu Sengupta, Alessandro Silva, and Mukund Vengalattore. Colloquium: Nonequilibrium dynamics of closed interacting quantum systems. Rev. Mod. Phys., 83:863–883, Aug 2011. [32] Adolfo Del Campo and Wojciech H Zurek. Universality of phase transition dynamics: Topological defects from symmetry breaking. International Journal of Modern Physics A, 29(08):1430018, 2014. [33] Amit Dutta, Gabriel Aeppli, Bikas K Chakrabarti, Uma Divakaran, Thomas F Rosenbaum, and Diptiman Sen. Quantum phase transitions in transverse field spin models: from statistical physics to quantum information. Cambridge University Press, 2015. [34] A. Polkovnikov and V. Gritsev. Breakdown of the adiabatic limit in low-dimensional gapless systems. Nature Physics, 4:477, June 2008. [35] C. De Grandi, V. Gritsev, and A. Polkovnikov. Quench dynamics near a quantum critical point: Application to the sine-gordon model. Phys. Rev. B, 81:224301, Jun 2010. [36] C. De Grandi, V. Gritsev, and A. Polkovnikov. Quench dynamics near a quantum critical point. Phys. Rev. B, 81:012303, Jan 2010. [37] Shusa Deng, Gerardo Ortiz, and Lorenza Viola. Dynamical critical scaling and effective thermalization in quantum quenches: Role of the initial state. Phys. Rev. B, 83:094304, 2011. [38] Pierre Pfeuty. The one-dimensional ising model with a transverse field. Annals of Physics, 57(1):79 – 90, 1970. [39] J. E. Bunder and Ross H. McKenzie. Effect of disorder on quantum phase transitions in anisotropic xy spin chains in a transverse field. Phys. Rev. B, 60:344–358, Jul 1999. [40] Elliott Lieb, Theodore Schultz, and Daniel Mattis. Two soluble models of an antiferromagnetic chain. Annals of Physics, 16(3):407 – 466, 1961. [41] Andreas Hartmann, Victor Mukherjee, Wolfgang Niedenzu, and Wolfgang Lechner. Many-body quantum heat engines with shortcuts to adiabaticity. Phys. Rev. Res., 2:023145, May 2020. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Authors: Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Free fermionic model 2 Free fermionic model 3 Many body quantum Otto cycle 3 Many body quantum Otto cycle 4 Universal scalings in work output 4 Universal scalings in work output 5 Transverse Ising model as working medium 5 Transverse Ising model as working medium 6 Conclusion and References 6 Conclusion and References 6 Conclusion We construct a many body quantum Otto cycle with a WM that undergoes a quantum phase transition. The non-unitary strokes of the cycle are powered by finite temperature baths, while the unitary strokes involve driving the WM close to the critical point. This driving leads to nonadiabatic excitations which can be quantified using relative excess energy that follows universal scalings with the rate of driving as well as the temperature of the cold bath. The excess energy can be linked to the output work of the engine which thus manifests the universal scalings shown by the excess energy. Notably, we show that higher values of the cold bath temperature TC allows one to operate the engine close to the adiabatic limit for lower values of τ2 ≈ τmin, which further follows universal scaling relations. This raises interesting questions regarding the importance of control methods such as shortcuts to adiabaticity [41], or bath engineering [23], for finite temperature quantum heat engines. Furthermore, our results for one-dimensional transverse Ising model WM suggest the existence of an optimal value of the cold bath temperature TC > 0, for operating the QHE with high work output at high power. These counterintuitive results stem from the dominance of thermal fluctuations over quantum fluctuations in finite-temperature quantum critical heat engines, for higher bath temperatures. Acknowledgements . R.B.S. and U.D. acknowledge the use of HPC facility Chandra at IIT Palakkad. U.D. acknowledges support from SERB (SPG/2022/000708). V.M. acknowledges support from SERB through MATRICS (Project No. MTR/2021/000055) and a Seed Grant from IISER Berhampur. Acknowledgements Data Availability Statement. Any data that support the findings of this study are included within the article. Data Availability Statement. References [1] Jochen Gemmer, Mathias Michel, and G¨unter Mahler. Quantum thermodynamics: Emergence of thermodynamic behavior within composite quantum systems, volume 784. Springer, 2009. [2] Robert Alicki and Ronnie Kosloff. Introduction to Quantum Thermodynamics: History and Prospects. Springer International Publishing, 2018. [3] Sai Vinjanampathy and Janet Anders. Quantum thermodynamics. Contemporary Physics, 57:545, 2016. [4] Johannes Roßnagel, Samuel T. Dawkins, Karl N. Tolazzi, Obinna Abah, Eric Lutz, Ferdinand Schmidt-Kaler, and Kilian Singer. A single-atom heat engine. Science, 352(6283):325–329, 2016. [5] D. von Lindenfels, O. Grab, C. T. Schmiegelow, V. Kaushal, J. Schulz, Mark T. Mitchison, John Goold, F. Schmidt-Kaler, and U. G. Poschinger. Spin heat engine coupled to a harmonic-oscillator flywheel. Phys. Rev. Lett., 123:080602, Aug 2019. [6] John P. S. Peterson, Tiago B. Batalh˜ao, Marcela Herrera, Alexandre M. Souza, Roberto S. Sarthour, Ivan S. Oliveira, and Roberto M. Serra. Experimental characterization of a spin quantum heat engine. Phys. Rev. Lett., 123:240601, Dec 2019. [7] James Klatzow, Jonas N. Becker, Patrick M. Ledingham, Christian Weinzetl, Krzysztof T. Kaczmarek, Dylan J. Saunders, Joshua Nunn, Ian A. Walmsley, Raam Uzdin, and Eilon Poem. Experimental demonstration of quantum effects in the operation of microscopic heat engines. Phys. Rev. Lett., 122:110601, Mar 2019. [8] Obinna Abah and Eric Lutz. Optimal performance of a quantum otto refrigerator. Europhysics Letters, 113(6):60002, apr 2016. [9] Andreas Hartmann, Victor Mukherjee, Glen Bigan Mbeng, Wolfgang Niedenzu, and Wolfgang Lechner. Multi-spin counterdiabatic driving in many-body quantum otto refrigerators. Quantum, 4:377, 2020. [10] Thao P. Le, Jesper Levinsen, Kavan Modi, Meera M. Parish, and Felix A. Pollock. Spinchain model of a many-body quantum battery. Phys. Rev. A, 97:022106, Feb 2018. [11] Francesco Campaioli, Felix A. Pollock, and Sai Vinjanampathy. Quantum batteries - review chapter. arXiv: Quantum Physics, 2018. [12] Victor Montenegro, Marco G. Genoni, Abolfazl Bayat, and Matteo G. A. Paris. Quantum metrology with boundary time crystals. Communications Physics, 6(1):304, Oct 2023. [13] Karl Joulain, Jeremie Drevillon, Younes Ezzahri, and Jose Ordonez-Miranda. Quantum thermal transistor. Phys. Rev. Lett., 116:200601, May 2016. [14] Nikhil Gupt, Srijan Bhattacharyya, Bikash Das, Subhadeep Datta, Victor Mukherjee, and Arnab Ghosh. Floquet quantum thermal transistor. Phys. Rev. E, 106:024110, Aug 2022. [15] H. T. Quan, Yu-xi Liu, C. P. Sun, and Franco Nori. Quantum thermodynamic cycles and quantum heat engines. Phys. Rev. E, 76:031105, Sep 2007. [16] Ronnie Kosloff and Yair Rezek. The quantum harmonic otto cycle. Entropy, 19(4), 2017. [17] M. Campisi and R. Fazio. The power of a critical heat engine. Nat. Commun., 7:11895, 2016. [18] Thomas Fogarty and Thomas Busch. A many-body heat engine at criticality. Quantum Science and Technology, 6(1):015003, nov 2020. [19] Giulia Piccitto, Michele Campisi, and Davide Rossini. The ising critical quantum otto engine. New Journal of Physics, 2022. [20] Yu-Han Ma, Shan-He Su, and Chang-Pu Sun. Quantum thermodynamic cycle with quantum phase transition. Phys. Rev. E, 96:022143, Aug 2017. [21] Watanabe G. Yu YC Guan XW Chen, YY. and del Campo. An interaction-driven many particles quantum heat engine and its universal behavior. npj Quantum Inf, 5, 2019. [22] Revathy B.S, Victor Mukherjee, Uma Divakaran, and Adolfo del Campo. Universal finite-time thermodynamics of many-body quantum machines from kibble-zurek scaling. Phys. Rev. Res., 2:043247, 2020. [23] Revathy B.S, Victor Mukherjee, and Uma Divakaran. Bath engineering enhanced quantum critical engines. Entropy, 24(10), 2022. [24] Revathy B.S, Harsh Sharma, and Uma Divakaran. Improving performance of quantum heat engines by free evolution. arXiv preprint arXiv:2302.07003, 2023. [25] Jacek Dziarmaga. Dynamics of a quantum phase transition: Exact solution of the quantum ising model. Phys. Rev. Lett., 95:245701, Dec 2005. [26] Victor Mukherjee, Uma Divakaran, Amit Dutta, and Diptiman Sen. Quenching dynamics of a quantum xy spin- 1 2 chain in a transverse field. Phys. Rev. B, 76:174303, Nov 2007. [27] K. Sengupta, Diptiman Sen, and Shreyoshi Mondal. Exact results for quench dynamics and defect production in a two-dimensional model. Phys. Rev. Lett., 100:077204, Feb 2008. [28] Maximilian Keck, Simone Montangero, Giuseppe E Santoro, Rosario Fazio, and Davide Rossini. Dissipation in adiabatic quantum computers: lessons from an exactly solvable model. New Journal of Physics, 19(11):113029, nov 2017. [29] Souvik Bandyopadhyay, Sudarshana Laha, Utso Bhattacharya, and Amit Dutta. Exploring the possibilities of dynamical quantum phase transitions in the presence of a markovian bath. Scientific Reports, 8(1):11921, 2018. [30] Bogdan Damski and Wojciech H. Zurek. Adiabatic-impulse approximation for avoided level crossings: From phase-transition dynamics to landau-zener evolutions and back again. Phys. Rev. A, 73:063405, Jun 2006. [31] Anatoli Polkovnikov, Krishnendu Sengupta, Alessandro Silva, and Mukund Vengalattore. Colloquium: Nonequilibrium dynamics of closed interacting quantum systems. Rev. Mod. Phys., 83:863–883, Aug 2011. [32] Adolfo Del Campo and Wojciech H Zurek. Universality of phase transition dynamics: Topological defects from symmetry breaking. International Journal of Modern Physics A, 29(08):1430018, 2014. [33] Amit Dutta, Gabriel Aeppli, Bikas K Chakrabarti, Uma Divakaran, Thomas F Rosenbaum, and Diptiman Sen. Quantum phase transitions in transverse field spin models: from statistical physics to quantum information. Cambridge University Press, 2015. [34] A. Polkovnikov and V. Gritsev. Breakdown of the adiabatic limit in low-dimensional gapless systems. Nature Physics, 4:477, June 2008. [35] C. De Grandi, V. Gritsev, and A. Polkovnikov. Quench dynamics near a quantum critical point: Application to the sine-gordon model. Phys. Rev. B, 81:224301, Jun 2010. [36] C. De Grandi, V. Gritsev, and A. Polkovnikov. Quench dynamics near a quantum critical point. Phys. Rev. B, 81:012303, Jan 2010. [37] Shusa Deng, Gerardo Ortiz, and Lorenza Viola. Dynamical critical scaling and effective thermalization in quantum quenches: Role of the initial state. Phys. Rev. B, 83:094304, 2011. [38] Pierre Pfeuty. The one-dimensional ising model with a transverse field. Annals of Physics, 57(1):79 – 90, 1970. [39] J. E. Bunder and Ross H. McKenzie. Effect of disorder on quantum phase transitions in anisotropic xy spin chains in a transverse field. Phys. Rev. B, 60:344–358, Jul 1999. [40] Elliott Lieb, Theodore Schultz, and Daniel Mattis. Two soluble models of an antiferromagnetic chain. Annals of Physics, 16(3):407 – 466, 1961. [41] Andreas Hartmann, Victor Mukherjee, Wolfgang Niedenzu, and Wolfgang Lechner. Many-body quantum heat engines with shortcuts to adiabaticity. Phys. Rev. Res., 2:023145, May 2020. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Quantum Critical Engine at Finite Temperatures: Conclusion and References

Authors:
(1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author;
(2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India;
(3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction 2 Free fermionic model 3 Many body quantum Otto cycle 4 Universal scalings in work output 5 Transverse Ising model as working medium 6 Conclusion and References 5 Transverse Ising model as working medium We demonstrate the results derived in the previous section using the prototypical model of transverse Ising model (TIM) as the WM of the Otto cycle. The Hamiltonian of TIM is given by When written in momentum (k) space using the basis |0, 0⟩, |k, 0⟩, |0, −k⟩, |k, −k⟩, the Hamiltonian takes the form During the unitary strokes of the QOC, the transverse field is changed from h1 to h2 in the B → C stroke using the driving protocol and vice versa in the D → A stroke using the protocol For TIM, the value of the critical exponents are ν = z = 1 which gives One can also obtain the expression for excess defects or excess energy by integrating the analytical expression given in Eq. 18 where pk is given by the Landau Zener probability which in our case takes the form This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Authors: Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Free fermionic model 2 Free fermionic model 3 Many body quantum Otto cycle 3 Many body quantum Otto cycle 4 Universal scalings in work output 4 Universal scalings in work output 5 Transverse Ising model as working medium 5 Transverse Ising model as working medium 6 Conclusion and References 6 Conclusion and References 5 Transverse Ising model as working medium We demonstrate the results derived in the previous section using the prototypical model of transverse Ising model (TIM) as the WM of the Otto cycle. The Hamiltonian of TIM is given by When written in momentum (k) space using the basis |0, 0⟩, |k, 0⟩, |0, −k⟩, |k, −k⟩, the Hamiltonian takes the form During the unitary strokes of the QOC, the transverse field is changed from h1 to h2 in the B → C stroke using the driving protocol and vice versa in the D → A stroke using the protocol For TIM, the value of the critical exponents are ν = z = 1 which gives One can also obtain the expression for excess defects or excess energy by integrating the analytical expression given in Eq. 18 where pk is given by the Landau Zener probability which in our case takes the form This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Quantum Critical Engine at Finite Temperatures: Transverse Ising Model as Working Medium

Authors:
(1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author;
(2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India;
(3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction 2 Free fermionic model 3 Many body quantum Otto cycle 4 Universal scalings in work output 5 Transverse Ising model as working medium 6 Conclusion and References 4 Universal scalings in work output The two unitary strokes of the quantum Otto cycle involve driving the Hamiltonian of the WM from one parameter to another. During this driving, the quantum critical point may or may not be crossed. Let us quickly revisit universal scalings in the non-equilibrium dynamics of a quantum system which is initially prepared in the ground state of the Hamiltonian, and is driven through the critical point linearly with a speed 1/τ . The diverging relaxation time close to the CP results in loss of adiabaticity, and thus generation of defects (excitations) no matter how slowly the CP is crossed [30–33]. The density of such defects nex follows a universal power law with the rate of driving where the power is determined by the critical exponents and dimensionality of the system, and is given by Here nex denotes the defect density, ν is the exponent associated with correlation length and z is the dynamical exponent with d being the dimensionality of the system. This scaling between the defect density and the rate of driving is called the Kibble-Zurek scaling which connects the equilibrium critical exponents with the non-equilibrium dynamics. However, this scaling gets modified when the driving starts from a thermal equilibrium state as opposed to the ground state of the system; In Refs. [34–37], the authors consider the case when the driving starts from the critical point and obtain a scaling of defects as a function of temperature and the driving rate, which we present below. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Authors: Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Free fermionic model 2 Free fermionic model 3 Many body quantum Otto cycle 3 Many body quantum Otto cycle 4 Universal scalings in work output 4 Universal scalings in work output 5 Transverse Ising model as working medium 5 Transverse Ising model as working medium 6 Conclusion and References 6 Conclusion and References 4 Universal scalings in work output The two unitary strokes of the quantum Otto cycle involve driving the Hamiltonian of the WM from one parameter to another. During this driving, the quantum critical point may or may not be crossed. Let us quickly revisit universal scalings in the non-equilibrium dynamics of a quantum system which is initially prepared in the ground state of the Hamiltonian, and is driven through the critical point linearly with a speed 1/τ . The diverging relaxation time close to the CP results in loss of adiabaticity, and thus generation of defects (excitations) no matter how slowly the CP is crossed [30–33]. The density of such defects n ex follows a universal power law with the rate of driving where the power is determined by the critical exponents and dimensionality of the system, and is given by ex Here n ex denotes the defect density, ν is the exponent associated with correlation length and z is the dynamical exponent with d being the dimensionality of the system. This scaling between the defect density and the rate of driving is called the Kibble-Zurek scaling which connects the equilibrium critical exponents with the non-equilibrium dynamics. However, this scaling gets modified when the driving starts from a thermal equilibrium state as opposed to the ground state of the system; In Refs. [34–37], the authors consider the case when the driving starts from the critical point and obtain a scaling of defects as a function of temperature and the driving rate, which we present below. ex This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Quantum Critical Engine at Finite Temperatures: Universal Scalings in Work Output

Authors:
(1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author;
(2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India;
(3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction 2 Free fermionic model 3 Many body quantum Otto cycle 4 Universal scalings in work output 5 Transverse Ising model as working medium 6 Conclusion and References 3 Many body quantum Otto cycle We now describe the quantum Otto cycle (QOC) which consist of four strokes (also shown in Fig.1): (ii) Stroke B → C: The WM is disconnected from the hot bath and α is changed from α1 to α2 using the driving protocol, The evolution being a unitary evolution is given by the von-Neumann equation of motion: In this work, we shall focus on α2 = αc, the critical value, for the reasons that will be explained later. (iii) Stroke C → D: The WM with α = α2 is next connected to the cold bath at a temperature TC till τC so that it reaches the thermal state at D given by (iv) Stroke D → A: In this last stroke, the WM is disconnected from the cold bath, and α is changed back to α1 from α2 using to reach A through unitary dynamics and thus the cycle repeats. Energies at the end of each stroke i is calculated using the equation We characterize the engine performance using the quantities efficiency and power which are computed as This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Authors: Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Free fermionic model 2 Free fermionic model 3 Many body quantum Otto cycle 3 Many body quantum Otto cycle 4 Universal scalings in work output 4 Universal scalings in work output 5 Transverse Ising model as working medium 5 Transverse Ising model as working medium 6 Conclusion and References 6 Conclusion and References 3 Many body quantum Otto cycle We now describe the quantum Otto cycle (QOC) which consist of four strokes (also shown in Fig.1): (ii) Stroke B → C: The WM is disconnected from the hot bath and α is changed from α1 to α2 using the driving protocol, The evolution being a unitary evolution is given by the von-Neumann equation of motion: In this work, we shall focus on α2 = αc, the critical value, for the reasons that will be explained later. (iii) Stroke C → D: The WM with α = α2 is next connected to the cold bath at a temperature TC till τC so that it reaches the thermal state at D given by (iv) Stroke D → A: In this last stroke, the WM is disconnected from the cold bath, and α is changed back to α1 from α2 using to reach A through unitary dynamics and thus the cycle repeats. Energies at the end of each stroke i is calculated using the equation We characterize the engine performance using the quantities efficiency and power which are computed as This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Quantum Critical Engine at Finite Temperatures: Many Body Quantum Otto Cycle

Authors:
(1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author;
(2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India;
(3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction 2 Free fermionic model 3 Many body quantum Otto cycle 4 Universal scalings in work output 5 Transverse Ising model as working medium 6 Conclusion and References Abstract We construct a quantum critical Otto engine that is powered by finite temperature baths. We show that the work output of the engine shows universal power law behavior that depends on the critical exponents of the working medium, as well as on the temperature of the cold bath. Furthermore, higher temperatures of the cold bath allows the engine to approach the limit of adiabatic operation for smaller values of the time period, while the corresponding power shows a maximum at an intermediate value of the cold bath temperature. These counterintuitive results stems from thermal excitations dominating the dynamics at higher temperatures. Keywords: Quantum heat engines, quantum phase transitions, Kibble Zurek scaling 1 Introduction Quantum thermodynamics is a rapidly progressing field aimed at understanding the thermodynamics at the quantum level [1, 2]. The field is getting much attention from the scientific community not just because it provides a link between quantum mechanics and thermodynamics but also because it aids in the development of nanoscale devices that aims to harness the potential benefits of the “quantumness” in their working medium (WM) [3]. The recent advances in the experiments such as trapped ions or ultracold atoms [4, 5], using NMR techniques [6], nitrogen vacancy centres in diamond [7] have made the realization of quantum devices possible. Among the various quantum devices studied, for example, quantum refrigerators [8, 9], quantum batteries [10, 11], quantum sensors [12], quantum thermal transistors [13, 14], etc., our work focusses on quantum heat engines that follow the quantum Otto cycle [15, 16]. The effect of phase transitions in quantum engines has been studied in Refs. [17–24]. While some of these studies concentrated on how to improve the performance of quantum engines with respect to its efficiency and power [23, 24], some focused on showcasing universality in their working [21, 22]. For instance, in Ref. [22], the authors showed that the work output of the engine up to an additive constant follows a universal power law governed by the driving speed with which the quantum critical points are crossed, where the exponent of the power law is determined by the critical points (CP) crossed. However, they considered the case of relaxing bath that takes the WM close to its ground state so that Kibble Zurek mechanism becomes relevant. On the other hand, in this paper, we specifically use thermal baths at different temperatures and study the effect of thermal excitations on the scalings of the work output. As discussed before, we prepare a many body quantum heat engine using a free fermionic model as WM that undergoes a quantum phase transition. We describe the details of the free fermionic model in Section 2, elaborate on the many body quantum Otto cycle that the engine follows in Section 3, followed by the universal scalings shown by the engine in Section 4. We demonstrate our results using the transverse field Ising model in Section 5 and finally conclude in Section 6. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Authors: Authors: (1) Revathy B S, Raman Research Institute, Bengaluru, 560080, Karnataka, India and Corresponding author; (2) Victor Mukherjee, Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India; (3) Uma Divakaran, Department of Physics, Indian Institute of Technology Palakkad, Palakkad, 678623, Kerala, India. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Free fermionic model 2 Free fermionic model 3 Many body quantum Otto cycle 3 Many body quantum Otto cycle 4 Universal scalings in work output 4 Universal scalings in work output 5 Transverse Ising model as working medium 5 Transverse Ising model as working medium 6 Conclusion and References 6 Conclusion and References Abstract We construct a quantum critical Otto engine that is powered by finite temperature baths. We show that the work output of the engine shows universal power law behavior that depends on the critical exponents of the working medium, as well as on the temperature of the cold bath. Furthermore, higher temperatures of the cold bath allows the engine to approach the limit of adiabatic operation for smaller values of the time period, while the corresponding power shows a maximum at an intermediate value of the cold bath temperature. These counterintuitive results stems from thermal excitations dominating the dynamics at higher temperatures. Keywords : Quantum heat engines, quantum phase transitions, Kibble Zurek scaling Keywords 1 Introduction Quantum thermodynamics is a rapidly progressing field aimed at understanding the thermodynamics at the quantum level [1, 2]. The field is getting much attention from the scientific community not just because it provides a link between quantum mechanics and thermodynamics but also because it aids in the development of nanoscale devices that aims to harness the potential benefits of the “quantumness” in their working medium (WM) [3]. The recent advances in the experiments such as trapped ions or ultracold atoms [4, 5], using NMR techniques [6], nitrogen vacancy centres in diamond [7] have made the realization of quantum devices possible. Among the various quantum devices studied, for example, quantum refrigerators [8, 9], quantum batteries [10, 11], quantum sensors [12], quantum thermal transistors [13, 14], etc., our work focusses on quantum heat engines that follow the quantum Otto cycle [15, 16]. The effect of phase transitions in quantum engines has been studied in Refs. [17–24]. While some of these studies concentrated on how to improve the performance of quantum engines with respect to its efficiency and power [23, 24], some focused on showcasing universality in their working [21, 22]. For instance, in Ref. [22], the authors showed that the work output of the engine up to an additive constant follows a universal power law governed by the driving speed with which the quantum critical points are crossed, where the exponent of the power law is determined by the critical points (CP) crossed. However, they considered the case of relaxing bath that takes the WM close to its ground state so that Kibble Zurek mechanism becomes relevant. On the other hand, in this paper, we specifically use thermal baths at different temperatures and study the effect of thermal excitations on the scalings of the work output. As discussed before, we prepare a many body quantum heat engine using a free fermionic model as WM that undergoes a quantum phase transition. We describe the details of the free fermionic model in Section 2, elaborate on the many body quantum Otto cycle that the engine follows in Section 3, followed by the universal scalings shown by the engine in Section 4. We demonstrate our results using the transverse field Ising model in Section 5 and finally conclude in Section 6. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Quantum Critical Engine at Finite Temperatures: Abstract and Introduction

MIAMI, FL, June 21st, 2024/Chainwire/--Beyond market trends, FLOKI has emerged as a significant entity in the meme coin sector. Its success is driven by practical applications, a robust presence, and a dedicated community. A key factor behind FLOKI's prominence is its strong community support. Notable mentions include a tweet from Elon Musk's parody account with 207k followers, stating, “FLOKI objectively offers much better utility than other memecoins on the market.” Crypto analyst Crypto_Scofield also praised FLOKI's rapid development, noting its leadership among major meme coins like $PEPE, $WIF, and $BONK. Social Media and Community Engagement Data from LunarCrush, a social media analytics tool, shows that in the last six months, FLOKI’s social interactions skyrocketed by 320%, and its social dominance was up 109%. Social Dominance measures the "share of voice" across social media, as per LunarCrush. FLOKI has been actively pursuing new developments to strengthen its ecosystem. The team introduced a trading bot and achieved major listings, like Kraken adding FLOKI to its exchange listing roadmap, just in the last 24 hours. FLOKI introduced a Telegram-based trading bot tool that enables holders to trade any token on the BNB Chain network. Public availability is expected in mid-June. The service will later be expanded to the Ethereum and Base blockchains. The bot, requiring FLOKI tokens for use, aims to increase token demand through a fee structure that supports buying pressure. The bot charges a 1% fee on every transaction, with 50% of the collected fees used to buy FLOKI on the open market, thus contributing to buying pressure. Some other examples of utility include FLOKI launching a decentralized domain name service on the BNB Chain mainnet. This service allows users to create .FLOKI domain names, representing their on-chain identities. FLOKI roadmap shows that it is in talks to offer digital banking accounts in partnership with a licensed fintech firm. Users can link these accounts to FLOKI debit cards for smooth transactions in euros, US dollars, and more. Another major project in progress is the launch of Valhalla, FLOKI's play-to-earn NFT gaming metaverse. Furthermore, FLOKIFi Locker Protocol has also been integrated into DEXTools. Collaborations with Inverse Finance and Venus Protocol allow users to stake their FLOKI tokens and use them as collateral for borrowing major cryptocurrencies like BTC and ETH. The partnership goes beyond the ecosystem, i.e IRL events too. Looking ahead, FLOKI's marketing efforts are likely to be even more impactful than in the past. As explained in a tweet by Sabre, Advisor, Creative Marketing Director, FLOKI, the past marketing partnerships are likely to be eclipsed by what's to come. https://x.com/SabreEthereum/status/1798750647937900695 Sports Partnerships To name a few, FLOKI became the sleeve sponsor for Kerala Blasters FC, a popular soccer team in India. Along with being the shirt sponsor of The India Maharajas. This partnership introduces the FLOKI brand to over 700 million cricket fans globally. FLOKI and its sister token, TokenFi, also formed a strategic marketing partnership with two legendary cricket teams, the Manipal Tigers and the Bhilwara Kings. The team also partnered with Spanish soccer team Cádiz CF, part of the La Liga division. The FLOKI logo features prominently on the jerseys and LED displays of SSC Napoli and at events hosted by World Table Tennis (WTT), reaching over half a billion Tennis fans. Last but not the least, FLOKI appeared on the Alfa Romeo F1 Team ORLEN cars in 2022 throughout the season. Marketing and Strategic Partnerships FLOKI has launched several aggressive marketing campaigns to boost adoption and awareness worldwide. The platform anticipates that upcoming initiatives will surpass the impact of previous efforts. In Nigeria, the team announced a three-month campaign targeting the country's high percentage of crypto users, including advertising on major news platforms and business publications and collaborating with 16 digital influencers to reach over 93 million people. In Vietnam, FLOKI initiated a comprehensive campaign aimed at the crypto user base with extensive advertising and local events to engage the community and build brand loyalty. Vietnam, which ranks 15th in global population, also has one of the highest crypto adoption rates. FLOKI has also made strides in Turkey, featuring prominently on television with commercials on popular channels to leverage Turkey's large and active crypto community. In the Middle East and North Africa (MENA) region, FLOKI's marketing initiatives include high-profile campaigns in countries like the UAE, where crypto adoption is rapidly growing. In the United States, FLOKI executed high-visibility billboard campaigns, notably in New York City's Times Square prime location beside the Nasdaq billboard in Times Square, capturing the attention of hundreds of thousands of passersby. FLOKI’s educational initiatives include regular AMA sessions. These sessions provide transparency and allow direct engagement with the community. https://www.youtube.com/embed/x4BIDZljzQ8?embedable=true With TokenFi joining its ecosystem, FLOKI plans to tap into the growing tokenization industry, projected to be worth $16 trillion by 2030, according to BlackRock CEO Larry Fink. Summarizing FLOKI’s strategy, Sabre explains it well: “attract retail investors, run impactful marketing campaigns, and leave a lasting impression.” Financial and Ecosystem Milestones Meanwhile, the FlokiFi platform has a total value locked (TVL) of $110.56 million secured through 2,372 locks. It supports 16 different blockchains and has locked 86 NFTs. Moreover, over 2.3 trillion FLOKI tokens, valued at $436.61 million, have been secured in the FLOKI staking program. In a show of strong support, DWF Labs said it will purchase $12 million in FLOKI tokens. This purchase will be a mix of market acquisitions and tokens from the Floki treasury. As of 2024, FLOKI has been one of the top performers, rising more than sixfold year-to-date. About 86% of FLOKI holders are in profit, according to data from IntoTheBlock. About Floki Floki is the people’s cryptocurrency and utility token of the Floki Ecosystem. Floki aims to become the world’s most known and most used cryptocurrency and intends to achieve this ambitious goal through a focus on utility, philanthropy, community, and marketing. Floki currently has 490,000+ holders and a strong brand recognized by billions of people worldwide due to its strategic marketing partnerships. Website Twitter Contact Community Relations Officer Pedro Vidal Floki Marketing@Floki.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here, MIAMI, FL, June 21st, 2024/Chainwire/--Beyond market trends, FLOKI has emerged as a significant entity in the meme coin sector. Its success is driven by practical applications, a robust presence, and a dedicated community. A key factor behind FLOKI's prominence is its strong community support. Notable mentions include a tweet from Elon Musk's parody account with 207k followers, stating, “FLOKI objectively offers much better utility than other memecoins on the market.” Crypto analyst Crypto_Scofield also praised FLOKI's rapid development, noting its leadership among major meme coins like $PEPE, $WIF, and $BONK. tweet tweet praised praised Social Media and Community Engagement Data from LunarCrush , a social media analytics tool, shows that in the last six months, FLOKI’s social interactions skyrocketed by 320%, and its social dominance was up 109%. Social Dominance measures the "share of voice" across social media, as per LunarCrush. LunarCrush LunarCrush FLOKI has been actively pursuing new developments to strengthen its ecosystem. The team introduced a trading bot and achieved major listings, like Kraken adding FLOKI to its exchange listing roadmap, just in the last 24 hours. introduced introduced adding adding FLOKI introduced a Telegram-based trading bot tool that enables holders to trade any token on the BNB Chain network. Public availability is expected in mid-June. The service will later be expanded to the Ethereum and Base blockchains. The bot, requiring FLOKI tokens for use, aims to increase token demand through a fee structure that supports buying pressure. The bot charges a 1% fee on every transaction, with 50% of the collected fees used to buy FLOKI on the open market, thus contributing to buying pressure. Some other examples of utility include FLOKI launching a decentralized domain name service on the BNB Chain mainnet. This service allows users to create .FLOKI domain names, representing their on-chain identities. include include FLOKI roadmap shows that it is in talks to offer digital banking accounts in partnership with a licensed fintech firm. Users can link these accounts to FLOKI debit cards for smooth transactions in euros, US dollars, and more. Another major project in progress is the launch of Valhalla, FLOKI's play-to-earn NFT gaming metaverse. shows shows launch launch Furthermore, FLOKIFi Locker Protocol has also been integrated into DEXTools. Collaborations with Inverse Finance and Venus Protocol allow users to stake their FLOKI tokens and use them as collateral for borrowing major cryptocurrencies like BTC and ETH. The partnership goes beyond the ecosystem, i.e IRL events too. Looking ahead, FLOKI's marketing efforts are likely to be even more impactful than in the past. As explained in a tweet by Sabre, Advisor, Creative Marketing Director, FLOKI, the past marketing partnerships are likely to be eclipsed by what's to come. tweet tweet https://x.com/SabreEthereum/status/1798750647937900695 https://x.com/SabreEthereum/status/1798750647937900695 https://x.com/SabreEthereum/status/1798750647937900695 Sports Partnerships To name a few, FLOKI became the sleeve sponsor for Kerala Blasters FC, a popular soccer team in India. Along with being the shirt sponsor of The India Maharajas. This partnership introduces the FLOKI brand to over 700 million cricket fans globally. FLOKI and its sister token, TokenFi, also formed a strategic marketing partnership with two legendary cricket teams, the Manipal Tigers and the Bhilwara Kings. The team also partnered with Spanish soccer team Cádiz CF, part of the La Liga division. The FLOKI logo features prominently on the jerseys and LED displays of SSC Napoli and at events hosted by World Table Tennis (WTT), reaching over half a billion Tennis fans. Last but not the least, FLOKI appeared on the Alfa Romeo F1 Team ORLEN cars in 2022 throughout the season. Marketing and Strategic Partnerships FLOKI has launched several aggressive marketing campaigns to boost adoption and awareness worldwide. The platform anticipates that upcoming initiatives will surpass the impact of previous efforts. In Nigeria, the team announced a three-month campaign targeting the country's high percentage of crypto users, including advertising on major news platforms and business publications and collaborating with 16 digital influencers to reach over 93 million people. announced announced In Vietnam, FLOKI initiated a comprehensive campaign aimed at the crypto user base with extensive advertising and local events to engage the community and build brand loyalty. Vietnam, which ranks 15th in global population, also has one of the highest crypto adoption rates. FLOKI has also made strides in Turkey, featuring prominently on television with commercials on popular channels to leverage Turkey's large and active crypto community. In the Middle East and North Africa (MENA) region, FLOKI's marketing initiatives include high-profile campaigns in countries like the UAE, where crypto adoption is rapidly growing. strides strides marketing marketing In the United States, FLOKI executed high-visibility billboard campaigns, notably in New York City's Times Square prime location beside the Nasdaq billboard in Times Square, capturing the attention of hundreds of thousands of passersby. executed executed FLOKI’s educational initiatives include regular AMA sessions. These sessions provide transparency and allow direct engagement with the community. https://www.youtube.com/embed/x4BIDZljzQ8?embedable=true https://www.youtube.com/embed/x4BIDZljzQ8?embedable=true With TokenFi joining its ecosystem, FLOKI plans to tap into the growing tokenization industry, projected to be worth $16 trillion by 2030, according to BlackRock CEO Larry Fink. Summarizing FLOKI’s strategy, Sabre explains it well: “attract retail investors, run impactful marketing campaigns, and leave a lasting impression.” Summarizing FLOKI’s strategy, Sabre explains it well: “attract retail investors, run impactful marketing campaigns, and leave a lasting impression.” Financial and Ecosystem Milestones Meanwhile, the FlokiFi platform has a total value locked (TVL) of $110.56 million secured through 2,372 locks. It supports 16 different blockchains and has locked 86 NFTs. Moreover, over 2.3 trillion FLOKI tokens, valued at $436.61 million, have been secured in the FLOKI staking program. FlokiFi FlokiFi In a show of strong support, DWF Labs said it will purchase $12 million in FLOKI tokens. This purchase will be a mix of market acquisitions and tokens from the Floki treasury. As of 2024, FLOKI has been one of the top performers, rising more than sixfold year-to-date. About 86% of FLOKI holders are in profit, according to data from IntoTheBlock. purchase purchase data data About Floki Floki is the people’s cryptocurrency and utility token of the Floki Ecosystem. Floki aims to become the world’s most known and most used cryptocurrency and intends to achieve this ambitious goal through a focus on utility, philanthropy, community, and marketing. Floki currently has 490,000+ holders and a strong brand recognized by billions of people worldwide due to its strategic marketing partnerships. Website Website Twitter Twitter Contact Community Relations Officer Pedro Vidal Floki Marketing@Floki.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here, This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here , here

FLOKI Unveils New Developments And Strategic Marketing Initiatives

This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeffrey Ouyang-Zhang, UT Austin (2) Daniel J. Diaz, UT Austin (3) Adam R. Klivans, UT Austin (4) Philipp Krähenbühl, UT Austin Table of Links Abstract & Introduction Related Works Preliminary Methodology Results Limitations Conclusion, Acknowledgements, & References Experimental Setting & Additional Results 7 Conclusion We present a method that efficiently scales thermodynamic stability prediction from single mutations to higher-order mutations. Our key insight is that the effects of mutations on the same protein are correlated. Thus, for a target protein, it suffices to run a deep backbone once and decode the effect of all mutations simultaneously using a shallow decoder. With the AlphaFold model as our backbone, our method outperforms existing methods on a variety of single and multiple mutation benchmarks. Our method scales to millions of mutations with minimal computational overhead and runs in a fraction of the time it would take prior works. 8 Acknowledgements This work is supported by the NSF AI Institute for Foundations of Machine Learning (IFML). References [1] Randomization of genes by pcr mutagenesis. Genome research 2(1), 28–33 (1992) 2 [2] Adams, J.P., Brown, M.J., Diaz-Rodriguez, A., Lloyd, R.C., Roiban, G.D.: Biocatalysis: A Pharma Perspective. Advanced Synthesis and Catalysis 361(11), 2421–2432 (2019). https://doi.org/10.1002/adsc.201900424 1 [3] Ahdritz, G., Bouatta, N., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T.J., Berenberg, D., Fisk, I., Zanichelli, N., Zhang, B., Nowaczynski, A., Wang, B., Stepniewska-Dziubinska, M.M., Zhang, S., Ojewole, A., Guney, M.E., Biderman, S., Watkins, A.M., Ra, S., Lorenzo, P.R., Nivon, L., Weitzner, B., Ban, Y.E.A., Sorger, P.K., Mostaque, E., Zhang, Z., Bonneau, R., AlQuraishi, M.: Openfold: Retraining alphafold2 yields new insights into its learning mechanisms and capacity for generalization. bioRxiv (2022). https://doi.org/10.1101/2022.11.20.517210 6 [4] Arnold, F.H.: Design by directed evolution. Accounts of chemical research 31(3), 125–131 (1998) 1, 2, 3 [5] Bell, E.L., Finnigan, W., France, S.P., Green, A.P., Hayes, M.A., Hepworth, L.J., Lovelock, S.L., Niikura, H., Osuna, S., Romero, E., Ryan, K.S., Turner, N.J., Flitsch, S.L.: Biocatalysis. Nature Reviews Methods Primers 1(1), 1–21 (2021). https://doi.org/10.1038/s43586-021-00044-z 1 [6] Benevenuta, S., Pancotti, C., Fariselli, P., Birolo, G., Sanavia, T.: An antisymmetric neural network to predict free energy changes in protein variants. Journal of Physics D: Applied Physics 54(24), 245403 [7] Benevenuta, S., Pancotti, C., Fariselli, P., Birolo, G., Sanavia, T.: An antisymmetric neural network to predict free energy changes in protein variants. Journal of Physics D: Applied Physics 54(24), 245403 (2021) 8 [8] Benevenuta, S., Birolo, G., Sanavia, T., Capriotti, E., Fariselli, P.: Challenges in predicting stabilizing variations: An exploration. Frontiers in Molecular Biosciences 9, 1075570 (2023) 5, 6, 17 [9] Brückner, A., Polge, C., Lentze, N., Auerbach, D., Schlattner, U.: Yeast two-hybrid, a powerful tool for systems biology. International journal of molecular sciences 10(6), 2763–2788 (2009) 1 [10] Cadet, F., Saavedra, E., Syren, P.O., Gontero, B.: Machine learning, epistasis, and protein engineering: From sequence-structure-function relationships to regulation of metabolic pathways. Frontiers in Molecular Biosciences 9, 1098289 (2022) 1 [11] Capriotti, E., Fariselli, P., Casadio, R.: I-mutant2. 0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic acids research 33(suppl_2), W306–W310 (2005) 3, 8 [12] Chen, K., Arnold, F.H.: Tuning the activity of an enzyme for unusual environments: sequential random mutagenesis of subtilisin e for catalysis in dimethylformamide. Proceedings of the National Academy of Sciences 90(12), 5618–5622 (1993) 1, 3 [13] Chen, T., Gong, C., Diaz, D.J., Chen, X., Wells, J.T., Wang, Z., Ellington, A., Dimakis, A., Klivans, A., et al.: Hotprotein: A novel framework for protein thermostability prediction and editing. In: The Eleventh International Conference on Learning Representations (2022) 3 [14] Chen, Y., Lu, H., Zhang, N., Zhu, Z., Wang, S., Li, M.: Premps: Predicting the impact of missense mutations on protein stability. PLoS computational biology 16(12), e1008543 (2020) 3, 8 [15] Cheng, J., Randall, A., Baldi, P.: Prediction of protein stability changes for single-site mutations using support vector machines. Proteins: Structure, Function, and Bioinformatics 62(4), 1125–1132 (2006) 3, 8 [16] Dehghanpoor, R., Ricks, E., Hursh, K., Gunderson, S., Farhoodi, R., Haspel, N., Hutchinson, B., Jagodzinski, F.: Predicting the effect of single and multiple mutations on protein structural stability. Molecules 23(2), 251 (2018) 4, 7 [17] Dehouck, Y., Kwasigroch, J.M., Gilis, D., Rooman, M.: Popmusic 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC bioinformatics 12(1), 1–12 (2011) 3, 8 [18] Diaz, D.J., Gong, C., Ouyang-Zhang, J., Loy, J.M., Wells, J.T., Yang, D., Ellington, A.J., Dimakis, A., Klivans, A.R.: Stability oracle: A structure-based graph-transformer for identifying stabilizing mutations (2023). https://doi.org/10.1101/2023.05.15.540857 3, 4, 7, 8, 16, 17, 18, 19 [19] Diaz, D.J., Kulikova, A.V., Ellington, A.D., Wilke, C.O.: Using machine learning to predict the effects and consequences of mutations in proteins. Current Opinion in Structural Biology 78, 102518 (2023) 3 [20] d’Oelsnitz, S., Diaz, D.J., Acosta, D.J., Schechter, M.W., Minus, M.B., Howard, J.R., Do, H., Loy, J., Alper, H., Ellington, A.D.: Synthetic microbial sensing and biosynthesis of amaryllidaceae alkaloids. bioRxiv pp. 2023–04 (2023) 3 [21] d’Oelsnitz, S., Kim, W., Burkholder, N.T., Javanmardi, K., Thyer, R., Zhang, Y., Alper, H.S., Ellington, A.D.: Using fungible biosensors to evolve improved alkaloid biosyntheses. Nature Chemical Biology 18(9), 981–989 (2022) 1 [22] Ellefson, J.W., Gollihar, J., Shroff, R., Shivram, H., Iyer, V.R., Ellington, A.D.: Synthetic evolutionary origin of a proofreading reverse transcriptase. Science 352(6293), 1590–1593 (2016) 1 [23] Feng, X., Sanchis, J., Reetz, M.T., Rabitz, H.: Enhancing the efficiency of directed evolution in focused enzyme libraries by the adaptive substituent reordering algorithm. Chemistry–A European Journal 18(18), 5646–5654 (2012) 3 [24] Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J.K., Brock, K., Gal, Y., Marks, D.S.: Disease variant prediction with deep generative models of evolutionary data. Nature 599(7883), 91–95 (2021) 9 [25] Gebauer, M., Skerra, A.: Engineered protein scaffolds as next-generation therapeutics. Annual Review of Pharmacology and Toxicology 60, 391–415 (2020). https://doi.org/10.1146/annurev-pharmtox-010818- 021118 1 [26] Gerasimavicius, L., Livesey, B.J., Marsh, J.A.: Correspondence between functional scores from deep mutational scans and predicted effects on protein stability. bioRxiv pp. 2023–02 (2023) 9 [27] Giver, L., Gershenson, A., Freskgard, P.O., Arnold, F.H.: Directed evolution of a thermostable esterase. Proceedings of the National Academy of Sciences 95(22), 12809–12813 (1998) 2, 3 [28] Hie, B.L., Shanker, V.R., Xu, D., Bruun, T.U., Weidenbacher, P.A., Tang, S., Wu, W., Pak, J.E., Kim, P.S.: Efficient evolution of human antibodies from general protein language models. Nature Biotechnology (2023) 3 [29] Jaroszewicz, W., Morcinek-Orłowska, J., Pierzynowska, K., Gaffke, L., W˛egrzyn, G.: Phage display and other peptide display technologies. FEMS Microbiology Reviews 46(2), fuab052 (2022) 1 [30] Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems (TOIS) 20(4), 422–446 (2002) 6, 17 [31] Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al.: Highly accurate protein structure prediction with alphafold. Nature 596(7873), 583–589 (2021) 2, 3, 4, 6, 9, 10 [32] Kellogg, E.H., Leaver-Fay, A., Baker, D.: Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins: Structure, Function, and Bioinformatics 79(3), 830–838 (2011) 3 [33] Klabunde, T., Petrassi, H.M., Oza, V.B., Raman, P., Kelly, J.W., Sacchettini, J.C.: Rational design of potent human transthyretin amyloid disease inhibitors. Nature structural biology 7(4), 312–321 (2000) 1, 2 [34] Kouba, P., Kohout, P., Haddadi, F., Bushuiev, A., Samusevich, R., Sedlar, J., Damborsky, J., Pluskal, T., Sivic, J., Mazurenko, S.: Machine learning-guided protein engineering. ACS Catalysis 13, 13863–13895 (2023) 3 [35] Laimer, J., Hofer, H., Fritz, M., Wegenkittl, S., Lackner, P.: Maestro-multi agent stability prediction upon point mutations. BMC bioinformatics 16(1), 1–13 (2015) 3, 4, 7, 8 [36] Li, B., Yang, Y.T., Capra, J.A., Gerstein, M.B.: Predicting changes in protein thermodynamic stability upon point mutation with deep 3d convolutional neural networks. PLoS computational biology 16(11), e1008291 (2020) 3, 4, 8 [37] Li, G., Panday, S.K., Alexov, E.: Saafec-seq: a sequence-based method for predicting the effect of single point mutations on protein thermodynamic stability. International journal of molecular sciences 22(2), 606 (2021) 3 [38] Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., et al.: Language models of protein sequences at the scale of evolution enable accurate structure prediction. bioRxiv (2022) 3, 4, 6, 9, 10, 18 [39] Loell, K., Nanda, V.: Marginal protein stability drives subcellular proteome isoelectric point. Proceedings of the National Academy of Sciences 115(46), 11778–11783 (2018) 1 [40] Lu, H., Diaz, D.J., Czarnecki, N.J., Zhu, C., Kim, W., Shroff, R., Acosta, D.J., Alexander, B.R., Cole, H.O., Zhang, Y., et al.: Machine learning-aided engineering of hydrolases for pet depolymerization. Nature 604(7907), 662–667 (2022) 3 [41] Meghwanshi, G.K., Kaur, N., Verma, S., Dabi, N.K., Vashishtha, A., Charan, P.D., Purohit, P., Bhandari, H.S., Bhojak, N., Kumar, R.: Enzymes for pharmaceutical and therapeutic applications. Biotechnology and Applied Biochemistry 67(4), 586–601 (2020). https://doi.org/10.1002/bab.1919 1 [42] Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., Rives, A.: Language models enable zero-shot prediction of the effects of mutations on protein function. Advances in Neural Information Processing Systems 34, 29287–29303 (2021) 9, 18 [43] Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., Steinegger, M.: Colabfold: making protein folding accessible to all. Nature methods 19(6), 679–682 (2022) 6 [44] Montanucci, L., Capriotti, E., Frank, Y., Ben-Tal, N., Fariselli, P.: Ddgun: an untrained method for the prediction of protein stability changes upon single and multiple point variations. BMC bioinformatics 20, 1–10 (2019) 3, 4, 7, 8, 16, 18 [45] Montanucci, L., Savojardo, C., Martelli, P.L., Casadio, R., Fariselli, P.: On the biases in predictions of protein stability changes upon variations: the inps test case. Bioinformatics 35(14), 2525–2527 (2019) 16 [46] Musdal, Y., Govindarajan, S., Mannervik, B.: Exploring sequence-function space of a poplar glutathione transferase using designed information-rich gene variants. Protein Engineering, Design and Selection 30(8), 543–549 (2017) 3 [47] Nijkamp, E., Ruffolo, J., Weinstein, E.N., Naik, N., Madani, A.: Progen2: exploring the boundaries of protein language models. arXiv preprint arXiv:2206.13517 (2022) 9 [48] Nikam, R., Kulandaisamy, A., Harini, K., Sharma, D., Gromiha, M.M.: Prothermdb: thermodynamic database for proteins and mutants revisited after 15 years. Nucleic acids research 49(D1), D420–D424 (2021) 2, 16 [49] Notin, P., Dias, M., Frazer, J., Hurtado, J.M., Gomez, A.N., Marks, D., Gal, Y.: Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. In: International Conference on Machine Learning. pp. 16990–17017. PMLR (2022) 2, 3, 9, 16, 18 [50] Notin, P.M., Van Niekerk, L., Kollasch, A.W., Ritter, D., Gal, Y., Marks, D.: Trancepteve: Combining family-specific and family-agnostic models of protein sequences for improved fitness prediction. bioRxiv pp. 2022–12 (2022) 9 [51] Nutschel, C., Fulton, A., Zimmermann, O., Schwaneberg, U., Jaeger, K.E., Gohlke, H.: Systematically scrutinizing the impact of substitution sites on thermostability and detergent tolerance for bacillus subtilis lipase a. Journal of chemical information and modeling 60(3), 1568–1584 (2020) 9 [52] Paik, I., Ngo, P.H., Shroff, R., Diaz, D.J., Maranhao, A.C., Walker, D.J., Bhadra, S., Ellington, A.D.: Improved bst dna polymerase variants derived via a machine learning approach. Biochemistry (2021) 3 [53] Pak, M.A., Markhieva, K.A., Novikova, M.S., Petrov, D.S., Vorobyev, I.S., Maksimova, E.S., Kondrashov, F.A., Ivankov, D.N.: Using alphafold to predict the impact of single mutations on protein stability and function. Plos one 18(3), e0282689 (2023) 3 [54] Pancotti, C., Benevenuta, S., Birolo, G., Alberini, V., Repetto, V., Sanavia, T., Capriotti, E., Fariselli, P.: Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset. Briefings in Bioinformatics 23(2), bbab555 (2022) 2, 8, 16, 17 [55] Pancotti, C., Benevenuta, S., Repetto, V., Birolo, G., Capriotti, E., Sanavia, T., Fariselli, P.: A deep-learning sequence-based method to predict protein stability changes upon genetic variations. Genes 12(6), 911 (2021) 8 [56] Pires, D.E., Ascher, D.B., Blundell, T.L.: Duet: a server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic acids research 42(W1), W314–W319 (2014) 3, 8 [57] Pires, D.E., Ascher, D.B., Blundell, T.L.: mcsm: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 30(3), 335–342 (2014) 8 [58] Pucci, F., Schwersensky, M., Rooman, M.: Artificial intelligence challenges for predicting the impact of mutations on protein stability. Current opinion in structural biology 72, 161–168 (2022) 17 [59] Qiu, Y., Wei, G.W.: Persistent spectral theory-guided protein engineering. Nature Computational Science pp. 1–15 (2023) 3, 6, 17 [60] Rao, R.M., Liu, J., Verkuil, R., Meier, J., Canny, J., Abbeel, P., Sercu, T., Rives, A.: Msa transformer. In: International Conference on Machine Learning. pp. 8844–8856. PMLR (2021) 3, 9, 10 [61] Riesselman, A.J., Ingraham, J.B., Marks, D.S.: Deep generative models of genetic variation capture the effects of mutations. Nature methods 15(10), 816–822 (2018) 9 [62] Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., Fergus, R.: Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. PNAS (2019). https://doi.org/10.1101/622803 3, 7 [63] Rodrigues, C.H., Pires, D.E., Ascher, D.B.: Dynamut: predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic acids research 46(W1), W350–W355 (2018) 3, 8 [64] Rodrigues, C.H., Pires, D.E., Ascher, D.B.: Dynamut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations. Protein Science 30(1), 60–69 (2021) 3, 4, 7 [65] Sanavia, T., Birolo, G., Montanucci, L., Turina, P., Capriotti, E., Fariselli, P.: Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine. Computational and Structural Biotechnology Journal 18, 1968–1979 (2020). https://doi.org/10.1016/j.csbj.2020.07.011, https://doi.org/10.1016/j.csbj.2020.07.011 6, 17 [66] Savojardo, C., Fariselli, P., Martelli, P.L., Casadio, R.: Inps-md: a web server to predict stability of protein variants from sequence and structure. Bioinformatics 32(16), 2542–2544 (2016) 3, 8 [67] Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., Serrano, L.: The foldx web server: an online force field. Nucleic acids research 33(suppl_2), W382–W388 (2005) 3, 7, 8, 18 [68] Shroff, R., Cole, A.W., Diaz, D.J., Morrow, B.R., Donnell, I., Annapareddy, A., Gollihar, J., Ellington, A.D., Thyer, R.: Discovery of novel gain-of-function mutations guided by structure-based deep learning. ACS synthetic biology 9(11), 2927–2935 (2020) 3 [69] Starr, T.N., Thornton, J.W.: Epistasis in protein evolution. Protein science 25(7), 1204–1218 (2016) 1 [70] Steinegger, M., Soding, J.: Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature biotechnology 35(11), 1026–1028 (2017) 6 [71] Stemmer, W.P.: Rapid evolution of a protein in vitro by dna shuffling. Nature 370(6488), 389–391 (1994) 2, 3 [72] Tsuboyama, K., Dauparas, J., Chen, J., Laine, E., Mohseni Behbahani, Y., Weinstein, J.J., Mangan, N.M., Ovchinnikov, S., Rocklin, G.J.: Mega-scale experimental analysis of protein folding stability in biology and protein design. bioRxiv pp. 2022–12 (2022) 1, 2, 6, 16 [73] Tung, J.W., Heydari, K., Tirouvanziam, R., Sahaf, B., Parks, D.R., Herzenberg, L.A., Herzenberg, L.A.: Modern flow cytometry: a practical approach. Clinics in laboratory medicine 27(3), 453–468 (2007) 1 [74] Umerenkov, D., Shashkova, T.I., Strashnov, P.V., Nikolaev, F., Sindeeva, M., Ivanisenko, N.V., Kardymon, O.L.: Prostata: Protein stability assessment using transformers. bioRxiv pp. 2022–12 (2022) 3, 4, 6, 7, 8, 18, 19 [75] Wittmann, B.J., Johnston, K.E., Wu, Z., Arnold, F.H.: Advances in machine learning for directed evolution. Current opinion in structural biology 69, 11–18 (2021) 3 [76] Wittmann, B.J., Yue, Y., Arnold, F.H.: Informed training set design enables efficient machine learningassisted directed protein evolution. Cell Systems 12(11), 1026–1045 (2021) 3 [77] Worth, C.L., Preissner, R., Blundell, T.L.: Sdm—a server for predicting effects of mutations on protein stability and malfunction. Nucleic acids research 39(suppl_2), W215–W222 (2011) 3, 8 [78] Wu, S., Snajdrova, R., Moore, J.C., Baldenius, K., Bornscheuer, U.T.: Biocatalysis: Enzymatic Synthesis for Industrial Applications. Angewandte Chemie - International Edition 60(1), 88–119 (2021). https://doi.org/10.1002/anie.202006648 1 [79] Yeung, N., Lin, Y.W., Gao, Y.G., Zhao, X., Russell, B.S., Lei, L., Miner, K.D., Robinson, H., Lu, Y.: Rational design of a structural and functional nitric oxide reductase. Nature 462(7276), 1079–1082 (2009) 1, 2 [80] Zhou, B., Lv, O., Yi, K., Xiong, X., Tan, P., Hong, L., Wang, Y.G.: Accurate and definite mutational effect prediction with lightweight equivariant graph neural networks. arXiv preprint arXiv:2304.08299 (2023) 3 [81] Zhou, Y., Pan, Q., Pires, D.E., Rodrigues, C.H., Ascher, D.B.: Ddmut: predicting effects of mutations on protein stability using deep learning. Nucleic Acids Research p. gkad472 (2023) 3

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