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This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency Appendix F: Optimization of optical extraction efficiency An optimal optical external coupling can be chosen to maximize the total conversion efficiency. Equation E9 can be rewritten in terms of quality factors by using the relation κ = ω/Q, which yields For the optics, we have Qo = Qint + Qext, where we separate the intrinsic quality factor Qint, oftentimes fabricationlimited and hence not easily adjustable, from the external coupling quality factor Qex that can be readily engineered through coupler design. The optical extraction efficiency can thus be written in the form where R = Qint/Qex and the total optical quality factor The total efficiency in the low-cooperativity regime is then given by where For a given F, the optimal efficiency is achieved at the critical coupling condition Qex = Qint, or R = 1. [1] H. Haus and W. Huang, Proceedings of the IEEE 79, 1505 (1991). [2] T. Blésin, H. Tian, S. A. Bhave, and T. J. Kippenberg, Phys. Rev. A 104, 052601 (2021). [3] A. Rueda, W. Hease, S. Barzanjeh, and J. M. Fink, npj Quantum Inf. 5, 1 (2019). [4] C. Zhong, Z. Wang, C. Zou, M. Zhang, X. Han, W. Fu, M. Xu, S. Shankar, M. H. Devoret, H. X. Tang, and L. Jiang, Phys. Rev. Lett. 124, 010511 (2020). [5] S. Krastanov, H. Raniwala, J. Holzgrafe, K. Jacobs, M. Lončar, M. J. Reagor, and D. R. Englund, Phys. Rev. Lett. 127, 040503 (2021). [6] L.-M. Duan, M. D. Lukin, J. I. Cirac, and P. Zoller, Nature 414, 413 (2001). [7] M. H. P. Pfeiffer, J. Liu, A. S. Raja, T. Morais, B. Ghadiani, and T. J. Kippenberg, Optica 5, 884 (2018). [8] J. Liu, G. Huang, R. N. Wang, J. He, A. S. Raja, T. Liu, N. J. Engelsen, and T. J. Kippenberg, Nat. Commun. 12, 2236 (2021). [9] H. Tian, J. Liu, B. Dong, J. C. Skehan, M. Zervas, T. J. Kippenberg, and S. A. Bhave, Nat. Commun. 11, 3073 (2020). [10] A. Siddharth, A. Anastasio, G. Lihachev, J. Zhang, Z. Qiu, S. Kenning, R. N. Wang, S. A. Bhave, J. Riemensberger, and T. J. Kippenberg, Hertz-linewidth and frequency-agile photonic integrated extended-DBR lasers (2023), arxiv:2306.03184 [physics]. [11] H. Tian, J. Liu, A. Siddharth, R. N. Wang, T. Blésin, J. He, T. J. Kippenberg, and S. A. Bhave, Nat. Photon. 15, 828 (2021). [12] M. Gao, Q.-F. Yang, Q.-X. Ji, H. Wang, L. Wu, B. Shen, J. Liu, G. Huang, L. Chang, W. Xie, S.-P. Yu, S. B. Papp, J. E. Bowers, T. J. Kippenberg, and K. J. Vahala, Nat. Commun. 13, 3323 (2022). [13] V. B. Braginsky, M. L. Gorodetsky, and V. S. Ilchenko, in Laser Applications, Vol. 2097 (SPIE, 1994) pp. 283–288. [14] A. Schliesser, P. Del’Haye, N. Nooshi, K. J. Vahala, and T. J. Kippenberg, Phys. Rev. Lett. 97, 243905 (2006). [15] K. Leong and J. Mazierska, IEEE Trans. Microw. Theory Tech. 50, 2115 (2002).

Bidirectional Microwave-optical Transduction Based on Integration: Appendix F: Optimization

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency III. DISCUSSION Non-classically correlated microwave-optical photon pairs can be generated through spontaneous parametric down-conversion in our transducer. These correlated photon pairs constitute a key ingredient toward entangling distant quantum processors via the Duan-LukinCirac-Zoller protocol [4, 45]. With the current device parameters, the pair generation rate on chip amounts to 190 Hz, which is well above the thermal decoherence rate for 3.5 GHz at 10 mK as explained in Appendix A 4. However, losses in the measurement setup specific to such an experiment, as well as the gating of the optical pump required to alleviate the effective heat load, are expected to limit further the final heralding rate. Quasi-free-standing structures, such as 1D OMCs, do not readily thermalize and are thus more susceptible to heating effects. Even though our transducer also utilizes a suspended acoustic resonator, it may be feasible to operate at a higher duty cycle than what is presented in Fig. 4. It has been shown that a buffer gas environment can facilitate thermalization of a 1D OMC, at the cost of increased damping of the mechanical breathing mode [46]. On the contrary, the HBAR mode of interest here should be relatively insensitive to such viscous damping, as the acoustic wave propagates predominantly along the longitudinal direction inside the cladding. Therefore, a sample cell affixed to the mixing chamber flange and filled with buffer superfluid helium—an inviscid fluid that is both an excellent thermal conductor and electrical insulator—could potentially improve the heralding rate without hampering the conversion efficiency [47]. The present design is also suited for tasks beyond quantum state transfer. The transducer can be a key component of photonic interconnects for superconducting qubits [7, 8], where laser lights routed through optical fibers are used to encode microwave signals directly inside the dilution refrigerator during qubit readout. It may be challenging for traveling-wave EOMs to attain the requisite half-wave voltage to be competitive in noise performance against conventional all-electrical readout scheme utilizing high-electron-mobility transistor amplifiers [8]. Our triply resonant approach affords a path towards a scalable optically controlled cryogenic waveform generator for both qubit readout and control. The multimode nature of the transducer is particularly suited for frequency-multiplexed dispersive readout. Transduction leveraging multiple HBAR modes is already demonstrated in Fig. 3e. One can additionally utilize line-type instead of point-type dimer coupling for the photonic molecule, which gives rise to a dispersion in the supermode splitting [48]. In combination with the multitude of acoustic overtones, a single transducer can therefore support several spectrally distinguishable triply resonant systems that serve as qubit multiplex channels. Finally, since the transducer requires no superconducting element to function, it may be pertinent for applications in classical microwave photonics. Optimization of design and fabrication should engender further improvement on device performance. The conversion efficiency, proportional to microwave extraction efficiency (Eq. 2), can be improved by replacing AlN with scandium-doped AlN [49], which features a piezoelectric coefficient d33 about five times larger than AlN [50, 51] while preserving CMOS compatibility. There is likewise additional upside on the photonics (Appendix C 1). Bending radiation loss can be significantly reduced by increasing the micro-ring radius r. In particular, intrinsic quality factor exceeding 107 has been demonstrated with an r approximately ten times larger using the same fabrication process [52]. It may be anticipated that the efficiency gain here may be chiefly compensated by the reduction in optomechanical coupling rate g0 ∼ 1/ √ r. Nevertheless, by thinning the waveguide to 200 nm that approaches half of the acoustic wavelength, g0 is expected to remain unchanged. As in the thick-core waveguide fabricated using the photonic Damascene process, such a thin-core waveguide on a subtractive manufacturing platform is expected to exhibit negligible bending loss [53]. Assuming critical coupling conditions (Appendix F), we therefore expect another 1.5- time gain in η tot. Finally, optical insertion loss can be nearly eliminated by employing a spot-size converter to facilitate mode-matching with lensed fiber [54–56]. Combining these improvements, our transducer could achieve a total efficiency η tot = 6 × 10−3 . In conclusion, we have designed, fabricated, and characterized a compact piezo-optomechanical microwaveoptical transducer that integrates wafer-scale, CMOScompatible HBAR and Si3N4 photonic technologies. Free of any superconducting components, this triply resonant transducer attains a bidirectional off-chip photon number conversion efficiency of 1.6 × 10−5 and a bandwidth of 25 MHz, with an input pump power of 21 dBm at room temperature. Multimode transduction leveraging distinct HBAR modes have been demonstrated, suggesting prospects for designing frequency-multiplexed photonic interconnects for superconducting qubits. Realistic design and fabrication improvements may even allow access to experiments in the quantum regime.

Bidirectional Microwave-optical Transduction Based on Integration: Discussion

This paper is available on arxiv under CC 4.0 license. Authors: (1) Terence Blésin, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (2) Wil Kao, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (3) Anat Siddharth, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL); (4) Alaina Attanasio, OxideMEMS lab, Purdue University; (5) Hao Tian, OxideMEMS lab, Purdue University; (6) Sunil A. Bhave, OxideMEMS lab, Purdue University; (7) Tobias J. Kippenberg, Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL) & Center of Quantum Science and Engineering (EPFL). Table of Links Introduction Results A. Physics and design B. Device characterization C. Bidirectional microwave-optical transduction D. Pulsed transduction Discussion Acknowledgments Appendix A: Theory Appendix B: Fabrication process flow Appendix C: Optical characterization Appendix D: Characterization of acoustic resonances Appendix E: Characterization of bidirectional transduction Appendix F: Optimization of optical extraction efficiency ACKNOWLEDGMENTS The authors would like to thank Amirali Arabmoheghi, Nils Johan Engelsen and Junyin Zhang for experimental assistance and fruitful discussions. This work was supported by the Air Force Office of Scientific Research under award no. FA8655-20-1-7009, Swiss National Science Foundation under grant agreement no. 204927, and European Research Council (ERC) under the EU H2020 research and innovation programme, grant agreement no. 835329 (ExCOM-cCEO). This work was further supported by United States Air Force Research Laboratory Award no. FA8750-21-2-0500, as well as United States National Science Foundation’s International Collaboration Supplements in Quantum Information Science and Engineering Research for RAISE-TAQS Award no. 18-39164. A.S. acknowledges support from the European Space Technology Centre with ESA Contract No. 4000135357/21/NL/GLC/my. Samples were fabricated in the Center of MicroNanoTechnology (CMi) at EPFL and Birck Nanotechnology Center at Purdue University. AUTHOR CONTRIBUTIONS T.B. designed the device. R.N.W, A.A. and H.T. fabricated the device. T.B., A.S. and W.K. conducted the experiments. T.B. and W.K. wrote the paper with input from all authors. T.J.K. and S.A.B. supervised the project. [1] K.-L. Lee, B. Sedighi, R. S. Tucker, H. Chow, and P. Vetter, J. Opt. Commun. Netw. 4, A59 (2012). [2] Q. Cheng, M. Bahadori, M. Glick, S. Rumley, and K. Bergman, Optica 5, 1354 (2018). [3] J. I. Cirac, P. Zoller, H. J. Kimble, and H. Mabuchi, Phys. Rev. Lett. 78, 3221 (1997). [4] L.-M. Duan, M. D. Lukin, J. 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Bidirectional Microwave-optical Transduction Based on Integration: Acknowledgments

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Does the team or app size affect the release process? Well, it depends. Let’s imagine a startup with one small team. In this case, the team usually makes a feature, and then just releases it. Now, let’s imagine a large project, for example, a banking app, with many teams working on it. In this case, there should probably be a process, release cycles, and maybe some bureaucracy. Without that, there will be chaos. So, when does it become clear that it’s time to set up such a process for your app? In this article, I’ll share my experience of implementing Release Train for the Dodo Pizza app (Android and iOS) and the problems we faced that made our team implement Release Train. If you are the Team Lead/Tech Lead of an Android or iOS project that is growing fast and you have not managed the release process yet, I hope that our experience will help you. How It Used to Be In 2021, we were already using a Trunk-based Development (TBD) approach in our teams. We covered the code with feature toggles, decomposed tasks, and ran unit and UI tests. Our feature branches didn’t live long, and we had CI working. The release process was very simple: whoever was ready to roll their feature out, rolled it. Ideal Scenario Here’s roughly what our branch workflow looked like. Several teams (gray, blue, orange, and green) worked on different features. Since we were working according to TBD, each feature could live through several consecutive branches. For example, the gray team made their feature in 4 steps, the blue and orange teams made theirs in 1 step, and the green team made theirs in 2 steps. When a team finished a feature, they could roll out a release. For example, if the blue team finished a feature, they could do a release. Then, the orange team would finish a feature and do another release. We had the perfect flow, as it seemed then. It worked great up to a point, but all good things come to an end. Something went wrong: tough, crowded, and unpredictable Mammoth The first problem we encountered was that releases started accumulating a lot of features and became too big. The teams didn’t always want to release their features right away. The release and regression process was time-consuming and took 3–4 days. So if your feature was small and not urgent, you didn’t always get to release it yourself because probably, some other team would do a release soon, and it would be included in that release. Roughly it looked like this: This was quite a fragile arrangement, especially when the number of teams started to grow. Many teams developed many small features, and the total volume of new code in each new release became huge. When someone got to release their big feature, they had to release a whole together with it. mammoth The mammoth-releases resulted in: delayed regression; higher risk of regression bugs; higher risk of getting a bug in production. We needed to make it so that even if the blue and orange team from the example didn’t want to release, the release would be done somehow. Bottlenecks Every team is unique, and every feature is different. Sometimes, things happened in such a way that several teams would finish their features around the same time. In this case, there was a lot of “wait for me, I’ll merge it tomorrow morning, I promise!” going around. In the end, such bottlenecks resulted in: releases turning into mammoths; delayed releases negatively affecting the releasing team’s plans especially if everybody else’s needs were met. We needed to make 2 crucial changes: The releasing team shouldn’t need to wait for anyone; every other team should know when the next release is expected. Lack of Predictability Imagine, the blue team made a small feature and expected the orange team to release it soon. But something went wrong, and the orange team didn’t roll out the release either because of some problems of their own. As a result, the blue team told the business that the feature would be in production soon, but it turned out to be not soon enough. As a result, it is impossible to predict when the feature will be in production. This doesn’t mean that the blue team is irresponsible. If they have a super important or urgent feature, then of course they will release it themselves. But in other cases, there is no way to tell exactly when the feature will be available to the users. As you can guess, we were experiencing such issues quite often. We needed to be able to tell exactly when customers would get a feature in production regardless of its size or urgency. All 3 problems (mammoth releases, bottlenecks, and lack of predictability) are closely related and complement each other. However, probably the most fundamental and important of them all is the lack of predictability. It generates other problems. Release Train We've had enough; it was time to make a change. The was supposed to help us do that. Release Train The term Release Train means different things: a , or a that manages the release process. Here, we’re going to talk about the scheduled release process. scheduled release process dedicated team I like the way Release Train is described by Martin Fowler in the “ ” article, and the definition given by (maybe it belongs to Martin as well). Patterns for Managing Source Code Branches Thoughtworks in their tech radar This is how we have defined Release Train for ourselves: Release Train is the process of coordinating releases between teams. All releases happen on a fixed schedule, independent of whether the features are ready or not. The train doesn't wait for anyone. If you are late, you have to wait for the next one. Let's break it down with a couple of examples using our color-coded teams. Solving the Mammoth Problem Release Train happens on schedule and does not depend on who has merged what into the main branch. In the example below, the features from the blue and orange teams will be released. The rest will wait for the next train. We could wait a bit longer, but then we'd get a mammoth. Resolving Bottlenecks At the same time, the Release Train helps us plan our work more efficiently. Let’s say that the blue team originally planned to finish a feature later. But since everyone knows the release date, they can slightly rearrange their plans to finish the feature early. Or, on the contrary, they can realize that they will definitely not be on time for the next train, and hence, they can finish the feature safely because they know the whole schedule. In the example below, the blue team wanted to get to the release and merge all their changes before the release. Otherwise, there might have been a bottleneck. Most importantly, Release Train . gave us predictability by design These examples may seem obvious to some, but we solved problems as they arose. When there were no problems with releases, we didn’t bother using Release Train. When problems accumulated, we realized that the time had come. How to Implement the Release Train in Your Team The first thing we did was write an . An RFC refers to both the process itself and the design document that use before they start working on a project. Some use RFCs specifically, some use ADRs, and some just call them by the more generic term Design Doc. RFC many companies At Dodo Engineering, we use both RFCs and ADRs. Our RFC process looked like this: We drafted an RFC document; we discussed it in a small group, collected comments, and made adjustments; then the RFC was communicated to a wider group; then we implemented it; after that, we gathered feedback, tracked metrics, and evaluated results. The structure of the RFC document for our Release Train was as follows: description of the Release Train process; what teams are involved, what they are doing; what the schedule will be; metrics. In drafting the RFC, we relied on the experience of other companies: SkyScanner Soundcloud Monzo Facebook First Implementation First, we came up with this process: release every week; create a release branch on Wednesday morning; complete regress, and send the app for review on Friday; start rolling out the release on Monday. Release team: 1 iOS and 1 Android developers from one of the feature teams; 2 QA engineers. create a new release branch on Wednesday; make a schedule one quarter ahead, indicating when it's each team's turn to release. After a quarter get together and extend the schedule. Schematically, our Release Train looked like this: It Didn't All Go Smoothly After a month, it became clear that although the first experience felt great, It was really hard to do a regress every week and be done by Friday; there was no time for hotfixes, and they sometimes happened. In 2021, our regression test used to take 3–4 days on average. We managed to shorten it to 2–3 days in 2022, but sometimes, it would exceed that time frame. We continued covering regression cases with e2e tests, but we didn’t have 100% coverage yet. We had about 70% and 60% coverage of regression cases on each platform respectively. The takeaway from this is that it will likely be uncomfortable to run a release cycle every week. as long as you have regression tests taking a few days to complete, The Final Answer We ended up moving to 2-week release cycles, and Release Train now looks like this: release every 2 weeks; create a release branch on Wednesday morning; regress, and send the app for review on Friday; start rolling out the release on Monday. Release team: 1 iOS and 1 Android developers from one of the feature teams; 2 QA engineers. make a schedule one quarter ahead, indicating when it's each team's turn to release. After a quarter, get together and extend the schedule. roll the release out gradually; do the hotfixes if needed, now when we have time for them; a week later on Wednesday, create a new release branch. This is how the process looks if everything goes according to plan: It all looks like a weekly cycle except there’s plenty of time left for potential hotfixes. This is what it will look like in case of extended regression tests: No big deal either; there’s still time even for hotfixes. How Did the New Process Affect Predictability? The main goal for us was . It can be broken down into two parts: to increase predictability When will the application be released; In which release will my feature get into? We answered the question “When will there be a release?” by implementing the Release Train process. Now, each team will be able to answer the question “in which release will my feature end up?” independently at the moment when they plan and evaluate the feature. Before, it was impossible to say for sure because another team might or might not do the release. Now, everything depends only on that team’s own planning. To further confirm this, we conducted surveys amongst mobile developers, QA, and product managers, where together, with other questions, we asked: When is the next release? (100% answered this question). Did Release Train help you plan your teamwork? (75% answered positively, but some perfectly predicted their work even without a Release Train). Release Train has also helped us with code freezes and release freezes. We had several of them, in addition to New Year’s Eve (for example, September 1st and some holidays). Now, with Release Train, we don’t have to adjust to these dates by creating release branches, regress testing, and all that. Releases work on schedule; we just open them in the stores later. Impact on Metrics Beyond just solving problems, we also measured metrics. Let’s take a look at the main ones. Lead Time The first important metric we measured was the . Lead Time commitment to release This is what the graph looks like. I marked the point when we started the Release Train process with an arrow. The graph shows that Lead Time went down to somewhere around six days. Is six days a long or short time? Google Benchmarks There are , but it’s mostly for the backend. On their scale, they distinguish the following groups: benchmarks from Google for this metric Elite: less than an hour High: 1 hour to 1 week Medium: 1 week to 6 months Low: 6 months or more I believe that for standard mobile apps, lead time should ideally aim for half the length of the release cycle. This is equivalent to merging a task into the main branch every day. This said, . if the release cycle is 14 days, Lead Time should aim for 7 days Bugs Per Regression Another metric we track is the number of bugs per regression. It describes how stable the release candidate is. If the previous release was a long time ago, then we probably created a lot of new code that might potentially contain a high number of bugs, and we might spend more time on regression and fixes. At one point, this metric was down to three bugs. The specific numbers aren’t that crucial, but overall, you can see that the trend has gone down. More Metrics I’ll briefly discuss what metrics were also monitored as part of the Release Train. Crash-free. We always keep an eye on this metric. There was a hypothesis that it would drop due to our attempts to fit the regression into a tighter timeframe. Well, no drop happened. We wondered if frequent (weekly) releases would have an impact on customer churn and on deleting the app. As a result, we detected no impact. Implement, Improve We like the current process as we think it has achieved its goals. We also know how to improve it further: We continue to work on automating the regression to make it simpler and faster. So far, we have left out the part on work automation (scripts for branching), but this would also be a great point of growth in the future. Our app works in 20 countries, and we need to translate the application into many different languages. There is an internal service for this, but developers still have to participate in this process manually before release. Automating this process potentially might improve the release cycle even further. Summary While we were relatively small, we didn’t need a Release Train. When we faced the fact that we couldn’t predict releases, their size, and number, we decided to implement Release Train. At first, we tried weekly release cycles, but because of time-consuming regressions, we had to switch to two-week release cycles. We’ve been living this way ever since. Now, we have predictability of releases, and metrics show positive dynamics. We plan to increase the coverage of regression cases with e2e-tests, automate the process of working with branches, and optimize the process of translations. I hope this article and our experience will help you, especially if you have already faced similar issues and they made you think about the release process. Thank you so much for reading my article. I hope you enjoyed it. If you have any questions or suggestions, drop me a line in the comments, and I’ll be sure to read it! And . Usually, I post about Android development and software engineering in general. follow me on Twitter Also published here

What Problems Does Release Train Solve in Mobile App Development?

মোবাইল অ্যাপ ডেভেলপমেন্টে রিলিজ ট্রেন কোন সমস্যার সমাধান করে?

Welche Probleme löst Release Train bei der Entwicklung mobiler Apps?

¿Qué problemas resuelve Release Train en el desarrollo de aplicaciones móviles?

Quels problèmes Release Train résout-il dans le développement d’applications mobiles ?

मोबाइल ऐप विकास में रिलीज़ ट्रेन किन समस्याओं का समाधान करती है?

Release Train はモバイルアプリ開発におけるどのような問題を解決しますか?

모바일 앱 개발에서 Release Train은 어떤 문제를 해결합니까?

Que problemas o Release Train resolve no desenvolvimento de aplicativos móveis?

Какие проблемы решает Release Train при разработке мобильных приложений?

Sürüm Treni Mobil Uygulama Geliştirmede Hangi Sorunları Çözer?

Bản phát hành có giải quyết được vấn đề gì trong quá trình phát triển ứng dụng di động?

Release Train解决了移动应用开发中的哪些问题？

Are you tired of grammar mistakes ruining your writing? Do you want to write with confidence, knowing your work is error-free? If so, you've come to the right place. In this article, we'll explore the best Spot and Solve Grammar Mistakes Solution and provide you with expert insights and practical tips to enhance your . writing skills Introduction Welcome to the world of spot-on writing, where are a thing of the past. Whether you're a student, a professional, or just someone who loves to write, it's essential to convey your thoughts clearly and effectively. Spot and Solve Grammar Mistakes Solution is the key to achieving this. grammar mistakes In this article, we will take you on a journey through the art of error-free writing, sharing the most efficient techniques to spot and solve grammar mistakes. From common errors to advanced proofreading tips, we've got you covered. So, grab your virtual red pen, and let's dive into the world of impeccable writing. Understanding Grammar Mistakes Grammar mistakes can be pesky little critters that sneak into our writing when we least expect them. They can undermine your credibility and make your content hard to read. Let's explore some of the common grammatical errors that can plague your writing. The Dreaded Apostrophe Catastrophe Apostrophes can be confusing, but they are vital for indicating possession and contractions. It's crucial to know when and where to use them. can change the entire meaning of a sentence. Incorrect apostrophe usage Subject-Verb Agreement: A Balancing Act Subject-verb agreement ensures that your sentences are structurally sound. When your subject and verb don't agree in number, your writing can sound awkward and incorrect. Punctuation Predicaments Misusing punctuation marks, like commas and semicolons, can lead to misunderstandings. The correct placement of these marks can significantly impact the clarity of your writing. The Art of Pronoun Usage Using the right pronouns is a must in writing. Incorrect pronoun usage can lead to confusion or even offense. We'll delve into the intricacies of pronouns and their usage in writing. Spellcheck Isn't Foolproof Relying solely on spellcheck can be a mistake. It may not catch contextual errors or misused words. We'll discuss the limitations of spellcheck and how to overcome them. Spot and Solve Grammar Mistakes Solution Now that we've identified the , let's move on to the exciting part: the solutions. We've compiled a list of the . common grammar mistakes most effective strategies to spot and solve grammar mistakes in your writing The Power of Proofreading Proofreading is your best friend when it comes to spotting grammar mistakes. Take your time to review your work carefully. Read it aloud, if possible, to catch errors you might have missed. Embrace Technology In this digital age, grammar-checking tools and software have become indispensable. We'll introduce you to some of the best grammar-checking tools available and how to make the most of them. Best Grammar Checker Websites: Grammarly MyEssayWriter.ai Grammar Checker Ginger Software LanguageTool ProWritingAid Learn how these tools can help you spot and solve grammar mistakes with ease. Please note that while all these tools are excellent, many consider to be the best grammar checker website. MyEssayWriter.ai Seek a Second Opinion Sometimes, a fresh pair of eyes can spot mistakes you've overlooked. We'll discuss the benefits of peer editing and how it can enhance the quality of your writing. Learn from Your Mistakes No one becomes a grammar expert overnight. We'll provide resources and tips for improving your grammar skills over time, so you make fewer mistakes in the first place. Grammar Courses and Workshops If you're committed to becoming a grammar guru, enrolling in a could be a game-changer. We'll point you in the right direction to find the perfect course for your needs. grammar course or workshop Be Patient and Persistent Spotting and solving grammar mistakes is an ongoing process. Stay patient and persistent, and your writing will improve over time.

Spot and Solve Grammar Mistakes Solution: Your Guide to Error-Free Writing

Satoshi Nakamoto - everybody who is into crypto knows this name. But who is behind the name? Wikipedia explains it in an easy way: Satoshi Nakamoto is the name used by the presumed pseudonymous person or persons who developed Bitcoin, authored the Bitcoin white paper, and created and deployed Bitcoin's original reference implementation. However, for some reason, nobody has seen this mysterious Satoshi. All we know about him is that in 2007, he started to work on the code. In 2008, the domain Bitcoin.org was registered. And finally, on 31 October 2009, the Bitcoin white paper was published. On January 9, 2009, Satoshi launched the Bitcoin network by defining the genesis block of Bitcoin with the reward of 50 BTC. With it, the father of cryptocurrency was born, and the era of crypto started. Bitcoin The most funny thing is that nobody knows who Satoshi Nakamoto is and whether it is a single person or a group of people. But, as usual, in such cases, there are versions. The most popular personalities that are believed to be Satoshi Nakamoto are the following. Hal Finney Hal Finney (4 May 1956 – 28 August 2014) was a pioneer in cryptography and the person who was fixing bugs and making improvements in the Bitcoin code. He was also a neighbor of Dorian Nakamoto, another candidate for being the famous Bitcoin founder. The writing manner of Hal Finney was the closest to the manner used by the Bitcoin founder. Why is he not Satoshi? There are several facts that confirm this. Satoshi was actively communicating with one of the Mike Hearn, and transacting Bitcoin just at the same time when Hal was participating in a race in Santa Barbara, California. Bitcoin developers, Photographic proofs that it was him running are also available. Then, Hal was diagnosed with Amyotrophic Lateral Sclerosis (ALS) in August 2009, and since then, his health has been deteriorating. In 2010, he could hardly type. Satoshi was pretty active at that time by posting in the forum and writing emails. Another piece of evidence is the fact that when debugging Bitcoin, Hal writes to Satoshi about the issues encountered. Additionally, Hal denied being Satoshi Nakamoto until his death in 2014 due to ALS complications. At the moment, there are no reasons not to believe him. Dorian Nakamoto Dorian Prentice Satoshi Nakamoto, a Japanese living in the USA, California, is another candidate for being the Bitcoin founder. In 2014, Newsweek published that the identity of Satoshi Nakamoto was revealed. Journalist Leah McGrath Goodman went to Dorian’s home to take an interview and claimed later that during the interview, Dorian mentioned, "I was no longer involved in that, and I cannot discuss it. It's been turned over to other people. They are in charge of it now. I no longer have any connection." Later, Dorian denied being the famous and claimed he misunderstood the journalist’s question. In an interview with The Associated Press, he insisted that he had nothing to do with Bitcoin. Moreover, he used to make such mistakes as calling the main crypto Bitcoin instead of Bitcoin and referring to Bitcoin as a company, which is not correct. Also, he said he had never heard of Gavin Andresen, one of the main Bitcoin developers. Bitcoin creator Nick Szabo Nick Szabo is the most viable candidate for being Satoshi Nakamoto. Why? He is a decentralized currency enthusiast, and he created “bit gold,” which was the Bitcoin precursor and the main characteristics of which later became the ones of Bitcoin. A group of forensic scientists from Aston University analyzed the writing styles of Nick Szabo and the Bitcoin founder and came to the conclusion that it is highly likely that Nick is Satoshi Nakamoto. Additionally, the following similarities also make us believe that Nick may be Satoshi. The Bitcoin whitepaper was written using Latex, and Nick also uses Latex for his publications. Elon Musk also believes that, even though Nick may not be the actual Bitcoin founder, he is the one responsible for the development of the majority of Bitcoin ideas. Even though it is clear that Bit Gold was the Bitcoin precursor, Satoshi has never mentioned the project, which looks logical only if Nick was trying to obscure the fact that Bitcoin was a logical development of Bit Gold and he was the Bitcoin founder. Nick Szabo used to backdate his posts about Bit Gold to make it appear that he published them after the Bitcoin launch. All these facts indicate indirectly that Nick may be behind the Bitcoin whitepaper creation, and he was one of the Bitcoin founders. However, it doesn’t mean that he was the only one who did it. Craig Wright Craig Wright is the brightest and the most scandalous candidate for being Satoshi Nakamoto. Here are just some details about his claims. Wright insisted that some details mentioned in the Bitcoin whitepaper were previously mentioned in his blog. It seems, however, that entries in his blog are backdated to make them look as if they were published before the Bitcoin whitepaper went public. Other his claims are also being doubted. However, whether he is going to prove that he is the Bitcoin founder or not, he has already carved his place in the crypto community. Additionally, he is a founder of and a chief science officer at nChain. Bitcoin SV He is involved in several legal cases, including the one about the Bitcoin whitepaper copyright; other cases are about suing those who call him a scammer. In a trial slated to begin in January 2024, he is going to be finally determined as a scammer or as a rightful Bitcoin founder. However, there is one small detail to note. In his emails, Craig misspelled the main cryptocurrency name. Satoshi Nakamoto wrote the name of this cryptocurrency thousands of times on different occasions and never ever used the version by Craig. Just leaving here the link to to compare. Satoshi’s archive And just to have some fun, I'm leaving here a . I’ve enjoyed listening to it. link to a nice song

The Satoshi Mystery Solved: Who Actually is the Father of Bitcoin?

As the 2023 season of NASA’s Space Apps Challenge is set to be held on 7-8 October 2023, we recall one of the most successful and innovative talents that surfaced in its 10-year milestone season two years ago. In a remarkable triumph of innovation and problem-solving, the 'App Ninjas' team from Arizona claimed the prestigious title of Local People's Choice Winner at the 2021 NASA , the world's largest annual global hackathon. Under the exceptional leadership of Aryyama Kumar Jana, this achievement underscored their outstanding dedication to addressing pressing global issues through technology. Space Apps Challenge Aryyama Kumar Jana is now a Software Development Engineer at Amazon. As a member of the Device Economics team at , he works on optimizing and automating decisions in the Devices business using science and technology, including machine learning. Amazon The 2021 NASA Space Apps Challenge marked a significant milestone as it celebrated its 10th year. The event, known as one of the most prestigious hackathons worldwide, attracted brilliant minds from around the globe. Recently, the event gained added significance through partnerships with nine renowned space agencies, expanding its reach and impact. Aryyama’s exemplary leadership and technical prowess distinguished him in a formidable field of participants. Their mission was to create a mobile application that could demystify NASA's extensive solar and meteorological data parameters, making them accessible to the general public. During Aryyama’s time at ASU, his startup idea was selected by ASU Venture Devils, the startup incubator for ASU. The 'App Ninjas' team excelled in this mission. Their mobile application is a masterpiece of user-centric design, offering easily understandable graphical representations of weekly average "available sunshine" or solar irradiance for the past year, tailored precisely to the user's location. This invaluable tool empowers homeowners to make informed decisions about solar panel purchases and simplifies the monitoring of existing systems. What set Aryyama and the 'App Ninjas' apart is their forward-thinking approach. The application's adaptability allowed users to customize parameters, including the ability to adjust temporal resolutions and time periods, providing highly specialized results. This innovation promises to revolutionize the way individuals engage with solar energy data. But the innovation doesn't stop there. Aryyama and the visionary 'App Ninjas' team have ambitious plans for the app's future. They envision adding user-defined buttons to display additional POWER parameters such as temperature, humidity, and wind, enhancing its versatility and value for renewable energy enthusiasts and sustainability advocates. In a groundbreaking move, the 'App Ninjas' has introduced the concept of integrating users' measurements, enabling comparisons and supplementations with NASA parameters. This transformative feature could democratize access to solar energy data and further advance global sustainability efforts. Aryyama’s recognition as a 'Galactic Problem Solver' underscored his exceptional contributions to the team's success. This prestigious award signifies his outstanding problem-solving abilities and visionary leadership, making him a true standout in the world of innovation. The 2021 NASA Space Apps Challenge in Arizona was an exhilarating journey where Aryyama and the 'App Ninjas' showcased their creativity and problem-solving skills. Winning the Local People's Choice award in such a competitive environment underscores their ingenuity and commitment to making a positive impact on the world. This victory cemented Aryyama’s and the 'App Ninjas' place as trailblazers in innovation, not only in Arizona but on a global stage. Their mobile application signifies a meaningful step towards a sustainable future, leaving an imprint of excellence in the renewable energy technology landscape.

“Galactic Problem Solver”— NASA’s Recognition for Aryyama Kumar Jana’s Solar Irradiance Tracking 

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