Grillo is a social enterprise that develops and deploys advanced and low-cost earthquake early-warnings around the world

Grillo

grillo

This paper is under CC 4.0 license. available on arxiv : Authors (1) Linda Pigureddu, University of Turin, Italy, 332079@edu.unito.it; (2) Cristina Gena, Dept. of Computer Science, University of Turin, Italy, cristina.gena@unito.it. Table of Links Introduction The Project Memes Conclusion and References CONCLUSION The analysis of the interactions and dialogues that took place during the therapeutic laboratory for autonomies endorsed with the inclusion of the Pepper robot provided an interesting perspective on the communication dynamics of children in the lab. The results obtained showed the effectiveness of the robot in promoting autonomy and functional acquisition, demonstrating that the therapeutic method based on assistive robotics is a valuable resource to support the rehabilitation needs of communication and social skills of autistic children. The analysis highlights new possibilities for their engagement and active participation as a co-designer, as we already experienced in the past with neuro-typical children [19], representing an innovative opportunity to promote the development and wellness of autistic people and opening to new perspectives for therapeutic intervention more aware of the needs of autistic minds. In conclusion, this paper highlights the need to provide the robot with the ability of adapt to the children’s peculiarity and features and sharing her/his vocabulary, especially by recognizing and using memes during interactions, to inspire greater trust in children and allowing the use of common slang, already in use with classmates, allowing them to consider Pepper a peer and effectively insert it in the role of mediator. In addition, it would allow children to use a simplified type of communication during meetings to compensate for the typical communication deficits associated with autism spectrum disorders. In the future we will work in this directions (co-design, user’s adaptations, using and recognizing memes during children-robot communication) having the robot able to communicate and express adapting to user tastes and preferences and and try again to field a real-world evaluation that takes into account the effectiveness of different levels of user adaptation [23]. REFERENCE [1] M. Biondi e M. Maj, DSM-5: diagnostic and statistical manual of mental disorders: text revision, 5. ed. Milano: Raffaello Cortina. [2] E. Hollander, R. Hagerman, e C. Ferretti, Textbook of Autism Spectrum Disorders, Second Edition. American Psychiatric Association Publishing, 2022. [3] ISS - Istituto superiore di sanità, «Osservatorio Nazionale Autismo», OssNA. https://osservatorionazionaleautismo.iss.it [4] P. Pennisi et al., «Autism and social robotics: A systematic review», Autism Research, vol. 9, fasc. 2, pp. 165–183, 2016, doi: 10.1002/aur.1527. [5] S. Shamsuddin, H. Yussof, S. Mohamed, e F. A. Hanapiah, «Design and Ethical Concerns in Robotic Adjunct Therapy Protocols for Children with Autism», Procedia Computer Science, vol. 42, pp. 9–16, gen. 2014, doi: 10.1016/j.procs.2014.11.027. [6] K. Dautenhahn e I. Werry, «Towards Interactive Robots in Autism Therapy: Background, Motivation and Challenges», Pragmatics & Cognition, vol. 12, pp. 1–35, giu. 2004, doi: 10.1075/pc.12.1.03dau. [7] C. Li, Q. Jia, e Y. Feng, «Human-Robot Interactoin Design for Robot-Assisted Intervention for Children with Autism Based on E-S Theory», in 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China: IEEE, ago. 2016, pp. 320–324. doi: 10.1109/IHMSC.2016.103. [8] A. Peca et al., «Robot Enhanced Therapy for Children with Autism Disorders: Measuring Ethical Acceptability», IEEE Technol. Soc. Mag., vol. 35, fasc. 2, pp. 54–66, giu. 2016, doi: 10.1109/MTS.2016.2554701. [9] B. Robins, K. Dautenhahn, e J. Dubowski, «Does appearance matter in the interaction of children with autism with a humanoid robot?», IS, vol. 7, fasc. 3, pp. 479–512, nov. 2006, doi:10.1075/is.7.3.16rob. [10] E. S. Kim, «Robots for social skills therapy in autism: evidence and designs toward clinical utility», 2013, doi: 10.13140/2.1.3038.2083. [11] «Autismo», Neuraxpharm Italia. https://www.neuraxpharm.com/it/disordini/autismo [12] L. Hull, K. V. Petrides, e W. Mandy, «The Female Autism Phenotype and Camouflaging: a Narrative Review», Rev J Autism Dev Disord, vol. 7, fasc. 4, pp. 306–317, dic. 2020, doi: 10.1007/s40489-020-00197-9. [13] D. Cohen et al., «NAO, a humanoid robot as a therapeutic mediator for young people with autism». [14] Cristina Gena, Claudio Mattutino, Andrea Maieli, Elisabetta Miraglio, Giulia Ricciardiello, Rossana Damiano, Alessandro Mazzei: Autistic Children's Mental Model of an Humanoid Robot. UMAP (Adjunct Publication) 2021: 128-129 [15] Rossana Damiano, Cristina Gena, Andrea Maieli, Claudio Mattutino, Alessandro Mazzei, Elisabetta Miraglio, Giulia Ricciardiello: UX Personas for Defining Robot's Character and Personality 213-216. IUI Workshops 2022: 213-216. [16] Cristina Gena, Claudio Mattutino, Stefania Brighenti, Andrea Meirone, Francesco Petriglia, Loredana Mazzotta, Federica Liscio, Matteo Nazzario, Valeria Ricci, Camilla Quarato, Cesare Pecone, Giuseppe Piccinni: et al., Sugar, Salt & Pepper - Humanoid robotics for autism, in Joint Proceedings of the ACM IUI 2021 Workshops co-located with 26th ACM Conference on Intelligent User Interfaces (ACM IUI 2021), College Station, United States, April 13-17, 2021, D. Glowacka e V. R. Krishnamurthy, A c. di, in CEUR Workshop Proceedings, vol. 2903. CEUR-WS.org, 2021. [17] Cristina Gena, Rossana Damiano, Claudio Mattutino, Alessandro Mazzei, Andrea Meirone, Loredana Mazzotta, Matteo Nazzario, Valeria Ricci, Stefania Brighenti, Federica Liscio, Francesco Petriglia: Preliminary results of a therapeutic lab for promoting autonomies in autistic children. arXiv, doi: 10.48550/arXiv.2305.02982. [18] B. C. Carbo, «The Use of Social Stories with Individuals with Autism Spectrum Disorders», University of Delaware ProQuest Dissertations Publishing, Newark, DE, 2005. [Online]. Disponibile su: https://udspace.udel.edu/server/api/core/bitstreams/cd5e8850-e57c-41d2-a7b4ac5e25a4b659/content [19] V. Cietto, C. Gena, I. Lombardi, C. Mattutino and C. Vaudano, "Co-designing with kids an educational robot," 2018 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), Genova, Italy, 2018, pp. 139-140, doi: 10.1109/ARSO.2018.8625810. [20] A. Mousa, «Sherry Turkle, Reclaiming Conversation. The Power of Talk in a Digital Age», Questions de communication, fasc. 34, Art. fasc. 34, dic. 2018, doi:10.4000/questionsdecommunication.16891. [21] H.-M. Chiang e M. Carter, «Spontaneity of Communication in Individuals with Autism», J Autism Dev Disord, vol. 38, fasc. 4, pp. 693–705, apr. 2008, doi: 10.1007/s10803-007-0436-7. [22] Gena, C., Cena, F., Vernero, F., Grillo, P. The evaluation of a social adaptive website for cultural events. User Model User-Adap Inter 23, 89–137 (2013) https://doi.org/10.1007/s11257-012-9129-9 [23] Gena, Cristina, and Ilaria Torre. "The importance of adaptivity to provide onboard services: A preliminary evaluation of an adaptive tourist information service onboard vehicles." Applied Artificial Intelligence 18.6 (2004): 549-580.

The Power of Memes: The Pepper Robot as a Communicative Aid for Autistic Children - Conclusion

Quality-Diversity through AI Feedback: Appendix

Revolutionizing Creative Text Generation with Quality-Diversity through AI Feedback

THE CRICKET: THE BURROW; THE EGG

: Jupiter in Ultraviolet from Hubble The APoD for 10/16/2018 : The sky is not the limit ☁️ Me : Pics or I don’t believe you Stranger 🙄 Based on the above conversation, I’m going to show you how to get NASA’s highly coveted (APoD) with 🌑 ⭐️ Astronomy Picture of the Day Standard Library If you’re not familiar with , we’re a serverless platform that enables (yes, ! Calling all PMs, designers, marketing teams, sales people, oh and engineers… 🙋) to build, ship, and integrate scalable APIs in a matter of seconds. With the help of our online text editor, , you can do all of this right from your browser. Standard Library everyone everyone Code.xyz Requirements account (free) Standard Library API key (free) NASA provides a plethora of API templates that are readily usable. In order to retrieve the APoD API template, go to and search for 💁 Standard Library Code.xyz astronomy-picture-of-the-day by @eileenzhong APoD template Name your API: Name your API One last step before sending the APoD to your phone! You’ll need to properly configure the file. You’ll notice that there are two sets of variables; however, we will be deploying our code to the environment, so we will only need to provide the variables. env.json dev dev First, insert your Standard Library library token ( ): STDLIB_LIBRARY_TOKEN Right click to access this context menu Then, insert your API key. When finished, your file should look like this: NASA env.json That’s all the setup you have to do! To run the program, click “Debug” to bring the parameter input area into view. Enter your phone number, including the country code: Don’t forget the country code! When you click “Run”, you’ll receive NASA’s astronomy picture of the day! 🚀 That’s not even the coolest part. We don’t want to click “Run” every single day to get this text delivered to our phone. Thankfully, has a task scheduler where you can customize a schedule to auto_magic_ally invoke your newly created API. Standard Library In , click the “Tasks” tab. You can then enter a phone number and customize a schedule to your liking: Code.xyz Create a scheduled task for your API Suggestion: schedule the task to execute once a day since APoD is an acronym for astronomy picture of the day , and hence, the picture won’t change throughout the day. When you’re happy with the settings, click the blue “Schedule Task” button to schedule your API. That’s it! If you know one ☝️, two ✌️, or five 🖐 other people who would appreciate a daily astronomy picture, you can schedule a different task (one for each phone number) for all of them. Bonus section If you’ve made it this far, you deserve a reward 🌈 Now, don’t get too excited (actually — do get excited 🙃). With just 2 more lines of code, you can send your friend a random quote along with the APoD! APoD and a random Paul Jacques Grillo quote We’ll be using WordPress’s API to pull a randomized quote: [http://quotesondesign.com/wp-json/posts?filter[orderby]=rand&filter[posts_per_page]=1](http://quotesondesign.com/wp-json/posts?filter[orderby]=rand&filter[posts_per_page]=1.) . Add lines and to send a random quote with the APoD text 11 12 Don’t worry about all the RegEx (regular expressions) sitting on line 12. The WordPress API just returns some HTML tags that won’t play nicely in an MMS 📱 Alternatively, you can just replace the value of the variable with any chunk of text you’d like to send your friend: quote const quote = "Hi friend." Enjoy!

Text Yourself the NASA Picture of the Day with Standard Library and Node.js

Authors:
(1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu);
(2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Table of Links Abstract and I. Introduction II. Overview of PING System and Gridlock III. Design Insights and Educational Impact IV. Conclusion and References II. OVERVIEW OF PING SYSTEM AND GRIDLOCK A. Intelligent Tutoring in the PING System This section provides a non-technical overview of the PING system and, more specifically, the ITS we integrate for personalized learning. For more technical information, we ask that readers refer to our other recent publication [11, 12]. The PING system is designed as a modular intelligent tutoring system (ITS) that can be integrated with any serious game (SG). To make the ITS modular, the system developer first partitions the game’s educational domain into certain subject-specific sections, referred to here as content blocks. Within each content block, the student can then be tested and supported on a specific topic. For example, a game dealing with math might have a block for multiplication and a block for division. Then, as the student interacts with game components and completes in-game content, the system receives a constant feed of that student’s performance, as well as any relevant data. For example, Gridlock collects timing information on how long students took to complete sections. In addition, Gridlock also records a score on each section, various data on mouse movement and keyboard interaction, as well as estimated emotional response through facial emotion recognition and webcam images. All of this data is collected through quizzes and mini-games, which are covered in the next section. Then, the data are fed into a set of reinforcement learning agents, each of which is trained to support a student on a specific content block [12]. With reinforcement learning, the system learns optimal behavior by exploring choices, so the system needs some student interaction before providing acceptable student support. But after initial training, the reinforcement learning back-end allows the system to adapt to trends and changes in student behavior without any human intervention. To learn, reinforcement learning requires feedback about the quality of the decisions made. As such, student performance is used as a “reward” for the agent. If a student performs better after receiving assistance from the system, the system receives a positive reward, encouraging the chosen assistance for future students. Likewise, if a student performs poorly after receiving some assistance, the specific assistance chosen will be less likely to appear to similar students in the future. B. Gridlock Gridlock is a game that has been iteratively redesigned and improved over the past several years. Through in-classroom testing, pilot evaluations, and student feedback, numerous changes have been made to improve both the usability of the game and to make the game more enjoyable and educational for students. The game places students in the position to redesign the logic controller for a traffic light, which is a typical lab assignment for courses that teach digital logic design. When the game starts, students are quizzed on their entry knowledge to establish a benchmark for future assistance. Then, students explore a 3D environment and unlock a door by completing tasks specific to the content the game intends to teach. The tasks given to students vary between concepts. For example, one block focuses on the specific design specifications students need for the traffic light problem. In this section, students must complete a miniature design problem which tests their ability to set the order of the traffic light changes. This problem is structured more as a puzzle or a minigame, and students’ number of actions, time taken, and correctness of their submitted solution all give the system insight into the student’s knowledge of this section’s content. Then, the system can give the student feedback to bolster their content knowledge. As shown in Figure 1, students are given dialogue from the game, videos, images, and other relevant materials based on their performance. Once students have mastered the concepts presented by the game, they are tasked with programming their final traffic light logic and submitting it into the game. To help minimize instructor time used by this assignment, the game has full functionality to let students program and debug and simulate their solutions, all within the game, as shown in Figure 2 and Figure 3. With this approach, students have a seamless experience, and the game can observe student performance as they write their solutions. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Authors: Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); Dept. of Electrical and Computer Engineering, Rowan University, (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Dept. of Electrical and Computer Engineering, Rowan University, Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Overview of PING System and Gridlock II. Overview of PING System and Gridlock III. Design Insights and Educational Impact III. Design Insights and Educational Impact IV. Conclusion and References IV. Conclusion and References II. OVERVIEW OF PING SYSTEM AND GRIDLOCK A. Intelligent Tutoring in the PING System A. Intelligent Tutoring in the PING System This section provides a non-technical overview of the PING system and, more specifically, the ITS we integrate for personalized learning. For more technical information, we ask that readers refer to our other recent publication [11, 12]. The PING system is designed as a modular intelligent tutoring system (ITS) that can be integrated with any serious game (SG). To make the ITS modular, the system developer first partitions the game’s educational domain into certain subject-specific sections, referred to here as content blocks. Within each content block, the student can then be tested and supported on a specific topic. For example, a game dealing with math might have a block for multiplication and a block for division. Then, as the student interacts with game components and completes in-game content, the system receives a constant feed of that student’s performance, as well as any relevant data. For example, Gridlock collects timing information on how long students took to complete sections. In addition, Gridlock also records a score on each section, various data on mouse movement and keyboard interaction, as well as estimated emotional response through facial emotion recognition and webcam images. All of this data is collected through quizzes and mini-games, which are covered in the next section. Then, the data are fed into a set of reinforcement learning agents, each of which is trained to support a student on a specific content block [12]. With reinforcement learning, the system learns optimal behavior by exploring choices, so the system needs some student interaction before providing acceptable student support. But after initial training, the reinforcement learning back-end allows the system to adapt to trends and changes in student behavior without any human intervention. To learn, reinforcement learning requires feedback about the quality of the decisions made. As such, student performance is used as a “reward” for the agent. If a student performs better after receiving assistance from the system, the system receives a positive reward, encouraging the chosen assistance for future students. Likewise, if a student performs poorly after receiving some assistance, the specific assistance chosen will be less likely to appear to similar students in the future. B. Gridlock B. Gridlock Gridlock is a game that has been iteratively redesigned and improved over the past several years. Through in-classroom testing, pilot evaluations, and student feedback, numerous changes have been made to improve both the usability of the game and to make the game more enjoyable and educational for students. The game places students in the position to redesign the logic controller for a traffic light, which is a typical lab assignment for courses that teach digital logic design. When the game starts, students are quizzed on their entry knowledge to establish a benchmark for future assistance. Then, students explore a 3D environment and unlock a door by completing tasks specific to the content the game intends to teach. Gridlock The tasks given to students vary between concepts. For example, one block focuses on the specific design specifications students need for the traffic light problem. In this section, students must complete a miniature design problem which tests their ability to set the order of the traffic light changes. This problem is structured more as a puzzle or a minigame, and students’ number of actions, time taken, and correctness of their submitted solution all give the system insight into the student’s knowledge of this section’s content. Then, the system can give the student feedback to bolster their content knowledge. As shown in Figure 1, students are given dialogue from the game, videos, images, and other relevant materials based on their performance. Once students have mastered the concepts presented by the game, they are tasked with programming their final traffic light logic and submitting it into the game. To help minimize instructor time used by this assignment, the game has full functionality to let students program and debug and simulate their solutions, all within the game, as shown in Figure 2 and Figure 3. With this approach, students have a seamless experience, and the game can observe student performance as they write their solutions. 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

Enhancing Learning with Gamified Instruction: Overview of PING System and Gridlock

What if I ask you what you want to have right now? If I were you, I would surely say hamburger. Nothing is better than a juicy, cheesy, perfectly grilled hamburger. However, not everyone can make a perfectly grilled hamburger. You need to nail every step and cook the meat at the right temperature to make a perfect hamburger. This article is the ultimate hamburger grilling guide with step by step process to make the perfect hamburger. Step 1 Let’s start with the meat selection. When it comes to meat, it is always best to select a fatty cut. High-fat content meat is perfect for the juiciness and the moisture of the burger. To be precise, 15-20% fat content will give the best-grilled patty for your hamburger. However, ground chuck beef is highly recommended and will give the best for making hamburgers. Step 2 We have to make proper and perfect patties. Hence, the preparation of the patty is very vital. To begin with, the patties should be refrigerated to make it stick together. It is better to keep the mass of the patty between 170-200 grams; it will give a perfect bun to meat ratio.  You need to marinate the patty with soya sauce, chili paste and a pinch of lime. Make sure to marinate the patty for 5-6 hours before grilling it. Step 3 You need to prepare the grill before you start grilling the patty. Olive oil should be brushed over the grills so that it does not stick with the patties. To add flavor, you can even use butter to sear the meat on all sides. A common mistake that everyone makes is that they press the patty too hard; don’t press it too hard as it makes the patty dry. The best way to grill is to let the patty sear for 2-3 minutes on each side, and then flip it. It is best to flip it only once, and hence, the searing time needs to be properly matained. The cooking approach To overview the whole cooking process, it is always best to use a thermometer. It allows checking the internal temperature of the burger. Moreover, maintaining the temperature allows the patty to be moist and juicy. Now, finish cooking the patty in the oven; the burger needs to be cooked within (365-400) degrees. However, try to maintain the internal temperature to be around 160 degrees. Additionally, cooking time varies according to the burger type. For instance: Rare: 4-5 minutes Medium-Rare: 6-7 minutes Medium: 8-9 minutes Once the patty is cooked, let it sit for 3-5 minutes. Then use dressing and sauce of your choice and enjoy the perfectly cooked patty, and a delicious hamburger. It is never hard to make the perfect hamburger if one just knows the little tricks. Moreover, religiously following the above steps, one can avoid the little mistakes to experience the juicy, tender, and flavourful taste that everyone wants in a perfectly grilled hamburger. So, when you have friends or family coming over, or a casual gathering, make sure to cook the ultimate grilled hamburger and awe-struck everyone with its amazing taste. Thanks to for share my article. hackernoon.com Related Post: 7 best 6 burner Gas grill review

The Ultimate Hamburger Grilling Guide

Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing: Conclusion, and References

Authors:
(1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu);
(2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Table of Links Abstract and I. Introduction II. Overview of PING System and Gridlock III. Design Insights and Educational Impact IV. Conclusion and References IV. CONCLUSION This paper presents findings from the ongoing development and testing of a personalized intelligent tutoring system integrated into an educational serious game. With our student observation results and design insights, we hope to provide some ideas and direction for other researchers in this area who are interested in creating this type of system. We also show some preliminary results on the educational efficacy of the proposed system. Looking ahead, additional work will be focused on further verifying the efficacy of the system, as well as gathering additional feedback from students and educators to further refine the final product. REFERENCES [1]. D. A. Schwinn, C. S. Cooper, and J. E. Robillard, “putting students at the center: Moving beyond time-variable one-size-fits-all medical education to true individualization,” Advances in Medical Education and Practice, vol. Volume 10, pp. 109–112, 2019. doi: 10.2147/amep.s187946 [2]. S. Yang, H. Tian, L. Sun, and X. Yu, “From one-size-fits-all teaching to adaptive learning: The crisis and solution of education in the era of ai,” Journal of Physics: Conference Series, vol. 1237, no. 4, p. 042039, 2019. doi: 10.1088/1742-6596/1237/4/042039 [3]. S. Olson, Grand Challenges for Engineering Imperatives, Prospects, and Priorities: Summary of A Forum. Washington, D.C: National Academies Press, 2016. [4]. T.-C. Lin, “An investigation of the relationship between in-class and outof-class efforts on student learning: Empirical evidence and strategy suggestion,” Journal of the Scholarship of Teaching and Learning, vol. 16, no. 4, pp. 14–32, 2016. doi: 10.14434/josotl.v16i4.20028 [5]. E. Mousavinasab et al., “Intelligent tutoring systems: A systematic review of characteristics, applications, and evaluation methods,” Interactive Learning Environments, vol. 29, no. 1, pp. 142–163, 2018. doi: 10.1080/10494820.2018.1558257 [6]. F. Martin and D. U. Bolliger, “Engagement matters: Student perceptions on the importance of engagement  strategies in the Online Learning Environment,” Online Learning, vol. 22, no. 1, 2018. doi: 10.24059/olj.v22i1.1092 [7]. G. Hookham and K. Nesbitt, “A systematic review of the definition and measurement of engagement in serious games,” Proceedings of the Australasian Computer Science Week Multiconference, 2019. doi: 10.1145/3290688.3290747 [8]. M.-T. Wang and T. L. Hofkens, “Beyond classroom academics: A schoolwide and multi-contextual perspective on student engagement in school,” Adolescent Research Review, vol. 5, no. 4, pp. 419–433, 2019. doi: 10.1007/s40894-019-00115-z [9]. M. Beyyoudh, M. K. Idrissi, and S. Bennani, “A new approach of integrating serious games in intelligent tutoring systems,” Learning and Analytics in Intelligent Systems, pp. 85–91, 2019. doi: 10.1007/978-3- 030-36778-7_10 [10]. F. Laamarti, M. Eid, and A. El Saddik, “An overview of serious games,” International Journal of Computer Games Technology, vol. 2014, pp. 1– 15, 2014. doi: 10.1155/2014/358152 [11]. J. Liang, Y. Tang, R. Hare, B. Wu and F. -Y. Wang, "A LearningEmbedded Attributed Petri Net to Optimize Student Learning in a Serious Game," in IEEE Transactions on Computational Social Systems, 2022, doi: 10.1109/TCSS.2021.3132355. [12]. R. Hare and Y. Tang, "Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2047-2055, April 2023, doi: 10.1109/TSMC.2022.3227919. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Authors: Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); Dept. of Electrical and Computer Engineering, Rowan University, (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Dept. of Electrical and Computer Engineering, Rowan University, Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Overview of PING System and Gridlock II. Overview of PING System and Gridlock III. Design Insights and Educational Impact III. Design Insights and Educational Impact IV. Conclusion and References IV. Conclusion and References IV. CONCLUSION This paper presents findings from the ongoing development and testing of a personalized intelligent tutoring system integrated into an educational serious game. With our student observation results and design insights, we hope to provide some ideas and direction for other researchers in this area who are interested in creating this type of system. We also show some preliminary results on the educational efficacy of the proposed system. Looking ahead, additional work will be focused on further verifying the efficacy of the system, as well as gathering additional feedback from students and educators to further refine the final product. REFERENCES [1]. D. A. Schwinn, C. S. Cooper, and J. E. Robillard, “putting students at the center: Moving beyond time-variable one-size-fits-all medical education to true individualization,” Advances in Medical Education and Practice, vol. Volume 10, pp. 109–112, 2019. doi: 10.2147/amep.s187946 [2]. S. Yang, H. Tian, L. Sun, and X. Yu, “From one-size-fits-all teaching to adaptive learning: The crisis and solution of education in the era of ai,” Journal of Physics: Conference Series, vol. 1237, no. 4, p. 042039, 2019. doi: 10.1088/1742-6596/1237/4/042039 [3]. S. Olson, Grand Challenges for Engineering Imperatives, Prospects, and Priorities: Summary of A Forum. Washington, D.C: National Academies Press, 2016. [4]. T.-C. Lin, “An investigation of the relationship between in-class and outof-class efforts on student learning: Empirical evidence and strategy suggestion,” Journal of the Scholarship of Teaching and Learning, vol. 16, no. 4, pp. 14–32, 2016. doi: 10.14434/josotl.v16i4.20028 [5]. E. Mousavinasab et al., “Intelligent tutoring systems: A systematic review of characteristics, applications, and evaluation methods,” Interactive Learning Environments, vol. 29, no. 1, pp. 142–163, 2018. doi: 10.1080/10494820.2018.1558257 [6]. F. Martin and D. U. Bolliger, “Engagement matters: Student perceptions on the importance of engagement  strategies in the Online Learning Environment,” Online Learning, vol. 22, no. 1, 2018. doi: 10.24059/olj.v22i1.1092 [7]. G. Hookham and K. Nesbitt, “A systematic review of the definition and measurement of engagement in serious games,” Proceedings of the Australasian Computer Science Week Multiconference, 2019. doi: 10.1145/3290688.3290747 [8]. M.-T. Wang and T. L. Hofkens, “Beyond classroom academics: A schoolwide and multi-contextual perspective on student engagement in school,” Adolescent Research Review, vol. 5, no. 4, pp. 419–433, 2019. doi: 10.1007/s40894-019-00115-z [9]. M. Beyyoudh, M. K. Idrissi, and S. Bennani, “A new approach of integrating serious games in intelligent tutoring systems,” Learning and Analytics in Intelligent Systems, pp. 85–91, 2019. doi: 10.1007/978-3- 030-36778-7_10 [10]. F. Laamarti, M. Eid, and A. El Saddik, “An overview of serious games,” International Journal of Computer Games Technology, vol. 2014, pp. 1– 15, 2014. doi: 10.1155/2014/358152 [11]. J. Liang, Y. Tang, R. Hare, B. Wu and F. -Y. Wang, "A LearningEmbedded Attributed Petri Net to Optimize Student Learning in a Serious Game," in IEEE Transactions on Computational Social Systems, 2022, doi: 10.1109/TCSS.2021.3132355. [12]. R. Hare and Y. Tang, "Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2047-2055, April 2023, doi: 10.1109/TSMC.2022.3227919. 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

Enhancing Learning with Gamified Instruction: Conclusion and References

Authors:
(1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu);
(2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Table of Links Abstract and I. Introduction II. Overview of PING System and Gridlock III. Design Insights and Educational Impact IV. Conclusion and References III. DESIGN INSIGHTS AND EDUCATIONAL IMPACT A. Design Insights and Student Feedback When discussing design changes made in the game with regards to student feedback, the iterations focused on three main areas: Interface design, student testing, and tutorials/student assistance. 1) Interface Design For interface design, we had a few main considerations. First, the target audience for the game is highly generalized, and thus includes players who are and are not familiar with virtual games. For players who are familiar with virtual games, certain aspects such as a standard control scheme and familiar interface elements are enough for players to understand the goals, controls, and objective of the game. For example, the updated game features a system of objective markers shown in Figure 4, where students receive a visual indication of their current objective. Even among students who were less familiar with virtual games, a visual indicator marking their objective helps to significantly improve their ability to navigate the virtual environment. Outside of marking objectives, it was also very important to have both a stated objective and a guide on the control scheme of the game. For Gridlock, students can open up a “how to play” screen at any time that indicates the control scheme of the game. Additionally, students can also access a “current objectives” screen that has a written statement of their current objective. Among players experienced with virtual games, these features were largely ignored. However, their inclusion is crucial to help players who are unfamiliar with navigating virtual game environments. One final aspect that also helped students navigate the environment was “notification” markers on certain in-game utilities. For example, when students start the game, they have a notification on one of the menu options. This incentivizes them to click this option, thus taking them to the assistance menu, as shown in Figure 5. This way, students are already familiarized with this menu before it becomes relevant, allowing them to have a smoother experience in the latter parts of the game. 2) Student Testing Another modification that was well-received among students was the replacement of some testing methods with interactive mini-games or puzzles. In each of these basic games, students receive an instructional prompt before participating. These games are designed in such a way that we can gather data on the students’ knowledge while they play. For example, as shown in Figure 6, students are tested on their ability to convert binary numbers through a game where they are tasked with matching binary numbers to their decimal counterparts as quickly as possible. 3) Student Assistance Finally, the in-game tutorials were also modified as per student feedback. In prior iterations, accessing the personalized assistance provided to students was required for students to progress. Furthermore, the assistance was specifically tailored to a student’s issues. In the updated version of the game, the assistance is general-purpose, but students are directed to a specific area that addresses their issues. This way, students have the option to manually explore additional help materials. Additionally, students now must manually access the help materials, which prevents the help prompts from breaking the flow of the game. If a student is performing poorly, the game can also put a notification marker on the help menu to encourage the student to request help. With all these changes, students have much more freedom to either accept the help as given, explore additional content at their own pace, or to ignore the help entirely and attempt to solve problems on their own. This gives students guidance if they need it, and the freedom to explore and solve problems without being limited or interrupted by the system. B. Educational Impact Preliminary results for an evaluation of educational efficacy use a pre-test and a post-test administered before and after students played the game, respectively. These tests asked students to design a state machine for a vending machine; a task which has a similar required skill set as the traffic light problem in the game. Figure 7 shows a comparison of score improvement among students from our pre-test to the post-test. Students were graded on both assignments on a scale from 0 to 1, and the graph compares four scenarios: 1) The updated game with PING system integration, modified based on student feedback as per the design statements outlined in this paper (𝑁 = 13); 2) The prior version of the game with PING system integration (𝑁 = 20); 3) An old version of Gridlock that did not use the PING system (𝑁 = 14); and 4) Students who did not play the game (𝑁 = 8). As shown, students who played the updated game had greater improvement over students who played the prior version of the game. Additionally, there were a fewer number of students who regressed or showed little improvement compared to past classroom evaluations. This is likely due to the new quality of life features and easier-to-access help documentation making it both easier and less frustrating for students to learn with Gridlock. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Authors: Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); Dept. of Electrical and Computer Engineering, Rowan University, (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Dept. of Electrical and Computer Engineering, Rowan University, Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Overview of PING System and Gridlock II. Overview of PING System and Gridlock III. Design Insights and Educational Impact III. Design Insights and Educational Impact IV. Conclusion and References IV. Conclusion and References III. DESIGN INSIGHTS AND EDUCATIONAL IMPACT A. Design Insights and Student Feedback A. Design Insights and Student Feedback When discussing design changes made in the game with regards to student feedback, the iterations focused on three main areas: Interface design, student testing, and tutorials/student assistance. 1) Interface Design 1) Interface Design For interface design, we had a few main considerations. First, the target audience for the game is highly generalized, and thus includes players who are and are not familiar with virtual games. For players who are familiar with virtual games, certain aspects such as a standard control scheme and familiar interface elements are enough for players to understand the goals, controls, and objective of the game. For example, the updated game features a system of objective markers shown in Figure 4, where students receive a visual indication of their current objective. Even among students who were less familiar with virtual games, a visual indicator marking their objective helps to significantly improve their ability to navigate the virtual environment. Outside of marking objectives, it was also very important to have both a stated objective and a guide on the control scheme of the game. For Gridlock, students can open up a “how to play” screen at any time that indicates the control scheme of the game. Additionally, students can also access a “current objectives” screen that has a written statement of their current objective. Among players experienced with virtual games, these features were largely ignored. However, their inclusion is crucial to help players who are unfamiliar with navigating virtual game environments. One final aspect that also helped students navigate the environment was “notification” markers on certain in-game utilities. For example, when students start the game, they have a notification on one of the menu options. This incentivizes them to click this option, thus taking them to the assistance menu, as shown in Figure 5. This way, students are already familiarized with this menu before it becomes relevant, allowing them to have a smoother experience in the latter parts of the game. 2) Student Testing 2) Student Testing Another modification that was well-received among students was the replacement of some testing methods with interactive mini-games or puzzles. In each of these basic games, students receive an instructional prompt before participating. These games are designed in such a way that we can gather data on the students’ knowledge while they play. For example, as shown in Figure 6, students are tested on their ability to convert binary numbers through a game where they are tasked with matching binary numbers to their decimal counterparts as quickly as possible. 3) Student Assistance 3) Student Assistance Finally, the in-game tutorials were also modified as per student feedback. In prior iterations, accessing the personalized assistance provided to students was required for students to progress. Furthermore, the assistance was specifically tailored to a student’s issues. In the updated version of the game, the assistance is general-purpose, but students are directed to a specific area that addresses their issues. This way, students have the option to manually explore additional help materials. Additionally, students now must manually access the help materials, which prevents the help prompts from breaking the flow of the game. If a student is performing poorly, the game can also put a notification marker on the help menu to encourage the student to request help. With all these changes, students have much more freedom to either accept the help as given, explore additional content at their own pace, or to ignore the help entirely and attempt to solve problems on their own. This gives students guidance if they need it, and the freedom to explore and solve problems without being limited or interrupted by the system. B. Educational Impact B. Educational Impact Preliminary results for an evaluation of educational efficacy use a pre-test and a post-test administered before and after students played the game, respectively. These tests asked students to design a state machine for a vending machine; a task which has a similar required skill set as the traffic light problem in the game. Figure 7 shows a comparison of score improvement among students from our pre-test to the post-test. Students were graded on both assignments on a scale from 0 to 1, and the graph compares four scenarios: 1) The updated game with PING system integration, modified based on student feedback as per the design statements outlined in this paper (𝑁 = 13); 2) The prior version of the game with PING system integration (𝑁 = 20); 3) An old version of Gridlock that did not use the PING system (𝑁 = 14); and 4) Students who did not play the game (𝑁 = 8). As shown, students who played the updated game had greater improvement over students who played the prior version of the game. Additionally, there were a fewer number of students who regressed or showed little improvement compared to past classroom evaluations. This is likely due to the new quality of life features and easier-to-access help documentation making it both easier and less frustrating for students to learn with Gridlock. 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

Enhancing Learning with Gamified Instruction: Design Insights and Educational Impact

Authors:
(1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu);
(2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Table of Links Abstract and I. Introduction II. Overview of PING System and Gridlock III. Design Insights and Educational Impact IV. Conclusion and References Abstract This work-in-progress research-to-practice paper provides ongoing results from the development and testing of a personalized learning system integrated into a serious game. Given limited instructor resources, the use of computerized systems to help tutor students offers a way to provide higher quality education and to improve educational efficacy. Personalized learning systems like the one proposed in this paper offer an accessible solution. Furthermore, by combining such a system with a serious game, students are further engaged in interacting with the system. The proposed learning system combines expert-driven structure and lesson planning with computational intelligence methods and gamification to provide students with a fun and educational experience. As the project is ongoing from past years, numerous design iterations have been made on the system based on feedback from students and classroom observations. Using computational intelligence, the system adaptively provides support to students based on data collected from both their in-game actions and by estimating their emotional state from webcam images. For our evaluation, we focus on student data gathered from in-classroom testing in relevant courses, with both educational efficacy results and student observations. To demonstrate the effect of our proposed system, students in an early electrical engineering course were instructed to interact with the system in place of their standard lab assignments. The system would then measure and help them improve their background knowledge before allowing them to complete the lab assignment. As they played through the game, we observed their interactions with the system to gather insights for future developments, which are presented in this work. Additionally, we demonstrate the system’s educational efficacy through early prepost-test results from students who played the game with and without the personalized learning system integration. Keywords—Gamification, Educational Software, Learning Technology, Higher Education, Engineering Education I. INTRODUCTION Despite many recent advancements and paradigm shifts worldwide, classroom education still plays a pivotal role in equipping students with the skills and knowledge required to join the workforce and address real problems. However, larger classrooms can struggle to address student needs and concerns, especially when those needs heavily deviate from the standard one-size-fits-all lesson plan [1, 2]. And as classroom sizes increase, it becomes impossible for instructors to provide support to each individual student based on their needs and background [3]. As such, students often need to resort to excessive studying, self-learning, or external tutoring to gain much-needed knowledge [4]. To take some burden off the instructor while still limiting learning to class time, one possible solution is intelligent tutoring systems (ITSs). ITSs are educational systems that leverage student data to provide personalized educational support and tutoring to students [5]. Unlike human tutors, these systems are not limited by time and resource constraints and can support many students at the same time. By collecting data such as student performance, student actions when interacting with the system, or results on graded assignments, these systems can make informed decisions about what support a student needs to succeed. Furthermore, recent trends in artificial intelligence (AI) and data mining have further bolstered the accuracy and ability of these systems to provide effective support. However, while these systems can provide appropriate support, they can often struggle with engaging students. This is especially prevalent when students are interacting with systems that require them to read large sections of content, watch extended videos, or otherwise undergo non-active learning [6]. Maintaining this engagement is crucial for effective learning. [7, 8]. The solution to the issue of student engagement, as addressed in this paper, is to combine an ITS with a serious game (SG) [9]. Serious games are virtual or physical games that focus primarily on education, training, or other non-entertainment purposes [10]. Due to the gamification aspect of SGs, students are often significantly more engaged in lessons. In a well-designed game, students may not even realize that they are learning. Additionally, virtual SGs have added benefits in that they can create environments, visualizations, or interactive scenarios that students would not otherwise experience through standard lectures or videos. This paper presents results from the ongoing development of Gridlock, a serious game that focuses on binary logic, simple programming, and digital system design. Gridlock is designed to be run in tandem with a lab assignment where students are tasked with designing the logic controller for a traffic light. This task is the goal of the game, and is a common lab assignment for early students in electrical engineering, computer engineering, and computer science. Gridlock further supports student learning by combining a serious game environment with an ITS that supports students on various topics relevant to the overarching task. Using reinforcement learning, an AI method, the system automatically adjusts what support it provides based on student responses. This support then manifests as pop-up prompts, hints, adjusted content, and enabling or disabling certain areas in the game based on student performance. We refer to the combined system as the personalized instruction and need-aware gaming (PING) system, and while this paper focuses on Gridlock, the system is designed as a modular, general-purpose approach for any SG. As the contribution of this work, we provide design insights for both our ITS and our virtual game environment based on both observations of student interactions with the system and interview sessions with students where they provided direct feedback. We also provide educational results from inclassroom testing to verify the educational efficacy of our proposed system. To that end, Section II of this paper gives a more detailed overview of our ITS, Gridlock, and the support provided in the game. Section III provides our development insights and educational results, followed by conclusions in Section IV. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Authors: Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); Dept. of Electrical and Computer Engineering, Rowan University, (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Dept. of Electrical and Computer Engineering, Rowan University, Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Overview of PING System and Gridlock II. Overview of PING System and Gridlock III. Design Insights and Educational Impact III. Design Insights and Educational Impact IV. Conclusion and References IV. Conclusion and References Abstract This work-in-progress research-to-practice paper provides ongoing results from the development and testing of a personalized learning system integrated into a serious game. Given limited instructor resources, the use of computerized systems to help tutor students offers a way to provide higher quality education and to improve educational efficacy. Personalized learning systems like the one proposed in this paper offer an accessible solution. Furthermore, by combining such a system with a serious game, students are further engaged in interacting with the system. The proposed learning system combines expert-driven structure and lesson planning with computational intelligence methods and gamification to provide students with a fun and educational experience. As the project is ongoing from past years, numerous design iterations have been made on the system based on feedback from students and classroom observations. Using computational intelligence, the system adaptively provides support to students based on data collected from both their in-game actions and by estimating their emotional state from webcam images. For our evaluation, we focus on student data gathered from in-classroom testing in relevant courses, with both educational efficacy results and student observations. This work-in-progress research-to-practice paper provides ongoing results from the development and testing of a personalized learning system integrated into a serious game. Given limited instructor resources, the use of computerized systems to help tutor students offers a way to provide higher quality education and to improve educational efficacy. Personalized learning systems like the one proposed in this paper offer an accessible solution. Furthermore, by combining such a system with a serious game, students are further engaged in interacting with the system. The proposed learning system combines expert-driven structure and lesson planning with computational intelligence methods and gamification to provide students with a fun and educational experience. As the project is ongoing from past years, numerous design iterations have been made on the system based on feedback from students and classroom observations. Using computational intelligence, the system adaptively provides support to students based on data collected from both their in-game actions and by estimating their emotional state from webcam images. For our evaluation, we focus on student data gathered from in-classroom testing in relevant courses, with both educational efficacy results and student observations. To demonstrate the effect of our proposed system, students in an early electrical engineering course were instructed to interact with the system in place of their standard lab assignments. The system would then measure and help them improve their background knowledge before allowing them to complete the lab assignment. As they played through the game, we observed their interactions with the system to gather insights for future developments, which are presented in this work. Additionally, we demonstrate the system’s educational efficacy through early prepost-test results from students who played the game with and without the personalized learning system integration. To demonstrate the effect of our proposed system, students in an early electrical engineering course were instructed to interact with the system in place of their standard lab assignments. The system would then measure and help them improve their background knowledge before allowing them to complete the lab assignment. As they played through the game, we observed their interactions with the system to gather insights for future developments, which are presented in this work. Additionally, we demonstrate the system’s educational efficacy through early prepost-test results from students who played the game with and without the personalized learning system integration. Keywords—Gamification, Educational Software, Learning Technology, Higher Education, Engineering Education Keywords—Gamification, Educational Software, Learning Technology, Higher Education, Engineering Education I. INTRODUCTION Despite many recent advancements and paradigm shifts worldwide, classroom education still plays a pivotal role in equipping students with the skills and knowledge required to join the workforce and address real problems. However, larger classrooms can struggle to address student needs and concerns, especially when those needs heavily deviate from the standard one-size-fits-all lesson plan [1, 2]. And as classroom sizes increase, it becomes impossible for instructors to provide support to each individual student based on their needs and background [3]. As such, students often need to resort to excessive studying, self-learning, or external tutoring to gain much-needed knowledge [4]. To take some burden off the instructor while still limiting learning to class time, one possible solution is intelligent tutoring systems (ITSs). ITSs are educational systems that leverage student data to provide personalized educational support and tutoring to students [5]. Unlike human tutors, these systems are not limited by time and resource constraints and can support many students at the same time. By collecting data such as student performance, student actions when interacting with the system, or results on graded assignments, these systems can make informed decisions about what support a student needs to succeed. Furthermore, recent trends in artificial intelligence (AI) and data mining have further bolstered the accuracy and ability of these systems to provide effective support. However, while these systems can provide appropriate support, they can often struggle with engaging students. This is especially prevalent when students are interacting with systems that require them to read large sections of content, watch extended videos, or otherwise undergo non-active learning [6]. Maintaining this engagement is crucial for effective learning. [7, 8]. The solution to the issue of student engagement, as addressed in this paper, is to combine an ITS with a serious game (SG) [9]. Serious games are virtual or physical games that focus primarily on education, training, or other non-entertainment purposes [10]. Due to the gamification aspect of SGs, students are often significantly more engaged in lessons. In a well-designed game, students may not even realize that they are learning. Additionally, virtual SGs have added benefits in that they can create environments, visualizations, or interactive scenarios that students would not otherwise experience through standard lectures or videos. This paper presents results from the ongoing development of Gridlock , a serious game that focuses on binary logic, simple programming, and digital system design. Gridlock is designed to be run in tandem with a lab assignment where students are tasked with designing the logic controller for a traffic light. This task is the goal of the game, and is a common lab assignment for early students in electrical engineering, computer engineering, and computer science. Gridlock further supports student learning by combining a serious game environment with an ITS that supports students on various topics relevant to the overarching task. Using reinforcement learning, an AI method, the system automatically adjusts what support it provides based on student responses. This support then manifests as pop-up prompts, hints, adjusted content, and enabling or disabling certain areas in the game based on student performance. We refer to the combined system as the personalized instruction and need-aware gaming (PING) system, and while this paper focuses on Gridlock, the system is designed as a modular, general-purpose approach for any SG. Gridlock Gridlock Gridlock As the contribution of this work, we provide design insights for both our ITS and our virtual game environment based on both observations of student interactions with the system and interview sessions with students where they provided direct feedback. We also provide educational results from inclassroom testing to verify the educational efficacy of our proposed system. To that end, Section II of this paper gives a more detailed overview of our ITS, Gridlock , and the support provided in the game. Section III provides our development insights and educational results, followed by conclusions in Section IV. Gridlock 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

Enhancing Learning with Gamified Instruction: Abstract and Introduction

I Asked Gemini 1.5 Pro About AGI Twice. This Was Its Shocking Answer.

Economists Agree, Politicians Don't — So Guess Who Loses

অর্থনীতিবিদরা একমত, রাজনীতিবিদরা না - তাই আপনি হারান

Ökonomen sind sich einig, Politiker nicht – also verlieren Sie

Los economistas están de acuerdo, los políticos no, así que usted pierde

Les économistes sont d’accord, les politiciens non – donc vous perdez

अर्थशास्त्री सहमत हैं, राजनेता नहीं - इसलिए आप हारे

経済学者は同意するが、政治家は同意しない — だから負ける

경제학자들은 동의하지만 정치인들은 그렇지 않습니다. 그래서 당신은 패합니다.

Os economistas concordam, os políticos não - então você perde

Экономисты согласны, политики — нет, поэтому вы проигрываете

Ekonomistler Kabul Ediyor, Politikacılar Katılmıyor - Yani Kaybediyorsunuz

Các nhà kinh tế đồng ý, các chính trị gia thì không - Vậy là bạn thua

经济学家同意，政客不同意——所以你输了

Supernormal Stimuli: Are We Amusing Ourselves to Death?

অতিপ্রাকৃতিক উদ্দীপনা: আমরা কি মৃত্যুর জন্য মজা করছি?

Übernatürliche Reize: Amüsieren wir uns zu Tode?

Estímulos sobrenaturales: ¿nos estamos divirtiendo hasta la muerte?

Stimuli supranormaux : sommes-nous en train de nous amuser à mourir ?

अलौकिक उत्तेजनाएँ: क्या हम अपने आप को मौत तक मनोरंजन कर रहे हैं?

超常刺激：私たちは死ぬほど楽しんでいるのか？

초자연적 자극: 우리는 죽을 때까지 즐겁게 지내고 있나요?

Estímulos supranormais: estamos nos divertindo até a morte?

Сверхъестественные стимулы: развлекаемся ли мы до смерти?

Olağanüstü Uyarıcılar: Kendimizi Ölesiye Eğlendiriyor muyuz?

Kích thích siêu thường: Chúng ta có đang vui vẻ đến chết không?

超常刺激：我们是不是正在自娱自乐？

The Evolution of Data Center Cooling: From Air-Based Methods to Free Cooling

This paper is available on arxiv under CC 4.0 license. Authors: (1) Zhouqiang Jiang, Meetyou AI Lab, (2) Bowen Wang, Institute for Datability Science, Osaka University, (3) Tong Xiang, Meetyou AI Lab, (4) Zhaofeng Niu, Department of Computer Science, Qufu Normal University, (5) Hong Tang, Department of Information Engineering, East China Jiaotong University, (6) Guangshun Li, Department of Computer Science, Qufu Normal University, (7) Liangzhi Li, Meetyou AI Lab. Table of Links Abstract & Introduction Related Work Method Experiments Conclusion & References V. CONCLUSION In this paper, we proposed CatMAE for self-supervised video representation learning. It leverages a concatenated information channel masking strategy to address the limitations posed by cube masking and enhances the capability to capture continuous and long-term motion compared to asymmetric masking. Our experimental results demonstrate superior performance in comparison to state-of-the-art methods across both video segmentation and action recognition tasks. One distinctive feature of our training pipeline is the propagation of reconstruction information from the initial frame throughout the entire video sequence. This theoretically unlimited propagation showcases CatMAE’s potential to learn long-term video representations. Our future work focuses on extending the application of CatMAE to realworld scenarios involving embodied agents, such as robots. REFERENCES [1] K. He, X. Chen, S. Xie, Y. Li, P. Dollar, and R. Girshick, “Masked ´ autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 16 000–16 009. [2] Z. Tong, Y. Song, J. Wang, and L. Wang, “Videomae: Masked autoencoders are data-efficient learners for self-supervised video pretraining,” Advances in neural information processing systems, vol. 35, pp. 10 078–10 093, 2022. [3] C. Feichtenhofer, Y. Li, K. He et al., “Masked autoencoders as spatiotemporal learners,” Advances in neural information processing systems, vol. 35, pp. 35 946–35 958, 2022. [4] A. Gupta, J. Wu, J. Deng, and L. Fei-Fei, “Siamese masked autoencoders,” arXiv preprint arXiv:2305.14344, 2023. [5] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020. [6] E. H. Adelson and J. R. Bergen, “Spatiotemporal energy models for the perception of motion,” Josa a, vol. 2, no. 2, pp. 284–299, 1985. [7] J. Carreira and A. Zisserman, “Quo vadis, action recognition? a new model and the kinetics dataset,” in proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 6299–6308. [8] Z. Zhang and D. Tao, “Slow feature analysis for human action recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 3, pp. 436–450, 2012. [9] J. Pont-Tuset, F. Perazzi, S. Caelles, P. Arbelaez, A. Sorkine-Hornung, ´ and L. Van Gool, “The 2017 davis challenge on video object segmentation,” arXiv preprint arXiv:1704.00675, 2017. [10] W. Lotter, G. Kreiman, and D. Cox, “Deep predictive coding networks for video prediction and unsupervised learning,” arXiv preprint arXiv:1605.08104, 2016. [11] M. Mathieu, C. Couprie, and Y. LeCun, “Deep multi-scale video prediction beyond mean square error,” arXiv preprint arXiv:1511.05440, 2015. [12] Q. Zhou, X. Liang, K. Gong, and L. Lin, “Adaptive temporal encoding network for video instance-level human parsing,” in Proceedings of the 26th ACM international conference on Multimedia, 2018, pp. 1527– 1535. [13] H. Jhuang, J. Gall, S. Zuffi, C. Schmid, and M. J. Black, “Towards understanding action recognition,” in Proceedings of the IEEE international conference on computer vision, 2013, pp. 3192–3199. [14] P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Proceedings of the 25th international conference on Machine learning, 2008, pp. 1096–1103. [15] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, and L. Bottou, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.” Journal of machine learning research, vol. 11, no. 12, 2010. [16] D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2536–2544. [17] R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14. Springer, 2016, pp. 649–666. [18] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018. [19] H. Bao, L. Dong, S. Piao, and F. Wei, “Beit: Bert pre-training of image transformers,” arXiv preprint arXiv:2106.08254, 2021. [20] X. Dong, J. Bao, T. Zhang, D. Chen, W. Zhang, L. Yuan, D. Chen, F. Wen, N. Yu, and B. Guo, “Peco: Perceptual codebook for bert pre-training of vision transformers,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 1, 2023, pp. 552– 560. [21] C. Wei, H. Fan, S. Xie, C.-Y. Wu, A. Yuille, and C. Feichtenhofer, “Masked feature prediction for self-supervised visual pre-training,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 14 668–14 678. [22] Z. Xie, Z. Zhang, Y. Cao, Y. Lin, J. Bao, Z. Yao, Q. Dai, and H. Hu, “Simmim: A simple framework for masked image modeling,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 9653–9663. [23] J. Zhou, C. Wei, H. Wang, W. Shen, C. Xie, A. Yuille, and T. Kong, “ibot: Image bert pre-training with online tokenizer,” arXiv preprint arXiv:2111.07832, 2021. [24] M. Chen, A. Radford, R. Child, J. Wu, H. Jun, D. Luan, and I. Sutskever, “Generative pretraining from pixels,” in International conference on machine learning. PMLR, 2020, pp. 1691–1703. [25] J. Walker, C. Doersch, A. Gupta, and M. Hebert, “An uncertain future: Forecasting from static images using variational autoencoders,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VII 14. Springer, 2016, pp. 835–851. [26] A. Gupta, S. Tian, Y. Zhang, J. Wu, R. Mart´ın-Mart´ın, and L. FeiFei, “Maskvit: Masked visual pre-training for video prediction,” arXiv preprint arXiv:2206.11894, 2022. [27] C. Vondrick, H. Pirsiavash, and A. Torralba, “Anticipating visual representations from unlabeled video,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 98– 106. [28] D. Pathak, R. Girshick, P. Dollar, T. Darrell, and B. Hariharan, ´ “Learning features by watching objects move,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2701–2710. [29] N. Srivastava, E. Mansimov, and R. Salakhudinov, “Unsupervised learning of video representations using lstms,” in International conference on machine learning. PMLR, 2015, pp. 843–852. [30] X. Wang, A. Jabri, and A. A. Efros, “Learning correspondence from the cycle-consistency of time,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 2566–2576. [31] X. Wang and A. Gupta, “Unsupervised learning of visual representations using videos,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 2794–2802. [32] C. Vondrick, A. Shrivastava, A. Fathi, S. Guadarrama, and K. Murphy, “Tracking emerges by colorizing videos,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 391–408. [33] B. Fernando, H. Bilen, E. Gavves, and S. Gould, “Self-supervised video representation learning with odd-one-out networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 3636–3645. [34] H.-Y. Lee, J.-B. Huang, M. Singh, and M.-H. Yang, “Unsupervised representation learning by sorting sequences,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 667–676. [35] I. Misra, C. L. Zitnick, and M. Hebert, “Shuffle and learn: unsupervised learning using temporal order verification,” in Computer Vision– ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer, 2016, pp. 527–544. [36] D. Wei, J. J. Lim, A. Zisserman, and W. T. Freeman, “Learning and using the arrow of time,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 8052–8060. [37] R. Hadsell, S. Chopra, and Y. LeCun, “Dimensionality reduction by learning an invariant mapping,” in 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06), vol. 2. IEEE, 2006, pp. 1735–1742. [38] S. Becker and G. E. Hinton, “Self-organizing neural network that discovers surfaces in random-dot stereograms,” Nature, vol. 355, no. 6356, pp. 161–163, 1992. [39] K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 9729–9738. [40] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning. PMLR, 2020, pp. 1597–1607. [41] X. Chen and K. He, “Exploring simple siamese representation learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 15 750–15 758. [42] J.-B. Grill, F. Strub, F. Altche, C. Tallec, P. Richemond, ´ E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar et al., “Bootstrap your own latent-a new approach to self-supervised learning,” Advances in neural information processing systems, vol. 33, pp. 21 271–21 284, 2020. [43] M. Caron, H. Touvron, I. Misra, H. Jegou, J. Mairal, P. Bojanowski, ´ and A. Joulin, “Emerging properties in self-supervised vision transformers,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 9650–9660. [44] M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin, “Unsupervised learning of visual features by contrasting cluster assignments,” Advances in neural information processing systems, vol. 33, pp. 9912–9924, 2020. [45] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017. [46] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778. [47] I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101, 2017. [48] E. Hoffer, T. Ben-Nun, I. Hubara, N. Giladi, T. Hoefler, and D. Soudry, “Augment your batch: Improving generalization through instance repetition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 8129–8138. [49] A. Jabri, A. Owens, and A. Efros, “Space-time correspondence as a contrastive random walk,” Advances in neural information processing systems, vol. 33, pp. 19 545–19 560, 2020. [50] X. Chen, H. Fan, R. Girshick, and K. He, “Improved baselines with momentum contrastive learning,” arXiv preprint arXiv:2003.04297, 2020. [51] J. Xu and X. Wang, “Rethinking self-supervised correspondence learning: A video frame-level similarity perspective,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 10 075–10 085.

Concatenated Masked Autoencoders as Spatial-Temporal Learner: Conclusion & References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References APPENDIX A. EXCLUDED WAVELENGTH REGIONS Here, we explicitly define the wavelength regions that were excluded from our analysis for having severe telluric contamination or insufficient signal-to-noise ratios (especially at the edges of the spectral orders). The excluded regions for the IGRINS data are shown in Table 3. REFERENCES Airapetian, V. S., Glocer, A., Khazanov, G. V., et al. 2017, ApJL, 836, L3, doi: 10.3847/2041-8213/836/1/L3 Artigau, E., Kouach, D., Donati, J.-F., et al. 2014, in ´ Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9147, Ground-based and Airborne Instrumentation for Astronomy V, ed. S. K. Ramsay, I. S. McLean, & H. Takami, 914715, doi: 10.1117/12.2055663 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f Batalha, N. E., Mandell, A., Pontoppidan, K., et al. 2017, PASP, 129, 064501, doi: 10.1088/1538-3873/aa65b0 Berta-Thompson, Z. K., Irwin, J., Charbonneau, D., et al. 2015, Nature, 527, 204, doi: 10.1038/nature15762 Birkby, J. L. 2018, arXiv e-prints, arXiv:1806.04617. https://arxiv.org/abs/1806.04617 Bolmont, E., Selsis, F., Owen, J. E., et al. 2017, MNRAS, 464, 3728, doi: 10.1093/mnras/stw2578 Bourrier, V., de Wit, J., Bolmont, E., et al. 2017a, AJ, 154, 121, doi: 10.3847/1538-3881/aa859c Bourrier, V., Ehrenreich, D., Wheatley, P. J., et al. 2017b, A&A, 599, L3, doi: 10.1051/0004-6361/201630238 Brogi, M., Line, M., Bean, J., D´esert, J. M., & Schwarz, H. 2017, ApJL, 839, L2, doi: 10.3847/2041-8213/aa6933 Brogi, M., & Line, M. R. 2019, AJ, 157, 114, doi: 10.3847/1538-3881/aaffd3 Brogi, M., Snellen, I. A. G., de Kok, R. J., et al. 2013, ApJ, 767, 27, doi: 10.1088/0004-637X/767/1/27 Caballero, J. A., Gonzalez-Alvarez, E., Brady, M., et al. 2022, arXiv e-prints, arXiv:2206.09990. https://arxiv.org/abs/2206.09990 Carnall, A. C. 2017, arXiv e-prints, arXiv:1705.05165. https://arxiv.org/abs/1705.05165 Chiavassa, A., & Brogi, M. 2019, A&A, 631, A100, doi: 10.1051/0004-6361/201936566 de Wit, J., Wakeford, H. R., Gillon, M., et al. 2016, Nature, 537, 69, doi: 10.1038/nature18641 de Wit, J., Wakeford, H. R., Lewis, N. K., et al. 2018, Nature Astronomy, 2, 214, doi: 10.1038/s41550-017-0374-z Deibert, E. K., de Mooij, E. J. W., Jayawardhana, R., et al. 2021a, AJ, 161, 209, doi: 10.3847/1538-3881/abe768 —. 2021b, ApJL, 919, L15, doi: 10.3847/2041-8213/ac2513 Demory, B.-O., Gillon, M., de Wit, J., et al. 2016, Nature, 532, 207, doi: 10.1038/nature17169 Esteves, L. J., de Mooij, E. J. W., Jayawardhana, R., Watson, C., & de Kok, R. 2017, AJ, 153, 268, doi: 10.3847/1538-3881/aa7133 Gandhi, S., Brogi, M., & Webb, R. K. 2020, MNRAS, 498, 194, doi: 10.1093/mnras/staa2424 Giacobbe, P., Brogi, M., Gandhi, S., et al. 2021, Nature, 592, 205, doi: 10.1038/s41586-021-03381-x Gibson, N. P., Nugroho, S. K., Lothringer, J., Maguire, C., & Sing, D. K. 2022, MNRAS, 512, 4618, doi: 10.1093/mnras/stac091 Gibson, N. P., Merritt, S., Nugroho, S. K., et al. 2020, MNRAS, 493, 2215, doi: 10.1093/mnras/staa228 Gressier, A., Mori, M., Changeat, Q., et al. 2022, A&A, 658, A133, doi: 10.1051/0004-6361/202142140 Grimm, S. L., & Heng, K. 2015, The Astrophysical Journal, 808, 182, doi: 10.1088/0004-637X/808/2/182 Grimm, S. L., Malik, M., Kitzmann, D., et al. 2021, ApJS, 253, 30, doi: 10.3847/1538-4365/abd773 Hargreaves, R. J., Gordon, I. E., Rey, M., et al. 2020, ApJS, 247, 55, doi: 10.3847/1538-4365/ab7a1a Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2 Helling, C. 2019, Annual Review of Earth and Planetary Sciences, 47, 583, doi: 10.1146/annurev-earth-053018-060401 Herman, M. K., de Mooij, E. J. W., Nugroho, S. K., Gibson, N. P., & Jayawardhana, R. 2022, AJ, 163, 248, doi: 10.3847/1538-3881/ac5f4d Hirano, T., Kuzuhara, M., Kotani, T., et al. 2020, Publications of the Astronomical Society of Japan, 72, doi: 10.1093/pasj/psaa085 Jindal, A., de Mooij, E. J. W., Jayawardhana, R., et al. 2020, AJ, 160, 101, doi: 10.3847/1538-3881/aba1eb Jones, A., Noll, S., Kausch, W., Szyszka, C., & Kimeswenger, S. 2013, A&A, 560, A91, doi: 10.1051/0004-6361/201322433 Kempton, E. M. R., Bean, J. L., Louie, D. R., et al. 2018, PASP, 130, 114401, doi: 10.1088/1538-3873/aadf6f Khalafinejad, S., Molaverdikhani, K., Blecic, J., et al. 2021, A&A, 656, A142, doi: 10.1051/0004-6361/202141191 Kite, E. S., & Schaefer, L. 2021, ApJL, 909, L22, doi: 10.3847/2041-8213/abe7dc Kotani, T., Tamura, M., Nishikawa, J., et al. 2018, in Ground-based and Airborne Instrumentation for Astronomy VII, ed. C. J. Evans, L. Simard, & H. Takami, Vol. 10702, International Society for Optics and Photonics (SPIE), 296 – 306, doi: 10.1117/12.2311836 Kreidberg, L. 2015, PASP, 127, 1161, doi: 10.1086/683602 Kreidberg, L., Koll, D. D. B., Morley, C., et al. 2019, Nature, 573, 87, doi: 10.1038/s41586-019-1497-4 Kuzuhara, M., Hirano, T., Kotani, T., et al. 2018, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 10702, Ground-based and Airborne Instrumentation for Astronomy VII, ed. C. J. Evans, L. Simard, & H. Takami, 1070260, doi: 10.1117/12.2311832 Lee, J.-J., & Gullikson, K. 2016, plp: v2.1 alpha 3, v2.1-alpha.3, Zenodo, doi: 10.5281/zenodo.56067 Libby-Roberts, J. E., Berta-Thompson, Z. K., Diamond-Lowe, H., et al. 2022, AJ, 164, 59, doi: 10.3847/1538-3881/ac75de Mace, G., Sokal, K., Lee, J.-J., et al. 2018, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 10702, Ground-based and Airborne Instrumentation for Astronomy VII, ed. C. J. Evans, L. Simard, & H. Takami, 107020Q, doi: 10.1117/12.2312345 Madhusudhan, N., Ag´undez, M., Moses, J. I., & Hu, Y. 2016, SSRv, 205, 285, doi: 10.1007/s11214-016-0254-3 Miguel, Y. 2019, MNRAS, 482, 2893, doi: 10.1093/mnras/sty2803 Molli`ere, P., & Snellen, I. A. G. 2019, A&A, 622, A139, doi: 10.1051/0004-6361/201834169 Molli`ere, P., Wardenier, J. P., van Boekel, R., et al. 2019, A&A, 627, A67, doi: 10.1051/0004-6361/201935470 Morris, B. M., Delrez, L., Brandeker, A., et al. 2021, A&A, 653, A173, doi: 10.1051/0004-6361/202140892 Mugnai, L. V., Modirrousta-Galian, D., Edwards, B., et al. 2021, AJ, 161, 284, doi: 10.3847/1538-3881/abf3c3 Mugnai, L. V., Modirrousta-Galian, D., Edwards, B., et al. 2021, AJ, 161, 284, doi: 10.3847/1538-3881/abf3c3 Neves Ribeiro do Amaral, L., Barnes, R., Segura, A., & Luger, R. 2022, arXiv e-prints, arXiv:2203.10127. https://arxiv.org/abs/2203.10127 Noll, S., Kausch, W., Barden, M., et al. 2012, A&A, 543, A92, doi: 10.1051/0004-6361/201219040 Nugroho, S. K., Kawahara, H., Gibson, N. P., et al. 2021, ApJL, 910, L9, doi: 10.3847/2041-8213/abec71 Oliva, E., Origlia, L., Scuderi, S., et al. 2015, A&A, 581, A47, doi: 10.1051/0004-6361/201526291 Ortenzi, G., Noack, L., Sohl, F., et al. 2020, Scientific Reports, 10, 10907, doi: 10.1038/s41598-020-67751-7 Owen, J. E., Shaikhislamov, I. F., Lammer, H., Fossati, L., & Khodachenko, M. L. 2020, SSRv, 216, 129, doi: 10.1007/s11214-020-00756-w Park, C., Jaffe, D. T., Yuk, I.-S., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9147, Ground-based and Airborne Instrumentation for Astronomy V, ed. S. K. Ramsay, I. S. McLean, & H. Takami, 91471D, doi: 10.1117/12.2056431 Peacock, S., Barman, T., Shkolnik, E. L., et al. 2020, ApJ, 895, 5, doi: 10.3847/1538-4357/ab893a Quirrenbach, A., Amado, P. J., Caballero, J. A., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9147, Ground-based and Airborne Instrumentation for Astronomy V, ed. S. K. Ramsay, I. S. McLean, & H. Takami, 91471F, doi: 10.1117/12.2056453 Ramirez, R. M., & Kaltenegger, L. 2014, ApJL, 797, L25, doi: 10.1088/2041-8205/797/2/L25 Ridden-Harper, A. R., Snellen, I. A. G., Keller, C. U., et al. 2016, A&A, 593, A129, doi: 10.1051/0004-6361/201628448 Rogers, J. G., & Owen, J. E. 2021, MNRAS, 503, 1526, doi: 10.1093/mnras/stab529 Rogers, L. A., Bodenheimer, P., Lissauer, J. J., & Seager, S. 2011, ApJ, 738, 59, doi: 10.1088/0004-637X/738/1/59 Rousselot, P., Lidman, C., Cuby, J. G., Moreels, G., & Monnet, G. 2000, A&A, 354, 1134 Schaefer, L., & Fegley, B. 2009, ApJL, 703, L113, doi: 10.1088/0004-637X/703/2/L113 Shkolnik, E. L., & Barman, T. S. 2014, AJ, 148, 64, doi: 10.1088/0004-6256/148/4/64 Sim, C. K., Le, H. A. N., Pak, S., et al. 2014, Advances in Space Research, 53, 1647, doi: 10.1016/j.asr.2014.02.024 Sing, D. K., Fortney, J. J., Nikolov, N., et al. 2016, Nature, 529, 59, doi: 10.1038/nature16068 Snellen, I. A. G., Brandl, B. R., de Kok, R. J., et al. 2014, Nature, 509, 63, doi: 10.1038/nature13253 Snellen, I. A. G., de Kok, R. J., de Mooij, E. J. W., & Albrecht, S. 2010, Nature, 465, 1049, doi: 10.1038/nature09111 Snellen, I. A. G., de Kok, R. J., le Poole, R., Brogi, M., & Birkby, J. 2013, ApJ, 764, 182, doi: 10.1088/0004-637X/764/2/182 Swain, M. R., Estrela, R., Roudier, G. M., et al. 2021, AJ, 161, 213, doi: 10.3847/1538-3881/abe879 Tamura, M., Suto, H., Nishikawa, J., et al. 2012, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 8446, Ground-based and Airborne Instrumentation for Astronomy IV, ed. I. S. McLean, S. K. Ramsay, & H. Takami, 84461T, doi: 10.1117/12.925885 Tamuz, O., Mazeh, T., & Zucker, S. 2005, MNRAS, 356, 1466, doi: 10.1111/j.1365-2966.2004.08585.x Thompson, M. A., Telus, M., Schaefer, L., et al. 2021, Nature Astronomy, 5, 575, doi: 10.1038/s41550-021-01338-8 Trifonov, T., Caballero, J. A., Morales, J. C., et al. 2021, Science, 371, 1038, doi: 10.1126/science.abd7645 Tsiaras, A., Rocchetto, M., Waldmann, I. P., et al. 2016, ApJ, 820, 99, doi: 10.3847/0004-637X/820/2/99 Vanderspek, R., Huang, C. X., Vanderburg, A., et al. 2019, ApJL, 871, L24, doi: 10.3847/2041-8213/aafb7a Vidal-Madjar, A., Lecavelier des Etangs, A., D´esert, J. M., et al. 2003, Nature, 422, 143, doi: 10.1038/nature01448 Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2 Wakeford, H. R., Lewis, N. K., Fowler, J., et al. 2019, AJ, 157, 11, doi: 10.3847/1538-3881/aaf04d Woitke, P., Helling, C., Hunter, G. H., et al. 2018, A&A, 614, A1, doi: 10.1051/0004-6361/201732193 Wunderlich, F., Godolt, M., Grenfell, J. L., et al. 2019, A&A, 624, A49, doi: 10.1051/0004-6361/201834504 Yurchenko, S. N., & Tennyson, J. 2014, MNRAS, 440, 1649, doi: 10.1093/mnras/stu326 Zhang, M., Knutson, H. A., Wang, L., et al. 2021, AJ, 161, 181, doi: 10.3847/1538-3881/abe382 Zhang, Z., Zhou, Y., Rackham, B. V., & Apai, D. 2018, AJ, 156, 178, doi: 10.3847/1538-3881/aade4f

High-Resolution Transmission Spectroscopy of the Terrestrial Exoplanet GJ 486b: Appendix & Reference

This paper is under CC 4.0 license. available on arxiv Authors: (1) Prajwal Niraula, Department of Earth, Atmospheric and Planetary Sciences; (2) Julien de Wit, Department of Earth, Atmospheric and Planetary Sciences; (3) Benjamin V. Rackham, Department of Earth, Atmospheric and Planetary Sciences; (4) Elsa Ducrot, Astrobiology Research Unit, University of Li`ege; (5) Artem Burdanov, Department of Earth, Atmospheric and Planetary Sciences; (6) Ian J. M. Crossfield, Kansas University Department of Physics and Astronomy; (7) Valerie Van Grootel´, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (8) Catriona Murray, 5Cavendish Laboratory; (9) Lionel J. Garcia, Astrobiology Research Unit, University of Li`ege; (10) Roi Alonso, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna; (11) Corey Beard, Department of Physics & Astronomy, The University of California; (12) Yilen Gomez Maqueo Chew, Instituto de Astronom´ıa, Universidad Nacional Aut´onoma de M´exico, Ciudad Universitaria; (13) Laetitia Delrez, Astrobiology Research Unit, University of Li`ege, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege & 0Observatoire de lUniversit´e de Gen`eve; (14) Brice-Olivier Demory, University of Bern, Center for Space and Habitability; (15) Benjamin J. Fulton, NASA Exoplanet Science Institute/Caltech-IPAC; (16) Michael Gillon, Astrobiology Research Unit, University of Li`ege; (17) Maximilian N. Gunther, Department of Physics, and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology; (18) Andrew W. Howard, California Institute of Technology; (19) Howard Issacson, Department of Astronomy, University of California Berkeley; (20) Emmanuel Jehin, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (21) Peter P. Pedersen, Cavendish Laboratory; (22) Francisco J. Pozuelos, Astrobiology Research Unit, University of Li`ege & Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (23) Didier Queloz, Cavendish Laboratory; (24) Rafael Rebolo-Lopez, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna'; (25) Sairam Lalitha, School of Physics & Astronomy, University of Birmingham; (26) Daniel Sebastian, Astrobiology Research Unit, University of Li`ege (27) Samantha Thompson, Cavendish Laboratory; (28) Amaury H.M.J. Triaud, School of Physics & Astronomy, University of Birmingham. Table of Links Abstract & Introduction Observation Analysis and Validation Future Prospects & References 4. FUTURE PROSPECTS The search for transiting planets around small stars has been motivated in large part by their potential for atmospheric characterization. Owing to the size and proximity of its host, K2-315b is thus one of the few known terrestrial exoplanets possibly amenable for atmospheric characterization in the next two decades. In order to quantify and contextualize its prospects for atmospheric study, we followed the same approach as for TRAPPIST-1 in Gillon et al. (2016) (see de Wit & Seager 2013), focusing here on all known terrestrial planets. We selected terrestrial planets as planets with a reported radius below 1.6 R⊕ in the NASA Exoplanet Archive[4] (Rogers 2015; Fulton et al. 2017). We thus derive the amplitude of the planets’ signals in transmission as: where Rp is the planetary radius, R∗ is the stellar radius, and heff is the effective atmospheric height, µ is the atmospheric mean molecular mass, T is the atmospheric temperature and g is the local gravity. We assume heff to cover seven atmospheric scale heights, µ the atmospheric mean molecular mass to be 20 amu, and the atmospheric temperature to be the equilibrium temperature for a Bond albedo of 0. For the planets with missing masses, we estimated g using the model of Chen & Kipping (2017). The signal amplitudes are reported in Figure 7 together with the SNR relative to TRAPPIST-1 b’s, calculated by scaling the signal amplitude with the hosts’ brightness in J band. We find that K2-315b fares closely to the outer planets of TRAPPIST-1 in terms of potential for atmospheric exploration with JWST— its warmer and thus larger atmosphere compensating for its larger star. In fact, its relative SNR for transmission spectroscopy is half those of TRAPPIST-1 f–h, meaning that assessing the presence of a µ ∼ 20 atmosphere around the planet would require of the order of 40 transits—four times the ∼10 transits required for a similar assessment for TRAPPIST-1 f–h (Lustig-Yaeger et al. 2019). K2-315b is thus at the very edge of JWST’s capability for atmospheric characterization, mostly due to its “large” host star. The derivation above allows us to rank planets in terms of relative potential for atmospheric characterization, assuming a similar atmospheric scenario. In practice, a significant difference in atmospheric mean molecular mass, surface pressure, and/or cloud/haze altitude will strongly affect the actual potential of a planet for characterization (Lustig-Yaeger et al. 2019). For example, in some cases clouds could render the characterization of the favorable TRAPPIST-1 e difficult even with the generation of instruments following the JWST (i.e., LUVOIR, Pidhorodetska et al. 2020). With an estimated radial velocity semi-amplitude of 1.3 m s−1 (assuming a mass comparable to that of Earth), the planet could be accessible for mass measurements using modern ultra-precise radial velocity instruments. Such possibilities and a ranking amongst the 10 best-suited Earth-sized planets for atmospheric study, EPIC 249631677 b will therefore play an important role in the upcoming era of comparative exoplanetology for terrestrial worlds. It will surely be a prime target for the generation of observatories to follow JWST and bring the field fully into this new era. This project makes use of publicly available K2 data. P.N. would like to acknowledge funding for Kerr Fellowship and Elliot Fellowship at MIT. J.d.W. and MIT gratefully acknowledge financial support from the Heising-Simons Foundation, Dr. and Mrs. Colin Masson and Dr. Peter A. Gilman for Artemis, the first telescope of the SPECULOOS network situated in Tenerife, Spain. B.V.R. thanks the Heising-Simons Foundation for support. V.V.G. is a F.R.S.-FNRS Research Associate. M.G. and E.J. are F.R.S.-FNRS Senior Research Associates. This work was also partially supported by a grant from the Simons Foundation (PI Queloz, grant number 327127), and funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) ERC Grant Agreement Number 336480, from the ARC grant for Concerted Research Actions financed by the Wallonia-Brussels Federation, from the Balzan Prize Foundation, from F.R.S-FNRS (Research Project ID T010920F), from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement n◦ 803193/BEBOP), and from a Leverhulme Trust Research Project Grant n◦ RPG-2018-418. Acknowledgments: Keck:I (HIRES), Kepler, MAST (HLSP, K2), SNO, SSO Facilities: astropy (Astropy Collaboration et al. 2013, 2018), batman (Kreidberg 2015), emcee (Foreman-Mackey et al. 2013), isochrones (Morton 2015), prose (Garcia et al. in prep.), SPOCK (Sebastian et al. in prep.), transitleastsquares (Hippke & Heller 2019), w¯otan (Hippke et al. 2019) Software: REFERENCES Allard, F., Homeier, D., & Freytag, B. 2012, Philosophical Transactions of the Royal Society of London Series A, 370, 2765, doi: 10.1098/rsta.2011.0269 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f Bensby, T., Feltzing, S., & Oey, M. S. 2014, A&A, 562, A71, doi: 10.1051/0004-6361/201322631 Borucki, W. J., Koch, D., Basri, G., et al. 2010, Science, 327, 977, doi: 10.1126/science.1185402 Buchner, J., Georgakakis, A., Nandra, K., et al. 2014, A&A, 564, A125, doi: 10.1051/0004-6361/201322971 Burdanov, A., Delrez, L., Gillon, M., & Jehin, E. 2018, SPECULOOS Exoplanet Search and Its Prototype on TRAPPIST, 130, doi: 10.1007/978-3-319-55333-7 130 Chen, J., & Kipping, D. 2017, ApJ, 834, 17, doi: 10.3847/1538-4357/834/1/17 Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102, doi: 10.3847/0004-637X/823/2/102 Christiansen, J. L., Crossfield, I. J. M., Barentsen, G., et al. 2018, AJ, 155, 57, doi: 10.3847/1538-3881/aa9be0 Chubak, C., Marcy, G., Fischer, D. A., et al. 2012, arXiv e-prints. https://arxiv.org/abs/1207.6212 Cutri, R. M., Skrutskie, M. F., van Dyk, S., et al. 2003, VizieR Online Data Catalog, II/246 Cutri et al., R. M. 2013, VizieR Online Data Catalog, II/328 de Wit, J., & Seager, S. 2013, Science, 342, 1473, doi: 10.1126/science.1245450 Delrez, L., Gillon, M., Queloz, D., et al. 2018, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 10700, Proc. SPIE, 107001I, doi: 10.1117/12.2312475 Dotter, A. 2016, ApJS, 222, 8, doi: 10.3847/0067-0049/222/1/8 Dressing, C. D., & Charbonneau, D. 2013, ApJ, 767, 95, doi: 10.1088/0004-637X/767/1/95 Esselstein, R., Aigrain, S., Vanderburg, A., et al. 2018, ApJ, 859, 167, doi: 10.3847/1538-4357/aac20e Fernandes, C. S., Van Grootel, V., Salmon, S. J. A. J., et al. 2019, ApJ, 879, 94, doi: 10.3847/1538-4357/ab2333 Feroz, F., Hobson, M. P., & Bridges, M. 2009, MNRAS, 398, 1601, doi: 10.1111/j.1365-2966.2009.14548.x Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067 Fressin, F., Torres, G., Charbonneau, D., et al. 2013, ApJ, 766, 81, doi: 10.1088/0004-637X/766/2/81 Fulton, B. J., Petigura, E. A., Howard, A. W., et al. 2017, AJ, 154, 109, doi: 10.3847/1538-3881/aa80eb Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2018, A&A, 616, A1, doi: 10.1051/0004-6361/201833051 Garcia, L. J., Gillon, M., & SPECULOOS. in prep. Gillon, M., Jehin, E., Fumel, A., Magain, P., & Queloz, D. 2013, in European Physical Journal Web of Conferences, Vol. 47, European Physical Journal Web of Conferences, 03001, doi: 10.1051/epjconf/20134703001 Gillon, M., Jehin, E., Lederer, S. M., et al. 2016, Nature, 533, 221, doi: 10.1038/nature17448 Gillon, M., Triaud, A. H. M. J., Demory, B.-O., et al. 2017, Nature, 542, 456, doi: 10.1038/nature21360 Gillon, M., Meadows, V., Agol, E., et al. 2020, arXiv e-prints, arXiv:2002.04798. https://arxiv.org/abs/2002.04798 Goldreich, P., & Soter, S. 1966, Icarus, 5, 375, doi: 10.1016/0019-1035(66)90051-0 G¨unther, M. N., Pozuelos, F. J., Dittmann, J. A., et al. 2019, Nature Astronomy, 3, 1099, doi: 10.1038/s41550-019-0845-5 G¨unther, M. N., Zhan, Z., Seager, S., et al. 2020, AJ, 159, 60, doi: 10.3847/1538-3881/ab5d3a Hardegree-Ullman, K. K., Cushing, M. C., Muirhead, P. S., & Christiansen, J. L. 2019, AJ, 158, 75, doi: 10.3847/1538-3881/ab21d2 Hippke, M., David, T. J., Mulders, G. D., & Heller, R. 2019, AJ, 158, 143, doi: 10.3847/1538-3881/ab3984 Hippke, M., & Heller, R. 2019, A&A, 623, A39, doi: 10.1051/0004-6361/201834672 Howard, A. W., Johnson, J. A., Marcy, G. W., et al. 2010, ApJ, 721, 1467, doi: 10.1088/0004-637X/721/2/1467 Howell, S. B., Sobeck, C., Haas, M., et al. 2014, PASP, 126, 398, doi: 10.1086/676406 Huber, D., Bryson, S. T., Haas, M. R., et al. 2016, ApJS, 224, 2, doi: 10.3847/0067-0049/224/1/2 Ilin, E., Schmidt, S. J., Davenport, J. R. A., & Strassmeier, K. G. 2019, A&A, 622, A133, doi: 10.1051/0004-6361/201834400 Jao, W.-C., Henry, T. J., Gies, D. R., & Hambly, N. C. 2018, ApJL, 861, L11, doi: 10.3847/2041-8213/aacdf6 Jehin, E., Gillon, M., Queloz, D., et al. 2018, The Messenger, 174, 2, doi: 10.18727/0722-6691/5105 Kass, R. E., & Raftery, A. E. 1995, Journal of the American Statistical Association, 90, 773 Kipping, D. M. 2013, MNRAS, 435, 2152, doi: 10.1093/mnras/stt1435 Kolbl, R., Marcy, G. W., Isaacson, H., & Howard, A. W. 2015, AJ, 149, 18, doi: 10.1088/0004-6256/149/1/18 Kostov, V. B., Schlieder, J. E., Barclay, T., et al. 2019, AJ, 158, 32, doi: 10.3847/1538-3881/ab2459 Kreidberg, L. 2015, PASP, 127, 1161, doi: 10.1086/683602 Lasker, B. M., Sturch, C. R., McLean, B. J., et al. 1990, AJ, 99, 2019, doi: 10.1086/115483 Luger, R., Agol, E., Kruse, E., et al. 2016, AJ, 152, 100, doi: 10.3847/0004-6256/152/4/100 Lustig-Yaeger, J., Meadows, V. S., & Lincowski, A. P. 2019, AJ, 158, 27, doi: 10.3847/1538-3881/ab21e0 Mann, A. W., Dupuy, T., Kraus, A. L., et al. 2019, ApJ, 871, 63, doi: 10.3847/1538-4357/aaf3bc Morris, S. L. 1985, ApJ, 295, 143, doi: 10.1086/163359 Morton, T. D. 2015, isochrones: Stellar model grid package. http://ascl.net/1503.010 Mulders, G. D., Pascucci, I., & Apai, D. 2015, ApJ, 814, 130, doi: 10.1088/0004-637X/814/2/130 Murray, C. A., Delrez, L., Pedersen, P. P., et al. 2020, MNRAS, 495, 2446, doi: 10.1093/mnras/staa1283 Niraula, P., Redfield, S., de Wit, J., et al. 2018, arXiv e-prints, arXiv:1812.09227. https://arxiv.org/abs/1812.09227 Niraula, P., Redfield, S., Dai, F., et al. 2017, AJ, 154, 266, doi: 10.3847/1538-3881/aa957c Ochsenbein, F., Bauer, P., & Marcout, J. 2000, A&AS, 143, 23, doi: 10.1051/aas:2000169 Patra, K. C., Winn, J. N., Holman, M. J., et al. 2017, AJ, 154, 4, doi: 10.3847/1538-3881/aa6d75 Paxton, B., Bildsten, L., Dotter, A., et al. 2011, ApJS, 192, 3, doi: 10.1088/0067-0049/192/1/3 Paxton, B., Cantiello, M., Arras, P., et al. 2013, ApJS, 208, 4, doi: 10.1088/0067-0049/208/1/4 Paxton, B., Marchant, P., Schwab, J., et al. 2015, ApJS, 220, 15, doi: 10.1088/0067-0049/220/1/15 Pidhorodetska, D., Fauchez, T., Villanueva, G., Domagal-Goldman, S., & Kopparapu, R. K. 2020, arXiv e-prints, arXiv:2001.01338. https://arxiv.org/abs/2001.01338 Quinn, S. N., Becker, J. C., Rodriguez, J. E., et al. 2019, AJ, 158, 177, doi: 10.3847/1538-3881/ab3f2b Rabus, M., Lachaume, R., Jord´an, A., et al. 2019, MNRAS, 484, 2674, doi: 10.1093/mnras/sty3430 Reid, I. N., Brewer, C., Brucato, R. J., et al. 1991, PASP, 103, 661, doi: 10.1086/132866 Reiners, A., Joshi, N., & Goldman, B. 2012, AJ, 143, 93, doi: 10.1088/0004-6256/143/4/93 Rodriguez, D. 2016, Dr-Rodriguez/Kinematics-App: Stellar Kinematics V1.0, v1.0, Zenodo, doi: 10.5281/zenodo.192159 Rodriguez, J. E., Vanderburg, A., Eastman, J. D., et al. 2018, AJ, 155, 72, doi: 10.3847/1538-3881/aaa292 Rogers, L. A. 2015, ApJ, 801, 41, doi: 10.1088/0004-637X/801/1/41 Seager, S., & Mall´en-Ornelas, G. 2003, ApJ, 585, 1038, doi: 10.1086/346105 Sebastian, D., Gillon, M., & SPECULOOS. in prep. Stassun, K. G., & Torres, G. 2018, ApJ, 862, 61, doi: 10.3847/1538-4357/aacafc Stassun, K. G., Oelkers, R. J., Paegert, M., et al. 2019, AJ, 158, 138, doi: 10.3847/1538-3881/ab3467 Van Grootel, V., Fernandes, C. S., Gillon, M., et al. 2018, ApJ, 853, 30, doi: 10.3847/1538-4357/aaa023 Vanderburg, A., & Johnson, J. A. 2014, PASP, 126, 948, doi: 10.1086/678764 Vanderburg, A., Johnson, J. A., Rappaport, S., et al. 2015, Nature, 526, 546, doi: 10.1038/nature15527 Vogt, S. S., Allen, S. L., Bigelow, B. C., et al. 1994, in Proc. SPIE, Vol. 2198, Instrumentation in Astronomy VIII, ed. D. L. Crawford & E. R. Craine, 362 Winn, J. N. 2010, Exoplanet Transits and Occultations, ed. S. Seager, 55–77 Yee, S. W., Petigura, E. A., & von Braun, K. 2017, ArXiv e-prints. https://arxiv.org/abs/1701.00922 [4] https://exoplanetarchive.ipac.caltech.edu

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