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NASA Scientists "Howl in Terror" as They Discovered Disastrous Flaw in $5 Billion Spacecraft About to Launch

Howl

howl

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The Cyclicality of the Crypto Markets Shows Its Hand Once Again

Are Brain-Computer Interfaces at Risk of Mass Cyberattacks?

ব্রেন-কম্পিউটার ইন্টারফেসগুলি কি ব্যাপক সাইবার আক্রমণের ঝুঁকিতে রয়েছে?

Besteht bei Brain-Computer-Interfaces die Gefahr massiver Cyberangriffe?

¿Las interfaces cerebro-computadora están en riesgo de sufrir ciberataques masivos?

Les interfaces cerveau-ordinateur sont-elles exposées à des cyberattaques de masse ?

क्या मस्तिष्क-कम्प्यूटर इंटरफेस पर बड़े पैमाने पर साइबर हमलों का खतरा है?

脳コンピューターインターフェースは大規模なサイバー攻撃の危険にさらされているか?

뇌-컴퓨터 인터페이스가 대규모 사이버공격의 위험에 처해 있는가?

As interfaces cérebro-computador correm risco de ataques cibernéticos em massa?

Подвержены ли интерфейсы «мозг-компьютер» риску массовых кибератак?

Beyin-Bilgisayar Arayüzleri Kitlesel Siber Saldırı Riski Altında mı?

Giao diện não-máy tính có nguy cơ bị tấn công mạng hàng loạt không?

脑机接口是否存在遭受大规模网络攻击的风险？

Towards the Automation of Book Typesetting: Acknowledgments and References

Authors:
(1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu);
(2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu);
(3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Appendix and References Appendix References Allen, J.; Howland, B.; Mobius, M.; Rothschild, D.; and Watts, D. J. 2020. Evaluating the fake news problem at the scale of the information ecosystem. Science Advances, 6(14): eaay3539. Barnard, S. R. 2018. Citizens at the Gates. Twitter, Networked Publics and The Transformation of American Journalism. Cham, Sveitsi: Palgrave Macmillan. Cheung, Y.-W.; and Lai, K. S. 1995. Lag Order and Critical Values of the Augmented Dickey–Fuller Test. Journal of Business & Economic Statistics, 13(3): 277–280. Chinn, S.; Hart, P. S.; and Soroka, S. 2020. Politicization and Polarization in Climate Change News Content, 1985-2017. Science Communication, 42(1): 112–129. Dash, S.; Mishra, D.; Shekhawat, G.; and Pal, J. 2022. Divided We Rule: Influencer Polarization on Twitter during Political Crises in India. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 16: 135–146. Demszky, D.; Movshovitz-Attias, D.; Ko, J.; Cowen, A.; Nemade, G.; and Ravi, S. 2020. GoEmotions: A dataset of fine-grained emotions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4040–4054. Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, 4171–4186. Dutta, S.; Ma, J.; and Choudhury, M. D. 2018. Measuring the Impact of Anxiety on Online Social Interactions. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 12(1): 584–587. Ebeling, R.; Saenz, C. A. C.; Nobre, J. C.; and Becker, K. 2022. Analysis of the Influence of Political Polarization in the Vaccination Stance: The Brazilian COVID-19 Scenario. Proceedings of the International AAAI Conference on Web and Social Media, 16: 159–170. Entman, R. M. 2003. Cascading activation: Contesting the White House’s frame after 9/11. Political Communication,, 20(4): 415– 432. Eveland, W. P.; Seo, M.; and Marton, K. 2002. Learning From the News in Campaign 2000: An Experimental Comparison of TV News, Newspapers, and Online News. Media Psychology, 4(4): 353–378. Garimella, K.; Smith, T.; Weiss, R.; and West, R. 2021. Political Polarization in Online News Consumption. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 15: 152–162. Gentzkow, M.; Shapiro, J. M.; and Taddy, M. 2019. Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech. Econometrica, 87(4): 1307–1340. Granger, C. W. J. 1980. Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2: 329–352. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change. Conference on Empirical Methods in Natural Language Processing., 2116–2121. Hoey, M. 2005. Lexical Priming: A new theory of words and language. London: Routledge. Horning, M. 2018. The pundit problem: A look at bias and negativity in cable news coverage as the 2016 election came to a close. The 2016 American Presidential Campaign and the News, 77–99. Jang, S. M.; and Hart, P. S. 2015. Polarized frames on “climate change” and “global warming” across countries and states: Evidence from Twitter big data. Global Environmental Change, 32: 11–17. Kim, E.; Lelkes, Y.; and McCrain, J. 2022. Measuring dynamic media bias. Proceedings of the National Academy of Sciences, 119(32): e2202197119. Kutuzov, A.; Øvrelid, L.; Szymanski, T.; and Velldal, E. 2018. Diachronic word embeddings and semantic shifts: a survey. In Proceedings of the 27th International Conference on Computational Linguistics, 1384–1397. McCombs, M. 1997. Building Consensus: The News Media’s Agenda-Setting Roles. Political Communication, 14(4): 433–443. McLeod, J. M.; Scheufele, D. A.; and Moy, P. 1999. Community, Communication, and Participation: The Role of Mass Media and Interpersonal Discussion in Local Political Participation. Political Communication, 16(3): 315–336. Muise, D.; Hosseinmardi, H.; Howland, B.; Mobius, M.; Rothschild, D.; and Watts, D. J. 2022. Quantifying partisan news diets in Web and TV audiences. Science Advances, 8(28): eabn0083. Papacharissi, Z. 2009. Journalism and citizenship. Routledge London. Polignano, M.; Basile, V.; Basile, P.; Gabrieli, G.; Vassallo, M.; and Bosco, C. 2022. A hybrid lexicon-based and neural approach for explainable polarity detection. Information Processing & Management, 59(5): 103058. Recuero, R.; Soares, F. B.; and Gruzd, A. 2020. Hyperpartisanship, Disinformation and Political Conversations on Twitter: The Brazilian Presidential Election of 2018. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 14: 569–578. Rogstad, I. 2016. Is Twitter just rehashing? Intermedia agenda setting between Twitter and mainstream media. Journal of Information Technology & Politics, 13(2): 142–158. Russell Neuman, W.; Guggenheim, L.; Mo Jang, S.; and Bae, S. Y. 2014. The Dynamics of Public Attention: Agenda-Setting Theory Meets Big Data. Journal of Communication, 64(2): 193–214. Sap, M.; Card, D.; Gabriel, S.; Choi, Y.; and Smith, N. A. 2019. The Risk of Racial Bias in Hate Speech Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 1668–1678. Scheufele, D. A. 2000. Agenda-setting, priming, and framing revisited: Another look at cognitive effects of political communication. Mass communication & society, 3(2-3): 297–316. Sundararajan, M.; Taly, A.; and Yan, Q. 2017. Axiomatic Attribution for Deep Networks. In Proceedings of the 34th International Conference on Machine Learning, 3319–3328. Tuba, M.; Akashe, S.; and Joshi, A., eds. 2020. Information and Communication Technology for Sustainable Development, volume 933. Advances in Intelligent Systems and Computing. Westfall, J.; Van Boven, L.; Chambers, J. R.; and Judd, C. M. 2015. Perceiving Political Polarization in the United States: Party Identity Strength and Attitude Extremity Exacerbate the Perceived Partisan Divide. Perspectives on Psychological Science, 10(2): 145– 158. Yang, M.; Wen, X.; Lin, Y.-R.; and Deng, L. 2017. Quantifying Content Polarization on Twitter. In 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC), 299– 308. Zhang, T.; Kishore, V.; Wu, F.; Weinberger, K. Q.; and Artzi, Y. 2020. BERTScore: Evaluating Text Generation with BERT. arXiv:1904.09675. This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Authors: Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Discussion and Ethics Statement Appendix and References Appendix and References Appendix References Allen, J.; Howland, B.; Mobius, M.; Rothschild, D.; and Watts, D. J. 2020. Evaluating the fake news problem at the scale of the information ecosystem. Science Advances, 6(14): eaay3539. Barnard, S. R. 2018. Citizens at the Gates. Twitter, Networked Publics and The Transformation of American Journalism. Cham, Sveitsi: Palgrave Macmillan. Cheung, Y.-W.; and Lai, K. S. 1995. Lag Order and Critical Values of the Augmented Dickey–Fuller Test. Journal of Business & Economic Statistics, 13(3): 277–280. Chinn, S.; Hart, P. S.; and Soroka, S. 2020. Politicization and Polarization in Climate Change News Content, 1985-2017. Science Communication, 42(1): 112–129. Dash, S.; Mishra, D.; Shekhawat, G.; and Pal, J. 2022. Divided We Rule: Influencer Polarization on Twitter during Political Crises in India. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 16: 135–146. Demszky, D.; Movshovitz-Attias, D.; Ko, J.; Cowen, A.; Nemade, G.; and Ravi, S. 2020. GoEmotions: A dataset of fine-grained emotions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4040–4054. Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, 4171–4186. Dutta, S.; Ma, J.; and Choudhury, M. D. 2018. Measuring the Impact of Anxiety on Online Social Interactions. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 12(1): 584–587. Ebeling, R.; Saenz, C. A. C.; Nobre, J. C.; and Becker, K. 2022. Analysis of the Influence of Political Polarization in the Vaccination Stance: The Brazilian COVID-19 Scenario. Proceedings of the International AAAI Conference on Web and Social Media, 16: 159–170. Entman, R. M. 2003. Cascading activation: Contesting the White House’s frame after 9/11. Political Communication,, 20(4): 415– 432. Eveland, W. P.; Seo, M.; and Marton, K. 2002. Learning From the News in Campaign 2000: An Experimental Comparison of TV News, Newspapers, and Online News. Media Psychology, 4(4): 353–378. Garimella, K.; Smith, T.; Weiss, R.; and West, R. 2021. Political Polarization in Online News Consumption. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 15: 152–162. Gentzkow, M.; Shapiro, J. M.; and Taddy, M. 2019. Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech. Econometrica, 87(4): 1307–1340. Granger, C. W. J. 1980. Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2: 329–352. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change. Conference on Empirical Methods in Natural Language Processing., 2116–2121. Hoey, M. 2005. Lexical Priming: A new theory of words and language. London: Routledge. Horning, M. 2018. The pundit problem: A look at bias and negativity in cable news coverage as the 2016 election came to a close. The 2016 American Presidential Campaign and the News, 77–99. Jang, S. M.; and Hart, P. S. 2015. Polarized frames on “climate change” and “global warming” across countries and states: Evidence from Twitter big data. Global Environmental Change, 32: 11–17. Kim, E.; Lelkes, Y.; and McCrain, J. 2022. Measuring dynamic media bias. Proceedings of the National Academy of Sciences, 119(32): e2202197119. Kutuzov, A.; Øvrelid, L.; Szymanski, T.; and Velldal, E. 2018. Diachronic word embeddings and semantic shifts: a survey. In Proceedings of the 27th International Conference on Computational Linguistics, 1384–1397. McCombs, M. 1997. Building Consensus: The News Media’s Agenda-Setting Roles. Political Communication, 14(4): 433–443. McLeod, J. M.; Scheufele, D. A.; and Moy, P. 1999. Community, Communication, and Participation: The Role of Mass Media and Interpersonal Discussion in Local Political Participation. Political Communication, 16(3): 315–336. Muise, D.; Hosseinmardi, H.; Howland, B.; Mobius, M.; Rothschild, D.; and Watts, D. J. 2022. Quantifying partisan news diets in Web and TV audiences. Science Advances, 8(28): eabn0083. Papacharissi, Z. 2009. Journalism and citizenship. Routledge London. Polignano, M.; Basile, V.; Basile, P.; Gabrieli, G.; Vassallo, M.; and Bosco, C. 2022. A hybrid lexicon-based and neural approach for explainable polarity detection. Information Processing & Management, 59(5): 103058. Recuero, R.; Soares, F. B.; and Gruzd, A. 2020. Hyperpartisanship, Disinformation and Political Conversations on Twitter: The Brazilian Presidential Election of 2018. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 14: 569–578. Rogstad, I. 2016. Is Twitter just rehashing? Intermedia agenda setting between Twitter and mainstream media. Journal of Information Technology & Politics, 13(2): 142–158. Russell Neuman, W.; Guggenheim, L.; Mo Jang, S.; and Bae, S. Y. 2014. The Dynamics of Public Attention: Agenda-Setting Theory Meets Big Data. Journal of Communication, 64(2): 193–214. Sap, M.; Card, D.; Gabriel, S.; Choi, Y.; and Smith, N. A. 2019. The Risk of Racial Bias in Hate Speech Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 1668–1678. Scheufele, D. A. 2000. Agenda-setting, priming, and framing revisited: Another look at cognitive effects of political communication. Mass communication & society, 3(2-3): 297–316. Sundararajan, M.; Taly, A.; and Yan, Q. 2017. Axiomatic Attribution for Deep Networks. In Proceedings of the 34th International Conference on Machine Learning, 3319–3328. Tuba, M.; Akashe, S.; and Joshi, A., eds. 2020. Information and Communication Technology for Sustainable Development, volume 933. Advances in Intelligent Systems and Computing. Westfall, J.; Van Boven, L.; Chambers, J. R.; and Judd, C. M. 2015. Perceiving Political Polarization in the United States: Party Identity Strength and Attitude Extremity Exacerbate the Perceived Partisan Divide. Perspectives on Psychological Science, 10(2): 145– 158. Yang, M.; Wen, X.; Lin, Y.-R.; and Deng, L. 2017. Quantifying Content Polarization on Twitter. In 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC), 299– 308. Zhang, T.; Kishore, V.; Wu, F.; Weinberger, K. Q.; and Artzi, Y. 2020. BERTScore: Evaluating Text Generation with BERT. arXiv:1904.09675. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Methodological Considerations in Semantic Polarization Research

Orca 2: Enhancing Reasoning in Smaller Language Models - Conclusions and References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Vitor da Fonseca, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa;
(2) Tiago Barreiro, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa and 2ECEO, Universidade Lus´ofona;
(3) Nelson J. Nunes, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa. Table of Links Abstract and Intro
Coupling Dark Energy to Neutrinos
Impact of the Coupling on Perturbations and Observables
Parameter Estimation
Discussion
Acknowledgment, Appendix, and References ACKNOWLEDGMENTS The authors would like to thank C. van de Bruck and D. F. Mota for the fruitful discussions. This work is supported by the Funda¸c˜ao para a Ciˆencia e a Tecnologia (FCT) through the research grants UIDB/04434/2020 and UIDP/04434/2020, and the BEYLA project PTDC/FIS-AST/0054/2021. V.d.F. acknowledges FCT support through fellowship 2022.14431.BD. Appendix A: Parameter constraints for the MaVaN and the uncoupled scalar field models In the appendix we show the triangular plots of the analysis made with the Plk18+BAO data for the MaVaN model (β free) and the uncoupled case (β = 0), i.e. no interaction between the quintessence component and the neutrino fluid. References [1] J. Lesgourgues and S. Pastor, Phys. Rept. 429, 307 (2006), arXiv:astro-ph/0603494. [2] M. Gerbino and M. Lattanzi, Frontiers in Physics 5 (2018), 10.3389/fphy.2017.00070. [3] Y. Fukuda et al. (Super-Kamiokande), Phys. Rev. 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Authors: (1) Vitor da Fonseca, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa; (2) Tiago Barreiro, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa and 2ECEO, Universidade Lus´ofona; (3) Nelson J. Nunes, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Vitor da Fonseca, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa; (2) Tiago Barreiro, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa and 2ECEO, Universidade Lus´ofona; (3) Nelson J. Nunes, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa. Table of Links Abstract and Intro Coupling Dark Energy to Neutrinos Impact of the Coupling on Perturbations and Observables Parameter Estimation Discussion Acknowledgment, Appendix, and References Abstract and Intro Abstract and Intro Coupling Dark Energy to Neutrinos Coupling Dark Energy to Neutrinos Impact of the Coupling on Perturbations and Observables Impact of the Coupling on Perturbations and Observables Parameter Estimation Parameter Estimation Discussion Discussion Acknowledgment, Appendix, and References Acknowledgment, Appendix, and References ACKNOWLEDGMENTS The authors would like to thank C. van de Bruck and D. F. Mota for the fruitful discussions. This work is supported by the Funda¸c˜ao para a Ciˆencia e a Tecnologia (FCT) through the research grants UIDB/04434/2020 and UIDP/04434/2020, and the BEYLA project PTDC/FIS-AST/0054/2021. V.d.F. acknowledges FCT support through fellowship 2022.14431.BD. 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专业编剧分享与人工智能合作写作的利弊

The ideology serves as a powerful mechanism not only for uniting individuals with similar interests but also for exchanging experiences and ideas, embodying the principle that sharing is indeed caring. is a great example of how a community of enthusiasts can change the world. Its are highly impressive and we can claim that most of the Internet works on Linux’s horsepower. open source Linux usage statistics Bad programmers worry about the code. Good programmers worry about data structures and their relationships. Linus Torvalds Managing an open-source project carries significant weight, particularly as its popularity grows: it brings forth the spotlight on technical expertise and architectural choices. Popular projects could be a treasure trove of knowledge for anyone who wants to boost their skills. Let's delve deeply into a few notable projects. Why focus on open source? First of all, it embodies top-tier coding standards, integrating best practices championed by industry leaders. My firm belief lies in the notion that genuine programmers are fueled by passion and curiosity. Why not learn something new and integrate it into our daily practice? Secondly, hacking code could take your skills to a whole new level if you are consistent and tenacious. Last but not least, it could help you jump right into the project you like: find new buddies, brand yourself as a skilled developer, and get the job of your dreams. Help the community to solve their problems, and the reward will not keep you waiting. Take your cup of coffee, let’s get straight to work. Linux Kernel The very heart of the Linux system — its kernel. Indeed, this is not merely a singular project, but rather a constellation of diverse projects unified by a central idea: tying up different parts of hardware and providing a sustainable, secure API for the end users. The Linux codebase is huge and contains a lot of vendor-specific code, but it is well-organized and structured. I strongly recommend everyone interested to read . It might be a bit outdated but it will foster a solid understanding of how the kernel internals work. The kernel version I have on the table is 6.3. this Radix tree for memory management At the very beginning of the main.c we can spot definitions of some functions like . It is responsible for initializing the data structure that is used in memory management processes. radix_tree_init In the codebase, the radix_tree_root struct is an alias for a wrapper above the struct. It a lock (pthread mutex); xa_flags — the enum that tells how memory allocations will be performed; and the pointer to xa_head. XArray holds gfp_t struct xarray {
	spinlock_t	xa_lock;
/* private: The rest of the data structure is not to be used directly. */
	gfp_t		xa_flags;
	void __rcu *	xa_head;
}; typedef enum {
	GFP_KERNEL,
	GFP_ATOMIC,
	__GFP_HIGHMEM,
	__GFP_HIGH
} gfp_t; If all of the entries in the array are NULL, xa_head is a NULL pointer. If the only non-NULL entry in the array is at index 0, xa_head is that entry. If any other entry in the array is non-NULL, xa_head points to a xa_node — the internal struct that handles offset, counts, slots, and other fields. struct xa_node {
	unsigned char	shift;		/* Bits remaining in each slot */
	unsigned char	offset;		/* Slot offset in parent */
	unsigned char	count;		/* Total entry count */
	unsigned char	nr_values;	/* Value entry count */
	struct xa_node __rcu *parent;	/* NULL at top of tree */
	struct xarray	*array;		/* The array we belong to */
	union {
		struct list_head private_list;	/* For tree user */
		struct rcu_head	rcu_head;	/* Used when freeing node */
	};
	void __rcu	*slots[XA_CHUNK_SIZE];
	union {
		unsigned long	tags[XA_MAX_MARKS][XA_MARK_LONGS];
		unsigned long	marks[XA_MAX_MARKS][XA_MARK_LONGS];
	};
}; The radix tree data structure contains “nodes” that link with each other based on the int key (index). The node contains several slots, each of which can handle a pointer. The main idea here is to keep a tree wide, not deep — it is effective for fast addressing. When the function is called by a user (it can be a device driver), the following actions occur: radix_tree_insert __radix_create_tree creates a slot in the tree if it is necessary, based on the passed index which is used to check the size of the tree. Or raise an error if there is no available memory; insert_entities pass an item to the slot; some asserts check that offsets are applied correctly to the data structure. Wherever a user of the tree is going to look up a specific page, they need to look up it with an index. The lookup phase is quite efficient because of the broad nature of the structure. It is based on the key that is modified on the user side. Take a look at the for more insight into how it works. article Users of this data structure include GPU and network device drivers, which maintain memory page pointers in a structured format rather than juggling them. It's like having a pocket dimension! This is where Linus's earlier quote comes into play. Consider the essence of the data you handle; aim for efficiency, compactness, and cleanliness in its usage, regardless of the language or project you're involved in. In 2021 Liam Howlett (no, another Liam) and Matthew Wilcox introduced data structure. I encourage you to read more about the intentions, motivation, and realization details and . the maple tree here here Kubernetes Let's direct our steps to the point of the architectural design. One of the most famous orchestrators, invented by Google and spread around the world at rocket pace — Kubernetes. There are a lot of fairly new resources out there covering the internal details, for example here is a that you can start exploring as you delve deeper into the details. repository The whole point of this article is to get some useful insights that answer not just “how” but “why”. Understanding the motivation is crucial, in my opinion, that is how we can “catch a mindset” to re-implement all those cool staff in our daily job. Let’s briefly recall what the Kubernetes is. This is the system that manages containers within the applications and provides orchestration features such as scaling and high availability. Kubernetes’ main components are the API service, the scheduler, etcd — the key-value storage, the controller service, and the agent service — “kubelete” on each node to manage containers. Guys from developed it to utilize the compute resources of their cloud . Their main intention was a “great container management system”. Google as efficiently as possible Architectural highlights The very first thing I’d like to highlight is the distributed nature of execution. To synchronize all of the user’s entities states the orchestrator uses etcd — the key-value storage which information in a tree data structure. It is a common pattern that presents a to manage different states between multiple entities, with one slight difference: the storage is also used to “instruct” all of the components about the final cluster’s state. keeps So the whole system uses the storage as a source of truth to keep maintaining the desired state of a cluster. source of truth One more nice architectural decision is the — container runtime interface. CRI It allows Kubernetes to work not just with Docker but many others (CRI-O, Dockerd) by defining the , just like one from RFC. The kubelet agent utilizes GRPC to connect with a runtime, so in case one would like to implement its own runtime, they should implement the exact interface on their side. The concept aims to loosen the connection between the kubelet agent and a runtime which manages containers. interaction protocol At the end let’s briefly look at the “sidecar” pattern that was presented by third-party services like and has recently been within the core Kubernetes functionality. Istio implemented The whole idea is to embed a service to the main application that helps to handle some specific cases such as proxy or load-balancing. Kubernetes manages this container as a whole different entity of a pod: the container can the entire pod status; restarting and updating separately of the main application; resources with the main app. influence share The main point here is that the main application may know nothing about the sidecar, while the behavior of the entire service can be changed. Bless Open Source, we how new functionality was implemented by the community. can trace PostgreSQL PostgreSQL is not just a database but a whole platform. After the client connects to the server a new is established. The query goes through many while executed: parsing, the rewriting system, planning and executing. backend process stages The PSQL server has a bunch of that allow developers to interact with different internal components. For example, we can our scan provider aimed, for some optimizations at each stage. Or create an . Even though the overall process required for specific knowledge and the documentation could be a bit knotty, interfaces write index add-on the loosely coupled architecture empowers developers to vastly expand functionality. There are many that we can use within the platform and the “bloom index” is one of them. extensions realization Bloom index I faced a problem of growing indexes back in the day. The task was to implement the custom search filter: every column in the database and the combination of it could be a key for a search. Building indexes for each and every column could be a bad decision because of the complexity and size. For example, if we are sure that item==2 is False there is no reason to check all other fields. I remembered the — the probabilistic data structure that could return a false-positive result (because of hash collision) and be quite sure when the data is absent. As I’ve mentioned earlier PSQL has an extension for this data structure. The very first idea that comes to mind is to reduce the dimension of possible variations removing the query values that we are sure are not in the database. Bloom filter Bloom filter in a nutshell — it’s just a bit array. Every item would be hashed with a certain amount of different functions that translates it to some index. That index will set the exact bit in the filter’s array. In the case of 3 different functions, an item would set 3 different bits. At the lookup phase, the query would be also hashed and compared within the bits in the array. If at least one of the bits is not set, we can be sure the item is absent. If all of them were set, it is still possible that the item was not placed — that is because each bit we have found could refer to other items — the collision takes place. Check out this great of how it works within the examples. explanation To start using the bloom index within the PSQL we need to create it properly: CREATE INDEX bloom_idx_bar ON foo.bar USING bloom (id,dept_id,zipcode) WITH (length=80, col1=4, col2=2, col3=4); where “length” — is the length of the array of bits for each row; and the “col” numbers — describe how many bits would be occupied by the column (how many functions would be applied). By increasing these numbers we can reduce false-positive shots. Of index creation in PSQL is a bit complicated, we should understand internal data structures and protocols; let’s try to make the observation short and sweet. The function promotes the data structure’s functions as an IndexAmRoutine pointer — a pointer to the , that represents an API for the index access methods. blhandler struct The function creates the new index: allocate memory pages, scan the table for tuples that will be indexed and in the structure. blbuild BloomState typedef struct BloomState
{
	FmgrInfo	hashFn[INDEX_MAX_KEYS];
	Oid			collations[INDEX_MAX_KEYS];
	BloomOptions opts;			/* copy of options on index's metapage */
	int32		nColumns;

	/*
	 * sizeOfBloomTuple is index-specific, and it depends on reloptions, so
	 * precompute it
	 */
	Size		sizeOfBloomTuple;
} BloomState; When we insert data to the index, we pass it in the form of an array within the array of booleans that marks NULL values to skip it at the indexing phase. The BloomFormTuple function forms the BloomTuple struct from the data — this is the part where the data would be signed. The tuple would be passed to a memory page after that phase. BloomTuple *
BloomFormTuple(BloomState *state, ItemPointer iptr, Datum *values, bool *isnull)
{
	int			i;
	BloomTuple *res = (BloomTuple *) palloc0(state->sizeOfBloomTuple);

	res->heapPtr = *iptr;

	/* Blooming each column */
	for (i = 0; i < state->nColumns; i++)
	{
		/* skip nulls */
		if (isnull[i])
			continue;

		signValue(state, res->sign, values[i], i);
	}

	return res;
} At the scan phase the forms the struct which contains the index’s data and the makes the TIDBitmap struct from it — the internal data structure that holds item pointers — reading BloomState page by page. blbeginscan IndexScanDesc blgetbitmap I hope the explanation isn't too confusing, PSQL internals are quite specific. Bloom filter is a pretty common data structure, PSQL uses the same approach for some purposes. It is also broadly used in many different situations: from cache-hitting cases to implementations. internal kernel packet filter More to know In the article, I’ve just lifted the veil of the shiny open-source world. There are many more great implementations you could meet in the wild: and — if you’d like to catch the magic of the “fair” resource allocation; — to understand the simultaneous editing in Google Docs. CFS CFQ operational transform and CRDT The whole software world is in motion: sidecars and operators to be the new model for software delivery; eBPF is in a to change the service-mesh post. claimed hurry Take a look at the elegance of the great things’ internals.

Algorithms in the Wild: Reusable Insights and Strategies from the Open Source Community

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Ke Hong, Tsinghua University & Infinigence-AI; (2) Guohao Dai, Shanghai Jiao Tong University & Infinigence-AI; (3) Jiaming Xu, Shanghai Jiao Tong University & Infinigence-AI; (4) Qiuli Mao, Tsinghua University & Infinigence-AI; (5) Xiuhong Li, Peking University; (6) Jun Liu, Shanghai Jiao Tong University & Infinigence-AI; (7) Kangdi Chen, Infinigence-AI; (8) Yuhan Dong, Tsinghua University; (9) Yu Wang, Tsinghua University. Table of Links Abstract & Introduction Backgrounds Asynchronized Softmax with Unified Maximum Value Flat GEMM Optimization with Double Buffering Heuristic Dataflow with Hardware Resource Adaption Evaluation Related Works Conclusion & References 8 Conclusions We propose FlashDecoding++, a fast Large Language Model inference engine in this paper. FlashDecoding++ accelerates mainstream LLMs with multiple hardware backend support. FlashDecoding++ proposes three novel designs: the asynchronized softmax with unified max value, the flat GEMM optimization with double buffering, and the heuristic dataflow with hardware resource adaption, achieving up to × and × speedup on NVIDIA and AMD GPUs compared with Hugging Face implementations. FlashDecoding++ also achieves an average of × speedup compared with state-of-the-art LLM inference engines, FlashDecoding, on various LLMs. 4.86 2.18 1.37 References [1] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023. [2] Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. Large language models in medicine. Nature medicine, 29(8):1930–1940, 2023. [3] Rohan Anil, Andrew M. 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Up-to-date chatgpt statistics and user numbers [oct 2023]. [Online], 2023. https://nerdynav.com/ chatgpt-statistics. [8] AFZAL AHMAD DYLAN PATEL. The inference cost of search disruption - large language model cost analysis. [Online], 2023. https://www.semianalysis.com/p/the-inference-cost-of-search-disruption. [9] Reza Yazdani Aminabadi, Samyam Rajbhandari, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Olatunji Ruwase, Shaden Smith, Minjia Zhang, Jeff Rasley, et al. Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale. In SC22: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1–15. IEEE, 2022. [10] Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Beidi Chen, Percy Liang, Christopher Re, Ion Stoica, and Ce Zhang. Flexgen: High-throughput generative inference of large language models with a single gpu. 2023. 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FlashDecoding++: Faster Large Language Model Inference on GPUs: Conclusion & References

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