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Reddit Stock Has 22% Upside, According to 1 Wall Street Analyst @themotleyfool #stocks $RDDT

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Are React and RTK Query a New Easy Way for Redux?

Come April 2 and the imposition of the reciprocal tariff regime by the United States on nations around the world including ours, there is bound to be tremendous economic uncertainty facing the Indian nation. Though India is not as economically integrated with the US as the other major economies of the world like China, the EU, Canada, and Mexico, there are bound to be serious ramifications of this upending of international trade conventions on it as well. India has two options before it. It can choose to ride it out by letting the reciprocal tariffs roll and play themselves out, or it can take a hard look at whether its policy of inordinately high tariffs on imported goods is actually doing its economy more harm than good. While India is definitely better positioned than most other major countries to stare the bully in the face and not back down on tariffs, the question that it needs to ask is if doing so actually suits the interests of the Indian economy and its people. High tariffs on imported goods make local industry complacent and lazy, in the process harming the interests of the consumers - the common people of India. There is no incentive on the part of local industry to compete with the best in the world in terms of quality, features, and price. This, in turn, stifles innovativeness and the development of new age products that offer the consumers the best value for their money. Remember the kind of cars India used to have in the old license-raj era of high import duties on practically every product under the sun. raj Once the license raj was dismantled, the old Ambassadors and Premier Padminis with their sputtering engines and vintage bodies became relics of the past, which had no place on Indian roads. As a matter of fact, a veritable automobile revolution swept the land with the vast middle class section of the population, which barely managed to buy a scooter, thought nothing of buying the leading car brands of the world. raj India today stands on the threshold of becoming a major global power and a developed country, but for that, it needs to expose its economy to more comprehensive trade with major economies like the U.S., the U.K., and the European Union.  We could sign separate trade deals with all of them to facilitate and expedite the process if needed, but it cannot be business as usual. The latter approach will find India always lagging behind and failing to realize its full potential. Reducing tariffs will help Indian companies become more competitive globally as they will be required to enhance quality and improve efficiency in order to survive in the new dispensation. This will grow India’s exports in a major way. The Indian consumers, on the other hand, will have access to much better quality goods at more reasonable prices. India’s current share of global exports is a mere 1.8%[[1]] This is dismal for a country seeking to become a global economic superpower in the near future. Dismantling the high tariff regime might be the big bang reform that India needs to kickstart and rev up its economy for exponential growth. [1]https://economictimes.indiatimes.com/news/economy/foreign-trade/indias-share-in-global-trade-doubled-since-2005-comparatively-moderated-in-last-10-years-report/articleshow/116676603.cms?from=mdr Feature Photo by Markus Winkler: https://www.pexels.com/photo/usa-tariffs-concept-with-scrabble-tiles-30855417/

Is Reducing Tariffs Good for India?

Table of Links Abstract and 1 Introduction 2 Related Works 3 Preliminaries 3.1 Fair Supervised Learning and 3.2 Fairness Criteria 3.3 Dependence Measures for Fair Supervised Learning 4 Inductive Biases of DP-based Fair Supervised Learning 4.1 Extending the Theoretical Results to Randomized Prediction Rule 5 A Distributionally Robust Optimization Approach to DP-based Fair Learning 6 Numerical Results 6.1 Experimental Setup 6.2 Inductive Biases of Models trained in DP-based Fair Learning 6.3 DP-based Fair Classification in Heterogeneous Federated Learning 7 Conclusion and References Appendix A Proofs Appendix B Additional Results for Image Dataset 7 Conclusion In this work, we attempted to demonstrate the inductive biases of in-processing fair learning algorithms aiming to achieve demographic parity (DP). We also proposed a distributionally robust optimization scheme to reduce the biases toward the majority sensitive attribute. An interesting future direction to our work is to search for similar biases in pre-processing and post-processing fair learning methods. Also, the theoretical comparison between different dependence measures such as mutual information, Pearson correlation, and the maximal correlation on the inductive bias levels will be an interesting topic for future exploration. Finally, characterizing the trade-off between accuracy, fairness violation, and biases toward the majority subgroups will help to better understand the costs of DP-based fair learning. References [1] Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference, pages 214–226, 2012. (Cited on page 1.) [2] Moritz Hardt, Eric Price, and Nati Srebro. Equality of opportunity in supervised learning. Advances in neural information processing systems, 29, 2016. (Cited on pages 1, 3, and 4.) [3] Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma. Fairness-aware learning through regularization approach. In 2011 IEEE 11th International Conference on Data Mining Workshops, pages 643–650. IEEE, 2011. (Cited on page 3.) [4] Ashkan Rezaei, Rizal Fathony, Omid Memarrast, and Brian Ziebart. Fairness for robust log loss classification. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 5511–5518, 2020. (Cited on page 3.) [5] Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. Mitigating unwanted biases with adversarial learning. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pages 335–340, 2018. (Cited on page 3.) [6] Jaewoong Cho, Gyeongjo Hwang, and Changho Suh. A fair classifier using mutual information. In 2020 IEEE International Symposium on Information Theory (ISIT), pages 2521–2526. IEEE, 2020. (Cited on pages 3, 8, and 19.) [7] Yuji Roh, Kangwook Lee, Steven Whang, and Changho Suh. Fr-train: A mutual information-based approach to fair and robust training. In International Conference on Machine Learning, pages 8147–8157. PMLR, 2020. (Cited on page 3.) [8] Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi. Fairness constraints: Mechanisms for fair classification. In Artificial intelligence and statistics, pages 962–970. PMLR, 2017. (Cited on page 3.) [9] Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, and Ed H Chi. Putting fairness principles into practice: Challenges, metrics, and improvements. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pages 453–459, 2019. (Cited on page 3.) [10] Flavien Prost, Hai Qian, Qiuwen Chen, Ed H Chi, Jilin Chen, and Alex Beutel. Toward a better trade-off between performance and fairness with kernel-based distribution matching. arXiv preprint arXiv:1910.11779, 2019. (Cited on page 3.) [11] Jaewoong Cho, Gyeongjo Hwang, and Changho Suh. A fair classifier using kernel density estimation. Advances in neural information processing systems, 33:15088–15099, 2020. (Cited on pages 3, 8, 9, and 19.) [12] Jérémie Mary, Clément Calauzenes, and Noureddine El Karoui. Fairness-aware learning for continuous attributes and treatments. In International Conference on Machine Learning, pages 4382–4391. PMLR, 2019. (Cited on pages 3 and 8.) [13] Sina Baharlouei, Maher Nouiehed, Ahmad Beirami, and Meisam Razaviyayn. Renyi fair inference. arXiv preprint arXiv:1906.12005, 2019. (Cited on pages 3, 8, and 9.) [14] Vincent Grari, Boris Ruf, Sylvain Lamprier, and Marcin Detyniecki. Fairness-aware neural renyi minimization for continuous features. arXiv preprint arXiv:1911.04929, 2019. (Cited on page 3.) [15] Vincent Grari, Oualid El Hajouji, Sylvain Lamprier, and Marcin Detyniecki. Learning unbiased representations via rényi minimization. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part II 21, pages 749–764. Springer, 2021. (Cited on page 3.) [16] Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, and Ahmad Beirami. A stochastic optimization framework for fair risk minimization. Transactions on Machine Learning Research, 2022. (Cited on page 3.) [17] Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. Certifying and removing disparate impact. In proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pages 259–268, 2015. (Cited on page 3.) [18] Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. Learning fair representations. In International conference on machine learning, pages 325–333. PMLR, 2013. (Cited on page 3.) [19] Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney. Optimized pre-processing for discrimination prevention. Advances in neural information processing systems, 30, 2017. (Cited on page 3.) [20] Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger. On fairness and calibration. Advances in neural information processing systems, 30, 2017. (Cited on page 3.) [21] Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. Fairness without demographics in repeated loss minimization. In International Conference on Machine Learning, pages 1929–1938. PMLR, 2018. (Cited on page 3.) [22] Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, and Michael Jordan. Robust optimization for fairness with noisy protected groups. Advances in neural information processing systems, 33:5190–5203, 2020. (Cited on page 3.) [23] Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed Chi. Fairness without demographics through adversarially reweighted learning. Advances in neural information processing systems, 33:728–740, 2020. (Cited on page 3.) [24] Hongseok Namkoong and John C Duchi. Stochastic gradient methods for distributionally robust optimization with f-divergences. Advances in neural information processing systems, 29, 2016. (Cited on page 7.) [25] Dimitris Bertsimas, Melvyn Sim, and Meilin Zhang. Adaptive distributionally robust optimization. Management Science, 65(2):604–618, 2019. (Cited on page 7.) [26] Hamed Rahimian and Sanjay Mehrotra. Distributionally robust optimization: A review. arXiv preprint arXiv:1908.05659, 2019. (Cited on page 7.) [27] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Large-scale celebfaces attributes (celeba) dataset. Retrieved August, 15(2018):11, 2018. (Cited on page 8.) [28] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. (Cited on pages 8 and 19.) [29] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273–1282. PMLR, 2017. (Cited on page 11.) [30] Gustavo L Gilardoni. On the minimum f-divergence for given total variation. Comptes rendus. Mathématique, 343(11-12):763–766, 2006. (Cited on page 17.) [31] Shahab Asoodeh, Fady Alajaji, and Tamás Linder. On maximal correlation, mutual information and data privacy. In 2015 IEEE 14th Canadian workshop on information theory (CWIT), pages 27–31. IEEE, 2015. (Cited on page 17.) This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors:
(1) Haoyu LEI, Department of Computer Science and Engineering, The Chinese University of Hong Kong (hylei22@cse.cuhk.edu.hk);
(2) Amin Gohari, Department of Information Engineering, The Chinese University of Hong Kong (agohari@ie.cuhk.edu.hk);
(3) Farzan Farnia, Department of Computer Science and Engineering, The Chinese University of Hong Kong (farnia@cse.cuhk.edu.hk). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Works 2 Related Works 3 Preliminaries 3.1 Fair Supervised Learning and 3.2 Fairness Criteria 3.1 Fair Supervised Learning and 3.2 Fairness Criteria 3.3 Dependence Measures for Fair Supervised Learning 3.3 Dependence Measures for Fair Supervised Learning 4 Inductive Biases of DP-based Fair Supervised Learning 4 Inductive Biases of DP-based Fair Supervised Learning 4.1 Extending the Theoretical Results to Randomized Prediction Rule 4.1 Extending the Theoretical Results to Randomized Prediction Rule 5 A Distributionally Robust Optimization Approach to DP-based Fair Learning 5 A Distributionally Robust Optimization Approach to DP-based Fair Learning 6 Numerical Results 6.1 Experimental Setup 6.1 Experimental Setup 6.2 Inductive Biases of Models trained in DP-based Fair Learning 6.2 Inductive Biases of Models trained in DP-based Fair Learning 6.3 DP-based Fair Classification in Heterogeneous Federated Learning 6.3 DP-based Fair Classification in Heterogeneous Federated Learning 7 Conclusion and References 7 Conclusion and References Appendix A Proofs Appendix A Proofs Appendix B Additional Results for Image Dataset Appendix B Additional Results for Image Dataset 7 Conclusion In this work, we attempted to demonstrate the inductive biases of in-processing fair learning algorithms aiming to achieve demographic parity (DP). We also proposed a distributionally robust optimization scheme to reduce the biases toward the majority sensitive attribute. An interesting future direction to our work is to search for similar biases in pre-processing and post-processing fair learning methods. Also, the theoretical comparison between different dependence measures such as mutual information, Pearson correlation, and the maximal correlation on the inductive bias levels will be an interesting topic for future exploration. Finally, characterizing the trade-off between accuracy, fairness violation, and biases toward the majority subgroups will help to better understand the costs of DP-based fair learning. References [1] Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference, pages 214–226, 2012. (Cited on page 1.) [2] Moritz Hardt, Eric Price, and Nati Srebro. Equality of opportunity in supervised learning. Advances in neural information processing systems, 29, 2016. (Cited on pages 1, 3, and 4.) [3] Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma. Fairness-aware learning through regularization approach. In 2011 IEEE 11th International Conference on Data Mining Workshops, pages 643–650. IEEE, 2011. (Cited on page 3.) [4] Ashkan Rezaei, Rizal Fathony, Omid Memarrast, and Brian Ziebart. Fairness for robust log loss classification. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 5511–5518, 2020. (Cited on page 3.) [5] Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. Mitigating unwanted biases with adversarial learning. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pages 335–340, 2018. (Cited on page 3.) [6] Jaewoong Cho, Gyeongjo Hwang, and Changho Suh. A fair classifier using mutual information. In 2020 IEEE International Symposium on Information Theory (ISIT), pages 2521–2526. IEEE, 2020. (Cited on pages 3, 8, and 19.) [7] Yuji Roh, Kangwook Lee, Steven Whang, and Changho Suh. Fr-train: A mutual information-based approach to fair and robust training. In International Conference on Machine Learning, pages 8147–8157. PMLR, 2020. (Cited on page 3.) [8] Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi. Fairness constraints: Mechanisms for fair classification. In Artificial intelligence and statistics, pages 962–970. PMLR, 2017. (Cited on page 3.) [9] Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, and Ed H Chi. Putting fairness principles into practice: Challenges, metrics, and improvements. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pages 453–459, 2019. (Cited on page 3.) [10] Flavien Prost, Hai Qian, Qiuwen Chen, Ed H Chi, Jilin Chen, and Alex Beutel. Toward a better trade-off between performance and fairness with kernel-based distribution matching. arXiv preprint arXiv:1910.11779, 2019. (Cited on page 3.) [11] Jaewoong Cho, Gyeongjo Hwang, and Changho Suh. A fair classifier using kernel density estimation. Advances in neural information processing systems, 33:15088–15099, 2020. (Cited on pages 3, 8, 9, and 19.) [12] Jérémie Mary, Clément Calauzenes, and Noureddine El Karoui. Fairness-aware learning for continuous attributes and treatments. In International Conference on Machine Learning, pages 4382–4391. PMLR, 2019. (Cited on pages 3 and 8.) [13] Sina Baharlouei, Maher Nouiehed, Ahmad Beirami, and Meisam Razaviyayn. Renyi fair inference. arXiv preprint arXiv:1906.12005, 2019. (Cited on pages 3, 8, and 9.) [14] Vincent Grari, Boris Ruf, Sylvain Lamprier, and Marcin Detyniecki. Fairness-aware neural renyi minimization for continuous features. arXiv preprint arXiv:1911.04929, 2019. (Cited on page 3.) [15] Vincent Grari, Oualid El Hajouji, Sylvain Lamprier, and Marcin Detyniecki. Learning unbiased representations via rényi minimization. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part II 21, pages 749–764. Springer, 2021. (Cited on page 3.) [16] Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, and Ahmad Beirami. A stochastic optimization framework for fair risk minimization. Transactions on Machine Learning Research, 2022. (Cited on page 3.) [17] Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. Certifying and removing disparate impact. In proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pages 259–268, 2015. (Cited on page 3.) [18] Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. Learning fair representations. In International conference on machine learning, pages 325–333. PMLR, 2013. (Cited on page 3.) [19] Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney. Optimized pre-processing for discrimination prevention. Advances in neural information processing systems, 30, 2017. (Cited on page 3.) [20] Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger. On fairness and calibration. Advances in neural information processing systems, 30, 2017. (Cited on page 3.) [21] Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. Fairness without demographics in repeated loss minimization. In International Conference on Machine Learning, pages 1929–1938. PMLR, 2018. (Cited on page 3.) [22] Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, and Michael Jordan. Robust optimization for fairness with noisy protected groups. Advances in neural information processing systems, 33:5190–5203, 2020. (Cited on page 3.) [23] Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed Chi. Fairness without demographics through adversarially reweighted learning. Advances in neural information processing systems, 33:728–740, 2020. (Cited on page 3.) [24] Hongseok Namkoong and John C Duchi. Stochastic gradient methods for distributionally robust optimization with f-divergences. Advances in neural information processing systems, 29, 2016. (Cited on page 7.) [25] Dimitris Bertsimas, Melvyn Sim, and Meilin Zhang. Adaptive distributionally robust optimization. Management Science, 65(2):604–618, 2019. (Cited on page 7.) [26] Hamed Rahimian and Sanjay Mehrotra. Distributionally robust optimization: A review. arXiv preprint arXiv:1908.05659, 2019. (Cited on page 7.) [27] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Large-scale celebfaces attributes (celeba) dataset. Retrieved August, 15(2018):11, 2018. (Cited on page 8.) [28] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. (Cited on pages 8 and 19.) [29] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273–1282. PMLR, 2017. (Cited on page 11.) [30] Gustavo L Gilardoni. On the minimum f-divergence for given total variation. Comptes rendus. Mathématique, 343(11-12):763–766, 2006. (Cited on page 17.) [31] Shahab Asoodeh, Fady Alajaji, and Tamás Linder. On maximal correlation, mutual information and data privacy. In 2015 IEEE 14th Canadian workshop on information theory (CWIT), pages 27–31. IEEE, 2015. (Cited on page 17.) This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors: (1) Haoyu LEI, Department of Computer Science and Engineering, The Chinese University of Hong Kong (hylei22@cse.cuhk.edu.hk); (2) Amin Gohari, Department of Information Engineering, The Chinese University of Hong Kong (agohari@ie.cuhk.edu.hk); (3) Farzan Farnia, Department of Computer Science and Engineering, The Chinese University of Hong Kong (farnia@cse.cuhk.edu.hk). Authors: Authors: (1) Haoyu LEI, Department of Computer Science and Engineering, The Chinese University of Hong Kong (hylei22@cse.cuhk.edu.hk); (2) Amin Gohari, Department of Information Engineering, The Chinese University of Hong Kong (agohari@ie.cuhk.edu.hk); (3) Farzan Farnia, Department of Computer Science and Engineering, The Chinese University of Hong Kong (farnia@cse.cuhk.edu.hk).

How to Reduce Majority Bias in AI Models

Authors:
(1) Todd K. Moon, Electrical and Computer Engineering Department, Utah State University, Logan, Utah;
(2) Jacob H. Gunther, Electrical and Computer Engineering Department, Utah State University, Logan, Utah. Table of Links Abstract and 1 Introduction and Background 2 Statistical Parsing and Extracted Features 3 Parse Tree Features 4 Classifier 5 Dimension Reduction 6 The Federalist Papers 6.1 Sanditon 7 Conclusions, Discussion, and Future Work A. A Brief Introduction to Statistical Parsing B. Dimension Reduction: Some Mathematical Details References B Dimension Reduction: Some Mathematical Details This material is drawn from [27]. The trace of Sw provides a measure of the clustering of the feature for each class around their respective centroids, Note that Sw, being the sum of the outer product of n terms, generically has rank min(n, m). In the work here, the dimension of the feature vectors m is very large, so that rank(Sw) = n; Sw is singular. Similarly, tr(Sb) measures the total distance between cluster centroids and the overall centroid, A measure of cluster quality which measures the degree to which tr(Sw) is small and tr(Sb) is large is To express the algorithm, the following matrices are defined. The scatter matrices Sw, Sb and Sm can be expressed in terms of the matrices That is, Hm, Hb and Hm form factors of the respective scatter matrices. The algorithm for computing G is shown below. (adapted from Algorithm 1 of [27]). References [1] R. Lord, “de Morgan and the Statistical Study of Literary Style,” Biometrica, vol. 3, p. 282, 1958. [2] A. Morton, Literary Detection. New York: Charles Scribner’s Sons, 1978. [3] T. Mendenhall, “A Mechanical Solution of a Literary Problem,” Popular Science Monthly, 1901. [4] C. D. Chretien, “A Statistical Method for Determining Authorship: The Junius Letters,” Languages, vol. 40, pp. 95–90, 1964. [5] D. Wishart and S. V. Leach, “A Multivariate Analysis of Platonic Prose Rhythm,” Computer Studies in the Humanities and Verbal Behavior, vol. 3, no. 2, pp. 109–125, 1972. [6] C. S. Brinegar, “Mark Twain and the Quintis Curtis Snodgrass Letters: A Statistical Test of Authorship,” Journal of the Americal Statistical Association, vol. 53, p. 85, 1963. [7] F. Mosteller and D. Wallace, Inference and Disputed Authorship: The Federalist. Reading, MA: Addison Wesley, 1964. [8] P. Hanus and J. Hagenauer, “Information Theory Helps Historians,” IEEE Information Theory Society Newsletter, vol. 55, p. 8, Sept. 2005. [9] D. Holmes, “The analysis of literary style — a review,” J. Royal Statistical Society, Series A, vol. 148, no. 4, pp. 328–341, 1985. [10] J. L. Hilton, “On Verifying Wordprint Studies: Book of Mormon Authorship,” Brigham Young University Studies, 1990. [11] D. Holmes, “A Stylometric Analysis of Mormon Scriptures and Related Texts,” Journal of the Royal Statistical Society, A, vol. 155, pp. 91–120, 1992. [12] K. Luyckx and W. Daelemans, “Shallow text analysis and machine learning for authorship attribution,” in Proceedings of the Fifteenth Meeting of Computational Linguistics in the Netherlands, pp. 149–160, 2005. [13] J. Grieve, “Quantitative authorship attribution: an evaluation of techniques,” Liter. Linguist. Comput., vol. 22, no. 3, pp. 251–270, 2007. [14] F. Iqbal, H. Binsalleeh, B. Fung, and M. Debbabi, “A unified data mining solution for authorship analysis in anonymous textual communication,” Inform. Sci, vol. 231, pp. 98–112, 2007. [15] E. Stamatos, “A survey of modern authorship attribution methods,” J. Am. Soc. Inform. Sci. Technol., vol. 60, no. 3, pp. 538–556, 2009. [16] C. Zhang, X. Wu, Z. Niu, and W. Ding, “Authorship identification from unstructured texts,” Knowledge-Based Systems, vol. 66, pp. 99–111, 2014. [17] I. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986. [18] S. Corbara, B. Chulvi, and A. Moreo, Experimental IR meets multilingality, multimodality, and interaction, vol. 13390 of Lecture Notes in Computer Science, ch. Rythmic and Psycholinguistic Features for Authorship Tasks in the Spanish Parliament: Evaluation and Analysis. Springer, 2022. [19] S. Corbara, C. C. Ferriols, P. Rosso, and A. Moreo, Natural Language Processing and Information Systems, vol. 13286 of Lecture Notes in Computer Science, ch. Investigating Topic-Agnostic Features for Authorship Tasks in Spanish Political Speeches. Springer, 2022. [20] M. Sanchez-Perez, I. Markov, H. Gomez-Adorno, and G. Sidorov, ´ Experimental IR Meets Multilinguality, Multimodality, and Interaction, vol. 10456 of Lecture Notes in Computer Science, ch. Comparison of Character n-grams and Lexical Features on Author, Gender, and Language Variety Identification on the Same Spanish News Corpus. Springer, 2017. [21] D. Klein and C. D. Manning, “Accurate unlexicalized parsing,” in Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, vol. 1, pp. 423–430, 2003. https://doi.org/10.3115/1075096.1075150. [22] M. P. Marcus, B. Santorini, and M. A. Marcinkiewicz, “Building a Large Annotated Corpus of English: The Penn Treebank,” Computational Linguisistics, vol. 19, no. 2, pp. 313–330, 1993. [23] A. Taylor, M. Marcus, and B. Santorini, “The Penn Treebank: An Overview.” https://www. researchgate.net/publication/2873803_The_Penn_Treebank_An_overview, 2003. [24] D. Klein and C. D. Mannning, “Accurate unlexicalized parsing,” in Proceedings of the 41st Meeting of the Association for Computational Linguisistics, pp. 423–430, 2003. [25] T. S. N. L. Group, “Software: Stanford parser.” https://nlp.stanford.edu/software/ lex-parser.html, 2020. [26] D. Juraksky and J. H. Martin, Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River, NJ: Prentice-Hall, 2009. [27] P. Howland, M. Jeon, and H. Park, “Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition,” SIAM J. Matrix Anal. Appl., vol. 25, no. 1, pp. 165–179, 2003. [28] T. K. Moon, P. Howland, and J. H. Gunther, “Document author classification using generalized discriminant analysis,” in SIAM Conference on Text Mining, (Baltimore, MD), May 23–25 2006. [29] P. Howland, J. Wang, and H. Park, “Solving the small sample size problem in face recognition using generalized discriminant analysis,” Pattern Recognition, vol. 39, pp. 277–287, 2006. [30] A. Hamilton, J. Madison, and J. Jay, “The Federalist,” in American State Papers (R. M. Hutchins, ed.), vol. 43 of Great Books of the Western World, pp. 29–266, Encyclopedia Britannica, Chicago ed., 1952. [31] A. Hamilton, J. Madison, and J. Jay, “The Federalist (machine readable).” http://www.gutenberg.org /etext/18. [32] P. Poplawski, A Jane Austen Encyclopedia. London: Aldwych Press, 1998. [33] J. Austen and A. Lady, Sanditon. London: Peter Davies, 1975. [34] D. Hopkinson, “Completions,” in The Jane Austen Companion (J. D. Grey, ed.), Macmillan, 1986. [35] “Sanditon (machine readable).” http://etext.lib.virginia.edu/toc/modeng/public/ AusSndt.html. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Todd K. Moon, Electrical and Computer Engineering Department, Utah State University, Logan, Utah; (2) Jacob H. Gunther, Electrical and Computer Engineering Department, Utah State University, Logan, Utah. Authors: Authors: (1) Todd K. Moon, Electrical and Computer Engineering Department, Utah State University, Logan, Utah; (2) Jacob H. Gunther, Electrical and Computer Engineering Department, Utah State University, Logan, Utah. Table of Links Abstract and 1 Introduction and Background Abstract and 1 Introduction and Background 2 Statistical Parsing and Extracted Features 2 Statistical Parsing and Extracted Features 3 Parse Tree Features 3 Parse Tree Features 4 Classifier 4 Classifier 5 Dimension Reduction 5 Dimension Reduction 6 The Federalist Papers 6 The Federalist Papers 6.1 Sanditon 6.1 Sanditon 7 Conclusions, Discussion, and Future Work 7 Conclusions, Discussion, and Future Work A. A Brief Introduction to Statistical Parsing A. A Brief Introduction to Statistical Parsing B. Dimension Reduction: Some Mathematical Details B. Dimension Reduction: Some Mathematical Details References References B Dimension Reduction: Some Mathematical Details This material is drawn from [27]. The trace of Sw provides a measure of the clustering of the feature for each class around their respective centroids, Note that Sw, being the sum of the outer product of n terms, generically has rank min(n, m). In the work here, the dimension of the feature vectors m is very large, so that rank(Sw) = n; Sw is singular. Similarly, tr(Sb) measures the total distance between cluster centroids and the overall centroid, A measure of cluster quality which measures the degree to which tr(Sw) is small and tr(Sb) is large is To express the algorithm, the following matrices are defined. The scatter matrices Sw, Sb and Sm can be expressed in terms of the matrices That is, Hm, Hb and Hm form factors of the respective scatter matrices. The algorithm for computing G is shown below. (adapted from Algorithm 1 of [27]). References [1] R. Lord, “de Morgan and the Statistical Study of Literary Style,” Biometrica, vol. 3, p. 282, 1958. [2] A. Morton, Literary Detection. New York: Charles Scribner’s Sons, 1978. [3] T. Mendenhall, “A Mechanical Solution of a Literary Problem,” Popular Science Monthly, 1901. [4] C. D. Chretien, “A Statistical Method for Determining Authorship: The Junius Letters,” Languages, vol. 40, pp. 95–90, 1964. [5] D. Wishart and S. V. Leach, “A Multivariate Analysis of Platonic Prose Rhythm,” Computer Studies in the Humanities and Verbal Behavior, vol. 3, no. 2, pp. 109–125, 1972. [6] C. S. Brinegar, “Mark Twain and the Quintis Curtis Snodgrass Letters: A Statistical Test of Authorship,” Journal of the Americal Statistical Association, vol. 53, p. 85, 1963. [7] F. Mosteller and D. Wallace, Inference and Disputed Authorship: The Federalist. Reading, MA: Addison Wesley, 1964. [8] P. Hanus and J. Hagenauer, “Information Theory Helps Historians,” IEEE Information Theory Society Newsletter, vol. 55, p. 8, Sept. 2005. [9] D. Holmes, “The analysis of literary style — a review,” J. Royal Statistical Society, Series A, vol. 148, no. 4, pp. 328–341, 1985. [10] J. L. Hilton, “On Verifying Wordprint Studies: Book of Mormon Authorship,” Brigham Young University Studies, 1990. [11] D. Holmes, “A Stylometric Analysis of Mormon Scriptures and Related Texts,” Journal of the Royal Statistical Society, A, vol. 155, pp. 91–120, 1992. [12] K. Luyckx and W. Daelemans, “Shallow text analysis and machine learning for authorship attribution,” in Proceedings of the Fifteenth Meeting of Computational Linguistics in the Netherlands, pp. 149–160, 2005. [13] J. Grieve, “Quantitative authorship attribution: an evaluation of techniques,” Liter. Linguist. Comput., vol. 22, no. 3, pp. 251–270, 2007. [14] F. Iqbal, H. Binsalleeh, B. Fung, and M. Debbabi, “A unified data mining solution for authorship analysis in anonymous textual communication,” Inform. Sci, vol. 231, pp. 98–112, 2007. [15] E. Stamatos, “A survey of modern authorship attribution methods,” J. Am. Soc. Inform. Sci. Technol., vol. 60, no. 3, pp. 538–556, 2009. [16] C. Zhang, X. Wu, Z. Niu, and W. Ding, “Authorship identification from unstructured texts,” Knowledge-Based Systems, vol. 66, pp. 99–111, 2014. [17] I. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986. [18] S. Corbara, B. Chulvi, and A. Moreo, Experimental IR meets multilingality, multimodality, and interaction, vol. 13390 of Lecture Notes in Computer Science, ch. Rythmic and Psycholinguistic Features for Authorship Tasks in the Spanish Parliament: Evaluation and Analysis. Springer, 2022. [19] S. Corbara, C. C. Ferriols, P. Rosso, and A. Moreo, Natural Language Processing and Information Systems, vol. 13286 of Lecture Notes in Computer Science, ch. Investigating Topic-Agnostic Features for Authorship Tasks in Spanish Political Speeches. Springer, 2022. [20] M. Sanchez-Perez, I. Markov, H. Gomez-Adorno, and G. Sidorov, ´ Experimental IR Meets Multilinguality, Multimodality, and Interaction, vol. 10456 of Lecture Notes in Computer Science, ch. Comparison of Character n-grams and Lexical Features on Author, Gender, and Language Variety Identification on the Same Spanish News Corpus. Springer, 2017. [21] D. Klein and C. D. Manning, “Accurate unlexicalized parsing,” in Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, vol. 1, pp. 423–430, 2003. https://doi.org/10.3115/1075096.1075150. [22] M. P. Marcus, B. Santorini, and M. A. Marcinkiewicz, “Building a Large Annotated Corpus of English: The Penn Treebank,” Computational Linguisistics, vol. 19, no. 2, pp. 313–330, 1993. [23] A. Taylor, M. Marcus, and B. Santorini, “The Penn Treebank: An Overview.” https://www. researchgate.net/publication/2873803_The_Penn_Treebank_An_overview, 2003. [24] D. Klein and C. D. Mannning, “Accurate unlexicalized parsing,” in Proceedings of the 41st Meeting of the Association for Computational Linguisistics, pp. 423–430, 2003. [25] T. S. N. L. Group, “Software: Stanford parser.” https://nlp.stanford.edu/software/ lex-parser.html, 2020. [26] D. Juraksky and J. H. Martin, Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River, NJ: Prentice-Hall, 2009. [27] P. Howland, M. Jeon, and H. Park, “Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition,” SIAM J. Matrix Anal. Appl., vol. 25, no. 1, pp. 165–179, 2003. [28] T. K. Moon, P. Howland, and J. H. Gunther, “Document author classification using generalized discriminant analysis,” in SIAM Conference on Text Mining, (Baltimore, MD), May 23–25 2006. [29] P. Howland, J. Wang, and H. Park, “Solving the small sample size problem in face recognition using generalized discriminant analysis,” Pattern Recognition, vol. 39, pp. 277–287, 2006. [30] A. Hamilton, J. Madison, and J. Jay, “The Federalist,” in American State Papers (R. M. Hutchins, ed.), vol. 43 of Great Books of the Western World, pp. 29–266, Encyclopedia Britannica, Chicago ed., 1952. [31] A. Hamilton, J. Madison, and J. Jay, “The Federalist (machine readable).” http://www.gutenberg.org /etext/18. [32] P. Poplawski, A Jane Austen Encyclopedia. London: Aldwych Press, 1998. [33] J. Austen and A. Lady, Sanditon. London: Peter Davies, 1975. [34] D. Hopkinson, “Completions,” in The Jane Austen Companion (J. D. Grey, ed.), Macmillan, 1986. [35] “Sanditon (machine readable).” http://etext.lib.virginia.edu/toc/modeng/public/ AusSndt.html. 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

A Mathematical Overview of Dimension Reduction in Text Classification

Reducing Memory Overhead in AI Models

Expertise Delivered Frontend, Backend & QA Industry Insurance Client Brief Our client, HealthPro Insurance, is a premier Medicare insurance broker, ranked among the top 5 in the United States by Healthcare.gov. Their comprehensive offerings include specialized support for health insurance, Dental, Vision, and long-term life insurance, ensuring their clients receive the best possible care and coverage. HealthPro provides valuable subsidies to employers, individual consumers, and nonprofits. They aim to enhance the accessibility and provision of insurance for all US citizens. Project Scope Our client managed their website and peripheral services on legacy processes and infrastructure. Their unoptimized processes and abrupt resource investments made them pay through their nose. They wanted to introduce automation with DevOps practices to optimize their system’s performance, flexibility, efficiency, and data security. HealthPro’s project scope revolves around the following attributes. Distribute server load and processes across multiple domains.
Switch to private cloud from public to prevent cyber attacks like DDoS, resulting in data breaches.
Creating two different environments for production and staging can mitigate the risks of compromised user experiences when experimenting with or upgrading systems. Distribute server load and processes across multiple domains. Switch to private cloud from public to prevent cyber attacks like DDoS, resulting in data breaches. Creating two different environments for production and staging can mitigate the risks of compromised user experiences when experimenting with or upgrading systems. The Challenge Due to hosting all services in a single public cloud and relying solely on credential protection, our client faced server overloading and security vulnerabilities. This also increased the platform’s instability, posing limitations with hosting the production and staging environments. The above issues compromised the performance of their services, increased costs, and created data risk, hindering their accessibility and scalability. Another prime challenge was switching all their domains and sub-domains to AWS and implementing autoscaling to optimize costs. Our client’s services used MariaDP as a database, and a significant challenge was to plan a systematic switch from MariaDP to MySQL while keeping their data intact. Why Maruti Techlabs? One of our consultants, who was acquainted with our client's challenges and with our expertise, advocated for our services. Subsequently, the consultant and the client's Chief of Sales, who served as the primary point of contact, designed a questionnaire to evaluate our approach. After thoroughly understanding the client's expectations through the questionnaire, our team of experts brainstormed various solutions. We developed a detailed workflow outlining the implementation of these solutions, focusing on enhancing security, improving stability, and optimizing costs. Our proven DevOps expertise in server administration and maintenance, implementing and maintaining CI/CD pipelines, cloud migration, integrating DevSecOps, and more, supported by the consultant's confidence in our capabilities, secured the project for us. The proposed workflow and solutions demonstrated our ability to meet the client's needs effectively, ultimately earning their trust and business. DevOps expertise CI/CD pipelines cloud migration Solution A phase-wise approach was chosen to implement the required solutions to ensure uninterrupted services for the client's customers. Here's how we implemented the perfect tech infrastructure and optimized costs for our client: 1. Isolating Staging and Production Environment 1. Isolating Staging and Production Environment Firstly, we isolated the whole(one) unit into two different environments for ‘Production’ and ‘Staging.’ This helped us offer a seamless customer experience in production, allowing our client to experiment with new features, load and regression testing, and introducing new upgrades with security in staging. 2. Transitioning from Public to Private Cloud 2. Transitioning from Public to Private Cloud Our next step was building the staging environment by creating a new Virtual Private Cloud (VPC) with private and public subnets. 3. Creating Data Back-Up 3. Creating Data Back-Up We then transferred the database from the old VPC’s public subnet to the new VPC’s private subnet. We did this by taking a snapshot or backing up the data, creating a copy in the new VPC, and restoring it to the private subnet. Additionally, to allow the server to access the database seamlessly, we assigned roles to the EC2 server and upgraded its database security rules. 4. Cost Optimization 4. Cost Optimization As a final step, we optimized costs for our client by implementing automatic scaling with AWS’s Aurora Serverless. Aurora Serverless offers the convenience of auto-scaling CPU and memory usage based on traffic. For instance, if our clients don’t have weekly traffic, they do not need to pay for that period. We achieved this by migrating from MariaDB to MySQL, as Aurora supports both database engines. We then transferred the migrated data to Aurora Serverless. Communication and Collaboration We conducted a one-week discovery phase during which our team members communicated extensively to understand their current solution's limitations and expectations concerning their desired solution. We allocated two team members, one DevOps manager and one project manager, to execute this project’s specifics end-to-end. For the entirety of the project, our primary point of contact was our client’s ‘Chief of Sales.’ Here are the platforms we majorly use for communication: Slack - Quick communication,
Google Meet - Daily updates and queries, roll-outs
Email - Official channel to share contracts, weekly updates, and more. Slack - Quick communication, Google Meet - Daily updates and queries, roll-outs Email - Official channel to share contracts, weekly updates, and more. Technology Stack Result Implementing our DevOps solutions significantly improved the client's infrastructure and operational efficiency. Reduced on-platform search time from 8 minutes to under 1 minute.
We achieved a 300% boost in application performance over previous versions.
Our solution facilitated a 50% decrease in database and server management costs.
Optimized costs by implementing auto-scaling with AWS Aurora Serverless.
The device system performs automated backups and recovery in case of a crash or security threat.
In case of a sudden server load, the system sends alert signals through email and Slack.
Migration to a private cloud offers end-to-end data encryption. Reduced on-platform search time from 8 minutes to under 1 minute. We achieved a 300% boost in application performance over previous versions. Our solution facilitated a 50% decrease in database and server management costs. Optimized costs by implementing auto-scaling with AWS Aurora Serverless. The device system performs automated backups and recovery in case of a crash or security threat. In case of a sudden server load, the system sends alert signals through email and Slack. Migration to a private cloud offers end-to-end data encryption. Our Development Process We follow Agile, Lean, & DevOps best practices to create a superior prototype that brings your users’ ideas to fruition through collaboration & rapid execution. Our top priority is quick reaction time & accessibility. We really want to be your extended team, so apart from the regular meetings, you can be sure that each of our team members is one phone call, email, or message away.

Reducing Server and Database Costs by 50% for an Insurance Broker using AWS

تخفيض تكاليف الخادم وقاعدة البيانات بنسبة 50% لشركة وساطة تأمين تستخدم AWS

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AWS ব্যবহারকারী বীমা ব্রোকারের জন্য সার্ভার এবং ডাটাবেস খরচ ৫০% কমানো

Reducción de costos de servidor y base de datos en un 50% para un corredor de seguros que utiliza AWS

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Kokitisa ba Coûts ya Serveur na Base de Données na 50% pona Correcteur d'Assurance oyo azali kosalela AWS

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ንሓደ AWS ዝጥቀም ናይ መድሕን ደላላይ ዋጋ ሰርቨርን ዳታቤዝን ብ50% ምንካይ

Authors:
(1) Andrew Draganov, Aarhus University and All authors contributed equally to this research;
(2) David Saulpic, Université Paris Cité & CNRS;
(3) Chris Schwiegelshohn, Aarhus University. Table of Links Abstract and 1 Introduction 2 Preliminaries and Related Work 2.1 On Sampling Strategies 2.2 Other Coreset Strategies 2.3 Coresets for Database Applications 2.4 Quadtree Embeddings 3 Fast-Coresets 4 Reducing the Impact of the Spread 4.1 Computing a crude upper-bound 4.2 From Approximate Solution to Reduced Spread 5 Fast Compression in Practice 5.1 Goal and Scope of the Empirical Analysis 5.2 Experimental Setup 5.3 Evaluating Sampling Strategies 5.4 Streaming Setting and 5.5 Takeaways 6 Conclusion 7 Acknowledgements 8 Proofs, Pseudo-Code, and Extensions and 8.1 Proof of Corollary 3.2 8.2 Reduction of k-means to k-median 8.3 Estimation of the Optimal Cost in a Tree 8.4 Extensions to Algorithm 1 References 4 Reducing the Impact of the Spread We now show how one can replace the linear time-dependency on log ∆ with a logarithmic one (i.e., log ∆ → log(log ∆)). Overview of the Approach Without loss of generality, we assume that the smallest pairwise distance is at least 1, and ∆ is a (known) upper-bound on the diameter of the input. To remove the log ∆ dependency, we proceed by producing a substitute dataset P ′ that has a lower-bound on its minimum distance and an upper-bound on its maximum distance. We then show that, with overwhelming likelihood, reasonable solutions on P ′ have the same cost as solutions on P up to an additive error. In order to produce the substitute dataset P ′ , we first find a crude upper-bound on the cost of the optimal solution to the clustering task on P. We then create a grid such that, for every cluster in the optimal solution, the cluster is entirely contained in a grid cell with overwhelming likelihood – in some sense, points in different grid cell do not interact. We can then freely move these grid cells around without affecting the cost of the solution, as long as they stay far enough away so that points in different cells still do not interact. Thus, we can constrain the diameter of P ′ to be small with respect to the quality of the approximate solution. We show later how to constrain the minimum distance to be comparable to the quality of this approximate solution as well. We will focus this section on the simpler k-median problem but show how to reduce k-means to this case in Section 8.2. 4.1 Computing a crude upper-bound As described, we start by computing an approximate solution U such that OPT ≤ U ≤ poly(n)· OPT. For this, the first step is to embed the input into a quadtree. This embedding has two key properties. First, distances are preserved up to a multiplicative factor O(d log ∆), and therefore the k-median cost is preserved up to this factor as well. Second, the metric is a hierarchically separated tree: it can be represented with a tree, where points of P are the leafs. The distance between two points is then given by the depth of their lowest common ancestor – if it is at depth ℓ, their distance is √ d∆2−ℓ . Our first lemma shows that finding the first level of the tree for which the input lies in k + 1 disjoint subtrees provides us with the desired approximation. We prove this in Section 8.3. A direct consequence is that the first level of the tree for which at least k + 1 cells are non empty allows us to compute an O(n)-approximation for k-median on the tree metric. Since the tree metric approximates the original Euclidean metric up to O(d log ∆), this is therefore an O(nd log ∆)-approximation to k-median in the Euclidean space. To turn this observation into an algorithm, one needs to count the number of non-empty cells at a given level ℓ: for each point, we identify the cell that contains it using modulo operations. Furthermore, we count the number of distinct non-empty cells using a standard dictionary data structure. This is done in time O˜(nd), and pseudo-code is given Algorithm 2. Using a binary search on the O(log ∆) many levels then gives the following result: Given this crude approximate solution, it remains to create a substitute dataset P ′ that satisfies two requirements: 1 First, P ′ must have spread linear in the quality of the approximate solution. If this holds, Algorithm 1 on P ′ will take O˜(nd log log ∆) time. 2 Second, any reasonable solution on P ′ should be roughly equivalent to a corresponding solution on P. This would mean that running Algorithm 1 on P ′ gives us a valid coreset for P. 4.2 From Approximate Solution to Reduced Spread Proposition 4.4. Let P ′ be the dataset produced by Algorithm 3. It holds that: Combining the algorithm of Lemma 4.2, which gives a bound on U, with Lemma 4.5 brings us to the following theorem: To summarize, we have shown that one can, in O˜(nd) time, find a modified dataset P ′ that preserves the cost of corresponding k-means and k-median solutions from P. Importantly, this P ′ has spread that is logarithmic in the spread of P. As a result, one can apply Algorithm 1 onto P ′ in O˜(nd) time without compromising the compression quality with respect to P. Lastly, this compression on P ′ can be re-formatted onto P in O˜(nd) time. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Andrew Draganov, Aarhus University and All authors contributed equally to this research; (2) David Saulpic, Université Paris Cité & CNRS; (3) Chris Schwiegelshohn, Aarhus University. Authors: Authors: (1) Andrew Draganov, Aarhus University and All authors contributed equally to this research; (2) David Saulpic, Université Paris Cité & CNRS; (3) Chris Schwiegelshohn, Aarhus University. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Preliminaries and Related Work 2.1 On Sampling Strategies 2.1 On Sampling Strategies 2.2 Other Coreset Strategies 2.2 Other Coreset Strategies 2.3 Coresets for Database Applications 2.3 Coresets for Database Applications 2.4 Quadtree Embeddings 2.4 Quadtree Embeddings 3 Fast-Coresets 3 Fast-Coresets 4 Reducing the Impact of the Spread 4 Reducing the Impact of the Spread 4.1 Computing a crude upper-bound 4.1 Computing a crude upper-bound 4.2 From Approximate Solution to Reduced Spread 4.2 From Approximate Solution to Reduced Spread 5 Fast Compression in Practice 5 Fast Compression in Practice 5.1 Goal and Scope of the Empirical Analysis 5.1 Goal and Scope of the Empirical Analysis 5.2 Experimental Setup 5.2 Experimental Setup 5.3 Evaluating Sampling Strategies 5.3 Evaluating Sampling Strategies 5.4 Streaming Setting and 5.5 Takeaways 5.4 Streaming Setting and 5.5 Takeaways 6 Conclusion 6 Conclusion 7 Acknowledgements 7 Acknowledgements 8 Proofs, Pseudo-Code, and Extensions and 8.1 Proof of Corollary 3.2 8 Proofs, Pseudo-Code, and Extensions and 8.1 Proof of Corollary 3.2 8.2 Reduction of k-means to k-median 8.2 Reduction of k-means to k-median 8.3 Estimation of the Optimal Cost in a Tree 8.3 Estimation of the Optimal Cost in a Tree 8.4 Extensions to Algorithm 1 8.4 Extensions to Algorithm 1 References References 4 Reducing the Impact of the Spread We now show how one can replace the linear time-dependency on log ∆ with a logarithmic one (i.e., log ∆ → log(log ∆)). Overview of the Approach Without loss of generality, we assume that the smallest pairwise distance is at least 1, and ∆ is a (known) upper-bound on the diameter of the input. To remove the log ∆ dependency, we proceed by producing a substitute dataset P ′ that has a lower-bound on its minimum distance and an upper-bound on its maximum distance. We then show that, with overwhelming likelihood, reasonable solutions on P ′ have the same cost as solutions on P up to an additive error. Overview of the Approach In order to produce the substitute dataset P ′ , we first find a crude upper-bound on the cost of the optimal solution to the clustering task on P. We then create a grid such that, for every cluster in the optimal solution, the cluster is entirely contained in a grid cell with overwhelming likelihood – in some sense, points in different grid cell do not interact. We can then freely move these grid cells around without affecting the cost of the solution, as long as they stay far enough away so that points in different cells still do not interact. Thus, we can constrain the diameter of P ′ to be small with respect to the quality of the approximate solution. We show later how to constrain the minimum distance to be comparable to the quality of this approximate solution as well. We will focus this section on the simpler k-median problem but show how to reduce k-means to this case in Section 8.2. 4.1 Computing a crude upper-bound As described, we start by computing an approximate solution U such that OPT ≤ U ≤ poly(n)· OPT. For this, the first step is to embed the input into a quadtree. This embedding has two key properties. First, distances are preserved up to a multiplicative factor O(d log ∆), and therefore the k-median cost is preserved up to this factor as well. Second, the metric is a hierarchically separated tree: it can be represented with a tree, where points of P are the leafs. The distance between two points is then given by the depth of their lowest common ancestor – if it is at depth ℓ, their distance is √ d∆2−ℓ . Our first lemma shows that finding the first level of the tree for which the input lies in k + 1 disjoint subtrees provides us with the desired approximation. We prove this in Section 8.3. A direct consequence is that the first level of the tree for which at least k + 1 cells are non empty allows us to compute an O(n)-approximation for k-median on the tree metric. Since the tree metric approximates the original Euclidean metric up to O(d log ∆), this is therefore an O(nd log ∆)-approximation to k-median in the Euclidean space. To turn this observation into an algorithm, one needs to count the number of non-empty cells at a given level ℓ: for each point, we identify the cell that contains it using modulo operations. Furthermore, we count the number of distinct non-empty cells using a standard dictionary data structure. This is done in time O˜(nd), and pseudo-code is given Algorithm 2. Using a binary search on the O(log ∆) many levels then gives the following result: Given this crude approximate solution, it remains to create a substitute dataset P ′ that satisfies two requirements: 1 First, P ′ must have spread linear in the quality of the approximate solution. If this holds, Algorithm 1 on P ′ will take O˜(nd log log ∆) time. 2 Second, any reasonable solution on P ′ should be roughly equivalent to a corresponding solution on P. This would mean that running Algorithm 1 on P ′ gives us a valid coreset for P. 4.2 From Approximate Solution to Reduced Spread Proposition 4.4. Let P ′ be the dataset produced by Algorithm 3. It holds that: Proposition 4.4. Let P ′ be the dataset produced by Algorithm 3. It holds that: Combining the algorithm of Lemma 4.2, which gives a bound on U, with Lemma 4.5 brings us to the following theorem: To summarize, we have shown that one can, in O˜(nd) time, find a modified dataset P ′ that preserves the cost of corresponding k-means and k-median solutions from P. Importantly, this P ′ has spread that is logarithmic in the spread of P. As a result, one can apply Algorithm 1 onto P ′ in O˜(nd) time without compromising the compression quality with respect to P. Lastly, this compression on P ′ can be re-formatted onto P in O˜(nd) time. 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

Reducing Spread Impact in Clustering Algorithms

How 3D-Printed Metamaterials Could Revolutionize Noise Reduction

Author:
(1) A.J. Santos, B.A. (UTSA), J.D. (STCL), Department of Private International Law, Ankara Yıldırım Beyazıt University, Faculty of Law (ajsantos@protonmail.com). Table of Links Abstract and I. Introduction II. Award Form and Content III. Electronic Signatures, Writing Requirement, and Authentication A. Is it possible for an Arbitrator o Digitally Sign an Arbitral Award
B. How could an Electronic Award be Considered to be "In Writing"
C. Could a Multisig E-Award be Duly Authenticated IV. Can Multisig Arbitration be Seen as an Autonomous Legal Order? V. Conclusion Appendix A: Multisig Transaction on Bitcoin Testnet Example Appendix B: Breakdown of BTC Stored in 2/3 Multisig Accounts Abstract: With blockchain technology, information is recorded in a permanent distributed ledger that is maintained by multiple computers in a peer-to-peer network. There is no central authority that can alter records or change network consensus rules. Such technology could be utilized for voting, title transfers, issuance of company shares, document notarization, but currently, the most popular use-case are virtual currencies. An interesting feature that some virtual currencies have is a multisignature (“multisig”) protocol that requires the electronic signatures from more than one private key to initiate a transfer of funds. Raw data of a multisig transaction may be recognized as an arbitral award under the New York Convention, where the law of England is the lex arbitri and parties have opted-out of a reasoned award. I. INTRODUCTION Multisig is a trust-less method for conducting electronic transactions that could possibly be used for international trade. For instance, a seller and buyer can set-up a multisig account and nominate a third party. If goods are delivered, funds are released from the multisig account with the signatures of the buyer and seller. In case of a dispute, the third party could adjudicate the dispute and sign the release of funds in favor of the prevailing party. However, the legal effect of the act of releasing funds from a multisig scheme has not been fully explored. It is of vital importance for resolved disputes to be final and binding. Businesses operate across many different jurisdictions and having an innovative method to finally resolve disputes would help decrease transactional costs and bolster certainty in an electronic trading environment. For this to occur, multisig needs to be re-imagined not as a simple transaction, but an arbitration process. The resulting multisig electronic award would have res judicata effect, where parties can rely on multisig to further the development of a case or to stop the reopening of a matter which has already been decided in a previous proceeding[2]. The res judicata effect of awards is an integral part of international commercial arbitration, which is evident in Article III of the New York Convention that requires courts in contracting states to recognize awards as binding[3]. This paper will determine whether a multisig transaction, arising out of an arbitration agreement, can be viewed as an electronic arbitral award that is capable of recognition under international arbitration law. In Part I, the legal framework for the form and content requirements of an arbitral award will be discussed. In Part II, we will touch on the law of electronic signatures and consider the authentication requirement of the New York Convention; and finally, in Part III, we will look at the possibility of characterizing multisig arbitration as an autonomous legal order. It is assumed the reader has a basic understanding of blockchain technology and asymmetric cryptography. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 4.0 INTERNATIONAL license. [2] Schaffstein, Silja (2016) The Doctrine of Res Judicata Before International Commercial Arbitral Tribunals, Oxford, Oxford University Press, pg. 210; Trans-Lex.org ‘No. XIII.4.5 - Conclusive and preclusive effect of awards; res judicata’ https://www.trans-lex.org/970070 l.a.d. 04/18/2019. [3] Id. Author: (1) A.J. Santos, B.A. (UTSA), J.D. (STCL), Department of Private International Law, Ankara Yıldırım Beyazıt University, Faculty of Law (ajsantos@protonmail.com). Author: Author: (1) A.J. Santos, B.A. (UTSA), J.D. (STCL), Department of Private International Law, Ankara Yıldırım Beyazıt University, Faculty of Law (ajsantos@protonmail.com). Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Award Form and Content II. Award Form and Content III. Electronic Signatures, Writing Requirement, and Authentication A. Is it possible for an Arbitrator o Digitally Sign an Arbitral Award B. How could an Electronic Award be Considered to be "In Writing" C. Could a Multisig E-Award be Duly Authenticated A. Is it possible for an Arbitrator o Digitally Sign an Arbitral Award A. Is it possible for an Arbitrator o Digitally Sign an Arbitral Award B. How could an Electronic Award be Considered to be "In Writing" B. How could an Electronic Award be Considered to be "In Writing" C. Could a Multisig E-Award be Duly Authenticated C. Could a Multisig E-Award be Duly Authenticated IV. Can Multisig Arbitration be Seen as an Autonomous Legal Order? IV. Can Multisig Arbitration be Seen as an Autonomous Legal Order? V. Conclusion V. Conclusion Appendix A : Multisig Transaction on Bitcoin Testnet Example Appendix A Appendix A : Multisig Transaction on Bitcoin Testnet Example Appendix B : Breakdown of BTC Stored in 2/3 Multisig Accounts Appendix B Appendix B : Breakdown of BTC Stored in 2/3 Multisig Accounts Abstract : With blockchain technology, information is recorded in a permanent distributed ledger that is maintained by multiple computers in a peer-to-peer network. There is no central authority that can alter records or change network consensus rules. Such technology could be utilized for voting, title transfers, issuance of company shares, document notarization, but currently, the most popular use-case are virtual currencies. An interesting feature that some virtual currencies have is a multisignature (“multisig”) protocol that requires the electronic signatures from more than one private key to initiate a transfer of funds. Raw data of a multisig transaction may be recognized as an arbitral award under the New York Convention, where the law of England is the lex arbitri and parties have opted-out of a reasoned award. Abstract I. INTRODUCTION Multisig is a trust-less method for conducting electronic transactions that could possibly be used for international trade. For instance, a seller and buyer can set-up a multisig account and nominate a third party. If goods are delivered, funds are released from the multisig account with the signatures of the buyer and seller. In case of a dispute, the third party could adjudicate the dispute and sign the release of funds in favor of the prevailing party. However, the legal effect of the act of releasing funds from a multisig scheme has not been fully explored. It is of vital importance for resolved disputes to be final and binding. Businesses operate across many different jurisdictions and having an innovative method to finally resolve disputes would help decrease transactional costs and bolster certainty in an electronic trading environment. For this to occur, multisig needs to be re-imagined not as a simple transaction, but an arbitration process. The resulting multisig electronic award would have res judicata effect, where parties can rely on multisig to further the development of a case or to stop the reopening of a matter which has already been decided in a previous proceeding[2]. The res judicata effect of awards is an integral part of international commercial arbitration, which is evident in Article III of the New York Convention that requires courts in contracting states to recognize awards as binding[3]. This paper will determine whether a multisig transaction, arising out of an arbitration agreement, can be viewed as an electronic arbitral award that is capable of recognition under international arbitration law. In Part I, the legal framework for the form and content requirements of an arbitral award will be discussed. In Part II, we will touch on the law of electronic signatures and consider the authentication requirement of the New York Convention; and finally, in Part III, we will look at the possibility of characterizing multisig arbitration as an autonomous legal order. It is assumed the reader has a basic understanding of blockchain technology and asymmetric cryptography. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 4.0 INTERNATIONAL license. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 4.0 INTERNATIONAL license. available on arxiv available on arxiv [2] Schaffstein, Silja (2016) The Doctrine of Res Judicata Before International Commercial Arbitral Tribunals, Oxford, Oxford University Press, pg. 210; Trans-Lex.org ‘No. XIII.4.5 - Conclusive and preclusive effect of awards; res judicata’ https://www.trans-lex.org/970070 l.a.d. 04/18/2019. Trans-Lex.org https://www.trans-lex.org/970070 [3] Id.

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