Kareer

kareer

Authors:
(1) Yingxu He, Department of Computer Science National University of Singapore {e0139128@u.nus.edu};
(2) Qiqi Sun, College of Life Sciences Nankai University {sunqiqi1018@gmail.com}. Table of Links Abstract and Intro
Methodology
References References [1] Armen Aghajanyan, Bernie Huang, Candace Ross, Vladimir Karpukhin, Hu Xu, Naman Goyal, Dmytro Okhonko, Mandar Joshi, Gargi Ghosh, Mike Lewis, and Luke Zettlemoyer. CM3: A causal masked multimodal model of the internet. CoRR, abs/2201.07520, 2022. [2] Jian Ding, Nan Xue, Gui-Song Xia, Xiang Bai, Wen Yang, Michael Yang, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, and Liangpei Zhang. Object detection in aerial images: A large-scale benchmark and challenges. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1–1, 2021. [3] Jon Kleinberg and Eva Tardos. Algorithm Design. Addison-Wesley Longman Publishing Co., Inc., USA, 2005. [4] Darius Lam, Richard Kuzma, Kevin McGee, Samuel Dooley, Michael Laielli, Matthew Klaric, Yaroslav Bulatov, and Brendan McCord. xview: Objects in context in overhead imagery. CoRR, abs/1802.07856, 2018. [5] Junnan Li, Dongxu Li, Caiming Xiong, and Steven C. H. Hoi. BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation. CoRR, abs/2201.12086, 2022. [6] Xiaoqiang Lu, Binqiang Wang, Xiangtao Zheng, and Xuelong Li. Exploring models and data for remote sensing image caption generation. IEEE Transactions on Geoscience and Remote Sensing, 56(4):2183–2195. [7] OpenAI. Introducing chatgpt, Nov 2022. [8] Ramakrishna Vedantam, C. Lawrence Zitnick, and Devi Parikh. Cider: Consensus-based image description evaluation, 2015. [9] Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, and Lijuan Wang. Git: A generative image-to-text transformer for vision and language, 2022. [10] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models, 2023. [11] Xi Ye and Greg Durrett. The unreliability of explanations in few-shot prompting for textual reasoning, 2022. [12] Lili Yu, Bowen Shi, Ramakanth Pasunuru, Benjamin Muller, Olga Golovneva, Tianlu Wang, Arun Babu, Binh Tang, Brian Karrer, Shelly Sheynin, Candace Ross, Adam Polyak, Russell Howes, Vasu Sharma, Puxin Xu, Hovhannes Tamoyan, Oron Ashual, Uriel Singer, Shang-Wen Li, Susan Zhang, Richard James, Gargi Ghosh, Yaniv Taigman, Maryam Fazel-Zarandi, Asli Celikyilmaz, Luke Zettlemoyer, and Armen Aghajanyan. Scaling autoregressive multi-modal models: Pretraining and instruction tuning, 2023. [13] Wenqi Zhang, Yongliang Shen, Weiming Lu, and Yueting Zhuang. Data-copilot: Bridging billions of data and humans with autonomous workflow, 2023. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Yingxu He, Department of Computer Science National University of Singapore {e0139128@u.nus.edu}; (2) Qiqi Sun, College of Life Sciences Nankai University {sunqiqi1018@gmail.com}. Authors: Authors: (1) Yingxu He, Department of Computer Science National University of Singapore {e0139128@u.nus.edu}; (2) Qiqi Sun, College of Life Sciences Nankai University {sunqiqi1018@gmail.com}. Table of Links Abstract and Intro Methodology References Abstract and Intro Abstract and Intro Methodology Methodology References References References [1] Armen Aghajanyan, Bernie Huang, Candace Ross, Vladimir Karpukhin, Hu Xu, Naman Goyal, Dmytro Okhonko, Mandar Joshi, Gargi Ghosh, Mike Lewis, and Luke Zettlemoyer. CM3: A causal masked multimodal model of the internet. CoRR, abs/2201.07520, 2022. [2] Jian Ding, Nan Xue, Gui-Song Xia, Xiang Bai, Wen Yang, Michael Yang, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, and Liangpei Zhang. Object detection in aerial images: A large-scale benchmark and challenges. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1–1, 2021. [3] Jon Kleinberg and Eva Tardos. Algorithm Design. Addison-Wesley Longman Publishing Co., Inc., USA, 2005. [4] Darius Lam, Richard Kuzma, Kevin McGee, Samuel Dooley, Michael Laielli, Matthew Klaric, Yaroslav Bulatov, and Brendan McCord. xview: Objects in context in overhead imagery. CoRR, abs/1802.07856, 2018. [5] Junnan Li, Dongxu Li, Caiming Xiong, and Steven C. H. Hoi. BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation. CoRR, abs/2201.12086, 2022. [6] Xiaoqiang Lu, Binqiang Wang, Xiangtao Zheng, and Xuelong Li. Exploring models and data for remote sensing image caption generation. IEEE Transactions on Geoscience and Remote Sensing, 56(4):2183–2195. [7] OpenAI. Introducing chatgpt, Nov 2022. [8] Ramakrishna Vedantam, C. Lawrence Zitnick, and Devi Parikh. Cider: Consensus-based image description evaluation, 2015. [9] Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, and Lijuan Wang. Git: A generative image-to-text transformer for vision and language, 2022. [10] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models, 2023. [11] Xi Ye and Greg Durrett. The unreliability of explanations in few-shot prompting for textual reasoning, 2022. [12] Lili Yu, Bowen Shi, Ramakanth Pasunuru, Benjamin Muller, Olga Golovneva, Tianlu Wang, Arun Babu, Binh Tang, Brian Karrer, Shelly Sheynin, Candace Ross, Adam Polyak, Russell Howes, Vasu Sharma, Puxin Xu, Hovhannes Tamoyan, Oron Ashual, Uriel Singer, Shang-Wen Li, Susan Zhang, Richard James, Gargi Ghosh, Yaniv Taigman, Maryam Fazel-Zarandi, Asli Celikyilmaz, Luke Zettlemoyer, and Armen Aghajanyan. Scaling autoregressive multi-modal models: Pretraining and instruction tuning, 2023. [13] Wenqi Zhang, Yongliang Shen, Weiming Lu, and Yueting Zhuang. Data-copilot: Bridging billions of data and humans with autonomous workflow, 2023. 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

Towards Automatic Satellite Images Captions Generation Using LLMs: References

স্বয়ংক্রিয় স্যাটেলাইট ইমেজের দিকে ক্যাপশন জেনারেশন ইউজিং এলএলএম: রেফারেন্স

Auf dem Weg zur automatischen Generierung von Bildunterschriften für Satellitenbilder mithilfe von LLMs: Referenzen

Hacia la generación automática de subtítulos de imágenes satelitales mediante LLM: referencias

Vers la génération automatique de légendes d'images satellites à l'aide de LLM : références

एलएलएम का उपयोग करके स्वचालित उपग्रह चित्र कैप्शन निर्माण की ओर: संदर्भ

LLM を使用した衛星画像のキャプションの自動生成に向けて: 参考文献

LLM을 사용한 자동 위성 이미지 캡션 생성을 향하여: 참고 문헌

Rumo à geração automática de legendas de imagens de satélite usando LLMs: referências

На пути к автоматическому созданию подписей к спутниковым изображениям с использованием LLM: ссылки

Yüksek Lisans Programlarını Kullanarak Otomatik Uydu Görüntüleri Altyazıları Oluşturmaya Doğru: Referanslar

Hướng tới tạo chú thích hình ảnh vệ tinh tự động bằng LLM: Tài liệu tham khảo

使用 LLM 自动生成卫星图像标题：参考文献

Authors:
(1) Zengyi Qin, MIT & MyShell.ai and (email: qinzy@mit.edu);
(2) Wenliang Zhao, Tsinghua University;
(3) Xumin Yu, Tsinghua University;
(4) Xin Sun, MyShell.ai; Table of Links Abstract and Introduction ApproachExperiment Discussion and References 4 Discussion OpenVoice demonstrates remarkable instance voice cloning capabilities and is more flexible than previous approaches in terms of voice styles and languages. The intuition behind the approach is that it is relatively easy to train a base speaker TTS model to control the voice styles and languages, as long as we do not require the model to have the ability to clone the tone color of the reference speaker. Therefore, we proposed to decouple the tone color cloning from the remaining voice styles and the language, which we believe is the foundational design principle of OpenVoice. In order to facilitate future research, we make the source code and model weights publicly available. References [1] I. P. Association. Handbook of the International Phonetic Association: A guide to the use of the International Phonetic Alphabet. Cambridge University Press, 1999. [2] E. Casanova, J. Weber, C. D. Shulby, A. C. Junior, E. Gölge, and M. A. Ponti. Yourtts: Towards zero-shot multi-speaker tts and zero-shot voice conversion for everyone. In International Conference on Machine Learning, pages 2709–2720. PMLR, 2022. [3] CoquiAI. Xtts taking text-to-speech to the next level. Technical Blog, 2023. [4] W.-N. Hsu, B. Bolte, Y.-H. H. Tsai, K. Lakhotia, R. Salakhutdinov, and A. Mohamed. Hubert: Self-supervised speech representation learning by masked prediction of hidden units. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29:3451–3460, 2021. [5] J. Kim, S. Kim, J. Kong, and S. Yoon. Glow-tts: A generative flow for text-to-speech via monotonic alignment search. Advances in Neural Information Processing Systems, 33:8067– 8077, 2020. [6] J. Kim, J. Kong, and J. Son. Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech. In International Conference on Machine Learning, pages 5530–5540. PMLR, 2021. [7] J. Kong, J. Kim, and J. Bae. Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. Advances in Neural Information Processing Systems, 33:17022–17033, 2020. [8] M. Le, A. Vyas, B. Shi, B. Karrer, L. Sari, R. Moritz, M. Williamson, V. Manohar, Y. Adi, J. Mahadeokar, et al. Voicebox: Text-guided multilingual universal speech generation at scale. arXiv preprint arXiv:2306.15687, 2023. [9] J. Li, W. Tu, and L. Xiao. Freevc: Towards high-quality text-free one-shot voice conversion. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE, 2023. [10] M. Müller. Dynamic time warping. Information retrieval for music and motion, pages 69–84, 2007. [11] A. Polyak, Y. Adi, J. Copet, E. Kharitonov, K. Lakhotia, W.-N. Hsu, A. Mohamed, and E. Dupoux. Speech resynthesis from discrete disentangled self-supervised representations. arXiv preprint arXiv:2104.00355, 2021. [12] D. Rezende and S. Mohamed. Variational inference with normalizing flows. In International conference on machine learning, pages 1530–1538. PMLR, 2015. [13] P. Senin. Dynamic time warping algorithm review. Information and Computer Science Department University of Hawaii at Manoa Honolulu, USA, 855(1-23):40, 2008. [14] B. van Niekerk, M.-A. Carbonneau, J. Zaïdi, M. Baas, H. Seuté, and H. Kamper. A comparison of discrete and soft speech units for improved voice conversion. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6562– 6566. IEEE, 2022. [15] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. [16] C. Wang, S. Chen, Y. Wu, Z. Zhang, L. Zhou, S. Liu, Z. Chen, Y. Liu, H. Wang, J. Li, et al. Neural codec language models are zero-shot text to speech synthesizers. arXiv preprint arXiv:2301.02111, 2023. [17] D. Yang, S. Liu, R. Huang, G. Lei, C. Weng, H. Meng, and D. Yu. Instructtts: Modelling expressive tts in discrete latent space with natural language style prompt. arXiv preprint arXiv:2301.13662, 2023. [18] Z. Zhang, L. Zhou, C. Wang, S. Chen, Y. Wu, S. Liu, Z. Chen, Y. Liu, H. Wang, J. Li, et al. Speak foreign languages with your own voice: Cross-lingual neural codec language modeling. arXiv preprint arXiv:2303.03926, 2023. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Zengyi Qin, MIT & MyShell.ai and (email: qinzy@mit.edu); (2) Wenliang Zhao, Tsinghua University; (3) Xumin Yu, Tsinghua University; (4) Xin Sun, MyShell.ai; Authors: Authors: (1) Zengyi Qin, MIT & MyShell.ai and (email: qinzy@mit.edu); (2) Wenliang Zhao, Tsinghua University; (3) Xumin Yu, Tsinghua University; (4) Xin Sun, MyShell.ai; Table of Links Abstract and Introduction Abstract and Introduction Approach Experiment Approach Experiment Discussion and References Discussion and References 4 Discussion OpenVoice demonstrates remarkable instance voice cloning capabilities and is more flexible than previous approaches in terms of voice styles and languages. The intuition behind the approach is that it is relatively easy to train a base speaker TTS model to control the voice styles and languages, as long as we do not require the model to have the ability to clone the tone color of the reference speaker. Therefore, we proposed to decouple the tone color cloning from the remaining voice styles and the language, which we believe is the foundational design principle of OpenVoice. In order to facilitate future research, we make the source code and model weights publicly available. References [1] I. P. Association. Handbook of the International Phonetic Association: A guide to the use of the International Phonetic Alphabet. Cambridge University Press, 1999. [2] E. Casanova, J. Weber, C. D. Shulby, A. C. Junior, E. Gölge, and M. A. Ponti. Yourtts: Towards zero-shot multi-speaker tts and zero-shot voice conversion for everyone. In International Conference on Machine Learning, pages 2709–2720. PMLR, 2022. [3] CoquiAI. Xtts taking text-to-speech to the next level. Technical Blog, 2023. [4] W.-N. Hsu, B. Bolte, Y.-H. H. Tsai, K. Lakhotia, R. Salakhutdinov, and A. Mohamed. Hubert: Self-supervised speech representation learning by masked prediction of hidden units. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29:3451–3460, 2021. [5] J. Kim, S. Kim, J. Kong, and S. Yoon. Glow-tts: A generative flow for text-to-speech via monotonic alignment search. Advances in Neural Information Processing Systems, 33:8067– 8077, 2020. [6] J. Kim, J. Kong, and J. Son. Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech. In International Conference on Machine Learning, pages 5530–5540. PMLR, 2021. [7] J. Kong, J. Kim, and J. Bae. Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. Advances in Neural Information Processing Systems, 33:17022–17033, 2020. [8] M. Le, A. Vyas, B. Shi, B. Karrer, L. Sari, R. Moritz, M. Williamson, V. Manohar, Y. Adi, J. Mahadeokar, et al. Voicebox: Text-guided multilingual universal speech generation at scale. arXiv preprint arXiv:2306.15687, 2023. [9] J. Li, W. Tu, and L. Xiao. Freevc: Towards high-quality text-free one-shot voice conversion. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE, 2023. [10] M. Müller. Dynamic time warping. Information retrieval for music and motion, pages 69–84, 2007. [11] A. Polyak, Y. Adi, J. Copet, E. Kharitonov, K. Lakhotia, W.-N. Hsu, A. Mohamed, and E. Dupoux. Speech resynthesis from discrete disentangled self-supervised representations. arXiv preprint arXiv:2104.00355, 2021. [12] D. Rezende and S. Mohamed. Variational inference with normalizing flows. In International conference on machine learning, pages 1530–1538. PMLR, 2015. [13] P. Senin. Dynamic time warping algorithm review. Information and Computer Science Department University of Hawaii at Manoa Honolulu, USA, 855(1-23):40, 2008. [14] B. van Niekerk, M.-A. Carbonneau, J. Zaïdi, M. Baas, H. Seuté, and H. Kamper. A comparison of discrete and soft speech units for improved voice conversion. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6562– 6566. IEEE, 2022. [15] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. [16] C. Wang, S. Chen, Y. Wu, Z. Zhang, L. Zhou, S. Liu, Z. Chen, Y. Liu, H. Wang, J. Li, et al. Neural codec language models are zero-shot text to speech synthesizers. arXiv preprint arXiv:2301.02111, 2023. [17] D. Yang, S. Liu, R. Huang, G. Lei, C. Weng, H. Meng, and D. Yu. Instructtts: Modelling expressive tts in discrete latent space with natural language style prompt. arXiv preprint arXiv:2301.13662, 2023. [18] Z. Zhang, L. Zhou, C. Wang, S. Chen, Y. Wu, S. Liu, Z. Chen, Y. Liu, H. Wang, J. Li, et al. Speak foreign languages with your own voice: Cross-lingual neural codec language modeling. arXiv preprint arXiv:2303.03926, 2023. 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

OpenVoice: Versatile Instant Voice Cloning - Discussion and References

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Ehud Shapiro,  Department of Computer Science and Applied Math, Weizmann Institute of Science, Israel and ehud.shapiro@weizmann.ac.il. Table of Links Abstract and Introduction
Grassroots Protocols
Grassroots Implementation of Grassroots Dissemination
Applications of Grassroots Dissemination Supporting Digital Sovereignty
Future Work & Discussion
References
A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems
B. All-to-All Dissemination is not Grassroots
C. Proofs References 1 Martin R Albrecht, Jorge Blasco, Rikke Bjerg Jensen, and Lenka Mareková. Mesh messaging in large-scale protests: Breaking bridgefy. In Cryptographers’ Track at the RSA Conference, pages 375–398. Springer, 2021. 2 Juan Benet. Ipfs-content addressed, versioned, p2p file system. arXiv preprint arXiv:1407.3561, 2014. 3 Sonja Buchegger, Doris Schiöberg, Le-Hung Vu, and Anwitaman Datta. Peerson: P2p social networking: early experiences and insights. In Proceedings of the Second ACM EuroSys Workshop on Social Network Systems, pages 46–52, 2009. 4 Vitalik Buterin et al. A next-generation smart contract and decentralized application platform. white paper, 3(37), 2014. 5 Manuel Castells. The city and the grassroots: a cross-cultural theory of urban social movements. Number 7. Univ of California Press, 1983. 6 Miguel Castro, Barbara Liskov, et al. Practical byzantine fault tolerance. In OsDI, volume 99, pages 173–186, 1999. 7 Gregory Chockler, Roie Melamed, Yoav Tock, and Roman Vitenberg. Constructing scalable overlays for pub-sub with many topics. In Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing, pages 109–118, 2007. 8 Gregory Chockler, Roie Melamed, Yoav Tock, and Roman Vitenberg. Spidercast: a scalable interest-aware overlay for topic-based pub/sub communication. In Proceedings of the 2007 inaugural international conference on Distributed event-based systems, pages 14–25, 2007. 9 Bram Cohen. Incentives build robustness in bittorrent. In Workshop on Economics of Peer-to-Peer systems, volume 6, pages 68–72. Berkeley, CA, USA, 2003. 10 Alan Demers, Karin Petersen, Mike Spreitzer, Doug Terry, Marvin Theimer, and Brent Welch. The bayou architecture: Support for data sharing among mobile users. In 1994 First Workshop on Mobile Computing Systems and Applications, pages 2–7. IEEE, 1994. 11 Bitcoin Developer. P2p network, Retrieved 20222. URL: https://developer.bitcoin.org/ devguide/p2p_network.html. 12 Ethereum Developer. go-ethereum/params/bootnodes.go, Retrieved 2023. URL: https://github.com/ethereum/go-ethereum/blob/master/params/bootnodes.go. 13 Adem Efe Gencer, Soumya Basu, Ittay Eyal, Robbert van Renesse, and Emin Gün Sirer. Decentralization in bitcoin and ethereum networks. In International Conference on Financial Cryptography and Data Security, pages 439–457. Springer, 2018. 14 Gabriele Goldacker. Digitale souveränität [white paper of kompetenzzentrum öffentliche it (öfit)]. retrieved april 20, 2021, 2017. 15 Idit Keidar, Eleftherios Kokoris-Kogias, Oded Naor, and Alexander Spiegelman. All you need is dag, 2021. arXiv:2102.08325. 16 Idit Keidar, Oded Naor, and Ehud Shapiro. Cordial miners: A family of simple and efficient consensus protocols for every eventuality. arXiv preprint arXiv:2205.09174, 2022. 17 Antony Lewis. A gentle introduction to self-sovereign identity. 2017. URL: https://bitsonblocks.net/2017/05/17/a-gentle-introduction-to-self-sovereign-identity/. 18 Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system, 2019. 19 Romualdo Pastor-Satorras, Claudio Castellano, Piet Van Mieghem, and Alessandro Vespignani. Epidemic processes in complex networks. Reviews of modern physics, 87(3):925, 2015. 20 Julia Pohle and Thorsten Thiel. Digital sovereignty. Internet Policy Review: Journal on Internet Regulation, 9(4):1–19, 2020. 21 Aravindh Raman, Sagar Joglekar, Emiliano De Cristofaro, Nishanth Sastry, and Gareth Tyson. Challenges in the decentralised web: The mastodon case. In Proceedings of the Internet Measurement Conference, pages 217–229, 2019. 22 Vinay Setty, Maarten van Steen, Roman Vitenberg, and Spyros Voulgaris. Poldercast: Fast, robust, and scalable architecture for p2p topic-based pub/sub. In ACM/IFIP/USENIX International Conference on Distributed Systems Platforms and Open Distributed Processing, pages 271–291. Springer, 2012. 23 Devavrat Shah et al. Gossip algorithms. Foundations and Trends® in Networking, 3(1):1–125, 2009. 24 Ehud Shapiro. Multiagent transition systems: Protocol-stack mathematics for distributed computing. arXiv preprint arXiv:2112.13650, 2021. 25 Ehud Shapiro. Sovereign personal cryptocurrencies: A grassroots foundation for a digital economy. arXiv preprint arXiv:2202.05619, 2022. 26 Marc Shapiro, Nuno Preguiça, Carlos Baquero, and Marek Zawirski. Conflict-free replicated data types. In Symposium on Self-Stabilizing Systems, pages 386–400. Springer, 2011. 27 Robert Shostak, Marshall Pease, and Leslie Lamport. The byzantine generals problem. ACM Transactions on Programming Languages and Systems, 4(3):382–401, 1982. 28 Ion Stoica, Robert Morris, David Karger, M Frans Kaashoek, and Hari Balakrishnan. Chord: A scalable peer-to-peer lookup service for internet applications. ACM SIGCOMM computer communication review, 31(4):149–160, 2001. 29 Maofan Yin, Dahlia Malkhi, Michael K Reiter, Guy Golan Gueta, and Ittai Abraham. Hotstuff: Bft consensus with linearity and responsiveness. In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, pages 347–356, 2019. 30 Shoshana Zuboff. The age of surveillance capitalism: The fight for a human future at the new frontier of power: Barack Obama’s books of 2019. Profile books, 2019. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ehud Shapiro,  Department of Computer Science and Applied Math, Weizmann Institute of Science, Israel and ehud.shapiro@weizmann.ac.il. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Ehud Shapiro,  Department of Computer Science and Applied Math, Weizmann Institute of Science, Israel and ehud.shapiro@weizmann.ac.il. Table of Links Abstract and Introduction Grassroots Protocols Grassroots Implementation of Grassroots Dissemination Applications of Grassroots Dissemination Supporting Digital Sovereignty Future Work & Discussion References A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems B. All-to-All Dissemination is not Grassroots C. Proofs Abstract and Introduction Abstract and Introduction Grassroots Protocols Grassroots Protocols Grassroots Implementation of Grassroots Dissemination Grassroots Implementation of Grassroots Dissemination Applications of Grassroots Dissemination Supporting Digital Sovereignty Applications of Grassroots Dissemination Supporting Digital Sovereignty Future Work & Discussion Future Work & Discussion References References A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems B. All-to-All Dissemination is not Grassroots B. All-to-All Dissemination is not Grassroots C. Proofs C. Proofs References 1 Martin R Albrecht, Jorge Blasco, Rikke Bjerg Jensen, and Lenka Mareková. Mesh messaging in large-scale protests: Breaking bridgefy. In Cryptographers’ Track at the RSA Conference, pages 375–398. Springer, 2021. 2 Juan Benet. Ipfs-content addressed, versioned, p2p file system. arXiv preprint arXiv:1407.3561, 2014. 3 Sonja Buchegger, Doris Schiöberg, Le-Hung Vu, and Anwitaman Datta. Peerson: P2p social networking: early experiences and insights. In Proceedings of the Second ACM EuroSys Workshop on Social Network Systems, pages 46–52, 2009. 4 Vitalik Buterin et al. A next-generation smart contract and decentralized application platform. white paper, 3(37), 2014. 5 Manuel Castells. The city and the grassroots: a cross-cultural theory of urban social movements. Number 7. Univ of California Press, 1983. 6 Miguel Castro, Barbara Liskov, et al. Practical byzantine fault tolerance. In OsDI, volume 99, pages 173–186, 1999. 7 Gregory Chockler, Roie Melamed, Yoav Tock, and Roman Vitenberg. Constructing scalable overlays for pub-sub with many topics. In Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing, pages 109–118, 2007. 8 Gregory Chockler, Roie Melamed, Yoav Tock, and Roman Vitenberg. Spidercast: a scalable interest-aware overlay for topic-based pub/sub communication. In Proceedings of the 2007 inaugural international conference on Distributed event-based systems, pages 14–25, 2007. 9 Bram Cohen. Incentives build robustness in bittorrent. In Workshop on Economics of Peer-to-Peer systems, volume 6, pages 68–72. Berkeley, CA, USA, 2003. 10 Alan Demers, Karin Petersen, Mike Spreitzer, Doug Terry, Marvin Theimer, and Brent Welch. The bayou architecture: Support for data sharing among mobile users. In 1994 First Workshop on Mobile Computing Systems and Applications, pages 2–7. IEEE, 1994. 11 Bitcoin Developer. P2p network, Retrieved 20222. URL: https://developer.bitcoin.org/ devguide/p2p_network.html. 12 Ethereum Developer. go-ethereum/params/bootnodes.go, Retrieved 2023. URL: https://github.com/ethereum/go-ethereum/blob/master/params/bootnodes.go. 13 Adem Efe Gencer, Soumya Basu, Ittay Eyal, Robbert van Renesse, and Emin Gün Sirer. Decentralization in bitcoin and ethereum networks. In International Conference on Financial Cryptography and Data Security, pages 439–457. Springer, 2018. 14 Gabriele Goldacker. Digitale souveränität [white paper of kompetenzzentrum öffentliche it (öfit)]. retrieved april 20, 2021, 2017. 15 Idit Keidar, Eleftherios Kokoris-Kogias, Oded Naor, and Alexander Spiegelman. All you need is dag, 2021. arXiv:2102.08325. 16 Idit Keidar, Oded Naor, and Ehud Shapiro. Cordial miners: A family of simple and efficient consensus protocols for every eventuality. arXiv preprint arXiv:2205.09174, 2022. 17 Antony Lewis. A gentle introduction to self-sovereign identity. 2017. URL: https://bitsonblocks.net/2017/05/17/a-gentle-introduction-to-self-sovereign-identity/. 18 Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system, 2019. 19 Romualdo Pastor-Satorras, Claudio Castellano, Piet Van Mieghem, and Alessandro Vespignani. Epidemic processes in complex networks. Reviews of modern physics, 87(3):925, 2015. 20 Julia Pohle and Thorsten Thiel. Digital sovereignty. Internet Policy Review: Journal on Internet Regulation, 9(4):1–19, 2020. 21 Aravindh Raman, Sagar Joglekar, Emiliano De Cristofaro, Nishanth Sastry, and Gareth Tyson. Challenges in the decentralised web: The mastodon case. In Proceedings of the Internet Measurement Conference, pages 217–229, 2019. 22 Vinay Setty, Maarten van Steen, Roman Vitenberg, and Spyros Voulgaris. Poldercast: Fast, robust, and scalable architecture for p2p topic-based pub/sub. In ACM/IFIP/USENIX International Conference on Distributed Systems Platforms and Open Distributed Processing, pages 271–291. Springer, 2012. 23 Devavrat Shah et al. Gossip algorithms. Foundations and Trends® in Networking, 3(1):1–125, 2009. 24 Ehud Shapiro. Multiagent transition systems: Protocol-stack mathematics for distributed computing. arXiv preprint arXiv:2112.13650, 2021. 25 Ehud Shapiro. Sovereign personal cryptocurrencies: A grassroots foundation for a digital economy. arXiv preprint arXiv:2202.05619, 2022. 26 Marc Shapiro, Nuno Preguiça, Carlos Baquero, and Marek Zawirski. Conflict-free replicated data types. In Symposium on Self-Stabilizing Systems, pages 386–400. Springer, 2011. 27 Robert Shostak, Marshall Pease, and Leslie Lamport. The byzantine generals problem. ACM Transactions on Programming Languages and Systems, 4(3):382–401, 1982. 28 Ion Stoica, Robert Morris, David Karger, M Frans Kaashoek, and Hari Balakrishnan. Chord: A scalable peer-to-peer lookup service for internet applications. ACM SIGCOMM computer communication review, 31(4):149–160, 2001. 29 Maofan Yin, Dahlia Malkhi, Michael K Reiter, Guy Golan Gueta, and Ittai Abraham. Hotstuff: Bft consensus with linearity and responsiveness. In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, pages 347–356, 2019. 30 Shoshana Zuboff. The age of surveillance capitalism: The fight for a human future at the new frontier of power: Barack Obama’s books of 2019. Profile books, 2019.

Grassroots Distributed Systems for Digital Sovereignty: References

The Angular CDK Overlay is a handy, powerful, and fairly complex feature that can be used to quickly create modals and pop-ups. In my about the CDK Overlay, I covered the basics needed to create both a modal using the global position strategy and a pop-up using the flexible connected to position strategy. I’d suggest that if you’re unfamiliar with the Overlay module at all, you go check out that post first because the demos in this video will build off the examples we built and concepts that we learned. In this post, we are going to learn about different ways to control how a pop-up gets positioned when using a connected overlay because there’s a lot to it, and it’s all pretty cool. previous post We’ll learn how we can specify a desired position and then provide fallbacks when it can’t fit. We’ll learn how to force a pop-up into view when it doesn’t correctly fit within any of the provided positions. And lastly, we’ll look at how to handle a pop-up when it’s too large to display within the viewport. Alright, let’s get to it! https://youtu.be/IpRQBtBN3iU?embedable=true Understanding How the CDK Connected Overlay Positioning Works When learning how the connected overlay works, we can’t really do much if we don’t understand how positioning works. So, let’s start with that. If we recall from my previous post about overlays, the connected overlay has an origin, which is the item that we will position relative to, and the overlay, which is the pop-up we will be positioning. With the Overlay, we essentially define an attachment point on the origin, and then we define an attachment point on the overlay. Then, it will attempt to position the overlay based on these attachment points. Understanding the CDK and How it’s Used to Create Overlay Positions ConnectedPositionPair Essentially, the attachment point for the overlay gets placed at that point on the attachment point for the origin. These attachment points are created with an object known as a . The most important properties of this object are: ConnectedPositionPair originX originY overlayX overlayY These are what make up the attachment points for both the origin and the overlay. When dealing with the x-axis, the possible values are: originX and overlayX Possible Values “start”: The left most edge of either the origin or overlay “center”: The horizontal midpoint of either the origin or overlay “end”: The right most edge of either the origin or overlay And for the y-axis, the possible values are: originY and overlayY Possible Values “top”: The topmost edge of the origin or overlay “center”: The vertical midpoint of the origin or overlay “bottom”: The bottommost edge of the origin or overlay So, for example, a value of originX: start, originY: bottom, overlayX: start, and overlayY: top would result in an overlay with its top left corner attached to the bottom left corner of the origin. In contrast, a value of originX: end, originY: top, overlayX: end, and overlayY: bottom would result in an overlay with its bottom right corner attached to the top right corner of the origin. How Fallback Positions Work With the CDK Connected Overlay So, what we do is pass an array of these connected position pairs to the overlay in the order that we prefer to have them applied. The overlay will then attempt to position itself in the first position, but if it can’t fit within the viewport, it will fall to the next position. And, if that doesn’t fit it will fall to the next position. Now, if none of the positions fit, it will determine which one fits best and then use it. So, there’s quite a lot of complexity there that we just don’t need to worry about. It’s all been figured out for us already. The Directive Default Positions cdkConnectedOverlay It’s important to note that when using the directive, as we will in this post, there are four default positions provided that mimic a standard dropdown: cdkConnectedOverlay From the left downward From the left upward From the right downward From the right upward So, if you don’t need anything different, you won’t need to provide any positions yourself. Ok, how about an example? Here, we have this app listing out some notable NBA players. When we click the button on the side, we have the pop-up that we created in the previous post on the overlay that shows more information about each player. We can see that it’s opening downward from the right edge because it’s the only position that works for this particular item. And if we open a pop-up for an item lower in the list, it will open from the right edge upward because, in this case, it’s the only position that works. Providing a Custom Position to the Directive Using the Input cdkConnectedOverlay cdkConnectedOverlayPositions But what if we prefer for the pop-up to open vertically centered from the left edge? Well, this would mean that we need to provide our own custom positions. For this, we need to use the input. This input accepts an array of position pairs. cdkConnectedOverlayPositions So, we’ll add an object. Our originX value will be “start” to position from the left edge. Our originY value will be “center” to position from the vertical midpoint. Our overlayX will be “end” to position from the right edge. And our overlayY value will be “center” to position from the vertical midpoint of the overlay. <ng-template
 cdkConnectedOverlay
 ...
 [cdkConnectedOverlayPositions]="[
 {
 originX: 'start',
 originY: 'center',
 overlayX: 'end',
 overlayY: 'center'
 }
 ]">
 ...
</ng-template> Nice, so now it’s opening with the overlay centered to the left of the origin. But I think we need a few more positions because the ones at the top and bottom will get cut off if they don’t fit within the viewport. So, we need to add another position that will open from the left top downward, and we need to add a position that will open from the left upward. Providing Fallback Positions When Needed So, we’ll add another object with originX “start”, originY “top”, overlayX “end”, and overlayY top. This will fix our issue for items at the top. <ng-template
 cdkConnectedOverlay
 ...
 [cdkConnectedOverlayPositions]="[
 {
 originX: 'start',
 originY: 'center',
 overlayX: 'end',
 overlayY: 'center'
 },
 {
 originX: 'start',
 originY: 'top',
 overlayX: 'end',
 overlayY: 'top'
 }
 ]">
 ...
</ng-template> Then, we’ll add another object for our bottom position where we’ll use originX “start”, originY “bottom”, overlayX “end”, and overlayY “bottom”. <ng-template
 cdkConnectedOverlay
 ...
 [cdkConnectedOverlayPositions]="[
 {
 originX: 'start',
 originY: 'center',
 overlayX: 'end',
 overlayY: 'center'
 },
 {
 originX: 'start',
 originY: 'top',
 overlayX: 'end',
 overlayY: 'top'
 },
 {
 originX: 'start',
 originY: 'bottom',
 overlayX: 'end',
 overlayY: 'bottom'
 }
 ]">
 ...
</ng-template> Nice, the one at the top opens downward like we want, the one at the bottom opens upward, and the pop-ups that can be centered will remain centered. How to Add an X and Y Offset to an Overlay When Defining a Custom Position Something else we can do here is offset the overlay on both the x and y axes. Right now, the right edge of our pop-up butts into the left edge of our origin. Let’s say we want to push it ten pixels away from the origin so that there will be some space between them. To do this, we can add an offsetX property to all of our positions. Since we want to offset to the left ten pixels, we’ll add a value of negative ten. <ng-template
 cdkConnectedOverlay
 ...
 [cdkConnectedOverlayPositions]="[
 {
 ...
 offsetX: -10
 },
 {
 ...
 offsetX: -10
 },
 {
 ...
 offsetX: -10
 }
 ]">
 ...
</ng-template> This will move our overlay backward, away from the origin element. We can also add an offset in the y direction with the offsetY property. Let’s offset our upward overlay negative ten pixels as well. This will push it up from the bottom edge of the origin. <ng-template
 cdkConnectedOverlay
 ...
 [cdkConnectedOverlayPositions]="[
 {
 ...
 offsetX: -10
 },
 {
 ...
 offsetX: -10
 },
 {
 ...
 offsetX: -10,
 offsetY: -10
 }
 ]">
 ...
</ng-template> Nice, so this overlay is now offset to the left, away from the origin. And the overlay at the bottom is offset up to ten pixels as well. How to Push an Overlay Into the View When it Doesn’t Fit Using the input cdkConnectedOverlayPush Another issue that we have is that Kareem’s pop-up gets cut off because it has a lot of content that doesn’t fit within the viewport, no matter which direction it opens. Well, the directive has an input that will allow us to push this pop-up into the view when it doesn’t fit. To do this we add the input and give it a value of true. cdkConnectedOverlay cdkConnectedOverlayPush <ng-template
 cdkConnectedOverlay
 ...
 cdkConnectedOverlayPush="true">
 ...
</ng-template> Now, when we open Kareem’s pop-up again, it’s been pushed downward to reveal all of the content. How cool is that? Ensure Space Between the Overlay and the Viewport With the Input cdkConnectedOverlayViewportMargin Something that I don’t love about this, though, is that the pop-up runs right into the top edge of the viewport. But guess what? We can fix this with another input. We can use the input. Let’s give it a value of seven pixels. cdkConnectedOverlayViewportMargin <ng-template
 cdkConnectedOverlay
 ...
 [cdkConnectedOverlayViewportMargin]="7">
 ...
</ng-template> Nice, so now it opens with some space. That feels better, right? So, hopefully, this deep dive into Connected Overlay position concepts has been helpful. Keep an eye out for future posts on other aspects of the Overlay Module. Found This Helpful? If you found this article helpful and want to show some love, you can always buy me a coffee! Want to See It in Action? Check out the demo code and examples of these techniques in the Stackblitz example below. If you have any questions or thoughts, don’t hesitate to leave a comment. https://stackblitz.com/edit/stackblitz-starters-7hjutg?ctl=1&embed=1&file=src%2Fplayer%2Fplayer.component.html&embedable=true Also published . here

CDK Overlay Positioning in Angular: Advanced Techniques

In the latest version of Angular, we’re getting what is known as the new Control Flow syntax for our component templates. It will replace older directives for things like , , and *ngIf/else ngSwitch *ngFor In this post, we’re going to learn exactly what this change means for us and how to use it. Alright, let’s get to it. https://youtu.be/nUEERAOZKwg?embedable=true So, for those of us that are used to building things in Angular, we’re used to the structural directive , and the concept of using an for the else condition when needed. Well, we don’t need this anymore with the new Control Flow syntax. *ngIf ng-template Also, we no longer need to import the to use them. Let’s take a look at an example. CommonModule Angular Control Flow Syntax for If/Else Conditions Ok, here in our component we have this player object consisting of some data about NBA player LeBron James. app.component.ts protected player: Player = {
 name: 'LeBron James',
 games: 1421,
 points: 38652,
 fieldGoalPercentage: 0.505,
 threePointPercentage: 0.345,
 imageName: 'lebron-james'
}; If we were to imagine that, in the real world, data like this may come from an API request where it may contain data or just be undefined. We would then need to conditionally use it within our template. In this case, we want to show our player component and pass it the player data only when it’s been defined. So, we need to use an if condition. For the new syntax, it’s similar to other templating languages like PHP, razor, and others. We start with the symbol, then in parentheses, we add our condition. Here, it will be if our player value is truthy, and then we wrap whatever conditional item. In this case, it’s our player component, with a player input, and that’s it. @ app.component.html @if (player) {
 <app-player [player]="player"></app-player>
} Right now, of course, it’s showing because we have our data hard-coded. So, let’s go clear it out. protected player?: Player; And, when we save, now we can see that it’s properly removed when no data is provided. If we want to show a message when we have no data, we need to add an else statement. To do this, we just need to add another symbol, then the word "else", then a new set of curly braces where we wrap our message. @ @if (player) {
 <app-player [player]="player"></app-player>
} @else {
 No player exists at this time
} Then, when we save this, we see that our message is properly displayed because we now have no data. So, that’s the new syntax replacing the old . *ngIf/else Angular Control Flow Syntax for Switch Statements Next up, we have the new syntax for switch statements. We no longer need a wrapping element or in order to use an directive and each of its cases. In this example, we have a select control where we can choose Lebron, Kareem, or leave it as the default player. ng-container ngSwitch And, when we look at our data, we have three player objects, player one, LeBron, player two, Kareem, and player three, Karl Malone. protected player1: Player = {
 name: 'LeBron James',
 games: 1421,
 points: 38652,
 fieldGoalPercentage: 0.505,
 threePointPercentage: 0.345,
 imageName: 'lebron-james'
};
protected player2: Player = {
 name: 'Kareem Abdul-Jabbar',
 games: 1560,
 points: 38387,
 fieldGoalPercentage: 0.559,
 threePointPercentage: 0.056,
 imageName: 'kareem-abdul-jabbar'
};
protected player3: Player = {
 name: 'Karl Malone',
 games: 1476,
 points: 36928,
 fieldGoalPercentage: 0.516,
 threePointPercentage: 0.274,
 imageName: 'karl-malone'
}; What we want to do in the template is show the appropriate player to match the value of our form control. This control will be a value of a string matching either Lebron, Kareem, or Default. protected options = new FormControl<'LeBron' | 'Kareem' | 'Default'>('Default', { nonNullable: true }); So, back in our template, we’ll begin by adding the symbol followed by the word "switch", then in parentheses, the value that we’ll want to switch on, in this case, that will be our options control value. @ @switch (options.value) {} Now, for our first case, we add the symbol, then the word "case", followed by the value, LeBron for this case, in parentheses. Then, within curly braces, we add our player component with the player input, in this case, it will be player one because that was Lebron in our data. @ @switch (options.value) {
 @case ('LeBron') {
 <app-player [player]="player1"></app-player>
 }
} Now, we can simply copy all of this, paste it, then change the case to Kareem, and the input to player two this time for Kareem’s data. @switch (options.value) {
 @case ('LeBron') {
 <app-player [player]="player1"></app-player>
 }
 @case ('Kareem') {
 <app-player [player]="player2"></app-player>
 }
} And then, finally, we can add our default case. We’ll add the player component one last time here, and we'll pass it to player three for Karl Malone. @switch (options.value) {
 @case ('LeBron') {
 <app-player [player]="player1"></app-player>
 }
 @case ('Kareem') {
 <app-player [player]="player2"></app-player>
 }
 @default {
 <app-player [player]="player3"></app-player>
 }
} Ok, now when we save, we see Karl Malone since he was the default, and our control value is set to default when initialized. If we change to Lebron, the view is properly updated. And likewise with Kareem. So, that’s the new switch syntax; up next, we have the final example in this post, a for loop. Angular Control Flow Syntax for For Loops When adding for loops in angular, we used to need an element or for our directive. Then we would need to create a variable from a list of options, and then much of the time, we needed to add a function to help with performance. Well, it looks similar now but a little different. ng-container *ngFor trackby In this example, our list of players is larger and consists of all of these items. players.ts export const players = [
 {
 name: 'LeBron James',
 games: 1421,
 points: 38652,
 fieldGoalPercentage: 0.505,
 threePointPercentage: 0.345,
 imageName: 'lebron-james'
 },
 {
 name: 'Kareem Abdul-Jabbar',
 games: 1560,
 points: 38387,
 fieldGoalPercentage: 0.559,
 threePointPercentage: 0.056,
 imageName: 'kareem-abdul-jabbar'
 },
 {
 name: 'Karl Malone',
 games: 1476,
 points: 36928,
 fieldGoalPercentage: 0.516,
 threePointPercentage: 0.274,
 imageName: 'karl-malone'
 },
 {
 name: 'Kobe Bryant',
 games: 1346,
 points: 33643,
 fieldGoalPercentage: 0.447,
 threePointPercentage: 0.329,
 imageName: 'kobe-bryant'
 },
 {
 name: 'Michael Jordan',
 games: 1072,
 points: 32292,
 fieldGoalPercentage: 0.497,
 threePointPercentage: 0.327,
 imageName: 'michael-jordan'
 },
 {
 name: 'Dirk Nowitzki',
 games: 1522,
 points: 31560,
 fieldGoalPercentage: 0.471,
 threePointPercentage: 0.38,
 imageName: 'dirk-nowitzki'
 },
 {
 name: 'Wilt Chamberlain',
 games: 1045,
 points: 31419,
 fieldGoalPercentage: 0.54,
 threePointPercentage: 0,
 imageName: 'wilt-chamberlain'
 },
 {
 name: 'Shaquille O\'Neal',
 games: 1207,
 points: 28596,
 fieldGoalPercentage: 0.582,
 threePointPercentage: 0.045,
 imageName: 'shaquille-oneal'
 },
 {
 name: 'Carmelo Anthony',
 games: 1260,
 points: 28289,
 fieldGoalPercentage: 0.447,
 threePointPercentage: 0.355,
 imageName: 'carmelo-anthony'
 },
 {
 name: 'Moses Malone',
 games: 1329,
 points: 27409,
 fieldGoalPercentage: 0.491,
 threePointPercentage: 0.10,
 imageName: 'moses-malone'
 },
 {
 name: 'Elvin Hayes',
 games: 1303,
 points: 27313,
 fieldGoalPercentage: 0.452,
 threePointPercentage: 0.147,
 imageName: 'elvin-hayes'
 }
]; We want to list these players out in the template with a for loop. So, we start by adding the symbol again. Then, the word "for" is followed by parentheses. In these parentheses, we create our variable, we’ll call it "player." @ Then, like the old , we use the word "of", followed by our variable for our list of data, in this case, players. *ngFor @for (player of players) {
} Now, one of the key differences in the new syntax is that a track expression is mandatory. So, if we were to save this, we get an error letting us know what we’re missing. So, we need to add a semi-colon, followed by the word "track." Then we need to provide a unique value to track, something like an ID, but in our case, we’ll use the player name. @for (player of players; track player.name) {
} Ok, all that’s left now is to add our player component and pass it our player data. @for (player of players; track player.name) {
 <app-player [player]="player"></app-player>
} And now when we save, there’s our list of players. Angular For Loop Empty Template Now, what if our list was empty, and we wanted to show a message? Well, let’s clear out our list data. If we were to save right now, we'd just have a blank screen. To show a message in this case, back in our template, we can add an empty scenario for our for loop. And inside, we can add our message. @for (player of players) {
 ...
}
@empty {
 There are no players to display at this time
} Now, when we save, we see our message. Pretty easy, right? Angular For Loop Additional Properties Now, for those of you familiar with the old , you may be wondering about all of the old properties that we had available: *ngFor index first last even odd count Well, they all still exist in the new syntax too. We just add them much like we did before. We use the word "let" followed by the variable name we want to use, then equals and the name of the value prefixed with a dollar sign. @for (player of players;
 track player.name;
 let index = $index;
 let first = $first;
 let last = $last;
 let even = $even;
 let odd = $odd;
 let count = $count) {
 ...
} So, after adding all of these, let's say we want to display the count in a message before the first item in the list. So, we add a condition for the first item, then add a message with the string interpolated value for the list count. @if (first) {
 There are {{ count }} players in the list
} When we save, now we can see the message with the count before all items. And, let’s say we want to display the list item number next to the player name. We can do this by passing the item index to the player component. <app-player
 ...
 [index]="index"
 ...>
</app-player> Internally, this component adds one to its index value and places the string interpolated value before the name. We could also add some zebra striping by binding a class, even, to all of the even-numbered list items. <app-player
 ...
 [class.even]="even"
 ...>
</app-player> Now, we can see all of the even items are a little darker. And finally, we can highlight the first and last list items by binding a first and last class to those variables. <app-player
 ...
 [class.first]="first"
 [class.last]="last"
 ...>
</app-player> Here, we can see the first item gets a red border. And, when we scroll down to the bottom, the last item gets a blue border. Pretty straightforward, right? So, I think this is a pretty nice change. It’s pretty similar to what we’re used to but it’s a little more simple. It doesn’t require as many elements, s, and s, and it also doesn’t require any imports which is pretty nice. ng-container ng-template I guess, some things are simpler, some things are easier, and some things are just different. The Angular Control Flow Migration Schematic For those of you who may be worried about making this change to an existing codebase, don’t be. To migrate all of your existing s, s, and s, you can simply use the migration schematic which should hopefully update them without much work. *ngIf ngSwitch *ngFor ng g @angular/core:control-flow-migration Ok, so that’s pretty much all I have for Angular’s new control flow syntax. Want to See It in Action? Check out the demo code and examples of these techniques in the stackblitz example below. If you have any questions or thoughts, don’t hesitate to leave a comment. https://stackblitz.com/edit/ttnwwm?ctl=1&embed=1&file=src%2Fapp%2Fapp.component.ts&embedable=true

A Beginner-Friendly Guide On How to Use Angular's New Control Flow Syntax

This article was originally published on by Meribah Knight, WPLN/Nashville Public Radio. ProPublica This article was produced in partnership with , a former member of ProPublica’s Local Reporting Network. to get stories like this one as soon as they are published. WPLN Nashville Public Radio Sign up for Dispatches It has been a little over three years since I began my reporting on juvenile justice in Tennessee. Until then, I hadn’t paid much attention to juvenile courts. For a reporter, they’re difficult to cover with any kind of intimacy. They are shrouded in secrecy in a way adult courts are not. The records are sealed. The proceedings are mostly private. And it’s for good reason: The dumb stuff you do as a kid shouldn’t follow you into adulthood. But this privacy has its downside, because it can shield the adults in charge from accountability. And as I soon found out, juvenile justice in the state does need someone — maybe a reporter — to pay attention. Tennessee has 98 juvenile courts and even more juvenile judges. Those judges have a lot of discretion, making decisions on everything from whether to take a case to whether a kid should get locked up and for how long. What’s more, in Tennessee, kids have no right to a jury trial. So, there’s really no check to a judge’s authority in a case — they decide how to interpret the facts and the law. “It’s like the Wild West out here,” one juvenile defense lawyer told me. “Each judge is its own county, some are hard on crime, some are progressive.” I can tell you from my reporting, he’s not wrong. Along with my colleague Ken Armstrong, I embarked on this story, now a podcast, to try to see inside one county’s juvenile court system, where an all-powerful judge and the jailer she appointed were playing by their own rules and the children were caught in the middle. But I learned about other juvenile justice systems along the way. In a county about 20 miles northeast of Nashville, I observed a hearing on a case involving a group of kids, one armed with a BB gun, who had stolen a phone and car keys from a teen couple. The hearing was for a 16-year-old girl who had held onto the stolen phone during the robbery. The assistant district attorney, known for his tough-on-crime approach, had charged her with aggravated robbery and filed a motion to have her case transferred to adult court. Despite pleadings by her attorney — this was the girl’s first offense, she’d been questioned by police without a parent or guardian present and was never read her Miranda rights — the judge granted the transfer. That means that this case, and any subsequent infraction, no matter how minor, would go straight to adult court, where the girl would face the same penalty as a grown-up. When the hearing ended, the girl, who stood less than 5 feet tall, was handcuffed and taken away with a bond set at $10,000. “I about died,” her lawyer told me afterwards. The woman who conceived of juvenile court at the turn of the 20th century, a Chicago social worker named Jane Addams, believed that children could be corrected and rehabilitated because of their young age. And she believed that juvenile courts could step in to help make rehabilitation happen, acting in loco parentis — in place of the parent. Which meant making decisions in the best interest of the child. That was the hope for Sharieka Frazier, the mother of a boy named Quinterrius Frazier, who was featured in our original investigation. They lived in Rutherford County, Tennessee, and when Quinterrius was in his early teens, he started running away, disappearing for a day or longer and hanging out with older kids. It worried Sharieka no end, so she started to track his phone. She’d go knock on doors looking for him. But as Quinterrius got older, his mother began to lose her grip on him. He stopped playing basketball, a sport that had kept him focused and engaged. And he started hanging out with older kids who spent their free time on less wholesome activities. So Sharieka turned to the juvenile justice system for help. She called the police and took out a runaway petition (treated as a warrant) on Quinterrius to get him back home. “I just figured anything’s worth trying at this point,” Sharieka told me. “I was desperate. I didn’t have any help. I didn’t know what else to do. So that was where I turned.” Sharieka isn’t an outlier. I came across a number of cases where it was parents or relatives who made that first call to police, hoping law enforcement would step in to help. As years passed, Quinterrius got sucked deeper and deeper into the juvenile justice system. Rehabilitation didn’t materialize. But a life of incarceration did. When you spend time in juvenile court, you can still see the remnants of this rehabilitative mission. It’s there in the lingo of the court: There are “petitions” or “summons” instead of “warrants,” “juvenile delinquents” instead of “criminal defendants.” Courts make “determinations” on cases rather than handing down “convictions.” There are juvenile “detention centers” instead of juvenile “jails,” youth “development centers” instead of youth “prisons.” But after years of covering the juvenile court system, I’ve come to realize that this belief in treating kids differently isn’t much more than semantics. During my reporting, I saw what amounted to a carbon copy of the adult system. In Rutherford County, where our new podcast takes place, children were wrongfully arrested and jailed illegally for years. At least hundreds, likely thousands of kids, were stripped of their civil rights, arrested and held in jail when their alleged offenses didn’t meet the state’s legal criteria for incarcerating children. Some were then placed in solitary confinement. (You can .) But just how much this juvenile justice system has strayed from Addams’ ideals, and the ways that the officials and court staff are complicit in that, is what kept me reporting on this story for years after the were published. read our 2021 investigation ProPublica articles I needed to clearly understand exactly how this had happened and to let you, the listener, hear directly from the people responsible, the people impacted and the people who tried to fight back. Because this wasn’t some state secret. Some of what was happening to kids in Rutherford County was known to officials. Years before its juvenile court was mired in lawsuits, the federal government had cited it for keeping kids locked up for too long. The county’s juvenile judge, Donna Scott Davenport, typically sentenced kids to two to 10 days in jail for cursing in the courtroom, which was common. Davenport was reprimanded for it, which led to a loss of grant money and some bad PR, but she didn’t seem bothered. “Was I in violation?” she told the local paper. “Heck yes. But am I going to allow a child to cuss anyone out? Heck no.” A 2020 report by Human Rights for Kids, a nonprofit that investigates and reports on the human rights of children, in the country for its inability to protect the rights of children in the justice system. ranked Tennessee one of the worst states When our reporting on Davenport’s behavior came out, the local university where she was an adjunct lecturer . Later she announced she would not seek reelection and . cut ties with her retired at the end of her term “Despite what you may hear about in the media, there is no such thing as ‘scaring a child straight,’” said Kathy Sinback, the longtime administrator in Davidson County’s juvenile court. “The evidence shows that children struggling with behavioral issues have the best outcomes when they are provided with support, encouragement, and positive opportunities — not isolation, fear and shame.” I heard this same sentiment from the young people I spoke to. I met with one young man who was first arrested for truancy at 12. It was right after his mother died of a drug overdose and he was sent to live with a grandmother he barely knew. He wound up spending a decade in prison. “I wasn’t really a bad kid,” he told me, “ I was just a hurt kid.” When I asked a few longtime juvenile court lawyers and administrators what they’d tell families who found themselves in the crosshairs of this system, their advice was to pay attention, ask for representation and don’t expect harsh punishments to fix a kid’s bad behavior. Chris Kleiser, a public defender of kids in Knox County, recommended asking for a lawyer for your child “at the earliest possible stage. And that includes if law enforcement wants to speak to your child before charges are ever brought.” But even access to a lawyer isn’t guaranteed. Recently, the dearth of lawyers available to judges has hit a crisis point in Tennessee. According to the state’s administrative office of the courts, nearly half of all cases with a court-appointed lawyer are in juvenile court, and finding lawyers willing to take these cases is harder than ever. The juvenile judge in Henry County said her list of available lawyers is the shortest it has been in her 17 years on the bench. What’s more, Tennessee’s reimbursement rate for these lawyers — $50 an hour — is the lowest in the country and it hasn’t changed since 1994. Addams conceived of juvenile court with a clear-eyed mission to rehabilitate the child. In Tennessee : Decisions must be “in the best interest of the child.” But, as one lawyer put it to me, “that means whatever the judge thinks.” the juvenile delinquency statute is explicit Some places have explored newer and more child-focused approaches. In Nashville, for example, juvenile Judge Sheila Calloway launched Tennessee’s first restorative justice program for youth offenders, through which children accused of crimes as serious as aggravated burglary, felony theft or even homicide can be diverted away from the court system completely. Instead of meting out justice in a courtroom, a community-based organization guides conversations between victims and offenders, working toward truth and reconciliation and making meaningful amends. “The less we use draconian measures, the more successful we are,” Calloway said when the program launched in 2018. Davenport, who was featured in the podcast, did not subscribe to that point of view. Over and over she said, “​​We don’t punish our children at all. It’s all about treatment.” “I want the children that come in front of me to leave better than they came in,” she said. But Davenport’s actions belied her words. And Tennessee is moving further in Davenport’s direction: Today, state lawmakers are working to make harsher sentences for children more accessible to judges. Waiting in the wings for the next legislative session are a flurry of and increase juvenile punishments. bills that would make it easier to transfer a child to adult court The bills have been criticized by , the and . And the concerns raised about the bills also showed up in my conversations with lawyers, judges and experts, not to mention dozens of people jailed as kids: , educational disruptions, mental health issues, trauma and a . juvenile attorneys ACLU national experts increases in recidivism boatload of other negative outcomes “You want to believe that you can trust your justice system and your judges and with your children,” said Karerra Brewington, who was arrested as a child and whose brother was jailed repeatedly for much of his youth. “But you know, it ruined my life, it ruined my brother’s life.” Another young man, Dylan Geerts, was 15 when he was illegally jailed by Rutherford County for breaking into unlocked cars and stealing a small radio, some loose change, a hat, a phone case and cologne. Dylan had never been arrested before, let alone jailed. But when police took him to the Rutherford County Juvenile Detention Center, staff there locked him up for four days. He proceeded to unravel emotionally and mentally. I spoke with Dylan eight years after his incarceration. He’d become the named plaintiff in a class-action lawsuit against Rutherford County over its illegal policy that jailed so many kids, including himself. (Eventually that suit was settled and Dylan got $25,000 from it.) I asked him what he thought of Davenport’s statement that children should leave the system better than when they arrived. He looked right at me, shook his head and said, “They’re not coming out better than they went in.” Photo by on AbsolutVision Unsplash

I Spent 3 Years Investigating What Happens to the Kids of Rutherford: Here's What I Uncovered

আমি রাদারফোর্ডের বাচ্চাদের কী ঘটে তা তদন্ত করতে 3 বছর কাটিয়েছি: এখানে আমি যা আবিষ্কার করেছি

Ich habe drei Jahre lang untersucht, was mit den Kindern von Rutherford passiert: Folgendes habe ich herausgefunden

Pasé 3 años investigando qué les sucede a los niños de Rutherford: esto es lo que descubrí

J'ai passé 3 ans à enquêter sur ce qui arrive aux enfants de Rutherford : voici ce que j'ai découvert

रदरफोर्ड के बच्चों के साथ क्या होता है इसकी जांच करने में मैंने 3 साल बिताए: यहां बताया गया है कि मैंने क्या उजागर किया

ラザフォードの子供たちに何が起こったのかを 3 年かけて調査しました: これが私が明らかにしたことです

나는 러더퍼드의 아이들에게 무슨 일이 일어나는지 조사하는 데 3년을 보냈습니다. 제가 알아낸 내용은 다음과 같습니다.

Passei 3 anos investigando o que acontece com as crianças de Rutherford: aqui está o que descobri

Я провел три года, расследуя, что происходит с детьми Резерфорда: вот что я обнаружил

Rutherford'un Çocuklarına Ne Olduğunu Araştırmak İçin 3 Yılımı Harcadım: İşte Ortaya Çıkardıklarım

Tôi đã dành 3 năm để điều tra những gì xảy ra với những đứa trẻ ở Rutherford: Đây là những gì tôi đã khám phá ra

我花了三年时间调查卢瑟福孩子们的遭遇：这就是我发现的

This paper is under CC 4.0 license. available on arxiv Authors: (1) Jingjing Wang, School of Computing; (2) Joshua Luo, The Westminster Schools; (3) Grace Yang, South Windsor High School; (4) Allen Hong, D.W. Daniel High School; (5) Feng Luo, School of Computing. Table of Links Abstract & Introduction Related Work Methodology Experiment Results Discussion References Abstract This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of spillovers on a single network. We provide an econometric framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global treatment effect. We show that the optimal clustering can be approximated as the solution of a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes easy-to-check conditions to choose between a cluster or individual-level randomization. We illustrate the method’s properties using unique network data from the universe of Facebook’s users and existing network data from a field experiment. : Experimental Design, Spillover Effects, Causal Inference, Cluster Designs. JEL Codes: C10, C14, C31, C54 Keywords 1 Introduction Consider a (large) population of n individuals connected under a single observed network. Researchers are interested in conducting an experiment to estimate the global average treatment effect, i.e., the difference between the average effect of treating all versus none of the individuals in the population. Treating an individual may generate spillovers to her friends in the network. To capture such effects, researchers conduct a cluster experiment. Individuals are first partitioned into clusters. Within a cluster, either all units are assigned to the treatment or all units are assigned to the control group. Finally, researchers estimate treatment effects by taking a difference between the average outcomes of treated and control units (possibly adjusting for baseline covariates). The cluster design does not require modeling the dependence of individual outcomes on neighbors’ assignments, but it requires a choice of clusters and some assumptions on the extend of the spillovers along the network. For example, cluster experiments on online platforms require choosing a partition of the social network, and field experiments require choosing the unit of randomization, such as villages or regions. This raises the question of how many and which clusters to use in experiments. Typical approaches in economic research assume prior knowledge of many independent clusters. There are many settings where this information is not available, and instead units in the population have different degrees of connections.[1] This paper provides an econometric framework to choose when and how to design the clusters in cluster experiments. Different from existing clustering algorithms geared towards community detection, we motivate the choice of the clusters based on the task of estimating global treatment effects. The choice of clustering must balance two competing objectives: the larger the clusters (and the smaller the number of clusters), the smaller the bias of the estimated global effect, but the larger its variance. We introduce an algorithmic procedure – entitled Causal Clustering – to choose the clustering that minimizes a weighted combination of the worst-case bias and variance as a function of the network and clusters. The worst-case approach encodes uncertainty over the dependence of individual outcomes on neighbors’ assignments. We study (i) whether to run a cluster-level instead of individual-level randomization; (ii) how to cluster individuals (and how many clusters to use). We focus on a class of models where spillover effects are small relative to the outcomes’ variance but possibly non-negligible for inference. This is formalized in a novel framework of local asymptotics where individual outcomes depend arbitrarily on neighbors’ treatments, and, as n grows, spillovers from neighbors (and possibly also direct effects) converge to zero, but at an arbitrary slow rate (e.g., slower than n −1/2 ). This framework encodes the researchers’ uncertainty on the presence (and magnitude) of spillover effects by modeling first-order neighbors’ effect local to zero, with the convergence rate capturing the expected magnitude of spillovers.[2] The local asymptotic framework we study is consistent with settings with small (but non-negligible) treatment and spillover effects, typical, for instance, in online experiments [e.g., Karrer et al., 2021]. We characterize optimal clustering as a function of the expected magnitude of the largest spillover effects that the experiment can generate. The largest size of the spillover effects is a key input in our algorithms, whose characterization, in practice, is necessary for the design of the experiment but can be challenging. This parameter can be informed by previous experiments, in the same spirit of minimum detectable effects used in power analysis [e.g. Baird et al., 2018], or using some particular modeling assumptions. We provide guidance to practitioners in Section 6. Our analysis proceeds as follows. First, we provide a formal characterization of the worstcase bias and variance. We show that the worst-case bias is closely related to a particular notion of between-clusters connectedness, defined as the per-individual average number of friends in other clusters. The worst-case variance can potentially be an arbitrary function of within-clusters covariances and between-clusters covariances: individuals in the same cluster have identical assignments and in different clusters may share common neighbors. We show that the variance only depends on the average squared cluster size, up to an asymptotically negligible error. This result formalizes the intuition that a larger number of clusters, with a small variance in cluster size, decreases the variance of the estimator. We draw the implications of these results for choosing between a cluster experiment (for a given clustering) or assigning treatments independently between individuals (i.e., Bernoulli design or completely randomized design). Suppose the magnitude of the spillover effects is smaller than the square root of the number of clusters. In that case, the variance component dominates the bias, and a Bernoulli design is preferred (where a Bernoulli design is a special case of a cluster design with clusters containing a single unit). Vice-versa, a cluster design is preferred if the bias dominates the variance. Intuitively, because our objective trades off the bias and variance of the estimator, whenever the number of clusters is small, it is best to run a Bernoulli design for any value of spillover effects local to zero. On the other hand, if the number of clusters is sufficiently large, and the cluster design appropriately controls the bias of the estimator, a cluster design is preferred. We provide practitioners with a simple decision rule between cluster and Bernoulli designs that only depends on the number of clusters and the expected spillover effects’ magnitude. We then turn to the design of the optimal clustering, where the choice is not whether to run a cluster or Bernoulli design, but rather which clustering to use. The choice of optimal clustering reduces to a novel penalized minimum cut optimization problem, with a penalty that depends on the variation in clusters’ size. The program admits a convenient formulation as a sequence of trace-optimization problems, each solved via off-the-shelf semidefinite programming algorithms. We provide an empirical application using unique network data from the universe of Facebook users. We show that our procedure provides an explicit ranking between different clustering algorithms implemented at scale at Facebook. We also illustrate trade-offs in the choice of the clustering algorithm and properties of the graph. A second application using network data from Cai et al. [2015] illustrates the method’s advantages for choosing clusters in field experiments. We show that the choice of the clusters based on village identity is sub-optimal in this application, because the number of village clusters is too small relative to the optimal clustering. We present an alternative choice of clusters. This paper connects to the literature on experimental design, causal inference with spillover effects, and clustering. Existing methods for experimental designs with spillover effects include cluster and saturation designs, studied in Baird et al. [2018], Basse and Feller [2016], Karrer et al. [2021], Pouget-Abadie [2018], Taylor and Eckles [2018], Viviano [2020b], among others. These papers provide an analysis of the properties of particular designs for a given clustering or clustering algorithm. Different from the current paper, these references either do not study the question of the optimal clustering algorithm for experimental design, or only provide heuristic comparisons of different clustering methods. A particular class of clustering algorithms is ε-net clustering algorithms – which consist of assigning sequentially individuals in the same neighborhood to the same cluster [Eckles et al., 2017, Ugander et al., 2013]. Variants of these algorithms have been recently studied in Leung [2022] for spatial spillovers and Faridani and Niehaus [2022] in more general nonEuclidean spaces. These papers provide an optimal rate of the clusters’ size as a function of how interference decays in space for approximately unbiased estimators. Here, instead, we provide an explicit trade-off and novel characterization of the bias and variance, leveraging local asymptotics. It allows us to characterize the optimal clustering as the solution of a trace-optimization program (different from ε-net clustering). The comparison of a cluster and Bernoulli designs through local asymptotics is also a novel contribution. Additional references of experiments with networks in the absence of cluster experiments are Basse and Airoldi [2018b], and Jagadeesan et al. [2020] for estimating direct instead of global average treatment effects studied here; Kang and Imbens [2016] who study encouragement designs, without focusing on the problem of mean-squared error optimal designs; Viviano [2020a] for experiments on networks using information from a pilot study; Basse and Airoldi [2018a] discuss limitations of design-based causal inference under interference. None of these paper study (optimal) cluster designs. The literature on treatment effects under network interference includes Aronow and Samii [2017], Hudgens and Halloran [2008], Manski [2013], Leung [2020], Athey et al. [2018], Goldsmith-Pinkham and Imbens [2013], S¨avje et al. [2021], Ogburn et al. [2017], Manresa [2013], Li and Wager [2022], Leung [2022], among others. The assumption that an individual depends on first-order degree connections is most closely related to Leung [2020].3 None of the above references study experimental designs. Finally, we relate more broadly to the literature on clustering in economics – see Wooldridge [2003], Leung [2023], Abadie et al. [2017] and references therein – with the difference that here we focus on optimal clustering, rather than taking the clusters as given – and the literature on graph clustering, including Von Luxburg [2007], Newman [2013a,b], Lei [2019], Lei et al. [2020], Li et al. [2022], and references therein. Different from this last set of papers on graph clustering, this paper focuses on treatment effect estimation, instead of community detection. The remainder of the paper is organized as follows: Section 2 presents the setup; Section
3 characterizes the bias and variance and contrast cluster and Bernoulli designs; Section
4 formalizes the optimization problem for the optimal clustering; Section 5 presents two
applications and numerical studies and Section 6 contains recommendations for practice. [1] For example, when using villages as clusters, individuals may interact in the same and nearby villages. See Egger et al. [2022] for an example in cash-transfer programs. [2] The assumption of spillovers within first-order neighbors can be relaxed here by assuming that higher order spillovers are an order of magnitude smaller than first-order spillovers.

Is GPT Powerful Enough to Analyze the Emotions of Memes?: Abstract & Intro

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Dihia LANASRI, OMDENA, New York, USA; (2) Juan OLANO, OMDENA, New York, USA; (3) Sifal KLIOUI, OMDENA, New York, USA; (4) Sin Liang Lee, OMDENA, New York, USA; (5) Lamia SEKKAI, OMDENA, New York, USA. Table of Links Abstract & Introduction Background Related Work Our Methodology Experiments and Results Conclusion, Acknowledgments & References 6 Conclusion The importance of hate speech detection on social networks has encouraged many researchers to build solutions (corpora and classifiers) to detect suspect messages. The literature review shows that most works are interested in text in structured languages like English, French, Arabic, etc. However, few works deal with dialects, mainly the Algerian one, which is known for its complexity and variety. To fill in the gap, we propose in this paper a complete NLP approach to detect hate speech in the Algerian dialect. We built an annotated corpus of more than 13,5K documents, which is used to evaluate various deep learning architectures. The obtained results are very promising, where the most accurate was the DzaraShield . Looking ahead, there is significant potential to enhance inference speed, particularly for the Dziribert-based and multilingual models. While this project primarily focused on Arabic characters, our next step will be to address the dialect when written in Latin characters. Embracing both Arabic and Latin characters will more accurately capture the nuances of the written Algerian dialect. Finally, we plan to expand our corpus size and explore alternative deep-learning architectures. 7 Acknowledgments We would like to thank every person who has contributed to this project: Micha Freidin, Viktor Ivanenko, Piyush Aaryan, Yassine Elboustani, Tasneem Elyamany, Cephars Bonacci, Nolan Wang and Lydia Khelifa Chibout. We would also like to thank Omdena organization for giving us this valuable opportunity References Sean MacAvaney, Hao-Ren Yao, Eugene Yang, Katina Russell, Nazli Goharian, and Ophir Frieder. Hate speech detection: Challenges and solutions. PloS one, 14(8):e0221152, 2019. Mirela P Bogdani, Federico Faloppa, and Xheni Karaj. Beyond definitions. a call for action against hate speech in albania. a comprehensive study november 2021. 2021. JT Nockleby. hate speech in encyclopedia of the american constitution. electronic journal of academic and special librarianship. 2000. Imane Guellil, Ahsan Adeel, Faical Azouaou, Mohamed Boubred, Yousra Houichi, and Akram Abdelhaq Moumna. Ara-women-hate: An annotated corpus dedicated to hate speech detection against women in the arabic community. In Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference, pages 68–75, 2022. Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. Automated hate speech detection and the problem of offensive language. In Proceedings of the international AAAI conference on web and social media, volume 11, pages 512–515, 2017. Paula Fortuna and Sérgio Nunes. A survey on automatic detection of hate speech in text. ACM Computing Surveys (CSUR), 51(4):1–30, 2018. Anna Schmidt and Michael Wiegand. A survey on hate speech detection using natural language processing. In Proceedings of the fifth international workshop on natural language processing for social media, pages 1–10, 2017. Delphine Battistelli, Cyril Bruneau, and Valentina Dragos. Building a formal model for hate detection in french corpora. Procedia Computer Science, 176:2358–2365, 2020. Fatimah Alkomah and Xiaogang Ma. A literature review of textual hate speech detection methods and datasets. Information, 13(6):273, 2022. Flor Miriam Plaza-del Arco, M Dolores Molina-González, L Alfonso Urena-López, and M Teresa Martín-Valdivia. Comparing pre-trained language models for spanish hate speech detection. Expert Systems with Applications, 166: 114120, 2021. Nuha Albadi, Maram Kurdi, and Shivakant Mishra. Are they our brothers? analysis and detection of religious hate speech in the arabic twittersphere. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 69–76. IEEE, 2018. Hala Mulki, Hatem Haddad, Chedi Bechikh Ali, and Halima Alshabani. L-hsab: A levantine twitter dataset for hate speech and abusive language. In Proceedings of the third workshop on abusive language online, pages 111–118, 2019. Fréha Mezzoudj, Mourad Loukam, and Fatma Zohra Belkredim. Arabic algerian oranee dialectal language modelling oriented topic. International Journal of Informatics and Applied Mathematics, 2(2):1–14, 2019. Oussama Boucherit and Kheireddine Abainia. Offensive language detection in under-resourced algerian dialectal arabic language. arXiv preprint arXiv:2203.10024, 2022. Djamila Menifi, Wiam Moussa, and Ahmed Cherif Mazari. Transfer Learning and Deep Learning for Multilingual Algerian Dialect Hate Speech Detection. PhD thesis, 2022. Adel Abdelli, Fayçal Guerrouf, Okba Tibermacine, and Belkacem Abdelli. Sentiment analysis of arabic algerian dialect using a supervised method. In 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS), pages 1–6. IEEE, 2019. Imane Guellil, Ahsan Adeel, Faical Azouaou, Mohamed Boubred, Yousra Houichi, and Akram Abdelhaq Moumna. Sexism detection: The first corpus in algerian dialect with a code-switching in arabic/french and english. arXiv preprint arXiv:2104.01443, 2021. Ona De Gibert, Naiara Perez, Aitor García-Pablos, and Montse Cuadros. Hate speech dataset from a white supremacy forum. arXiv preprint arXiv:1809.04444, 2018. Areej Al-Hassan and Hmood Al-Dossari. Detection of hate speech in social networks: a survey on multilingual corpus. In 6th international conference on computer science and information technology, volume 10, pages 10–5121, 2019. Naaima Boudad, Rdouan Faizi, Rachid Oulad Haj Thami, and Raddouane Chiheb. Sentiment analysis in arabic: A review of the literature. Ain Shams Engineering Journal, 9(4):2479–2490, 2018. Nizar Y Habash. Introduction to Arabic natural language processing. Springer Nature, 2022. Abdul-Baquee M Sharaf and Eric Atwell. Qurana: Corpus of the quran annotated with pronominal anaphora. In Lrec, pages 130–137, 2012. Kheireddine Abainia, Kenza Kara, and Tassadit Hamouni. A new corpus and lexicon for offensive tamazight language detection. In Proceedings of the 7th International Workshop on Social Media World Sensors, pages 1–6, 2022. Ahmed Cherif Mazari and Hamza Kheddar. Deep learning-based analysis of algerian dialect dataset targeted hate speech, offensive language and cyberbullying. International Journal of Computing and Digital Systems, 2023. Imane Guellil, Ahsan Adeel, Faical Azouaou, Sara Chennoufi, Hanene Maafi, and Thinhinane Hamitouche. Detecting hate speech against politicians in arabic community on social media. International Journal of Web Information Systems, 16(3):295–313, 2020. Djamila Mohdeb, Meriem Laifa, Fayssal Zerargui, and Omar Benzaoui. Evaluating transfer learning approach for detecting arabic anti-refugee/migrant speech on social media. Aslib Journal of Information Management, 74(6): 1070–1088, 2022. Reem ALBayari and Sherief Abdallah. Instagram-based benchmark dataset for cyberbullying detection in arabic text. Data, 7(7):83, 2022. Monirah A Al-Ajlan and Mourad Ykhlef. Optimized twitter cyberbullying detection based on deep learning. In 2018 21st Saudi Computer Society National Computer Conference (NCC), pages 1–5. IEEE, 2018. Batoul Haidar, Maroun Chamoun, and Ahmed Serhrouchni. Arabic cyberbullying detection: Enhancing performance by using ensemble machine learning. In 2019 international conference on internet of things (ithings) and ieee green computing and communications (greencom) and ieee cyber, physical and social computing (cpscom) and ieee smart data (smartdata), pages 323–327. IEEE, 2019. Hamdy Mubarak, Kareem Darwish, and Walid Magdy. Abusive language detection on arabic social media. In Proceedings of the first workshop on abusive language online, pages 52–56, 2017. Azalden Alakrot, Liam Murray, and Nikola S Nikolov. Dataset construction for the detection of anti-social behaviour in online communication in arabic. Procedia Computer Science, 142:174–181, 2018. Hatem Haddad, Hala Mulki, and Asma Oueslati. T-hsab: A tunisian hate speech and abusive dataset. In International conference on Arabic language processing, pages 251–263. Springer, 2019. Hamdy Mubarak, Ammar Rashed, Kareem Darwish, Younes Samih, and Ahmed Abdelali. Arabic offensive language on twitter: Analysis and experiments. arXiv preprint arXiv:2004.02192, 2020. Hala Mulki and Bilal Ghanem. Let-mi: an arabic levantine twitter dataset for misogynistic language. arXiv preprint arXiv:2103.10195, 2021. Rehab Duwairi, Amena Hayajneh, and Muhannad Quwaider. A deep learning framework for automatic detection of hate speech embedded in arabic tweets. Arabian Journal for Science and Engineering, 46:4001–4014, 2021. Wassen Aldjanabi, Abdelghani Dahou, Mohammed AA Al-qaness, Mohamed Abd Elaziz, Ahmed Mohamed Helmi, and Robertas Damaševicius. Arabic offensive and hate speech detection using a cross-corpora multi-task learning ˇ model. In Informatics, volume 8, page 69. MDPI, 2021. Batoul Haidar, Maroun Chamoun, and Ahmed Serhrouchni. A multilingual system for cyberbullying detection: Arabic content detection using machine learning. Advances in Science, Technology and Engineering Systems Journal, 2(6): 275–284, 2017. Ahmed Abdelali, Kareem Darwish, Nadir Durrani, and Hamdy Mubarak. Farasa: A fast and furious segmenter for arabic. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Demonstrations, pages 11–16, 2016. Amine Abdaoui, Mohamed Berrimi, Mourad Oussalah, and Abdelouahab Moussaoui. Dziribert: a pre-trained language model for the algerian dialect. arXiv preprint arXiv:2109.12346, 2021. Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, and Sayak Paul. Peft: State-of-the-art parameterefficient fine-tuning methods. https://github.com/huggingface/peft, 2022. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533, 2022. E Moatez Billah Nagoudi, A Elmadany, and M Abdul-Mageed. Arat5: Text-to-text transformers for arabic language understanding and generation. arXiv preprint arXiv:2109.12068, 2021.

Hate Speech Detection in Algerian Dialect Using Deep Learning: Conclusion,  Acknowledgments

Authors: (1) Davide Viviano, Department of Economics, Harvard University; (2) Lihua Lei, Graduate School of Business, Stanford University; (3) Guido Imbens, Graduate School of Business and Department of Economics, Stanford University; (4) Brian Karrer, FAIR, Meta; (5) Okke Schrijvers, Meta Central Applied Science; (6) Liang Shi, Meta Central Applied Science. Table of Links Abstract & Introduction Setup (When) should you cluster? Choosing the cluster design Empirical illustration and numerical studies Recommendations for practice References A) Notation B) Endogenous peer effects C) Proofs C Proofs Throughout the proofs, expectations are conditional on the adjacency matrix A. C.1 Proof of Lemma 3.1 We have C.2 Proof of Lemma 3.2 C.3 Proof of Lemma 3.3 We consider the case where two units are in the same or different clusters separately. We will refer to µi(Di , D−i) as µi(D) for notational convenience. Following the same steps as for the case where i, j are in different clusters, accounting for Equation (27), the proof completes. C.4 Proof of Lemma 3.4 other units are not zero for individuals in the sets Bi , Gi defined in Lemma 3.2. where the first inequality is due to Cauchy-Schwarz inequality and last equality follows from Assumption 5. The proof completes after collecting the terms. C.5 Proof of Theorem 3.5 C.6 Proof of Theorem 3.6 C.7 Proof of Theorem 4.1 The bias follows directly from Lemma 3.1. We now discuss the variance component. Under Lemmas 3.2, 3.3, and following Equations (28), (29), we can write C.8 Proof of Theorem 4.2 C.9 Proof of Theorem 4.3 This paper is under CC 1.0 license. available on arxiv

Proofs and Insights into Causal Clustering Experiments under Network Interference 

Authors: (1) Davide Viviano, Department of Economics, Harvard University; (2) Lihua Lei, Graduate School of Business, Stanford University; (3) Guido Imbens, Graduate School of Business and Department of Economics, Stanford University; (4) Brian Karrer, FAIR, Meta; (5) Okke Schrijvers, Meta Central Applied Science; (6) Liang Shi, Meta Central Applied Science. Table of Links Abstract & Introduction Setup (When) should you cluster? Choosing the cluster design Empirical illustration and numerical studies Recommendations for practice References A) Notation B) Endogenous peer effects C) Proofs B Endogenous peer effects This paper is under CC 1.0 license. available on arxiv

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