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Norma

norma

HackerNoon editorial team has launched to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. ! this interview series with women in tech Share your story today Tell us about yourself! Thank you for having me! As a leading Product Manager at InDrive, a global ride-hailing service, I’m passionate about making transportation more accessible and efficient. With experience in the tech industry, surpassing more than five years, I’ve spearheaded product launches across 17 countries, impacting the lives of millions of users. Recently, I was honored to serve as a jury member for a 2023 Globee Women in Business Awards and the AI for Humanity Hackathon organized by Burning Heroes. As of 2024, I lead a talented team developing international ride-hailing services at InDrive, collaborating with colleagues from over 15 countries to ensure a seamless experience for riders and drivers worldwide. In particular, I specialize in optimizing driver-sender matches, creating solutions for users to make better deals within our platform, and using the latest cutting-edge technology, such as machine learning and economic principles. Such an approach enhances customer experience and platform efficiency – a win-win for everyone. Why did you choose this field in the first place? I’ve always been fascinated by technology’s potential to solve real-world problems and, most importantly, improve people’s lives. Plus, the fast-paced nature of tech keeps things exciting for me, with new challenges and opportunities to learn emerging technologies. In other words, the field offers a constant learning curve. Besides that, I'm particularly drawn to the collaborative culture where knowledge is shared openly within the tech community. This allows me to create solutions practically for every industry — medicine, AI, real estate — that can impact millions of people worldwide, which truly motivates me. What tech are you most excited/passionate about right now and why? While large language models (LLM) are the most anticipated development, several other technological advancements capture my interest. Here are a few areas I find particularly promising: New technologies, such as advanced biosensors and generic testing, tremendous possibilities for detecting severe diseases like cancer and autoimmune disorders at earlier stages. Additionally, the growing role of big data and its practical interpretation from medical scans (MRI and CT) can lead to better diagnoses, personalized treatment plans, and improved preventative care. In fact, several innovative startups are working in this direction, and I'm eager to see their progress. Healthcare advancements: offer Fully automating back-office processes through AI would benefit tech companies, manufacturers, and other industries. While it may seem ordinary, solving this narrow problem could have a massive impact on industry and enterprises’ efficiency and productivity. AI-powered enterprise automation: Platforms allowing users to create websites without coding are changing how people access technology creation. Recently, an AI tool code and communicated as a software engineer! Just imagine building mobile apps, data dashboards, or even simple workflow tools—all without writing a single line of code. These no-code tools and numerous pilot engineering tools are slowly transforming the landscape, empowering people, and reducing production costs in the digital world. Development through no-code tools: wrote Beyond my interest in the field, the increasing complexity of transportation technologies gives me goosebumps—from self-driving cars and trucks to AI optimization and next-generation predictive analytics. Future of transportation: What tech are you most worried about right now and why? One primary concern about generative AI is the potential misuse of personal data to train these models. Although data privacy regulations are strengthening globally, there’s a lingering suspicion that corporations can still misuse personal data to gain an unfair advantage in the market. This raises a crucial question: Are these regulations compelling, and can the non-tech person understand them? What are your hobbies and interests outside of tech? I enjoy various activities that challenge me mentally and physically in my spare time. Take, for instance, tennis. It is a great way to hone my focus and develop a winning mindset. Its practice also taught me the importance of teamwork and effective conflict resolution. At the same time, psychology is another area I’m passionate about. Currently, I’m delving into Jungian Analysis, which helps me communicate with platform users unbiasedly and glean deeper insights from our conversations. This knowledge and my understanding of cultural differences foster greater empathy — a crucial skill when working on a product used in over twenty countries. I find it helpful to take conversation pauses, ask thoughtful questions, and practice active listening to bridge cultural gaps. Let's talk about breaking the glass ceiling. What were the biggest challenges you faced as a woman in tech, and how did you deal with them? Undoubtedly, the glass ceiling in tech remains a persistent issue, and statistics paint a clear picture – men significantly outnumber women. For instance, women for 25.3% of Google’s new tech hiring in 2023, compared to 20.8% in 2018. At the same time, Microsoft’s workforce to 33.1% in 2023, up from 29.2% in 2018. Other companies, such as Facebook and Apple, showed similar trends. The numbers reveal a stark reality: only one in three women work in the , compared to management, consulting, legal, or finance. accounted rose tech sector Several factors contribute to this disparity. For example, societal expectations can differ for men and women, with women often taking on greater childcare and household responsibilities. Additionally, tech can feel like a male-dominated environment, lacking role models for female leadership and fostering unconscious bias against women leading times in a traditionally masculine style. To navigate these challenges, I choose teams with inclusive cultures and actively support other women in tech. Practically speaking, I am an active participant in communities like Women in Tech that offer invaluable support networks where we can share experiences, empower each other, and curb any misogyny. Though a topic of debate, positive discrimination holds potential in the current landscape. When equally qualified candidates exist, a diverse slate can highlight potential unconscious bias within homogenous environments. The most significant challenge is ascending the corporate ladder within a male-dominated hierarchy. While some advise leveraging charisma to stand out, a more sustainable approach lies in achieving goals through shared objectives and mutual respect. Focusing solely on gender differences can reinforce bias. In contrast, a business-oriented approach fosters collaboration and paves the way for advancement. Any questionable misogynistic story/situation you faced/handled and you want to share with the HackerNoon Fam? While I may not have personally faced misogyny, it's essential to acknowledge that these situations still exist. We've all likely witnessed or heard about them. That's why standing with those who challenge inequality and fight for a more inclusive space is crucial. What's your biggest achievement that you're really proud of? One of my most significant achievements was leading the launch of intercity delivery product, which is now the leader of the mobility market in its country. It resulted in 47.4 million monthly active users by 2023. I managed five teams with numerous stakeholders and oversaw all aspects of the project, from product research to market assessment to financial planning. The product went live in over 100 cities nationwide within several months. In your opinion, why do we see this huge gender gap in the tech industry, and how can we reduce it? As mentioned earlier, the significant gender gap in tech stems from societal expectations and the industry's current culture. To bridge this gap, we need a two-pronged approach. First, we need to cultivate a more inclusive culture within tech. Initiatives like mentorship programs and fostering diverse hiring practices can help. Second, we need to address societal expectations. Normalizing women in tech and providing them with the support they need to balance work and life can create a more equitable future for the industry. Who is your tech idol? Why? Tech is full of inspiring figures, and a few people who stand out for me are Julie Zhuo, Lenny Rachitski, and Paul Graham. Julie's journey from intern to VP of design at Facebook, especially as a minority woman, is a powerful testament to her talent and perseverance. Her recent article on navigating company politics completely changed my perspective on workplace conflicts and empowered me to be more constructive. Sharing valuable insights while in leadership demonstrates her commitment to giving back and helping countless professionals. Lenny Rachitski's insightful product management blog and interviews with industry leaders offer a goldmine of knowledge for anyone in the field. His ability to spark discussions relevant to global communities makes his work exceptionally valuable. I deeply appreciate his contributions - these insights from experienced professionals are invaluable in the market. Finally, Paul Graham's writing on is a constant source of motivation. I often revisit it for inspiration. great work Do you have any advice for aspiring girls who want to join the field? Make sure your passion for this field is the driving force behind your decision. It can be challenging without support, so I recommend building a strong community around you. Look for mentors, participate in online forums, or attend industry events to connect with others. Get real-world experience through volunteering or internships related to your field. The stronger your network and the more you learn, the more opportunities will come. However, it's essential to find something you enjoy. Remember, your career path is yours to define. Don't be afraid to explore and change course if needed. And, finally, believe in yourself – no one can tell you what's impossible.

Normalizing Women In Tech: Interview With Ksenia Mayorova, Leading Product Manager At InDrive

It seems that everything we have been doing for the last four years is talking about the elusive ‘new normal.’ First, it was the COVID-19 pandemic, then, it was our global shift to the remote mode, after that it was living in a world filled with economic and political uncertainty. And here we are, talking about it once again. What is the ‘new normal’ for 2024, and will we be able to overcome everything the year will throw at us? AI gains even more importance. In 2023, AI occupied the headlines in almost every media. And we thought the craze would die down in a few short months. As time goes on, we see AI becoming not only THE technology to watch but also feeling cozier and cozier in our minds and our work processes. Today, it is used to , , and even (the last one is appalling). Nevertheless, AI is seeping into our everyday lives more, and even the laggards (it’s a word now) of laggards can no longer ignore its impact on the world. decipher ancient scrolls enhance student creativity cheat people out of their money I think 2024 will become the year of not only further AI development but also AI regulation. The process in the US and EU, so more countries are likely to follow suit. has already started Low well-being rates According to the recent report by , “The percentage of Americans who evaluate their lives well enough to be considered "thriving" on Gallup’s Life Evaluation Index averaged a subdued 52.1% in 2023, higher than only the Great Recession era reading in 2008-2009 (50.2%) and the first year of the COVID-19 pandemic in 2020 (50.1%). [...] Between 2015 and 2019, and in 2021, more than 55% were thriving.” Gallup Economic, political, and ecological downturns have, sadly, become a part of our everyday lives. In fact, sometimes it seems our world is on fire, and there is nothing we can do about it. No wonder our mental health and well-being rates are so low. As bad as it may seem, there are things I find to be the most helpful when I am feeling down: Communication — even a simple work call can be therapeutic because it can help us switch our attention to something other than our personal state. Books — ever since childhood, I have considered books one of the best ways, if not to escape, than to enhance our reality. Reading is one of my favorite hobbies, and it helps me relax and almost forget about everything going on around me. Sports — whatever it may be, running, squash, yoga, or sports- has been a part of my personal and even work life. Scientific also supports this approach. It has been proven that sport improves mental health, decreases the rates of ‘psychological ill-being’, and improves interpersonal communication. Win-win. data Doing something for myself — whether it is traveling, studying, going to the gym, or watching a movie when I have the time, I have found dedicating at least 30 minutes a day to my personal needs fills me up with energy and brings me the inspiration I need to face any challenge that the world might throw at us. 2024 started off with low well-being rates, and we should do everything we can to bring the numbers up and meet 2025 on a high note. Teams will no longer tolerate toxic leaders “We know our worth” — this is the attitude professionals have been coming to for several years. In 2024, leaders who have been displaying a tendency to be may be in for an unpleasant surprise. Even despite all the , teams no longer want to put up with toxicity, micromanagement, arrogance, irritability, and bullying from their managers and leaders. Our world is filled with possibilities and, for that matter, vacancies for professionals in different fields and with different skill sets. toxic tech layoffs 2024 is the year for managers to stop perceiving their toxicity as something they should hold on to. It is also nothing to be proud of, so business leaders need to either adjust their attitude or watch their teams slip through their fingers. Burn- and rust-out rates are off the charts Toxic managers, the lack of stability, constant technological disruptions, and low mental health rates have led us to extremely high rates of and out. If 2024 numbers remain as high as they were in 2023 or, even worse, will keep on increasing, we will end up with the entire global workforce unable to perform, grow, develop their skills, and bring new and modern solutions to the table. burning rusting Leaders have to address the trend this year and make necessary adjustments to make sure their teams have all the conditions they need to perform well and keep their mental health in check. 2024 has brought new challenges for us to address. But if my years of working in the tech industry taught me anything, it is that we usually come to the right decision, overcome the challenges posed ahead of us, and come out even more capable. That is one of my goals for this year.

What is the 'New Normal' for 2024

Authors: (1) Chenhao Shi, Shanghai Jiao Tong University; (2) Ruibang Liu, Shanghai Jiao Tong University; (3) Guoqiang Li†, Shanghai Jiao Tong University. Table of Links Abstract & Introduction Preliminaries Normalization Algorithm Evaluation Conclusion & References IV. EVALUATION In this section, we introduced the self-designed benchmark used in this paper. We evaluated the effectiveness of the paradigm generation algorithm by analyzing the test results and the intermediate outputs. A. Benchmark Design To evaluate the proposed algorithm in this paper, we implemented the entire process of paradigm generation explained in the former section using Python to verify its results. Due to the lack of related research, this field has no comprehensive benchmark. Therefore, we summarized some rules for generating equivalent R1CS constraint groups based on the logic the mainstream Circom compiler used to create R1CS and designed a more comprehensive benchmark based on these rules. The benchmark includes the following main categories depending on the reflected situation: Replacement of variable order in R1CS. Transformation of constraint order in R1CS. Introduction of multiple new variables in a single linear constraint in R1CS. Introduction of new variables in multiple linear constraints in R1CS, with shared new variables. Merging and splitting of constraints in R1CS. The different categories in the benchmark correspond to the other reasons for generating equivalent R1CS. Each category contains 2-3 basic R1CS constraints. To comprehensively test the robustness and correctness of the algorithm, 5-6 equivalent R1CS constraint groups are generated for each R1CS based on the respective reasons. The equivalent constraint groups of each constraint group are paired and inputted into the algorithm to verify whether the algorithm can generate consistent and R1CS-compliant output results defined in definition II.5, when processing different equivalent constraint groups. The publicly available data set used in this study can be found at the following GitHub repository: https://github. com/Ash1sc/R1CS normalization benchmark. This repository contains the raw data that was utilized for testing purposes. It is important to note that the data set is licensed under the GNU General Public License version 3.0 (GPL-3.0). This license allows for the data set and scripts to be freely distributed, modified, and used, with the condition that any derived works are also licensed under the GPL-3.0 and that the original copyright and license information is retained. If you would like to learn more about the details of the GPL-3.0, please visit https://www.gnu.org/licenses/gpl-3.0.en.html. B. Result Evaluation Table I shows the result of the experiments. Observation shows that the generated paradigms meet the requirements of the R1CS paradigm mentioned above and have equal semantics as the original R1CS constraint groups. Through analysis of the intermediate outputs at each stage of the conversion process, it was found that for equivalent R1CS constraint groups generated by reordering constraints, the only difference in the resulting data flow graphs lies in the order in which nodes representing intermediate variables are created. This is due to different processing orders of each constraint during traversal, leading to other orders of introducing intermediate variables in each constraint. For equivalent R1CS constraint groups generated by variable replacement, the differences lie in the order in which RNodes representing initial variables in the R1CS are developed and in the order of addition in the summation chain structure caused by differences in variable order in linear constraints. However, these changes do not affect the selected tile set, and the same data flow graph is obtained after abstraction. In the final step of the algorithm, we proposed a novel variable ordering method to solve the sorting confusion issue when multiple variables are introduced to a linear constraint. Experimental results demonstrate that our algorithm is capable of correctly identifying these variables and produces a variable mapping sequence that conforms to the definition. After the abstraction of the data flow graph, the splitting and merging of linear constraints can lead to changes in the order of addition in the summation chain. However, this issue is resolved. When equivalent R1CS constraint groups are created through the merging and splitting of constraints, the only discrepancy in the resulting data flow graphs is with regards to the vertices representing intermediate variables. These vertices can either represent existing variables or intermediate variables introduced during the data flow graph’s creation. Nevertheless, the structure of the data flow graph remains unaffected. This paper is under CC BY 4.0 DEED license. available on arxiv

Insights from R1CS Normalization Benchmark Analysis

Authors: (1) Chenhao Shi, Shanghai Jiao Tong University; (2) Ruibang Liu, Shanghai Jiao Tong University; (3) Guoqiang Li†, Shanghai Jiao Tong University. Table of Links Abstract & Introduction Preliminaries Normalization Algorithm Evaluation Conclusion & References V. CONCLUSION In this paper, we propose an algorithm based on data flow analysis to construct a paradigm for R1CS. The correctness and equivalence of R1CS have long been challenging to study due to the diversity and flexibility of constraint construction methods. Our algorithm aims to eliminate the differences between equivalent R1CS constraint systems through a series of abstraction processes. We introduce ordering methods for internal variables and constraints in R1CS, which provide reference information for the final paradigm generation. Through experimental results, we demonstrate that our algorithm can identify equivalent R1CS resulting from constraint merging, intermediate variable selection, and variable and constraint reordering, and transform them into a unique paradigm. Therefore, R1CS plays an indispensable part in zk-SNARKS. Further work includes establishing rules for merging constraints and a more comprehensive benchmark. This requires us to conduct more in-depth research and exploration of the generation rules of R1CS to improve the algorithm. REFERENCES [1] S. Goldwasser, S. Micali, and C. Rackoff, “The knowledge complexity of interactive proof-systems,” in Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio Micali, 2019, pp. 203–225. [2] Zcash company, https://z.cash/, 2014. [3] E. B. Sasson, A. Chiesa, C. Garman, et al., “Zerocash: Decentralized anonymous payments from bitcoin,” in 2014 IEEE symposium on security and privacy, IEEE, 2014, pp. 459–474. [4] G. Drevon, J-r1cs – a json lines format for r1cs, 2019. [5] E. Albert, M. Belles-Mu ´ noz, M. Isabel, C. Rodrı ˜ eguez- ´ Nu´nez, and A. Rubio, “Distilling constraints in zero- ˜ knowledge protocols,” in Computer Aided Verification: 34th International Conference, CAV 2022, Haifa, Israel, August 7–10, 2022, Proceedings, Part I, Springer, 2022, pp. 430–443. [6] E. Ben-Sasson, A. Chiesa, M. Riabzev, N. Spooner, M. Virza, and N. P. Ward, “Aurora: Transparent succinct arguments for r1cs,” in Advances in Cryptology– EUROCRYPT 2019: 38th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Darmstadt, Germany, May 19–23, 2019, Proceedings, Part I 38, Springer, 2019, pp. 103–128. [7] J. Lee, S. Setty, J. Thaler, and R. Wahby, “Lineartime and post-quantum zero-knowledge snarks for r1cs,” Cryptology ePrint Archive, 2021. [8] A. Golovnev, J. Lee, S. Setty, J. Thaler, and R. S. Wahby, “Brakedown: Linear-time and post-quantum snarks for r1cs,” Cryptology ePrint Archive, 2021. [9] Y. Muzi, F. Muyue, B. Gu, et al., Semantic comparison method and device between a kind of source code and binary code, 2019. [10] G. Li, L. Zhongqi, Y. Dongsheng, and Y. Liu, Compiling method from intermediate language (il) program to c language program of instruction list, 2013. [11] Z. Yongsheng, C. Zhiyong, C. Rongtao, and W. Zhili, A kind of assembly language is to the code conversion method of higher level language and device, 2015. [12] E. Ben-Sasson, A. Chiesa, E. Tromer, and M. Virza, “Succinct {non-interactive} zero knowledge for a von neumann architecture,” in 23rd USENIX Security Symposium (USENIX Security 14), 2014, pp. 781–796. [13] V. Buterin, “Quadratic arithmetic programs: From zero to hero,” URl: https://medium. com/@ VitalikButerin/quadratic-arithmetic-programs-fromzero-to-hero-f6d558cea649, 2016. [14] H. Vollmer, Introduction to circuit complexity: a uniform approach. Springer Science & Business Media, 1999. [15] J. Groth, “On the size of pairing-based non-interactive arguments,” in Advances in Cryptology–EUROCRYPT 2016: 35th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Vienna, Austria, May 8-12, 2016, Proceedings, Part II 35, Springer, 2016, pp. 305–326. [16] J. B. Dennis, “First version of a data flow procedure language,” in Programming Symposium: Proceedings, Colloque sur la Programmation Paris, April 9–11, 1974, Springer, 2005, pp. 362–376. [17] P. C. Treleaven, D. R. Brownbridge, and R. P. Hopkins, “Data-driven and demand-driven computer architecture,” ACM Computing Surveys (CSUR), vol. 14, no. 1, pp. 93–143, 1982. [18] W. Xing and A. Ghorbani, “Weighted pagerank algorithm,” in Proceedings. Second Annual Conference on Communication Networks and Services Research, 2004., IEEE, 2004, pp. 305–314. [19] L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: Bring order to the web,” Technical report, stanford University, Tech. Rep., 1998. This paper is under CC BY 4.0 DEED license. available on arxiv

Data-Flow-Based Normalization Generation Algorithm of R1CS: Conclusion & References

Authors: (1) Chenhao Shi, Shanghai Jiao Tong University; (2) Ruibang Liu, Shanghai Jiao Tong University; (3) Guoqiang Li†, Shanghai Jiao Tong University. Table of Links Abstract & Introduction Preliminaries Normalization Algorithm Evaluation Conclusion & References III. NORMALIZATION ALGORITHM In this section, we formally introduce various steps of the normalization generation algorithm and several data structures defined within the algorithm. A. Construction of RNode Graph Our study presents a novel data structure, RNode, that represents variable relationships within an R1CS arithmetic circuit. This structure, , facilitates efficient problem-solving by tracking interconnections among variables, which is formally defined as follows, RNode An RNode is a node of two types in the data flow graph constructed in this normalization algorithm. Definition III.1. RNode = ConstNode ∪ V arNode ConstNode = {ConstV alue, Operation, F ather, Child} V arNode = {Operation, F ather, Child} where ∀c is a ConstNode ∩ c.Operation = Null, c.child = ∅ ∀c is a ConstNode ∩ c.Operation ∈ {Add, Mul}, c.f ather = ∅ RNodes can be categorized into two types based on the variables they represent. The first category represents the original variables in the solution vector of the R1CS and the intermediate variables produced during the construction of the arithmetic circuit. The second category represents the constants in the data flow graph. Each RNode includes both an operator and a computed result, which store the calculation method between its two parent nodes and represent the calculated result of the subtree rooted in itself. The generation of the RNode Graph involves 3 main stages: Transform each constraint into an equation of a ∗ b = c, as required by the R1CS constraints. 2) Convert each constraint in the original R1CS constraint into an equation. 3) Organize the resulting equations containing common sub-expressions into a DAG-structured expression tree. The core logic of the RNode Graph generation algorithm is similar to the procedure we previously mentioned for constructing the RNode Graph. The RNode is a data structure used to store information about the variables in an R1CS during the construction of the RNode Graph. Unlike typical nodes in an expression tree, each RNode stores information about both the variable and operator involved in a given operation, allowing each operator’s output to be considered an intermediate variable and making it more closely aligned with the properties of an R1CS constraint set. In practice, the merging and splitting of constraints poses a challenge in determining the equivalence of R1CS constraint sets. However, our RNode graph generation algorithm has observed that this merging or splitting does not lead to substantial differences. When constraints are merged, one variable is subtracted from the original constraint set. For instance, the merging process can be exemplified by the following example. However, the subtracted variable will be added back into the RNode Graph as an intermediate node in the sum-product expression during the construction of the RNode Graph. The reverse is also true. Further abstraction of the RNode Graph is required to eliminate the difference observed in the graphs generated by equivalent R1CS due to the different variable ordering in the constraint set. The main difference is observed in the order of addition in constructing the expression of continuous addition. At this stage, we do not have sufficient information to determine the sequential execution order. During the algorithm execution flow, two variables are randomly selected and added together, resulting in a different structure in the graph. B. Tile Selection Here, we categorize tiles into three types and give the formal definition of tile. Definition III.2. Tile is a tree-like subgraph of the RNode Graph, representing a constraint in R1CS. Quadratic: Tiles with the form x ∗ y = z, where x, y, and z are variables. MulLinear: Tiles whose root is obtained by multiplying its two parents, with at least one of the parents being a constant. 3) AddLinear: Tiles whose root is obtained by adding its two parents. Tile is essentially a set of linear equations generated by applying certain constraints to variables in the R1CS. We can use tiles as building blocks to construct a normalized R1CS that is both correct and scalable. During tile selection, we divide the data flow graph from the previous step into 3 types. While AddLinear and MulLinear tiles are generated by linear constraints and are essentially linear tiles, their logical processing differs significantly. Hence, we discuss them as two separate types. We temporarily put aside the constraint merging step until we obtain more information about the tree in subsequent steps. If there is a need to generate merged formulas later, it can be achieved simply by applying a fixed algorithm to the unmerged formulas. The implementation of the tile selection algorithm is relatively simple. When the RNode graph is generated, we select a node with no successors and use it as the root to partition a subgraph from the RNode Graph as a tile. We then remove this tile from the RNode Graph and repeat this loop until the entire graph is partitioned. Detailed code will posted in github. The difference between data flow graphs generated by equivalent R1CS constraint systems lies in the order of node additions when processing linear tiles. However, the nodes added within a linear constraint, when considered as a set, are equivalent. This implies that the different addition order only affects the traversal order of the nodes. Therefore, if we consider the selected linear tiles as the products of the selected nodes and their respective coefficients, the chosen sets of linear tiles from equivalent R1CS constraint systems are the same. In other words, there is no distinction between the selected sets of tiles for equivalent R1CS constraint systems. Revised paragraph: The difference between data flow graphs generated by equivalent R1CS constraint systems lies in the order of node additions when processing linear tiles. However, the nodes added within a linear constraint, when considered as a set, are equivalent. This implies that the different addition order only affects the traversal order of the nodes. Therefore, the selected sets of linear tiles from equivalent R1CS constraint systems are the same if we consider the selected linear tiles as the products of the selected nodes and their respective coefficients. In other words, there is no distinction between the selected sets of tiles for equivalent R1CS constraint systems. C. Graph Abstraction We further abstract the data flow graph based on the selected tiles to eliminate the differences between various equivalent R1CS constraint systems. Specifically, we abstract linear tiles using a new node. By doing so, we mask the difference in addition order within linear tiles in the RNode Graph and transform the relationship between external nodes and specific nodes within the linear tile into a relationship between external nodes and the tile to which the particular node belongs. In this abstracted data flow graph, the types of edges are as follows: Non-linear tile abstract node to non-linear tile abstract node: The two vertices already existed in the original  RNode graph. This edge type remains consistent with the original RNode graph. 2) Non-linear tile abstract node to linear tile abstract node: This edge type exists only if there are non-abstract nodes in the linear tile represented by the abstract node. 3) Linear tile abstract node to linear tile abstract node: This edge type exists only if the two abstract nodes represent linear tiles that share common non-abstract nodes. D. Tile Weight Calculation In this step, we use the Weighted PageRank algorithm to calculate the scores of each vertex in the data flow graph. The previous steps eliminated the differences in the data flow graphs generated by equivalent R1CS through the abstraction of linear tiles. In the following step, constraints are generated on a tile-by-tile basis, and a criterion for tile order is proposed to sort the generated constraints. In the algorithm proposed in this paper, the Weighted PageRank algorithm is used to calculate the weights of each node in the abstracted data flow graph, which are then used as the basis for calculating the weights of the corresponding constraints for each tile. Compared to the traditional PageRank algorithm, this algorithm assigns weights to every edge in the graph and adjusts the iterative formula for node weights. In the Weighted PageRank algorithm, as mentioned in the former section, the formula for calculating node scores is defined as follows: In this algorithm, the primary purpose of using the Weighted PageRank algorithm is to reduce the symmetry of the abstracted data flow graph. The structure of the graph is significantly simplified in the previous step through the simplification of linear tiles. However, some structures in the graph still contain symmetric nodes. If general algorithms are used to calculate the weights of nodes, these symmetric nodes may be assigned the same weight, which can lead to problems in subsequent constraint generation and sorting. To address this issue, the Weighted PageRank algorithm is employed to calculate weights for different nodes. This helps increase the asymmetry of the graph and minimize the occurrence of nodes with the same score. In this algorithm, the iterative formula for scores in the Weighted PageRank algorithm is further adjusted. The node weights retained in the original data flow graph are set to 1, while for nodes abstracted from linear constraints, their weights are calculated through a series of steps. Finally, the variance of the normalized coefficients is utilized as the weight of the abstract node representing the linear constraint. In this algorithm, the iterative formula for the node score in the Weighted PageRank algorithm is given by: The scores for each node are calculated to determine the ranking of each constraint in the R1CS form. E. Constraint Generation In this step, we generate constraints in descending order of weight, with tiles as the unit, starting with quadratic constraints and followed by linear constraints. In this phase, a portion of the variable ordering was determined through the quadratic tile. In the tripartite matrix group of the R1CS paradigm, the three-row vectors corresponding to quadratic constraints each have only one non-zero coefficient. Still, in matrices A and B, the two-row vectors corresponding to the same constraint are equivalent in the constraint representation, consistent with the commutativity of multiplication. This results in two equivalent expressions for the same quadratic constraint. Figure 9 illustrates an example of equivalent terms for a quadratic constraint. In the abstract data flow graph, all vertices representing variables appearing in quadratic constraints are retained, making it possible to determine the choice of non-zero coefficients in the corresponding row vectors of matrices A and B in the tripartite matrix group of the R1CS paradigm based on the weights of each variable. The variables with higher weights are assigned to matrix A and given smaller indices in the variable mapping. The sorting rules for the ordering of variables that appear in quadratic constraints can be summarized as follows: Sort variables based on the highest weight value among all quadratic constraints in which they appear, such that variables with higher weight values have smaller indices in the variable mapping. 2) For variables that appear in the same constraint and have the same highest weight value sort them based on their scores in the Weighted PageRank algorithm for the nodes in the data flow graph corresponding to the variables. Variables with higher node scores will have smaller indices in the variable mapping and be assigned to the corresponding row vector in matrix A of the tripartite matrix group. F. Adjustment of Linear Constraints At this stage, the partition and ordering of constraints within a constraint group have been established, and the ordering of variables that appeared in quadratic constraints has also been determined. However, it is necessary to adjust the order of newly introduced variables in linear tiles in this step. As the example below demonstrates, introducing multiple new variables within a linear tile can result in disorderly sorting. This is because, in the previous actions, the specific structures of linear tile constraints were abstracted to eliminate differences in the RNode Graph. Therefore, we introduce a new method to order the newly introduced variables in linear tiles. For each new variable introduced in a linear tile, its weight is calculated as The new variables are sorted based on their weights. If the weights are the same, the coefficients of the variable in its linear tile are considered for comparison. The appearance of new variables in other linear tiles, to some extent, reflects their importance in the entire constraint group. Additionally, suppose certain new variables only appear in their constraints. In that case, their weights will be zero, and their ordering will only affect the constraints generated by their linear tile without changing the ordering of other constraints. Therefore, they can be sorted in descending order based on their coefficients alone. This paper is under CC BY 4.0 DEED license. available on arxiv

Streamlining R1CS Normalization: A Guide to the Normalization Algorithm

Authors: (1) Chenhao Shi, Shanghai Jiao Tong University; (2) Ruibang Liu, Shanghai Jiao Tong University; (3) Guoqiang Li†, Shanghai Jiao Tong University. Table of Links Abstract & Introduction Preliminaries Normalization Algorithm Evaluation Conclusion & References Abstract The communities of blockchains and distributed ledgers have been stirred up by the introduction of zeroknowledge proofs (ZKPs). Originally designed to solve privacy issues, ZKPs have now evolved into an effective remedy for scalability concerns and are applied in Zcash (internet money like Bitcoin). To enable ZKPs, Rank-1 Constraint Systems (R1CS) offer a verifier for bi-linear equations. To accurately and efficiently represent R1CS, several language tools like Circom, Noir, and Snarky have been proposed to automate the compilation of advanced programs into R1CS. However, due to the flexible nature of R1CS representation, there can be significant differences in the compiled R1CS forms generated from circuit language programs with the same underlying semantics. To address this issue, this paper uses a data-flow-based R1CS paradigm algorithm, which produces a standardized format for different R1CS instances with identical semantics. By using the normalized R1CS format circuits, the complexity of circuits’ verification can be reduced. In addition, this paper presents an R1CS normalization algorithm benchmark, and our experimental evaluation demonstrates the effectiveness and correctness of our methods. —Zero-knowledge proof; Rank-1 constraint systems; Data flow graph; ZKP programming; Normalization Index Terms I. INTRODUCTION (ZKPs) are increasingly recognized for their importance in modern cryptography [1], as one and more cryptographic communities seek to address some of the blockchain’s most significant challenges: privacy and scalability. It is also the essential technique in Zcash [2], [3]. From both user’s and developer’s perspectives, the heightened emphasis on information privacy and security has led to a greater appreciation for the privacy advantages offered by zero-knowledge proofs. As decentralized finance (DeFi) usage grows, zero-knowledge applications that provide scalability and privacy advantages will have more opportunities to increase industry-wide adoption. However, not all computational problems can be directly addressed using zero-knowledge proofs. Instead, we must transform the issue into the correct form of computation. The rank-1 constraint system (R1CS) describes the execution of statements written in high-level programming languages and is used by many ZKP applications, but there is no standard way of representing them [4]. Circom is a novel domain-specific language for transforming computational problems into R1CS format circuits.[5] In the specific process of a first-order zero-knowledge proof, we first convert the problem into a computational problem in Circom, then into R1CS format circuits. Zero-knowledge proofs Due to the flexible nature of R1CS representation and variation of program organizations and compiler optimization levels, there can be significant differences in the compiled R1CS forms generated from circuit language programs with the same underlying semantics, which leads to difficulties in further ZKP program analysis and verification. This paper proposes a data-flow-based algorithm for generating normalization of R1CS, enabling the conversion of different R1CS constraints into a normal form, facilitating the determination of equivalence and correctness. To achieve this, the algorithm starts by transforming an R1CS into a data flow graph structure resembling an expression tree. It then segments and abstracts the data flow graph, eliminating differences between equivalent R1CS constraints that may arise from the generation process. Finally, sorting rules are proposed to sort the constraints and variables within R1CS, ultimately resulting in a unique normal form for equivalent R1CS. Moreover, we classify and summarize the reasons and characteristics of the different equivalent R1CS generated, based on the constraint generation logic of mainstream compilers and the expressiveness of R1CS. In addition, based on the identified reasons for producing equivalent R1CS, we create a relatively complete benchmark. Our proposed algorithm, which can pass all test cases in the benchmark, demonstrates that equivalent R1CS can be converted into a unique and identical canonical form under various circumstances. This work contributes to R1CS optimization by providing a novel algorithm for generating canonical forms of equivalent R1CS constraints. Our algorithm can eliminate unnecessary redundancy and normalize representation, thus it can improve existing methods and facilitates the analysis of equivalence and correctness. Furthermore, the effectiveness and practicality of the proposed algorithm are demonstrated through our comprehensive benchmark. Eli et al. design, implement, and evaluate a zero-knowledge succinct non-interactive argument (SNARG) R1CS [6]. Historically, research investigating the factors associated with R1CS has focused on satisfiability. In paper[7], where the prover incurs finite field operations to prove the satisfiability of an n-sized R1CS instance [7]. Alexander et al. introduce Brakedown, the first built system that provides linear-time SNARKs for NP [8]. Related Work Considering Circom and R1CS format circuits as two languages before and after compilation, research on the generation of the R1CS paradigm is more akin to research on semantic consistency in the compilation. Currently, the patent applications and research papers propose ideas and solutions for generating compilation paradigms in other languages, mainly exploring data flow [9], syntax tree [10], or semantic mapping [11] aspects. These studies offer crucial insights into the fundamental information semantically identical programs entail in the compilation process. However, due to the inherent constraints embedded within the R1CS form, this paper ultimately elects to use data flow as a starting point for research. The paper is organized as follows: In the next section, a brief preliminary review of zero-knowledge proof and related tools. Section 3 provides the process of the proposed algorithm in this paper. The technical exposition in Section 4 explains in detail the logic of the critical steps and their formal description. Section 5 presents the specific categories of benchmarks and their corresponding experimental results. Lastly, Section 6 concludes the present study. Paper Organization This paper is under CC BY 4.0 DEED license. available on arxiv

Data-Flow-Based Normalization Generation Algorithm of R1CS

A Norman woman, Amante by name

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The TechBeat: AB Testing on Small Sample Sizes with Non-Normal Distributions (9/25/2023)

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The TechBeat: AB Testing on Small Sample Sizes with Non-Normal Distributions (9/23/2023)

In this article, we will explore the intricacies of AB testing on small sample sizes, which can be valuable in B2B settings or products with a limited user base that still want to conduct experiments. Many who have dealt with AB tests in large internet companies are accustomed to having a wealth of data, and the sample size for experiments can be enormous. Even if the key metric is not normally distributed, which often happens, it doesn't pose significant challenges. With large samples, you can even afford excess modifications. For example, observations can be grouped into buckets, and the average metric values within these buckets can be calculated. You can then work with the distribution of these averages, which tends to be close to normal due to the Central Limit Theorem. The topic of experiments on big data has been well-studied recently, with many established approaches and best practices. However, experiments in B2B can significantly differ from what we're accustomed to. They bring us back to the world of small data, where experiments need to be conducted with a few thousand or even hundreds of observations, and their distribution may be even more skewed than in user data. All the methods listed below are applicable to samples of any size, but they gain greater value in small samples. Non-Parametric Tests The non-normality of the distribution is, of course, a minor issue. Using non-parametric tests can be very helpful here. Instead of using Student's t-test, consider using the Mann-Whitney test, for example. If you haven't encountered non-parametric tests before, it's a good idea to get familiar with them sooner rather than later. The advantage of non-parametric tests is that they don't require assumptions about data distribution and can be used when the distribution is not normal or when the data contains outliers. Non-parametric tests are based on data ranks rather than values. Data is ranked, and various statistical methods are then used to test hypotheses about differences between samples. There are plenty of resources on this topic, ranging from very basic to more scientific. For example, one of the most cited works on this topic was published in 1957: , and more accessible descriptions can be found on the web Link to the paper It's worth noting that the Mann-Whitney test doesn't work well with ties, meaning a large number of repeated values in the sample, so you should check if this situation applies before using it. Outliers Any modification of the sample is a contentious and responsible decision, especially with small samples. Removing even a few observations can significantly impact the results, and there's no guarantee it's an improvement rather than a deterioration of the signal. However, if you've decided that something needs to be done with outliers, consider alternatives to outright removal. One such technique is Winsorization, which replaces extreme values with the most extreme values within specified criteria: . Link to Winsorization Variance Reduction Additionally, it may be appropriate to apply one of the accepted methods for reducing variance. A good example of such a technique is CUPED, which emerged at Microsoft a decade ago. In its simplest form, the main idea is to compare the change in a metric before and after instead of the metric itself for groups A and B. You can find a canonical work on this topic here: . Link to the paper Formation of A and B Groups All the methods mentioned above are valuable for improving the quality of results, but in the case of small samples, they may not help if you don't pay sufficient attention to the logic of how A and B groups are allocated early on. The usual random allocation to groups, which is canonical for tests on large data, can be detrimental in our case. For example, we may doom an experiment to failure by putting the two largest companies in the same group. The solution to this problem is quite simple: random pairwise allocation. Sort the list from largest to smallest and then randomly assign neighbors to groups A or B. It might look something like this: 1000 - A 930 - B 800 - B 730 - A 520 - B 511 - A In Conclusion All the above should help you conduct experiments with small data, but the main recommendation is the same for all experiments: first, make sure the experiment is necessary in the first place, and only then delve into all the mathematical and quasi-mathematical magic.

AB Testing on Small Sample Sizes with Non-Normal Distributions

In the world of database design, normal forms play a vital role in ensuring data integrity, eliminating redundancy, and maintaining efficient data storage. Understanding the different types of normal forms is essential for anyone involved in designing and optimizing relational databases. Each normal form represents a level of data normalization, with higher forms indicating a more refined and well-structured database schema. In this guide, we will explore the different types of normal forms, from the first normal form (1NF) to higher levels such as third normal form (3NF) and beyond. By gaining a clear understanding of these normal forms, you will be equipped with the knowledge to create well-designed, efficient, and scalable databases. In database design, normal forms are guidelines that help organize and structure data in a relational database. There are different levels of normal forms, each addressing specific aspects of data normalization. Let's explore the different types of normal forms: First Normal Form (1NF): 1NF ensures atomicity and eliminates repeating groups. Each column in a table contains only atomic (indivisible) values. There are no repeating groups or arrays of values in any column. Each row is uniquely identifiable with a primary key. Second Normal Form (2NF): 2NF builds upon 1NF and eliminates partial dependencies. All non-key attributes depend on the entire primary key, not just part of it. If a table has composite primary keys, each non-key attribute must depend on the entire composite key. Third Normal Form (3NF): 3NF builds upon 2NF and eliminates transitive dependencies. No non-key attribute depends on another non-key attribute. Any non-key attribute should depend only on the primary key or other candidate keys. Boyce-Codd Normal Form (BCNF): BCNF is a stronger version of 3NF, focusing on functional dependencies. It eliminates all non-trivial dependencies where a non-key attribute determines another non-key attribute. BCNF is more stringent in enforcing dependencies than 3NF. Fourth Normal Form (4NF): 4NF addresses multi-valued dependencies. It ensures that each non-key attribute is dependent on the entire primary key and not on subsets of it. It eliminates redundancy arising from multi-valued dependencies. Fifth Normal Form (5NF) or Project-Join Normal Form (PJNF): 5NF deals with the elimination of join dependencies. It decomposes tables to ensure that no join dependency exists between any subset of candidate keys. These are the commonly recognized normal forms in database design. However, it's important to note that higher normal forms the Greibach normal form (beyond 3NF) are not always necessary or practical for every situation. The level of normalization depends on the specific requirements, complexity, and trade-offs of the database design. like By adhering to the appropriate normal forms, database designers can achieve better data organization, integrity, and performance. It's essential to understand these normal forms and apply them judiciously based on the specific needs of each database application. The advantages of normal forms in database design are numerous and contribute to the overall efficiency, integrity, and maintainability of a database. Here are some key advantages: Normalization reduces data redundancy and ensures that data is stored consistently. By organizing data into separate tables and eliminating duplicate information, the risk of inconsistent or conflicting data is minimized. This promotes data integrity and improves the reliability of the database. Data Integrity: Normal forms help eliminate data redundancy by breaking down information into smaller, logical units. Redundant data not only wastes storage space but also introduces the possibility of inconsistencies and update anomalies. Normalization reduces redundancy by storing data only once and referencing it through relationships between tables. Elimination of Data Redundancy: Greibach normal form allows efficient storage and retrieval of data. By breaking down data into smaller tables based on functional dependencies, queries can be targeted to specific tables rather than scanning large amounts of data. This improves query performance and enhances the overall efficiency of the database. Efficient Data Storage and Retrieval: Normalized databases make it easier to update and modify data. With well-defined relationships between tables, changes in data can be made in a single location without affecting other parts of the database. This reduces the likelihood of inconsistencies and makes database maintenance and updates more manageable. Simplified Updates and Modifications: Normalization provides a flexible foundation for database growth and expansion. As the database evolves, new tables and relationships can be added without disrupting the existing structure. Normalized databases are adaptable to changing business requirements, allowing for easier integration of new features or functionalities. Scalability and Flexibility: The chomsky normal form helps maintain data consistency by enforcing referential integrity through relationships and constraints. This ensures that data dependencies are properly managed, and only valid and consistent data can be stored in the database. Referential integrity constraints prevent data inconsistencies and maintain the accuracy of the database. Improved Data Consistency: Normalization provides a structured approach to database design, making it easier to understand and maintain. By following the guidelines of normal forms, designers can create clear and logical data models that are easier to work with and comprehend. This simplifies ongoing maintenance, troubleshooting, and future enhancements to the database. Simplified Database Design and Maintenance: You now possess a solid understanding of the progression from first normal form (1NF) to higher forms such as second normal form (2NF) and third normal form (3NF). Normalization is a crucial concept that helps ensure data integrity, eliminate redundancy, and improve the overall efficiency of database systems. Remember, the of the Chomsky normal form is to design a database schema that minimizes data duplication and provides a clear structure for efficient data storage and retrieval. While achieving higher normal forms can increase data integrity and optimize storage, it's important to strike a balance between normalization and practicality. Over-normalization can lead to complex join operations and performance issues, so it's essential to consider the specific requirements and trade-offs of each situation. goal By applying the principles of normalization and understanding the advantages and limitations of each normal form, you will be well-equipped to design robust and scalable databases. Regularly reviewing and refining your database schema as new requirements arise is crucial to maintaining an optimized data model. Keep exploring and deepening your knowledge of database design principles, as well as staying updated on emerging trends and best practices. As you gain experience and expertise, you will become proficient in designing efficient and reliable database systems. Good luck on your journey to mastering database normalization!

Different Types of Normal Form

Rilla of Ingleside by L. M. Montgomery, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . NORMAN DOUGLAS SPEAKS OUT IN MEETING CHAPTER XX. NORMAN DOUGLAS SPEAKS OUT IN MEETING "Where are you wandering, Anne o' mine?" asked the doctor, who even yet, after twenty-four years of marriage, occasionally addressed his wife thus when nobody was about. Anne was sitting on the veranda steps, gazing absently over the wonderful bridal world of spring blossom, Beyond the white orchard was a copse of dark young firs and creamy wild cherries, where the robins were whistling madly; for it was evening and the fire of early stars was burning over the maple grove. Anne came back with a little sigh. "I was just taking relief from intolerable realities in a dream, Gilbert—a dream that all our children were home again—and all small again—playing in Rainbow Valley. It is always so silent now—but I was imagining I heard clear voices and gay, childish sounds coming up as I used to. I could hear Jem's whistle and Walter's yodel, and the twins' laughter, and for just a few blessed minutes I forgot about the guns on the Western front, and had a little false, sweet happiness." The doctor did not answer. Sometimes his work tricked him into forgetting for a few moments the Western front, but not often. There was a good deal of grey now in his still thick curls that had not been there two years ago. Yet he smiled down into the starry eyes he loved—the eyes that had once been so full of laughter, and now seemed always full of unshed tears. Susan wandered by with a hoe in her hand and her second best bonnet on her head. "I have just finished reading a piece in the Enterprise which told of a couple being married in an aeroplane. Do you think it would be legal, doctor dear?" she inquired anxiously. "I think so," said the doctor gravely. "Well," said Susan dubiously, "it seems to me that a wedding is too solemn for anything so giddy as an aeroplane. But nothing is the same as it used to be. Well, it is half an hour yet before prayer-meeting time, so I am going around to the kitchen garden to have a little evening hate with the weeds. But all the time I am strafing them I will be thinking about this new worry in the Trentino. I do not like this Austrian caper, Mrs. Dr. dear." "Nor I," said Mrs. Blythe ruefully. "All the forenoon I preserved rhubarb with my hands and waited for the war news with my soul. When it came I shrivelled. Well, I suppose I must go and get ready for the prayer-meeting, too." Every village has its own little unwritten history, handed down from lip to lip through the generations, of tragic, comic, and dramatic events. They are told at weddings and festivals, and rehearsed around winter firesides. And in these oral annals of Glen St. Mary the tale of the union prayer-meeting held that night in the Methodist Church was destined to fill an imperishable place. The union prayer-meeting was Mr. Arnold's idea. The county battalion, which had been training all winter in Charlottetown, was to leave shortly for overseas. The Four Winds Harbour boys belonging to it from the Glen and over-harbour and Harbour Head and Upper Glen were all home on their last leave, and Mr. Arnold thought, properly enough, that it would be a fitting thing to hold a union prayer-meeting for them before they went away. Mr. Meredith having agreed, the meeting was announced to be held in the Methodist Church. Glen prayer-meetings were not apt to be too well attended, but on this particular evening the Methodist Church was crowded. Everybody who could go was there. Even Miss Cornelia came—and it was the first time in her life that Miss Cornelia had ever set foot inside a Methodist Church. It took no less than a world conflict to bring that about. "I used to hate Methodists," said Miss Cornelia calmly, when her husband expressed surprise over her going, "but I don't hate them now. There is no sense in hating Methodists when there is a Kaiser or a Hindenburg in the world." So Miss Cornelia went. Norman Douglas and his wife went too. And Whiskers-on-the-moon strutted up the aisle to a front pew, as if he fully realized what a distinction he conferred upon the building. People were somewhat surprised that he should be there, since he usually avoided all assemblages connected in any way with the war. But Mr. Meredith had said that he hoped his session would be well represented, and Mr. Pryor had evidently taken the request to heart. He wore his best black suit and white tie, his thick, tight, iron-grey curls were neatly arranged, and his broad, red round face looked, as Susan most uncharitably thought, more "sanctimonious" than ever. "The minute I saw that man coming into the Church, looking like that, I felt that mischief was brewing, Mrs. Dr. dear," she said afterwards. "What form it would take I could not tell, but I knew from face of him that he had come there for no good." The prayer-meeting opened conventionally and continued quietly. Mr. Meredith spoke first with his usual eloquence and feeling. Mr. Arnold followed with an address which even Miss Cornelia had to confess was irreproachable in taste and subject-matter. And then Mr. Arnold asked Mr. Pryor to lead in prayer. Miss Cornelia had always averred that Mr. Arnold had no gumption. Miss Cornelia was not apt to err on the side of charity in her judgment of Methodist ministers, but in this case she did not greatly overshoot the mark. The Rev. Mr. Arnold certainly did not have much of that desirable, indefinable quality known as gumption, or he would never have asked Whiskers-on-the-moon to lead in prayer at a khaki prayer-meeting. He thought he was returning the compliment to Mr. Meredith, who, at the conclusion of his address, had asked a Methodist deacon to lead. Some people expected Mr. Pryor to refuse grumpily—and that would have made enough scandal. But Mr. Pryor bounded briskly to his feet, unctuously said, "Let us pray," and forthwith prayed. In a sonorous voice which penetrated to every corner of the crowded building Mr. Pryor poured forth a flood of fluent words, and was well on in his prayer before his dazed and horrified audience awakened to the fact that they were listening to a pacifist appeal of the rankest sort. Mr. Pryor had at least the courage of his convictions; or perhaps, as people afterwards said, he thought he was safe in a church and that it was an excellent chance to air certain opinions he dared not voice elsewhere, for fear of being mobbed. He prayed that the unholy war might cease—that the deluded armies being driven to slaughter on the Western front might have their eyes opened to their iniquity and repent while yet there was time—that the poor young men present in khaki, who had been hounded into a path of murder and militarism, should yet be rescued— Mr. Pryor had got this far without let or hindrance; and so paralysed were his hearers, and so deeply imbued with their born-and-bred conviction that no disturbance must ever be made in a church, no matter what the provocation, that it seemed likely that he would continue unchecked to the end. But one man at least in that audience was not hampered by inherited or acquired reverence for the sacred edifice. Norman Douglas was, as Susan had often vowed crisply, nothing more or less than a "pagan." But he was a rampantly patriotic pagan, and when the significance of what Mr. Pryor was saying fully dawned on him, Norman Douglas suddenly went berserk. With a positive roar he bounded to his feet in his side pew, facing the audience, and shouted in tones of thunder: "Stop—stop—STOP that abominable prayer! What an abominable prayer!" Every head in the church flew up. A boy in khaki at the back gave a faint cheer. Mr. Meredith raised a deprecating hand, but Norman was past caring for anything like that. Eluding his wife's restraining grasp, he gave one mad spring over the front of the pew and caught the unfortunate Whiskers-on-the-moon by his coat collar. Mr. Pryor had not "stopped" when so bidden, but he stopped now, perforce, for Norman, his long red beard literally bristling with fury, was shaking him until his bones fairly rattled, and punctuating his shakes with a lurid assortment of abusive epithets. "You blatant beast!"—shake—"You malignant carrion"—shake—"You pig-headed varmint!"—shake—"you putrid pup"—shake—"you pestilential parasite"—shake—"you—Hunnish scum"—shake—"you indecent reptile—you—you—" Norman choked for a moment. Everybody believed that the next thing he would say, church or no church, would be something that would have to be spelt with asterisks; but at that moment Norman encountered his wife's eye and he fell back with a thud on Holy Writ. "You whited sepulchre!" he bellowed, with a final shake, and cast Whiskers-on-the-moon from him with a vigour which impelled that unhappy pacifist to the very verge of the choir entrance door. Mr. Pryor's once ruddy face was ashen. But he turned at bay. "I'll have the law on you for this," he gasped. "Do—do," roared Norman, making another rush. But Mr. Pryor was gone. He had no desire to fall a second time into the hands of an avenging militarist. Norman turned to the platform for one graceless, triumphant moment. "Don't look so flabbergasted, parsons," he boomed. "You couldn't do it—nobody would expect it of the cloth—but somebody had to do it. You know you're glad I threw him out—he couldn't be let go on yammering and yodelling and yawping sedition and treason. Sedition and treason—somebody had to deal with it. I was born for this hour—I've had my innings in church at last. I can sit quiet for another sixty years now! Go ahead with your meeting, parsons. I reckon you won't be troubled with any more pacifist prayers." But the spirit of devotion and reverence had fled. Both ministers realized it and realized that the only thing to do was to close the meeting quietly and let the excited people go. Mr. Meredith addressed a few earnest words to the boys in khaki—which probably saved Mr. Pryor's windows from a second onslaught—and Mr. Arnold pronounced an incongruous benediction, at least he felt it was incongruous, for he could not at once banish from his memory the sight of gigantic Norman Douglas shaking the fat, pompous little Whiskers-on-the-moon as a huge mastiff might shake an overgrown puppy. And he knew that the same picture was in everybody's mind. Altogether the union prayer-meeting could hardly be called an unqualified success. But it was remembered in Glen St. Mary when scores of orthodox and undisturbed assemblies were totally forgotten. "You will never, no, never, Mrs. Dr. dear, hear me call Norman Douglas a pagan again," said Susan when she reached home. "If Ellen Douglas is not a proud woman this night she should be." "Norman Douglas did a wholly indefensible thing," said the doctor. "Pryor should have been let severely alone until the meeting was over. Then later on, his own minister and session should deal with him. That would have been the proper procedure. Norman's performance was utterly improper and scandalous and outrageous; but, by George,"—the doctor threw back his head and chuckled, "by George, Anne-girl, it was satisfying." About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. L. M. Montgomery (2003). Rilla of Ingleside. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/3796/pg3796-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

NORMAN DOUGLAS SPEAKS OUT IN MEETING

The Noonification: A Nullish Coalescing (in JS) Tutorial for Normal People (6/24/2023)

Hey there! Have you ever been frustrated trying to handle null or undefined values in your JavaScript code? Nullish coalescing is a new feature that helps make that a whole lot easier. In this post, we’ll explain nullish coalescing. That means simple terms, fun examples, and no complicated jargon. By the end, you’ll be writing clean, concise JS code in no time. So what exactly is nullish coalescing? Basically, it’s a short, simple way to provide a default value when something is null or undefined. Instead of having messy nested ternaries or logical OR statements, you can use the double question mark operator (??) to coalesce to a default. Sound confusing? Don’t worry, we’ll show you how it works with some easy-to-follow examples. Buckle up, this is going to be a fun ride! What Exactly Is the Nullish Coalescing Operator?? Have you ever gotten stuck dealing with null values in JavaScript? I know I have. Well, my friend, let me introduce you to the nullish coalescing operator (??). This little guy is going to make your life so much easier. So what exactly is the nullish coalescing operator? Basically, it's a shortcut to provide a default value when something could be null or undefined. Instead of writing: let name = obj.name || 'default name'; You can now simply write: let name = obj.name ?? 'default name'; The ?? operator will return the left side if it's not nullish (meaning null or undefined), otherwise, it will return the right side. So in the example above, if is not null or undefined, name will be set to . But if is null or undefined, name will be 'default name'. obj.name obj.name obj.name Pretty handy, right? Some other examples: let age = ageInput ?? 25; // Sets age to ageInput if not nullish, otherwise 25

let email = user.email ?? 'no email provided';

let hobbies = obj.hobbies ?? []; // Sets hobbies to obj.hobbies if not nullish, otherwise an empty array I hope this helps simplify some of your conditional logic in JavaScript. A Real-World Example We Can All Relate To Have you ever gone to pay for something, only to realize you're a few bucks short? We've all been there. Nullish coalescing is like finding a few extra dollars in your pocket so you can actually afford what you want to buy. Let's say you head to your favorite coffee shop for your usual morning brew. You go up to the counter with $3 in your hand, ready to order. But when the barista tells you your total, it comes out to $3.50 after tax. is like suddenly remembering you have another dollar in your pocket, so you can still get your coffee. Phew! Nullish coalescing In JavaScript, is a way to provide a default value in case something resolves to null or undefined. For example, say you have a function that may or may not return a value: nullish coalescing function getCoffeePrice() {

// Returns $3.50 or undefined

}

let price = getCoffeePrice() ?? '$3.50'; Here, if returns undefined, will be by default thanks to the nullish coalescing operator. So you're guaranteed to have a price for your coffee either way. getCoffeePrice() price '$3.50' ?? Nullish coalescing allows your code to be more resilient by providing fallback default values when variables are null or undefined. Just like finding that extra dollar in your pocket at the coffee shop, it ensures you have what you need to complete the transaction. Pretty handy, right? Now you can rest easy knowing you understand nullish coalescing like a pro. Happy coding and enjoy your coffee! Using Nullish Coalescing in JavaScript The nullish coalescing operator (??) returns its left operand if it's not nullish; otherwise, it returns its right operand. Confused? Let's break that down. A "nullish" value is either null or undefined. So the operator checks if the left value is nullish, and if so, returns the right value instead. ?? For example: const name = bob ?? 'anonymous'; This will assign the value of name to 'bob' if bob is defined. If bob is undefined or null, it will assign the string 'anonymous' instead. Why Is This Useful? Without the ?? operator, you'd have to do this: const name = bob !== null && bob !== undefined ? bob : 'anonymous'; The ?? operator gives us a shorter, cleaner syntax to essentially do the same thing. It can be used anywhere in JavaScript - in , , object properties, etc. Anywhere you'd normally use the || (or) operator for default values, you can now use ?? for a "nullish" default value. variables function arguments A Few Examples: const foo = null ?? 'default string';

// foo is 'default string'

const bar = 0 ?? 42;

// bar is 0

const qux = 0 || 42;

// qux is 42 - || returns first truthy value

function doSomething(x = 0) {

x = x ?? 10;

// If x is nullish, set default of 10

console.log(x);

}

doSomething(null); // Prints 10

doSomething(5); // Prints 5 I hope this helps explain the nullish coalescing operator! Why Nullish Coalescing Is Better Than OR (||) The OR (||) Operator Will Return the First Truthy Value. The OR (||) operator works by returning the first truthy value it encounters. If both values are falsy, it will return the last value. For example: let name = null;

let user = name || 'John'; // user is 'John' Here, is null (falsy), so gets the second value, 'John'. name user The Nullish Coalescing Operator (??) Is Smarter. The Nullish Coalescing Operator (??) was introduced in ES2020 and is a bit smarter than OR (||). It will return the first defined value (not null/undefined). So if the first value is an empty string, 0, or false, it will return that value. For example: let name = '';

let user = name ?? 'John'; // user is '' Here, is an empty string, so gets (the first defined value). name user name The Nullish Coalescing Operator allows us to provide default values in a cleaner way. We can set default values for null/undefined values without worrying about falsy values like 0, empty strings, false, etc. For example, say we have a function that returns a user's age, but sometimes the age is null: function getAge() {

return age; // could be null

}

let userAge = getAge() || 30; // Uses OR, not ideal Here, if is 0, '', or false, would incorrectly be 30. age userAge Using the Nullish Coalescing Operator fixes this: let userAge = getAge() ?? 30; // Uses ?? Now, will only be 30 if is null or undefined. Any other falsy value like 0 will be used. userAge age So in summary, use the Nullish Coalescing Operator (??) instead of OR (||) when you want to provide a default value for null/undefined, but not for falsy values like 0, empty strings, or false. Your code will be cleaner and less prone to bugs! Common Questions About Nullish Coalescing So you've heard about this fancy new Nullish Coalescing operator (??) in JavaScript and have some questions. No worries, I've got you covered! What Data Types Does Nullish Coalescing Work With? Nullish coalescing works with any data type in JavaScript, including: Numbers: 0 ?? 1 // Returns 1 Strings: '' ?? 'Hello' // Returns 'Hello' Booleans: false ?? true // Returns true Objects: {} ?? {name: 'John'} // Returns {name: 'John'} Arrays: [] ?? [1, 2, 3] // Returns [1, 2, 3] And more! What's the Difference Between || (OR) and ?? (Nullish Coalescing) Operators? The || (OR) operator will return the right operand if the left operand is falsy (false, 0, empty string, null, undefined, NaN). The ?? (Nullish Coalescing) operator will only return the right operand if the left operand is null or undefined. So the key difference is that ?? considers 0 and empty strings to be valid values, whereas || does not. When Should I Use ?? Over ||? Use when you want to provide a default value for null or undefined, but consider 0 and empty strings to be valid values. ?? Use || when you want to provide a fallback for any "falsy" value. Hope this helps clear up some of those burning questions you had about the Nullish Coalescing operator! Let me know if you have any other questions. Conclusion Now you know all about nullish coalescing and how it works in JavaScript. Pretty simple, right? This little operator can save you time and make your code cleaner by providing default values in a concise way. So go forth and coalesce nullish values to your heart's content! You're well on your way to mastering modern JavaScript features. Happy Coding!

A Nullish Coalescing (in JS) Tutorial for Normal People

সাধারণ মানুষের জন্য একটি শূন্য কোলেসিং (জেএস-এ) টিউটোরিয়াল

Ein Nullish Coalescing (in JS) Tutorial für normale Menschen

Un tutorial de fusión nula (en JS) para personas normales

Un tutoriel sur la coalescence nulle (en JS) pour les personnes normales

सामान्य लोगों के लिए एक नुलिश कोलेसिंग (जेएस में) ट्यूटोरियल

普通の人のための Nullish Coalescing (JS) チュートリアル

일반 사람들을 위한 Nullish Coalescing(JS) 튜토리얼

Um Tutorial Nullish Coalescing (em JS) para pessoas normais

Normal İnsanlar için Nullish Birleştirme (JS'de) Eğitimi

Hướng dẫn hợp nhất Nullish (trong JS) cho người bình thường

适合普通人的空合并（JS 语言）教程

You are working on a project to create a for a school. This database should contain details about the student including class, subjects, and teachers. Bearing in mind that a student would learn multiple subjects and may have multiple teachers. If you are to present this, it would look like the table below: database Student Id Student Name Age Year Courses Course id 001 Benson 19 Year 2 MathsEconomicsGrammarGeography 20013 002 Mary 18 Year 2 MathHistoryGeographyLiterature 20014 003 Dennis 20 Year 3 BiologyPhysicsHistoryMath 20015 In this table we represented all the required data, however, using this format allows you to assign more than one value to a cell in a table, which causes a repetition of data and is considered an anomaly. As a database grows, anomalies can lead to data integrity problems, You would need a method to represent data correctly, thereby eliminating anomalies. What is Normalization? In simple terms, is a system that allows you to organize data in a way that prevents repetition. It breaks larger tables into smaller tables by removing duplicate data from its relational tables. normalization This allows you to: Reduce data redundancy and, in turn, improve Integrity eliminate Insertion, Update, and Deletion Anomalies To understand the concept of normalization, you would need to know what keys are and how relationships work in a database. Keys Keys refer to a column or a set of columns in a database table that uniquely identifies each row. They play a crucial role in establishing relationships between tables and ensuring data integrity. Some Commonly used keys are: Primary key Primary keys are columns that uniquely identify rows in a table. For instance, the first row in your student table. Student Id Student Name Age Year Courses Course id 001 Benson 19 Year 2 MathsEconomicsGrammarGeography 20013 002 Mary 18 Year 2 MathHistoryGeographyLiterature 20014 003 Dennis 20 Year 3 BiologyPhysicsHistoryMath 20015 The student id can be considered a primary key in this case as it is used to identify each student and is distinct through the table. are very similar to primary keys and can be used interchangeably. Candidate Keys are basically a set of primary keys in a table which are in this case Student ID and Course ID. Super Keys Foreign Keys Foreign keys serve as references to the primary keys of other tables. This establishes a relationship between the two tables within a database Forms of Normalization Normalization follows a series of stages called Normal forms. To fully understand normal forms, consider them as set rules that should be followed. Here is a list of the Normal Forms in SQL: 1NF (First Normal Form) 2NF (Second Normal Form) 3NF (Third Normal Form) BCNF (Boyce-Codd Normal Form) 4NF (Fourth Normal Form) 5NF (Fifth Normal Form) 6NF (Sixth Normal Form) First Normal Form (INF) First Normal form aims to solve the problem of atomicity (multiple values in a cell). The rules in the First Normal form state that: Each cell should contain a single value. Each record needs to be unique Now, if you apply the rules of  First Normal Form to our table,  you would reduce the value of the subject column to one per cell. Student Id Student Name Age Year Course Course id 001 Benson 19 Year 2 Economics 20013 002 Mary 18 Year 2 History 20014 003 Dennis 20 Year 3 Biology 20015 Second Normal Form(2NF) For a table to be in the second normal form; it has to be in First Normal Form(1NF) It shouldn't possess partial dependency. This means each column in the table should be fully dependent on the primary key. Let’s take a look at the student table; Student Id Student Name Age Year Course Course id 001 Benson 19 Year 2 Economics 20013 002 Mary 18 Year 2 History 20014 003 Dennis 20 Year 3 Biology 20015 In this table, the non-key columns(Student Name, age, year, course ) are all attributes of the student which are identified by the studentID. But, the course id only depends on the course, which is a part of the primary key(Student ID ). Hence, this table violates the second normal form(2NF) To achieve the  Second Normal Form, you need to divide the table based on the dependency: Student Id Student Name Age Year Course 001 Benson 19 Year 2 Economics 002 Mary 18 Year 2 History 003 Dennis 20 Year 3 Biology Course Course id Economics 20013 History 20014 Biology 20015 Using the Second Normal Form (2NF), the Course column now entirely depends on the primary keys of Student and Course Table respectively. Third Normal Form (3NF) Now, you have created a separate table hereby eliminating partial dependencies on your table. Let’s assume the teacher of your students, just scored their recent paper and wants to store the record in your table. The table would be updated to: Student Id Student Name Age Year Course Course Score 001 Benson 19 Year 2 Economics 88 002 Mary 18 Year 2 History 92 003 Dennis 20 Year 3 Biology 85 In this table, there are no partial dependencies. However, Course, Age, and Year are directly dependent on the Student Id. Although the Course Score is also dependent on the student Id, It is first dependent on the course. Student Id   →  Course Student Id → Course → Course Id This means that the table has a transitive dependency and hence does not meet the third normal form requirement. To bring the table to a third normal form, it; has to be in 2NF Should have no transitive functional dependencies. To convert the 2NF table into 3NF, you would need to split the table based on transitive functional dependency. Student Id Student Name Age Year Course 001 Benson 19 Year 2 Economics 002 Mary 18 Year 2 History 003 Dennis 20 Year 3 Biology Course Course Score Economics 88 History 92 Biology 85 Boyce-Codd Normal Form(BCNF) Boyce-Codd  Normal Form is the superior version of 3NF and is also known as 3.5 NF. It is used to tackle certain types of anomalies which are not resolved with 3NF. To resolve a table in Boyce-Codd normal form. The table has to Be in a Third Normal Form(3NF). Have each attribute on the right side of a dependency depend wholly primary key of the table. Let's consider an example where the given table does not satisfy BCNF. Student Id Student Name Age Course Instructor 001 Benson 19 Economics Dr. Smith 002 Mary 18 History Dr. Johnson 003 Dennis 20 Biology Dr. Lee In this case, the dependency Course → Instructor violates BCNF because Course is not a superkey. This means that the dependency does not rely on the entire Student ID. To achieve BCNF, we need to split the table into two separate tables: Student table Student Id Student Name Age 001 Benson 19 002 Mary 18 003 Dennis 20 Course table Course Course Instructor Economics Dr. Smith History Dr. Johnson Biology Dr. Lee In the modified version, we reference the primary key from the Course table as a foreign key in the Student table. Although these are the most used form of Normalization. There are other forms such as; Fourth Normal Form(4NF) In 4NF, a table should satisfy the following two conditions: It must already be in 3NF. It should not have any multi-valued dependencies. Fifth Normal Form (5NF) For a table to be in the Fifth Normal Form(5NF), It must already be in 4NF. It should not have any non-trivial join dependencies.

Understanding Normalization: A Guide to Effective Database Design

An AI Article Not Really About AI “You’re soaking in it.” That’s the sense THE innovation conversation of 2023 gives me about AI (aka generative computing, , , et al). It’s everywhere. The topic of seemingly every current and upcoming tech conference panel? Yes. In daily headlines and non-stop LinkedIn comments? Sure. Core to every pitch deck presently? Of course. Heck, AI will probably be the rage at non-tech trade shows soon enough ( anyone?), and kindergarten kids will declare wanting to be prompt engineers when they grow up. machine/deep learning LLMs Coat Hanger Innovation Summit ‘23 If you listen to the conversations, AI promises a new age that will disrupt our lives in ways we are only beginning to fathom, with impacts beyond the public’s initial forays into ChatGPT-powered term papers and other outputs from its rivals and relatives. Whether this new age is one of machine-driven empowerment and progress, one where dystopian fears are realized and truths are eroded, or some combination thereof is now speculated upon widely and loudly. What’s struck me as much as the predictive scenarios and prospective use cases for AI is the pace at which it and its disruptive potential has entered the . It feels as if this has mainstreamed faster than any prior innovation, including the Internet. It’s one thing to observe folk in the tech world ret-con their creds (and keywords) to claim long-held expertise in and intent to incorporate AI into their platforms, but when late-night talk shows center bits around the technology, elderly, tech-averse relatives speak knowingly of the topic, students come home counseled about ChatGPT and academic integrity, and .ai domains proliferate, we’re seemingly up (or down) whatever curve consultants use to describe their innovation frameworks. Kudos to AI’s PR lobby. zeitgeist We Believe How is it that we have so quickly accepted an AI-infused future as a fait accompli? Recall that past disruptive technologies and services – such as the Web, mobile, e-commerce, and social media – certainly had their boosters and made news about their impact potential when they emerged, but the foreboding sense of how each would profoundly change our personal and professional lives simply wasn’t omnipresent. Instead, prior innovations needed to prove their transformative effects. This required some mix of accomplishments, including but not limited to HW/SW innovation and evolution, infrastructure deployment, acquisition of trust, and user scaling to achieve network effects. As with many technologies that seem to suddenly break through in the public consciousness, foundational work on AI has been underway for decades. Still, AI in all its flavors is still quite early in its mass commercialization. In Internet-equivalent evolutionary time, we are perhaps at the stage when one would be using an NCSA Mosaic browser on a 9600 baud dial-up modem and wait hours to download a Quicktime movie trailer. Sure, it was very cool for its time, but not a capability set that was widely recognized for its game-changing potential. Yet AI is already heralded as the catalyst for a new age. On the surface this is understandable. Today’s AI engines are accessible to the masses and produce highly demonstrable and, equally important, relatable deliverables. Via our phone or laptop, we can readily generate that term paper (though we shouldn’t), home repair instructions, or an article like this [ed. note: no Large Language Models were harmed in the making of this essay]. We can use a few words to prompt images like the ones in this article or generate almost-Hollywood-level VFX for our media creations. And we are entertained by “new” works emulating our favorite musicians, leaving labels and lawyers grappling with a new era of sampling and copyright challenges. For some, AI is already touching their lives directly in more profound ways, as AI models drawing upon massive data sets diagnose otherwise missed cancers and guide physicians’ treatment options toward improved efficacy and away from unnecessary therapies. Further, these AI use cases are enabled by and benefit greatly from prior waves of disruption – such as the Web, mobile, and distributed compute and social communications infrastructures. These technologies enable the amplification of AI, fueling its mainstreaming by disseminating the news, hype, and speculation about our AI future instantly to all corners of the globe. In ways unmatched by the announcements of earlier developments, a crescendo of all things AI dominates our screens. That may explain our mass awareness, but what’s to account for our forgone conclusion about AI’s coming transformative impact? Surely we must have a reason to believe it will be more than ChatGPT-powered scripts and faux-Oasis tracks. I believe we do, and it's simply this: we have normalized disruption. It starts with our technology experiences of the past few decades. After experiencing how the Web, mobile, et al have transformed so many aspects of our lives, they have seasoned us to expect disruption and step-level evolution. Whether we are old enough to remember payphones or have been born into the age of screens and instant everything, when it comes to technology, we have become conditioned to believe. Life, Interrupted Recent, non-technological disruptions have also contributed to our acclimation, demonstrating that change is not only possible, but it can be fast, massive, and tremendously impactful on our daily lives. Today’s politics of course, whatever your beliefs, have brought to the fore stresses and divisions at levels never before felt so strongly or amplified and perpetuated so greatly. While one can readily make the case that these realities have always been present in the fabric of America, it’s hard to deny that the disruption brought about by Trump’s election was stark and impactful and entirely unexpected as the outcome of a Presidential election. No longer. Above all though is COVID. The pandemic was and remains the uber-disruptor on every level of society, a wave that crashed upon each of us directly, laying bare the precariousness of our norms. More than anything it shook our perception of society, safety, and our future in ways with which we are still grappling. In our (barely) post-pandemic state, we have come to understand and expect that disruption is to be expected and its impacts can be immense and potentially permanent, whether we’re speaking of the shift to remote work, an erosion of trust in assumed “solids” such as science and supply chains, or an overall instillment of fear in the new and unknown. Accept The Unexpected Today, whether we’ve seen a wave or three of disruption break upon our shores, or have only lived through the world-shaking events of recent years, I suggest that we have come to internalize an understanding that in our connected world, every successive disruption – technological, political, medical or otherwise – holds the potential to more greatly, strongly, and quickly flip our scripts and change our lives. Through this lens, I can better understand why we are so bullish on AI and have essentially priced its proliferation into our societal P&L. It’s a broad set of disruptive technologies. They appear to work. We expect they will only get better as we have seen with other technologies. And we are now freshly conditioned to expect their disruption to be massive, if unpredictable.

Have We Normalized Disruption?

He did not return to camp as Norman of Torn had ordered

The Outlaw of Torn by Edgar Rice Burroughs is part of the HackerNoon Books Series. You can jump to any chapter in this book here . Chapter XVII The Outlaw of Torn Chapter XVII When those of the royalists who had not deserted the King and fled precipitately toward the coast had regained the castle and the Priory, the city was turned over to looting and rapine. In this, Norman of Torn and his men did not participate, but camped a little apart from the town until daybreak the following morning, when they started east, toward Dover. They marched until late the following evening, passing some twenty miles out of their way to visit a certain royalist stronghold. The troops stationed there had fled, having been apprised some few hours earlier, by fugitives, of the defeat of Henry’s army at Lewes. Norman of Torn searched the castle for the one he sought, but, finding it entirely deserted, continued his eastward march. Some few miles farther on, he overtook a party of deserting royalist soldiery, and from them he easily, by dint of threats, elicited the information he desired: the direction taken by the refugees from the deserted castle, their number, and as close a description of the party as the soldiers could give. Again he was forced to change the direction of his march, this time heading northward into Kent. It was dark before he reached his destination, and saw before him the familiar outlines of the castle of Roger de Leybourn. This time, the outlaw threw his fierce horde completely around the embattled pile before he advanced with a score of sturdy ruffians to reconnoiter. Making sure that the drawbridge was raised, and that he could not hope for stealthy entrance there, he crept silently to the rear of the great building and there, among the bushes, his men searched for the ladder that Norman of Torn had seen the knavish servant of My Lady Claudia unearth, that the outlaw might visit the Earl of Buckingham, unannounced. Presently they found it, and it was the work of but a moment to raise it to the sill of the low window, so that soon the twenty stood beside their chief within the walls of Leybourn. Noiselessly, they moved through the halls and corridors of the castle until a maid, bearing a great pasty from the kitchen, turned a sudden corner and bumped full into the Outlaw of Torn. With a shriek that might have been heard at Lewes, she dropped the dish upon the stone floor and, turning, ran, still shrieking at the top of her lungs, straight for the great dining hall. So close behind her came the little band of outlaws that scarce had the guests arisen in consternation from the table at the shrill cries of the girl than Norman of Torn burst through the great door with twenty drawn swords at his back. The hall was filled with knights and gentlewomen and house servants and men-at-arms. Fifty swords flashed from fifty scabbards as the men of the party saw the hostile appearance of their visitors, but before a blow could be struck, Norman of Torn, grasping his sword in his right hand, raised his left aloft in a gesture for silence. “Hold!” he cried, and, turning directly to Roger de Leybourn, “I have no quarrel with thee, My Lord, but again I come for a guest within thy halls. Methinks thou hast as bad taste in whom thou entertains as didst thy fair lady.” “Who be ye, that thus rudely breaks in upon the peace of my castle, and makes bold to insult my guests?” demanded Roger de Leybourn. “Who be I! If you wait, you shall see my mark upon the forehead of yon grinning baboon,” replied the outlaw, pointing a mailed finger at one who had been seated close to De Leybourn. All eyes turned in the direction that the rigid finger of the outlaw indicated, and there indeed was a fearful apparition of a man. With livid face he stood, leaning for support against the table; his craven knees wabbling beneath his fat carcass; while his lips were drawn apart against his yellow teeth in a horrid grimace of awful fear. “If you recognize me not, Sir Roger,” said Norman of Torn, drily, “it is evident that your honored guest hath a better memory.” At last the fear-struck man found his tongue, and, though his eyes never left the menacing figure of the grim, iron-clad outlaw, he addressed the master of Leybourn; shrieking in a high, awe-emasculated falsetto: “Seize him! Kill him! Set your men upon him! Do you wish to live another moment, draw and defend yourselves for he be the Devil of Torn, and there be a great price upon his head. “Oh, save me, save me! for he has come to kill me,” he ended in a pitiful wail. The Devil of Torn! How that name froze the hearts of the assembled guests. The Devil of Torn! Slowly the men standing there at the board of Sir Roger de Leybourn grasped the full purport of that awful name. Tense silence for a moment held the room in the stillness of a sepulchre, and then a woman shrieked, and fell prone across the table. She had seen the mark of the Devil of Torn upon the dead brow of her mate. And then Roger de Leybourn spoke: “Norman of Torn, but once before have you entered within the walls of Leybourn, and then you did, in the service of another, a great service for the house of Leybourn; and you stayed the night, an honored guest. But a moment since, you said that you had no quarrel with me. Then why be you here? Speak! Shall it be as a friend or an enemy that the master of Leybourn greets Norman of Torn; shall it be with outstretched hand or naked sword?” “I come for this man, whom you may all see has good reason to fear me. And when I go, I take part of him with me. I be in a great hurry, so I would prefer to take my great and good friend, Peter of Colfax, without interference; but, if you wish it otherwise; we be a score strong within your walls, and nigh a thousand lie without. What say you, My Lord?” “Your grievance against Peter of Colfax must be a mighty one, that you search him out thus within a day’s ride from the army of the King who has placed a price upon your head, and from another army of men who be equally your enemies.” “I would gladly go to hell after Peter of Colfax,” replied the outlaw. “What my grievance be matters not. Norman of Torn acts first and explains afterward, if he cares to explain at all. Come forth, Peter of Colfax, and for once in your life, fight like a man, that you may save your friends here from the fate that has found you at last after two years of patient waiting.” Slowly, the palsied limbs of the great coward bore him tottering to the center of the room, where gradually a little clear space had been made; the men of the party forming a circle, in the center of which stood Peter of Colfax and Norman of Torn. “Give him a great draught of brandy,” said the outlaw, “or he will sink down and choke in the froth of his own terror.” When they had forced a goblet of the fiery liquid upon him, Peter of Colfax regained his lost nerve enough so that he could raise his sword arm and defend himself and, as the fumes circulated through him, and the primal instinct of self-preservation asserted itself, he put up a more and more creditable fight, until those who watched thought that he might indeed have a chance to vanquish the Outlaw of Torn. But they did not know that Norman of Torn was but playing with his victim, that he might make the torture long, drawn out, and wreak as terrible a punishment upon Peter of Colfax, before he killed him, as the Baron had visited upon Bertrade de Montfort because she would not yield to his base desires. The guests were craning their necks to follow every detail of the fascinating drama that was being enacted before them. “God, what a swordsman!” muttered one. “Never was such swordplay seen since the day the first sword was drawn from the first scabbard!” replied Roger de Leybourn. “Is it not marvellous!” Slowly but surely was Norman of Torn cutting Peter of Colfax to pieces; little by little, and with such fiendish care that, except for loss of blood, the man was in no way crippled; nor did the outlaw touch his victim’s face with his gleaming sword. That he was saving for the fulfillment of his design. And Peter of Colfax, cornered and fighting for his life, was no marrowless antagonist, even against the Devil of Torn. Furiously he fought; in the extremity of his fear, rushing upon his executioner with frenzied agony. Great beads of cold sweat stood upon his livid brow. And then the gleaming point of Norman of Torn flashed, lightning-like, in his victim’s face, and above the right eye of Peter of Colfax was a thin vertical cut from which the red blood had barely started to ooze ere another swift move of that master sword hand placed a fellow to parallel the first. Five times did the razor point touch the forehead of Peter of Colfax, until the watchers saw there, upon the brow of the doomed man, the seal of death, in letters of blood—NT. It was the end. Peter of Colfax, cut to ribbons yet fighting like the maniac he had become, was as good as dead, for the mark of the Outlaw of Torn was upon his brow. Now, shrieking and gibbering through his frothy lips, his yellow fangs bared in a mad and horrid grin, he rushed full upon Norman of Torn. There was a flash of the great sword as the outlaw swung it to the full of his mighty strength through an arc that passed above the shoulders of Peter of Colfax, and the grinning head rolled upon the floor, while the loathsome carcass, that had been a baron of England, sunk in a disheveled heap among the rushes of the great hall of the castle of Leybourn. A little shudder passed through the wide-eyed guests. Some one broke into hysterical laughter, a woman sobbed, and then Norman of Torn, wiping his blade upon the rushes of the floor as he had done upon another occasion in that same hall, spoke quietly to the master of Leybourn. “I would borrow yon golden platter, My Lord. It shall be returned, or a mightier one in its stead.” Leybourn nodded his assent, and Norman of Torn turned, with a few words of instructions, to one of his men. The fellow gathered up the head of Peter of Colfax, and placed it upon the golden platter. “I thank you, Sir Roger, for your hospitality,” said Norman of Torn, with a low bow which included the spellbound guests. “Adieu.” Thus followed by his men, one bearing the head of Peter of Colfax upon the platter of gold, Norman of Torn passed quietly from the hall and from the castle. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Edgar Rice Burroughs (1995). The Outlaw of Torn. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/369/pg369-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

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