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Although there are numerous predictions for the Ethereum all-time high price for this year, I believe there is room for a reasonable price projection with fundamental analysis. I am NOT a Quan or pretend to be one so that’s why I’m not using mathematical models, but qualitative indicators in this model. I will be using reputable sources to support my arguments and I will also be playing my own devil’s advocate against each one of my conclusions. Please, feel free to reach out to me if you have any questions, disagreements or just want to wish me a good day on @ Twitter EdosophiaBTC Similarly, I will keep this simple and brief so please enjoy it. It might get a bit technical at times so please just click on the links for quick reading on the concepts you might not be familiar with. Time will tell whether I’m right or wrong, so let it be my judge. The Edosophia Model for Ethereum True value derives from the usefulness of an invention to solve a problem. Similarly, Ethereum derives its value not only from (for more info on , please read by ) due to the several factors such as the explosive growth of and the future burning mechanism reducing supply, but also from its utility use case to solve a crucial problem in the global financial market. This problem can be simplified in just a few words; Eliminating third parties with the invention of . scarcity scarcity Bitcoin Stock-To-Flow PlanB DeFi EIP 1559 Smart Contracts The best counter-argument against this premise is that Ethereum is too slow to get rid of third parties and as we are experiencing right now, Gwei prices are skyrocketing due to the congestion of the network. Another rebuttal against Ethereum abilities to solve the third party issue is that it is not necessary to get rid of third parties in the first place. Lastly, there is another counter statement against Ethereum potential which is thriving competitors in the space such as Cardano and Polkadot which claim to be the next wave of blockchains. I can make the case that the first problem is just temporary, the second one is just not true, and the third is the weakest argument of all these rebuttals. Firstly, are currently being staked on Ethereum 2.0 as of Jan 5th of 2021 to solve this scalability problem. We should be seen this issue solved in the last phase sometime in 2022. 2B Dollars “ Docking ” Secondly, third parties are incredibly bureaucratic and slow with respect to transactions and processes, ask . As a consequence, third parties add up unnecessary costs to the bottom line and do NOT always pursue the best outcome for everyone, . Top US Banking Regulators I don’t even need a reference to prove this point Lastly, Ethereum is the Tesla of blockchains, although newcomers are trying to come up with better innovative ideas, the truth is that thanks to network effects, Ethereum has a solid ground against these other blockchains. If the best value proposition of other blockchain competitors (VeChain, Cardano, Stellar, Dot, my dog) is that they are interoperable with Ethereum and scale better, these networks won’t be taking significant market share due to Google’s effect (being first in the market and creating a new sub-industry where other copycats try to catch up). I could go on but the point is that ETH 2.0 solves the third party problem and each individual counter-argument is not strong enough against this premise. Since we can agree on this now, the next phase is knowing the market value for solving the third party problem. It is hard to estimate the market value for transactions and fees (third parties) in the financial service industry. It is in the multiple of trillions of dollars globally and Ethereum has barely been above 100 billion dollars as of today. However, thanks to big institutions, we have some metrics that we can discuss to assess this valuation. For one, has over 3.5 billion dollars worth of ETH at the time of this article. Similarly, has shown that large wallets are accumulating ETH on-chain at an exponential rate. We even have future instrument launching Feb 8, something that institutions are patiently waiting for. Lastly, the amount of ETH in exchanges has been heavily decreasing and put in smart contracts for staking and liquidity mining as we saw in the DeFi Summer of 2020. This trend has only grown since then and many consider this new financial system will take over the old one in this decade. Greyscale Santiment CME ETH There is a clear sign that scarcity is also playing a factor in the appreciation of ETH as an asset class just like Bitcoin, although in a different way as previously mentioned. ETH, unlike Bitcoin, has more use cases than just being a stored of value. Bitcoin can’t be used as a currency due to scalability issues, but ETH is being used as such by supporting DeFi protocols. Total Value locked in DeFi 40x in less than a year from the first quarter of 2020, check for info on this. Bitcoin just teased 4x in 2020. DeFi Pulse Similarly, thanks to the beacon chain for ETH 2.0, billions of dollars worth of ETH are currently being locked and staked until the Docking stage in 2022. You guessed it, less circulating supply. Despite all these metrics shown for how much demand there is for Ethereum, there is one that is by far the most bullish of all, demand for . stablecoins Ethereum has now become the most common layer for settlements in stablecoins and the has said that banks can now use stablecoins for settlement. The value of interbank transfers alone are in the TRILLIONS.. thank you ETH :) OCC Okay one last juicy fact before my prediction, let’s talk about This is an improvement proposal for transactions on the ETH blockchain. It means that the current bit base model for transactions will be replaced for a set base one. Similar to how burns its token, ETH will use a similar mechanism reducing supply circulating of ETH that could offset the 18 million coins created per year. EIP 1559 . Synthetix Conclusion I hope you find this article useful. Time will tell whether this model for Ethereum is right or wrong but in the meantime, follow me on @ for more content about crypto. Twitter EdosophiaBTC My Prediction for Ethereum Price Is — Between $8K to $12K Disclaimer: Not intended to constitute legal, tax, accounting or investment advice. Also published at https://edosophia.medium.com/the-edosophia-macro-model-for-ethereum-valuation-7d80294be212

Ethereum Valuation Using the Edosophia Macro Model 

Hateful Meme Detection: Leveraging PVLMs for Zero-shot VQA Probing

This paper is available on arxiv under CC 4.0 license. Authors: (1) Muzhaffar Hazman, University of Galway, Ireland; (2) Susan McKeever, Technological University Dublin, Ireland; (3) Josephine Griffith, University of Galway, Ireland. Table of Links Abstract and Introduction Related Works Methodology Results Limitations and Future Works Conclusion, Acknowledgments, and References A Hyperparameters and Settings B Metric: Weighted F1-Score C Architectural Details D Performance Benchmarking E Contingency Table: Baseline vs. Text-STILT 6 Conclusion In this work, we addressed the challenge of training multimodal meme sentiment classifiers on a limited number of labelled memes by incorporating unimodal sentiment analysis data. We did so by proposing the first instance of STILT that applies unimodal intermediate tasks to a multimodal target task. Specifically, we tested image-only and text-only sentiment classification as intermediate tasks in training a meme sentiment classifier. We showed that this approach worked – unimodal text improved meme classification performance to a statistically significant degree. This novel approach allowed us to train a meme classifier that outperforms meme-only finetuning with only 60% as many labelled meme samples. As possible explanations for our observations, we discuss apparent similarities and differences in the roles of image and text modalities between unimodal and multimodal sentiment analysis tasks. Acknowledgments This work was conducted with the financial support of the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224; and the provision of computational facilities and support from the Irish Centre for High-End Computing (ICHEC). References Christian Bauckhage. 2011. Insights into internet memes. Proceedings of the International AAAI Conference on Web and Social Media, 5(1):42–49. Efrat Blaier, Itzik Malkiel, and Lior Wolf. 2021. 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Image sentiment polarity - dataset. Available at https://data.world/ crowdflower/image-sentiment-polarity. Accessed: 2023-01-15. Baishan Duan and Yuesheng Zhu. 2022. Browallia at memotion 2.0 2022 : Multimodal memotion analysis with modified ogb strategies (short paper). In DeFactify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Noam Gal, Limor Shifman, and Zohar Kampf. 2016. “it gets better”: Internet memes and the construction of collective identity. New Media & Society, 18(8):1698–1714. Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, and Ivan Marsic. 2018. Hybrid Attention based Multimodal Network for Spoken Language Classification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2379–2390, Santa Fe, New Mexico, USA. Association for Computational Linguistics. Muzhaffar Hazman, Susan McKeever, and Josephine Griffith. 2023. Meme sentiment analysis enhanced with multimodal spatial encoding and face embedding. In Artificial Intelligence and Cognitive Science, pages 318–331, Cham. Springer Nature Switzerland. Junguang Jiang, Yang Shu, Jianmin Wang, and Mingsheng Long. 2022. Transferability in deep learning: A survey. arXiv preprint, arXiv:2201.05867. Vishal Keswani, Sakshi Singh, Suryansh Agarwal, and Ashutosh Modi. 2020. IITK at SemEval-2020 task 8: Unimodal and bimodal sentiment analysis of Internet memes. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1135–1140, Barcelona (online). International Committee for Computational Linguistics. Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, et al. 2020. The hateful memes challenge: Detecting hate speech in multimodal memes. Advances in Neural Information Processing Systems, 33:2611–2624. Liisi Laineste and Piret Voolaid. 2017. Laughing across borders: Intertextuality of internet memes. 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In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Jason Phang, Thibault Fevry, and Samuel R. Bowman. ´ 2019. Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks. arXiv preprint, arXiv:1811.01088. Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019. WiC: the word-in-context dataset for evaluating context-sensitive meaning representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1267–1273, Minneapolis, Minnesota. Association for Computational Linguistics. Clifton Poth, Jonas Pfeiffer, Andreas Ruckl ¨ e, and Iryna ´ Gurevych. 2021. What to pre-train on? Efficient intermediate task selection. 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MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4439–4455, Punta Cana, Dominican Republic. Association for Computational Linguistics. Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, and Samuel R. Bowman. 2020. Intermediate-task transfer learning with pretrained language models: When and why does it work? In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5231–5247, Online. Association for Computational Linguistics. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, et al. 2021. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748–8763. PMLR. Sathyanarayanan Ramamoorthy, Nethra Gunti, Shreyash Mishra1, S Suryavardan, Aishwarya Reganti, Parth Patwa, Amitava Das, Tanmoy Chakraborty, Amit Sheth, Asif Ekbal, and Chaitanya Ahuja. 2022. Memotion 2: Dataset on sentiment and emotion analysis of memes. In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Elad Segev, Asaf Nissenbaum, Nathan Stolero, and Limor Shifman. 2015. Families and Networks of Internet Memes: The Relationship Between Cohesiveness, Uniqueness, and Quiddity Concreteness. Journal of Computer-Mediated Communication, 20(4):417–433. Lanyu Shang, Yang Zhang, Yuheng Zha, Yingxi Chen, Christina Youn, and Dong Wang. 2021. AOMD: An Analogy-Aware Approach to Offensive Meme Detection on Social Media. Inf. Process. Manage., 58(5). Chhavi Sharma, Deepesh Bhageria, William Scott, Srinivas PYKL, Amitava Das, Tanmoy Chakraborty, et al. 2020. SemEval-2020 task 8: Memotion analysisthe visuo-lingual metaphor! In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 759–773, Barcelona (online). International Committee for Computational Linguistics. Shivam Sharma, Mohd Khizir Siddiqui, Md. Shad Akhtar, and Tanmoy Chakraborty. 2022. Domainaware self-supervised pre-training for label-efficient meme analysis. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 792–805, Online only. Association for Computational Linguistics. Limor Shifman. 2014. The cultural logic of photo-based meme genres. Journal of Visual Culture, 13(3):340–358. Pranaydeep Singh, Aaron Maladry, and Els Lefever. 2022. Combining language models and linguistic information to label entities in memes. 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Innovative Strategies in Meme Sentiment Analysis: The Impact of Unimodal Training

In the era of widespread adoption of AI models, the data science community exhibits a rich diversity of backgrounds, fostering an environment conducive to innovation. Collaborative efforts stemming from this diversity continue to propel us toward the envisioned future of a decade or two ago. However, innovation often follows incremental patterns and may require decades before yielding tangible commercial benefits. Among industries adept at translating ideas into tangible products, automotive manufacturers stand out, boasting a deep familiarity with research and development processes and advanced algorithms. This proficiency is evident in the innovation pipeline for super and hypercars, drawing inspiration from advancements in rocket science. Leveraging extensive simulations and field tests, these manufacturers consistently push the boundaries of performance and safety in vehicle design. Major manufacturers have achieved breakthroughs with torque vectoring systems by capitalizing on established development pipelines and harnessing the capabilities of reinforcement learning. The late 2010s witnessed a surge in commercial vehicles featuring these systems, attributed partly to their association with electric and hybrid vehicles and partly to the appeal of enhanced safety. While the full extent of their influence on driver experience remains to be realized, these systems have already demonstrated remarkable efficacy, particularly in high-performance vehicles. So, what exactly is a torque vectoring system, and how is this innovation shaping the landscape of automotive manufacturing? What is Torque Vectoring In Torque Vectoring Systems (TVS), torque denotes a rotational force applied to a vehicle’s differentials or generated by independent electric motors on its wheels. These systems aim to regulate this rotational force to enhance speed, stability, handling, balance, and other performance aspects. A compelling illustration of the protracted nature of innovation is evident in the evolution of torque vectoring systems, with the first cars equipped with Torque Vectoring Systems (TVS) debuting in the 1990s, pioneered by Mitsubishi. Initially known as Active Yaw Control, Mitsubishi integrated this technology into vehicles like the 1996 Mitsubishi Lancer Evolution IV GSR models. The initial implementation relied on steering, throttle inputs, and g-force sensors to mitigate torque steer, a phenomenon where engine torque influences steering behavior. Initially geared towards racing applications, these systems have since evolved to cater to broader audiences, with companies like Rimac continuing to employ them for racing. In contrast, others emphasize safety and enhanced handling features for everyday drivers. Furthermore, advancements driven by the pursuit of eco-friendly transportation have led to the utilization of independent electric motors in torque vectoring systems, further expanding their applicability. Commercial Applications Ricardo (Honorable Mention) In 2006, Ricardo, a renowned engineering consultancy with over a century of experience, introduced the concept of torque vectoring. Ricardo rolled out its initial torque vectoring project centered around the Audi A6 4.2L, intending to license its technology to interested automakers. Unlike Mitsubishi’s Active Yaw Control system and similar counterparts, Ricardo’s torque vectoring approach prioritized safety and stability, showcasing a different aspect of the technology. Limited information is currently available on Ricardo’s involvement in assisting manufacturers in developing these systems for commercially available vehicles. However, they have publicly provided engineering support to Chinese students building cars for the global multi-event Formula SAE race in 2019. Rimac When discussing electric vehicles, Tesla typically takes the spotlight regarding market dominance. However, Rimac stands out as a company dedicated to crafting high-performance electric hypercars, where they excel. Just ask Elon… ( t) X pos Rimac burst onto the scene in January 2016 with their inaugural concept car, the Concept_One prototype. This marked the debut of their torque vectoring system (TVS) in an all-wheel-drive fully electric vehicle. Spearheaded by Tomislav Šimunić, Rimac’s TVS development pushed the boundaries by employing individual motors on each wheel. Moreover, they implemented reinforcement algorithms utilizing data from various sensors, including accelerometers, gyroscopes, steering angle, and wheel speed sensors. This data is then used to predict torque distribution to optimize vehicle stability. https://www.youtube.com/shorts/i4jVW29gbDc?embedable=true Currently, Rimac boasts one of the fastest and safest hypercars on the market. Their innovation has led to unprecedented control, resulting in their car achieving the title of the fastest production car ever in 21 acceleration and deceleration categories. Furthermore, their TVS is so reliable that their drivers can cruise hands-free at speeds exceeding 230 mph while enjoying gelato! Toyota In 2018, Toyota introduced their Dynamic Torque Vectoring AWD and Electric 4WD systems, expanding the scope of this technology beyond on-road safety and control to encompass off-road safety features and enhanced fuel efficiency. https://www.youtube.com/watch?v=LCqCa7PP1gE&embedable=true The video above showcases Toyota’s emphasis on “proactive torque transfer,” allowing for seamless adjustment of torque distribution when one or more wheels lose traction, enabling drivers to navigate various terrains more effectively. https://youtu.be/d1UG1a38diw?embedable=true Furthermore, Toyota optimizes torque distribution to enhance fuel-efficiency, shifting from AWD to 2WD when conditions permit, directing torque predominantly to the front wheels and thus cutting fuel consumption. Their limited content suggests the employment of a Dynamic Control System designed for stability, taking into account challenging driving scenarios. Moreover, the system can evaluate tire traction levels. These functionalities resonate with the research findings discussed below, showcasing the seamless transition from research concepts to practical implementation in the automotive industry. Research Review Conceptual Models — Bicycle, Vehicle, Tire Model Following Occam’s razor principle, simplicity reigns supreme when initiating modeling endeavors, with complexity introduced only when its benefits are worthwhile. This philosophy holds for TVS as well. Early TVS adopted a bicycle model, allowing researchers to streamline vehicle dynamics assumptions by confining movements to two wheels. This simplified framework facilitated the study of lateral vehicle dynamics during various events like braking, acceleration, and cornering under typical conditions. Key inputs into the bicycle model include yaw rate, lateral acceleration, steering angle, and velocity. Yaw rate denotes the vehicle’s rotation speed around its vertical axis, aiding in determining its orientation during maneuvers like cornering. Lateral acceleration captures sideways movements perpendicular to the vehicle’s direction of travel. Steering angle refers to the front wheel angle, typically in relation to the rear wheel positioning, while velocity represents the vehicle’s speed. For more details, check out this article explaining a simple bicycle model. https://dingyan89.medium.com/simple-understanding-of-kinematic-bicycle-model-81cac6420357?embedable=true Simplicity in modeling often comes with assumptions that may not hold, especially in challenging driving scenarios. Extreme conditions like rain, snow, or rough off-road terrain expose the limitations of the traditional bicycle model. The assumption of symmetry between the left and right wheels hinders the accurate analysis of lateral dynamics across all four wheels. To address this, researchers upgraded to a more comprehensive 4-wheel vehicle model. One such model, proposed in the 2023 journal article by Hui Deng, Youqun Zhao, Fen Lin, and Qiuwei Wang, boasts seven degrees of freedom. This expanded model captures all four wheels’ lateral, longitudinal, rotational, tilting, leaning, and spinning movements. However, it still falls short in capturing driving dynamics such as suspension behavior and tire-road interactions. Although suspension dynamics may be indirectly represented within the model, a separate tire model is necessary to capture tire-road interactions accurately. Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy & Safety² Tire models typically include parameters like tire force and slip angles. In the journal article by Michele Vignati, Edoardo Sabbioni, and Federico Cheli, researchers describe a simple tire model that incorporates lateral and longitudinal dynamics, slip angles, and slippage. Like the seven degrees of freedom model indirectly addresses suspension dynamics, the tire model considers slippage influenced by latent variables such as surface texture, conditions, and angles. This comprehensive approach allows researchers to analyze vehicle dynamics across various conditions and terrains, enhancing the torque vectoring system’s adaptability and performance. A Torque Vectoring Control for Enhancing Vehicle Performance in Drifting³ Control Schemes/Implementations In Torque Vectoring Systems (TVS) development, a significant portion of research revolves around employing Reinforcement Learning techniques, leveraging real-time data collected from various measurement devices. These solutions are predominantly implemented in all-wheel drive, four-wheel drive hybrid, and electric vehicles. Typically, the training of these systems takes place within simulated environments. However, in certain instances, researchers conduct road tests and utilize live test outcomes to validate simulation results. Torque Vectoring System Testing While specific details about testing TVS are often limited, researchers generally have three options: entirely computer-based simulations, hardware-in-the-loop testing combining simulation with real parts, and full-scale vehicle road tests. Computer-based simulations offer cost-effective evaluation of TVS with precise control over various driving conditions. Hardware-in-the-loop testing balances simulation and road tests, providing insights aligned with real-world performance. Although the most expensive, road tests offer the most reliable results by implementing TVS fully, enabling the most accurate evaluation of model performance. In the study by Shayan Taherian, Sampo Kuutti, Marco Visca, and Saber Fallah, computer-based simulations were utilized to test their RL algorithm across diverse environments. The researchers employed an adaptive algorithm based on Deep Deterministic Policy Gradient to update TVS control parameters as it learns from the environment. Testing across various velocity and friction environments allowed validation of their adaptive algorithm, particularly in high-speed/low-velocity scenarios. Self-adaptive Torque Vectoring Controller Using Reinforcement Learning⁴ Similarly, in the research by Wei Xu, Hong Chen, Haiyan Zhao, and Bingtao Ren, real-time Simulink simulations were leveraged to validate their model predictive controller. This controller predicts future system states and optimizes for multiple energy efficiency and safety objectives. Tests were conducted using an electric vehicle model with a 120 km/h velocity and an “adhesion coefficient” of .85, measuring tire friction between tire and ground. Torque Optimization Control for Electric Vehicles with Four In-Wheel Motors Equipped with Regenerative Braking System⁵ Powertrain & Fuel Types The specific problem addressed by torque vectoring systems heavily influences the powertrain choice and fuel types utilized in their implementations. Traditional gasoline and diesel vehicles necessitate mechanical implementations due to a single torque source, leading to complex mechanical differentials, challenges in power distribution, and overall less efficiency compared to hybrid and electric vehicle setups, which enable independent motor implementations. Similarly, All-Wheel Drive (AWD) and Four-Wheel Drive (4WD) powertrains offer superior advantages over other options. These configurations enhance stability, versatility, and energy efficiency. The work of Jeongmin Cho and Kunsoo Huh, in their study , underscores the stability and versatility benefits of AWD vehicles. By distributing power to all four wheels, AWD enhances the effectiveness of torque vectoring systems, granting increased control over each wheel. This translates to improved vehicle dynamics during cornering, acceleration, and deceleration. Torque Vectoring System Design for Hybrid Electric–All-Wheel Drive Vehicle⁶ Meta Control Algorithms In Reinforcement Learning (RL), an agent decides actions based on the current situation to maximize a reward function. It learns which actions yield the highest rewards by assessing the outcomes of its actions. In torque vectoring algorithms, auto manufacturers define different reward functions based on the specific goals of their TVS, which vary depending on the type of vehicles used, such as sports cars versus mini-vans. These goals include increased stability, controllability, fuel economy, cornering performance, and optimizing various vehicle dynamics behaviors. One example of a multi-objective implementation is outlined in the paper by Jae-Young Park, Seung-Jin Heo, and Daeoh Kang. Their algorithm features a supervisory controller that selects between an “agile” mode and a “safe” mode based on vehicle and driving conditions. Agile mode prioritizes driver controllability, while safe mode focuses on stability. Controllability refers to the synchronization between driver steering input and vehicle direction, while stability relates to the vehicle’s resistance to deviations or disturbances. Inputs such as vehicle speed, steering angle, yaw rate, lateral acceleration, and wheel slip guide the supervisory controller’s decision-making process. Development of Torque Vectoring Control Algorithm for Front Wheel Driven Dual Motor System and Evaluation of Vehicle Dynamics Performance⁷ Another example is presented in the paper by Hui Deng, Youqun Zhao, Fen Lin, and Qiuwei Wang. Here, the researchers optimize for safety, economy, and driver load. Economy involves assessing motor efficiency and energy savings compared to vehicles without torque vectoring systems. Safety focuses on vehicle stability, while driver load considers the physical and cognitive load based on the correlation between the driver’s steering wheel angle and the vehicle’s longitudinal acceleration. Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy and Safety⁸ The algorithm includes an active safety controller and a torque allocation layer. The active safety controller calculates total longitudinal acceleration and additional “yaw moment” values based on driving conditions, applying safety and driver load penalties. These values are then fed to the torque allocation layer, which distributes torque to all four wheels. Additionally, the researchers implement a tire model using a Fibonacci tree optimization algorithm and a vehicle model with 7 degrees of freedom to enhance system performance. The , research done by Park et al., aims to enhance the driver’s experience through improved smoothness and convergence. Their TVS, designed for commercial application, underwent rigorous validation through real vehicle experiments conducted under various conditions. Torque Vectoring Algorithm of Electronic-Four-Wheel Drive Vehicles for Enhancement of Cornering Performance⁹ The researchers implemented a smooth sliding controller, GPS, and sensor inputs to generate desired yaw moment values. These values are then utilized by the torque allocation algorithm, which employs a daisy-chaining allocation method to distribute torque efficiently. The goal is to optimize the convergence from current yaw moment values to target values, thereby reducing the instability during this transition. Conclusion The evolution of torque vectoring systems underscores the transformative power of innovation within the automotive industry. From pioneering beginnings to cutting-edge applications, these systems have revolutionized vehicle dynamics, speed, stability, and handling. As automotive manufacturers continue to push the boundaries of technology and leverage advancements in advanced algorithms, the future holds promise for further improvements in safety, performance, and driver experience. However, hurdles remain in integrating this technology into internal combustion engine vehicles, and ongoing research and development efforts are required to overcome them. With continued dedication to innovation, torque vectoring systems are poised to shape the future landscape of automotive manufacturing, driving us toward a new era of mobility. Research Reference Martin Dendaluce Jahnke et al., “Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles,” 8, no. 2 (February 22, 2019): 250 Electronics Huifan Deng et al., “Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy and Safety,” 11, no. 4 (April 6, 2023): 459 Machines Michele Vignati, Edoardo Sabbioni, and Federico Cheli, “A Torque Vectoring Control for Enhancing Vehicle Performance in Drifting,” 7, no. 12 (December 5, 2018): 394 Electronics Shayan Taherian et al., “Self-Adaptive Torque Vectoring Controller Using Reinforcement Learning,” , September 19, 2021 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) Wei Xu et al., “Torque Optimization Control for Electric Vehicles with Four In-Wheel Motors Equipped with Regenerative Braking System,” 57 (February 2019): 95–108 Mechatronics Jeongmin Cho and Kunsoo Huh, “Torque Vectoring System Design for Hybrid Electric–All Wheel Drive Vehicle,” 234, no. 10–11 (April 7, 2020): 2680–92 Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering Jae-Young Park, Seung-Jin Heo, and Dae-Oh Kang, “Development of Torque Vectoring Control Algorithm for Front Wheel Driven Dual Motor System and Evaluation of Vehicle Dynamics Performance,” 21, no. 5 (October 2020): 1283–91 International Journal of Automotive Technology Huifan Deng et al., “Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy and Safety,” 11, no. 4 (April 6, 2023): 459 Machines Giseo Park et al., “Torque Vectoring Algorithm of Electronic-Four-Wheel Drive Vehicles for Enhancement of Cornering Performance,” 69, no. 4 (April 2020): 3668–79 IEEE Transactions on Vehicular Technology

Driving Innovation: The Science of Torque Vectoring Systems

Transductive Learning for Textual Few-Shot: Limitations, Acknowledgements, & References

581 Stories To Learn About Non Fiction

Authors: (1) Davide Viviano, Department of Economics, Harvard University; (2) Lihua Lei, Graduate School of Business, Stanford University; (3) Guido Imbens, Graduate School of Business and Department of Economics, Stanford University; (4) Brian Karrer, FAIR, Meta; (5) Okke Schrijvers, Meta Central Applied Science; (6) Liang Shi, Meta Central Applied Science. Table of Links Abstract & Introduction Setup (When) should you cluster? Choosing the cluster design Empirical illustration and numerical studies Recommendations for practice References A) Notation B) Endogenous peer effects C) Proofs References Alberto Abadie, Susan Athey, Guido W Imbens, and Jeffrey Wooldridge. When should you adjust standard errors for clustering? Technical report, National Bureau of Economic Research, 2017. Vivi Alatas, Abhijit Banerjee, Rema Hanna, Benjamin A Olken, and Julia Tobias. Targeting the poor: evidence from a field experiment in indonesia. American Economic Review, 102 (4):1206–1240, 2012. Peter M Aronow and Cyrus Samii. Estimating average causal effects under general interference, with application to a social network experiment. The Annals of Applied Statistics, 11(4):1912–1947, 2017. Susan Athey, Dean Eckles, and Guido W Imbens. Exact p-values for network interference. Journal of the American Statistical Association, 113(521):230–240, 2018. Sarah Baird, J Aislinn Bohren, Craig McIntosh, and Berk Ozler. Optimal design of experi- ¨ ments in the presence of interference. Review of Economics and Statistics, 100(5):844–860, 2018. G Basse and A Feller. Analyzing multilevel experiments in the presence of peer effects. arXiv preprint arXiv, 1608, 2016. Guillaume W Basse and Edoardo M Airoldi. Limitations of design-based causal inference and a/b testing under arbitrary and network interference. Sociological Methodology, 48(1): 136–151, 2018a. Guillaume W Basse and Edoardo M Airoldi. Model-assisted design of experiments in the presence of network-correlated outcomes. Biometrika, 105(4):849–858, 2018b. Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10):P10008, 2008. Paul S Bradley, Kristin P Bennett, and Ayhan Demiriz. Constrained k-means clustering. Microsoft Research, Redmond, 20(0):0, 2000. Yann Bramoull´e, Habiba Djebbari, and Bernard Fortin. Identification of peer effects through social networks. Journal of econometrics, 150(1):41–55, 2009. Emily Breza, Arun G Chandrasekhar, Tyler H McCormick, and Mengjie Pan. Using aggregated relational data to feasibly identify network structure without network data. American Economic Review, 110(8):2454–2484, 2020. Jing Cai, Alain De Janvry, and Elisabeth Sadoulet. Social networks and the decision to insure. American Economic Journal: Applied Economics, 7(2):81–108, 2015. Aureo De Paula, Seth Richards-Shubik, and Elie Tamer. Identifying preferences in networks ´ with bounded degree. Econometrica, 86(1):263–288, 2018. Dean Eckles, Brian Karrer, and Johan Ugander. Design and analysis of experiments in networks: Reducing bias from interference. Journal of Causal Inference, 5(1), 2017. Dennis Egger, Johannes Haushofer, Edward Miguel, Paul Niehaus, and Michael Walker. General equilibrium effects of cash transfers: experimental evidence from kenya. Econometrica, 90(6):2603–2643, 2022. Stefan Faridani and Paul Niehaus. Rate-optimal linear estimation of average global effects. arXiv preprint arXiv:2209.14181, 2022. Paul Goldsmith-Pinkham and Guido W Imbens. Social networks and the identification of peer effects. Journal of Business & Economic Statistics, 31(3):253–264, 2013. Keisuke Hirano and Jack R Porter. Asymptotics for statistical treatment rules. Econometrica, 77(5):1683–1701, 2009. David Holtz, Ruben Lobel, Inessa Liskovich, and Sinan Aral. Reducing interference bias in online marketplace pricing experiments. arXiv preprint arXiv:2004.12489, 2020. Daniel Hong, Hyunwoo Lee, and Alex Wei. Optimal solutions and ranks in the max-cut sdp. arXiv preprint arXiv:2109.02238, 2021. Michael G Hudgens and M Elizabeth Halloran. Toward causal inference with interference. Journal of the American Statistical Association, 103(482):832–842, 2008. Ravi Jagadeesan, Natesh S Pillai, and Alexander Volfovsky. Designs for estimating the treatment effect in networks with interference. 2020. Igor Kabiljo, Brian Karrer, Mayank Pundir, Sergey Pupyrev, Alon Shalita, Alessandro Presta, and Yaroslav Akhremtsev. Social hash partitioner: a scalable distributed hypergraph partitioner. arXiv preprint arXiv:1707.06665, 2017. Hyunseung Kang and Guido Imbens. Peer encouragement designs in causal inference with partial interference and identification of local average network effects. arXiv preprint arXiv:1609.04464, 2016. Brian Karrer, Liang Shi, Monica Bhole, Matt Goldman, Tyrone Palmer, Charlie Gelman, Mikael Konutgan, and Feng Sun. Network experimentation at scale. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 3106– 3116, 2021. Toru Kitagawa and Guanyi Wang. Who should get vaccinated? individualized allocation of vaccines over sir network. Journal of Econometrics, 2021. Lihua Lei. Unified ℓ2→∞ eigenspace perturbation theory for symmetric random matrices. arXiv preprint arXiv:1909.04798, 2019. Lihua Lei, Xiaodong Li, and Xingmei Lou. Consistency of spectral clustering on hierarchical stochastic block models. arXiv preprint arXiv:2004.14531, 2020. Michael P Leung. Treatment and spillover effects under network interference. Review of Economics and Statistics, 102(2):368–380, 2020. Michael P Leung. Causal inference under approximate neighborhood interference. Econometrica, 90(1):267–293, 2022. Michael P Leung. Network cluster-robust inference. Econometrica, 91(2):641–667, 2023. Shuangning Li and Stefan Wager. Random graph asymptotics for treatment effect estimation under network interference. The Annals of Statistics, 50(4):2334–2358, 2022. Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Koen Van den Berge, Purnamrita Sarkar, Peter J Bickel, and Elizaveta Levina. Hierarchical community detection by recursive partitioning. Journal of the American Statistical Association, 117(538):951–968, 2022. Shuyang Ling and Thomas Strohmer. Certifying global optimality of graph cuts via semidefinite relaxation: A performance guarantee for spectral clustering. Foundations of Computational Mathematics, 20(3):367–421, 2020. Elena Manresa. Estimating the structure of social interactions using panel data. Unpublished Manuscript. CEMFI, Madrid, 2013. Charles F Manski. Identification of endogenous social effects: The reflection problem. The review of economic studies, 60(3):531–542, 1993. Charles F Manski. Identification of treatment response with social interactions. The Econometrics Journal, 16(1):S1–S23, 2013. Karthik Muralidharan and Paul Niehaus. Experimentation at scale. Journal of Economic Perspectives, 31(4):103–24, 2017. Mark EJ Newman. Community detection and graph partitioning. Europhysics Letters, 103 (2):28003, 2013a. Mark EJ Newman. Spectral methods for community detection and graph partitioning. Physical Review E, 88(4):042822, 2013b. Elizabeth L Ogburn, Oleg Sofrygin, Ivan Diaz, and Mark J van der Laan. Causal inference for social network data. arXiv preprint arXiv:1705.08527, 2017. Jean Pouget-Abadie. Dealing with Interference on Experimentation Platforms. PhD thesis, 2018. Fredrik S¨avje, Peter Aronow, and Michael Hudgens. Average treatment effects in the presence of unknown interference. Annals of statistics, 49(2):673, 2021. Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Transactions on pattern analysis and machine intelligence, 22(8):888–905, 2000. Betsy Sinclair, Margaret McConnell, and Donald P Green. Detecting spillover effects: Design and analysis of multilevel experiments. American Journal of Political Science, 56(4):1055– 1069, 2012. Sean J Taylor and Dean Eckles. Randomized experiments to detect and estimate social influence in networks. In Complex Spreading Phenomena in Social Systems, pages 289– 322. Springer, 2018. Johan Ugander, Brian Karrer, Lars Backstrom, and Jon Kleinberg. Graph cluster randomization: Network exposure to multiple universes. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 329–337. ACM, 2013. Davide Viviano. Experimental design under network interference. arXiv preprint arXiv:2003.08421, 2020a. Davide Viviano. Policy choice in experiments with unknown interference. arXiv preprint arXiv:2011.08174, 2020b. Ulrike Von Luxburg. A tutorial on spectral clustering. Statistics and computing, 17:395–416, 2007. Jeffrey M Wooldridge. Cluster-sample methods in applied econometrics. American Economic Review, 93(2):133–138, 2003. This paper is under CC 1.0 license. available on arxiv

Causal Clustering: Design of Cluster Experiments Under Network Interference: References

Spoiler: Use to render DAGs written in the DOT language Vizdom.dev As a software dev, I’ve occasionally worked with DAGs — most of which involve visualizing them. In many cases, I found it difficult to get a quick rendering and found myself looking for tooling. Fortunately, there’s the trusty tool that can render graphs written in the DOT language — Perfect! Graphviz Except… It doesn’t work well on the web* I need to install it It doesn’t manage DOT/GV files online Considerations *Okay, Graphviz can be compiled to WebAssembly, and a few talented folks managed to build a few neat projects such as dreampuf’s , nikeee’s , and magjac’s — each with its own twist. I still struggle to embed these renderings without exporting them to an SVG (or other medium). technically editor edotor.net editor For example, I want to store my graph in by simply pasting a link that automatically renders the graph. Notion.so I could use which renders graphs pretty cleanly and works surprisingly well — it’s one of the many reasons why it has . I can also embed graphs in Notion/GitHub by using markdown, sweet! MermaidJS GitHub 64k+ starts on Unfortunately, I have to translate DOT into the flavor of markdown that Mermaid requires. For smaller textual representations, I can use ChatGPT to convert them for me, but it frequently makes grammatical mistakes, and the graph will refuse to render — making it unreliable as a source of automation. Then there is which is amazing for managing diagrams, but like my Mermaid woes, I cannot convert DOT to D2 reliably. I suppose if you’re going to be building diagrams by hand this is an acceptable, one-time cost. In general, it is a fantastic tool. Kudos! Terrastruct.com hand-built The Problem I want to render generated DOT outputs — none of the tooling seems to help me achieve that goal. programmatically I want to embed links that render graphs. I want to store the rendering online for updates or to visually compare it with other graphs. The Solution Yup — I built an for that 🎉 app …And it’s built entirely in Rust 🦀 with many thanks to Leptos tl;dr — a simple, purpose-built app to render DAGs online, fast Minimizing edge crossings in graph generation, a recognized NP-hard challenge is key to creating visually appealing graphs. The team at Terrastruct has published an outstanding delving into this topic. Inspired significantly by the DagreJS layout algorithm(s) and the insightful paper “ ¹”, I developed a variant in Rust. blog post A Technique for Drawing Directed Graphs This version is particularly effective for rendering hierarchical graphs, leveraging the intricate complexities of this problem. Rendering large DAGs (500+ nodes/edges) tends to be somewhat sluggish with Graphviz. Dagre + D3 ( and derivatives) end up quite a bit faster and offer great animations/styling customizations. Yet, I felt that they miss out on the features that DOT language provides — namely, the styling attributes are often only honored if you’re rending with Graphviz. d3-graphviz will produce results very similar to Dagre but aligned more toward the DOT specification and style behaviors of Graphviz. However, it does not have feature parity with Graphviz, and statements/attributes that aren’t supported are simply ignored. Vizdom I believe it is a good compromise for the kinds of attributes programmatically generated DOT will consume. Vizdom Features 🚀 Blazing fast rendering 💾 Store graphs in links 0️⃣ Zero dependencies and no installation required Rendering Speed Well, it’s an apples-to-oranges comparison. Graphviz still produces visualizations and supports more , so I am subjectively comparing against Graphviz’s DOT engine, scoping it down to just DAGs, and even scoping it down to a list of narrow features of the DOT language. great layout engines Vizdom further Yes, I know it’s terrible to compare years of experience and tooling around the 30+ year streak that is Graphviz, but for my narrow use-case, it is crazy fast. Here are some wall times on my M1 Macbook Pro for rendering with ~400 nodes: a fairly large graph Native Graphviz: ~30 seconds Chrome (WASM) Graphviz: crashes* Chrome (Javascript) DagreJS: ~10 seconds Chrome (WASM) : Vizdom ~1 second** * It crashes because the emscripten flag, ALLOW_MEMORY_GROWTH=1 , is not set, therefore limiting the total memory to 16MB. This can be fixed, but I have not found an online project that can handle the graph in question. ** The example graph is rendered by selecting Example 14 in the editor view. Refreshing the page will result in you refreshing the page and getting a 414 because the URI that persists in the graph is too large. I am working on a better solution to store larger graphs. Store Graphs in Links You’ll notice when you make any changes to the DOT file, the URL immediately updates a few query parameters to store the graph and layout options, allowing you to always reference a copy of your data just by storing the link! There is a catch — large-ish graphs tend to make the URI too large for AWS (where is currently hosted) and other load balancers. Vizdom I’m currently working on a solution to handle larger graphs, but in the meantime, I’ve included a few ways to persist and store graphs: Copy an embeddable link that automatically renders a view that maximizes the graph. Copy an iframe snippet to embed in any application that supports rendering them. Or simply download the rendered SVG. Here’s an example of what the editor view looks like of the : pprof trace for deflate Go to the , and load Editor View Example 42 Diff View Occasionally, I found myself trying to compare two graphs that I’ve generated, so while I was at it, I added a to visually see changes between two different graphs. diff viewer Color Legend: Red: Deleted Orange: Modified Green: Added Here’s a few snaps: Click me: changing a node ID from “e” to “f” Click me: adding “cluster=true” Future Direction is still a work in progress, and as it continues to evolve, I am aware that the journey of perfecting this tool is as dynamic as the field of graph visualization itself. While I am proud of what has achieved so far, I am even more excited about its potential. That’s where you, the reader and potential user, come in. Vizdom Vizdom If you have any feedback, please feel free to message me over on or . Gitter Discord Thanks for reading — If you enjoyed this article, please ! follow [1]: E. R. Gansner, E. Koutsofios, S. C. North and K. . -P. Vo, “A technique for drawing directed graphs,” in IEEE Transactions on Software Engineering, vol. 19, no. 3, pp. 214–230, March 1993, doi: 10.1109/32.221135. Also published here

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