LONDON -- Japan&#x27;s Mitsui Sumitomo Insurance plans to roughly double its insurance underwriting capacity in the U.K. to 3 billion pounds ($3.78 billion) within five years, the company&#x27;s chief told Nikkei in an interview.

Mitsui Sumitomo Insurance to double U.K. underwriting, CEO says - Nikkei Asia

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Efficient Detection of Defects in Magnetic Labyrinthine Patterns: Conclusion and References

About Me I was born into a low-income household in Taiwan, raised by a single mother who struggled to support us after moving from Japan. Growing up, I experienced firsthand the financial instability that many families face. Despite the odds, I worked my way through university and eventually secured a job in Japan at SMBC Investment Banking, where I worked on high-profile deals with companies like Toyota, Sony, and Mitsubishi. On paper, I was successful—earning a top salary among my age group. But behind the scenes, I struggled with the same financial issues I had growing up, living paycheck to paycheck despite my high income. My experiences during this time, and the lessons I learned about financial habits and societal expectations, shaped my journey toward founding Syval—a company designed to disrupt the financial system and help Gen Z avoid the same struggles. What is Syval? Syval is a Data-as-a-Service (DaaS) platform designed to help Gen Z better understand their financial habits through data-driven insights. Unlike traditional financial tools that focus on budgeting or transactions, Syval dives deeper into the psychology behind spending. We empower users by giving them access to personalized data that helps them make informed decisions about their finances. Our goal is to disrupt the old financial systems created by previous generations and build a system that aligns with the unique challenges faced by younger generations. With Syval, users can track spending habits, understand the external influences that drive their financial behavior, and develop a value system based on what truly matters to them. Our platform not only helps people budget better, but also supports them in making long-term financial decisions that align with their goals and priorities. I. Introduction As mentioned, I grew up in a low-income household in Taiwan, raised by a single mother who worked tirelessly to support us. From a young age, I was acutely aware of financial instability, which influenced many of my decisions later in life. After university, I moved to Japan and, in 2020, secured a position at SMBC Investment Banking, working on major deals with global giants like Toyota, Sony, and Mitsubishi. My salary placed me among the top 1% in my age group and top 5% in Japan. On the surface, I had achieved what many people strive for—financial success. However, despite the high salary, I quickly fell into a pattern of reckless spending. I bought expensive items without thinking and spent lavishly on luxuries like $300 massages in Ginza, Tokyo’s most exclusive shopping district. I thought that money would bring me happiness, but instead, it only added stress to my life. Many of my peers in the banking sector had the same bad financial habits, despite earning significant incomes. We were all living paycheck to paycheck, which, in Japan, is quite rare. Over time, I realized that my financial struggles were deeply tied to a lack of self-understanding and a pursuit of happiness through external validation. Changing these habits took time, but the shift eventually inspired me to build Syval—a platform aimed at helping others break free from these damaging cycles. II. Life in Investment Banking: Financial Success vs. Personal Struggles In the world of investment banking, financial success is often equated with personal success. My peers and I earned more than we ever had, but beneath the surface, we were all struggling with the same issue—misaligned spending habits and misconceptions about wealth. Wealth, I realized, is not about how much money you make, but how you manage and value it. Society often defines wealth in terms of material possessions, but real financial stability comes from understanding what truly matters to you. In a capitalist society, the constant bombardment of marketing and societal pressure creates a false sense of value, pushing us to buy things we don’t need just to fit in or keep up appearances. This mindset led to unnecessary stress. We were earning top-tier salaries, but many of us couldn’t escape the cycle of paycheck-to-paycheck living. The external image of financial success did not match the internal struggles we were experiencing. III. Understanding Financial Struggles as a Gen Z As I reflect on my financial habits in investment banking, I see clear parallels with the financial struggles Gen Z faces today. I would spend freely, only to feel the pressure and anxiety at the end of each month when my balance ran low. This is a cycle many Gen Z individuals find themselves in, especially as they deal with the financial challenges unique to their generation—student debt, high living costs, and uncertain job prospects. Gen Z has grown up in a world where everything is designed to be addictive, from social media to consumer products. We’re constantly tempted by short-term rewards, opting for instant gratification instead of long-term planning. This mindset makes it difficult to delay gratification or save for bigger goals—what’s often referred to as the “Marshmallow Test.” The result? Financial habits that mirror those I had when I was younger, driven by external pressures rather than personal values. Through my own experience, I realized that financial behavior is heavily influenced by a person’s value system, which is shaped by peer pressure, social expectations, and a desire for acceptance. Traditional financial tools don’t address this deeper, psychological aspect of money management, which is why we need new solutions. IV. The Pivot to DaaS: Why Data Matters in Solving Financial Issues My decision to transition from investment banking to building Syval, a DaaS platform, was driven by my personal financial struggles. Even as a teenager, I had an entrepreneurial mindset—I started a comic club and sold goods at vendor shops. But it was my banking career that opened my eyes to the deeper financial issues people face, especially when their financial decisions are misaligned with their values. Data plays a crucial role in understanding financial habits. At Syval, we believe that data can help people see patterns in their spending that they may not be aware of. In today’s world, Gen Z is constantly surrounded by advertisements and influencers telling them what to buy. This creates a false sense of value, which leads to impulsive spending. By analyzing this data, we can understand the psychology behind financial decisions and help users figure out what truly matters to them. Syval’s mission is to go beyond simple budgeting. We want to help users understand their financial journeys, anticipate the challenges they’ll face, and prioritize what they truly value. This data-driven approach empowers long-term decision-making and prepares users for a future where they can make financial choices aligned with their personal goals. V. Why DaaS is the Future for Financial Wellness DaaS is the key to addressing Gen Z’s financial struggles. Too much data is hidden and used solely for commercial purposes, pushing people to make decisions based on temporary desires rather than long-term value. Syval aims to change this by providing users with data-driven insights that reveal the patterns behind their financial behavior. With real-time feedback, users can interact with the app and reflect on their spending habits. This helps them make better financial decisions, not just in the short term, but also in planning for their future. Unlike traditional fintech solutions, which focus on transactions, Syval uses data to address the deeper psychological factors that drive financial behavior. Data is the foundation of every financial decision—whether it’s borrowing money or negotiating a business deal, data influences every aspect of finance. But the current system, built by older generations, doesn’t reflect the realities that Gen Z faces today. This outdated system leads to Gen Z being labeled as “broke” because the financial rules don’t fit their experience. At Syval, we believe that data is the first step to creating a financial system that works for younger generations. By understanding what data is important and how it shapes financial habits, we can build a system that supports the financial well-being of Gen Z. VI. Lessons Learned from Investment Banking to DaaS Looking back on my journey from investment banking to DaaS, I’ve learned that the old financial model no longer works. The values I was taught—spend money to feel successful—didn’t align with the realities of today’s financial landscape. This left me feeling unhappy and stressed. The key to transforming financial habits is understanding the data behind those habits. With the right data, you can see the bigger picture and make better decisions. For Gen Z, this means using tools like Syval to think critically about their behavior and align their financial choices with their personal values. VII. The Road Ahead: Empowering Gen Z with DaaS The future of personal finance for Gen Z is data-driven. With Syval, users will have the chance to reflect on every financial decision they make, using data to build a value system that aligns with their long-term goals. This shift in perspective will empower Gen Z to break free from old financial systems that don’t serve them. We’re excited to launch Syval in October, and I encourage university students and young professionals to join us in shaping the future of finance. If you’re interested in becoming an early user, visit mysyval.com and sign up for early access. At Syval, we believe that the future of finance isn’t just about budgeting—it’s about understanding who you are, what you value, and using data to build a richer, more fulfilling life. About Me About Me I was born into a low-income household in Taiwan, raised by a single mother who struggled to support us after moving from Japan. Growing up, I experienced firsthand the financial instability that many families face. Despite the odds, I worked my way through university and eventually secured a job in Japan at SMBC Investment Banking, where I worked on high-profile deals with companies like Toyota, Sony, and Mitsubishi. On paper, I was successful—earning a top salary among my age group. But behind the scenes, I struggled with the same financial issues I had growing up, living paycheck to paycheck despite my high income. My experiences during this time, and the lessons I learned about financial habits and societal expectations, shaped my journey toward founding Syval—a company designed to disrupt the financial system and help Gen Z avoid the same struggles. What is Syval? What is Syval? Syval is a Data-as-a-Service (DaaS) platform designed to help Gen Z better understand their financial habits through data-driven insights. Unlike traditional financial tools that focus on budgeting or transactions, Syval dives deeper into the psychology behind spending. We empower users by giving them access to personalized data that helps them make informed decisions about their finances. Our goal is to disrupt the old financial systems created by previous generations and build a system that aligns with the unique challenges faced by younger generations. With Syval, users can track spending habits, understand the external influences that drive their financial behavior, and develop a value system based on what truly matters to them. Our platform not only helps people budget better, but also supports them in making long-term financial decisions that align with their goals and priorities. I. Introduction As mentioned, I grew up in a low-income household in Taiwan, raised by a single mother who worked tirelessly to support us. From a young age, I was acutely aware of financial instability, which influenced many of my decisions later in life. After university, I moved to Japan and, in 2020, secured a position at SMBC Investment Banking, working on major deals with global giants like Toyota, Sony, and Mitsubishi. My salary placed me among the top 1% in my age group and top 5% in Japan. On the surface, I had achieved what many people strive for—financial success. However, despite the high salary, I quickly fell into a pattern of reckless spending. I bought expensive items without thinking and spent lavishly on luxuries like $300 massages in Ginza, Tokyo’s most exclusive shopping district. I thought that money would bring me happiness, but instead, it only added stress to my life. Many of my peers in the banking sector had the same bad financial habits, despite earning significant incomes. We were all living paycheck to paycheck, which, in Japan, is quite rare. Over time, I realized that my financial struggles were deeply tied to a lack of self-understanding and a pursuit of happiness through external validation. Changing these habits took time, but the shift eventually inspired me to build Syval—a platform aimed at helping others break free from these damaging cycles. II. Life in Investment Banking: Financial Success vs. Personal Struggles In the world of investment banking, financial success is often equated with personal success. My peers and I earned more than we ever had, but beneath the surface, we were all struggling with the same issue—misaligned spending habits and misconceptions about wealth. Wealth, I realized, is not about how much money you make, but how you manage and value it. Society often defines wealth in terms of material possessions, but real financial stability comes from understanding what truly matters to you. In a capitalist society, the constant bombardment of marketing and societal pressure creates a false sense of value, pushing us to buy things we don’t need just to fit in or keep up appearances. This mindset led to unnecessary stress. We were earning top-tier salaries, but many of us couldn’t escape the cycle of paycheck-to-paycheck living. The external image of financial success did not match the internal struggles we were experiencing. III. Understanding Financial Struggles as a Gen Z As I reflect on my financial habits in investment banking, I see clear parallels with the financial struggles Gen Z faces today. I would spend freely, only to feel the pressure and anxiety at the end of each month when my balance ran low. This is a cycle many Gen Z individuals find themselves in, especially as they deal with the financial challenges unique to their generation—student debt, high living costs, and uncertain job prospects. Gen Z has grown up in a world where everything is designed to be addictive, from social media to consumer products. We’re constantly tempted by short-term rewards, opting for instant gratification instead of long-term planning. This mindset makes it difficult to delay gratification or save for bigger goals—what’s often referred to as the “Marshmallow Test.” The result? Financial habits that mirror those I had when I was younger, driven by external pressures rather than personal values. Through my own experience, I realized that financial behavior is heavily influenced by a person’s value system, which is shaped by peer pressure, social expectations, and a desire for acceptance. Traditional financial tools don’t address this deeper, psychological aspect of money management, which is why we need new solutions. IV. The Pivot to DaaS: Why Data Matters in Solving Financial Issues My decision to transition from investment banking to building Syval, a DaaS platform, was driven by my personal financial struggles. Even as a teenager, I had an entrepreneurial mindset—I started a comic club and sold goods at vendor shops. But it was my banking career that opened my eyes to the deeper financial issues people face, especially when their financial decisions are misaligned with their values. Data plays a crucial role in understanding financial habits. At Syval, we believe that data can help people see patterns in their spending that they may not be aware of. In today’s world, Gen Z is constantly surrounded by advertisements and influencers telling them what to buy. This creates a false sense of value, which leads to impulsive spending. By analyzing this data, we can understand the psychology behind financial decisions and help users figure out what truly matters to them. Syval’s mission is to go beyond simple budgeting. We want to help users understand their financial journeys, anticipate the challenges they’ll face, and prioritize what they truly value. This data-driven approach empowers long-term decision-making and prepares users for a future where they can make financial choices aligned with their personal goals. V. Why DaaS is the Future for Financial Wellness DaaS is the key to addressing Gen Z’s financial struggles. Too much data is hidden and used solely for commercial purposes, pushing people to make decisions based on temporary desires rather than long-term value. Syval aims to change this by providing users with data-driven insights that reveal the patterns behind their financial behavior. With real-time feedback, users can interact with the app and reflect on their spending habits. This helps them make better financial decisions, not just in the short term, but also in planning for their future. Unlike traditional fintech solutions, which focus on transactions, Syval uses data to address the deeper psychological factors that drive financial behavior. Data is the foundation of every financial decision—whether it’s borrowing money or negotiating a business deal, data influences every aspect of finance. But the current system, built by older generations, doesn’t reflect the realities that Gen Z faces today. This outdated system leads to Gen Z being labeled as “broke” because the financial rules don’t fit their experience. At Syval, we believe that data is the first step to creating a financial system that works for younger generations. By understanding what data is important and how it shapes financial habits, we can build a system that supports the financial well-being of Gen Z. VI. Lessons Learned from Investment Banking to DaaS Looking back on my journey from investment banking to DaaS, I’ve learned that the old financial model no longer works. The values I was taught—spend money to feel successful—didn’t align with the realities of today’s financial landscape. This left me feeling unhappy and stressed. The key to transforming financial habits is understanding the data behind those habits. With the right data, you can see the bigger picture and make better decisions. For Gen Z, this means using tools like Syval to think critically about their behavior and align their financial choices with their personal values. VII. The Road Ahead: Empowering Gen Z with DaaS The future of personal finance for Gen Z is data-driven. With Syval, users will have the chance to reflect on every financial decision they make, using data to build a value system that aligns with their long-term goals. This shift in perspective will empower Gen Z to break free from old financial systems that don’t serve them. We’re excited to launch Syval in October, and I encourage university students and young professionals to join us in shaping the future of finance. If you’re interested in becoming an early user, visit mysyval.com and sign up for early access. At Syval, we believe that the future of finance isn’t just about budgeting—it’s about understanding who you are, what you value, and using data to build a richer, more fulfilling life.

From Investment Banking to DaaS: My Journey of Understanding Financial Struggles as a Gen Z

Enabling Acoustic Audience Feedback in Large Virtual Events: Conclusions & References

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

FACT SHEET: Biden-⁠Harris Administration Announces Initiative to Bolster Cybersecurity of U.S. Ports Today, the Biden-Harris Administration will issue an Executive Order to bolster the security of the nation’s ports, alongside a series of additional actions that will strengthen maritime cybersecurity, fortify our supply chains and strengthen the United States industrial base. The Administration will also announce its intent to bring domestic onshore manufacturing capacity back to America to provide safe, secure cranes to U.S. ports – thanks to an over $20 billion investment in U.S. port infrastructure under President Biden’s Investing in America Agenda. Today’s actions are clear examples of the President’s work to invest in America, , and against 21st century threats – priorities his Administration has focused on relentlessly since taking office. secure the country’s supply chains strengthen the cybersecurity of our nation’s critical infrastructure America’s prosperity is directly linked to maritime trade and the integrated network of ports, terminals, vessels, waterways, and land-side connections that constitute the Nation’s Marine Transportation System (MTS). This complex system supports $5.4 trillion worth of economic activity each year, contributes to the employment of more than 31 million Americans, and supports nearly 95% of cargo entering the U.S. The security of our critical infrastructure remains a national imperative in an increasingly complex threat environment. MTS owners and operators rely on digital systems to enable their operations, to include ship navigation, the movement of cargo, engineering, safety, and security monitoring. These systems have revolutionized the maritime shipping industry and American supply chains by enhancing the speed and efficiency of moving goods to market, but the increasing digital interconnectedness of our economy and supply chains have also introduced vulnerabilities that, if exploited, could have cascading impacts on America’s ports, the economy, and everyday hard-working Americans. Today’s actions include: Now, the U.S. Coast Guard will have the express authority to respond to malicious cyber activity in the nation’s MTS by requiring vessels and waterfront facilities to mitigate cyber conditions that may endanger the safety of a vessel, facility, or harbor. The Executive Order will also institute mandatory reporting of cyber incidents – or active cyber threats – endangering any vessel, harbor, port, or waterfront facility. Additionally, the Coast Guard will now have the authority to control the movement of vessels that present a known or suspected cyber threat to U.S. maritime infrastructure, and be able to inspect those vessels and facilities that pose a threat to our cybersecurity. President Biden will sign an Executive Order to bolster the Department of Homeland Security’s authority to directly address maritime cyber threats, including through cybersecurity standards to ensure that American ports’ networks and systems are secure. Owners and operators of these cranes must acknowledge the directive and take a series of actions on these cranes and associated Information Technology (IT) and Operational Technology (OT) systems. This action is a vital step to securing our maritime infrastructure’s digital ecosystem and addresses several vulnerabilities that have been identified in the updated U.S. Maritime Advisory, 2024-00X – Worldwide Foreign Adversarial Technological, Physical, and Cyber Influence, that was released today. The U.S. Coast Guard will issue a Maritime Security Directive on cyber risk management actions for ship-to-shore cranes manufactured by the People’s Republic of China located at U.S. Commercial Strategic Seaports. Every day malicious cyber actors attempt to gain unauthorized access to MTS control systems and networks throughout the nation. The Proposed Rule will strengthen these digital systems by establishing minimum cybersecurity requirements that meet international and industry-recognized standards to best manage cyber threats. These actions build on prior actions by DHS including those taken by the Transportation Security Administration, and reflect the Administration’s commitment to leverage regulatory requirements in pursuit of safeguarding critical infrastructure. The U.S. Coast Guard has issued a Notice of Proposed Rulemakingon Cybersecurity in the Marine Transportation System. The Administration will invest over $20 billion, including through grants, into U.S. port infrastructure over the next 5 years through the President’s Investing in America Agenda, including the Bipartisan Infrastructure Law and the Inflation Reduction Act. As a result, a U.S.-based subsidiary of Mitsui E&S Co., Ltd (Japan), is planning to onshore U.S. manufacturing capacity for its crane production. PACECO has a deep history in the container shipping industry, manufacturing the first dedicated ship-to-shore container crane in 1958 as PACECO Inc., and it continued U.S.-based crane manufacturing until the late 1980s. PACECO intends to partner with other trusted manufacturing companies to bring port crane manufacturing capabilities back to the U.S. for the first time in 30 years, pending final site and partner selection.  The announcement is part of the Biden-Harris Administration’s fourth Investing in America tour, where White House and Administration officials are traveling across the country to highlight the impacts of the President’s Investing in America agenda on communities, families, small businesses and the United States’ economic and national security. It also follows-up on the White House Council on Supply Chain Resilience’s efforts to strengthen America’s supply chains, particularly by addressing supply chain risks resulting from threats and vulnerabilities inside U.S. ports. The Administration continues to deliver for the American people by rebuilding the U.S.’s industrial capacity to produce port cranes with trusted partners. PACECO Corp., This content was published on February 21, 2024 in whitehouse.gov.

Biden-Harris Administration Takes Bold Steps to Strengthen Cybersecurity of U.S. Ports

বিডেন-হ্যারিস প্রশাসন মার্কিন বন্দরগুলির সাইবার নিরাপত্তা জোরদার করার জন্য সাহসী পদক্ষেপ নেয়

Die Biden-Harris-Regierung unternimmt mutige Schritte zur Stärkung der Cybersicherheit von US-Häfen

La administración Biden-Harris toma medidas audaces para fortalecer la ciberseguridad de los puertos estadounidenses

L’administration Biden-Harris prend des mesures audacieuses pour renforcer la cybersécurité des ports américains

बिडेन-हैरिस प्रशासन ने अमेरिकी बंदरगाहों की साइबर सुरक्षा को मजबूत करने के लिए साहसिक कदम उठाए

バイデン・ハリス政権、米国港湾のサイバーセキュリティ強化に大胆な措置を講じる

Biden-Harris 행정부는 미국 항구의 사이버 보안을 강화하기 위해 대담한 조치를 취합니다.

Administração Biden-Harris toma medidas ousadas para fortalecer a segurança cibernética dos portos dos EUA

Администрация Байдена-Харриса предпринимает смелые шаги по усилению кибербезопасности портов США

Biden-Harris Yönetimi ABD Limanlarının Siber Güvenliğini Güçlendirmek İçin Cesur Adımlar Atıyor

Chính quyền Biden-Harris thực hiện các bước đi táo bạo để tăng cường an ninh mạng cho các cảng của Hoa Kỳ

拜登-哈里斯政府采取大胆步骤加强美国港口的网络安全

88 Stories To Learn About Music

Let's learn about via these 79 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Youtubers /Learn Repo 1. The State of AI in 2022: An End-of-Year Recap of the Machine Learning Industry An 8-minute AI rewind with results and limitations of all the hottest AI models shared in 2022! 2. An Interview with Quadruple Kaggle Grandmaster Chris Deotte from NVIDIA In this episode, I interview Chris Deotte, one of the very few Kaggle quadruple grandmasters. 3. How to Enable Autocomplete (and AI) in your Terminal Here's how I enhance my terminal with autocomplete and Artificial Intelligence 4. GPT-4 Explained: ChatGPT's Big Brother is Here to Disrupt Everything If you thought ChatGPT was good, just wait until you try GPT-4. 5. 5 Docker Desktop Alternatives For Windows and macOS users, Docker Desktop has been the main way to use Docker containers for many years. But how about now? 6. #FoundersConnect: Interview with Hanu Agbodje, the Founder and the CEO of Patricia In this episode, Peace Itimi interviews Hanu Agbodje, CEO of Patricia, on his journey as a tech entrepreneur and 7. Making the Case for Why React Native is Garbage Ex-Google/ex-Facebook TechLead presents the case against the React Native cross-platform mobile app framework.  Join my interview training prep here http://techinterviewpro.com/ 8. 8 BEST Crypto Wallets for Beginners If there’s one thing that every crypto investor needs to have, it’s a crypto wallet. There are all kinds of different crypto wallets you can use. 9. The New AI Model Lets You Generate Music via Text Prompt We recently covered a model able to imitate someone’s voice called VALL-E. Let’s jump a step further in the creative direction with this new AI called MusicLM. MusicLM allows you to generate music from a text description. 10. 6 Common Monetization Problems for Creators on YouTube If you are a video creator looking for ways to make your videos work hard, you are not wrong in worrying about Youtube monetization issues. Read on to know the how-tos and what-nots of monetizing your videos. 11. Top 10 Computer Vision Papers of 2020 This is a video of the 10 most interesting research papers on computer vision in 2020. 12. Google's PaLM-E (AI Robot) Can See and Understand Language Google's latest publication, PaLM-E, is an embodied multimodal language model. 13. OBS for Dummies : How to Record Your Screen Wait... OBS..I have heard it somewhere. 14. Top 8 Machine Learning Content Creators on YouTube Here are the top Machine Learning content creators on YouTube to follow for tutorials, deep learning, and more. 15. 'Why I don't eat out at restaurants, even as a millionaire' - TechLead Ex-Google Tech Lead reveals the secret to why he doesn't enjoy eating out at restaurants, even as a multi-millionaire. 16. How to Get Started Streaming on Twitch Twitch is a great place to learn in public. I'll cover tools, tips, and workflows to help you get started with streaming. 17. How to Turn Mockups Into Videos Instantly with This New AI Model GEN-1 is able to take a video and apply a completely different style onto it, just like that… 18. 5 Gaming YouTubers That are Worth a Sub There are far too many people sharing their opinions about gaming on the internet but not all of them are for us. These are some people who we enjoy the most. 19. Even Disney is Investing in AI: A Look at Face Re-Aging for Visual Effects Whether it be for fun in a Snapchat filter, for a movie, or even to remove a few riddles, we all have a utility in mind for being able to change our age in a picture. 20. 6 Vlogging Tips For Business Success in 2020 Vlogging has taken the world by storm in recent years. It's just a way of blogging, using videos instead of text to reach out, create an audience, and share information. 21. 5 Gaming YouTubers That are Worth a Sub There are far too many people sharing their opinions about gaming on the internet but not all of them are for us. These are some people who we enjoy the most. 22. Youtube Deletes Video About Bitcoin: Is This a Common Trend? You would think that Youtube would at least wait till the holidays are over to hit its creators with this news but apparently, bitcoin and all cryptocurrency talk is now concrete himself and a danger to society. A lot of YouTube creators woke up to find many of their crypto-related videos taken down because youtube sees them as violating their guidelines. 23. How to Use GitHub Copilot with Your Voice “Hey, GitHub” comes in the form of a VSCode extension and has many more capabilities accessible with your voice. 24. How to Ace ML Interviews: Get Your Foot in the AI Door An interview with the Director of Perception at Zoox, Ruijie (RJ) He, with the goal of demystifying what is a good profile to get an ML engineer job and perform 25. 3D Articulated Shape Reconstruction from Videos With LASR, you can generate 3D models of humans or animals moving using only a short video as input. 26. How to Set Up Aleph.im Virtual Machines Nearing the end of 2021, most blockchain dApps still have large parts of their stack that are centralized. With Aleph VMs, you can decentralize the rest. 27. Let’s Play Ethermore: Part 1 – Exploring The Ruins of Nio Cegod Ethermore's first-ever let’s play video is being used to showcase a possible new feature: AI-generated synthetic audio narration. 28. 5 Ways to Get More Likes on Your YouTube Videos Business owners and marketers around the globe know that YouTube is one of the best places to connect with their target audience. 29. The Facebook TransCoder Explained: Converting Coding Languages with AI This new model converts code from a programming language to another without any supervision! 30. The Best AI Articles of October 2022 Support vector machines, decision trees, and AI-generated content are some of the topics in the best AI articles of October. 31. ChatGPT Explained in 5 Minutes ChatGPT has taken over Twitter and pretty much the whole internet, thanks to its power and the meme potential it provides. 32. 5 Strategies To Grow Your YouTube Channel YouTube is the most popular online video platform as of now. Over hundreds of hours of content is uploaded every minute on this platform, and on average, the viewership crosses billions of hours every day. 33. YouTube Fake Crypto Giveaways Explained - Behind The Account Hacks Maybe you have noticed that there is a lot of fake Binance, Ethereum, Ripple and Bitcoin Cash streams on Youtube lately. The crazy part is that those live streams are on a big accounts with thousands of subscribers. 34. 8 Tools Every Data Scientists Should Use The most useful tools I use daily as a research scientist for finding and reading AI research papers 35. OpenAI's New Code Generator: GitHub Copilot (and Codex) You’ve probably heard of the recent Copilot tool by GitHub, which generates code for you. Find out how OpenAI's AI generates code from words 36. Introducing DALL·E: Inspired by GPT-3 and Image-GPT from OpenAI DALL-E from Open AI can generate images from text captions. It is very similar to GPT-3 and Image GPT and produces amazing results. 37. How to Optimize a YouTube Channel: 15 Effective Tips Competing on YouTube isn’t an easy task. Here are 15 effective ways to optimize your channel. Follow these ways to optimize now. 38. PSG is a New Task for AIs Requiring Higher Levels of Understanding Panoptic scene graph generation, or PSG, is a new problem task aiming to generate a more comprehensive graph representation of an image or scene based on panoptic segmentation rather than bounding boxes. It can be used to understand images and generate sentences describing what's happening. This may be the most challenging task for an AI! Learn more in the video... 39. Will Transformers Replace CNNs in Computer Vision? This video explains how transformer architecture can be applied to computer vision with a new paper called the Swin Transformer. 40. Galactica is an AI Model Trained on 120 Billion Parameters On November 15th, MetaAI and Papers with Code announced the release of Galactica, a game-changer, open-source large language model trained on scientific knowledge with 120 billion parameters. 41. DALL·E 2 Pre-Training Mitigations You’ve all seen amazing-looking images like these, entirely generated by an artificial intelligence model. I covered multiple approaches on my channel, like Craiyon, Imagen, and the most well-known, Dall-e 2.Most people want to try them and generate images from random prompts, but the majority of these models aren’t open-source, which means we, regular people like us, cannot use them freely. Why? This is what we will dive into in this video... 42. This AI Can Separate Speech, Music and Sound Effects from Movie Soundtracks Have you ever tuned in to a video or a TV show and the actors were completely inaudible, or the music was way too loud? Well, this problem, also called the cocktail party problem, may never happen again. Mitsubishi and Indiana University just published a new model as well as a new dataset tackling this task of identifying the right soundtrack. For example, if we take the same audio clip we just ran with the music way too loud, you can simply turn up or down the audio track you want to give more importance to the speech than the music. 43. Meta's Groundbreaking AI Film Maker: Make-A-Scene Meta AI’s new model make-a-video is out and in a single sentence: it generates videos from text. It’s not only able to generate videos, but it’s also the new state-of-the-art method, producing higher quality and more coherent videos than ever before! 44. How YouTubers Can Improve Their Marketing Strategies in 2021 2020 with its lockdown affected both video creation and consumption, and that resulted in new rules in video marketing. 45. How to Spot a DeepFake in 2021 How to Spot a Deep Fake in 2021. Breakthrough US Army technology using artificial intelligence to find deepfakes. 46. What is the InfiniteNature-Zero AI Model? This AI generates infinite new frames as if you would be flying into your image! 47. Efficient NeRFs for Real-Time Portrait Synthesis (RAD-NeRF) We’ve heard of deepfakes, we’ve heard of NeRFs, and we’ve seen these kinds of applications allowing you to recreate someone’s face and pretty much make him say whatever you want. 48. DeOldify can Colorize your Black & White Photos with Full Photorealistic Renders DeOldify is a technique to colorize and restore old black and white images or even film footage. It was developed by Jason Antic. 49. OpenAI’s DALL·E: Text-to-Image Generation Explained OpenAI just released the paper explaining how DALL-E works! It is called "Zero-Shot Text-to-Image Generation". 50. How to Talk to ChatGPT: An Intro to Prompt Engineering Prompting is pretty much the only skill you now require to be a master of these new large and powerful generative models such as ChatGPT. 51. This AI Prevents Bad Hair Days This AI can transfer your hair to see how it would look like before committing to the change. 52. VOGUE by Google, MIT, and UW: The AI-Powered Online Fitting Room Google used a modified StyleGAN2 architecture to create an online fitting room where you can automatically try-on any pants or shirts you want using only an ima 53. How Uber Uses AI to Improve Deliveries How can Uber deliver food and always arrive on time or a few minutes before? How do they match riders to drivers so that you can find a Uber? All that while also managing all the drivers?! always 54. How to Create Realistic Slow Motion Videos With AI TimeLens can understand the movement of the particles in-between the frames of a video to reconstruct what really happened at a speed even our eyes cannot see. 55. Text Embedding Explained: How AI Understands Words Large language models are a specific type of machine learning-based algorithm that understand and can generate language 56. How to Create Realistic Slow Motion Videos With AI TimeLens can understand the movement of the particles in-between the frames of a video to reconstruct what really happened at a speed even our eyes cannot see. 57. How Apple Recognizes People in Private Photos via Machine Learning Apple uses machine learning to directly recognize people in private photos on your iPhones and iPad, without having access to your images for training. 58. Is Subjective Beauty Something We Can Model with AI? This AI reads your brain to generate personally attractive faces. It generates images containing optimal values for personal attractive features. 59. Using AI to Detect and Count Plastic Waste in the Ocean A deep-learning-based algorithm that is able to detect and quantify floating garbage from aerial images of the ocean. 60. Imagic: AI Image Editing from Text Commands This week’s paper may just be your next favorite model to date. 61. Google Brain's New Model Imagen is Even More Impressive than Dall-E 2 If you thought Dall-e 2 had great results, wait until you see what this new model from Google Brain can do. Dalle-e is amazing but often lacks realism, and this is what the team attacked with this new model called Imagen. They share a lot of results on their project page as well as a benchmark, which they introduced for comparing text-to-image models, where they clearly outperform Dall-E 2, and previous image generation approaches. Learn more in the video... 62. Combining CNNs, GANs, and Transformers to Outperform Image-GPT Researchers combined the efficiency of GANs and convolutional approaches with the expressivity of transformers to outperform Open AI's Image-GPT 63. Introducing Total Relighting by Google In a new paper titled Total Relighting, a research team at Google presents a novel per-pixel lighting representation in a deep learning framework. 64. What is Data Science like at NVIDIA? What is it like to work in big companies like NVIDIA, and how to get there? 65. How to Create Realistic Slow Motion Videos With AI TimeLens can understand the movement of the particles in-between the frames of a video to reconstruct what really happened at a speed even our eyes cannot see. 66. What is OpenAI's Whisper Model? Have you ever dreamed of a good transcription tool that would accurately understand what you say and write it down? Not like the automatic YouTube translation tools… I mean, they are good but far from perfect. Just try it out and turn the feature on for the video, and you’ll see what I’m talking about. 67. An Intro to eDiffi: NVIDIA's New SOTA Image Synthesis Model eDiffi, NVIDIA's most recent model, generates better-looking and more accurate images than all previous approaches like DALLE 2 or Stable Diffusion. 68. How Waymo Combines Lidar and Cameras for 3D Object Detection Let us talk about how autonomous vehicles 'see' the world. 69. NVIDIA and Tel Aviv University's Approach to Conditioning Text-to-Image Models Text-to-Image models like DALLE or stable diffusion are really cool and allow us to generate fantastic pictures with a simple text input. But would it be even cooler to give them a picture of you and ask it to turn it into a painting? Imagine being able to send any picture of an object, person, or even your cat, and ask the model to transform it into another style like turning yourself into a cyborg of into your preferred artistic style or adding it to a new scene. 70. New SOTA Image Captioning: ClipCap We’ve seen AI generate images from other images using GANs. Then, there were models able to generate questionable images using text. In early 2021, DALL-E was published, beating all previous attempts to generate images from text input using CLIP, a model that links images with text as a guide. A very similar task called image captioning may sound really simple but is, in fact, just as complex. It is the ability of a machine to generate a natural description of an image. 71. Secrets to Grow a Small Business Without Spending Money Ex-Google Tech Lead shares his experience in growing a business from 0 to millions of users fast, and his top tips for you to get going too. 72. What is General Video Recognition? We’ve seen AI generate text, then generate images and most recently even generate short videos 73. Would You Swipe Right for an AI Profile? Most of us are convinced that we can dissociate humans from machines, but is it really the case?  Would you swipe right for an AI-generated profile? 74. Convolutional Neural Networks (CNNs) Explained What is a convolutional neural network and explanation of one of the best and most used state-of-the-art CNN architecture in 2020: DenseNet. 75. How to Make $40K Per Month in Your 20's: Building a Side-Business Ex-Google Tech Lead talks about building a side-hustle and making $40K/month in your 20's. 76. Top 5 AI Articles of February 2022 Every Data Scientist Should Read Here are the five best articles related to artificial intelligence in February, hoping they will make you want to learn more and visit their website. 77. How to Get Started With AI in 2021 and Keep Up with Latest Innovations in ML How to start machine learning & Ways to keep up with the latest developments in Machine Learning. 78. How to Get Started With AI in 2021 and Keep Up with Latest Innovations in ML How to start machine learning & Ways to keep up with the latest developments in Machine Learning. 79. 'How I got a $500K job at Facebook (as a software engineer)' - TechLead Ex-Google TechLead explains the interview process at Facebook for staff software engineer.  (FYI, this video is a little more technical in nature.) Thank you for checking out the 79 most read stories about Youtubers on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

79 Stories To Learn About Youtubers

123 Stories To Learn About Youtube Transcripts

Hakuhodo Inc., in partnership with Mazda and Mitsubishi Estate, announces the Web3 Global Hackathon 2023, aiming to merge the future of driving with decentralized digital innovation and revitalize Tokyo's landscapes. The event invites global creators to design Web3 services that make driving a rewarding experience and turn urban spaces into connected communities. Driving the Future: Web3's Intersection with Automobiles and Urban Landscapes Decentralization Takes the Wheel: Unpacking Web3's Potent Influence on Tomorrow's World Web3 isn't just digital jargon; it's the horizon of a new digital era. This next internet iteration, with its foundation in blockchain, promises more than just virtual coins. It holds the key to reshaping our cities, transport, and everyday interactions in the digital age. Leading Tokyo's innovative charge is Hakuhodo Inc., an integrated marketing juggernaut, as it announces the second of its web3 hackathon series – Web3 Global Hackathon 2023 set to commence on September 30. This grand tech challenge, championed by HAKUHODO KEY3, comes to life in collaboration with Hiroshima's automobile giant, Mazda, and Tokyo's architectural stalwart, Mitsubishi Estate. When three titans of their respective industries come together, they're bound to create ripples. Through HAKUHODO KEY3, Hakuhodo merges the innovation-driven minds of Mazda and Mitsubishi Estate, setting the stage for their next groundbreaking hackathon. Drive & Thrive: Mazda's Vision of Enriching Every Journey. With "Drive to Earn, Use for Fun", Mazda invites participants to transform every drive into an adventure. Envision a world where your car isn't just a mode of transport, but an interactive entity. Using Web3, vehicles could become sources of entertainment, with potential rewards for drivers and passengers alike. Vested Interest Disclosure: This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR For the uninitiated, web3 is the futuristic version of the internet, anchored by decentralized tech, including blockchains. But why does it matter? This next-generation digital landscape aims to immerse everyone in its fold, blurring the line between digital interactions and everyday life. Two key themes guide this hackathon: Drive to Earn, Use for Fun - Beyond just transportation, Mazda envisions an entertainment hub on wheels with web3 tech at its heart. Mazda's Vision: DAO Building in Tokyo - Revitalizing Tokyo's TOKYO TORCH district as a pulsating web3 city, promoting organic connections between its inhabitants. Mitsubishi Estate's Dream: Urban Evolution: Mitsubishi Estate's Blueprint for Tokyo's Metamorphosis. Mitsubishi Estate visualizes Tokyo's iconic Tokyo Torch district as more than just buildings and roads. They see an interactive hub where every corner buzzes with Web3's digital potential, turning urban spaces into vibrant, connected communities. Shape the Future:  Your Invitation to the Digital Frontier. The Web3 Global Hackathon 2023 isn't exclusive to tech wizards. Hakuhodo extends its invitation to global creators from diverse fields to craft the digital future. And with a rewarding prize pool in Astar tokens, innovation has never been more lucrative. Hackathon & Beyond: Seeding Today's Innovations for Tomorrow's Growth. It's not just about winning; it's about shaping the future. Beyond the accolades, winning teams have the opportunity to foster long-term collaborations, ensuring their innovations find a home in our daily lives. The Sei-katsu-sha Touch: Humanizing the Digital Wave. At its core, this hackathon strives to ensure the Web3 revolution touches every individual. Recognizing people for their unique lives and aspirations, the event is a nod to the "sei-katsu-sha" philosophy, underlining the importance of intertwining technology with human experience. Why Participate? Here's What's At Stake Participants have a chance to bag over 11 million yen in Astar tokens. And it's not just about the money. Winning teams get an opportunity for joint ventures and post-hackathon collaborations, bringing their innovations to life in the real world. Important Dates: - Kickoff: September 30 - North American Mazda Event: October 28 (Details to follow) - Submission Deadline: November 24, 23:59 JST - Final Pitch & Winners' Announcement: December 9 [Click here for the complete schedule and registration.] HAKUHODO KEY3 isn't settling for mediocrity. Their goal? To lead the world in pioneering web3 services by melding 'sei-katsu-sha' insights and boundless creativity. In essence, it's not just about technology; it's about enriching human experience. Get Involved! Engineers, creators, designers, and marketers – this is your chance to shape the future. Dive into this web3 frontier and craft the next wave of digital evolution. [ ] and be a part of this groundbreaking journey. Register now For more about HAKUHODO KEY3 and their ambitions, visit their [ ] official website Join us in this revolutionary journey, where the roads of imagination merge with the highways of technology, steering us towards an unprecedented future.

With Web3 Global Hackathon, Mazda Makes a Push For 'Drive-to-Earn'

Since well before the ICO craze, the market has been looking for narratives with the potential to 10x crypto’s market cap - decentralized finance, NFTs, hedging US dollar inflation, Layer 2s, Elon Musk, Microstrategy. The list goes on. Each narrative spurs an injection of new capital, developers, financial engineers, investors, influencers - creating new use cases and market cycles from which further narratives are born. Being early to these narratives is key — and with that being said, let’s talk about Real World Assets (RWA). What are real world assets (RWA)? In short RWAs refer to the tokenization of physical world assets. In this context, “asset” refers to capital assets - significant pieces of property such as homes, cars, commercial real estate, loan portfolios, stocks, and bonds. Even physical art like collectibles can be capital assets. By tokenizing these assets on-chain, RWAs essentially bridge the gap between decentralized finance ($47 billion TVL) and traditional finance (the market cap of Nasdaq stocks alone is $18 trillion). How will it bring value into crypto and what does it mean for the ecosystem? The crypto market as it stands today is largely circular. DeFi is akin to a closed financial system and most of its revenue is derived from endogenous sources i.e. swap fees, borrowing fees. By introducing real world assets onto the blockchain, RWAs promise to open up new avenues for capital, collateral, and revenue to flow into the crypto ecosystem. In turn, blockchain and DeFi building blocks have the potential to bring liquidity to historically illiquid assets, lower the cost of credit, widen the reach of access for credit and collateral, and bring transparency to what is currently an opaque black box of middlemen, wall street horse trading, and compounding inefficiencies. The cross-border, impartial nature of blockchain and smart contracts help put capital assets to use by people or organizations who would traditionally never have had the opportunity to do so, maximizing economic surplus (total welfare/productivity). It’s also an attractive proposition for institutions who tend to seek liquidity wherever they can and are constantly looking for ways to reduce inefficiencies. On-chain management of assets can dramatically reduce middle and back-office operations across firms and intermediaries. To put it in perspective, shaving just 0.5% of the cost from $5bn of transaction settlements would save $25m, a meaningful difference on the balance sheet. To top it all off, successful onboarding of RWAs can serve as a framework for tokenizing other physical assets, like inventory (supply chain management), identity, and taxes – all key building blocks and potential growth opportunities for crypto. What does it look like in its current form? And where is it most likely to start? The biggest use of RWAs to date has been stablecoins— which are essentially a tokenization of US treasury bonds into US dollar units on-chain. The introduction and rapid adoption of stablecoins have proven that there is a real and growing appetite for tokenized RWAs. However, stablecoins serve one basic function - to be a store of value used for earning and spending (making gainz 🚀 or getting rekt 🤡, depending on your perspective). RWAs like gold and stocks have also been tokenized on-chain, either as a different form of (pegged to the price of gold) or as a mirrored asset to be fractionalized and traded using blockchain rather than traditional settlement. stablecoin Projects that explicitly flaunt the “RWA” label, however, have mostly been a tokenization of credit. By “credit” we mean traditional interest bearing assets, like packaged real estate or automobile loans, which are collateralized and brought on-chain as: a)  interest bearing assets for the lender b)  a pool of liquidity, usually stablecoins, for the borrower By essentially securitizing these loans on-chain, they can be easily bought and sold, and come with built-in interest distribution mechanisms via smart contract logic. Securitization allows for the efficient transfer of risk (lenders), and allows people (borrowers) to raise capital and create businesses that provide goods and services— which ultimately allows society to grow and thrive. Real world yields, especially in today’s bearish regime, are often higher than yields that one can find on-chain, increasing the value proposition of RWAs that much more. What are some current RWA projects? MakerDAO ( ) is one of the leaders when it comes to RWAs. RWAs currently account for and recently onboarded a , a Pennsylvania chartered bank. Essentially MakerDAO will allow for HVB to access up to $100m in liquid DAI —which can be converted into fiat— in return for the equivalent in collateralized loans on commercial and residential real estate, e-commerce, industrial and consumer loans. This allows for another $100m in stablecoin to be put on-chain, while MakerDAO receives interest for continuing MakerDAO and DAI operations. $MKR 12.3% (about $907m) of DAI’s collateral $100m loan portfolio from Huntingdon Valley Bank (HVB) Centrifuge ( ) is another leader when it comes to bringing credit on-chain, with over , some of which account for the backing of DAI via MakerDAO. Centrifuge is an open financing platform that allows businesses and borrowers to create fully collateralized asset pools from which investors can provide liquidity in return for shares of the pool that return interest. Centrifuge aims to lower the cost of borrowing for businesses around the world and gives liquidity providers a source of yield while ensuring that they have legal recourse in the case of a default. The credit is also revolving, meaning investment and redemption (the withdrawal of invested capital) orders can come in at any time, and assets can be financed and repaid continuously. $CFG $206m in active loans GoldFinch ( ) aims to build credit markets for emerging markets, providing credit for businesses in Africa, Southeast Asia, and Latin America to the tune of . GoldFinch helps decentralize the auditing process by requiring borrowers to receive approval votes from auditors who stake GFI tokens and are either rewarded tokens or have their stake slashed depending on the outcome of the loan’s repayment. This ensures an alignment of incentives and has allowed funding of startups and impact investments all over the world. $GFI $103m active loans as of today Maple Finance ( ) is similar to GoldFinch and Centrifuge, with over $135m in active loans. Maple uses pool delegates to manage lending pools and enable these delegates to attract global capital and provide funding to a network of “premium” (i.e. vetted) borrowers, which are judged on their investment strategies, credit quality, reputation, and industry. Borrowers submit a loan proposal which is reviewed by pool delegates to calibrate interest rates and collateral ratios. $MPL Institutional Players that are using blockchain for RWA A very promising signal for RWAs has been rapidly growing interest from traditional institutional players. Some examples of recent RWA developments from the world of traditional finance: Feb 14 - Siemens issues its , worth $60m, on the first digital bond Polygon blockchain Feb 16 - Hong Kong issues it’s first tokenized green bond of $101m Apr 3 - Credit Agricole CIB and Sweden’s SEB begin to develop a blockchain-based platform for digital bonds Apr 14 - Bank of America like commodities, currencies, and equities as a key driver of digital asset adoption details tokenization of RWAs Apr 20 - Societe Generale introduces a , CoinVertable on Ethereum - the first institutional stablecoin deployed on a public blockchain Euro stablecoin Apr 26 - Mitsubishi UFG Trust and Banking Corporation (cosmos) as the communication protocol and the Corda blockchain infrastructure develops a digital securities platform using IBC What does the future look like for RWA? Given that tokenized real-world assets depend on the existence of parallel traditional financial institutions, their trust properties will likely never be the same as a DeFi ecosystem which solely deals in crypto-native assets. However, it remains a very compelling case as the market finds relative stability and more advanced data, onboarding, and custody solutions over time. RWAs will bring more holders and users to the ecosystem. While Web3 natives who already hold tokens are the clear initial product-market-fit of DeFi products, bringing DeFi to the traditional and institutional consumers will require assets and financial products that traditional users are familiar with and already own in one shape or another. The TVL in DeFi is a drop in the bucket compared to the hundreds of trillions available in traditional capital markets, and is similarly dwarfed by physical non-capital and even intangible assets mentioned earlier. Think about the concept of the , where most of the world’s transactions are done via some form of abstracted digital representation (your mobile app banking account, your social media identity, text messaging). If you continue along that trend and take it to its logical end within the context of crypto, it is pretty easy to envision a world where all assets are tokenized, mapped to the blockchain, and traded or transacted through the RWA process. With such a large opportunity and gap in the market - bringing another $1 trillion dollars of market cap to the market via RWAs seems like an easy reach. digital twin Author: Toby Fan, Web3 Strategist @ LunarCrush | Twitter LinkedIn

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