Product Recalls, Minimization Functions, Data Tracing, Cost Minimization
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01/11/2023
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21/10/2023
In the realm of software development and computer science, we encounter a range of concepts and practices that help build robust, efficient, and reliable systems.
Idempotent operations are those that, when performed multiple times, have the same effect as if they were performed once. This property is crucial in distributed systems to ensure consistency.
A monoid is a mathematical structure often used in functional programming. It consists of a set, an operation, and an identity element, and it helps in aggregating and combining data.
Decoupled systems are designed to have minimal interdependencies. In software architecture, decoupling components allows for greater flexibility and easier maintenance.
Dependency injection is a design pattern where a component's dependencies are provided externally rather than created internally. It helps make code more testable and modular.
A unit in computer science often refers to the smallest addressable data size for a particular system, such as a byte. It's fundamental for memory and storage management.
Functional programming is a paradigm that treats computation as the evaluation of mathematical functions. It emphasizes immutability and declarative code.
Asynchronous vs. parallel programming involves handling tasks that run concurrently. Asynchronous tasks don't necessarily run in parallel but allow for non-blocking operations, while parallel programming uses multiple processors to execute tasks simultaneously.
Thread locking is a synchronization mechanism used in multi-threaded applications to ensure that only one thread can access a specific resource at a time, preventing data corruption.
Eventual consistency is a property of distributed systems where, given time, all nodes in the system will converge to the same state, even after dealing with network delays or failures.
Exactly-once semantics is a guarantee in distributed data processing that ensures a particular operation is executed precisely once, avoiding duplicates or data loss.
Lambda vs. Kappa architecture refers to two distinct approaches for processing and storing big data. Lambda architecture combines batch and stream processing, while Kappa architecture simplifies it by using only stream processing.
Push vs. pull architectures involve how data is transmitted. In a push architecture, data is sent from the source to the recipient, while in a pull architecture, the recipient requests data from the source.
The write-audit-publish pattern is a software design pattern where data is first written, then audited for quality or compliance, and finally published for consumption. It's often used in data pipelines and data integration scenarios.
These concepts and practices are fundamental in software development, providing the tools and methodologies needed to build efficient, reliable, and scalable systems. Understanding and appropriately applying these concepts is essential for developing modern software solutions.
☀️🦅🇺🇸🧠🗺️🧬🌍🌏🌎🌉
03/10/2023
Here are 10 motivation and achievement-focused "spiritual reserves" that individuals can accumulate in life and how to cultivate them:
Drive and Ambition: Cultivate a strong desire to achieve your goals by setting clear, meaningful objectives and regularly revisiting your aspirations.
Self-Discipline: Develop self-discipline by creating daily routines, setting deadlines, and staying committed to your tasks and objectives.
Grit: Build grit by persisting through adversity, setbacks, and failures, viewing them as opportunities for growth rather than roadblocks.
Vision and Goal Clarity: Clearly define your vision and break it down into actionable goals, ensuring each step aligns with your ultimate vision.
Positive Affirmations: Use positive affirmations and self-talk to boost your confidence, motivation, and self-belief.
Visualization: Practice visualization techniques to mentally rehearse your successes and reinforce your motivation.
Accountability: Hold yourself accountable for your actions and progress, and consider working with an accountability partner or coach.
Continuous Learning: Stay motivated by continuously learning and seeking new knowledge and skills that align with your goals.
Resilience Training: Build resilience by intentionally putting yourself in challenging situations and learning from them.
Celebrating Milestones: Recognize and celebrate your achievements, no matter how small, to maintain motivation and build a sense of accomplishment.
These motivation and achievement-focused spiritual reserves are crucial for staying driven and reaching your goals. Regularly nurturing these reserves can help you overcome obstacles, maintain focus, and ultimately achieve the success you aspire to.
Does like mean the end of traditional dashboards? New capabilities like boost productivity and help reduce human bias. But people still want visual stories to support their decision-intelligence processes. Read more about the evolution of dashboards: https://social.ora.cl/6182uER9I
26/09/2023
Y. Huang, W. Zhang, K. Yang, W. Hou, Y. Huang, An optimal scheduling method for multi-energy hub systems using game theory. Energies 12(12), 2270 (2019)
R. Jing, M. Wang, H. Liang, X. Wang, N. Li, N. Shah, Y. Zhao, Multi-objective optimization of a neighborhood-level urban energy network: Considering game-theory inspired multi-benefit allocation constraints. Appl. Energy 231, 534–548 (2018)
C.S. Karavas, K. Arvanitis, G. Papadakis, A game theory approach to multi-agent decentralized energy management of autonomous polygeneration microgrids. Energies 10(11), 1756 (2017)
A. Laha, B. Yin, Y. Cheng, L.X. Cai, Y. Wang, Game theory based charging solution for networked electric vehicles: A location-aware approach. IEEE Trans. Veh. Technol. 68(7), 6352–6364 (2019)
Oracle Announces Next-Generation Fusion Data Intelligence Platform Oracle today announced the Fusion Data Intelligence Platform, a next-generation data, analytics, and AI platform that will help Oracle Fusion Cloud Applications customers achieve better business outcomes by combining data-driven insights with intelligent decisions and actions.
20/09/2023
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Our CEO Safra Catz and Uber CEO Dara Khosrowshahi unveil a groundbreaking partnership to revolutionize retail delivery services. https://social.ora.cl/6184PhTDi
18/09/2023
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Don't miss this one. Sign up for our next webinar on Sept. 19 to learn how to empower your industrial applications with . Gain insights from advanced use cases powered by the NVIDIA Jetson Orin in ruggedized environments. Register now: https://nvda.ws/46yPQwb
13/09/2023
Best Outcome
Join Gary Miller at to discover how Accenture, Marriott International, and Minerals Technologies Inc. have minimized risk and maximized their investments with our technology: https://social.ora.cl/6180PNYYs
07/09/2023
"Look for investments with catalysts that may assist directly in the realization of underlying value."
--- Seth Klarman Dell Technologies
07/09/2023
Overcoming sloth and unreliability at multiple levels within a system, community, or individually to achieve economic success with machine learning and intelligent technologies in a multi-agent system can be complex. Here's a 10-part analysis to address these challenges:
Define Clear Objectives:
Start by defining specific economic goals and objectives for your multi-agent system (MAS) using machine learning.
System Architecture:
Design a robust MAS architecture that accounts for redundancy, fault tolerance, and scalability.
Data Quality and Reliability:
Ensure high-quality and reliable data sources, cleaning, and preprocessing techniques to feed into the ML models.
Algorithm Selection:
Choose machine learning algorithms suited to your MAS objectives, considering factors like interpretability, scalability, and accuracy.
Model Training and Validation:
Implement rigorous model training and validation procedures to ensure reliable predictions.
Continuous Monitoring:
Set up continuous monitoring of your MAS to detect anomalies and deviations in real-time.
Feedback Loops:
Establish feedback loops to retrain and adapt your ML models as the environment and data change.
Incentive Structures:
Create incentive structures within your community or organization to motivate individuals to participate actively in the MAS.
Governance and Accountability:
Develop clear governance mechanisms and accountability frameworks to address sloth and unreliability issues.
Education and Training:
Invest in education and training programs to upskill individuals within the community or organization, ensuring they understand and can contribute effectively to the MAS.
By addressing these aspects comprehensively, you can mitigate issues related to sloth and unreliability in a multi-agent system powered by machine learning, ultimately increasing the likelihood of achieving economic success.
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Giving each department a clear focus and the appropriate resources to achieve its goals makes the diagnosis of resource allocations more straightforward and reduces job slip. As an example of how this works, at Bridgewater we have a Marketing Department (goal: to market) that is separate from our Client Service Department (goal: to service clients), even though they do similar things and there would be advantages to having them work together. But marketing and servicing clients are two distinct goals; if they were merged, the department head, salespeople, client advisors, analysts, and others would be giving and receiving conflicting feedback. If asked why clients were receiving relatively poor attention, the answer might be: "We have incentives to raise sales." If asked why they weren't making sales, the merged department might explain that they need to take care of their clients.
28/08/2023
📣 IT'S EMBEDDED WEEK! 📣 To kick us off, see why Oracle Analytics was named a Leader in the Nucleus Research 2022 Embedded Analytics Value Matrix. https://social.ora.cl/6187PmfPN
26/08/2023
Did you know we were ranked first in the Data Storytelling Use Case of the April 2023 Gartner Critical Capabilities for Analytics and Business Intelligence Platforms report? Find out why at https://social.ora.cl/6186PlVSo
Oracle Cloud
10/08/2023
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09/08/2023
1/9: 🤖 Exciting developments in multi-agent robotics! 🌐 Introducing a decentralized framework that harmonizes agents' actions based on time, place, role, purpose, mission, and goals. Enhanced collaboration for industries like logistics and disaster response!
2/9: 🚀 Revolutionizing teamwork in agriculture! 🌾 Our new decentralized multi-agent robotic framework maximizes collaboration. Robots align their efforts with planting seasons, field zones, roles, and crop goals. Efficiency meets sustainability! 🌱
3/9: 🏭 Industrial automation gets smarter! 🛠️ Our decentralized multi-agent robotic system optimizes factory efficiency. With real-time adaptation to tasks, locations, roles, and production targets, manufacturing has never been this synchronized! 🏭🤖
4/9: 🌊 Dive into the future of ocean exploration! 🌊 Decentralized multi-agent robotics ensure synchronized marine missions. Adapting to depths, environmental cues, roles, research objectives, and goals, we're unlocking mysteries of the deep! 🌊🤿
5/9: 🚗 Navigating the road to autonomy! 🛣️ Our decentralized multi-agent robotic approach propels self-driving cars forward. Seamlessly factoring in time, place, roles, objectives, and traffic conditions, safer and smarter roads are within reach! 🚗🤖
6/9: 🏥 Healthcare meets innovation! 🌡️ Introducing a decentralized multi-agent robotic framework for hospitals. Coordinating tasks, roles, schedules, medical objectives, and patient care goals, it's a prescription for enhanced healthcare! 🏥🤖
7/9: 🌆 Urban planning reimagined! 🏙️ Our decentralized multi-agent robotics system paves the way for smart cities. Balancing roles, purposes, timelines, objectives, and urban goals, it's the foundation for sustainable urban evolution! 🏙️🤖
8/9: 🌍 Conservation goes high-tech! 🦌 Embrace our decentralized multi-agent robotics for wildlife protection. Adapting to terrain, roles, ecological missions, and conservation goals, it's a game-changer for safeguarding our planet's biodiversity! 🌳🤖
9/9: 🛰️ Embarking on space exploration like never before! 🚀 Our decentralized multi-agent robotic framework propels missions to the cosmos. Coordinating roles, objectives, timeframes, and goals, it's a giant leap for collaborative space discovery! 🌌🤖
Microservices with Oracle Database? Let us show you how. https://social.ora.cl/6188PefQj
🦅👋👂🌉☀️👀👍
Autonomous detection of collective behaviors in swarms involves utilizing advanced algorithms and technology to observe and analyze the coordinated actions and patterns exhibited by groups of entities, such as robots, animals, or other agents. The reasoning behind this approach lies in the rich potential it offers for understanding, predicting, and influencing the behavior of these swarms.
Reasoning:
Complex System Understanding: Swarms are complex systems where individual agents' behaviors interact to create emergent group behaviors. Autonomous detection allows us to uncover hidden relationships and dependencies within these systems, leading to a deeper understanding of how swarms function.
Predictive Insights: By analyzing collective behaviors, we can develop predictive models that anticipate future actions or patterns. This capability is invaluable in various fields, from disaster response to market trends, enabling proactive decision-making.
Behavioral Variability: Swarms often display intricate variations in behavior due to factors like environment, communication, or individual differences. Autonomous detection helps identify and categorize these variations, contributing to a more nuanced understanding of swarm dynamics.
Benefits:
Resource Allocation: Understanding collective behaviors aids in optimizing resource allocation. For example, in agriculture, knowing how a swarm of drones behaves can lead to efficient dispersal of pesticides or nutrients across a field.
Efficient Routing: In transportation or logistics, detecting collective behaviors can help optimize route planning for delivery vehicles or traffic management systems, reducing congestion and travel time.
Environmental Monitoring: Swarms of sensors can be used to monitor environmental changes, such as pollution levels or wildlife movements. Autonomous detection enhances the accuracy and coverage of such monitoring efforts.
Search and Rescue: In disaster scenarios, swarms of robots can assist in search and rescue missions. Detecting collective behaviors enables these robots to collaborate effectively in locating survivors or navigating hazardous environments.
Innovation and Design: Observing collective behaviors can inspire innovative solutions and designs. Engineers can draw inspiration from natural swarm behaviors to create novel technologies and systems.
Scientific Discovery: Autonomous detection of collective behaviors can lead to new scientific insights. For example, studying the flocking patterns of birds might reveal principles applicable to fields like physics or sociology.
In essence, autonomous detection of collective behaviors empowers us to tap into the inherent intelligence of swarms, yielding a wealth of insights and benefits across various domains. By deciphering the intricate interplay of these behaviors, we unlock opportunities to enhance efficiency, responsiveness, and decision-making in complex systems.
08/08/2023
Simplicity in close relationships can be a powerful and transformative approach. By embracing simplicity, we prioritize authenticity, understanding, and spiritual depth over superficial complexities. When we keep things simple, we foster genuine connections built on trust, empathy, and shared values.
In a world filled with distractions and constant noise, simplicity allows us to focus on what truly matters: the core of the relationship. By avoiding unnecessary complications and drama, we create a safe space for vulnerability and open communication, enabling us to understand each other on a profound level.
Moreover, simplicity reduces misunderstandings and miscommunications, as we communicate clearly and directly. This leads to fewer conflicts and more effective problem-solving, strengthening the bond between individuals. It promotes a sense of ease and comfort, making it easier to navigate challenges together.
Simplicity in close relationships also encourages personal growth and self-awareness. When we embrace simplicity, we encourage each other to be authentic and true to ourselves.
This environment of acceptance allows us to explore our vulnerabilities and learn from our mistakes, fostering growth and resilience.
Furthermore, simplicity promotes balance and harmony within the relationship. By avoiding unnecessary complexities, we allocate more time and energy for shared experiences and quality time together. This cultivates a deeper sense of intimacy and fulfillment in the relationship, strengthening the spiritual connection.
In conclusion, simplicity in close relationships is not about avoiding complexities or challenges but rather about stripping away the unnecessary to reveal the essence of the connection. By embracing simplicity, we create a solid foundation for trust, understanding, and growth, enriching our lives and the lives of those we hold dear. ☀️
in PRA: Quantum Stirling heat engine operating in finite time. Read more now: https://go.aps.org/3Qgfj8F.
Long-Range Thinking (LRT):
LRT is a strategic approach that considers future consequences and outcomes in decision-making. To symbolize this concept, we can use a differential equation that represents the rate of change of forward-looking decisions over time t:
dLRT/dt = α * LRT
Here, α is a positive constant representing the influence of long-range thinking on decision-making. As LRT increases, the rate of change of forward-looking decisions also increases, leading to more thoughtful and far-sighted strategies.
Regret Minimization (RM):
RM involves making decisions that aim to minimize future regrets. To symbolize this concept, we can use a differential equation that represents the rate of change of regret reduction over time t:
dRM/dt = -β * RM
In this equation, β is a positive constant representing the effectiveness of regret minimization strategies. As RM increases, the rate of regret reduction decreases, implying that more proactive measures are being taken to avoid potential future regrets.
Database Performance (DP):
Database performance relates to the efficiency and responsiveness of a distributed database system. To symbolize this concept, we can use a differential equation that represents the rate of change of database performance over time t:
dDP/dt = γ * DP
Here, γ is a positive constant representing the factors influencing database performance. As DP increases, the rate of change of performance also increases, indicating that the database system is becoming more efficient and responsive.
Machine Learning (ML):
Machine learning involves algorithms and models that improve performance based on experience. To symbolize this concept, we can use a differential equation representing the learning rate over time t:
dML/dt = η * (target - ML)
In this equation, η is a positive constant representing the learning rate, and (target - ML) is the difference between the desired target performance and the current state of machine learning. As ML approaches the target, the learning rate decreases, implying that the system becomes more refined and accurate.
Insight and Wisdom for Success (IWS):
Insight and wisdom involve gaining valuable understanding and knowledge to achieve success. To symbolize this concept, we can use a differential equation representing the accumulation of insights and wisdom over time t:
dIWS/dt = ζ * IWS
Here, ζ is a positive constant representing the impact of gaining insights and wisdom on success. As IWS increases, the rate of insight and wisdom accumulation also increases, leading to better decision-making and overall success.
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🦅☀️🇺🇸👋🧭🌉🧠🧭
Methods for solving SAT
28/07/2023
Safra Catz’s keynote at will bring together some of the most successful and innovative organizations from around the world. Register now: https://social.ora.cl/6184PXL3K
20/07/2023
🦁☀️
Your chance to meet Max at next week's Belgian GP. Sign up to The Paddock and enter below 🇧🇪👇
win.gs/MGMVSPAPDKFB
18/07/2023
🦅🦅 Methods for solving knapsack problems
Recently published in : Experimental observation of longer trajectories than previously observed in high-order harmonic generation. Learn more: https://go.aps.org/3pGmhce.
18/07/2023
🦅🦅
Are you using our apps to achieve your sustainability targets and build a greener future? You may be eligible to win an Oracle Sustainability Award! Learn how: https://social.ora.cl/6183Pu4Ld
13/10/2022
End-to-end reinforcement learning
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Smart Cities / Regions / For Development / Community Building / Learning / Network Mobilization / Innovation / Market Integration
1. Austin, Texas Collaboration
2. Los Angeles, California Insight
3. San Diego, California Team Basics
4. Denver, Colorado Value Propositions
5. Houston, Texas Software Design
6. Chicago, Illinois Community Building
7. New York, New York SuperRadiance
8. London, United Kingdom Joy
9. Bangkok, Thailand Honor
10. Seoul Korea Good Faith
11. Italy / Brasil / Hong Kong Integrity
12. Berlin, Germany Mindfulness
13. Stockholm, Sweden ComplexSystems
14. Detroit, Michigan Multi Objective Optimization
15. Miami, Florida Family Science
16. Tampa, Florida Quantum Chemistry
17. Charlotte, NC Decision Support
18. Richmond, Virginia Sustainability
19. Boston, Massachusetts Learning and Improvement
20. Phoenix, AZ Intelligent Systems
21. Seattle, WA Smart City as a Service
22. Portland, OR Rational Unified Process
23. Vancouver, BC Quantum Optimization
24. Toronto, Ontario Health
25. Madrid, Spain Quality
26. Mumbai, India Apple Development
27. Kansas City, MO Microsoft Development
28. St. Petersburg, FL Edge Computing
29. Nashville, Tennessee SaaS
30. Netherlands, Israel, Australia
31. Atlanta, Georgia, Oracle
32. Tokyo, Japan Tesla Elon Musk
33. Paris, France Embedded Systems
34. Cape Town, South Africa Spiritual and Economic Linkages
35. Dubai, UAE Cognitive Computing
36. Minneapolis, MN Rural and Urban Linkages
37. Zurich, Germany Engineering, Development, Operations
38. Nordic Countries - Cognitive Computing, Learning Reasoning Optimization
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