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When Power Flow Models Go Off the Rails
DPFL methods vary in flexibility when selecting predictors and responses, impacting performance. Challenges like multicollinearity, zero predictors, and normalization constraints must be carefully managed for accurate power flow modeling.
Why Current Power Flow Models May Not Work in Real-World Scenarios
Current DPFL studies often rely on artificial data without noise, lack transparency in load fluctuation, and conduct limited comparative analyses. A broader, real-world-focused evaluation is needed.
How 44 Different Algorithms Compare in Power Flow Optimization
This evaluation of 44 DPFL methods explores the impact of constraints, introduces novel least squares and clustering-based approaches, and compares widely used physics-driven power flow linearization models.
New Study Reveals the Best AI Models for Power Grid Optimization
A large-scale benchmarking study of 44 DPFL methods identifies performance trends, practical limitations, and research gaps. This work supports method selection and outlines ten key future directions in DPFL.
If Life Is a Simulation, Do We Have an Exit Strategy?
This article explores Roman Yampolskiy's radical ideas on hacking reality, from AI-driven jailbreaks to rewiring human perception. Dive into the science, philosophy, and speculative methods of breaking free from a...
Ducho: A Unified Framework for Multimodal Feature Extraction in AI-Powered Recommendations
Ducho simplifies multimodal feature extraction for recommender systems with a modular architecture. Future plans include broader backend support, a unified extraction interface, and low-level feature extraction.
Ducho, the AI That Knows What You Think About That Toaster
Ducho processes textual features from Amazon’s dataset, distinguishing product descriptions from user reviews. Using multilingual BERT for sentiment analysis, it enhances recommendations by capturing nuanced customer interactions.
Making AI Recommendations Smarter with Visual, Text, and Audio Data
Ducho’s demos showcase its multimodal capabilities in fashion and music recommendation. Using deep learning models like VGG19, Xception, Sentence-BERT, and Hybrid Demucs, Ducho extracts and processes visual, textual, and audio...
Ducho’s Big Bet: A Unified Future for Multimodal AI
Ducho’s extraction pipeline automates multimodal feature processing using a YAML-based configuration and deep learning backends. To simplify deployment, Ducho offers a Docker image preloaded with CUDA and cuDNN, ensuring a...
A New Way to Extract Features for Smarter AI Recommendations
Ducho’s architecture consists of three key modules—Dataset, Extractor, and Runner—designed for efficient multimodal feature extraction. It supports TensorFlow, PyTorch, and Transformers, allowing flexible dataset processing, model selection, and YAML-based configuration...
A Unified Framework for Multimodal Feature Extraction in Recommendation Systems
Ducho is an open-source framework for extracting multimodal features in recommendation systems. It integrates TensorFlow, PyTorch, and Transformers, offering a configurable YAML-based extraction pipeline. A Docker image with CUDA support...
It is Cheaper to Grow than to Die
Staying stagnant costs more than investing in growth. Whether through steady linear progress or bold exponential leaps, investing in yourself is key to survival.
Lead Scoring 2.0: From Static Models to Dynamic Buyer Intent
Traditional lead scoring systems are based on the assumption that buyers go on a simple, linear journey. Dynamic scoring updates in real-time based on actual buyer behaviour, providing more relevant...
A DevOps Approach to AEM Packages: Automating Creation, Configuration, and More
The `create-remote-aem-pkg.sh script automates interactions with AEM’s Package Manager API, offering a structured approach to package creation, configuration, and distribution. Designed for developers and administrators, it replaces manual workflows with...
AI’s Non-Determinism, Hallucinations, And... Cats?
AI is like cats: sometimes they eat, sometimes ignore it, and sometimes they scratch you. ChatGPT’s answers result from a stochastic process rather than a rigid rule. It tends to...
The HackerNoon Newsletter: Futures of Ethereum II - Censorship Resistance (2/16/2025)
How are you, hacker? 🪐 What’s happening in tech today, February 16, 2025? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, we present you...
You Should Be Harder on Yourself, Despite What Self Care Gurus Say
Most successful people are simultaneously confident in their abilities and brutally honest about their shortcomings. Mediocrity isn't a position - it's a mindset. The moment you stop defending your current...
The TechBeat: Cybercrooks Are Using Fake Job Listings to Steal Crypto (2/16/2025)
How are you, hacker? 🪐Want to know what's trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set...
Design, Manufacturing and Open-Loop Control of a Soft Pneumatic Arm: Bending Experiments
PAUL’s bending experiments reveal a maximum deflection of 40°, maintaining flexibility even with extra segments. Compared to 80° Pneunet robots, PAUL’s controlled flexibility ensures adaptability for navigating obstacles and cluttered...
Soft Robots and Smart Movement
PAUL’s table-based kinematic model achieved an average error of 4.27 mm for direct kinematics and 10.78 mm for inverse kinematics, with redundancy in inflation combinations introducing uncertainties. Data collection was...