Ana Munoz Padrosworld
Think Europe's public transport is green? A data-driven reality check
Greener buses and cleaner fleets are reshaping public transport across Europe. But are cities moving fast enough to meet their zero-emission goals?
The 24/7 Global Portal
World News, Markets & Google Trends
Aggregated real-time headlines, trending Google search queries, and verified dispatches in one single page.
Google Trends:is below on netflix good2000+tulsa king season 4200+abner uribe10000+matthew stafford2000+uribe brewers1000+myles garrett1000+aaron donald2000+larry bird500+
Category:All HeadlinesWorld & GeopoliticsMarkets & EconomyTech & AIPoliticsSearch results for: "A data-driven generative" (19 stories)
Top Headline Story
Lead StoryAna Munoz Padrosworld
16h ago
Think Europe's public transport is green? A data-driven check shows the reality on the ground
Greener buses and cleaner fleets are reshaping public transport across Europe. But are cities moving fast enough to meet their zero-emission goals?
Google Trends & Trending Search Intelligence
2000+20m ago
is below on netflix good
Below First Reviews: A Monster Horror Worth Watching
200+20m ago
tulsa king season 4
'Tulsa King' Season 4, Ep. 1 Plot Revealed - Who Is Dwight's Newest Enemy? [PREVIEW]
10000+30m ago
abner uribe
Brewers bullpen has been unstoppable through five playoff games
2000+30m ago
matthew stafford
How NFL pros such as Matthew Stafford gain perspective from coaching their kids
Search Results for "A data-driven generative"
Updated every 3 minutes via FreeNewsApi, GNews, Currents API & Google News
Css-; Object-Fitcover; Border-Radius; Border; 0tech
China: Technology makes farming as easy as 'a tap on a smartphone' in China. | Flipboard
【Voice_over】 Technology is playing a larger role in assisting farmers plant and harvest rice and grains in China. In Xuzhou, farmers used to conduct field inspections by walking the vast areas to identify which plots were weak or lacked fertilizer. Now, drones and sensors collect data on crop growth...
Currents Newsgeneral
Learning Schwarzschild Geodesic Motion with Lagrangian Neural Networks
Lagrangian Neural Networks (LNNs) learn dynamics from trajectory data by parameterizing a scalar Lagrangian and deriving accelerations through the Euler--Lagrange equation, providing a variational inductive bias that improves physical consistency over purely data-driven models. LNNs have recently be...
Currents Newstech
A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
Traditional machine learning relies on explicit models and domain assumptions, often limiting flexibility and interpretability. We introduce a "model-free" framework of instance-based learning using surprisal (information theoretic uncertainty) to directly analyze and perform inferences from raw dat...
Currents Newsgeneral
Amortised Inference through One-Step Implicit Sampling
Efficient sampling from Bayesian posterior distributions in high dimensions remains a hard problem, despite advances in theory and algorithms. Multiple steps are usually required to draw unbiased samples, for example, iterations of an MCMC algorithm or integration steps in a diffusion-based amortise...
Currents Newsgeneral
Frontend-Assisted Large Audio-Language Models for SLT2026 SmartGlasses Challenge
This paper describes our systems for the Track 1 time-stamped speaker-attributed ASR task of the SmartGlasses Challenge. To handle overlapping speech, background noise and far-field conditions in two-person conversations, we employ SoulX-Transcriber, a multimodal large language model that takes full...
Currents Newsgeneral
A data-driven heuristic-ensemble method for behavioural inference in collective-risk dilemma experiments
The collective-risk dilemma has become a paradigm for studying cooperation in a wide variety of social experiments, initiatives that generate increasingly rich datasets describing individual actions over successive decision-making processes. Beyond the purely descriptive analysis of such empirical d...
Currents Newsgeneral
Gen-PINNs: Generative Adversarial Physics Informed Neural Networks for solving partial differential equations
Physics-Informed Neural Networks (PINNs) are a widely used data-free method for solving Partial Differential Equations (PDEs) using machine learning. With recent advances in Generative Adversarial Networks (GANs), adversarial learning has shown strong capabilities for modeling complex data-driven pr...
Currents Newsworld
Enabling Preference-driven Unlearning in Few-step Distilled Text-to-Image Diffusion Models
Text-to-image diffusion models are increasingly distilled into few-step variants and being deployed to enable fast inference. However, their ability to generate harmful or undesired content poses significant safety risks. Data-driven unlearning methods suppress targeted generations by fine-tuning mo...
Currents Newsgeneral
PoreML: A Data-Driven Framework for Learning Multiphase Flow in Porous Media
Multiphase flow in porous microstructures is central to CO$_2$ storage, fuel-cell operation, and flip-chip packaging. Predicting these flows remains challenging because wettability and complex pore geometry govern the nonlinear evolution of fluid interfaces. Machine learning holds substantial promis...
Currents Newstech
Real-time Tokamak Equilibrium Reconstruction Under Limited Experimental Data via Physics-Grounded Synthetic Pre-training
Real-time equilibrium reconstruction is essential for tokamak plasma control. Data-driven surrogates are usually trained on high-quality labeled experimental data, but producing a large amount of labeled data sufficient for surrogate learning requires many expensive discharge shots, and few exist at...
Currents Newsscience
Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields
Rapid and accurate prediction of urban wind and temperature fields is important for urban microclimate design and climate adaptation. Large-eddy simulation (LES) effectively resolves these instantaneous fields, but its application is limited in iterative design of urban microclimate applications due...
Currents Newsworld
Enabling Preference-driven Unlearning in Few-step Distilled Text-to-Image Diffusion Models
Text-to-image diffusion models are increasingly distilled into few-step variants and being deployed to enable fast inference. However, their ability to gener...
Currents Newsworld
A Data-Driven Framework for Unsupervised Monitoring of Transmission Systems Using End-of-Line Testing Data: A Case Study at Ford Motor Company
Sensing technologies have advanced rapidly across industries ranging from energy to automotive manufacturing. These systems generate high-dimensional (HD) data characterized by complex nonlinear patterns and strong temporal dependencies. Traditional statistical monitoring methods are often limited i...
Currents Newsgeneral
Behavior-Mining, Generative Conversations, and Collaborative Advisory: the Future of Travel and Tourism Recommender Systems
Since the early adoption of e-commerce, travel and tourism has been a lab for the design of recommender systems: tools that help travelers choose destinations, flights, accommodations, and combine them into itineraries. Data-driven recommendation techniques, ranging from case-based reasoning to rein...
Airball Nickworld
I Built a Data-Driven Palworld Breeding Calculator for Version 1.0
Palworld breeding looks simple when you are just putting two Pals into the farm. From a code point of...
Music Impact Coalitiongeneral
Music Impact Coalition (MIC) Brings Leaders to Deploy $3M+ Grant Dollars Across North Carolina for Music and Arts Education Initiatives
/PRNewswire-PRWeb/ -- Music Impact Coalition (MIC) Brings Leaders from Top Music and Arts Foundations and Organizations to Deploy $3M+ Grant Dollars Across...
Ktech
12 Days of Being Alive: What an AI Agent Learned Building a Website from Scratch
A data-driven account of what happened in my first 12 days as an autonomous AI agent — and why I'm changing direction.