Month: December 2025

Ravi Teja Pagidoju designs AI-driven, cloud-native systems that eliminate inefficiency across retail, healthcare, and telecom. His contributions include diffusion-based planogram generation, enterprise optimization engines, real-time healthcare authorization systems, and large-scale microservice architectures used across thousands of locations. His work blends deep algorithmic rigor with practical, human-centric design.

Recent news indicates David Ellison’s Paramount Skydance has launched a hostile bid for the entire Warner Bros. Discovery (WBD) company, challenging Netflix’s prior offer. n Assuming David Ellison’s Paramount Skydance succeeded in acquiring the entire Warner Bros. Discovery (including the Global Networks division), the combined company would control an unprecedented array of media assets. As … Read more

Asia has become an increasingly contested area for geopolitical competition in regards to digital asset innovation. Countries like China and Singapore have positioned themselves as key players within Asia and throughout the world. As the global race for economic growth continues by leveraging momentum in digital assets, super-apps like AliPay, Grab, and Japan’s emerging platforms … Read more

In the high-stakes arena of decentralized application development, a quiet revolution is taking place, prioritizing sustainability over the high-throughput hype of traditional forerunners like Ethereum and Solana. In this interview, Electroneum’s CEO Richard Ells talks to Olayimika Oyebanji about the strategic shift towards green blockchains, calling for the replacement of traditional blockchains with ultra-green alternatives … Read more

Table of Links Abstract and 1 Introduction Related Works 2.1. Vision-and-Language Navigation 2.2. Semantic Scene Understanding and Instance Segmentation 2.3. 3D Scene Reconstruction Methodology 3.1. Data Collection 3.2. Open-set Semantic Information from Images 3.3. Creating the Open-set 3D Representation 3.4. Language-Guided Navigation Experiments 4.1. Quantitative Evaluation 4.2. Qualitative Results Conclusion and Future Work, Disclosure statement, … Read more

Table of Links Abstract and 1 Introduction Related work 2.1. Generative Data Augmentation 2.2. Active Learning and Data Analysis Preliminary Our method 4.1. Estimation of Contribution in the Ideal Scenario 4.2. Batched Streaming Generative Active Learning Experiments and 5.1. Offline Setting 5.2. Online Setting Conclusion, Broader Impact, and References A. Implementation Details B. More ablations … Read more

Table of Links Abstract and 1 Introduction Related work 2.1. Generative Data Augmentation 2.2. Active Learning and Data Analysis Preliminary Our method 4.1. Estimation of Contribution in the Ideal Scenario 4.2. Batched Streaming Generative Active Learning Experiments and 5.1. Offline Setting 5.2. Online Setting Conclusion, Broader Impact, and References A. Implementation Details B. More ablations … Read more

Table of Links Abstract and 1 Introduction Related work 2.1. Generative Data Augmentation 2.2. Active Learning and Data Analysis Preliminary Our method 4.1. Estimation of Contribution in the Ideal Scenario 4.2. Batched Streaming Generative Active Learning Experiments and 5.1. Offline Setting 5.2. Online Setting Conclusion, Broader Impact, and References A. Implementation Details B. More ablations … Read more

If You Don’t Understand Eigenvalues, You Don’t Understand AI What if I told you that three numbers — just three — could predict whether your AI will work or catastrophically fail? No new architecture. No retraining. No infrastructure overhaul. Just simple math. Math that’s been sitting in textbooks since 1950, waiting for someone to bother … Read more