Simplifying the latest STEM breakthroughs for all.
Highlights: New quantization technique ‘ParoQuant’ enhances reasoning LLM efficiency. Introduces pairwise Givens rotations and channel-wise scaling for finer precision. Achieves 2.4% average accuracy improvement over previous methods. Less than 10%…
Highlights: Introduces SmolVLM2 variants for blind and low-vision accessibility research. Develops two novel evaluation frameworks – Multi-Context BLV and Navigational Assistance. Assesses 500M and 2.2B parameter models on AVCaps (outdoor)…
Highlights: New AI language ‘Regular Games’ (RG) introduces an automata-based approach to General Game Playing (GGP). The system uses a finite automaton to define game rules for greater computational efficiency.…
Highlights: Introduces a framework for safe planning in interactive environments such as self-driving vehicles among human actors. Combines iterative policy updates with adversarially robust conformal prediction (CP) to maintain safety…
Highlights: Introduces Regular Games (RG), a new automata-based framework for general game playing (GGP). Combines computational efficiency with a flexible design language for human and procedural game creation. Outperforms existing…
Highlights: Researchers Arastu Sharma and Rakesh Jain propose a portable, low-cost diagnostic device for detecting Tuberculosis (TB) and multi-drug resistant TB. The device integrates machine learning algorithms with nucleic acid…
Highlights: Researchers introduce a deep learning-powered holographic polarization microscope. Quantitative birefringence data extracted from a single polarization image using AI. Simplifies optical design—requires only one polarizer/analyzer pair. Achieves results comparable…
Highlights: Introduces a weakly supervised deep learning framework for prostate tissue microarray (TMA) classification. Uses Graph Convolutional Networks (GCNs) to model spatial cell organization and improve Gleason grading accuracy. Self-supervised…
Highlights: Researchers use synthetic image rendering to solve the data annotation bottleneck in deep learning. Achieves segmentation accuracy comparable to human-labeled datasets for metal-oxide nanoparticles. Opens new possibilities for automated…
Highlights: Researchers present a deep learning model for automatic heart abnormality detection using heart sound signals. The system combines raw heart sound input with MFCC features using a dual stream…