In recent years, large language models (LLMs) have revolutionized the field of natural language processing. However, they often suffer from knowledge gaps and hallucinations. Graph retrieval-augmented generation (GraphRAG) enhances LLM reasoning by integrating structured knowledge from external graphs. However, we identify two key challenges that plague GraphRAG:(1) Retrieving noisy and irrelevant information can degrade performance and (2)Excessive reliance on external knowledge suppresses the model's intrinsic reasoning. To address these issues, we propose GraphRAG-FI (Filtering and Integration), consisting of GraphRAG-Filtering and GraphRAG-Integration. GraphRAG-Filtering employs a two-stage filtering mechanism to refine retrieved information. GraphRAG-Integration employs a logits-based selection strategy to balance external knowledge from GraphRAG with the LLM's intrinsic reasoning,reducing over-reliance on retrievals. Experiments on knowledge graph QA tasks demonstrate that GraphRAG-FI significantly improves reasoning performance across multiple backbone models, establishing a more reliable and effective GraphRAG framework.

In this July 30, 2019, photo, Paul Hildreth, emergency operations coordinator for the Fulton County School District, works in the emergency operations center at the Fulton County School District Administration Center in Atlanta. Artificial Intelligence is transforming surveillance cameras from passive sentries into active observers that can immediately spot a gunman, alert retailers when someone is shoplifting and help police quickly find suspects. Schools, such as the Fulton County School District, are among the most enthusiastic adopters of the technology. Paul Hildreth peered at a display of dozens of images from security cameras surveying his Atlanta school district and settled on one showing a woman in a bright yellow shirt walking a hallway. A mouse click instructed the artificial intelligence-equipped system to find other images of the woman, and it immediately stitched them into a video narrative of where she was currently, where she had been and where she was going.

Paul Hildreth peered at a display of dozens of images from security cameras surveying his Atlanta school district and settled on one showing a woman in a bright yellow shirt walking a hallway. A mouse click instructed the artificial intelligence-equipped system to find other images of the woman, and it immediately stitched them into a video narrative of where she was currently, where she had been and where she was going. There was no threat, but Hildreth's demonstration showed what's possible with AI-powered cameras. If a gunman were in one of his schools, the cameras could quickly identify the shooter's location and movements, allowing police to end the threat as soon as possible, said Hildreth, emergency operations coordinator for the Fulton County School District. AI is transforming surveillance cameras from passive sentries into active observers that can identify people, suspicious behavior and guns, amassing large amounts of data that help them learn over time to recognize mannerisms, gait and dress.

In this Thursday, July 11, 2019, photograph, United States Department of Agriculture intern Alex Olsen prepares to place down a drone at a research farm northeast of Greeley, Colo. After a brief, snaking flight above the field, the drone landed and the researchers removed a handful of memory cards. Back at their computers, they analyzed the images for signs the corn was stressed from a lack of water. This U.S. Department of Agriculture station outside Greeley and other sites across the Southwest are experimenting with drones, specialized cameras and other technology to squeeze the most out of every drop of water in the Colorado River – a vital but beleaguered waterway that serves an estimated 40 million people. Should they still be able to use it?

I'm Evan, co-founder and CTO of Fuzzy.ai. Our team is very excited to be opening our doors for new registration today. These rules are optimized over time due to feedback from production use. We've applied this technique to a number of interesting business cases -- fraud detection, recommendations, content optimization, and dynamic pricing. Our Web-based developer environment makes it easy to design an agent quickly, and our SDKs for different programming languages make integrating with our REST API pretty painless.

Your meat may soon be prepared by a robot butcher. Sadly, it won't be an android in a striped apron behind the meat counter at your local store, asking you in a metallic voice how you'd like your steak cut today, sir/ma'am? These robots will replace workers at meat-packing factories, and not a moment too soon. The meat-packing company JBS is part of the world's largest beef processor, and in its Greeley, Colorado plant, it is experimenting with robots on the production line. In order to automate the processing of the meat, JBS has invested in a New Zealand robot company called Scott Technology.

This paper considers a multiple regression model and compares, under full model hypothesis, analytically as well as by simulation, the performance characteristics of some popular penalty estimators such as ridge regression, LASSO, adaptive LASSO, SCAD, and elastic net versus Least Squares Estimator, restricted estimator, preliminary test estimator, and Stein-type estimators when the dimension of the parameter space is smaller than the sample space dimension. We find that RR uniformly dominates LSE, RE, PTE, SE and PRSE while LASSO, aLASSO, SCAD, and EN uniformly dominates LSE only. Further, it is observed that neither penalty estimators nor Stein-type estimator dominate one another.

We propose an improved LASSO estimation technique based on Stein-rule. We shrink classical LASSO estimator using preliminary test, shrinkage, and positive-rule shrinkage principle. Simulation results have been carried out for various configurations of correlation coefficients ($r$), size of the parameter vector ($\beta$), error variance ($\sigma^2$) and number of non-zero coefficients ($k$) in the model parameter vector. Several real data examples have been used to demonstrate the practical usefulness of the proposed estimators. Our study shows that the risk ordering given by LSE $>$ LASSO $>$ Stein-type LASSO $>$ Stein-type positive rule LASSO, remains the same uniformly in the divergence parameter $\Delta^2$ as in the traditional case.