Fox News chief Washington correspondent Mike Emanuel has the latest on Transportation Secretary Sean Duffy's statements about recent air traffic control incidents on'Special Report.' Transportation Secretary Sean Duffy recently announced that artificial intelligence (AI) is being used to detect and address air traffic risks, following a slew of near-misses and fatal plane crashes across the country. Duffy told FOX 5 DC that officials are implementing AI to "identify and address potential air traffic risks nationwide," potentially aiding in preventing tragedies like the fatal Jan. 29 midair collision at Ronald Reagan Washington National Airport (DCA) that claimed the lives of 67 people. Following the Potomac River crash, which involved a commercial plane and an Army Black Hawk helicopter, Duffy announced a plan to build a new "state-of-the-art" traffic control system that will equip locations with better technology to reduce outages, improve efficiency and reinforce safety. Duffy told FOX 5 that when investigators were looking into how to prevent collisions, they asked themselves, "Are there any other DCAs out there?" Transportation Secretary Sean Duffy speaks during a news conference following up on the issuance of the National Transportation Safety Board preliminary report on the mid-air collision near Ronald Reagan Washington National Airport, on Tuesday, March 11.

Transportation Secretary Sean Duffy delves into his take on DEI, DOGE, infrastructure projects and his first weeks in his new role on'My View with Lara Trump.' Transportation Secretary Sean Duffy announced plans to bolster airport air traffic control systems with the latest technology over the next four years, while also using artificial intelligence (AI) to identify "hot spots" where close encounters between aircraft occur frequently. The announcement came after an update on an investigation into a crash near Ronald Reagan Washington National Airport in Arlington, Virginia, when a U.S. Army helicopter and an American Airlines-operated passenger jet collided over the Potomac River Jan. 29. "We're here because 67 souls lost their lives on Jan. 29," Duffy told reporters Tuesday, noting that the National Transportation Safety Board (NTSB) unveiled its preliminary findings into the crash earlier in the day. The findings noted that, over the last 2½ years, there have been 85 near misses or close calls at Reagan National. Close calls were identified as incidents when there are less than 200 feet of vertical separation and 1,500 feet of lateral separation between aircraft.

Radioguided surgery, such as sentinel lymph node biopsy, relies on the precise localization of radioactive targets by non-imaging gamma/beta detectors. Manual radioactive target detection based on visual display or audible indication of gamma level is highly dependent on the ability of the surgeon to track and interpret the spatial information. This paper presents a learning-based method to realize the autonomous radiotracer detection in robot-assisted surgeries by navigating the probe to the radioactive target. We proposed novel hybrid approach that combines deep reinforcement learning (DRL) with adaptive robotic scanning. The adaptive grid-based scanning could provide initial direction estimation while the DRL-based agent could efficiently navigate to the target utilising historical data. Simulation experiments demonstrate a 95% success rate, and improved efficiency and robustness compared to conventional techniques. Real-world evaluation on the da Vinci Research Kit (dVRK) further confirms the feasibility of the approach, achieving an 80% success rate in radiotracer detection. This method has the potential to enhance consistency, reduce operator dependency, and improve procedural accuracy in radioguided surgeries.

Objectives: Our research adopts computational techniques to analyze disease outbreaks weekly over a large geographic area while maintaining local-level analysis by incorporating relevant high-spatial resolution cultural and environmental datasets. The abundance of data about disease outbreaks gives scientists an excellent opportunity to uncover patterns in disease spread and make future predictions. However, data over a sizeable geographic area quickly outpace human cognition. Our study area covers a significant portion of the African continent (about 17,885,000 km2). The data size makes computational analysis vital to assist human decision-makers. Methods: We first applied global and local spatial autocorrelation for malaria, cholera, meningitis, and yellow fever case counts. We then used machine learning to predict the weekly presence of these diseases in the second-level administrative district. Lastly, we used machine learning feature importance methods on the variables that affect spread. Results: Our spatial autocorrelation results show that geographic nearness is critical but varies in effect and space. Moreover, we identified many interesting hot and cold spots and spatial outliers. The machine learning model infers a binary class of cases or none with the best F1 score of 0.96 for malaria. Machine learning feature importance uncovered critical cultural and environmental factors affecting outbreaks and variations between diseases. Conclusions: Our study shows that data analytics and machine learning are vital to understanding and monitoring disease outbreaks locally across vast areas. The speed at which these methods produce insights can be critical during epidemics and emergencies.

Deep reinforcement learning (DRL) has been extensively applied to Multi-Unmanned Aerial Vehicle (UAV) network (MUN) to effectively enable real-time adaptation to complex, time-varying environments. Nevertheless, most of the existing works assume a stationary user distribution (UD) or a dynamic one with predicted patterns. Such considerations may make the UD-specific strategies insufficient when a MUN is deployed in unknown environments. To this end, this paper investigates distributed user connectivity maximization problem in a MUN with generalization to arbitrary UDs. Specifically, the problem is first formulated into a time-coupled combinatorial nonlinear non-convex optimization with arbitrary underlying UDs. To make the optimization tractable, a multi-agent CNN-enhanced deep Q learning (MA-CDQL) algorithm is proposed. The algorithm integrates a ResNet-based CNN to the policy network to analyze the input UD in real time and obtain optimal decisions based on the extracted high-level UD features. To improve the learning efficiency and avoid local optimums, a heatmap algorithm is developed to transform the raw UD to a continuous density map. The map will be part of the true input to the policy network. Simulations are conducted to demonstrate the efficacy of UD heatmaps and the proposed algorithm in maximizing user connectivity as compared to K-means methods.

Every day, the company fields searches on topics like suicide, sexual assault, and domestic abuse. But Google wants to do more to direct people to the information they need, and says new AI techniques that better parse the complexities of language are helping. Specifically, Google is integrating its latest machine learning model, MUM, into its search engine to "more accurately detect a wider range of personal crisis searches." The company unveiled MUM at its IO conference last year, and has since used it to augment search with features that try to answer questions connected to the original search. In this case, MUM will be able to spot search queries related to difficult personal situations that earlier search tools could not, says Anne Merritt, a Google product manager for health and information quality.

Pervasiveness of tracking devices and enhanced availability of spatially located data has deepened interest in using them for various policy interventions, through computational data analysis tasks such as spatial hot spot detection. In this paper, we consider, for the first time to our best knowledge, fairness in detecting spatial hot spots. We motivate the need for ensuring fairness through statistical parity over the collective population covered across chosen hot spots. We then characterize the task of identifying a diverse set of solutions in the noteworthiness-fairness trade-off spectrum, to empower the user to choose a trade-off justified by the policy domain. Being a novel task formulation, we also develop a suite of evaluation metrics for fair hot spots, motivated by the need to evaluate pertinent aspects of the task. We illustrate the computational infeasibility of identifying fair hot spots using naive and/or direct approaches and devise a method, codenamed {\it FiSH}, for efficiently identifying high-quality, fair and diverse sets of spatial hot spots. FiSH traverses the tree-structured search space using heuristics that guide it towards identifying effective and fair sets of spatial hot spots. Through an extensive empirical analysis over a real-world dataset from the domain of human development, we illustrate that FiSH generates high-quality solutions at fast response times.

Police departments around the world have been experimenting with forms of place-based data-driven proactive policing for over two decades. Modern incarnations of such systems are commonly known as hot spot predictive policing. These systems predict where future crime is likely to concentrate such that police can allocate patrols to these areas and deter crime before it occurs. Previous research on fairness in predictive policing has concentrated on the feedback loops which occur when models are trained on discovered crime data, but has limited implications for models trained on victim crime reporting data. We demonstrate how differential victim crime reporting rates across geographical areas can lead to outcome disparities in common crime hot spot prediction models. Our analysis is based on a simulation patterned after district-level victimization and crime reporting survey data for Bogot\'a, Colombia. Our results suggest that differential crime reporting rates can lead to a displacement of predicted hotspots from high crime but low reporting areas to high or medium crime and high reporting areas. This may lead to misallocations both in the form of over-policing and under-policing.

But this year, the coronavirus pandemic complicates things further. To keep an eye on the matter, experts are using artificial intelligence to predict flu activity in metro areas across the country. Dr. Ravi Johar joined Friday's St. Louis on the Air to explain how the forecasting technology works. He is chief medical officer of UnitedHealthcare of Missouri. "Last year, we were able to put out a pretty powerful forecasting program that can determine where flu was increasing, where we thought a hot spot was going to be," he said.

Maria Viego and Cooper Glynn were thriving at their elementary schools. Maria, 10, adored the special certificates she earned volunteering to read to second-graders. Cooper, 9, loved being with his friends and how his teacher incorporated the video game Minecraft into lessons. But when their campuses shut down amid the COVID-19 pandemic, their experiences diverged dramatically. Maria is a student in the Coachella Valley Unified School District, where 90% of the children are from low-income families. She didn't have a computer, so she and her mother tried using a cellphone to access her online class, but the connection kept dropping, and they gave up after a week. She did worksheets until June, when she at last received a computer, but struggled to understand the work. Now, as school starts again online, she has told her mother she's frustrated and worried.