To reduce the redundancy in representation, this paper extends some recent work on diversity regularization in neural networks by proposing the so-called hyperspherical potential energy defined on the Euclidean distance or the angular distance, which, when minimized, helps to increase the diversity of the input weights of different neurons and hence reduce the redundancy. The regularized loss function contains two regularization terms corresponding to the hidden units and the output units, respectively.

Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has organized an annual influenza forecasting challenge with the motivation that accurate probabilistic forecasts could improve situational awareness and yield more effective public health actions. Starting with the 2021/22 influenza season, the forecasting targets for this challenge have been based on hospital admissions reported in the CDC's National Healthcare Safety Network (NHSN) surveillance system. Reporting of influenza hospital admissions through NHSN began within the last few years, and as such only a limited amount of historical data are available for this signal. To produce forecasts in the presence of limited data for the target surveillance system, we augmented these data with two signals that have a longer historical record: 1) ILI+, which estimates the proportion of outpatient doctor visits where the patient has influenza; and 2) rates of laboratory-confirmed influenza hospitalizations at a selected set of healthcare facilities. Our model, Flusion, is an ensemble that combines gradient boosting quantile regression models with a Bayesian autoregressive model. The gradient boosting models were trained on all three data signals, while the autoregressive model was trained on only the target signal; all models were trained jointly on data for multiple locations. Flusion was the top-performing model in the CDC's influenza prediction challenge for the 2023/24 season. In this article we investigate the factors contributing to Flusion's success, and we find that its strong performance was primarily driven by the use of a gradient boosting model that was trained jointly on data from multiple surveillance signals and locations. These results indicate the value of sharing information across locations and surveillance signals, especially when doing so adds to the pool of available training data.

Vector maps find widespread utility across diverse domains due to their capacity to not only store but also represent discrete data boundaries such as building footprints, disaster impact analysis, digitization, urban planning, location points, transport links, and more. Although extensive research exists on identifying building footprints and road types from satellite imagery, the generation of vector maps from such imagery remains an area with limited exploration. Furthermore, conventional map generation techniques rely on labor-intensive manual feature extraction or rule-based approaches, which impose inherent limitations. To surmount these limitations, we propose a novel method called HPix, which utilizes modified Generative Adversarial Networks (GANs) to generate vector tile map from satellite images. HPix incorporates two hierarchical frameworks: one operating at the global level and the other at the local level, resulting in a comprehensive model. Through empirical evaluations, our proposed approach showcases its effectiveness in producing highly accurate and visually captivating vector tile maps derived from satellite images. We further extend our study's application to include mapping of road intersections and building footprints cluster based on their area.

Emergence is a pregnant property in various fields. It is the fact for a phenomenon to appear surprisingly and to be such that it seems at first sight that it is not possible to predict its apparition. That is the reason why it has often been said that emergence is a subjective property relative to the observer. Some mathematical systems having very simple and deterministic rules nevertheless show emergent behavior. Studying these systems shed a new light on the subject and allows to define a new concept, computational irreducibility, which deals with behaviors that even though they are totally deterministic cannot be predicted without simulating them. Computational irreducibility is then a key for understanding emergent phenomena from an objective point of view that does not need the mention of any observer.

The Biden administration unveiled a set of far-reaching goals Tuesday aimed at averting harms caused by the rise of artificial intelligence systems, including guidelines for how to protect people's personal data and limit surveillance. The Blueprint for an AI Bill of Rights notably does not set out specific enforcement actions, but instead is intended as a White House call to action for the U.S. government to safeguard digital and civil rights in an AI-fueled world, officials said. "This is the Biden-Harris administration really saying that we need to work together, not only just across government, but across all sectors, to really put equity at the center and civil rights at the center of the ways that we make and use and govern technologies," said Alondra Nelson, deputy director for science and society at the White House Office of Science and Technology Policy. "We can and should expect better and demand better from our technologies." The office said the white paper represents a major advance in the administration's agenda to hold technology companies accountable, and highlighted various federal agencies' commitments to weighing new rules and studying the specific impacts of AI technologies.

The Blueprint for an AI Bill of Rights notably does not set out specific enforcement actions, but instead is intended as a White House call to action for the U.S. government to safeguard digital and civil rights in an AI-fueled world, officials said. "This is the Biden-Harris administration really saying that we need to work together, not only just across government, but across all sectors, to really put equity at the center and civil rights at the center of the ways that we make and use and govern technologies," said Alondra Nelson, deputy director for science and society at the White House Office of Science and Technology Policy. "We can and should expect better and demand better from our technologies." The office said the white paper represents a major advance in the administration's agenda to hold technology companies accountable, and highlighted various federal agencies' commitments to weighing new rules and studying the specific impacts of AI technologies. The document emerged after a year-long consultation with more than two dozen different departments, and also incorporates feedback from civil society groups, technologists, industry researchers and tech companies including Palantir and Microsoft.

"This is the Biden-Harris administration really saying that we need to work together, not only just across government, but across all sectors, to really put equity at the center and civil rights at the center of the ways that we make and use and govern technologies," said Alondra Nelson, deputy director for science and society at the White House Office of Science and Technology Policy. "We can and should expect better and demand better from our technologies." The office said the white paper represents a major advance in the administration's agenda to hold technology companies accountable, and highlighted various federal agencies' commitments to weighing new rules and studying the specific impacts of AI technologies. The document emerged after a year-long consultation with more than two dozen different departments, and also incorporates feedback from civil society groups, technologists, industry researchers and tech companies including Palantir and Microsoft. It suggests five core principles that the White House says should be built into AI systems to limit the impacts of algorithmic bias, give users control over their data and ensure that automated systems are used safely and transparently.

Representative image San Francisco: The US space agency has collaborated with Google to help local governments improve their monitoring and prediction of air quality. NASA and Google will develop advanced machine learning-based algorithms that link space data with Google Earth Engine data streams to generate high-resolution air quality maps in near real-time. "We're thrilled about our partnership with NASA to make daily air quality more actionable at a local level," said Rebecca Moore, director at Google Earth, Earth Engine and Outreach at Google. The results will create city-scale, near real-time estimation and forecasting of harmful pollutants, such as nitrogen dioxide and fine particulate matter. Google has incorporated two new NASA data sets into the Earth Engine Catalogue that are automatically updated daily.

The Department of Innovation & Technology (InnoTech) under Druk Holding & Investments (DHI), the commercial arm of the Royal Government of Bhutan, has partnered with Omdena, a global collaborative platform that makes AI for good accessible to all. This partnership is a step further in DHI InnoTech's mission to strategize technology and innovation pathways to enhance access and diffusion of emerging technologies, and build local capacity in the fields of science and technology. Omdena will assist InnoTech in hosting a global 2-week hackathon wherein InnoTech will identify key themes and issues that can be resolved using innovative AI/ML applications. Omdena will work with 50 AI engineers over an additional 8-week challenge to develop the idea or POC selected from the hackathon into a fully deployable algorithm. The pilot InnoTech-Omdena event will serve as a showcase for local institutions and the general public who are interested in AI/ML.

Connectivity has improved by leaps and bounds since the dawn of the 21st century, and that has in turn enabled the growth of many industries, analytics included. However, as much as bandwidth and latency may have improved across large distances, there is still no real substitute for the response time of local hardware, and when it comes to analyzing big data, sometimes that's really important. With masses of on board sensors and cameras, there is a ton of data to process at any given moment, helping the passengers avoid obstacles and stay within the bounds of the roadway. Sending that information to the cloud is doable, but in moments of danger where every millisecond counts, it's far better to make those decisions locally. Relying on a remote connection would also require such vehicles to remain in strong coverage areas such as major population centers, where the improved infrastructure of traditional transport networks makes driverless transit less useful than it might in somewhere more remote.