Kernel design for Multi-output Gaussian Processes (MOGP) has received increased attention recently. In particular, the Multi-Output Spectral Mixture kernel (MOSM) arXiv:1709.01298 approach has been praised as a general model in the sense that it extends other approaches such as Linear Model of Corregionalization, Intrinsic Corregionalization Model and Cross-Spectral Mixture. MOSM relies on Cram\'er's theorem to parametrise the power spectral densities (PSD) as a Gaussian mixture, thus, having a structural restriction: by assuming the existence of a PSD, the method is only suited for multi-output stationary applications. We develop a nonstationary extension of MOSM by proposing the family of harmonizable kernels for MOGPs, a class of kernels that contains both stationary and a vast majority of non-stationary processes. A main contribution of the proposed harmonizable kernels is that they automatically identify a possible nonstationary behaviour meaning that practitioners do not need to choose between stationary or non-stationary kernels. The proposed method is first validated on synthetic data with the purpose of illustrating the key properties of our approach, and then compared to existing MOGP methods on two real-world settings from finance and electroencephalography.

The Latent Dirichlet Allocation (LDA) model is a popular method for creating mixed-membership clusters. Despite having been originally developed for text analysis, LDA has been used for a wide range of other applications. We propose a new formulation for the LDA model which incorporates covariates. In this model, a negative binomial regression is embedded within LDA, enabling straight-forward interpretation of the regression coefficients and the analysis of the quantity of cluster-specific elements in each sampling units (instead of the analysis being focused on modeling the proportion of each cluster, as in Structural Topic Models). We use slice sampling within a Gibbs sampling algorithm to estimate model parameters. We rely on simulations to show how our algorithm is able to successfully retrieve the true parameter values and the ability to make predictions for the abundance matrix using the information given by the covariates. The model is illustrated using real data sets from three different areas: text-mining of Coronavirus articles, analysis of grocery shopping baskets, and ecology of tree species on Barro Colorado Island (Panama). This model allows the identification of mixed-membership clusters in discrete data and provides inference on the relationship between covariates and the abundance of these clusters.

Artificial intelligence (AI) isn't just transforming the world -- it's helping protect and preserve the future of the Amazon River. Rapid hydropower expansion has radically altered the Amazon River. When the natural flow of a river is altered, there are often serious, cascading changes. Now, AI and other computer science tools can help reduce these adverse and devastating effects on the environment, according to new research published in Science. FIU researcher Elizabeth Anderson was a part of a collaborative team of scientists from across the United States, Europe and South America who examined how cutting-edge technology can inform more sustainable and strategic planning.

It may sound obvious, perhaps even clichéd, but this mantra is something that must be remembered in ongoing political negotiations over Horizon Europe, which could see Switzerland and the UK excluded from EU research projects. We need more, not fewer, researchers collaborating to solve today's and tomorrow's challenges. By closely working with Swiss and British researchers, who have long played key roles, Horizon Europe projects will benefit – as they have in the past. This is the motivation behind ETH Zurich, which collaborates with IBM Research on nanotechnology, leading the Stick to Science campaign. This calls on all three parties – Switzerland, the UK and the EU – to try and solve the current stalemate and put Swiss and British association agreements in place.

In 2018, while the Argentine Congress was hotly debating whether to decriminalize abortion, the Ministry of Early Childhood in the northern province of Salta and the American tech giant Microsoft presented an algorithmic system to predict teenage pregnancy. They called it the Technology Platform for Social Intervention. Diego Jemio is a journalist, educator, and podcaster. He currently writes for the Clarín newspaper (Buenos Aires), Vértice (Mexico), and other media. He is the creator of the podcast Epistolar.

In 2020, IDRC and the Swedish International Development Cooperation Agency (Sida) launched the Artificial Intelligence for Development in Africa (IAPD Africa) program. This program aims to support the AI community and policymakers in developing responsible, ethical, and equitable AI that meets the continent's challenges, under the leadership of Africa. IDRC, the International Development Research Centre, was established in Canada in 1970 with a mission "to initiate, encourage, support and conduct research into the problems of the developing regions of the world and into the application of scientific, technical and other knowledge for the economic and social advancement of those regions . IDRC sees climate change and inequality, combined with the HIV/AIDS pandemic, as major obstacles to achieving the UN's sustainable development goals, and it is these challenges that it helps to address. While the center is headquartered in Ottawa, Canada, its five regional offices are located in India, Jordan, Kenya, Senegal, and Uruguay to be as close as possible to the researchers and projects it funds.

Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition. Nevertheless, resource-efficient GNN learning is a rarely explored topic despite its many benefits for edge computing and Internet of Things (IoT) applications. To improve this state of affairs, this work proposes efficient subgraph-level training via resource-aware graph partitioning (SUGAR). SUGAR first partitions the initial graph into a set of disjoint subgraphs and then performs local training at the subgraph-level. We provide a theoretical analysis and conduct extensive experiments on five graph benchmarks to verify its efficacy in practice. Our results show that SUGAR can achieve up to 33 times runtime speedup and 3.8 times memory reduction on large-scale graphs. We believe SUGAR opens a new research direction towards developing GNN methods that are resource-efficient, hence suitable for IoT deployment.

Consider a situation where a new patient arrives in the Intensive Care Unit (ICU) and is monitored by multiple sensors. We wish to assess relevant unmeasured physiological variables (e.g., cardiac contractility and output and vascular resistance) that have a strong effect on the patients diagnosis and treatment. We do not have any information about this specific patient, but, extensive offline information is available about previous patients, that may only be partially related to the present patient (a case of dataset shift). This information constitutes our prior knowledge, and is both partial and approximate. The basic question is how to best use this prior knowledge, combined with online patient data, to assist in diagnosing the current patient most effectively. Our proposed approach consists of three stages: (i) Use the abundant offline data in order to create both a non-causal and a causal estimator for the relevant unmeasured physiological variables. (ii) Based on the non-causal estimator constructed, and a set of measurements from a new group of patients, we construct a causal filter that provides higher accuracy in the prediction of the hidden physiological variables for this new set of patients. (iii) For any new patient arriving in the ICU, we use the constructed filter in order to predict relevant internal variables. Overall, this strategy allows us to make use of the abundantly available offline data in order to enhance causal estimation for newly arriving patients. We demonstrate the effectiveness of this methodology on a (non-medical) real-world task, in situations where the offline data is only partially related to the new observations. We provide a mathematical analysis of the merits of the approach in a linear setting of Kalman filtering and smoothing, demonstrating its utility.

Abstract-- Cross-view image matches have been widely explored on terrestrial image localization using aerial images from drones or satellites. This study expands the cross-view image match idea and proposes a cross-domain and cross-view localization framework. The method identifies the correlation between color aerial images and underwater acoustic images to improve the localization of underwater vehicles that travel in partially structured environments such as harbors and marinas. The approach is validated on a real dataset acquired by an underwater vehicle in a marina. The results show an improvement in the localization when compared to the dead reckoning of the vehicle.

The United States military just inched one step closer to bringing autonomous helicopters to the battlefield. Like most strange feats of advanced military technology, this one comes from The Pentagon's Defense Advanced Research Projects Agency, better known simply as "DARPA." On Tuesday, DARPA said a UH-60A Black Hawk helicopter outfitted with its experimental Aircrew Labor In-Cockpit Automation System (ALIAS) system safely completed a test flight without anyone in the chopper. The 30-minute test flight occurred over the weekend above a U.S. Army installation at Fort Campbell, Kentucky. DARPA describes its Aircrew Labor In-Cockpit Automation System (ALIAS) as a "tailorable, drop-in, removable kit," meant to add sophisticated automation to pre-built aircraft at a fraction of the cost of upgrading individual models with new, advanced avionics and software.