There are situations where data relevant to machine learning problems are distributed across multiple locations that cannot share the data due to regulatory, competitiveness, or privacy reasons. Machine learning approaches that require data to be copied to a single location are hampered by the challenges of data sharing. Federated Learning (FL) is a promising approach to learn a joint model over all the available data across silos. In many cases, the sites participating in a federation have different data distributions and computational capabilities. In these heterogeneous environments existing approaches exhibit poor performance: synchronous FL protocols are communication efficient, but have slow learning convergence and high energy cost; conversely, asynchronous FL protocols have faster convergence with lower energy cost, but higher communication. In this work, we introduce a novel energy-efficient Semi-Synchronous Federated Learning protocol that mixes local models periodically with minimal idle time and fast convergence. We show through extensive experiments over established benchmark datasets in the computer-vision domain as well as in real-world biomedical settings that our approach significantly outperforms previous work in data and computationally heterogeneous environments .

SUMMARY: Finding informative predictive features in high-dimensional biological case-control datasets is challenging. The Extreme Pseudo-Sampling (EPS) algorithm offers a solution to the challenge of feature selection via a combination of deep learning and linear regression models. First, using a variational autoencoder, it generates complex latent representations for the samples. Second, it classifies the latent representations of cases and controls via logistic regression. Third, it generates new samples (pseudo-samples) around the extreme cases and controls in the regression model. Finally, it trains a new regression model over the upsampled space. The most significant variables in this regression are selected. We present an open-source implementation of the algorithm that is easy to set up, use and customize. Our package enhances the original algorithm by providing new features and customizability for data preparation, model training and classification functionalities. We believe the new features will enable the adoption of the algorithm for a diverse range of datasets. AVAILABILITY AND IMPLEMENTATION: The software package for Python is available online at https://github.com/roohy/eps. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Many scientific prediction problems have spatiotemporal data- and modeling-related challenges in handling complex variations in space and time using only sparse and unevenly distributed observations. This paper presents a novel deep learning architecture, Deep learning predictions for LocATion-dependent Time-sEries data (DeepLATTE), that explicitly incorporates theories of spatial statistics into neural networks to addresses these challenges. In addition to a feature selection module and a spatiotemporal learning module, DeepLATTE contains an autocorrelation-guided semi-supervised learning strategy to enforce both local autocorrelation patterns and global autocorrelation trends of the predictions in the learned spatiotemporal embedding space to be consistent with the observed data, overcoming the limitation of sparse and unevenly distributed observations. During the training process, both supervised and semi-supervised losses guide the updates of the entire network to: 1) prevent overfitting, 2) refine feature selection, 3) learn useful spatiotemporal representations, and 4) improve overall prediction. We conduct a demonstration of DeepLATTE using publicly available data for an important public health topic, air quality prediction, in a well-studied, complex physical environment - Los Angeles. The experiment demonstrates that the proposed approach provides accurate fine-spatial-scale air quality predictions and reveals the critical environmental factors affecting the results.

The availability of massive datasets in genetics, neuroimaging, mobile health, and other subfields of biology and medicine promises new insights but also poses significant challenges. To realize the potential of big data in biomedicine, the National Institutes of Health launched the Big Data to Knowledge (BD2K) initiative, funding several centers of excellence in biomedical data analysis and a Training Coordinating Center (TCC) tasked with facilitating online and inperson training of biomedical researchers in data science. A major initiative of the BD2K TCC is to automatically identify, describe, and organize data science training resources available on the Web and provide personalized training paths for users. In this paper, we describe the construction of ERuDIte, the Educational Resource Discovery Index for Data Science, and its release as linked data. ERuDIte contains over 11,000 training resources including courses, video tutorials, conference talks, and other materials. The metadata for these resources is described uniformly using Schema.org. We use machine learning techniques to tag each resource with concepts from the Data Science Education Ontology, which we developed to further describe resource content. Finally, we map references to people and organizations in learning resources to entities in DBpedia, DBLP, and ORCID, embedding our collection in the web of linked data. We hope that ERuDIte will provide a framework to foster open linked educational resources on the Web.

Forecasting spatially correlated time series data is challenging because of the linear and non-linear dependencies in the temporal and spatial dimensions. Air quality forecasting is one canonical example of such tasks. Existing work, e.g., auto-regressive integrated moving average (ARIMA) and artificial neural network (ANN), either fails to model the non-linear temporal dependency or cannot effectively consider spatial relationships between multiple spatial time series data. In this paper, we present an approach for forecasting short-term PM2.5 concentrations using a deep learning model, the geo-context based diffusion convolutional recurrent neural network, GC-DCRNN. The model describes the spatial relationship by constructing a graph based on the similarity of the built environment between the locations of air quality sensors. The similarity is computed using the surrounding "important" geographic features regarding their impacts to air quality for each location (e.g., the area size of parks within a 1000-meter buffer, the number of factories within a 500-meter buffer). Also, the model captures the temporal dependency leveraging the sequence to sequence encoder-decoder architecture. We evaluate our model on two real-world air quality datasets and observe consistent improvement of 5%-10% over baseline approaches.

Air quality models are important for studying the impact of air pollutant on health conditions at a fine spatiotemporal scale. Existing work typically relies on area-specific, expert-selected attributes of pollution emissions (e,g., transportation) and dispersion (e.g., meteorology) for building the model for each combination of study areas, pollutant types, and spatiotemporal scales. In this paper, we present a data mining approach that utilizes publicly available OpenStreetMap (OSM) data to automatically generate an air quality model for the concentrations of fine particulate matter less than 2.5 μm in aerodynamic diameter at various temporal scales. Our experiment shows that our (domain-) expert-free model could generate accurate PM2.5 concentration predictions, which can be used to improve air quality models that traditionally rely on expert-selected input. Our approach also quantifies the impact on air quality from a variety of geographic features (i.e., how various types of geographic features such as parking lots and commercial buildings affect air quality and from what distance) representing mobile, stationary and area natural and anthropogenic air pollution sources. This approach is particularly important for enabling the construction of context-specific spatiotemporal models of air pollution, allowing investigations of the impact of air pollution exposures on sensitive populations such as children with asthma at scale.

According to the Centers for Disease Control, in the United States there are 6.8 million children living with asthma. Despite the importance of the disease, the available prognostic tools are not sufficient for biomedical researchers to thoroughly investigate the potential risks of the disease at scale. To overcome these challenges we present a big data integration and analysis infrastructure developed by our Data and Software Coordination and Integration Center (DSCIC) of the NIBIB-funded Pediatric Research using Integrated Sensor Monitoring Systems (PRISMS) program. Our goal is to help biomedical researchers to efficiently predict and prevent asthma attacks. The PRISMS-DSCIC is responsible for collecting, integrating, storing, and analyzing real-time environmental, physiological and behavioral data obtained from heterogeneous sensor and traditional data sources. Our architecture is based on the Apache Kafka, Spark and Hadoop frameworks and PostgreSQL DBMS. A main contribution of this work is extending the Spark framework with a mediation layer, based on logical schema mappings and query rewriting, to facilitate data analysis over a consistent harmonized schema. The system provides both batch and stream analytic capabilities over the massive data generated by wearable and fixed sensors.

We are interested in scalable data integration and data exchange under constraints/dependencies. In data exchange the problem is how to materialize a target database instance, satisfying the source-to-target and target dependencies, that provides the certain answers. In data integration, the problem is how to rewrite a query over the target schema into a query over the source schemas that provides the certain answers. In both these problems we make use of the chase algorithm, the main tool to reason with dependencies. Our first contribution is to introduce the frugal chase, which produces smaller universal solutions than the standard chase, still remaining polynomial in data complexity. Our second contribution is to use the frugal chase to scale up query answering using views under LAV weakly acyclic target constraints, a useful language capturing RDF/S. The latter problem can be reduced to query rewriting using views without constraints by chasing the source-to-target mappings with the target constraints. We construct a compact graph-based representation of the mappings and the constraints and develop an efficient algorithm to run the frugal chase on this representation. We show experimentally that our approach scales to large problems, speeding up the compilation of the dependencies into the mappings by close to 2 and 3 orders of magnitude, compared to the standard and the core chase, respectively. Compared to the standard chase, we improve online query rewriting time by a factor of 3, while producing equivalent, but smaller, rewritings of the original query.

In this paper we consider the problem of answering queries using views, which is important for data integration, query optimization, and data warehouses. We consider its simplest form, conjunctive queries and views, which already is NP-complete. Our context is data integration, so we search for maximally-contained rewritings. By looking at the problem from a graph perspective we are able to gain a better insight and develop an algorithm which compactly represents common patterns in the source descriptions, and (optionally) pushes some computation offline. This together with other optimizations result in an experimental performance about two orders of magnitude faster than current state-of-the-art algorithms, rewriting queries using over 10000 views within seconds.

OBJECTIVE: As biomedical technology becomes increasingly sophisticated, researchers can probe ever more subtle effects with the added requirement that the investigation of small effects often requires the acquisition of large amounts of data. In biomedicine, these data are often acquired at, and later shared between, multiple sites. There are both technological and sociological hurdles to be overcome for data to be passed between researchers and later made accessible to the larger scientific community. The goal of the Biomedical Informatics Research Network (BIRN) is to address the challenges inherent in biomedical data sharing. MATERIALS AND METHODS: BIRN tools are grouped into 'capabilities' and are available in the areas of data management, data security, information integration, and knowledge engineering. BIRN has a user-driven focus and employs a layered architectural approach that promotes reuse of infrastructure. BIRN tools are designed to be modular and therefore can work with pre-existing tools. BIRN users can choose the capabilities most useful for their application, while not having to ensure that their project conforms to a monolithic architecture. RESULTS: BIRN has implemented a new software-based data-sharing infrastructure that has been put to use in many different domains within biomedicine. BIRN is actively involved in outreach to the broader biomedical community to form working partnerships. CONCLUSION: BIRN's mission is to provide capabilities and services related to data sharing to the biomedical research community. It does this by forming partnerships and solving specific, user-driven problems whose solutions are then available for use by other groups.

Accurate and efficient integration of geospatial data is an important problem with applications in areas such as emergency response and urban planning. Some of the key challenges in supporting large-scale geospatial data integration are automatically computing the quality of the data provided by a large number of geospatial sources and dynamically providing high quality answers to the user queries based on a quality criteria supplied by the user. We describe a framework called the Quality-driven Geospatial Mediator (QGM) that supports efficient and accurate integration of geospatial data from a large number of sources. The key contributions of our framework are: (1) the ability to automatically estimate the quality of data provided by a source by using the information from another source of known quality, (2) representing the quality of data provided by the sources in a declarative data integration framework, and (3) a query answering technique that exploits the quality information to provide high quality geospatial data in response to user queries. Our experimental evaluation using over 1200 real-world sources shows that QGM can accurately estimate the quality of geospatial sources. Moreover, QGM provides better quality data in response to the user queries compared to the traditional data integration systems and does so with lower response time.

While the information resources on the Web are vast, the sources are often hard to nd, painful to use, and dicult to integrate. Wehavedeveloped the Heracles framework for building Web-based information assistants. This framework provides the infrastructure to rapidly construct new applications that extract information from multiple Web sources and interactively integrate the data using a dynamic, hierarchical constraint network. This paper describes the core technologies that comprise the framework, including information extraction, hierarchical template representation, and constraint propagation. In addition, we present an application of this framework, the ###### #########, which is an interactivetravel planning system. We also briey describe our experience using the same framework to build a second application, the ######### #########, which extracts and integrates geographic-related data about countries thorughout the world. We believe these types of information assistants provide a signicant step forward in fully exploiting the information available on the Internet. 1.

The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. Today, the only way to do this is to build specialized applications, which are time-consuming to develop and difficult to maintain. We have addressed this problem by creating the technology and tools for rapidly constructing information agents that extract, query, and integrate data from web sources. Our approach is based on a uniform representation that makes it simple and efficient to integrate multiple sources. Instead of building specialized algorithms for handling web sources, we have developed methods for mapping web sources into this uniform representation. This approach builds on work from knowledge representation, databases, machine learning and automated planning. The resulting system, called Ariadne, makes it fast and easy to build new information agents that access existing web sources. Ariadne also makes it easy to maintain these agents and incorporate new sources as they become available.

Domain-independent planning is a hard combinatorial problem. Taking into account plan quality makes the task even more difficult. This article introduces Planning by Rewriting (PbR), a new paradigm for efficient high-quality domain-independent planning. PbR exploits declarative plan-rewriting rules and efficient local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a high-quality plan. In addition to addressing the issues of planning efficiency and plan quality, this framework offers a new anytime planning algorithm. We have implemented this planner and applied it to several existing domains. The experimental results show that the PbR approach provides significant savings in planning effort while generating high-quality plans.

The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. Today, the only way to achieve this integration is by building specialized applications, which are time-consuming to develop and difficult to maintain. We are addressing this problem by creating the technology and tools for rapidly constructing information mediators that extract, query, and integrate data from web sources. The resulting system, called Ariadne, makes it feasible to rapidly build information mediators that access existing web sources.