Personal health libraries (PHLs) provide a single point of secure access to patients digital health data and enable the integration of knowledge stored in their digital health profiles with other sources of global knowledge. PHLs can help empower caregivers and health care providers to make informed decisions about patients health by understanding medical events in the context of their lives. This paper reports the implementation of a mobile health digital intervention that incorporates both digital health data stored in patients PHLs and other sources of contextual knowledge to deliver tailored recommendations for improving self-care behaviors in diabetic adults. We conducted a thematic assessment of patient functional and nonfunctional requirements that are missing from current EHRs based on evidence from the literature. We used the results to identify the technologies needed to address those requirements. We describe the technological infrastructures used to construct, manage, and integrate the types of knowledge stored in the PHL. We leverage the Social Linked Data (Solid) platform to design a fully decentralized and privacy-aware platform that supports interoperability and care integration. We provided an initial prototype design of a PHL and drafted a use case scenario that involves four actors to demonstrate how the proposed prototype can be used to address user requirements, including the construction and management of the PHL and its utilization for developing a mobile app that queries the knowledge stored and integrated into the PHL in a private and fully decentralized manner to provide better recommendations. The proposed PHL helps patients and their caregivers take a central role in making decisions regarding their health and equips their health care providers with informatics tools that support the collection and interpretation of the collected knowledge.

There are demographic biases in the SOTA CNN used for FR. Our BFW dataset serves as a proxy to measure bias across ethnicity and gender subgroups, allowing us to characterize FR performances per subgroup. We show performances are non-optimal when a single score threshold is used to determine whether sample pairs are genuine or imposter. Furthermore, actual performance ratings vary greatly from the reported across subgroups. Thus, claims of specific error rates only hold true for populations matching that of the validation data. We mitigate the imbalanced performances using a novel domain adaptation learning scheme on the facial encodings extracted using SOTA deep nets. Not only does this technique balance performance, but it also boosts the overall performance. A benefit of the proposed is to preserve identity information in facial features while removing demographic knowledge in the lower dimensional features. The removal of demographic knowledge prevents future potential biases from being injected into decision-making. Additionally, privacy concerns are satisfied by this removal. We explore why this works qualitatively with hard samples. We also show quantitatively that subgroup classifiers can no longer learn from the encodings mapped by the proposed.

Quality-of-Service prediction of web service is an integral part of services computing due to its diverse applications in the various facets of a service life cycle, such as service composition, service selection, service recommendation. One of the primary objectives of designing a QoS prediction algorithm is to achieve satisfactory prediction accuracy. However, accuracy is not the only criteria to meet while developing a QoS prediction algorithm. The algorithm has to be faster in terms of prediction time so that it can be integrated into a real-time recommendation or composition system. The other important factor to consider while designing the prediction algorithm is scalability to ensure that the prediction algorithm can tackle large-scale datasets. The existing algorithms on QoS prediction often compromise on one goal while ensuring the others. In this paper, we propose a semi-offline QoS prediction model to achieve three important goals simultaneously: higher accuracy, faster prediction time, scalability. Here, we aim to predict the QoS value of service that varies across users. Our framework consists of multi-phase prediction algorithms: preprocessing-phase prediction, online prediction, and prediction using the pre-trained model. In the preprocessing phase, we first apply multi-level clustering on the dataset to obtain correlated users and services. We then preprocess the clusters using collaborative filtering to remove the sparsity of the given QoS invocation log matrix. Finally, we create a two-staged, semi-offline regression model using neural networks to predict the QoS value of service to be invoked by a user in real-time. Our experimental results on four publicly available WS-DREAM datasets show the efficiency in terms of accuracy, scalability, fast responsiveness of our framework as compared to the state-of-the-art methods.

Episodic and semantic memory are critical components of the human memory model. The theory of complementary learning systems (McClelland et al., 1995) suggests that the compressed representation produced by a serial event (episodic memory) is later restructured to build a more generalized form of reusable knowledge (semantic memory). In this work we develop a new principled Bayesian memory allocation scheme that bridges the gap between episodic and semantic memory via a hierarchical latent variable model. We take inspiration from traditional heap allocation and extend the idea of locally contiguous memory to the Kanerva Machine, enabling a novel differentiable block allocated latent memory. In contrast to the Kanerva Machine, we simplify the process of memory writing by treating it as a fully feed forward deterministic process, relying on the stochasticity of the read key distribution to disperse information within the memory. We demonstrate that this allocation scheme improves performance in memory conditional image generation, resulting in new state-of-the-art conditional likelihood values on binarized MNIST ( 41.58 nats/image), binarized Omniglot ( 66.24 nats/image), as well as presenting competitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32 32. Memory is a central tenet in the model of human intelligence and is crucial to long-term reasoning and planning.

As Women in AI Education Ambassador for Australia Angela Kim told Women's Agenda: AI tech is evolving at the "speed of light", while much about machine learning models can be automated, human must be included in its creation – which means the potential for human bias. Women's Agenda spoke to Charles Sturt University, Associate Professor in Computer Science, Lihong Zheng, who has lectured in mathematics and computer science since 2008. Lihong believes encouraging women to pursue careers in STEM begins in early primary school and continues throughout high school. Lihong started her career at a time when there were even fewer women working in STEM, particularly in Australia. Great mentors and having more women leaders in technology and science make it more accessible for girls to pursue degrees in STEM.

Sure, evolution invented mammals that soar 200 feet through the air on giant flaps of skin and 3-foot-wide crabs that climb trees, but has it ever invented a four-legged animal with telescoping limbs? Meet the Dynamic Robot for Embodied Testing, aka DyRET, a machine that changes the length of its legs on the fly--not to creep out humans, but to help robots of all stripes not fall over so much. Writing today in the journal Nature Machine Intelligence, researchers in Norway and Australia describe how they got DyRET to learn how to lengthen or shorten its limbs to tackle different kinds of terrains. Then once they let the shape-shifting robot loose in the real world, it used that training to efficiently tread surfaces it had never seen before. "We can actually take the robot, bring it outside, and it will just start adapting," says computer scientist Tønnes Nygaard of the University of Oslo and the Norwegian Defence Research Establishment, the lead author on the paper.

ZEISS Germany, Nikon Instruments, Ariadne.ai, Mindpeak, Aiforia, Celly.AI Corporation, SVision LLC, Scopio Lab, AlexaPath, MicroscopeIT, Nanotronics, AiScope, Thermo Fisher, Ash Vision, Sigtuple, GoMicro, MantiScope, Cognex, Paige.AI, Motic, and Pleora Technologies among others are the players in the artificial intelligence microscopy market.Brooklyn, New York, March 10, 2021 (GLOBE NEWSWIRE) -- According to a new market research report published by Global Market Estimates, the Artificial Intelligence Microscopy Market will grow with a CAGR value of 7.2 percent from 2021 to 2026. The market for AI in microscopy will increase with the rising prevalence of infectious disease, cancer, and other disorders that require routine blood morphology analysis. Moreover, with the rising need for advanced live-cell imaging, cloud sharing, and efficient lab workflow, clubbed with the rising research activities in the field of drug testing and toxicology, the market will grow rapidly from 2020 to 2021. Browse 151 Market Data Tables and 111 Figures spread through 181 Pages and in-depth TOC on “Global Artificial Intelligence Microscopy Market - Forecast to 2026" https://www.globalmarketestimates.com/market-report/global-artificial-intelligence-microscopy-market-2824 Key Market Insights Optical or light microscopy is estimated to be the largest segment as per market share or market revenue generation from 2021 to 2026Cancer disease diagnosis and prevention is the major driving factor for this segment to grow rapidlyThe market for independent & private laboratories will be dominant from 2021 to 2026ZEISS Germany, Nikon Instruments, Ariadne.ai, Mindpeak, Aiforia, Celly.AI Corporation, SVision LLC, Scopio Lab, AlexaPath, MicroscopeIT, Nanotronics, AiScope, Thermo Fisher, Ash Vision, Sigtuple, GoMicro, MantiScope, Cognex, Paige.AI, Motic, and Pleora Technologies among others are the players in the artificial intelligence microscopy market Browse the Report @ https://www.globalmarketestimates.com/market-report/global-artificial-intelligence-microscopy-market-2824 Imaging Modalities Outlook (Revenue, USD Billion, 2019-2026) Optical MicroscopyElectron MicroscopyScanning Probe Microscopy Application Outlook (Revenue, USD Billion, 2019-2026) Clinical PathologyNeuron MorphologyCell BiologyPharmacology & ToxicologyOncologyOthers Product Type Outlook (Revenue, USD Billion, 2019-2026) AI-Enabled Cloud SoftwareAI-Enabled Microscopes End-User Outlook (Revenue, USD Billion, 2019-2026) Hospital LaboratoriesIndependent & Private LaboratoriesAcademic Research LabsPharmaceutical & Biotechnology LaboratoriesContract Research Organizations Regional Outlook (Revenue, USD Billion, 2019-2026) North America The U.S.CanadaMexico Europe GermanyUKFranceSpainItalyRest of Europe Asia Pacific ChinaIndiaJapanSouth KoreaAustraliaRest of APAC Central & South America BrazilArgentinaRest of CSA Middle East & Africa Saudi ArabiaUAERest of MEA Website: Global Market Estimates CONTACT: Contact: Yash Jain Email address: yash.jain@globalmarketestimates.com Phone Number: +16026667238

Document-level relation extraction aims to discover relations between entities across a whole document. How to build the dependency of entities from different sentences in a document remains to be a great challenge. Current approaches either leverage syntactic trees to construct document-level graphs or aggregate inference information from different sentences. In this paper, we build cross-sentence dependencies by inferring compositional relations between inter-sentence mentions. Adopting aggressive linking strategy, intermediate relations are reasoned on the document-level graphs by mention convolution. We further notice the generalization problem of NA instances, which is caused by incomplete annotation and worsened by fully-connected mention pairs. An improved ranking loss is proposed to attend this problem. Experiments show the connections between different mentions are crucial to document-level relation extraction, which enables the model to extract more meaningful higher-level compositional relations.

Hierarchical forecasting with intermittent time series is a challenge in both research and empirical studies. The overall forecasting performance is heavily affected by the forecasting accuracy of intermittent time series at bottom levels. In this paper, we present a forecasting reconciliation approach that treats the bottom level forecast as latent to ensure higher forecasting accuracy on the upper levels of the hierarchy. We employ a pure deep learning forecasting approach N-BEATS for continuous time series on top levels and a widely used tree-based algorithm LightGBM for the bottom level intermittent time series. The hierarchical forecasting with alignment approach is simple and straightforward to implement in practice. It sheds light on an orthogonal direction for forecasting reconciliation. When there is difficulty finding an optimal reconciliation, allowing suboptimal forecasts at a lower level could retain a high overall performance. The approach in this empirical study was developed by the first author during the M5 Forecasting Accuracy competition ranking second place. The approach is business orientated and could be beneficial for business strategic planning.

In the estimation of the causal effect under linear Structural Causal Models (SCMs), it is common practice to first identify the causal structure, estimate the probability distributions, and then calculate the causal effect. However, if the goal is to estimate the causal effect, it is not necessary to fix a single causal structure or probability distributions. In this paper, we first show from a Bayesian perspective that it is Bayes optimal to weight (average) the causal effects estimated under each model rather than estimating the causal effect under a fixed single model. This idea is also known as Bayesian model averaging. Although the Bayesian model averaging is optimal, as the number of candidate models increases, the weighting calculations become computationally hard. We develop an approximation to the Bayes optimal estimator by using Gaussian scale mixture distributions.