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.

Imbalanced classification tasks are widespread in many real-world applications. For such classification tasks, in comparison with the accuracy rate, it is usually much more appropriate to use non-decomposable performance measures such as the Area Under the receiver operating characteristic Curve (AUC) and the $F_\beta$ measure as the classification criterion since the label class is imbalanced. On the other hand, the minimax probability machine is a popular method for binary classification problems and aims at learning a linear classifier by maximizing the accuracy rate, which makes it unsuitable to deal with imbalanced classification tasks. The purpose of this paper is to develop a new minimax probability machine for the $F_\beta$ measure, called MPMF, which can be used to deal with imbalanced classification tasks. A brief discussion is also given on how to extend the MPMF model for several other non-decomposable performance measures listed in the paper. To solve the MPMF model effectively, we derive its equivalent form which can then be solved by an alternating descent method to learn a linear classifier. Further, the kernel trick is employed to derive a nonlinear MPMF model to learn a nonlinear classifier. Several experiments on real-world benchmark datasets demonstrate the effectiveness of our new model.

We consider the problem of reaching a propositional goal condition in fully-observable non-deterministic (FOND) planning under a general class of fairness assumptions that are given explicitly. The fairness assumptions are of the form A/B and say that state trajectories that contain infinite occurrences of an action a from A in a state s and finite occurrence of actions from B, must also contain infinite occurrences of action a in s followed by each one of its possible outcomes. The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state. We show that strong and strong-cyclic FOND planning, as well as QNP planning, a planning model introduced recently for generalized planning, are all special cases of FOND planning with fairness assumptions of this form which can also be combined. FOND+ planning, as this form of planning is called, combines the syntax of FOND planning with some of the versatility of LTL for expressing fairness constraints. A new planner is implemented by reducing FOND+ planning to answer set programs, and the performance of the planner is evaluated in comparison with FOND and QNP planners, and LTL synthesis tools.

Diabetes is currently one of the most common, dangerous, and costly diseases in the world that is caused by an increase in blood sugar or a decrease in insulin in the body. Diabetes can have detrimental effects on people's health if diagnosed late. Today, diabetes has become one of the challenges for health and government officials. Prevention is a priority, and taking care of people's health without compromising their comfort is an essential need. In this study, the Ensemble training methodology based on genetic algorithms are used to accurately diagnose and predict the outcomes of diabetes mellitus. In this study, we use the experimental data, real data on Indian diabetics on the University of California website. Current developments in ICT, such as the Internet of Things, machine learning, and data mining, allow us to provide health strategies with more intelligent capabilities to accurately predict the outcomes of the disease in daily life and the hospital and prevent the progression of this disease and it's many complications. The results show the high performance of the proposed method in diagnosing the disease, which has reached 98.8%, and 99% accuracy in this study.

In the age of the intelligent era, more and more innovative concepts emerge daily. One of them is undoubtedly the field of "artificial intelligence." In this case, specifically, "deep learning," a sub-branch of AI, will encounter us when we need to dive deeper into complex problems, and it is used in many areas today. We develop deep learning models to achieve specific tasks, and in some cases, surpass human, repetitive capabilities. In this article, we will explore how the GPU is used for deep learning with code examples.

A Data Scientist is responsible for extracting, manipulating, pre-processing and generating predictions out of data. In order to do so, he requires various statistical tools and programming languages. In this article, we will share some of the Data Science Tools used by Data Scientists to carry out their data operations. We will understand the key features of the tools, benefits they provide and comparison of various data science tools. Stay updated with latest technology trends Join DataFlair on Telegram!! Data Science has emerged out as one of the most popular fields of 21st Century. Companies employ Data Scientists to help them gain insights about the market and to better their products.