Automated mitotic detection in time-lapse phasecontrast microscopy provides us much information for cell behavior analysis, and thus several mitosis detection methods have been proposed. However, these methods still have two problems; 1) they cannot detect multiple mitosis events when there are closely placed. 2) they do not consider the annotation gaps, which may occur since the appearances of mitosis cells are very similar before and after the annotated frame. In this paper, we propose a novel mitosis detection method that can detect multiple mitosis events in a candidate sequence and mitigate the human annotation gap via estimating a spatiotemporal likelihood map by 3DCNN. In this training, the loss gradually decreases with the gap size between ground truth and estimation. This mitigates the annotation gaps. Our method outperformed the compared methods in terms of F1- score using a challenging dataset that contains the data under four different conditions.

Recurrent neural networks (RNNs) are more suitable for learning non-linear dependencies in dynamical systems from observed time series data. In practice all the external variables driving such systems are not known a priori, especially in economical forecasting. A class of RNNs called Error Correction Neural Networks (ECNNs) was designed to compensate for missing input variables. It does this by feeding back in the current step the error made in the previous step. The ECNN is implemented in Python by the computation of the appropriate gradients and it is tested on stock market predictions. As expected it out performed the simple RNN and LSTM and other hybrid models which involve a de-noising pre-processing step. The intuition for the latter is that de-noising may lead to loss of information.

Network management, whether for malfunction analysis, failure prediction, performance monitoring and improvement, generally involves large amounts of data from different sources. To effectively integrate and manage these sources, automatically finding semantic matches among their schemas or ontologies is crucial. Existing approaches on database matching mainly fall into two categories. One focuses on the schema-level matching based on schema properties such as field names, data types, constraints and schema structures. Network management databases contain massive tables (e.g., network products, incidents, security alert and logs) from different departments and groups with nonuniform field names and schema characteristics. It is not reliable to match them by those schema properties. The other category is based on the instance-level matching using general string similarity techniques, which are not applicable for the matching of large network management databases. In this paper, we develop a matching technique for large NEtwork MAnagement databases (NEMA) deploying instance-level matching for effective data integration and connection. We design matching metrics and scores for both numerical and non-numerical fields and propose algorithms for matching these fields. The effectiveness and efficiency of NEMA are evaluated by conducting experiments based on ground truth field pairs in large network management databases. Our measurement on large databases with 1,458 fields, each of which contains over 10 million records, reveals that the accuracies of NEMA are up to 95%. It achieves 2%-10% higher accuracy and 5x-14x speedup over baseline methods.

We find that the requirement of model interpretations to be faithful is vague and incomplete. Indeed, recent work refers to interpretations as unfaithful despite adhering to the available definition. Similarly, we identify several critical failures with the notion of textual highlights as faithful interpretations, although they adhere to the faithfulness definition. With textual highlights as a case-study, and borrowing concepts from social science, we identify that the problem is a misalignment between the causal chain of decisions (causal attribution) and social attribution of human behavior to the interpretation. We re-formulate faithfulness as an accurate attribution of causality to the model, and introduce the concept of "aligned faithfulness": faithful causal chains that are aligned with their expected social behavior. The two steps of causal attribution and social attribution *together* complete the process of explaining behavior, making the alignment of faithful interpretations a requirement. With this formalization, we characterize the observed failures of misaligned faithful highlight interpretations, and propose an alternative causal chain to remedy the issues. Finally, we the implement highlight explanations of proposed causal format using contrastive explanations.

Seoul – In a cramped office in eastern Seoul, Hwang Seungwon points a remote control toward a huge NASA-like overhead screen stretching across one of the walls. With each flick of the control, a colorful array of pie charts, graphs and maps reveals the search habits of thousands of South Korean senior citizens being monitored by voice-enabled "smart" speakers, an experimental remote care service the company says is increasingly needed during the coronavirus crisis. "We closely monitor for signs of danger, whether they are more frequently using search words that indicate rising states of loneliness or insecurity," said Hwang, director of a social enterprise established by SK Telecom to handle the service. Trigger words lead to a recommendation for a visit by local public health officials. As South Korea's government pushes to allow businesses to access vast amounts of personal information and to ease restrictions holding back telemedicine, tech firms could potentially find much bigger markets for their artificial intelligence and other emerging technologies.

You, a person who's currently on the English-speaking internet in The Year of The Pandemic, have definitely seen public service information about Covid-19. You've probably been unable to escape seeing quite a lot of it, both online and offline, from handwashing posters to social distancing tape to instructional videos for face covering. But if we want to avoid a pandemic spreading to all the humans in the world, this information also has to reach all the humans of the world--and that means translating Covid PSAs into as many languages as possible, in ways that are accurate and culturally appropriate. It's easy to overlook how important language is for health if you're on the English-speaking internet, where "is this headache actually something to worry about?" is only a quick Wikipedia article or WebMD search away. For over half of the world's population, people can't expect to Google their symptoms, nor even necessarily get a pamphlet from their doctor explaining their diagnosis, because it's not available in a language they can understand.

Malaysia 5.0 outlines a problem-solving approach to society's challenges and problems through the deployment and implementation of Fourth Industrial Revolution (IR4.0) The term "Society 5.0" describes the next stage of the evolution of societal communities, following the hunting society (Society 1.0), agricultural society (Society 2.0), industrial society (Society 3.0), and information society (Society 4.0). The key differentiation of Society 5.0 (the digital age) from Society 4.0 (the information age) is the convergence of the virtual world with the physical world. Covid-19 has accelerated the migration of society from physical infrastructures onto digital infrastructures, but Society 5.0 holds the promise to bring these back together through the use of IR4.0 technologies such as artificial intelligence (AI), internet of things (IoT), blockchain and digital assets (FinTech). A national IR4.0 policy is needed to create a new narrative for Malaysia as an innovation economy that can compete in a disruptive technology world, serve as a springboard into Asean, bridge Asia, the Middle East and Africa, as well as connect with the 1.8 billion Muslims worldwide.

In a rush to find solutions for the Covid-19 pandemic, researchers are deploying machine learning algorithms to trawl through data that might give us more clues about the virus. Some claim to have identified potential treatments based on the data, while others are using it to screen patients or identify those at highest risk. But, like their vaccine and drug counterparts, many of these algorithms are still unproven. With hundreds of research articles describing the use of artificial intelligence or machine learning -- many of them preprints -- it can be difficult to sort out which ones are most effective. "I've heard a lot of hype about machine learning being applied to battling Covid-19, but I haven't seen very many concrete examples where you could imagine in the short- or medium-term something that is going to have a substantial effect," said John Quackenbush, chair of the Department of Biostatistics at the Harvard T.H. Chan School of Public Health, in a phone interview.

Momentum is building in the drone industry according to latest data, drone industry insight (DII) has revealed. Venture capitalists are putting significant funding into the segment as world leverage on technologies and related infrastructure. According to the its 2019 report, DII stated that the global market will grow from $14 billion in 2018 to over $43 billion in 2024 at annual average growth rate of 20.5%. Specifically, the drone market which has been evolving, has become clearer as VC funding is focused mainly on drone delivery, security, and mining. This was a year on year growth of 67% with venture capitalist funding standing at $930 million.

This week, host Isaac Butler talks to documentary theater makers Jessica Blank and Erik Jensen, whose plays include The Exonerated, about the criminal justice system, and Coal Country, about the Upper Big Branch mine disaster in West Virginia. Blank and Jensen explain how documentary theater works, from interviews with subjects to the final product, where actors perform interview excerpts verbatim. After the interview, Isaac and co-host June Thomas discuss why documentary theater is such a great way to communicate important information to an audience. Send your questions about creativity and any other feedback to working@slate.com.