Tel-Aviv based start-up Aidoc, a leading provider of Artificial Intelligence solutions for radiologists, received US Food and Drug Administration (FDA) clearance for its AI solution that spots strokes (Large-Vessel Occlusion) in the brain during head CTA scans.An LVO is the blockage of vessels in the brain, and according to Ariella Shoham, Aidoc's vice president of marketing, the AI technology "uses deep learning to automatically look at every head CT before a patient has even left the imaging room. "It investigates the images to see if they show blocked blood vessels in the brain or bleeding (intracranial hemorrhages)," she explained. "If one of these time-critical conditions is found, Aidoc re-prioritizes the worklists of radiologists so that the urgent scan is looked at immediately and the patient can be treated quickly."Shoham said that Aidoc already received FDA clearances to identify and flag pulmonary embolism (blockages in the lungs) and cervical spine fractures (broken neck). "Other Aidoc solutions currently in clinical testing include identifying air in the abdomen," she continued. "Altogether, Aidoc is targeting the most common critical life-threatening conditions that make up 80% of all urgent cases on CT scans.

Brazil's government has big plans for AI, despite having come late to the party. In Oxford Insights' AI Readiness Index 2019, Brazil was ranked 40 out of 192 countries, a sign that the South American powerhouse is moving up in the AI world. The report looks at how ready countries are to take advantage of the AI technologies PwC forecasts will add $15 trillion to the global economy by 2030. The 2019 report also cautions that the "Global South could be left behind by the so-called fourth industrial revolution." But even as some of the planet's richest nations, including Canada, China, Germany, Japan, Singapore, and the U.S, have become recognized AI innovation hubs, according to studies by Deloitte and others, South America -- led by Brazil -- is rapidly emerging as a leader in AI-enabled businesses.

This work aims to study and explore the use of Gene Expression Programming (GEP) in solving the on-line Bin-Packing problem. The main idea is to show how GEP can automatically find acceptable heuristic rules to solve the problem efficiently and economically. One dimensional Bin-Packing problem is considered in the course of this work with the constraint of minimizing the number of bins filled with the given pieces. Experimental Data includes instances of benchmark test data taken from Falkenauer (1996) for One-dimensional Bin-Packing Problems. Results show that GEP can be used as a very powerful and flexible tool for finding interesting compact rules suited for the problem. The impact of functions is also investigated to show how they can affect and influence the success of rates when they appear in rules. High success rates are gained with smaller population size and fewer generations compared to previous work performed using Genetic Programming.

We investigate some algorithmic properties of closed set and half-space separation in abstract closure systems. Assuming that the underlying closure system is finite and given by the corresponding closure operator, we show that the half-space separation problem is NP-complete. In contrast, for the relaxed problem of maximal closed set separation we give a greedy algorithm using linear number of queries (i.e., closure operator calls) and show that this bound is sharp. For a second direction to overcome the negative result above, we consider Kakutani closure systems and prove that they are algorithmically characterized by the greedy algorithm. As one of the major potential application fields, we then focus on Kakutani closure systems over graphs and generalize a fundamental characterization result based on the Pasch axiom to graph structured partitioning of finite sets. In addition, we give a sufficient condition for Kakutani closure systems over graphs in terms of graph minors. For a second application field, we consider closure systems over finite lattices, present an adaptation of the generic greedy algorithm to this kind of closure systems, and consider two potential applications. We show that for the special case of subset lattices over finite ground sets, e.g., for formal concept lattices, its query complexity is only logarithmic in the size of the lattice. The second application is concerned with finite subsumption lattices in inductive logic programming. We show that our method for separating two sets of first-order clauses from each other extends the traditional approach based on least general generalizations of first-order clauses. Though our primary focus is on the generality of the results obtained, we experimentally demonstrate the practical usefulness of the greedy algorithm on binary classification problems in Kakutani and non-Kakutani closure systems.

Relation Extraction (RE), the task of detecting and characterizing semantic relations between entities in text, has gained much importance in the last two decades, mainly in the biomedical domain. Many papers have been published on Relation Extraction using supervised machine learning techniques. Most of these techniques rely on statistical methods, such as feature-based and tree-kernels-based methods. Such statistical learning techniques are usually based on a propositional hypothesis space for representing examples, i.e., they employ an attribute-value representation of features. This kind of representation has some drawbacks, particularly in the extraction of complex relations which demand more contextual information about the involving instances, i.e., it is not able to effectively capture structural information from parse trees without loss of information. In this work, we present OntoILPER, a logic-based relational learning approach to Relation Extraction that uses Inductive Logic Programming for generating extraction models in the form of symbolic extraction rules. OntoILPER takes profit of a rich relational representation of examples, which can alleviate the aforementioned drawbacks. The proposed relational approach seems to be more suitable for Relation Extraction than statistical ones for several reasons that we argue. Moreover, OntoILPER uses a domain ontology that guides the background knowledge generation process and is used for storing the extracted relation instances. The induced extraction rules were evaluated on three protein-protein interaction datasets from the biomedical domain. The performance of OntoILPER extraction models was compared with other state-of-the-art RE systems. The encouraging results seem to demonstrate the effectiveness of the proposed solution.

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The Artificial Intelligence For Healthcare Applications Market recently Published Global Market look into study with in excess of 100 industry enlightening work area and Figures spread through Pages and straightforward itemized TOC on "Artificial Intelligence For Healthcare Applications Market". The report provides information and the advancing business series information in the sector to the exchange. The report gives an idea associated with the advancement of this market development of significant players of this industry. An examination of this Artificial Intelligence For Healthcare Applications relies upon aims, which are of coordinated into market analysis, is incorporated into the reports. The global Artificial Intelligence For Healthcare Applications market is expected to grow at a CAGR of 43.5% from 2018 to reach USD 27.60 billion by 2025. Artificial intelligence (AI) in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data.

Probabilistic Quantum Memory (PQM) is a data structure that computes the distance from a binary input to all binary patterns stored in superposition on the memory. This data structure allows the development of heuristics to speed up artificial neural networks architecture selection. In this work, we propose an improved parametric version of the PQM to perform pattern classification, and we also present a PQM quantum circuit suitable for Noisy Intermediate Scale Quantum (NISQ) computers. We present a classical evaluation of a parametric PQM network classifier on public benchmark datasets. We also perform experiments to verify the viability of PQM on a 5-qubit quantum computer. Introduction Quantum Computing is a computational paradigm that has been harvesting increasing attention for decades now. Several quantum algorithms have time advantages over their best known classical counterparts [1, 2, 3, 4]. The current advances in quantum hardware are bringing us to the era of Noisy Intermediate-Scale Quantum (NISQ) computers [5]. The quest for quantum supremacy is the search for an efficient solution of a task in a quantum computer that current classical computers are not able to efficiently solve. Some authors argue that given the current state of the art, we will achieve quantum supremacy in the next few years [6]. One of the approaches to achieve this supremacy and to expand the potential applications of quantum computers is through quantum machine learning [7]. Machine learning (ML) [8] aims at developing automated ways for computers to learn a specific task from a given set of data samples.