In ontology-mediated querying, description logic (DL) ontologies are used to enrich incomplete data with domain knowledge which results in more complete answers to queries. However, the evaluation of ontology-mediated queries (OMQs) over relational databases is computationally hard. This raises the question when OMQ evaluation is efficient, in the sense of being tractable in combined complexity or fixed-parameter tractable. We study this question for a range of ontology-mediated query languages based on several important and widely-used DLs, using unions of conjunctive queries as the actual queries. For the DL ELHI extended with the bottom concept, we provide a characterization of the classes of OMQs that are fixed-parameter tractable. For its fragment EL extended with domain and range restrictions and the bottom concept (which restricts the use of inverse roles), we provide a characterization of the classes of OMQs that are tractable in combined complexity. Both results are in terms of equivalence to OMQs of bounded tree width and rest on a reasonable assumption from parameterized complexity theory. They are similar in spirit to Grohe's seminal characterization of the tractable classes of conjunctive queries over relational databases. We further study the complexity of the meta problem of deciding whether a given OMQ is equivalent to an OMQ of bounded tree width, providing several completeness results that range from NP to 2ExpTime, depending on the DL used. We also consider the DL-Lite family of DLs, including members that admit functional roles.

Physiological signals, such as the electrocardiogram and the phonocardiogram are very often corrupted by noisy sources. Usually, artificial intelligent algorithms analyze the signal regardless of its quality. On the other hand, physicians use a completely orthogonal strategy. They do not assess the entire recording, instead they search for a segment where the fundamental and abnormal waves are easily detected, and only then a prognostic is attempted. Inspired by this fact, a new algorithm that automatically selects an optimal segment for a post-processing stage, according to a criteria defined by the user is proposed. In the process, a Neural Network is used to compute the output state probability distribution for each sample. Using the aforementioned quantities, a graph is designed, whereas state transition constraints are physically imposed into the graph and a set of constraints are used to retrieve a subset of the recording that maximizes the likelihood function, proposed by the user. The developed framework is tested and validated in two applications. In both cases, the system performance is boosted significantly, e.g in heart sound segmentation, sensitivity increases 2.4% when compared to the standard approaches in the literature.

Many machine learning problems consider data where each instance is, itself, an unordered set of elements; i.e., such that each observation is a set. Data of this kind arises in a variety of applications, ranging from document modeling (Blei et al., 2003; Garnelo et al., 2018a) and multi-task learning (Zaheer et al., 2017; Edwards & Storkey, 2016; Liu et al., 2019) to 3D point cloud modeling (Li et al., 2018; Yang et al., 2019). In unsupervised settings, a dataset typically consists of a set of such sets, while in supervised learning, it consists of a set of (set, label) pairs. Modeling a distribution over a space of instances, where each instance is, itself, an unordered set of elements involves two key considerations: (1) the elements within a single instance are exchangeable, i.e., the elements are order invariant; and (2) the cardinalities of the instances (sets) vary, i.e., instances need not exhibit the same cardinality. Modeling both unconditional and conditional distributions over instances (sets) are relevant to consider, since these support unsupervised and supervised tasks respectively. For unconditional distribution modeling, there has been significant prior work on modeling set distributions, which has sought to balance competing needs to expand model flexibility and preserve tractability on the one hand, with respecting exchangeability and varying instance cardinalities on the other hand. However, managing these tradeoffs has proved to be quite difficult, and current approaches remain limited in different respects. For example, a particularly straightforward strategy for modeling distributions over instances x {x 1,..., x n } without assuming fixed cardinality is simply to use a recurrent neural network (RNNs) to encode instance probability auto-regressively via p (x) n

This paper presents a software implementation of a general framework for time series interpretation based on abductive reasoning. The software provides a data model and a set of algorithms to make inference to the best explanation of a time series, resulting in a description in multiple abstraction levels of the processes underlying the time series. As a proof of concept, a comprehensive knowledge base for the electrocardiogram (ECG) domain is provided, so it can be used directly as a tool for ECG analysis. This tool has been successfully validated in several noteworthy problems, such as heartbeat classification or atrial fibrillation detection.

Centralized state camera surveillance is but one component of a burgeoning practice of personal data collection paired with artificial intelligence (AI). Camera surveillance is not inherently unlawful and has long been used at border-crossings, airports, and other high-security areas. However, recent technological advances have contributed to the spread of a more intrusive form of video surveillance that includes powerful, if imperfect, facial recognition abilities and AI decision making. While the technology offers states the ability to, among other things, identify lost children, identify criminals, and monitor threats, the new capacity also raises significant human rights issues. The use of camera surveillance has grown with leaps in technology, including the introduction of videocassette recorders in the 1970s and the internet in the 1990s.

Prophecy Market Insights recently presented Artificial Intelligence (AI) In Supply Chain market report which provides reliable and sincere insights related to the various segments and sub-segments of the market. The market study throws light on the various factors that are projected to impact the overall dynamics of the Artificial Intelligence (AI) In Supply Chain market over the forecast period (2019-2029). The Artificial Intelligence (AI) In Supply Chain research study contains 100 market data Tables, Pie Chat, Graphs & Figures spread through Pages and easy to understand detailed analysis. This Artificial Intelligence (AI) In Supply Chain market research report estimates the size of the market concerning the information on key retailer revenues, development of the industry by upstream and downstream, industry progress, key highlights related to companies, along with market segments and application. Global Artificial Intelligence (AI) In Supply Chain market 2020-2030 in-depth study accumulated to supply latest insights concerning acute options.

Global Artificial Intelligence in Computer Networks Market Report 2019 – Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global Artificial Intelligence in Computer Networks industry. The report also covers segment data, including: type segment, industry segment, channel segment etc. cover different segment market size, both volume and value. Also cover different industries clients information, which is very important for the manufacturers. There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment. For competitor segment, the report includes global key players of Artificial Intelligence in Computer Networks as well as some small players.

This paper plays a part in two branches of the asset pricing literature, the multi-factor literature built on the Arbitrage Pricing Theory (APT) from Ross (1976) [1] and the Inter-temporal Capital Asset Pricing Model (ICAPM) from Merton (1973) [2] and to the growing literature related to the low-risk anomaly. First, we use the Adaptive Multi-Factor (AMF) model framework developed in Zhu et al. (2018) [3] in which both the APT and ICAPM are special cases under weaker conditions with three main added benefits: 1) It allows for a large number of risk factors to explain returns even though empirically a smaller subset of them is needed to explain returns, 2) The set of risk factors is different for different securities, and 3) The risk factors are Exchange Traded Funds (ETF) which are tradeable instruments. Second, the low-risk anomaly is an empirical asset pricing observation in which stocks with lower risk yield higher returns than stocks with higher risk. The two main measures for characterising risk in this context are volatility of returns and β derived from the Capital Asset Pricing Model (CAPM). Therefore, when mentioning the low-risk anomaly, we are referring to the low-volatility and the low-beta anomaly interchangeably.

Throughout the last years, machine learning techniques have been broadly encouraged in the context of deep learning architectures. An interesting algorithm denoted as Restricted Boltzmann Machine relies on energy- and probabilistic-based nature to tackle with the most diverse applications, such as classification, reconstruction, and generation of images and signals. Nevertheless, one can see they are not adequately renowned when compared to other well-known deep learning techniques, e.g., Convolutional Neural Networks. Such behavior promotes the lack of researches and implementations around the literature, coping with the challenge of sufficiently comprehending these energy-based systems. Therefore, in this paper, we propose a Python-inspired framework in the context of energy-based architectures, denoted as Learnergy. Essentially, Learnergy is built upon PyTorch for providing a more friendly environment and a faster prototyping workspace, as well as, possibility the usage of CUDA computations, speeding up their computational time.

The goal of this challenge is to test the potential of Reinforcement Learning (RL) to control electrical power transmission, in the most cost-effective manner, while keeping people and equipment safe from harm. Solving this challenge may have very positive impacts on society, as governments move to decarbonize the electricity sector and to electrify other sectors, to help reach IPCC climate goals. Existing software, computational methods and optimal powerflow solvers are not adequate for real-time network operations on short temporal horizons in a reasonable computational time. With recent changes in electricity generation and consumption patterns, system operation is moving to become more of a stochastic rather than a deterministic control problem. In order to overcome these complexities, new computational methods are required. The intention of this challenge is to explore RL as a solution method for electricity network control. There may be under-utilized, cost-effective flexibility in the power network that RL techniques can identify and capitalize on, that human operators and traditional solution techniques are unaware of or unaccustomed to. An RL agent that can act in conjunction, or in parallel with human network operators, will optimize grid security and reliability, allowing more renewable resources to be connected while minimizing the cost and maintaining supply to customers, and preventing damage to electrical equipment. Another aim of the project is to broaden the audience for the problem of electricity network control and to foster collaboration between experts in both the power systems community and the wider RL/ML community.