Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time. In a radiological setting, objects commonly have high within-class appearance variability whilst sharing certain characteristics across different classes, making their distinction even more difficult. As an example, markers of cerebral small vessel disease, such as enlarged perivascular spaces (EPVS) and lacunes, can be very varied in their appearance while exhibiting high inter-class similarity, making this task highly challenging for human raters. In this work, we investigate joint models of individual rater behaviour and multi-rater consensus in a deep learning setting, and apply it to a brain lesion object-detection task. Results show that jointly modelling both individual and consensus estimates leads to significant improvements in performance when compared to directly predicting consensus labels, while also allowing the characterization of human-rater consistency.

Fractals2019 started as a new experimental entry in the RoboCup Soccer 2D Simulation League, based on Gliders2d code base, and advanced to a team winning RoboCup-2019 championship. Our approach is centred on combinatorial optimisation methods, within the framework of Guided Self-Organisation (GSO), with the search guided by local constraints. We present examples of several tactical tasks based on the fully released Gliders2d code (version v2), including the search for an optimal assignment of heterogeneous player types, as well as blocking behaviours, offside trap, and attacking formations. We propose a new method, Dynamic Constraint Annealing, for solving dynamic constraint satisfaction problems, and apply it to optimise thermodynamic potential of collective behaviours, under dynamically induced constraints. 1 Introduction The RoboCup Soccer 2D Simulation League provides a rich dynamic environment, facilitated by the RoboCup Soccer Simulator (RCSS), aimed to test advances in decentralised collective behaviours of autonomous agents. The challenges include concurrent adversarial actions, computational nondetermin-ism, noise and latency in asynchronous perception and actuation, and limited processing time [1-9]. Over the years the progress of the League has been supported by several important base code releases, covering both low-level skills and standardised world models of simulated agents [10-13]. The release in 2010 of the base code of HELIOS team, agent2d-3.0.0, later upgraded to agent2d-3.1.1,

We present a comprehensive case study in the automated synth esis of list sorting algorithms: two main proofs produce the most popular sorting algorithms (min-so rt, quick-sort, insert-sort, merge-sort) and trigger all the proofs necessary for producing the needed au xiliary functions for inserting, splitting, and merging. This is a continuation of our work on exploring in pa rallel the theories of multisets, lists, and binary trees, for the purpose of developing proof method s for the synthesis of algorithms on these domains. In one related paper [12] we already investigated a lgorithms for deletion from lists and binary trees using multisets. We follow the proof-based approach to automated synthesis: first one proves automatically a synthesis conjecture which is based on the specification (input and output conditions) of the desired function, then the algorithm is extracted automatically from the proo f, in form of conditional rewrite rules. The theoretical basis and the correctness of this scheme is well -known [6] and we used earlier in [11, 15].

Abstractive summarization approaches based on Reinforcement Learning (RL) have recently been proposed to overcome classical likelihood maximization. RL enables to consider complex, possibly non-differentiable, metrics that globally assess the quality and relevance of the generated outputs. ROUGE, the most used summarization metric, is known to suffer from bias towards lexical similarity as well as from suboptimal accounting for fluency and readability of the generated abstracts. W e thus explore and propose alternative evaluation measures: the reported human-evaluation analysis shows that the proposed metrics, based on Question Answering, favorably compares to ROUGE - with the additional property of not requiring reference summaries. Training a RL-based model on these metrics leads to improvements (both in terms of human or automated metrics) over current approaches that use ROUGE as a reward.

Quality, architecture, and process are considered the keystones of software engineering. ISO defines them in three separate standards. However, their interaction has been scarcely studied, so far. The SQuAP model (Software Quality, Architecture, Process) describes twenty-eight main factors that impact on software quality in banking systems, and each factor is described as a relation among some characteristics from the three ISO standards. Hence, SQuAP makes such relations emerge rigorously, although informally. In this paper, we present SQuAP-Ont, an OWL ontology designed by following a well-established methodology based on the reuse of Ontology Design Patterns (i.e. SQuAP-Ont formalises the relations emerging from SQuAP to represent and reason via Linked Data about software engineering in a three-dimensional model consisting of quality, architecture, and process ISO characteristics. Industrial standards are widely used in the software engineering practice: they are built on preexisting literature and provide a common ground to scholars and practitioners to analyze, develop, and assess software systems. As far as software quality is concerned, the reference standard is the ISO/IEC 25010:2011 (ISO quality from now on), which defines the quality of software products and their usage (i.e., in-use quality). The ISO quality standard introduces eight characteristics that qualify a software product, and five characteristics that assess its quality in use. A characteristic is a parameter for measuring the quality of a software system-related aspect, e.g., reliability, usability, performance efficiency. The quantitative value associated with a characteristic is measured employing metrics that are dependent on the context of a specific software project and defined following the established literature.

Jacob Rafati, David C. Noelle Electrical Engineering and Computer Scinence Computational Cognitive Neuroscience Laboratory University of California, Merced 5200 North Lake Road, Merced, CA 95343 USA.Abstract Reinforcement learning (RL) algorithms allow artificial agents to improve their selection of actions to increase rewarding experiences in their environments. Temporal Di ff erence (TD) Learning - a model-free RL method - is a leading account of the midbrain dopamine system and the basal ganglia in reinforcement learning. These algorithms typically learn a mapping from the agent's current sensed state to a selected action (known as a policy function) via learning a value function (expected future rewards). TD Learning methods have been very successful on a broad range of control tasks, but learning can become intractably slow as the state space of the environment grows. This has motivated methods that learn internal representations of the agent's state, e ffectively reducing the size of the state space and restructuring state representations in order to support generalization. However, TD Learning coupled with an artificial neural network, as a function approximator, has been shown to fail to learn some fairly simple control tasks, challenging this explanation of reward-based learning. We hypothesize that such failures do not arise in the brain because of the ubiquitous presence of lateral inhibition in the cortex, producing sparse distributed internal representations that support the learning of expected future reward. The sparse conjunctive representations can avoid catastrophic interference while still supporting generalization. We provide support for this conjecture through computational simulations, demonstrating the benefits of learned sparse representations for three problematic classic control tasks: Puddle-world, Mountain-car, and Acrobot. Introduction Reinforcement learning (RL) - a class of machine learning problems - is learning how to map situations to actions so as to maximize numerical reward signals received during the experiences that an artificial agent has as it interacts with its environment (Sutton and Barto, 1998). The agent may also be seen as having a goal (or goals) related to the state of the environment. Humans and nonhuman animals' capability of learning highly complex skills by reinforcing appropriate behaviors with reward and the role of midbrain dopamine system in reward-based learning has been well described by a class of a model-free RL, called T emporal Difference (TD) Learning (Montague et al., 1996; Schultz et al., 1997). While TD Learning, by itself, certainly does not explain all observed RL phenomena, increasing evidence suggests that it is key to the brain's adaptive nature (Dayan and Niv, 2008). One of the challenges that arise in RL in real-world problems is that the state space can be very large.

Bilinear diagonal models for knowledge graph embedding (KGE), such as DistMult and ComplEx, balance expressiveness and computational efficiency by representing relations as diagonal matrices. Although they perform well in predicting atomic relations, composite relations (relation paths) cannot be modeled naturally by the product of relation matrices, as the product of diagonal matrices is commutative and hence invariant with the order of relations. In this paper, we propose a new bilinear KGE model, called BlockHolE, based on block circulant matrices. In BlockHolE, relation matrices can be non-commutative, allowing composite relations to be modeled by matrix product. The model is parameterized in a way that covers a spectrum ranging from diagonal to full relation matrices. A fast computation technique is developed on the basis of the duality of the Fourier transform of circulant matrices.

Megvii, one of four highly valued Chinese facial-recognition startups, has filed for an IPO. Most of its revenue comes from surveillance and security systems.

Machine learning and artificial intelligence will become the most defining technologies in banking and beyond, which led some of the most powerful institutions to seek partnerships, investments, and in-house developments to take advantage of application potential of machine learning and AI. Let's look at a collection of examples of how leading institutions are utilizing machine learning to unlock value from the vast data pools they command and continuously accumulate. Aetna has launched a new security system for its consumer mobile and web apps that, in something of a twist, makes passwords optional. Instead of a password or fingerprint being the only barrier to entry, Aetna's new behavior-based security system monitors user devices and how and where a consumer uses that machine. Consumers can add biometric protection available on their devices. That risk engine takes in data from many attributes of the device (software configuration, operating system version, etc.), in addition to benign attributes of consumer behavior (for example, how a mobile device is held when texting and location of the device), and matches these attributes against a device signature and a model based on previous behavior. The risk engine binds a consumer to one or more of the devices they typically use.

So, after importing and transforming the data into a readable format, we'll move to the next crucial step in Data Processing, which is Data Cleaning.