A retrospective comparative study of three data modelling techniques in anticoagulation therapy.

Three types of data modelling technique are applied retrospectively to individual patients’ anticoagulation therapy data to predict their future levels of anticoagulation. The results of the different models are compared and discussed relative to each other and previous similar studies. The conclusions of earlier papers are reinforced here using an extensive data set and continuously-updating neural network models are shown to predict future INR measurements best of the models presented here

A CNL for Contract-Oriented Diagrams

We present a first step towards a framework for defining and manipulating normative documents or contracts described as Contract-Oriented (C-O) Diagrams. These diagrams provide a visual representation for such texts, giving the possibility to express a signatory's obligations, permissions and prohibitions, with or without timing constraints, as well as the penalties resulting from the non-fulfilment of a contract. This work presents a CNL for verbalising C-O Diagrams, a web-based tool allowing editing in this CNL, and another for visualising and manipulating the diagrams interactively. We then show how these proof-of-concept tools can be used by applying them to a small example

Interpretable policies for reinforcement learning by empirical fuzzy sets

This paper proposes a method and an algorithm to implement interpretable fuzzy reinforcement learning (IFRL). It provides alternative solutions to common problems in RL, like function approximation and continuous action space. The learning process resembles that of human beings by clustering the encountered states, developing experiences for each of the typical cases, and making decisions fuzzily. The learned policy can be expressed as human-intelligible IF-THEN rules, which facilitates further investigation and improvement. It adopts the actor–critic architecture whereas being different from mainstream policy gradient methods. The value function is approximated through the fuzzy system AnYa. The state–action space is discretized into a static grid with nodes. Each node is treated as one prototype and corresponds to one fuzzy rule, with the value of the node being the consequent. Values of consequents are updated using the Sarsa() algorithm. Probability distribution of optimal actions regarding different states is estimated through Empirical Data Analytics (EDA), Autonomous Learning Multi-Model Systems (ALMMo), and Empirical Fuzzy Sets ( ε FS). The fuzzy kernel of IFRL avoids the lack of interpretability in other methods based on neural networks. Simulation results with four problems, namely Mountain Car, Continuous Gridworld, Pendulum Position, and Tank Level Control, are presented as a proof of the proposed concept

Differences in the transduction of canonical wnt signals demarcate effector and memory CD8 T cells with distinct recall proliferation capacity.

Protection against reinfection is mediated by Ag-specific memory CD8 T cells, which display stem cell-like function. Because canonical Wnt (Wingless/Int1) signals critically regulate renewal versus differentiation of adult stem cells, we evaluated Wnt signal transduction in CD8 T cells during an immune response to acute infection with lymphocytic choriomeningitis virus. Whereas naive CD8 T cells efficiently transduced Wnt signals, at the peak of the primary response to infection only a fraction of effector T cells retained signal transduction and the majority displayed strongly reduced Wnt activity. Reduced Wnt signaling was in part due to the downregulation of Tcf-1, one of the nuclear effectors of the pathway, and coincided with progress toward terminal differentiation. However, the correlation between low and high Wnt levels with short-lived and memory precursor effector cells, respectively, was incomplete. Adoptive transfer studies showed that low and high Wnt signaling did not influence cell survival but that Wnt high effectors yielded memory cells with enhanced proliferative potential and stronger protective capacity. Likewise, following adoptive transfer and rechallenge, memory cells with high Wnt levels displayed increased recall expansion, compared with memory cells with low Wnt signaling, which were preferentially effector-like memory cells, including tissue-resident memory cells. Thus, canonical Wnt signaling identifies CD8 T cells with enhanced proliferative potential in part independent of commonly used cell surface markers to discriminate effector and memory T cell subpopulations. Interventions that maintain Wnt signaling may thus improve the formation of functional CD8 T cell memory during vaccination

Component-Based Real-Time Operating System for Embedded Applications

Acceptance rate: 37%, Rank (CORE): AInternational audienceAs embedded systems must constantly integrate new functionalities, their developement cycles must be based on high-level abstractions, making the software design more flexible. CBSE provides an approach to these new requirements. However, low-level services provided by operating systems are an integral part of embedded applications, furthermore deployed on resource-limited devices. Therefore, the expected benefits of CBSE must not impact on the constraints imposed by the targetted domain, such as memory footprint, energy consumption, and execution time. In this paper, we present the componentization of a legacy industry-established Real-Time Operating System, and how component-based applications are built on top of it. We use the Think framework that allows to produce flexible systems while paying for flexibility only where desired. Performed experimentions show that the induced overhead is negligeable

Properties of the Volume Operator in Loop Quantum Gravity II: Detailed Presentation

The properties of the Volume operator in Loop Quantum Gravity, as constructed by Ashtekar and Lewandowski, are analyzed for the first time at generic vertices of valence greater than four. The present analysis benefits from the general simplified formula for matrix elements of the Volume operator derived in gr-qc/0405060, making it feasible to implement it on a computer as a matrix which is then diagonalized numerically. The resulting eigenvalues serve as a database to investigate the spectral properties of the volume operator. Analytical results on the spectrum at 4-valent vertices are included. This is a companion paper to arXiv:0706.0469, providing details of the analysis presented there.Comment: Companion to arXiv:0706.0469. Version as published in CQG in 2008. More compact presentation. Sign factor combinatorics now much better understood in context of oriented matroids, see arXiv:1003.2348, where also important remarks given regarding sigma configurations. Subsequent computations revealed some minor errors, which do not change qualitative results but modify some numbers presented her

Transition Radiation Spectroscopy with Prototypes of the ALICE TRD

We present measurements of the transition radiation (TR) spectrum produced in an irregular radiator at different electron momenta. The data are compared to simulations of TR from a regular radiator.Comment: 4 pages, 5 Figures, Proceedings for "TRDs for the 3rd millennium" (Sept. 4-7, 2003, Bari, Italy

From Demonstrations to Task-Space Specifications:Using Causal Analysis to Extract Rule Parameterization from Demonstrations

Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human-robot interaction. In this work, we show that it is possible to learn generative models for distinct user behavioural types, extracted from human demonstrations, by enforcing clustering of preferred task solutions within the latent space. We use these models to differentiate between user types and to find cases with overlapping solutions. Moreover, we can alter an initially guessed solution to satisfy the preferences that constitute a particular user type by backpropagating through the learned differentiable models. An advantage of structuring generative models in this way is that we can extract causal relationships between symbols that might form part of the user's specification of the task, as manifested in the demonstrations. We further parameterize these specifications through constraint optimization in order to find a safety envelope under which motion planning can be performed. We show that the proposed method is capable of correctly distinguishing between three user types, who differ in degrees of cautiousness in their motion, while performing the task of moving objects with a kinesthetically driven robot in a tabletop environment. Our method successfully identifies the correct type, within the specified time, in 99% [97.8 - 99.8] of the cases, which outperforms an IRL baseline. We also show that our proposed method correctly changes a default trajectory to one satisfying a particular user specification even with unseen objects. The resulting trajectory is shown to be directly implementable on a PR2 humanoid robot completing the same task.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0126