Weighted network analysis of high frequency cross-correlation measures

In this paper we implement a Fourier method to estimate high-frequency correlation matrices from small data sets. The Fourier estimates are shown to be considerably less noisy than the standard Pearson correlation measures and thus capable of detecting subtle changes in correlation matrices with just a month of data. The evolution of correlation at different time scales is analyzed from the full correlation matrix and its minimum spanning tree representation. The analysis is performed by implementing measures from the theory of random weighted networks. © 2007 The American Physical Society

Information theoretic approach to interactive learning

The principles of statistical mechanics and information theory play an important role in learning and have inspired both theory and the design of numerous machine learning algorithms. The new aspect in this paper is a focus on integrating feedback from the learner. A quantitative approach to interactive learning and adaptive behavior is proposed, integrating model- and decision-making into one theoretical framework. This paper follows simple principles by requiring that the observer's world model and action policy should result in maximal predictive power at minimal complexity. Classes of optimal action policies and of optimal models are derived from an objective function that reflects this trade-off between prediction and complexity. The resulting optimal models then summarize, at different levels of abstraction, the process's causal organization in the presence of the learner's actions. A fundamental consequence of the proposed principle is that the learner's optimal action policies balance exploration and control as an emerging property. Interestingly, the explorative component is present in the absence of policy randomness, i.e. in the optimal deterministic behavior. This is a direct result of requiring maximal predictive power in the presence of feedback.Comment: 6 page

Cross-correlation measures in the high-frequency domain

On a high-frequency scale the time series are not homogeneous, therefore standard correlation measures can not be directly applied to the raw data. To deal with this problem the time series have to be either homogenised through interpolation or methods that can handle raw non-synchronous time series need to be employed. This paper compares two traditional methods that use inter-polation with an alternative method applied directly to the actual time series. The three methods are tested on simulated data and actual trades time series

Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction

Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. Our design recognizes and takes advantage of the structural characteristics of text-based games. We first propose a contextualisation mechanism, based on accumulated reward, which simplifies the learning problem and mitigates partial observability. We then study different methods that rely on the notion that most actions are ineffectual in any given situation, following Zahavy et al.'s idea of an admissible action. We evaluate these techniques in a series of text-based games of increasing difficulty based on the TextWorld framework, as well as the iconic game Zork. Empirically, we find that these techniques improve the performance of a baseline deep reinforcement learning agent applied to text-based games.Comment: To appear in Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). Accepted for Oral presentatio

Numerical reconstruction of brain tumours

We propose a nonlinear Landweber method for the inverse problem of locating the brain tumour source (origin where the tumour formed) based on well-established models of reaction–diffusion type for brain tumour growth. The approach consists of recovering the initial density of the tumour cells starting from a later state, which can be given by a medical image, by running the model backwards. Moreover, full three-dimensional simulations are given of the tumour source localization on two types of data, the three-dimensional Shepp–Logan phantom and an MRI T1-weighted brain scan. These simulations are obtained using standard finite difference discretizations of the space and time derivatives, generating a simple approach that performs well

Novel foods in the European Union: Scientific requirements and challenges of the risk assessment process by the European Food Safety Authority

The European Food Safety Authority (EFSA) has been involved in the risk assessment of novel foods since 2003. The implementation of the current novel food regulation in 2018 rendered EFSA the sole entity of the European Union responsible for such safety evaluations. The risk assessment is based on the data submitted by applicants in line with the scientific requirements described in the respective EFSA guidance document. The present work aims to elaborate on the rationale behind the scientific questions raised during the risk assessment of novel foods, with a focus on complex mixtures and whole foods. Novel foods received by EFSA in 2003–2019 were screened and clustered by nature and complexity. The requests for additional or supplementary information raised by EFSA during all risk assessments were analyzed for identifying reoccurring issues. In brief, it is shown that applications concern mainly novel foods derived from plants, microorganisms, fungi, algae, and animals. A plethora of requests relates to the production process, the compositional characterization of the novel food, and the evaluation of the product's toxicological profile. Recurring issues related to specific novel food categories were noted. The heterogeneous nature and the variable complexity of novel foods emphasize the challenge to tailor aspects of the evaluation approach to the characteristics of each individual product. Importantly, the scientific requirements for novel food applications set by EFSA are interrelated, and only a rigorous and cross-cutting approach adopted by the applicants when preparing the respective application dossiers can lead to scientifically sound dossiers. This is the first time that an in-depth analysis of the experience gained by EFSA in the risk assessment of novel foods and of the reasoning behind the most frequent scientific requests by EFSA to applicants is made

Guidance on the scientific requirements for an application for authorisation of a novel food in the context of Regulation (EU) 2015/2283

The European Commission requested EFSA to update the scientific guidance for the preparation of applications for authorisation of novel foods, previously developed following the adoption of Regulation (EU) 2015/2283 on novel foods. This guidance document provides advice on the scientific information needed to be submitted by the applicant towards demonstrating the safety of the novel food. Requirements pertain to the description of the novel food, production process, compositional data, specifications, proposed uses and use levels and anticipated intake of the novel food. Furthermore, information needed in sections on the history of use of the novel food and/or its source, absorption, distribution, metabolism, excretion, toxicological information, nutritional information and allergenicity is also described. The applicant should integrate and interpret the data presented in the different sections to provide their overall considerations on how the information supports the safety of the novel food under the proposed conditions of use. Where potential health hazards have been identified, they are to be discussed in relation to the anticipated intake of the novel food and the proposed target populations. On the basis of the information provided, EFSA will assess the safety of the novel food under the proposed conditions of use