LUNAR: Cellular automata for drifting data streams

With the advent of fast data streams, real-time machine learning has become a challenging task, demanding many processing resources. In addition, they can be affected by the concept drift effect, by which learning methods have to detect changes in the data distribution and adapt to these evolving conditions. Several emerging paradigms such as the so-called Smart Dust, Utility Fog, or Swarm Robotics are in need for efficient and scalable solutions in real-time scenarios, and where usually computing resources are constrained. Cellular automata, as low-bias and robust-to-noise pattern recognition methods with competitive classification performance, meet the requirements imposed by the aforementioned paradigms mainly due to their simplicity and parallel nature. In this work we propose LUNAR, a streamified version of cellular automata devised to successfully meet the aforementioned requirements. LUNAR is able to act as a real incremental learner while adapting to drifting conditions. Furthermore, LUNAR is highly interpretable, as its cellular structure represents directly the mapping between the feature space and the labels to be predicted. Extensive simulations with synthetic and real data will provide evidence of its competitive behavior in terms of classification performance when compared to long-established and successful online learning methods

AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking

Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm to deal with Evolutionary Multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps us to understand interactions between related optimization tasks. A comprehensive experimental setup is designed to assess and compare the performance of AT-MFCGA to that of other renowned Evolutionary Multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed of 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regard to the superior quality of solutions provided by AT-MFCGA with respect to the rest of the methods, which are complemented by a quantitative examination of the genetic transferability among tasks throughout the search process

Exciton and negative trion dissociation by an external electric field in vertically coupled quantum dots

We study the Stark effect for an exciton confined in a pair of vertically coupled quantum dots. A single-band approximation for the hole and a parabolic lateral confinement potential are adopted which allows for the separation of the lateral center-of-mass motion and consequently for an exact numerical solution of the Schr\"odinger equation. We show that for intermediate tunnel coupling the external electric field leads to the dissociation of the exciton via an avoided crossing of bright and dark exciton energy levels which results in an atypical form of the Stark shift. The electric-field-induced dissociation of the negative trion is studied using the approximation of frozen lateral degrees of freedom. It is shown that in a symmetric system of coupled dots the trion is more stable against dissociation than the exciton. For an asymmetric system of coupled dots the trion dissociation is accompanied by a positive curvature of the recombination energy line as a function of the electric field.Comment: PRB - in prin

Impact of climate change on surface stirring and transport in the Mediterranean Sea

Understanding how climate change will affect oceanic fluid transport is crucial for environmental applications and human activities. However, a synoptic characterization of the influence of climate change on mesoscale stirring and transport in the surface ocean is missing. To bridge this gap, we exploit a high-resolution, fully coupled climate model of the Mediterranean basin using a Network Theory approach. We project significant increases of horizontal stirring and kinetic energies in the next century, likely due to increments of available potential energy. The future evolution of basin-scale transport patterns hints at a rearrangement of the main hydrodynamic provinces, defined as regions of the surface ocean that are well mixed internally but with minimal cross-flow across their boundaries. This results in increased heterogeneity of province sizes and stronger mixing in their interiors. Our approach can be readily applied to other oceanic regions, providing information for the present and future marine spatial planning.En prensa3,79

Integrals Over Polytopes, Multiple Zeta Values and Polylogarithms, and Euler's Constant

Let $T$ be the triangle with vertices (1,0), (0,1), (1,1). We study certain integrals over $T$, one of which was computed by Euler. We give expressions for them both as a linear combination of multiple zeta values, and as a polynomial in single zeta values. We obtain asymptotic expansions of the integrals, and of sums of certain multiple zeta values with constant weight. We also give related expressions for Euler's constant. In the final section, we evaluate more general integrals -- one is a Chen (Drinfeld-Kontsevich) iterated integral -- over some polytopes that are higher-dimensional analogs of $T$. This leads to a relation between certain multiple polylogarithm values and multiple zeta values.Comment: 19 pages, to appear in Mat Zametki. Ver 2.: Added Remark 3 on a Chen (Drinfeld-Kontsevich) iterated integral; simplified Proposition 2; gave reference for (19); corrected [16]; fixed typ

Establishing the optimum threshold value for haemoglobin in faecal immunochemical tests (FITs) for use in the primary care symptomatic population: South West Cancer Alliance FIT programme evaluation

This is the final version.Colorectal cancer is the fourth most common cancer in the UK, and the second leading cause of cancer-related deaths. Diagnosing colorectal cancer is difficult, as the symptoms are the same as many non-cancerous conditions. The NICE guideline NG12 (2015) recommends that patients consulting their GP with ‘alarm’ symptoms of colorectal cancer are urgently referred for colonoscopy. However, not all patients with colorectal cancer have these alarm symptoms. Many have vague low-risk symptoms that do not warrant a colonoscopy under NG12. In 2017, a new NICE guidance DG30 suggested that faecal immunochemical tests (FITs) are used for patients with these vague symptoms that could suggest colorectal cancer, but do not represent a great enough risk for an urgent referral. FITs measure the amount of haemoglobin (Hb) in a stool sample. A high level of Hb in a stool sample may suggest bleeding in the bowel caused by cancer. However, we don’t know how high Hb in the stool should be before the patient is offered a colonoscopy, when the patient has these vague symptoms. In this study, our primary aims are 1) to determine the optimum cut off point for Hb in FITs in a symptomatic primary care population, and 2) to estimate the diagnostic performance of FITs at detecting cancer in a symptomatic primary care population. In the South West, FITs have been in use since June 2018. We will collect data on all FITs performed in the region during the 18-month study period. This will include the amount of Hb present in the patients’ samples, whether or not they were referred for colonoscopy, patient demographic data, the type of FIT used, and whether or not the patient was diagnosed with colorectal cancer within one year of their FIT. We will also collect data on the number and type of referrals and diagnoses in the region during the study period, and the number of FITs ordered from primary care during that time. We estimate that around 30,000 FITs will be performed during the data collection period. This study will be complemented by a narrative review providing an overview of FIT use across the globe in primary care symptomatic patients, and a health economics study to evaluate the cost implications of FITs

Beyond Speculative Robot Ethics

In this article we develop a dialogue model for robot technology experts and designated users to discuss visions on the future of robotics in long-term care. Our vision assessment study aims for more distinguished and more informed visions on future robots. Surprisingly, our experiment also lead to some promising co-designed robot concepts in which jointly articulated moral guidelines are embedded. With our model we think to have designed an interesting response on a recent call for a less speculative ethics of technology by encouraging discussions about the quality of positive and negative visions on the future of robotics.

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability