Efficient Covariance Matrix Update for Variable Metric Evolution Strategies

International audienceRandomized direct search algorithms for continuous domains, such as Evolution Strategies, are basic tools in machine learning. They are especially needed when the gradient of an objective function (e.g., loss, energy, or reward function) cannot be computed or estimated efficiently. Application areas include supervised and reinforcement learning as well as model selection. These randomized search strategies often rely on normally distributed additive variations of candidate solutions. In order to efficiently search in non-separable and ill-conditioned landscapes the covariance matrix of the normal distribution must be adapted, amounting to a variable metric method. Consequently, Covariance Matrix Adaptation (CMA) is considered state-of-the-art in Evolution Strategies. In order to sample the normal distribution, the adapted covariance matrix needs to be decomposed, requiring in general $\Theta(n^3)$ operations, where $n$ is the search space dimension. We propose a new update mechanism which can replace a rank-one covariance matrix update and the computationally expensive decomposition of the covariance matrix. The newly developed update rule reduces the computational complexity of the rank-one covariance matrix adaptation to $\Theta(n^2)$ without resorting to outdated distributions. We derive new versions of the elitist Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the multi-objective CMA-ES. These algorithms are equivalent to the original procedures except that the update step for the variable metric distribution scales better in the problem dimension. We also introduce a simplified variant of the non-elitist CMA-ES with the incremental covariance matrix update and investigate its performance. Apart from the reduced time-complexity of the distribution update, the algebraic computations involved in all new algorithms are simpler compared to the original versions. The new update rule improves the performance of the CMA-ES for large scale machine learning problems in which the objective function can be evaluated fast

Ranges of control in the transcriptional regulation of Escherichia coli

<p>Abstract</p> <p>Background</p> <p>The positioning of genes in the genome is an important evolutionary degree of freedom for organizing gene regulation. Statistical properties of these distributions have been studied particularly in relation to the transcriptional regulatory network. The systematics of gene-gene distances then become important sources of information on the control, which different biological mechanisms exert on gene expression.</p> <p>Results</p> <p>Here we study a set of categories, which has to our knowledge not been analyzed before. We distinguish between genes that do not participate in the transcriptional regulatory network (i.e. that are according to current knowledge not producing transcription factors and do not possess binding sites for transcription factors in their regulatory region), and genes that via transcription factors either are regulated by or regulate other genes. We find that the two types of genes ("isolated" and "regulatory" genes) show a clear statistical repulsion and have different ranges of correlations. In particular we find that isolated genes have a preference for shorter intergenic distances.</p> <p>Conclusions</p> <p>These findings support previous evidence from gene expression patterns for two distinct logical types of control, namely digital control (i.e. network-based control mediated by dedicated transcription factors) and analog control (i.e. control based on genome structure and mediated by neighborhood on the genome).</p

The Schwarzhorn Amphibolite (Eastern Ratikon, Austria): an Early Cambrian intrusion in the Lower Austroalpine basement

The Alpine nappe stack in the Penninic-Austroalpine boundary zone in the Ratikon (Austria) contains a 4x1 km tectonic sliver of meta-diorite, known as the Schwarzhorn Amphibolite. It was deformed and metamorphosed in the amphibolite facies and is unconformably overlain by unmetamorphic Lower Triassic sandstone, indicating pre-Triassic metamorphism. Cataclastic deformation and brecciation of the amphibolite is related to normal faulting and block tilting during Jurassic rifting. Zircon dating of the Schwarzhorn Amphibolite using LA-ICP-MS gave a U-Pb age of 529+9/-8 Ma, interpreted as the crystallization age of the protolith. Geochemical characteristics indicate formation of the magmatic protolith in a supra-subduction zone setting. The Cambrian protolith age identifies the Schwarzhorn Amphibolite as a pre-Variscan element within the Austroalpine basement. Similar calc-alkaline igneous rocks of Late Neoproterozoic to Early Cambrian age are found in the Upper Austroalpine Silvretta Nappe nearby and in several other Variscan basement units of the Alps, interpreted to have formed in a peri-Gondwanan active-margin or island-arc setting

The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems

Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for developing socio-technological ensembles of humans and machines. Such systems possess the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Thus, the need for structured design knowledge for those systems arises. Following a taxonomy development method, this article provides three main contributions: First, we present a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline. Second, we envision hybrid intelligence systems and conceptualize the relevant dimensions for system design for the first time. Finally, we offer useful guidance for system developers during the implementation of such applications

On the dynamics of nitrite, nitrate and other biomarkers of nitric oxide production in inflammatory bowel disease

Nitrite and nitrate are frequently used surrogate markers of nitric oxide (NO) production. Using rat models of acute and chronic DSS-induced colitis we examined the applicability of these and other NO-related metabolites, in tissues and blood, for the characterization of inflammatory bowel disease. Global NO dynamics were assessed by simultaneous quantification of nitrite, nitrate, nitroso and nitrosyl species over time in multiple compartments. NO metabolite levels were compared to a composite disease activity index (DAI) and contrasted with measurements of platelet aggregability, ascorbate redox status and the effects of 5-aminosalicylic acid (5-ASA). Nitroso products in the colon and in other organs responded in a manner consistent with the DAI. In contrast, nitrite and nitrate, in both intra- and extravascular compartments, exhibited variations that were not always in step with the DAI. Extravascular nitrite, in particular, demonstrated significant temporal instabilities, ranging from systemic drops to marked increases. The latter was particularly evident after cessation of the inflammatory stimulus and accompanied by profound ascorbate oxidation. Treatment with 5-ASA effectively reversed these fluctuations and the associated oxidative and nitrosative stress. Platelet activation was enhanced in both the acute and chronic model. Our results offer a first glimpse into the systemic nature of DSS-induced inflammation and reveal a greater complexity of NO metabolism than previously envisioned, with a clear dissociation of nitrite from other markers of NO production. The remarkable effectiveness of 5-ASA to abrogate the observed pattern of nitrite instability suggests a hitherto unrecognized role of this molecule in either development or resolution of inflammation. Its possible link to tissue oxygen consumption and the hypoxia that tends to accompany the inflammatory process warrants further investigation

Assessment of nursing home residents in Europe: the Services and Health for ELderly in Long TERm care (SHELTER) study

Kaisu Pitkälä ryhmän SHELTER project jäsen. Jäseniä yhteensä 32. WOS:000300767900001Peer reviewe

ESA's Space-based Doppler Wind Lidar Mission Aeolus - First Wind and Aerosol Product Assessment Results

The European Space Agency (ESA) wind mission, Aeolus, hosts the first space-based Doppler Wind Lidar (DWL) world-wide. The primary mission objective is to demonstrate the DWL technique for measuring wind profiles from space, intended for assimilation in Numerical Weather Prediction (NWP) models. The wind observations will also be used to advance atmospheric dynamics research and for evaluation of climate models. Mission spinoff products are profiles of cloud and aerosol optical properties. Aeolus was launched on 22 August 2018, and the Atmospheric LAser Doppler INstrument (Aladin) instrument switch-on was completed with first high energy output in wind mode on 4 September 2018. The on-ground data processing facility worked excellent, allowing L2 product output in near-real-time from the start of the mission. First results from the wind profile product (L2B) assessment show that the winds are of very high quality, with random errors in the free Troposphere within (cloud/aerosol backscatter winds: 2.1 m/s) and larger (molecular backscatter winds: 4.3 m/s) than the requirements (2.5 m/s), but still allowing significant positive impact in first preliminary NWP impact experiments. The higher than expected random errors at the time of writing are amongst others due to a lower instrument outand input photon budget than designed. The instrument calibration is working well, and some of the data processing steps are currently being refined to allow to fully correct instrument alignment related drifts and elevated detector dark currents causing biases in the first data product version. The optical properties spin-off product (L2A) is being compared e.g. to NWP model clouds, air quality model forecasts, and collocated ground-based observations. Features including optically thick and thin particle and hydrometeor layers are clearly identified and are being validated