Learning Equations for Extrapolation and Control

We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task.Comment: 9 pages, 9 figures, ICML 201

Magnetodielectric detection of magnetic quadrupole order in Ba(TiO)Cu4_4(PO4_4)4_4 with Cu4_4O12_{12} square cupolas

In vortex-like spin arrangements, multiple spins can combine into emergent multipole moments. Such multipole moments have broken space-inversion and time-reversal symmetries, and can therefore exhibit linear magnetoelectric (ME) activity. Three types of such multipole moments are known: toroidal, monopole, and quadrupole moments. So far, however, the ME-activity of these multipole moments has only been established experimentally for the toroidal moment. Here, we propose a magnetic square cupola cluster, in which four corner-sharing square-coordinated metal-ligand fragments form a noncoplanar buckled structure, as a promising structural unit that carries an ME-active multipole moment. We substantiate this idea by observing clear magnetodielectric signals associated with an antiferroic ME-active magnetic quadrupole order in the real material Ba(TiO)Cu$_4$(PO$_4$)$_4$. The present result serves as a useful guide for exploring and designing new ME-active materials based on vortex-like spin arrangements.Comment: 4 figure

Independent data for transparent monitoring of greenhouse gas emissions from the land use sector – What do stakeholders think and need?

The agriculture, forestry and other land use (AFOLU) sectors contribute substantially to the net global anthropogenic greenhouse gas (GHG) emissions. To reduce these emissions under the Paris Agreement, effective mitigation actions are needed that require engagement of multiple stakeholders. Emission reduction also requires that accurate, consistent and comparable datasets are available for transparent reference and progress monitoring. Availability of free and open datasets and portals (referred to as independent data) increases, offering opportunities for improving and reconciling estimates of GHG emissions and mitigation options. Through an online survey, we investigated stakeholders’ data needs for estimating forest area and change, forest biomass and emission factors, and AFOLU GHG emissions. The survey was completed by 359 respondents from governmental, intergovernmental and non-governmental organizations, research institutes and universities, and public and private companies. These can be grouped into data users and data providers. Our results show that current open and freely available datasets and portals are only able to fulfil stakeholder needs to a certain degree. Users require a) detailed documentation regarding the scope and usability of the data, b) comparability between alternative data sources, c) uncertainty estimates for evaluating mitigation options, d) more region-specific and detailed data with higher accuracy for sub-national application, e) regular updates and continuity for establishing consistent time series. These requirements are found to be key elements for increasing overall transparency of data sources, definitions, methodologies and assumptions, which is required under the Paris Agreement. Raising awareness and improving data availability through centralized platforms are important for increasing engagement of data users. In countries with low capacities, independent data can support countries’ mitigation planning and implementation, and related GHG reporting. However, there is a strong need for further guidance and capacity development (i.e. ‘readiness support’) on how to make proper use of independent datasets. Continued investments will be needed to sustain programmes and keep improving datasets to serve the objectives of the many stakeholders involved in climate change mitigation and should focus on increased accessibility and transparency of data to encourage stakeholder involvement

Estimating above ground net biomass change in tropical and subtropical forests: refinement of IPCC default values using forest plot data

As countries advance in greenhouse gas (GHG) accounting for climate change mitigation, consistent estimates of above ground biomass (AGB) net change are needed for the tropics and subtropics. Countries with limited forest monitoring capabilities rely on 2006 IPCC default AGB net change values, which are averages per ecological zone, per continent. These previous defaults come from single studies, provide no uncertainty indications, and aggregate old secondary forests and old-growth forests. In this study, we update these default values using forest plot data. In comparison with previous estimates, new values include data published from 2006 onwards, are derived from multiple sites per global ecological zone, provide measures of variation, and divide forests >20 years old into older secondary forests and old-growth forests. We compiled 176 AGB chronosequences in secondary forests and AGB net change rates from 536 permanent plots in old-growth and managed or logged forests. In this dataset, across all continents and ecozones, AGB net change rates in younger secondary forests (go years) are higher than rates in older secondary (>20 years and ≤100 years) forests and managed or logged forests, which in turn are higher than rates in old-growth forests (> 100 years). Data availability is highest for North and South America, followed by Asia then Africa. We provide a rigorous and traceable refinement of the IPCC 2006 AGB net change default rates, identify which areas in the tropics and subtropics require more research on AGB change, and reflect on possibilities for improvement as more data becomes available

Self-organized control of an tendon driven arm by differential extrinsic plasticity

With the accelerated development of robot technologies, optimal control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of the history of sensor values, guided by the goals, intentions, objectives, learning schemes, and so forth. The idea is that the controller controls the world---the body plus its environment---as reliably as possible. This paper focuses on new lines of self-organization for developmental robotics. We apply the recently developed differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we observe a vast variety of self-organized behavior patterns: when left alone, the arm realizes pseudo-random sequences of different poses. By applying physical forces, the system can be entrained into definite motion patterns like wiping a table. Most interestingly, after attaching an object, the controller gets in a functional resonance with the object's internal dynamics, starting to shake spontaneously bottles half-filled with water or sensitively driving an attached pendulum into a circular mode. When attached to the crank of a wheel the neural system independently discovers how to rotate it. In this way, the robot discovers affordances of objects its body is interacting with

On northern-hemisphere wave patterns associated with winter rainfall events in China

During extended winter (November-April) 43% of the intraseasonal rainfall variability in China is explained by three spatial patterns of temporally coherent rainfall. These patterns were identified with Empirical Orthogonal Teleconnection (EOT) analysis of observed 1982-2007 pentad rainfall anomalies and connected to midlatitude disturbances. However, examination of individual strong EOT events shows that there is substantial inter-event variability in their dynamical evolution, which implies that precursor patterns found in regressions cannot serve as useful predictors. To understand the physical nature and origins of the extratropical precursors, the EOT technique is applied to six simulations of the Met Office Unified Model at horizontal resolutions of 200--40 km and with and without air-sea coupling. All simulations reproduce the observed precursor patterns in regressions, indicating robust underlying dynamical processes. Further investigation into the dynamics associated with observed patterns shows that Rossby wave dynamics can explain the large inter-event variability. The results suggest that the apparently slowly evolving or quasi-stationary waves in regression analysis are a statistical amalgamation of more rapidly propagating waves with a variety of origins and properties

Uso de ninhos de cupin como fonte de matéria orgânica em sistemas de produção agrosilviculturais na Amazônia

The growth of two annual crops, okra (Abelmoschus escutentus) and egg-plant (Solatium melongena) and one perennial crop, andiroba (Carapa guianensis, a native forest tree of Amazonia) under different treatments with organic manure derived from termite nest material of wood-feeding Nasutitermes species was tested (randomized block design). The use of 25-100 g of nest material gave no significant increase in okra productivity, and 25-200 g gave no significant response in andiroba. The combined use of NPK with 200 g of nest material gave a significant higher production in egg-plant (total number and total fresh weight of fruits) when compared to the control (without fertilizer) and to the treatment with NPK only.The results suggest the possibility to use termite nest material to enhance crop production in Amazonia, particularly in combination with low amounts of mineral fertilizer. Research lines for further investigations are outlined.Foi avaliado crescimento de duas espécies agriculturais anuais, quiabo (Abelmoschus esculentus) e berinjela (Solatium melongena), e de uma espécie perene, andiroba (Carapa guianensis, uma árvore nativa da Amazônia) sob diferentes tratamentos com matéria orgânica derivada de material de cupinzeiro de espécies xilófagas de Nasutitermes (desenho de bloco randomizado). O uso de 25-100 g de material de termiteiro não levou a um incremento significativo da produtividade em quiabo, e 25-200 g não resultou numa resposta significativa em andiroba. O uso combinado de NPK com 200 g de ninho de cupim resultou numa produção significantemente maior em S. melongena (número total e peso fresco total de frutos) se comparado com o controle (sem fertilizante nenhum) e com o tratamento de NPK apenas. Os resultados sugerem a possibilidade de usar material de cupinzeiro para melhorara produção agrossilvicultural na Amazônia, especialmente em combinação com pequenas quantidades de fertilizante mineral Linhas de pesquisa para futuras investigações são apresentadas

A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods

Temporal (serial) clustering of extreme precipitation events on sub-seasonal time scales is a type of compound event. It can cause large precipitation accumulations and lead to floods. We present a novel, count-based procedure to identify episodes of sub-seasonal clustering of extreme precipitation. We introduce two metrics to characterise the frequency of sub-seasonal clustering episodes and their relevance for large precipitation accumulations. The procedure does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this procedure to daily precipitation from the ERA5 reanalysis data set, we identify regions where sub-seasonal clustering occurs frequently and contributes substantially to large precipitation accumulations. The regions are the east and northeast of the Asian continent (north of Yellow Sea, in the Chinese provinces of Hebei, Jilin and Liaoning; North and South Korea; Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southwest of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the sub-seasonal time window (here 2–4 weeks). This procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different time scales (sub-seasonal to decadal). The code is available at the listed GitHub repository

Nonlinear decoding of a complex movie from the mammalian retina

Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed “pixel-by-pixel”. We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains