Toward Self-Referential Autonomous Learning of Object and Situation Models

Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach

A novel sea surface pCO₂-product for the global coastal ocean resolving trends over 1982–2020

In recent years, advancements in machine learning based interpolation methods have enabled the production of high-resolution maps of sea surface partial pressure of CO2 (pCO2) derived from observations extracted from databases such as the Surface Ocean CO2 Atlas (SOCAT). These pCO2-products now allow quantifying the oceanic air–sea CO2 exchange based on observations. However, most of them do not yet explicitly include the coastal ocean. Instead, they simply extend the open ocean values onto the nearshore shallow waters, or their spatial resolution is simply so coarse that they do not accurately capture the highly heterogeneous spatiotemporal pCO2 dynamics of coastal zones. Until today, only one global pCO2-product has been specifically designed for the coastal ocean (Laruelle et al., 2017). This product, however, has shortcomings because it only provides a climatology covering a relatively short period (1998–2015), thus hindering its application to the evaluation of the interannual variability, decadal changes and the long-term trends of the coastal air–sea CO2 exchange, a temporal evolution that is still poorly understood and highly debated. Here we aim at closing this knowledge gap and update the coastal product of Laruelle et al. (2017) to investigate the longest global monthly time series available for the coastal ocean from 1982 to 2020. The method remains based on a two-step Self-Organizing Maps and Feed-Forward Network method adapted for coastal regions, but we include additional environmental predictors and use a larger pool of training and validation data with ∼18 million direct observations extracted from the latest release of the SOCAT database. Our study reveals that the coastal ocean has been acting as an atmospheric CO2 sink of −0.40 Pg C yr−1 (−0.18 Pg C yr−1 with a narrower coastal domain) on average since 1982, and the intensity of this sink has increased at a rate of 0.06 Pg C yr−1 decade−1 (0.02 Pg C yr−1 decade−1 with a narrower coastal domain) over time. Our results also show that the temporal changes in the air–sea pCO2 gradient plays a significant role in the long-term evolution of the coastal CO2 sink, along with wind speed and sea-ice coverage changes that can also play an important role in some regions, particularly at high latitudes. This new reconstructed coastal pCO2-product (https://doi.org/10.25921/4sde-p068; Roobaert et al., 2023) allows us to establish regional carbon budgets requiring high-resolution coastal flux estimates and provides new constraints for closing the global carbon cycle.</p

Uncertainty in the global oceanic CO₂ uptake induced by wind forcing: quantification and spatial analysis

The calculation of the air–water CO2 exchange (FCO2) in the ocean not only depends on the gradient in CO2 partial pressure at the air–water interface but also on the parameterization of the gas exchange transfer velocity (k) and the choice of wind product. Here, we present regional and global-scale quantifications of the uncertainty in FCO2 induced by several widely used k formulations and four wind speed data products (CCMP, ERA, NCEP1 and NCEP2). The analysis is performed at a 1°  ×  1° resolution using the sea surface pCO2 climatology generated by Landschützer et al. (2015a) for the 1991–2011 period, while the regional assessment relies on the segmentation proposed by the Regional Carbon Cycle Assessment and Processes (RECCAP) project. First, we use k formulations derived from the global 14C inventory relying on a quadratic relationship between k and wind speed (k = c ⋅ U102; Sweeney et al., 2007; Takahashi et al., 2009; Wanninkhof, 2014), where c is a calibration coefficient and U10 is the wind speed measured 10 m above the surface. Our results show that the range of global FCO2, calculated with these k relationships, diverge by 12 % when using CCMP, ERA or NCEP1. Due to differences in the regional wind patterns, regional discrepancies in FCO2 are more pronounced than global. These global and regional differences significantly increase when using NCEP2 or other k formulations which include earlier relationships (i.e., Wanninkhof, 1992; Wanninkhof et al., 2009) as well as numerous local and regional parameterizations derived experimentally. To minimize uncertainties associated with the choice of wind product, it is possible to recalculate the coefficient c globally (hereafter called c∗) for a given wind product and its spatio-temporal resolution, in order to match the last evaluation of the global k value. We thus performed these recalculations for each wind product at the resolution and time period of our study but the resulting global FCO2 estimates still diverge by 10 %. These results also reveal that the Equatorial Pacific, the North Atlantic and the Southern Ocean are the regions in which the choice of wind product will most strongly affect the estimation of the FCO2, even when using c∗

Extensive Remineralization of Peatland‐Derived Dissolved Organic Carbon and Ocean Acidification in the Sunda Shelf Sea, Southeast Asia

Southeast Asia is a hotspot of riverine export of terrigenous organic carbon to the ocean, accounting for ∼10% of the global land-to-ocean riverine flux of terrigenous dissolved organic carbon (tDOC). While anthropogenic disturbance is thought to have increased the tDOC loss from peatlands in Southeast Asia, the fate of this tDOC in the marine environment and the potential impacts of its remineralization on coastal ecosystems remain poorly understood. We collected a multi-year biogeochemical time series in the central Sunda Shelf (Singapore Strait), where the seasonal reversal of ocean currents delivers water masses from the South China Sea first before (during Northeast Monsoon) and then after (during Southwest Monsoon) they have mixed with run-off from peatlands on Sumatra. The concentration and stable isotope composition of DOC, and colored dissolved organic matter spectra, reveal a large input of tDOC to our site during Southwest Monsoon. Using isotope mass balance calculations, we show that 60%–70% of the original tDOC input is remineralized in the coastal waters of the Sunda Shelf, causing seasonal acidification. The persistent CO2 oversaturation drives a CO2 efflux of 2.4–4.9 mol m−2 yr−1 from the Singapore Strait, suggesting that a large proportion of the remineralized peatland tDOC is ultimately emitted to the atmosphere. However, incubation experiments show that the remaining 30%–40% tDOC exhibits surprisingly low lability to microbial and photochemical degradation, suggesting that up to 20%–30% of peatland tDOC might be relatively refractory and exported to the open ocean

Coupling of evolution and learning to optimize a hierarchical object recognition model

Abstract. A key problem in designing artificial neural networks for visual object recognition tasks is the proper choice of the network architecture. Evolutionary optimization methods can help to solve this problem. In this work we compare different evolutionary optimization approaches for a biologically inspired neural vision system: Direct coding versus a biologically more plausible indirect coding using unsupervised local learning. A comparison to state-of-the-art recognition approaches shows the competitiveness of our approach.

A Synthesis of Global Coastal Ocean Greenhouse Gas Fluxes

International audienceThe coastal ocean contributes to regulating atmospheric greenhouse gas concentrations by taking up carbon dioxide (CO 2) and releasing nitrous oxide (N 2 O) and methane (CH 4). In this second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP2), we quantify global coastal ocean fluxes of CO 2 , N 2 O and CH 4 using an ensemble of global gap-filled observation-based products and ocean biogeochemical models. The global coastal ocean is a net sink of CO 2 in both observational products and models, but the magnitude of the median net global coastal uptake is ∼60% larger in models (-0.72 vs.-0.44 PgC year-1 , 1998-2018, coastal ocean extending to 300 km offshore or 1,000 m isobath with area of 77 million km 2). We attribute most of this model-product difference to the seasonality in sea surface CO 2 partial pressure at mid-and high-latitudes, where models simulate stronger winter CO 2 uptake. The coastal ocean CO 2 sink has increased in the past decades but the available time-resolving observation-based products and models show large discrepancies in the magnitude of this increase. The global coastal ocean is a major source of N 2 O (+0.70 PgCO 2-e year-1 in observational product and +0.54 PgCO 2-e year-1 in model median) and CH 4 (+0.21 PgCO 2-e year-1 in observational product), which offsets a substantial proportion of the coastal CO 2 uptake in the net radiative balance (30%-60% in CO 2-equivalents), highlighting the importance of considering the three greenhouse gases when examining the influence of the coastal ocean on climate