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

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.

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

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