Modeling the Temporal Dynamics of Visual Working Memory

Abstract Visual working memory (VWM) is one of the most crucial parts of the human cognitive system. Research focuses on the apparent limits in the capacity of this system and the reasons for them. So far only a few formal models exist that can account for the temporal dynamics of the amount of information stored in VWM. We propose a combination of the well established theory of visual attention (TVA) with a dynamic memory model, resulting in an iterative, probabilistic framework for VWM. The model includes a consolidation as well as a decay mechanism and employs the strength concept to quantify the availability of a certain memory trace. We evaluate the model on available change detection data

Pinning and switching of magnetic moments in bilayer graphene

We examine the magnetic properties of the localized states induced by lattice vacancies in bilayer graphene with an unrestricted Hartree-Fock calculation. We show that with realistic values of the parameters and for experimentally accessible gate voltages we can have a magnetic switching between an unpolarized and a fully polarized system.Comment: 9 pages, 4 figure

Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception

Learning classifier systems (LCSs) belong to a class of algorithms based on the principle of self-organization and have frequently been applied to the task of solving mazes, an important type of reinforcement learning (RL) problem. Maze problems represent a simplified virtual model of real environments that can be used for developing core algorithms of many real-world applications related to the problem of navigation. However, the best achievements of LCSs in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons of failure. We construct a new LCS agent that has a simpler and more transparent performance mechanism, but that can still solve mazes better than existing algorithms. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability of associative perception, adopted from psychology. To improve our understanding of the nature and structure of maze environments, we analyze mazes used in research for the last two decades, introduce a set of maze complexity characteristics, and develop a set of new maze environments. We then run our new LCS with associative perception through the old and new aliasing mazes, which represent partially observable Markov decision problems (POMDP) and demonstrate that it performs at least as well as, and in some cases better than, other published systems

Trained laser-patterned carbon as high-performance mechanical sensors

We describe the mechanical properties of turbostratically graphitized carbon films obtained by carbon laser-patterning (CLaP) and their application as bending or mechanical pressure sensors. Stable conductive carbonized films were imprinted on a flexible polyethylene terephthalate (PET) substrate by laser-induced carbonization. After initial gentle bending, i.e. training, these sponge-like porous films show a quantitative and reversible change in resistance upon bending or application of pressure in normal loading direction. Maximum response values of ΔR/R0 = 388% upon positive bending (tensile stress) and −22.9% upon negative bending (compression) are implicit for their high sensitivity towards mechanical deformation. Normal mechanical loading in a range between 0 and 500 kPa causes a response between ΔR/R0 = 0 and −15%. The reversible increase or decrease in resistance is attributed to compression or tension of the turbostratically graphitized domains, respectively. This mechanism is supported by a detailed microstructural and chemical high-resolution transmission electron microscopic analysis of the cross-section of the laser-patterned carbon

The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparative validation of GHG-CCI SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT CO₂ and CH₄ retrieval algorithm products with measurements from the TCCON

Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS (Thermal And Near-infrared Sensor for carbon Observations-Fourier Transform Spectrometer) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) instruments on board GOSAT (Greenhouse gases Observing SATellite) and ENVISAT (ENVIronmental SATellite), respectively, using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier transform spectrometers (FTSs) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the weighting function modified differential optical absorption spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen optimal estimation DOAS algorithm (BESD, University of Bremen), the iterative maximum a posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP, respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP, respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called round- robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "greenhouse gases" (GHGs). For XCO₂, all algorithms reach the precision requirements for inverse modelling (< 8 ppm), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4–2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding < 0.5 ppm threshold. For XCH₄, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fails to meet the < 34 ppb threshold for inverse modelling, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT XCH₄ precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the < 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 and 10.5 ppb, respectively

Validation and data characteristics of methane and nitrous oxide profiles observed by MIPAS and processed with Version 4.61 algorithm

The ENVISAT validation programme for the atmospheric instruments MIPAS, SCIAMACHY and GOMOS is based on a number of balloon-borne, aircraft, satellite and ground-based correlative measurements. In particular the activities of validation scientists were coordinated by ESA within the ENVISAT Stratospheric Aircraft and Balloon Campaign or ESABC. As part of a series of similar papers on other species [this issue] and in parallel to the contribution of the individual validation teams, the present paper provides a synthesis of comparisons performed between MIPAS CH4 and N2O profiles produced by the current ESA operational software (Instrument Processing Facility version 4.61 or IPF v4.61, full resolution MIPAS data covering the period 9 July 2002 to 26 March 2004) and correlative measurements obtained from balloon and aircraft experiments as well as from satellite sensors or from ground-based instruments. In the middle stratosphere, no significant bias is observed between MIPAS and correlative measurements, and MIPAS is providing a very consistent and global picture of the distribution of CH4 and N2O in this region. In average, the MIPAS CH4 values show a small positive bias in the lower stratosphere of about 5%. A similar situation is observed for N2O with a positive bias of 4%. In the lower stratosphere/upper troposphere (UT/LS) the individual used MIPAS data version 4.61 still exhibits some unphysical oscillations in individual CH4 and N2O profiles caused by the processing algorithm (with almost no regularization). Taking these problems into account, the MIPAS CH4 and N2O profiles are behaving as expected from the internal error estimation of IPF v4.61 and the estimated errors of the correlative measurements

Methane retrievals from Greenhouse Gases Observing Satellite (GOSAT) shortwave infrared measurements: Performance comparison of proxy and physics retrieval algorithms

We compare two conceptually different methods for determining methane column-averaged mixing ratios image from Greenhouse Gases Observing Satellite (GOSAT) shortwave infrared (SWIR) measurements. These methods account differently for light scattering by aerosol and cirrus. The proxy method retrieves a CO_2 column which, in conjunction with prior knowledge on CO_2 acts as a proxy for scattering effects. The physics-based method accounts for scattering by retrieving three effective parameters of a scattering layer. Both retrievals are validated on a 19-month data set using ground-based X_CH_4 at 12 stations of the Total Carbon Column Observing Network (TCCON), showing comparable performance: for the proxy retrieval we find station-dependent retrieval biases from −0.312% to 0.421% of X_CH_4 a standard deviation of 0.22% and a typical precision of 17 ppb. The physics method shows biases between −0.836% and −0.081% with a standard deviation of 0.24% and a precision similar to the proxy method. Complementing this validation we compared both retrievals with simulated methane fields from a global chemistry-transport model. This identified shortcomings of both retrievals causing biases of up to 1ings and provide a satisfying validation of any methane retrieval from space-borne SWIR measurements, in our opinion it is essential to further expand the network of TCCON stations