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

From perception to action: phase-locked gamma oscillations correlate with reaction times in a speeded response task

<p>Abstract</p> <p>Background</p> <p>Phase-locked gamma oscillations have so far mainly been described in relation to perceptual processes such as sensation, attention or memory matching. Due to its very short latency (≈90 ms) such oscillations are a plausible candidate for very rapid integration of sensory and motor processes.</p> <p>Results</p> <p>We measured EEG in 13 healthy participants in a speeded reaction task. Participants had to press a button as fast as possible whenever a visual stimulus was presented. The stimulus was always identical and did not have to be discriminated from other possible stimuli. In trials in which the participants showed a fast response, a slow negative potential over central electrodes starting approximately 800 ms before the response and highly phase-locked gamma oscillations over central and posterior electrodes between 90 and 140 ms after the stimulus were observed. In trials in which the participants showed a slow response, no slow negative potential was observed and phase-locked gamma oscillations were significantly reduced. Furthermore, for slow response trials the phase-locked gamma oscillations were significantly delayed with respect to fast response trials.</p> <p>Conclusion</p> <p>These results indicate the relevance of phase-locked gamma oscillations for very fast (not necessarily detailed) integration processes.</p

Resource-efficient incremental learning in very high dimensions

International audienceWe propose a three-layer neural architecture for incremental multi-class learning that remains resource-efficient even when the number of input dimensions is very high (≥ 1000). This so-called projection-prediction (PROPRE) architecture is strongly inspired by biological information processing in that it uses a prototype-based, topologically organized hidden layers trained with the SOM learning rule controlled by a global, task-related error signal. Furthermore, the SOM learning adapts only the weights of localized neural sub-populations that are similar to the input, which explicitly avoids the catastrophic forgetting effect of MLPs in case new input statistics are presented to the architecture. As the readout layer uses simple linear regression, the approach essentially applies locally linear models to " receptive fields " (RF) defined by SOM prototypes, whereas RF shape is implicitly defined by adjacent prototypes (which avoids the storage of covariance matrices that gets prohibitive for high input dimensionality). Both RF centers and shapes are jointly adapted w.r.t. input statistics and the classification task. Tests on the MNIST dataset show that the algorithm achieves compares favorably compared to the state-of-the-art LWPR algorithm at vastly decreased resource requirements

Biologically inspired incremental learning for high-dimensional spaces

International audienceWe propose an incremental, highly parallelizable, and constant-time complexity neural learning architecture for multi-class classification (and regression) problems that remains resource-efficient even when the number of input dimensions is very high (≥ 1000). This so-called projection-prediction (PROPRE) architecture is strongly inspired by biological information processing in that it uses a prototype-based, topologically organized hidden layer trained with the SOM learning rule that updates hidden layer weights whenever an error occurs. The SOM learning adapts only the weights of localized neural sub-populations that are similar to the input, which explicitly avoids the catastrophic forgetting effect of MLPs in case new input statistics are presented. The readout layer applies linear regression to hidden layer activities subjected to a transfer function, making the whole system capable of representing strongly non-linear decision boundaries. The resource-efficiency of the algorithm stems from the fact of approximating similarity in the input space by proximity in the SOM layer due to the topological SOM projection property. This avoids the storage of inter-cluster distances (quadratic in number of hidden layer) or input space covariance matrices (quadratic in input dimensions) as K-means, RBF or LWPR would have to do. Tests on the popular MNIST handwritten digit benchmark show that the algorithm compares favorably to state-of-the-art results, and parallelizability is demonstrated by analyzing the efficiency of a parallel GPU implementation of the architecture

Time Pressure Modulates Electrophysiological Correlates of Early Visual Processing

BACKGROUND: Reactions to sensory events sometimes require quick responses whereas at other times they require a high degree of accuracy-usually resulting in slower responses. It is important to understand whether visual processing under different response speed requirements employs different neural mechanisms. METHODOLOGY/PRINCIPAL FINDINGS: We asked participants to classify visual patterns with different levels of detail as real-world or non-sense objects. In one condition, participants were to respond immediately, whereas in the other they responded after a delay of 1 second. As expected, participants performed more accurately in delayed response trials. This effect was pronounced for stimuli with a high level of detail. These behavioral effects were accompanied by modulations of stimulus related EEG gamma oscillations which are an electrophysiological correlate of early visual processing. In trials requiring speeded responses, early stimulus-locked oscillations discriminated real-world and non-sense objects irrespective of the level of detail. For stimuli with a higher level of detail, oscillatory power in a later time window discriminated real-world and non-sense objects irrespective of response speed requirements. CONCLUSIONS/SIGNIFICANCE: Thus, it seems plausible to assume that different response speed requirements trigger different dynamics of processing

Terrestrial vegetation redistribution and carbon balance under climate change

BACKGROUND: Dynamic Global Vegetation Models (DGVMs) compute the terrestrial carbon balance as well as the transient spatial distribution of vegetation. We study two scenarios of moderate and strong climate change (2.9 K and 5.3 K temperature increase over present) to investigate the spatial redistribution of major vegetation types and their carbon balance in the year 2100. RESULTS: The world's land vegetation will be more deciduous than at present, and contain about 125 billion tons of additional carbon. While a recession of the boreal forest is simulated in some areas, along with a general expansion to the north, we do not observe a reported collapse of the central Amazonian rain forest. Rather, a decrease of biomass and a change of vegetation type occurs in its northeastern part. The ability of the terrestrial biosphere to sequester carbon from the atmosphere declines strongly in the second half of the 21(st )century. CONCLUSION: Climate change will cause widespread shifts in the distribution of major vegetation functional types on all continents by the year 2100

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

Incontri, scontri, confronti Appunti sulla ricezione della xilografia nordica in Italia tra XV e XX secolo

Germany, France, Italy: the attribution of the first woodcut images has long been debated between several countries, to gain the technological primacy of the invention of reproductive printmaking, before Gutenberg’s movable type printing. Today we know how difficult it is, if not impossible, to establish a place and a date of origin of image printing in Europe. Impossible and probably unimportant. Printing was a European phenomenon in the 15th century, and we may ask ourselves whether a northern woodcut beyond the Italian borders was intended as something different than an Italian one. The contrast between northern and southern prints, which has been claimed by art historians from Vasari until the half of the 20th century, seems to be denied by early modern Italian sources. For example, a German woodcut from the first decades of the 15th century and a Florentine painting from the end of the 14th century can coexist as models for the illumination of the same manuscript. This unpublished case study of two Florentine 15th-century illuminations shows how a European cultural horizon was more common than we think today, and how much woodcut has been a fundamental tool for this broadening of horizons, since its very beginning

Evaluation Procedure for the Determination of a macrokinetic Model for liquid/liquid Phase Reactions by calorimetric Experiments

In der chemischen Industrie können Betriebsstörungen nur dann sicher beherrscht und das Durchgehen einer Reaktion (Runaway) verhindert werden, wenn für die thermische Auslegung des Reaktors die thermodynamischen und -kinetischen Parameter der ablaufenden chemischen Reaktion bekannt sind und die Verfahrenssicherheit durch Anwendung von allgemeingültigen Sicherheitskriterien sichergestellt wird. Zur Berechnung der Sicherheitskriterien werden bei homogenen (einphasigen) Reaktionen thermodynamische und kinetische Parameter benötigt, welche üblicherweise durch Auswertung kalorimetrischer Messungen im Batchreaktor unter Einführung des thermischen Umsatzes bestimmt werden. Auf gleiche Weise können auch heterogene (mehrphasige) Reaktionen kalorimetrisch ausgewertet werden, wobei neben den thermodynamischen in diesem Fall die makrokinetischen Parameter erhalten werden. Weil sich die makrokinetische Reaktionsgeschwindigkeit je nach vorliegendem Reaktionsregime in unterschiedlicher Weise aus Mikrokinetik und Stofftransport zusammensetzt, ist die Kenntnis dieser Parameter allein nicht ausreichend, um die Sicherheitskriterien einer heterogenen Reaktion zufriedenstellend zu berechnen. Für ihre Berechnung müssen zum einen die mikrokinetischen Parameter und die den Stofftransport charakterisierenden Einflussparameter bekannt sein; zum anderen muss hierfür sowohl das im Laborreaktor vorhandene Reaktionsregime identifiziert als auch das im Betrieb zu erwartende Regime bestimmt werden. Als ein erster Schritt auf dem breiten Gebiet der heterogenen Reaktionen wurde im Rahmen der hier vorgestellten Dissertation eine wissenschaftlich begründete Vorgehensweise zur Parameterapproximierung in den verschiedenen Reaktionsregimen für flüssig/flüssig-Reaktionen entwickelt. Auf der Grundlage des für den isothermen flüssig/flüssig-Batchreaktor in gPROMSTM erstellten Simulationsmodells wurde ein Auswertungsverfahren zur Bestimmung der makrokinetischen Einflussparameter entwickelt mit dem die unbekannten Einflussparameter einfacher irreversibler flüssig/flüssig-Reaktionen 2. Ordnung durch Auswertung systematisch durchgeführter isothermer Batchversuche ermittelt werden können, nachdem das Reaktionsregime als kinetik-, transportkontrolliert oder spontan identifiziert wurde. Anhand der ermittelten makrokinetischen Einflussparameter unter Anwendung der auf flüssig/flüssig-Reaktionen übertragenen Äquivalenten-Isothermen-Zeit-Methode, kann der entsprechende kinetische Ansatz für das kinetik-, das transportkontrollierte oder das spontane Reaktionsregime berechnet werden. Wie anhand der vorliegenden Ergebnisse bezüglich der Rücksimulationen erkennbar ist, stellt die Äquivalente-Isotherme-Zeit-Methode eine gut gebräuchliche Methode zur Ermittlung der makrokinetischen Parameter bei flüssig/flüssig-Reaktionen dar. Bei der Auswertung von Simulations- und Rücksimulationskurven im kinetik- und transportkontrollierten Regime mit dieser Methode wurden Ergebnisse für die makrokinetischen Ansätze und die makrokinetischen Modelle erzielt, die um weniger als 1 % von den Ausgangswerten der Simulationen abweichen. Zudem kann anhand dieser Arbeit der Parameter P* der das Verhältnis aus Reaktionsgeschwindigkeit und Transportgeschwindigkeit wiedergibt, durch Anwendung der makrokinetischen Modelle ermittelt werden. Nach der Ermittlung weiterer Abhängigkeiten des Parameters von der Rührerdrehzahl bzw. der Phasengrenzfläche und der Temperatur kann der Parameter P* und zur sicherheitstechnischen Beurteilung von einfachen irreversiblen flüssig/flüssig-Reaktionen zweiter Ordnung herangezogen werden