Dynamic Adaptation on Non-Stationary Visual Domains

Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets. However, with large-scale or dynamic data sources, data from a defined domain is not usually available all at once. For instance, in a streaming data scenario, dataset statistics effectively become a function of time. We introduce a framework for adaptation over non-stationary distribution shifts applicable to large-scale and streaming data scenarios. The model is adapted sequentially over incoming unsupervised streaming data batches. This enables improvements over several batches without the need for any additionally annotated data. To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions. We apply our method to several adaptation benchmark datasets for classification and show improved classifier accuracy not only for the currently adapted batch, but also when applied on future stream batches. Furthermore, we show the applicability of our associative learning modifications to semantic segmentation, where we achieve competitive results

Clustering of syntactic and discursive information for the dynamic adaptation of Language Models

Presentamos una estrategia de agrupamiento de elementos de diálogo, de tipo semántico y discursivo. Empleando Latent Semantic Analysis (LSA) agru- pamos los diferentes elementos de acuerdo a un criterio de distancia basado en correlación. Tras seleccionar un conjunto de grupos que forman una partición del espacio semántico o discursivo considerado, entrenamos unos modelos de lenguaje estocásticos (LM) asociados a cada modelo. Dichos modelos se emplearán en la adaptación dinámica del modelo de lenguaje empleado por el reconocedor de habla incluido en un sistema de diálogo. Mediante el empleo de información de diálogo (las probabilidades a posteriori que el gestor de diálogo asigna a cada elemento de diálogo en cada turno), estimamos los pesos de interpolación correspondientes a cada LM. Los experimentos iniciales muestran una reducción de la tasa de error de palabra al emplear la información obtenida a partir de una frase para reestimar la misma frase

Ontology Change Management in Protégé

Ontology schemas tend to change and evolve over time to meet new requirements. This change may invalidate dependent applications if there is no dynamic adaptation to the changes made to underlying ontologies. Protégé, as a popular ontology development tool, should meet the challenges addressed by the evolving ontology. In this paper, we will briefly analyse the current ontology-change management in Protégé, and propose some extensions to facilitate change traceability by external application and services

Ontology Change Management in Protégé

Ontology schemas tend to change and evolve over time to meet new requirements. This change may invalidate dependent applications if there is no dynamic adaptation to the changes made to underlying ontologies. Protégé, as a popular ontology development tool, should meet the challenges addressed by the evolving ontology. In this paper, we will briefly analyse the current ontology-change management in Protégé, and propose some extensions to facilitate change traceability by external application and services

Dynamic Adaptation in Fly Motion Vision

Sensory neurons process and convey information about our surroundings, providing the physiological basis for how we interact with the external world. In order to understand neuronal responses we must identify the rules governing how sensory information is encoded. It was proposed more than fifty years ago that neural codes constitute efficient representations of the natural world (Attneave, 1954; Barlow, 1961). In an information maximization paradigm, an efficient coding strategy will match the encoded neural response to the statistics of the input signals. Adaptation of the stimulus-response function to the statistics of the stimulus is one way to efficiently encode a stimulus when the response range and resolution are limited compared to the entire range of stimulus probabilities (Laughlin, 1981). Recent work has indeed shown that adaptation to the input statistics can occur in real time (Smirnakis et al., 1997) and that this form of adaptation can be used to efficiently encode the stimulus and maximize information transmission (Brenner et al., 2000). In this work I examined the mechanisms of dynamic adaptation in fly motion vision. The H1-cell is a large field tangential cell of the blowfly visual system that responds to motion in a directionally selective way. It also adapts its response properties to the second order statistics of an apparent motion stimulus (Fairhall et al., 2001). I measured the adaptation of the H1-cell to the variance and temporal correlations of a Gaussian low-pass filtered velocity signal that directed a sine wave visual grating. I found that the H1-cell adapted the slope, or gain, and range of its input-output function to the variance of the velocity signal over two orders of magnitude. The H1-cell also adapted its response properties to the low-pass filter time constant of the velocity signal over one order of magnitude. I compared the adaptation between flies by normalizing the gain of the stimulus-response function by the gain of the stimulus-response function during steady-state firing properties. This “dynamic gain” decreased as the velocity variance increased and broadened to cover the larger range of velocities. In contrast, as the time constant of the velocity fluctuations increased, the dynamic gain increased. The results of these experiments were then compared with simulations of the correlation-type or Reichardt motion detector model. The Reichardt detector is an algorithmic model for motion detection that explains the behavior of directionally selective large-field tangential cells in flies including the H1-cell, as well as directionally selective motion vision in humans (Zanker, 1996; Borst and Egelhaaf, 1989). The Reichardt detector model showed the same adaptive properties as the H1-cell in response to the same stimuli. Reichardt detector adaptation occurred without changing any of the model parameters; it was an automatic function of the dynamics of the model. This suggested that the mathematical properties of the Reichardt detector provide a mechanism for adaptation in the H1-cell of the blowfly. This adaptation was further characterized in both the Reichardt detector model and the H1-cell. The time course of this form of velocity adaptation in the H1-cell was examined by switching between two different variances and two different low-pass filter time constants of the velocity signal. The H1-cell adapted to the statistics or the time course of the new velocity signal within two seconds after the switch. The Reichardt detector showed a similar time course for adaptation as in the experiments. The effect of the visual pattern on adaptation was also examined, using a square wave pattern in addition to the sine wave used previously. The visual pattern affects the output of an array of Reichardt motion detectors and may therefore affect adaptation in the system. The overall shape of the adaptation function with respect to the stimulus variance was not different between the two stimulus patterns. In the experiments, the H1-cell showed a consistently higher dynamic gain with a square wave pattern. The Reichardt detector model, however, had a lower dynamic gain when the square wave pattern was presented. After careful investigation of the potential causes of this discrepancy I found that the steady-state firing rate of the H1-cell saturated when a square wave pattern was used, thereby altering the normalization under experimental conditions that was not accounted for in the simulations. These results suggest that contrast saturation is an important feature of fly motion vision that has not been explained by the Reichardt detector model. The Reichardt detector provides an automatic mechanism and mathematical explanation for adaptation in the fly visual system involving the nature of the incoming visual signals and the non-linearity in the motion detector model. Interestingly, the gradient detector model, although it is also non-linear, does not display automatic adaptation. It remains to be seen whether this type of adaptation is prominent in other sensory systems and whether it leads to and efficient and accurate representation of the natural world

Dynamic adaptation to resource scarcity and backstop availability: theory and application to groundwater

In this paper we analyse the optimal management of a renewable resource (groundwater) with stock-dependent extraction cost and a backstop substitute, facing two-sector linear demands. Application to the Kiti region in Cyprus demonstrates the model’s performance and is used to test for the difference between optimal and myopic behaviour. It is found that the presence of a backstop resource diminishes the importance of optimal dynamic behaviour, whereas in the absence of backstop the optimal control solution yields a value for social welfare significantly larger than the myopic policy.backstop technology, endogenous adaptation, Gisser–Sanchez effect, groundwater resource management, multistage optimal control, Resource /Energy Economics and Policy,

Dynamic Adaptation of Temporal Event Correlation Rules

Temporal event correlation is essential to realizing self-managing distributed systems. Autonomic controllers often require that events be correlated across multiple components using rule patterns with timer-based transitions, e.g., to detect denial of service attacks and to warn of staging problems with business critical applications. This short paper discusses automatic adjustment of timer values for event correlation rules, in particular compensating for the variability of event propagation delays due to factors such as contention for network and server resources. We describe a corresponding Management Station architecture and present experimental studies on a testbed system that suggest that this approach can produce results at least as good as an optimal fixed setting of timer values

A Systematic Comparison of Music Similarity Adaptation Approaches

In order to support individual user perspectives and different retrieval tasks, music similarity can no longer be considered as a static element of Music Information Retrieval (MIR) systems. Various approaches have been proposed recently that allow dynamic adaptation of music similarity measures. This paper provides a systematic comparison of algorithms for metric learning and higher-level facet distance weighting on the MagnaTagATune dataset. A crossvalidation variant taking into account clip availability is presented. Applied on user generated similarity data, its effect on adaptation performance is analyzed. Special attention is paid to the amount of training data necessary for making similarity predictions on unknown data, the number of model parameters and the amount of information available about the music itself. 1