Showing papers on "Representation (systemics) published in 2012"

Translating the Statistical Representation of the Effects of Education Interventions Into More Readily Interpretable Forms

Abstract: Decisions Systems & Solutions to develop a report that assists with the translation of effect size statistics into more readily interpretable forms for practitioners, policymakers, and researchers. The views expressed in this report are those of the author and they do not necessarily represent the opinions and positions of the There are nine authors for this report with whom IES contracted to develop the discussion of the issues presented.

Trajectory-Based modeling of human actions with motion reference points

Abstract: Human action recognition in videos is a challenging problem with wide applications State-of-the-art approaches often adopt the popular bag-of-features representation based on isolated local patches or temporal patch trajectories, where motion patterns like object relationships are mostly discarded This paper proposes a simple representation specifically aimed at the modeling of such motion relationships We adopt global and local reference points to characterize motion information, so that the final representation can be robust to camera movement Our approach operates on top of visual codewords derived from local patch trajectories, and therefore does not require accurate foreground-background separation, which is typically a necessary step to model object relationships Through an extensive experimental evaluation, we show that the proposed representation offers very competitive performance on challenging benchmark datasets, and combining it with the bag-of-features representation leads to substantial improvement On Hollywood2, Olympic Sports, and HMDB51 datasets, we obtain 595%, 806% and 407% respectively, which are the best reported results to date

The (In)validity of Supercrip Representation of Paralympian Athletes

Abstract: This article provides a critical overview of the viability of the “supercrip” iconography as an appropriate representation of Paralympic athletes. It focuses on its validity as a vehicle for the em...

Improved dust representation in the Community Atmospheric Model

Abstract: Aerosol-climate interactions constitute one of the major sources of uncertainty in assessing changes in aerosol forcing in the anthropocene as well as understanding glacial-interglacial cycles. Here we focus on improving the representation of mineral dust in the Community Atmosphere Model and assessing the impacts of the improvements in terms of direct effects on the radiative balance of the atmosphere. We simulated the dust cycle using different parameterization sets for dust emission, size distribution, and optical properties. Comparing the results of these simulations with observations of concentration, deposition, and aerosol optical depth allows us to refine the representation of the dust cycle and its climate impacts. We propose a tuning method for dust parameterizations to allow the dust module to work across the wide variety of parameter settings which can be used within the Community Atmosphere Model. Our results include a better representation of the dust cycle, most notably for the improved size distribution. The estimated net top of atmosphere direct dust radiative forcing is −0.23 ± 0.14 W/m2 for present day and −0.32 ± 0.20 W/m2 at the Last Glacial Maximum. From our study and sensitivity tests, we also derive some general relevant findings, supporting the concept that the magnitude of the modeled dust cycle is sensitive to the observational data sets and size distribution chosen to constrain the model as well as the meteorological forcing data, even within the same modeling framework, and that the direct radiative forcing of dust is strongly sensitive to the optical properties and size distribution used.

Writing Against Othering

Abstract: Writing about the Other arouses questions of representation, and specifically the risk of Othering, that is, the risk of portraying the other essentially different, and translating this difference ...

Adaptive object tracking by learning background context

Abstract: One challenge when tracking objects is to adapt the object representation depending on the scene context to account for changes in illumination, coloring, scaling, etc. Here, we present a solution that is based on our earlier approach for object tracking using particle filters and component-based descriptors. We extend the approach to deal with changing backgrounds by using a quick training phase with user interaction at the beginning of an image sequence. During this phase, some background clusters are learned along with object representations for those clusters. Next, for the rest of the sequence the best fitting background cluster is determined for each frame and the corresponding object representation is used for tracking. Experiments show a particle filter adapting to background changes can efficiently track objects and persons in natural scenes and results in higher tracking results than the basic approach. Additionally, using an object tracker to follow the main character in video games, we were able to explain a large amount of eye fixations higher than other saliency models in terms of NSS score proving that tracking is an important top-down attention component.

Convex Multi-view Subspace Learning

Abstract: Subspace learning seeks a low dimensional representation of data that enables accurate reconstruction. However, in many applications, data is obtained from multiple sources rather than a single source (e.g. an object might be viewed by cameras at different angles, or a document might consist of text and images). The conditional independence of separate sources imposes constraints on their shared latent representation, which, if respected, can improve the quality of a learned low dimensional representation. In this paper, we present a convex formulation of multi-view subspace learning that enforces conditional independence while reducing dimensionality. For this formulation, we develop an efficient algorithm that recovers an optimal data reconstruction by exploiting an implicit convex regularizer, then recovers the corresponding latent representation and reconstruction model, jointly and optimally. Experiments illustrate that the proposed method produces high quality results.

Representation use and strategy choice in physics problem solving

Abstract: In this paper, we examine student success on three variants of a test item given in different representational formats (verbal, pictorial, and graphical), with an isomorphic problem statement. We confirm results from recent papers where it is mentioned that physics students' problem-solving competence can vary with representational format and that solutions can be triggered by particular details of the representation. Previous studies are complemented with a fine grained analysis of solution strategies. We find that students use different problem-solving strategies, depending on the representational format in which the problem is stated.

Compositionality of Rule Representations in Human Prefrontal Cortex

Abstract: Rules are widely used in everyday life to organize actions and thoughts in accordance with our internal goals. At the simplest level, single rules can be used to link individual sensory stimuli to their appropriate responses. However, most tasks are more complex and require the concurrent application of multiple rules. Experiments on humans and monkeys have shown the involvement of a frontoparietal network in rule representation. Yet, a fundamental issue still needs to be clarified: Is the neural representation of multiple rules compositional, that is, built on the neural representation of their simple constituent rules? Subjects were asked to remember and apply either simple or compound rules. Multivariate decoding analyses were applied to functional magnetic resonance imaging data. Both ventrolateral frontal and lateral parietal cortex were involved in compound representation. Most importantly, we were able to decode the compound rules by training classifiers only on the simple rules they were composed of. This shows that the code used to store rule information in prefrontal cortex is compositional. Compositional coding in rule representation suggests that it might be possible to decode other complex action plans by learning the neural patterns of the known composing elements.

The poor political representation of the poor in comparative perspective

Abstract: Due to diverging levels of political influence of various income groups, political institutions likely reflect the policy preferences of certain groups of citizens better than others, independently of their numerical weight. This runs counter the egalitarian principle of ‘one citizen, one vote’. The present article documents a general trend of underrepresentation of the preferences of relatively poor citizens both by parties and by governments across Western democracies, although important cross-national differences exist.

Clinical reasoning processes: unravelling complexity through graphical representation.

Abstract: Clinical reasoning is a core skill in medical practice, but remains notoriously difficult for students to grasp and teachers to nurture. To date, an accepted model that adequately captures the complexity of clinical reasoning processes does not exist. Knowledge-modelling software such as MOT Plus (Modelling using Typified Objects [MOT]) may be exploited to generate models capable of unravelling some of this complexity.

Augmented attribute representations

Abstract: We propose a new learning method to infer a mid-level feature representation that combines the advantage of semantic attribute representations with the higher expressive power of non-semantic features. The idea lies in augmenting an existing attribute-based representation with additional dimensions for which an autoencoder model is coupled with a large-margin principle. This construction allows a smooth transition between the zero-shot regime with no training example, the unsupervised regime with training examples but without class labels, and the supervised regime with training examples and with class labels. The resulting optimization problem can be solved efficiently, because several of the necessity steps have closed-form solutions. Through extensive experiments we show that the augmented representation achieves better results in terms of object categorization accuracy than the semantic representation alone.

Learning and development of embodied numerosity.

Abstract: Recent empirical evidence indicates that seemingly abstract numerical cognitions are rooted in sensory and bodily experiences. In particular in finger counting finger-based representations reflect a specific case of embodied cognition, we termed embodied numerosity. Furthermore, we suggest that finger-based representations should be considered a distinct representation of number (magnitude) and argue that this representation is activated automatically whenever we encounter a number. We discuss in what way such a theoretical framework can account for the associations of fingers and numbers observed so far. In the final part, we evaluate whether the concept of embodied numerosity should be generalized beyond finger-based representations with particular focus on whether bodily-sensory experiences (such as moving the whole body along the mental number line) may corroborate numerical capabilities. In a series of intervention studies, we consistently observed more pronounced training effects for our embodied numerosity trainings for different age groups, different digital media, different number ranges, and different control conditions. Taken together, we conclude that embodied representations of number (magnitude) exist, are not limited to finger-based representations, and influence number processing in a systematic and functional way that can be used to foster the efficiency of numerical trainings.

Models as make-believe

Abstract: In this paper I propose an account of representation for scientific models based on Kendall Walton’s “make-believe” theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, I demonstrate an important advantage my account has over other theories of scientific representation. All existing theories analyze scientific representation in terms of relations, such as similarity or denotation. By contrast, my account does not take representation in modeling to be essentially relational. For this reason, it can accommodate a group of models often ignored in discussions of scientific representation, namely models which are representational but which represent no actual object.

Advanced shape context for plant species identification using leaf image retrieval

Abstract: This paper presents a novel method for leaf species identification combining local and shape-based features. Our approach extends the shape context model in two ways. First of all, two different sets of points are distinguished when computing the shape contexts: the voting set, i.e. the points used to describe the coarse arrangement of the shape and the computing set containing the points where the shape contexts are computed. This representation is enriched by introducing local features computed in the neighborhood of the computing points. Experiments show the effectiveness of our approach.

Light-based finger gesture user interface

Abstract: A light-based finger gesture user interface for an electronic device including a housing for an electronic device, a display mounted in the housing, a cavity, separated from the display, penetrating two opposite sides of the housing, a detector mounted in the housing operative to detect an object inserted in the cavity, and a processor connected to the detector and to the display for causing the display to render a visual representation in response to output from the detector.

A 2.5-D representation of the human hand.

Abstract: Primary somatosensory maps in the brain represent the body as a discontinuous, fragmented set of two-dimensional (2-D) skin regions. We nevertheless experience our body as a coherent three-dimensional (3-D) volumetric object. The links between these different aspects of body representation, however, remain poorly understood. Perceiving the body's location in external space requires that immediate afferent signals from the periphery be combined with stored representations of body size and shape. At least for the back of the hand, this body representation is massively distorted, in a highly stereotyped manner. Here we test whether a common pattern of distortions applies to the entire hand as a 3-D object, or whether each 2-D skin surface has its own characteristic pattern of distortion. Participants judged the location in external space of landmark points on the dorsal and palmar surfaces of the hand. By analyzing the internal configuration of judgments, we produced implicit maps of each skin surface. Qualitatively similar distortions were observed in both cases. The distortions were correlated across participants, suggesting that the two surfaces are bound into a common underlying representation. The magnitude of distortion, however, was substantially smaller on the palmar surface, suggesting that this binding is incomplete. The implicit representation of the human hand may be a hybrid, intermediate between a 2-D representation of individual skin surfaces and a 3-D representation of the hand as a volumetric object.

Qualitative Spatio-Temporal Representation and Reasoning: Trends and Future Directions

Abstract: This chapter focuses on the topological and mereological relations, contact, and parthood, between spatiotemporal regions as axiomatized in so-called mereotopologies. Despite, or because of, their simplicity, a variety of different first-order axiomatizations have been proposed. This chapter discusses their underlying ontological choices and different ways of systematically looking at them. The chapter further gives an overview of the algebraic, topological, and graph-theoretic representations of mereotopological models which help to better understand the model-theoretic consequences of the various ontological choices. While much work on mereotopologies has been primarily theoretical, the focus started shifting towards applications and domain-specific extensions of mereotopology. These aspects will most likely guide the future direction of the field: How can mereotopologies be extended or otherwise adjusted to better suit practical needs? Moreover, the integration of mereotopology into more comprehensive and maybe more pragmatic ontologies of space and time remains another challenge in the field of region-based space.

Close Encounters with the Stirling Numbers of the Second Kind

Abstract: This is a short introduction to the theory of Stirling numbers of the second kind S(m, k) from the point of view of analysis. It is written as an historical survey centered on the representation of...

On the automorphy of l-adic Galois representations with small residual image With an appendix by Robert Guralnick, Florian Herzig, Richard Taylor and Jack Thorne

Abstract: We prove new automorphy lifting theorems for essentially conjugate self-dual Galois representations into GLn. Existing theorems require that the residual representation have ‘big’ image, in a certain technical sense. Our theorems are based on a strengthening of the Taylor–Wiles method which allows one to weaken this hypothesis.

Shape-Shifting Representation

Abstract: In this paper I aim to show why embracing and exploring the idea of shape-shifting representation can provide an important empirical and analytical focal point for our understanding of political representation, especially in such highly mediatised, narrowcast and panoptical times. Building upon recent innovations in theories of representation, I show in particular how a partial recasting of current analysis of taxonomies of representation (and aspects of the theoretical and motivational frames that give rise to them) can advance us towards a conception that (1) better captures the core practical dynamics of representation today, without (2) sacrificing a sense of the breadth and complexity of existing representative practices.

The spatial distribution of attention within and across objects.

Abstract: Attention operates to select both spatial locations and perceptual objects. However, the specific mechanism by which attention is oriented to objects is not well understood. We examined the means by which object structure constrains the distribution of spatial attention (i.e., a “grouped array”). Using a modified version of the Egly et al. object cuing task, we systematically manipulated within-object distance and object boundaries. Four major findings are reported: 1) spatial attention forms a gradient across the attended object; 2) object boundaries limit the distribution of this gradient, with the spread of attention constrained by a boundary; 3) boundaries within an object operate similarly to across-object boundaries: we observed object-based effects across a discontinuity within a single object, without the demand to divide or switch attention between discrete object representations; and 4) the gradient of spatial attention across an object directly modulates perceptual sensitivity, implicating a relatively early locus for the grouped array representation.

Learning optimal spike-based representations

Abstract: How can neural networks learn to represent information optimally? We answer this question by deriving spiking dynamics and learning dynamics directly from a measure of network performance. We find that a network of integrate-and-fire neurons undergoing Hebbian plasticity can learn an optimal spike-based representation for a linear decoder. The learning rule acts to minimise the membrane potential magnitude, which can be interpreted as a representation error after learning. In this way, learning reduces the representation error and drives the network into a robust, balanced regime. The network becomes balanced because small representation errors correspond to small membrane potentials, which in turn results from a balance of excitation and inhibition. The representation is robust because neurons become self-correcting, only spiking if the representation error exceeds a threshold. Altogether, these results suggest that several observed features of cortical dynamics, such as excitatory-inhibitory balance, integrate-and-fire dynamics and Hebbian plasticity, are signatures of a robust, optimal spike-based code.

Methods and apparatus for analyzing a social network

Abstract: Techniques are disclosed for analyzing a social network having a plurality of members, each member having declared a connection with each of one or more other members of the social network. Exemplary techniques include monitoring activity performed by members on the social network, the monitored activity including actions other than the declaring and undeclaring of connections between members. A graphical representation of at least a portion of the social network may be computed to include at least one indication of the monitored activity.

Embodying the Mind and Representing the Body

Abstract: Does the existence of body representations undermine the explanatory role of the body? Or do certain types of representation depend so closely upon the body that their involvement in a cognitive task implicates the body itself? In the introduction of this special issue we explore lines of tension and complement that might hold between the notions of embodiment and body representations, which remain too often neglected or obscure. To do so, we distinguish two conceptions of embodiment that either put weight on the explanatory role of the body itself or body representations. We further analyse how and to what extent body representations can be said to be embodied. Finally, we give an overview of the full volume articulated around foundational issues (How should we define the notion of embodiment? To what extent and in what sense is embodiment compatible with representationalism? To what extent and in what sense are sensorimotor approaches similar to behaviourism?) and their applications in several cognitive domains (perception, concepts, selfhood, social cognition).

The State-of-the-Art in Visual Object Tracking

Abstract: There is a broad range of applications of visual object tracking that motivate the interests of researchers worldwide. These include video surveillance to know the suspicious activity, sport video analysis to extract highlights, traffic monitoring to analyse traffic flow and human computer interface to assist visually challenged people. In general, the processing framework of object tracking in dynamic scenes includes the following stages: segmentation and modelling of interesting moving object, predicting possible location of candidate object in each frame, localization of object in each frame, generally through a similarity measure in feature space. However, tracking an object in a complex environment is a challenging task. This survey discusses some of the core concepts used in object tracking and present a comprehensive survey of efforts in the past to address this problem. We have also explored wavelet domain and found that it has great potential in object tracking as it provides a rich and robust representation of an object. Povzetek: Podan je pregled metod vizualnega sledenja objektov .

The roles of endstopped and curvature tuned computations in a hierarchical representation of 2D shape.

Abstract: That shape is important for perception has been known for almost a thousand years (thanks to Alhazen in 1083) and has been a subject of study ever since by scientists and phylosophers (such as Descartes, Helmholtz or the Gestalt psychologists). Shapes are important object descriptors. If there was any remote doubt regarding the importance of shape, recent experiments have shown that intermediate areas of primate visual cortex such as V2, V4 and TEO are involved in analyzing shape features such as corners and curvatures. The primate brain appears to perform a wide variety of complex tasks by means of simple operations. These operations are applied across several layers of neurons, representing increasingly complex, abstract intermediate processing stages. Recently, new models have attempted to emulate the human visual system. However, the role of intermediate representations in the visual cortex and their importance have not been adequately studied in computational modeling. This paper proposes a model of shape-selective neurons whose shape-selectivity is achieved through intermediate layers of visual representation not previously fully explored. We hypothesize that hypercomplex - also known as endstopped - neurons play a critical role to achieve shape selectivity and show how shape-selective neurons may be modeled by integrating endstopping and curvature computations. This model - a representational and computational system for the detection of 2-dimensional object silhouettes that we term 2DSIL - provides a highly accurate fit with neural data and replicates responses from neurons in area V4 with an average of 83% accuracy. We successfully test a biologically plausible hypothesis on how to connect early representations based on Gabor or Difference of Gaussian filters and later representations closer to object categories without the need of a learning phase as in most recent models.