Showing papers on "Representation (systemics) published in 2015"

From Work to Representation

Abstract: In this chapter we will be concentrating on one of the key processes in the ‘cultural circuit’ (see Du Gay et al., 1997, and the Introduction to this volume) – the practices of representation. The aim of this chapter is to introduce you to this topic, and to explain what it is about and why we give it such importance in cultural studies. The concept of representation has come to occupy a new and important place in the study of culture. Representation connects meaning and language to culture. But what exactly do people mean by it? What does representation have to do with culture and meaning? One common-sense usage of the term is as follows: ‘Representation means using language to say something meaningful about, or to represent, the world meaningfully, to other people.’ You may well ask, ‘Is that all?’ Well, yes and no. Representation is an essential part of the process by which meaning is produced and exchanged between members of a culture. It does involve the use of language, of signs and images which stand for or represent things. But this is a far from simple or straightforward process, as you will soon discover. How does the concept of representation connect meaning and language to culture? In order to explore this connection further, we will look at a number of different theories about how language is used to represent the world. Here we will be drawing a distinction between three different accounts or theories: the reflective, the intentional and the constructionist approaches to representation. Does language simply reflect a meaning which already exists out there in the world of objects, people and events (reflective)? Does language express only what the speaker or writer or painter wants to say, his or her personally intended meaning (intentional)? Or is meaning constructed in and through language (constructionist)? You will learn more in a moment about these three approaches. Most of the chapter will be spent exploring the constructionist approach, because it is this perspective which has had the most significant impact on cultural studies in recent years. This CHAPTER ONE

Role Discovery in Networks

Abstract: Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-cliques or nodes that act as bridges to different regions of the graph. Intuitively, two nodes belong to the same role if they are structurally similar. Roles have been mainly of interest to sociologists, but more recently, roles have become increasingly useful in other domains. Traditionally, the notion of roles were defined based on graph equivalences such as structural, regular, and stochastic equivalences. We briefly revisit these early notions and instead propose a more general formulation of roles based on the similarity of a feature representation (in contrast to the graph representation). This leads us to propose a taxonomy of three general classes of techniques for discovering roles that includes(i) graph-based roles, (ii) feature-based roles, and (iii) hybrid roles. We also propose a flexible framework for discovering roles using the notion of similarity on a feature-based representation. The framework consists of two fundamental components: (a) role featureconstruction and (b) role assignment using the learned feature representation. We discuss the different possibilities for discoveringfeature-based roles and the tradeoffs of the many techniques for computing them. Finally, we discuss potential applications and future directions and challenges.

Understanding tools: Task-oriented object modeling, learning and recognition

Abstract: In this paper, we present a new framework - task-oriented modeling, learning and recognition which aims at understanding the underlying functions, physics and causality in using objects as “tools”. Given a task, such as, cracking a nut or painting a wall, we represent each object, e.g. a hammer or brush, in a generative spatio-temporal representation consisting of four components: i) an affordance basis to be grasped by hand; ii) a functional basis to act on a target object (the nut), iii) the imagined actions with typical motion trajectories; and iv) the underlying physical concepts, e.g. force, pressure, etc. In a learning phase, our algorithm observes only one RGB-D video, in which a rational human picks up one object (i.e. tool) among a number of candidates to accomplish the task. From this example, our algorithm learns the essential physical concepts in the task (e.g. forces in cracking nuts). In an inference phase, our algorithm is given a new set of objects (daily objects or stones), and picks the best choice available together with the inferred affordance basis, functional basis, imagined human actions (sequence of poses), and the expected physical quantity that it will produce. From this new perspective, any objects can be viewed as a hammer or a shovel, and object recognition is not merely memorizing typical appearance examples for each category but reasoning the physical mechanisms in various tasks to achieve generalization.

The heterogeneity of mental representation: Ending the imagery debate

Abstract: The possible ways that information can be represented mentally have been discussed often over the past thousand years. However, this issue could not be addressed rigorously until late in the 20th century. Initial empirical findings spurred a debate about the heterogeneity of mental representation: Is all information stored in propositional, language-like, symbolic internal representations, or can humans use at least two different types of representations (and possibly many more)? Here, in historical context, we describe recent evidence that humans do not always rely on propositional internal representations but, instead, can also rely on at least one other format: depictive representation. We propose that the debate should now move on to characterizing all of the different forms of human mental representation.

Learning Social Relation Traits from Face Images

Abstract: Social relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. Motivated by psychological studies, we investigate if such fine grained and high-level relation traits can be characterised and quantified from face images in the wild. To address this challenging problem we propose a deep model that learns a rich face representation to capture gender, expression, head pose, and age-related attributes, and then performs pairwise-face reasoning for relation prediction. To learn from heterogeneous attribute sources, we formulate a new network architecture with a bridging layer to leverage the inherent correspondences among these datasets. It can also cope with missing target attribute labels. Extensive experiments show that our approach is effective for fine-grained social relation learning in images and videos.

Real-time conversational analytics facility

Abstract: Methods and systems are provided for receiving a communication, analyzing the communication in real-time or near real-time using a computer-based communications analytics facility for at least one of a language characteristic and an acoustic characteristic, wherein for analyzing the language characteristic of voice communications, the communication is converted to text using computer-based speech recognition, determining at least one of the category, the score, the sentiment, or the alert associated with the communication using the at least one language and/or acoustic characteristic, and providing a dynamic graphical representation of the at least one category, score, sentiment, or alert through a graphical user interface.

A distributed representation of internal time.

Abstract: This article pursues the hypothesis that a scale-invariant representation of history could support performance in a variety of learning and memory tasks. This representation maintains a conjunctive representation of what happened when that grows continuously less accurate for events further and further in the past. Simple behavioral models using a few operations, including scanning, matching and a "jump back in time" that recovers previous states of the history, describe a range of behavioral phenomena. These behavioral applications include canonical results from the judgment of recency task over short and long scales, the recency and contiguity effect across scales in episodic recall, and temporal mapping phenomena in conditioning. A growing body of neural data suggests that neural representations in several brain regions have qualitative properties predicted by the representation of temporal history. Taken together, these results suggest that a scale-invariant representation of temporal history may serve as a cornerstone of a physical model of cognition in learning and memory.

NASARI: a Novel Approach to a Semantically-Aware Representation of Items

Abstract: The semantic representation of individual word senses and concepts is of fundamental importance to several applications in Natural Language Processing. To date, concept modeling techniques have in the main based their representation either on lexicographic resources, such as WordNet, or on encyclopedic resources, such as Wikipedia. We propose a vector representation technique that combines the complementary knowledge of both these types of resource. Thanks to its use of explicit semantics combined with a novel cluster-based dimensionality reduction and an effective weighting scheme, our representation attains state-of-the-art performance on multiple datasets in two standard benchmarks: word similarity and sense clustering. We are releasing our vector representations at http://lcl.uniroma1.it/nasari/.

Interactive content generation

Abstract: Generation of interactive content. In an embodiment, a representation of candidate object(s) in content of a digital media asset are received. For each of the candidate object(s), feature(s) of the candidate object are compared to corresponding feature(s) of a plurality of reference objects to identify reference object(s) that match the candidate object. For each of the matched candidate object(s), a hotspot package is generated. The hotspot package may comprise a visual overlay which comprises information associated with the reference object(s) matched to the respective candidate object.

Single-view to Multi-view: Reconstructing Unseen Views with a Convolutional Network

Abstract: We present a convolutional network capable of generating images of a previously unseen object from arbitrary viewpoints given a single image of this object. The input to the network is a single image and the desired new viewpoint; the output is a view of the object from this desired viewpoint. The network is trained on renderings of synthetic 3D models. It learns an implicit 3D representation of the object class, which allows it to transfer shape knowledge from training instances to a new object instance. Beside the color image, the network can also generate the depth map of an object from arbitrary viewpoints. This allows us to predict 3D point clouds from a single image, which can be fused into a surface mesh. We experimented with cars and chairs. Even though the network is trained on artificial data, it generalizes well to objects in natural images without any modifications.

The Darboux transformation of the Kundu–Eckhaus equation

Abstract: We construct an analytical and explicit representation of the Darboux transformation (DT) for the KunduEckhaus (KE) equation. Such solution and n-fold DT Tn are given in terms of determinants whose...

Physical activity and workout monitor

Abstract: The present disclosure relates to devices and processes for monitoring attributes of a user's physical activity (e.g., workout) or inactivity, and to user interfaces (e.g., an activity indicator) for displaying the same. In some examples, a device determines whether physical activity corresponds to a first type based on a first set of criteria, and whether physical activity corresponds to a second type based on a second set of criteria. In some examples, the device controls an inactivity timer that measures user's inactivity. In some examples, the device displays a first visual representation of an attribute or amount of a first type of physical activity, and a second visual representation of an attribute or amount of a second type. In some examples, the device displays a third visual representation of an attribute or amount of a third type of activity. In some examples, the third visual representation corresponds to user's inactivity.

Contextual effects on educational attainment in individualised, scalable neighbourhoods: Differences across gender and social class:

Abstract: This paper analyses whether a multi-scale representation of geographical context based on statistical aggregates computed for individualised neighbourhoods can lead to improved estimates of neighbo ...

Language identification from short strings

Abstract: Systems and processes for language identification from short strings are provided. In accordance with one example, a method includes, at a first electronic device with one or more processors and memory, receiving user input including an n-gram and determining a similarity between a representation of the n-gram and a representation of a first language. The representation of the first language is based on an occurrence of each of a plurality of n-grams in the first language and an occurrence of each of the plurality of n-grams in a second language. The method further includes determining whether the similarity between the representation of the n-gram and the representation of the first language satisfies a threshold.

Stability patterns in representation theory

Abstract: We develop a comprehensive theory of the stable representation categories of several sequences of groups, including the classical and symmetric groups, and their relation to the unstable categories. An important component of this theory is an array of equivalences between the stable representation category and various other categories, each of which has its own flavor (representation theoretic, combinatorial, commutative algebraic, or categorical) and offers a distinct perspective on the stable category. We use this theory to produce a host of specific results: for example, the construction of injective resolutions of simple objects, duality between the orthogonal and symplectic theories, and a canonical derived auto-equivalence of the general linear theory.

Representation of Concepts as Frames

Abstract: Concepts can be represented as frames, i.e., recursive attribute-value structures. Frames assign unique values to attributes. Concepts can be classified into four groups with respect to both relationality and referential uniqueness: sortal, individual, proper relational, and functional concepts. The paper defines frames as directed graphs with labeled nodes and arcs and it discusses the graph structures of frames for sortal and relational concepts. It aims at a classification of frame graphs that reflects the given concept classification. By giving a new definition of type signatures, the status of attributes in frames is clarified and the connection between functional concepts, their sortal uses, and their associated attributes is explained.

Seeing-as, seeing-in, and pictorial representation

Abstract: In Art and its Objects I made two claims about pictorial representation. The first claim was that representation (as I shall call it for short) is to be understood through, though not exclusively through, a certain species of seeing, which may be thought of, but not, of course, defined, as the seeing appropriate to representations. Representation is not to be understood exclusively in these terms: at least for the reason that reference is also required to artifactuality. The second claim was that the seeing appropriate to representations is a species of a broader perceptual genus, for which I used – confusingly, I now recognize, but I shall stick to it – the term ‘representational seeing’. The present essay amplifies the second of these two claims. The nature of the perceptual species, or what is unique to the seeing appropriate to representations, is easier to characterize than the nature of the perceptual genus to which the species belongs, or what is common to all representational seeing. What is unique to the seeing appropriate to representations is this: that a standard of correctness applies to it and this standard derives from the intention of the maker of the representation, or ‘the artist’ as he is usually called – a practice harmless enough, so long as it is recognized that most representations are made by people who neither are nor claim to be artists. Naturally the standard of correctness cannot require that someone should see a particular representation in a particular way if even a fully informed and competent spectator could not see it that way. What the standard does is to select the correct perception of a representation out of possible perceptions of it, where possible perceptions are those open to spectators in possession of all the relevant skills and beliefs. If, through the incompetence, ignorance, or bad luck of the artist, the possible perceptions of a given representation do not include one that matches the artist' intention, there is, for that representation, no correct perception – and consequently (to invoke the first of the claims made in Art and its Objects ) nothing or no-one represented. The standard, it will be observed, applies both to representations of particular things and to the representations of things of a particular kind as the following examples bring out.

Learning the spatial semantics of manipulation actions through preposition grounding

Abstract: In this paper, we introduce an abstract representation for manipulation actions that is based on the evolution of the spatial relations between involved objects. Object tracking in RGBD streams enables straightforward and intuitive ways to model spatial relations in 3D space. Reasoning in 3D overcomes many of the limitations of similar previous approaches, while providing significant flexibility in the desired level of abstraction. At each frame of a manipulation video, we evaluate a number of spatial predicates for all object pairs and treat the resulting set of sequences (Predicate Vector Sequences, PVS) as an action descriptor. As part of our representation, we introduce a symmetric, time-normalized pairwise distance measure that relies on finding an optimal object correspondence between two actions. We experimentally evaluate the method on the classification of various manipulation actions in video, performed at different speeds and timings and involving different objects. The results demonstrate that the proposed representation is remarkably descriptive of the high-level manipulation semantics.

Trade Unions and Interest Representation in the Context of Globalisation

Abstract: This article begins by offering a framework for the analysis of the future of interest representation. Trade unions, explicitly or implicitly, have to answer three fundamental questions: whose inte...

Medical device with natural language processor

Abstract: An external medical device is provided. The external medical device includes a memory and circuitry, in communication with the memory, to receive input specifying at least in part at least one prompt relating to a health survey for a patient, the at least one prompt being customized based on to the patient; convert the at least one prompt to an audio representation; and perform the health survey by at least delivering to the patient the audio representation.

Representation errors and retrievals in linear and nonlinear data assimilation

Abstract: This article shows how one can formulate the representation problem starting from Bayes’ theorem. The purpose of this article is to raise awareness of the formal solutions, so that approximations can be placed in a proper context. The representation errors appear in the likelihood, and the different possibilities for the representation of reality in model and observations are discussed, including nonlinear representation probability density functions. Specifically, the assumptions needed in the usual procedure to add a representation error covariance to the error covariance of the observations are discussed, and it is shown that, when several sub-grid observations are present, their mean still has a representation error; so-called ‘superobbing’ does not resolve the issue. Connection is made to the off-line or on-line retrieval problem, providing a new simple proof of the equivalence of assimilating linear retrievals and original observations. Furthermore, it is shown how nonlinear retrievals can be assimilated without loss of information. Finally we discuss how errors in the observation operator model can be treated consistently in the Bayesian framework, connecting to previous work in this area.

Dental variation tracking and prediction

Abstract: Systems and methods are presented for evaluating a dental condition. A first digital representation of at least a portion of an oral cavity of a first patient is compared to a second digital representation of the oral cavity of the same patient. The first digital representation is representative of the oral cavity of the first patient at a first time and the second digital representation is representative of the oral cavity of the first patient at a second, later time. At least one clinically-significant difference between the first digital representation and the second digital representation are automatically identified and the first digital representation is displayed in a way that highlights the at least one clinically significant difference.

Representation Learning Using Multi-Task Deep Neural Networks

Abstract: A system may comprise one or more processors and memory storing instructions that, when executed by one or more processors, configure one or more processors to perform a number of operations or tasks, such as receiving a query or a document, and mapping the query or the document into a lower dimensional representation by performing at least one operational layer that shares at least two disparate tasks.