Image-based Recommendations on Styles and Substitutes

Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.Comment: 11 pages, 10 figures, SIGIR 201

Un paysage entièrement moderne : Artaud dans la Sierra Tarahumara

Un paysage entièrement moderne : Artaud dans la Sierra TarahumaraLes écrits surréalistes d'Antonin Artaud sur les Tarahumaras du nord-ouest du Mexique se conforment à certaines qualités contemporaines de l'ethnographie postmoderne : une impression que l'observation subjective est politiquement libératrice et une vision globale de l'humanité sans réelles distinctions culturelles. De récentes recherches sur le terrain dans la Sierra Tarahumara révèlent que l'ethnographie d'Artaud a été sérieusement déformée par le primitivisme romantique et par le mysticisme. Cependant, sa tendance à utiliser des métaphores du paysage pour décrire les conditions culturelles a été ( par inadvertance ) retenue par bon nombre de postmodernistes. Plus encore, malgré le fait que les postmodernes aient pour la plupart remplacé l'inconscient universel (et le mysticisme) par une union mondiale en évoquant les - droits •• de co-citoyenneté. ces deux genres d'ethnographie perpétuent le colonialisme culturel en imposant au monde des définitions euro-centriques de l'humanité.A Thorouahly Modem Landscape : Artaud in the Sierra TarahumaraArtaud's surrealist writings about the Tarahumara of Northwestern Mexico adhère to some contemporary qualities of postmodern ethnography : a sense that subjective observation is politically liberating and a global vision of humanity that dismisses cultural différence. Recent Tarahumara fieldwork reveals that Artaud's ethnography was seriously distorted by Romantic primitivism and mysticism. However. his tendency to use landscape metaphors to describe cultural conditions has been (inadvertently) retained by many postmodernists. Furthermore. although postmodernists hâve largely replaced the universal unconscious (and mysticism) with global union through the " rights " of co-citizenship. both types of ethnography continue cultural colonialism through global impositions of Euro-centric définitions of humanity

Mainstreaming ecosystem science in spatial planning practice : exploiting a hybrid opportunity space

This paper develops a framework for improved mainstreaming of ecosystem science in policy and decision-making within a spatial planning context. Ecosystem science is advanced as a collective umbrella to capture a body of work and approaches rooted in social-ecological systems thinking, spawning a distinctive ecosystem terminology: ecosystem approach, ecosystem services, ecosystem services framework and natural capital. The interface between spatial planning and ecosystem science is explored as a theoretical opportunity space to improve mainstreaming processes adapting Rogers’ (2003) diffusion model. We introduce the twin concepts of hooks (linking ecosystem science to a key policy or legislative term, duty or priority that relate to a particular user group) and ‘bridges’ (linking ecosystem science to a term, concept or policy priority that is used and readily understood across multiple groups and publics) as translational mechanisms in transdisciplinary mainstreaming settings. We argue that ecosystem science can be embedded into the existing work priorities and vocabularies of spatial planning practice using these hooks and bridges. The resultant framework for mainstreaming is then tested, drawing on research funded as part of the UK National Ecosystem Assessment Follow-On programme (2012-2014), within 4 case studies; each reflecting different capacities, capabilities, opportunities and barriers. The results reveal the importance of leadership, political buy in, willingness to experiment outside established comfort zones and social learning as core drivers supporting mainstreaming processes. Whilst there are still significant challenges in mainstreaming in spatial planning settings, the identification and use of hooks and bridges collectively, enables traction to be gained for further advances; moving beyond the status quo to generate additionality and potential behaviour change within different modes of mainstreaming practice. This pragmatic approach has global application to help improve the way nature is respected and taken account of in planning systems nationally and globally

Confirmation Sampling for Exact Nearest Neighbor Search

Locality-sensitive hashing (LSH), introduced by Indyk and Motwani in STOC ’98, has been an extremely influential framework for nearest neighbor search in high-dimensional data sets. While theoretical work has focused on the approximate nearest neighbor problem, in practice LSH data structures with suitably chosen parameters are used to solve the exact nearest neighbor problem (with some error probability). Sublinear query time is often possible in practice even for exact nearest neighbor search, intuitively because the nearest neighbor tends to be significantly closer than other data points. However, theory offers little advice on how to choose LSH parameters outside of pre-specified worst-case settings. We introduce the technique of confirmation sampling for solving the exact nearest neighbor problem using LSH. First, we give a general reduction that transforms a sequence of data structures that each find the nearest neighbor with a small, unknown probability, into a data structure that returns the nearest neighbor with probability 1−δ , using as few queries as possible. Second, we present a new query algorithm for the LSH Forest data structure with L trees that is able to return the exact nearest neighbor of a query point within the same time bound as an LSH Forest of Ω(L) trees with internal parameters specifically tuned to the query and data

Decision-theoretic planning with non-Markovian rewards

A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decision-theoretic planning, where many desirable behaviours are more naturally expressed a

Social network visualizations of streaming data: Design and use considerations

Abstract. Understanding networks of people linked by some common factor is an important task in many domains. Most commonly, a user creates a visualization of social interactions to see the patterns of interactions between individuals, and then used to find and identify important groups. Networks of individuals and links between them form graphs that vary with time and importance. Visualizing the changes in social networks over time is a non-trivial design task, imposing interesting demands on the visualization and interaction model. In this paper we briefly analyze the user requirements for interactive visualizations of streaming social network data. We find that these continuously updated, dynamic displays need: (1) controls that permit time-based control of the visualization, including pausing, restarting and variable speed playback of the data, (2) the ability to continue importing and processing streamed information even the display is paused, (3) a visually represented method to track changes in the displays over time, (4) interaction methods to allow drilldown from the visualization to original source data, and (5) information extraction from the displayed social network. We describe our visualization tool, SSNV, showing how it embodies these interaction requirements

Perfectionism and self-conscious emotions in British and Japanese students: Predicting pride and embarrassment after success and failure

Regarding self-conscious emotions, studies have shown that different forms of perfectionism show different relationships with pride, shame, and embarrassment depending on success and failure. What is unknown is whether these relationships also show cultural variations. Therefore, we conducted a study investigating how self-oriented and socially prescribed perfectionism predicted pride and embarrassment after success and failure comparing 363 British and 352 Japanese students. Students were asked to respond to a set of scenarios where they imagined achieving either perfect (success) or flawed results (failure). In both British and Japanese students, self-oriented perfectionism positively predicted pride after success and embarrassment after failure whereas socially prescribed perfectionism predicted embarrassment after success and failure. Moreover, in Japanese students, socially prescribed perfectionism positively predicted pride after success and self-oriented perfectionism negatively predicted pride after failure. The findings have implications for our understanding of perfectionism indicating that the perfectionism–pride relationship not only varies between perfectionism dimensions, but may also show cultural variations

Automatically generating streamlined constraint models with ESSENCE and CONJURE

Streamlined constraint reasoning is the addition of uninferred constraints to a constraint model to reduce the search space, while retaining at least one solution. Previously, effective streamlined models have been constructed by hand, requiring an expert to examine closely solutions to small instances of a problem class and identify regularities. We present a system that automatically generates many conjectured regularities for a given Essence specification of a problem class by examining the domains of decision variables present in the problem specification. These conjectures are evaluated independently and in conjunction with one another on a set of instances from the specified class via an automated modelling tool-chain comprising of Conjure, Savile Row and Minion. Once the system has identified effective conjectures they are used to generate streamlined models that allow instances of much larger scale to be solved. Our results demonstrate good models can be identified for problems in combinatorial design, Ramsey theory, graph theory and group theory - often resulting in order of magnitude speed-ups.Postprin

A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on temporal response functions (TRFs). In the current context, a TRF is a function that facilitates a mapping between features of sound streams and EEG responses. It has been shown that when the envelope of attended speech and EEG responses are used to derive TRF mapping functions, the TRF model predictions can be used to discriminate between attended and unattended talkers. However, the predictive performance of the TRF models is dependent on how the TRF model parameters are estimated. There exist a number of TRF estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different TRF estimation methods to classify attended speakers from multi-channel EEG data. The performance of the TRF estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams