基于门控联合池化自编码器的通用性文本表征

为了学习文本的语义表征,以往的研究者主要依赖于复杂的循环神经网络(recurrent neural networks, RNNs)和监督式学习方法。该文提出了一种门控联合池化自编码器(gated mean-max AAE)用于学习中英文的文本语义表征。该文的自编码器完全通过多头自注意力机制(multi-head self-attention mechanism)来构建编码器和解码器网络。在编码阶段,提出了均值—最大化(mean-max)联合表征策略,即同时运用平均池化(mean pooling)和最大池化(max pooling)操作来捕获输入文本中多样性的语义信息。为促使联合池化表征可以全面地指导重构过程,解码器采用门控操作进行动态关注。通过在大规模中英文未标注语料上训练模型,获得了高质量的句子编码器。在重构文本段落的实验中,该文模型在实验效果和计算效率上均超越了传统的RNNs模型。将公开训练好的文本编码器,使其可以方便地运用于后续的研究。     

Artificial neural networks for coordination and control: The portability of experiential representations

It is time to locate connectionist representation theory in the new wave of robotics research. The utility of representations developed in artificial neural networks (ANNs) during learning has been demonstrated in cognitive science research since the 1980s. The research reported here puts learned representations to work in a decentered control task, the disembodied arm problem, in which a mobile robot operates an arm fixed to a table to pick up objects. There is no physical linkage between the arm and the robot and so the robot's point of view must be decentered. This is done by developing a modular Artificial Neural Net system in three stages: (i) a classifier net is trained with laser scan data to output transformationally invariant position classes; (ii) an arm net is trained for picking up objects; (iii) an inter net is trained to communicate and coordinate the sensing and acting. The completed system is shown to create new nonsymbolic transformationally invariant representations in order to perform the effective generalization of decentered viewpoints.     

Learning to Perceive and Act by Trial and Error

This article considers adaptive control architectures that integrate active sensory-motor systems with decision systems based on reinforcement learning. One unavoidable consequence of active perception is that the agent's internal representation often confounds external world states. We call this phoenomenon perceptual aliasingand show that it destabilizes existing reinforcement learning algorithms with respect to the optimal decision policy. We then describe a new decision system that overcomes these difficulties for a restricted class of decision problems. The system incorporates a perceptual subcycle within the overall decision cycle and uses a modified learning algorithm to suppress the effects of perceptual aliasing. The result is a control architecture that learns not only how to solve a task but also where to focus its visual attention in order to collect necessary sensory information.     

The Self and Object Representations of Narcissistically Disturbed Children: An Empirical Investigation.

The aim of this study was to demonstrate a correlation between the clinical assessment of narcissistic personality disorder in children and underlying self and object relationships. D. Westen's Social Cognition and Object Relations Scale for the Thematic Apperception Test (1995; D. Westen, N. Lohr, K. Silk, K. Kerber, & S. Goodrich, 1985) was used to compare the object representations of two groups of referred latency-aged children, one for whom narcissistic issues were thought to be primary. Support for the clinical literature, which describes these children as lacking in empathy, struggling with self-esteem regulation, and in poor control of their impulses or aggression, was found. Additionally, a tendency to have a vulnerable sense of identity and to be preoccupied with seeking out and developing relationships with others was found to differentiate these children from peers with similar behavioral and diagnostic profiles. Results obtained have potential implications for the assessment and treatment of narcissistically disturbed children. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The silence of the library: Environment, situational norm, and social behavior.

On the basis of the idea that situational norms are mentally represented as associations between environments and normative behavior, it was proposed that an environment can automatically direct normative behavior. More specifically, when situational norms are well-established (e.g., when entering the library, one should be silent), an environment is capable of automatically activating mental representations of normative behavior and the behavior itself. In these experiments, participants were exposed to pictures of environments, and effects on accessibility of representations of normative behavior and on actual behavior were assessed. Results indicated that representations of behavior and actual behavior itself are activated automatically when (a) goals to visit the environment are active and (b) strong associations between environment and normative behavior are established. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Do 9-month-old infants expect distinct words to refer to kinds?

In 3 experiments, 9-month-old infants' expectations for what distinct count noun labels refer to were investigated. In Experiment 1, a box was opened to reveal 2 objects inside during familiarization: either 2 identical objects or 2 different objects. Test trials followed the same procedure, except before the box was opened, the contents were described using 2 distinct labels ("I see a wug! I see a dak!") or the same label twice ("I see a zav! I see a zav!"). Infants who heard a label repeated twice looked longer at 2 different objects versus 2 identical objects, whereas infants who heard 2 distinct labels showed a different pattern of looking. Experiments 2 and 3 presented infants with object pairs that only differed in shape or color, and it was found that infants expected the different-shaped (but not the different-colored) objects to be labeled by distinct count nouns. Because the property of shape is a cue to kind membership and the property of color is not, these results suggest that even at the beginning of word learning, infants may expect distinct labels to refer to distinct kinds of objects. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Boundary Constrained Swept Surfaces for Modelling and Animation

Due to their simplicity and intuitiveness, swept surfaces are widely used in many surface modelling applications. In this paper, we present a versatile swept surface technique called the boundary constrained swept surfaces. The most distinct feature is its ability to satisfy boundary constraints, including the shape and tangent conditions at the boundaries of a swept surface. This permits significantly varying surfaces to be both modelled and smoothly assembled, leading to the construction of complex objects. The representation, similar to an ordinary swept surface, is analytical in nature and thus it is light in storage cost and numerically very stable to compute. We also introduce a number of useful shape manipulation tools, such as sculpting forces, to deform a surface both locally and globally. In addition to being a complementary method to the mainstream surface modelling and deformation techniques, we have found it very effective in automatically rebuilding existing complex models. Model reconstruction is arguably one of the most laborious and expensive tasks in modelling complex animated characters. We demonstrate how our technique can be used to automate this process.     

Data-driven realizations of kernel and image representations and their application to fault detection and control system design

This paper deals with the data-driven design of observer-based fault detection and control systems. We first introduce the definitions of the data-driven forms of kernel and image representations. It is followed by the study of their identification. In the context of a fault-tolerant architecture, the design of observer-based fault detection, feed-forward and feedback control systems are addressed based on the data-driven realization of the kernel and image representations. Finally, the main results are demonstrated on the laboratory continuous stirred tank heater (CSTH) system.     

Approximate Symmetry Detection in Partial 3D Meshes

Symmetry is a common characteristic in natural and man‐made objects. Its ubiquitous nature can be exploited to facilitate the analysis and processing of computational representations of real objects. In particular, in computer graphics, the detection of symmetries in 3D geometry has enabled a number of applications in modeling and reconstruction. However, the problem of symmetry detection in incomplete geometry remains a challenging task. In this paper, we propose a vote‐based approach to detect symmetry in 3D shapes, with special interest in models with large missing parts. Our algorithm generates a set of candidate symmetries by matching local maxima of a surface function based on the heat diffusion in local domains, which guarantee robustness to missing data. In order to deal with local perturbations, we propose a multi‐scale surface function that is useful to select a set of distinctive points over which the approximate symmetries are defined. In addition, we introduce a vote‐based scheme that is aware of the partiality, and therefore reduces the number of false positive votes for the candidate symmetries. We show the effectiveness of our method in a varied set of 3D shapes and different levels of partiality. Furthermore, we show the applicability of our algorithm in the repair and completion of challenging reassembled objects in the context of cultural heritage.