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Implicit Bias Predicts Liking of Ingroup Members Who Are Comfortable With Intergroup Interaction.
We test a novel framework for how ingroup members are perceived during intergroup interaction. Across three experiments, we found that, above and beyond egalitarian attitudes and motivations, White observers' automatic responses to Blacks (i.e., their implicit anti-Black bias) shaped their affiliation toward ingroup targets who appeared comfortable engaging in interracial versus same-race interaction. White observers' implicit anti-Black bias negatively correlated with liking of White targets who were comfortable with Blacks (Experiments 1-3). The relationship between implicit bias and liking varied as a function of targets' nonverbal comfort in interracial interactions (Experiment 1). Specifically, implicit bias negatively correlated with liking of targets when targets' nonverbal behaviors revealed observers felt comfortable with interracial contact, irrespective of the nature of those behaviors (Experiment 2). Finally, the relationship between implicit bias and target liking was mediated by perceived similarity (Experiment 3). Theoretical implications for stigma-by-association, social network homogeneity, and extended contact are discussed
The use of implicit evidence for relevance feedback in web retrieval
In this paper we report on the application of two contrasting types of relevance feedback for web retrieval. We compare two systems; one using explicit relevance feedback (where searchers explicitly have to mark documents relevant) and one using implicit relevance feedback (where the system endeavours to estimate relevance by mining the searcher's interaction). The feedback is used to update the display according to the user's interaction. Our research focuses on the degree to which implicit evidence of document relevance can be substituted for explicit evidence. We examine the two variations in terms of both user opinion and search effectiveness
Removing the Stiffness of Elastic Force from the Immersed Boundary Method for the 2D Stokes Equations
The Immersed Boundary method has evolved into one of the most useful
computational methods in studying fluid structure interaction. On the other
hand, the Immersed Boundary method is also known to suffer from a severe
timestep stability restriction when using an explicit time discretization. In
this paper, we propose several efficient semi-implicit schemes to remove this
stiffness from the Immersed Boundary method for the two-dimensional Stokes
flow. First, we obtain a novel unconditionally stable semi-implicit
discretization for the immersed boundary problem. Using this unconditionally
stable discretization as a building block, we derive several efficient
semi-implicit schemes for the immersed boundary problem by applying the Small
Scale Decomposition to this unconditionally stable discretization. Our
stability analysis and extensive numerical experiments show that our
semi-implicit schemes offer much better stability property than the explicit
scheme. Unlike other implicit or semi-implicit schemes proposed in the
literature, our semi-implicit schemes can be solved explicitly in the spectral
space. Thus the computational cost of our semi-implicit schemes is comparable
to that of an explicit scheme, but with a much better stability property.Comment: 40 pages with 8 figure
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
This paper proposes a new neural architecture for collaborative ranking with
implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning})
is a novel metric learning approach for recommendation. More specifically,
instead of simple push-pull mechanisms between user and item pairs, we propose
to learn latent relations that describe each user item interaction. This helps
to alleviate the potential geometric inflexibility of existing metric learing
approaches. This enables not only better performance but also a greater extent
of modeling capability, allowing our model to scale to a larger number of
interactions. In order to do so, we employ a augmented memory module and learn
to attend over these memory blocks to construct latent relations. The
memory-based attention module is controlled by the user-item interaction,
making the learned relation vector specific to each user-item pair. Hence, this
can be interpreted as learning an exclusive and optimal relational translation
for each user-item interaction. The proposed architecture demonstrates the
state-of-the-art performance across multiple recommendation benchmarks. LRML
outperforms other metric learning models by in terms of Hits@10 and
nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover,
qualitative studies also demonstrate evidence that our proposed model is able
to infer and encode explicit sentiment, temporal and attribute information
despite being only trained on implicit feedback. As such, this ascertains the
ability of LRML to uncover hidden relational structure within implicit
datasets.Comment: WWW 201
Double (implicit and explicit) dependence of the electromagnetic field of an accelerated charge on time: Mathematical and physical analysis of the problem
We considered the electromagnetic field of a charge moving with a constant
acceleration along an axis. We found that this field obtained from the
Li\'enard-Wiechert potentials does not satisfy Maxwell equations if one
considers exclusively a retarded interaction (i.e. pure implicit dependence
this field on time). We show that if and only if one takes into account both
retarded interaction and direct interaction (so called "action-at-a-distance")
the field produced by an accelerated charge satisfies Maxwell equations.Comment: ReVTeX file, no figures, 12p
Explicit and Implicit Processes in Human Aversive Conditioning
The ability to adapt to a changing environment is central to an organism’s success. The process of associating two stimuli (as in associative conditioning) requires very little in the way of neural machinery. In fact, organisms with only a few hundred neurons show conditioning that is specific to an associated cue. This type of learning is commonly referred to as implicit learning. The learning can be performed in the absence of the subject’s ability to describe it. One example of learning that is thought to be implicit is delay conditioning. Delay conditioning consists of a single cue (a tone, for example) that starts before, and then overlaps with, an outcome (like a pain stimulus).
In addition to associating sensory cues, humans routinely link abstract concepts with an outcome. This more complex learning is often described as explicit since subjects are able to describe the link between the stimulus and outcome. An example of conditioning that requires this type of knowledge is trace conditioning. Trace conditioning includes a separation of a few seconds between the cue and outcome. Explicit learning is often proposed to involve a separate system, but the degree of separation between implicit associations and explicit learning is still debated.
We describe aversive conditioning experiments in human subjects used to study the degree of interaction that takes place between explicit and implicit systems. We do this in three ways. First, if a higher order task (in this case a working memory task) is performed during conditioning, it reduces not only explicit learning but also implicit learning. Second, we describe the area of the brain involved in explicit learning during conditioning and confirm that it is active during both trace and delay conditioning. Third, using functional magnetic resonance imaging (fMRI), we describe hemodynamic activity changes in perceptual areas of the brain that occur during delay conditioning and persist after the learned association has faded.
From these studies, we conclude that there is a strong interaction between explicit and implicit learning systems, with one often directly changing the function of the other.</p
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