Attentional Guidance from Multiple Working Memory Representations: Evidence from Eye Movements

Recent studies have shown that the representation of an item in visual working memory (VWM) can bias the deployment of attention to stimuli in the visual scene possessing the same features. When multiple item representations are simultaneously held in VWM, whether these representations, especially those held in a non-prioritized or accessory status, are able to bias attention, is still controversial. In the present study we adopted an eye tracking technique to shed light on this issue. In particular, we implemented a manipulation aimed at prioritizing one of the VWM representation to an active status, and tested whether attention could be guided by both the prioritized and the accessory representations when they reappeared as distractors in a visual search task. Notably, in Experiment 1, an analysis of first fixation proportion (FFP) revealed that both the prioritized and the accessory representations were able to capture attention suggesting a significant attentional guidance effect. However, such effect was not present in manual response times (RT). Most critically, in Experiment 2, we used a more robust experimental design controlling for different factors that might have played a role in shaping these findings. The results showed evidence for attentional guidance from the accessory representation in both manual RTs and FFPs. Interestingly, FFPs showed a stronger attentional bias for the prioritized representation than for the accessory representation across experiments. The overall findings suggest that multiple VWM representations, even the accessory representation, can simultaneously interact with visual attention

Explicit solutions for a class of nonlinear backward stochastic differential equations and their nodal sets

In this paper, we investigate a class of nonlinear backward stochastic differential equations (BSDEs) arising from financial economics, and give specific information about the nodal sets of the related solutions. As applications, we are able to obtain the explicit solutions to an interesting class of nonlinear BSDEs including the k-ignorance BSDE arising from the modeling of ambiguity of asset pricing

p-Laplace equations in conformal geometry

In this paper we introduce the p-Laplace equations for the intermediate Schouten curvature in conformal geometry. These p-Laplace equations provide more tools for the study of geometry and topology of manifolds. First, the positivity of the intermediate Schouten curvature yields the vanishing of Betti numbers on locally conformally flat manifolds as consequences of the B\"{o}chner formula as in the works of Nayatani and Guan-Lin-Wang. Secondly and more interestingly, when the intermediate Schouten curvature is nonnegative, these p-Laplace equations facilitate the geometric applications of p-superharmonic functions and the nonlinear potential theory. This leads to the estimates on Hausdorff dimension of singular sets and vanishing of homotopy groups that is inspired by and extends the work of Schoen-Yau. In the forthcoming paper we will present our results on the asymptotic behavior of p-superharmonic functions at singularities.Comment: 19 pag

On the asymptotic behavior of p-superharmonic functions at singularities

In this paper we develop the p-thinness and the p-fine topology for the asymptotic behavior of p-superharmonic functions at singular points. We consider these as extensions of earlier works on superharmonic functions in dimension 2, on the Riesz and Log potentials in higher dimensions,, and on p-harmonic functions. It is remarkable that, contrary to the above cases, the p-thinness for the singular behavior differs from the p-thinness for continuity by the Wiener criterion for p-superharmonic functions. As applications of asymptotic estimates of p-superharmonic functions, we also obtain asymptotic estimates of solutions to a class of fully nonlinear elliptic equations. This paper grows out of our recent papers on the potential theory in conformal geometry.Comment: 26 page

ALID: Scalable Dominant Cluster Detection

Detecting dominant clusters is important in many analytic applications. The state-of-the-art methods find dense subgraphs on the affinity graph as the dominant clusters. However, the time and space complexity of those methods are dominated by the construction of the affinity graph, which is quadratic with respect to the number of data points, and thus impractical on large data sets. To tackle the challenge, in this paper, we apply Evolutionary Game Theory (EGT) and develop a scalable algorithm, Approximate Localized Infection Immunization Dynamics (ALID). The major idea is to perform Localized Infection Immunization Dynamics (LID) to find dense subgraph within local range of the affinity graph. LID is further scaled up with guaranteed high efficiency and detection quality by an estimated Region of Interest (ROI) and a carefully designed Candidate Infective Vertex Search method (CIVS). ALID only constructs small local affinity graphs and has a time complexity of O(C(a^*+ {\delta})n) and a space complexity of O(a^*(a^*+ {\delta})), where a^* is the size of the largest dominant cluster and C << n and {\delta} << n are small constants. We demonstrate by extensive experiments on both synthetic data and real world data that ALID achieves state-of-the-art detection quality with much lower time and space cost on single machine. We also demonstrate the encouraging parallelization performance of ALID by implementing the Parallel ALID (PALID) on Apache Spark. PALID processes 50 million SIFT data points in 2.29 hours, achieving a speedup ratio of 7.51 with 8 executors

Discovery, Identification and Comparative Analysis of Non-Specific Lipid Transfer Protein (nsLtp) Family in Solanaceae

AbstractPlant non-specific lipid transfer proteins (nsLtps) have been reported to be involved in plant defense activity against bacterial and fungal pathogens. In this study, we identified 135 (122 putative and 13 previously identified) Solanaceae nsLtps, which are clustered into 8 different groups. By comparing with Boutrot’s nsLtp classification, we classified these eight groups into five types (I, II, IV, IX and X). We compared Solanaceae nsLtps with Arabidopsis and Gramineae nsLtps and found that (1) Types I, II and IV are shared by Solanaceae, Gramineae and Arabidopsis; (2) Types III, V, VI and VIII are shared by Gramineae and Arabidopsis but not detected in Solanaceae so far; (3) Type VII is only found in Gramineae whereas type IX is present only in Arabidopsis and Solanaceae; (4) Type X is a new type that accounts for 52.59% Solanaceae nsLtps in our data, and has not been reported in any other plant so far. We further built and compared the three-dimensional structures of the eight groups, and found that the major functional diversification within the nsLtp family could be predated to the monocot/dicot divergence, and many gene duplications and sequence variations had happened in the nsLtp family after the monocot/dicot divergence, especially in Solanaceae

Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images

Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing images, a greater range of objects and spatial patterns can be observed than ever before. This offers us a new opportunity for advancing the performance of land-use classification. In this paper, we first introduce an effective midlevel visual elements-oriented land-use classification method based on “partlets,” which are a library of pretrained part detectors used for midlevel visual elements discovery. Taking advantage of midlevel visual elements rather than low-level image features, a partlets-based method represents images by computing their responses to a large number of part detectors. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel framework to train coarse-to-fine shared intermediate representations, which are termed “sparselets,” from a large number of pretrained part detectors. This is achieved by building a single-hidden-layer autoencoder and a single-hidden-layer neural network with an L0-norm sparsity constraint, respectively. Comprehensive evaluations on a publicly available 21-class VHR land-use data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of this paper