Gelechiidae Moths Are Capable of Chemically Dissolving the Pollen of Their Host Plants

Background: Many insects feed on pollen surface lipids and contents accessible through the germination pores. Pollen walls, however, are not broken down because they consist of sporopollenin and are highly resistant to physical and enzymatic damage. Here we report that certain Microlepidoptera chemically dissolve pollen grains with exudates from their mouthparts. Methodology/Principal Findings: Field observations and experiments in tropical China revealed that two species of Deltophora (Gelechioidea) are the exclusive pollinators of two species of Phyllanthus (Phyllanthaceae) on which their larvae develop and from which the adults take pollen and nectar. DNA sequences placed the moths and plants phylogenetically and confirmed that larvae were those of the pollinating moths; molecular clock dating suggests that the moth clade is younger than the plant clade. Captive moths with pollen on their mouthparts after 2-3 days of starvation no longer carried intact grains, and SEM photographs showed exine fragments on their proboscises. GC-MS revealed cis-b-ocimene as the dominant volatile in leaves and flowers, but GC-MS analyses of proboscis extracts failed to reveal an obvious sporopollenindissolving compound. A candidate is ethanolamine, which occurs in insect hemolymphs and is used to dissolve sporopollenin by palynologists. Conclusions/Significance: This is the first report of any insect and indeed any animal chemically dissolving pollen

Online Deep Metric Learning

Metric learning learns a metric function from training data to calculate the similarity or distance between samples. From the perspective of feature learning, metric learning essentially learns a new feature space by feature transformation (e.g., Mahalanobis distance metric). However, traditional metric learning algorithms are shallow, which just learn one metric space (feature transformation). Can we further learn a better metric space from the learnt metric space? In other words, can we learn metric progressively and nonlinearly like deep learning by just using the existing metric learning algorithms? To this end, we present a hierarchical metric learning scheme and implement an online deep metric learning framework, namely ODML. Specifically, we take one online metric learning algorithm as a metric layer, followed by a nonlinear layer (i.e., ReLU), and then stack these layers modelled after the deep learning. The proposed ODML enjoys some nice properties, indeed can learn metric progressively and performs superiorly on some datasets. Various experiments with different settings have been conducted to verify these properties of the proposed ODML.Comment: 9 page

Theory of the Lattice Boltzmann Equation: Symmetry properties of Discrete Velocity Sets

In the lattice Boltzmann equation, continuous particle velocity space is replaced by a finite dimensional discrete set. The number of linearly independent velocity moments in a lattice Boltzmann model cannot exceed the number of discrete velocities. Thus, finite dimensionality introduces linear dependencies among the moments that do not exist in the exact continuous theory. Given a discrete velocity set, it is important to know to exactly what order moments are free of these dependencies. Elementary group theory is applied to the solution of this problem. It is found that by decomposing the velocity set into subsets that transform among themselves under an appropriate symmetry group, it becomes relatively straightforward to assess the behavior of moments in the theory. The construction of some standard two- and three-dimensional models is reviewed from this viewpoint, and procedures for constructing some new higher dimensional models are suggested

Fuzzy aesthetic semantics description and extraction for art image retrieval

AbstractMore and more digitized art images are accumulated and expanded in our daily life and techniques are needed to be established on how to organize and retrieve them. Though content-based image retrieval (CBIR) made great progress, current low-level visual information based retrieval technology in CBIR does not allow users to search images by high-level semantics for art image retrieval. We propose a fuzzy approach to describe and to extract the fuzzy aesthetic semantic feature of art images. Aiming to deal with the subjectivity and vagueness of human aesthetic perception, we utilize the linguistic variable to describe the image aesthetic semantics, so it becomes possible to depict images in linguistic expression such as ‘very action’. Furthermore, we apply neural network approach to model the process of human aesthetic perception and to extract the fuzzy aesthetic semantic feature vector. The art image retrieval system based on fuzzy aesthetic semantic feature makes users more naturally search desired images by linguistic expression. We report extensive empirical studies based on a 5000-image set, and experimental results demonstrate that the proposed approach achieves excellent performance in terms of retrieval accuracy