A multi-protein receptor-ligand complex underlies combinatorial dendrite guidance choices in C. elegans.

Ligand receptor interactions instruct axon guidance during development. How dendrites are guided to specific targets is less understood. The C. elegans PVD sensory neuron innervates muscle-skin interface with its elaborate dendritic branches. Here, we found that LECT-2, the ortholog of leukocyte cell-derived chemotaxin-2 (LECT2), is secreted from the muscles and required for muscle innervation by PVD. Mosaic analyses showed that LECT-2 acted locally to guide the growth of terminal branches. Ectopic expression of LECT-2 from seam cells is sufficient to redirect the PVD dendrites onto seam cells. LECT-2 functions in a multi-protein receptor-ligand complex that also contains two transmembrane ligands on the skin, SAX-7/L1CAM and MNR-1, and the neuronal transmembrane receptor DMA-1. LECT-2 greatly enhances the binding between SAX-7, MNR-1 and DMA-1. The activation of DMA-1 strictly requires all three ligands, which establishes a combinatorial code to precisely target and pattern dendritic arbors

The Distribution of Satellites Around Central Galaxies in a Cosmological Hydrodynamical Simulation

Observations have shown that the spatial distribution of satellite galaxies is not random, but rather is aligned with the major axes of central galaxies (CGs). The strength of the alignment is dependent on the properties of both the satellites and centrals. Theoretical studies using dissipationless N-body simulations are limited by their inability to directly predict the shape of CGs. Using hydrodynamical simulations including gas cooling, star formation, and feedback, we carry out a study of galaxy alignment and its dependence on the galaxy properties predicted directly from the simulations.We found that the observed alignment signal is well produced, as is the color dependence: red satellites and red centrals both show stronger alignments than their blue counterparts. The reason for the stronger alignment of red satellites is that most of them stay in the inner region of the dark matter halo where the shape of the CG better traces the dark matter distribution. The dependence of alignment on the color of CGs arises from the halo mass dependence, since the alignment between the shape of the central stellar component and the inner halo increases with halo mass. We also find that the alignment of satellites is most strongly dependent on their metallicity, suggesting that the metallicity of satellites, rather than color, is a better tracer of galaxy alignment on small scales. This could be tested in future observational studies.Comment: ApJ Letter, accepted. Four figures, no table. The resolution of Fig 1 was downgraded due to the limitation of file size. Updated to match the version in pres

Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks

Deeper and wider Convolutional Neural Networks (CNNs) achieve superior performance but bring expensive computation cost. Accelerating such over-parameterized neural network has received increased attention. A typical pruning algorithm is a three-stage pipeline, i.e., training, pruning, and retraining. Prevailing approaches fix the pruned filters to zero during retraining, and thus significantly reduce the optimization space. Besides, they directly prune a large number of filters at first, which would cause unrecoverable information loss. To solve these problems, we propose an Asymptotic Soft Filter Pruning (ASFP) method to accelerate the inference procedure of the deep neural networks. First, we update the pruned filters during the retraining stage. As a result, the optimization space of the pruned model would not be reduced but be the same as that of the original model. In this way, the model has enough capacity to learn from the training data. Second, we prune the network asymptotically. We prune few filters at first and asymptotically prune more filters during the training procedure. With asymptotic pruning, the information of the training set would be gradually concentrated in the remaining filters, so the subsequent training and pruning process would be stable. Experiments show the effectiveness of our ASFP on image classification benchmarks. Notably, on ILSVRC-2012, our ASFP reduces more than 40% FLOPs on ResNet-50 with only 0.14% top-5 accuracy degradation, which is higher than the soft filter pruning (SFP) by 8%.Comment: Extended Journal Version of arXiv:1808.0686

Enhancement of Heat-Cured Cement Paste with Tannic Acid

The Improvement of Cement-Based Materials\u27 Performance by Natural Organic Compounds Can Greatly Promote the Green and Sustainable Development of the Construction Industry. However, Such Compounds Are Not Widely Used Yet Because of their Retarding Effect on Cement. in This Study, the Retardation Effect of Tannic Acid (TA, a Well-Known Retarding Compound) is overcome and the Enhancing Effect is Achieved by Adding Less Than 0.1% Content and Curing Samples in Thermal Regime. Then the Mechanism of TA Enhancing Heat-Cured Cement Pastes is Studied Systematically. Mechanical Properties Results Suggest that Addition of 0.025% TA Can Reduce the Compressive and Flexural Strengths of Cement Pastes by Up to 3.4% and 17.1% under Normal Curing Regime at 3 Days, But Enhance These Two Strengths by More Than 11.4% and 34.6% after Thermal Curing, Respectively. XRD Patterns and TGA Analysis Indicate that, under Thermal Curing Regime, 0.025% TA Can Improve the Hydration Degree of Cement Where the Bound Water Content is Increased by 21.4%. SEM Observations and MIP Results Show that TA Can Compact the Microstructure and the Porosity is Decreased by More Than 7.0%. Furthermore, FTIR Spectrums Prove that TA Can Bond with Hydration Products. Molecular Dynamics Simulation Demonstrates that TA Cross-Links with Calcium Silicate Hydrates (C–S–H) through Ionic and Hydrogen Bonds, Which Could Increase the Tensile Strength by 12.5% and the Ultimate Strain by 100%

Soft filter pruning for accelerating deep convolutional neural networks

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference procedure of deep Convolutional Neural Networks (CNNs). Specifically, the proposed SFP enables the pruned filters to be updated when training the model after pruning. SFP has two advantages over previous works: (1) Larger model capacity. Updating previously pruned filters provides our approach with larger optimization space than fixing the filters to zero. Therefore, the network trained by our method has a larger model capacity to learn from the training data. (2) Less dependence on the pre-trained model. Large capacity enables SFP to train from scratch and prune the model simultaneously. In contrast, previous filter pruning methods should be conducted on the basis of the pre-trained model to guarantee their performance. Empirically, SFP from scratch outperforms the previous filter pruning methods. Moreover, our approach has been demonstrated effective for many advanced CNN architectures. Notably, on ILSCRC-2012, SFP reduces more than 42% FLOPs on ResNet-101 with even 0.2% top-5 accuracy improvement, which has advanced the state-of-the-art