Learning Task Relatedness in Multi-Task Learning for Images in Context

Multimedia applications often require concurrent solutions to multiple tasks. These tasks hold clues to each-others solutions, however as these relations can be complex this remains a rarely utilized property. When task relations are explicitly defined based on domain knowledge multi-task learning (MTL) offers such concurrent solutions, while exploiting relatedness between multiple tasks performed over the same dataset. In most cases however, this relatedness is not explicitly defined and the domain expert knowledge that defines it is not available. To address this issue, we introduce Selective Sharing, a method that learns the inter-task relatedness from secondary latent features while the model trains. Using this insight, we can automatically group tasks and allow them to share knowledge in a mutually beneficial way. We support our method with experiments on 5 datasets in classification, regression, and ranking tasks and compare to strong baselines and state-of-the-art approaches showing a consistent improvement in terms of accuracy and parameter counts. In addition, we perform an activation region analysis showing how Selective Sharing affects the learned representation.Comment: To appear in ICMR 2019 (Oral + Lightning Talk + Poster

Definitions of groove and hollowness of the infraorbital region and clinical treatment using soft-tissue filler

Clarification is needed regarding the definitions and classification of groove and hollowness of the infraorbital region depending on the cause, anatomical characteristics, and appearance. Grooves in the infraorbital region can be classified as nasojugal grooves (or folds), tear trough deformities, and palpebromalar grooves; these can be differentiated based on anatomical characteristics. They are caused by the herniation of intraorbital fat, atrophy of the skin and subcutaneous fat, contraction of the orbital part of the orbicularis oculi muscle or squinting, and malar bone resorption. Safe and successful treatment requires an optimal choice of filler and treatment method. The choice between a cannula and needle depends on various factors; a needle is better for injections into a subdermal area in a relatively safe plane, while a cannula is recommended for avoiding vascular compromise when injecting filler into a deep fat layer and releasing fibrotic ligamentous structures. The injection of a soft-tissue filler into the subcutaneous fat tissue is recommended for treating mild indentations around the orbital rim and nasojugal region. Reducing the tethering effect of ligamentous structures by undermining using a cannula prior to the filler injection is recommended for treating relatively deep and fine indentations. The treatment of mild prolapse of the intraorbital septal fat or broad flattening of the infraorbital region can be improved by restoring the volume deficiency using a relatively firm filler

Asymmetric Multi-task Learning Based on Task Relatedness and Loss

We propose a novel multi-task learning method that can minimize the effect of negative transfer by allowing asymmetric transfer between the tasks based on task relatedness as well as the amount of individual task losses, which we refer to as Asymmetric Multi-task Learning (AMTL). To tackle this problem, we couple multiple tasks via a sparse, directed regularization graph, that enforces each task parameter to be reconstructed as a sparse combination of other tasks, which are selected based on the task-wise loss. We present two different algorithms to solve this joint learning of the task predictors and the regularization graph. The first algorithm solves for the original learning objective using alternative optimization, and the second algorithm solves an approximation of it using curriculum learning strategy, that learns one task at a time. We perform experiments on multiple datasets for classification and regression, on which we obtain significant improvements in performance over the single task learning and symmetric multitask learning baselines

Learning Task Relatedness in Multi-Task Learning for Images in Context

Multimedia applications often require concurrent solutions to multiple tasks. These tasks hold clues to each-others solutions, however as these relations can be complex this remains a rarely utilized property. When task relations are explicitly defined based on domain knowledge multi-task learning (MTL) offers such concurrent solutions, while exploiting relatedness between multiple tasks performed over the same dataset. In most cases however, this relatedness is not explicitly defined and the domain expert knowledge that defines it is not available. To address this issue, we introduce Selective Sharing, a method that learns the inter-task relatedness from secondary latent features while the model trains. Using this insight, we can automatically group tasks and allow them to share knowledge in a mutually beneficial way. We support our method with experiments on 5 datasets in classification, regression, and ranking tasks and compare to strong baselines and state-of-the-art approaches showing a consistent improvement in terms of accuracy and parameter counts. In addition, we perform an activation region analysis showing how Selective Sharing affects the learned representation

Annealing effects of ZnO seed layers on structural and optical properties of ZnO nanorods grown on R-plane sapphire substrates by hydrothermal Method

ZnO nanorods were hydrothermally grown on R-plane sapphire substrates coated with an as-grown ZnO seed layer and with ZnO seed layers annealed at different temperatures. The effects of the seed-layer annealing temperature on the structural and optical properties of the ZnO nanorods were investigated using scanning electron microscopy, X-ray diffraction, and photoluminescence. ZnO nanorods and nanosheets grew on the as-prepared seed layer. Only ZnO nanorods grew on the ZnO seed layer annealed above 700 °C. The structural and optical properties of the ZnO nanorods were significantly enhanced when the seed layers were annealed at 700 °C. A cubic equation was used to establish the non-linear exciton radiative lifetime of the free exciton emission peak. Varshni's empirical equation fitting parameters were α = 4 × 10-3 eV/K, β = 1 × 104 K, and Eg(0) = 3.335 eV; the activation energy was ∼94.6 meV. Copyright © The Korean Institute of Metals and Materials1741sciescopuskc

Photoluminescence wavelength variation of monolayer MoS2 by oxygen plasma treatment

We performed nanoscale confocal photoluminescence (PL), Raman, and absorption spectral imaging measurements to investigate the optical and structural properties of molybdenum disulfide (MoS2) monolayers synthesized by chemical vapor deposition method and subjected to oxygen plasma treatment for 10 to 120 s under high vacuum(1.3 × 10−3 Pa). Oxygen plasma treatment induced red shifts of ~20 nmin the PL emission peaks corresponding to A and B excitons. Similarly, the peak positions corresponding to A and B excitons of the absorption spectra were red-shifted following oxygen plasma treatment. Based on the confocal PL, absorption, and Raman microscopy results, we suggest that the red-shifting of the A and B exciton peaks originated from shallow defect states generated by oxygen plasma treatment.1991sciescopu

Fast and scalable in-memory deep multitask learning via neural weight virtualization

This paper introduces the concept of Neural Weight Virtualization - which enables fast and scalable in-memory multitask deep learning on memory-constrained embedded systems. The goal of neural weight virtualization is two-fold: (1) packing multiple DNNs into a fixed-sized main memory whose combined memory requirement is larger than the main memory, and (2) enabling fast in-memory execution of the DNNs. To this end, we propose a two-phase approach: (1) virtualization of weight parameters for fine-grained parameter sharing at the level of weights that scales up to multiple heterogeneous DNNs of arbitrary network architectures, and (2) in-memory data structure and run-time execution framework for in-memory execution and context-switching of DNN tasks. We implement two multitask learning systems: (1) an embedded GPU-based mobile robot, and (2) a microcontroller-based IoT device. We thoroughly evaluate the proposed algorithms as well as the two systems that involve ten state-of-the-art DNNs. Our evaluation shows that weight virtualization improves memory efficiency, execution time, and energy efficiency of the multitask learning systems by 4.1x, 36.9x, and 4.2x, respectively

Facile Synthesis and Enhanced Ultraviolet Emission of ZnO Nanorods Prepared by Vapor-Confined Face-to-Face Annealing

In this study, we report a novel regrowth method of sol–gel-prepared ZnO films using a vapor-confined face-to-face annealing (VC-FTFA) technique in which mica was inserted between two films, followed by annealing with the FTFA method. The ZnO nanorods are regrown when zinc acetate dihydrate and zinc chloride (ZnCl₂) are used as the solvent, because these generate ZnCl₂ vapor. The near-band-edge emission intensity of the ZnO nanorods was enhanced through the VC-FTFA method, increasing significantly by a factor of 56 compared to that of ZnO films annealed in open air at 700 °C. Our method may provide a route toward the facile fabrication of ZnO nanorods

Effects of Ga concentration on the structural, electrical and optical properties of Ga-doped ZnO thin films grown by sol-gel method

Undoped ZnO and Ga-doped ZnO (GZO) thin films with different Ga concentrations were prepared by using the sol-gel spin-coating method. The surface morphologies and the growth orientations of the films were measured by using scanning electron microscopy and X-ray diffraction, respectively. The electrical properties were measured by using the Hall effect. The optical transmittances and reflectances of the films were measured as functions of the wavelength by UV-vis spectroscopy. The undoped ZnO thin films exhibited rough surfaces with particle-like structures. When Ga was incorporated, the particle sizes dramatically decreased without changes in the surface morphologies, and the c-axis growth orientations of the GZO thin films were significantly decreased. The optical transmittances clearly exhibited shifts in the band edge, and those in the visible range gradually increased with increasing Ga concentration. The absorption coefficients, refractive indices, extinction constants, dielectric constants, and optical conductivities of the films gradually decreased with increasing Ga concentration. © 2014 The Korean Physical Society.1451sciescopuskc