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

Modeling and simulation of thermoelectric generator (TEG) performance parameters evaluation

Nowadays, thermoelectric module is widely used for industrial application to produce electricity energy from wasted heat energy. This paper discusses thermoelectric generator (TEG) modeling and simulation to attain improvement. There are several methods to model the thermoelectric generator: finite element method (FEM) model, circuit equivalent model and mathematical/computer model. In order to perform FEM modeling analysis, any engineer should acquire high level of knowledge in material engineering. Circuit equivalent model is using electrical circuit analogy to describe heat transfer and electrical behavior in TEG. In this work, mathematical derivation from circuit equivalent model is used in computer model by using Simulink (Matlab). The result from computer model is an alternative model for electrical or electronics engineers to study and doing future improvements on TEG performance

Edge illumination X-ray phase tomography of multi-material samples using a single-image phase retrieval algorithm

In this paper we present a single-image phase retrieval algorithm for multi-material samples, developed for the edge illumination (EI) X-ray phase contrast imaging method. The theoretical derivation is provided, along with any assumptions made. The algorithm is evaluated quantitatively using both simulated and experimental results from a computed tomography (CT) scan using the EI laboratory implementation. Qualitative CT results are provided for a biological sample containing both bone and soft-tissue. Using a single EI image per projection and knowledge of the complex refractive index, the algorithm can accurately retrieve the interface between a given pair of materials. A composite CT slice can be created by splicing together multiple CT reconstructions, each retrieved for a different pair of materials

An investigation into morphological and physiological approaches to screen maize (Zea mays L.) hybrids for drought tolerance

A pot experiment was carried out in completely randomized design (CRD) having three replications to screen out six maize (Zea mays L.) hybrids viz; FH-810, 32-F-10, FH-782, 32-B-33, YH-1898, Monsanto-6525, R-2315 and R-3304 for drought tolerance. The study was carried out with objective to screen hybrids, when exposed to drought on the early phase of their vegetative growth. The moisture treatments comprised of 100% field capacity (FC), 75% FC and 50% FC. The results exhibited that all these hybrids varied substantially in their stability against drought tolerance. However, the results pertaining to interaction of maize hybrids with three moisture levels of 100% FC, 75% FC and 50% FC revealed that 32-F-10 performed comparatively better in contrast to other maize hybrids in plant height (79.74 cm, 47.02 cm and 41.65 cm), leaf area per plant (865.10 cm2, 405.7 cm2 and 178.60 cm2), relative water contents (81.23%, 69.79% and 65.98%), at 100%, 75% and 50% FC, respectively, while YH-1898 hybrid produced lowest values of these attributes in almost all water levels. However, a better stomatal conductance (gs), photosynthetic rate (A) and transpiration rate (E) were exhibited by 32-F-10 while YH-1898 revealed least gas-exchange values among all hybrids. The experimental results revealed that under drought conditions 32-F-10 performed best than all other maize hybrids and could be used for further investigation to screen out other drought tolerant-maize hybrids for maximum production

Microbial and heavy metal contaminant of antidiabetic herbal preparations formulated in Bangladesh

The aim of the current study was to evaluate microbial contamination in terms of microbial load (total aerobic count and total coliform count) and specific pathogenic bacteria (Salmonella spp., Escherichia coli, particularly Escherichia coli 0157) in thirteen antidiabetic herbal preparations (ADHPs) from Dhaka City. All the thirteen ADHPs had been found contaminated with fungi and different pathogenic bacteria. From the data, it is found that only two of these preparations (ADHP-1 and ADHP-12) complied with the safety limit (as stated in different Pharmacopoeias and WHO guidelines) evaluated by all different microbial counts. None of these herbal preparations could assure the safety as all of them were contaminated by fungi. The overall safety regarding heavy metal content (Zn, Cu, Mn, Cr, Cd, and Pb) was assured as none of them exceeded the safety limit of the daily intake. Microbial contaminants in these herbal preparations pose a potential risk for human health and care should be taken in every step involved in the preparation of these herbal preparations to assure safety.Rausan Zamir, Anowar Hosen, M. Obayed Ullah, and Nilufar Naha

Two Dimension Marginal Distributions of Crossing Time and Renewal Numbers Related to Two-Stage Erlang Processes

The two dimensional marginal transform, probability density and cumulative probability distribution functions for the random variables TξN (time taken by servers during vacations), ξN (number of vacations taken by servers) and Nη (number of customers or units arriving in the system) are derived by taking combinations of these random variables. One random variable is controlled at one time to determine the effect of the other two random variables simultaneously

A complementary view on the growth of directory trees

Trees are a special sub-class of networks with unique properties, such as the level distribution which has often been overlooked. We analyse a general tree growth model proposed by Klemm {\em et. al.} (2005) to explain the growth of user-generated directory structures in computers. The model has a single parameter $q$ which interpolates between preferential attachment and random growth. Our analysis results in three contributions: First, we propose a more efficient estimation method for $q$ based on the degree distribution, which is one specific representation of the model. Next, we introduce the concept of a level distribution and analytically solve the model for this representation. This allows for an alternative and independent measure of $q$. We argue that, to capture real growth processes, the $q$ estimations from the degree and the level distributions should coincide. Thus, we finally apply both representations to validate the model with synthetically generated tree structures, as well as with collected data of user directories. In the case of real directory structures, we show that $q$ measured from the level distribution are incompatible with $q$ measured from the degree distribution. In contrast to this, we find perfect agreement in the case of simulated data. Thus, we conclude that the model is an incomplete description of the growth of real directory structures as it fails to reproduce the level distribution. This insight can be generalised to point out the importance of the level distribution for modeling tree growth.Comment: 16 pages, 7 figure

Increased robustness and speed in low-dose phase-contrast tomography with laboratory sources

In this article we discuss three different developments in Edge Illumination (EI) X-ray phase contrast imaging (XPCi), all ultimately aimed at optimising EI computed tomography (CT) for use in different environments, and for different applications. For the purpose of reducing scan times, two approaches are presented; the reverse projection" acquisition scheme which allows a continuous rotation of the sample, and the single image" retrieval algorithm, which requires only one frame for retrieval of the projected phase map. These are expected to lead to a substantial reduction of EI CT scan times, a prospect which is likely to promote the translation of EI into several applications, including clinical. The last development presented is the "modified local" phase retrieval. This retrieval algorithm is specifically designed to accurately retrieve sample properties (absorption, refraction, scattering) in cases where high-resolution scans are required in non-ideal environments. Experimental results, using both synchrotron radiation and laboratory sources, are shown for the various approaches