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

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

Influence of Tillage and Poultry Manure on the Physical Properties of Grain and Yield Attributes of Spring Maize (Zea mays L.)

Grains are the economical part of maize that demand proper management practices to achieve the crop potential. The study explored the influence of different tillage practices and poultry manure levels on the grain length, breadth, area, grains weight per cob and grains yield per m2of maize at Agronomic Research Area, University of Agriculture, Faisalabad, Pakistan, during spring 2010 and 2011. The experiment was laid out in randomized complete block design with split plot arrangement, having four tillage practices as main plot treatments, zero tillage (direct seed sowing with dibbler), minimum tillage (one cultivation with normal cultivator followed by planking), conventional tillage (2–3 cultivations with normal cultivator followed by planking) and deep tillage (two deep ploughing with chisel plough + one cultivation with normal cultivator followed by planking). Sub plot treatments were three poultry manure levels; control (no poultry manure), poultry manure at the amount of 5 Mg ha-1 and poultry manure at 10 Mg ha-1. Data indicated that the deep tillage practice significantly improved the maize grain physical properties and yield over the other tillage practices in both years of study. Increasing order of poultry manure dose treatments produced the good and healthy seeds over the control treatment. A positive correlation between grain yield, physical properties of maize grain and grains weight per cob was recorded

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