How Government Policy and Demographics affect Money Demand Function in Bangladesh

Abstract. Money demand has a key position in macroeconomics generally and monetary economics particularly. The improved economic condition of any country is a sign of increasing money demand and deteriorating economic climate is a sign of decreasing money demand (Maravic & Palic, 2005). In this study, Autoregressive distributed lag (ARDL) approach of co-integration developed by Pesaran et al., (2001) is used to estimate the money demand function. Real interest rate, GDP per capita, exchange rate, fiscal deficit, urban and rural population are selected to determine money demand function in Bangladesh over the period from 1975-2013. The co-integration analysis reveals that interest rate and per capita GDP exerts significant effect upon money demand both in long run and short run as well. Both urban and rural population have significant effect on money demand in the long run and short run and money demand function is found stable over time.Keywords. Bangladesh, Money demand, Per Capita GDP, Real interest rate, Exchange rate, Fiscal deficit, Urban and Rural Population.JEL. E41, G18, N30

LOCL: Learning Object-Attribute Composition using Localization

This paper describes LOCL (Learning Object Attribute Composition using Localization) that generalizes composition zero shot learning to objects in cluttered and more realistic settings. The problem of unseen Object Attribute (OA) associations has been well studied in the field, however, the performance of existing methods is limited in challenging scenes. In this context, our key contribution is a modular approach to localizing objects and attributes of interest in a weakly supervised context that generalizes robustly to unseen configurations. Localization coupled with a composition classifier significantly outperforms state of the art (SOTA) methods, with an improvement of about 12% on currently available challenging datasets. Further, the modularity enables the use of localized feature extractor to be used with existing OA compositional learning methods to improve their overall performance.Comment: 20 pages, 7 figures, 11 tables, Accepted in British Machine Vision Conference 202

Charging infrastructure for commercial electric vehicles: Challenges and future works

The journey towards transportation electrification started with small electric vehicles (i.e., electric cars), which have enjoyed an increasing level of global interest in recent years. Electrification of commercial vehicles (e.g., trucks) seems to be a natural progression of this journey, and many commercial vehicle manufacturers have shifted their focus on medium- and heavy-duty vehicle electrification over the last few years. In this paper, we present a comprehensive review and analysis of the existing works presented in the literature on commercial vehicle charging. The paper starts with a brief discussion on the significance of commercial vehicle electrification, especially heavy- and medium-duty vehicles. The paper then reviews two major charging strategies for commercial vehicles, namely the return-to-base model and the on route charging model. Research challenges related to the return-to-base model are then analysed in detail. Next, different methods to charge commercial vehicles on route during their driving cycles are summarized. The paper then analyzes the challenging issues related to charging commercial vehicles at public charging stations. Future works relevant to these challenges are highlighted. Finally, the possibility of accommodating vehicle to grid technology for commercial vehicles is discussed

GTNet:Guided Transformer Network for Detecting Human-Object Interactions

The human-object interaction (HOI) detection task refers to localizing humans, localizing objects, and predicting the interactions between each human-object pair. HOI is considered one of the fundamental steps in truly understanding complex visual scenes. For detecting HOI, it is important to utilize relative spatial configurations and object semantics to find salient spatial regions of images that highlight the interactions between human object pairs. This issue is addressed by the novel self-attention based guided transformer network, GTNet. GTNet encodes this spatial contextual information in human and object visual features via self-attention while achieving state of the art results on both the V-COCO and HICO-DET datasets. Code will be made available online.Comment: pre-print, the work is in progres

A charging strategy for large commercial electric vehicle fleets

The popularity of Commercial Electric Vehicles (CEVs) has experienced a surge in recent years, particularly in urban vocational contexts, as a means of advancing towards the goal of attaining net-zero emissions by 2050. The return-to-base charging strategy, which involves charging CEVs at depots, has become a prevalent practice for smaller CEV fleets. Nevertheless, for larger CEV fleets, the limited charging capacity at depots presents a significant challenge, leading to a reliance on both limited depot charging infrastructure and public charging infrastructure. This reliance can have a substantial impact on both the operational costs and the sustainability of logistics services. To address these challenges, this study proposes a new charging strategy for managing the charging of large CEV fleets. The proposed strategy coordinates the charging of CEVs at depots and public charging stations. The strategy is formulated as a constraint optimization problem and takes into consideration operational schedules, demand charges, and the characteristics of public charging stations. The results of this study demonstrate the effectiveness of the proposed strategy in optimizing CEV charging at different stations, preserving the continuity of logistics services, and reducing total travel costs by 30% compared to existing solutions. This study offers a solution to the challenges faced by large CEV fleets in their efforts to achieve cost-effective and sustainable charging solutions

What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions

We propose a novel one-stage Transformer-based semantic and spatial refined transformer (SSRT) to solve the Human-Object Interaction detection task, which requires to localize humans and objects, and predicts their interactions. Differently from previous Transformer-based HOI approaches, which mostly focus at improving the design of the decoder outputs for the final detection, SSRT introduces two new modules to help select the most relevant object-action pairs within an image and refine the queries' representation using rich semantic and spatial features. These enhancements lead to state-of-the-art results on the two most popular HOI benchmarks: V-COCO and HICO-DET.Comment: CVPR 2022 Ora

Impact of Decmedetomidine on Opioid and Benzodiazepine Dosing Requirements in Children.

Poster presented at: Annual Update on Pediatric Cardiovascular Disease; February 2008; Scottsdale Arizona

Oxygen uptake in the brine shrimp artemia in relation to salinity

The rate of oxygen consumption of Artemia has decreased with decrease in salinity and in freshwater the 02 consumed was least. The probable reasons for such decrease have (been discussed