eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems

Recent advancements in software and hardware technologies have enabled the use of AI/ML models in everyday applications has significantly improved the quality of service rendered. However, for a given application, finding the right AI/ML model is a complex and costly process, that involves the generation, training, and evaluation of multiple interlinked steps (called pipelines), such as data pre-processing, feature engineering, selection, and model tuning. These pipelines are complex (in structure) and costly (both in compute resource and time) to execute end-to-end, with a hyper-parameter associated with each step. AutoML systems automate the search of these hyper-parameters but are slow, as they rely on optimizing the pipeline's end output. We propose the eTOP Framework which works on top of any AutoML system and decides whether or not to execute the pipeline to the end or terminate at an intermediate step. Experimental evaluation on 26 benchmark datasets and integration of eTOPwith MLBox4 reduces the training time of the AutoML system upto 40x than baseline MLBox.Comment: N

Reinforced Approximate Exploratory Data Analysis

Exploratory data analytics (EDA) is a sequential decision making process where analysts choose subsequent queries that might lead to some interesting insights based on the previous queries and corresponding results. Data processing systems often execute the queries on samples to produce results with low latency. Different downsampling strategy preserves different statistics of the data and have different magnitude of latency reductions. The optimum choice of sampling strategy often depends on the particular context of the analysis flow and the hidden intent of the analyst. In this paper, we are the first to consider the impact of sampling in interactive data exploration settings as they introduce approximation errors. We propose a Deep Reinforcement Learning (DRL) based framework which can optimize the sample selection in order to keep the analysis and insight generation flow intact. Evaluations with 3 real datasets show that our technique can preserve the original insight generation flow while improving the interaction latency, compared to baseline methods.Comment: Appears in the 37th AAAI Conference on Artificial Intelligence (AAAI), 202

Flux-resolved spectro-polarimetric evolution of the X-ray pulsar Her X-1 using IXPE

We conduct a spectro-polarimetric study of the accreting X-ray pulsar Hercules X-1 using observations with the Imaging X-ray Polarimetry Explorer (IXPE). IXPE monitored the source in three different Epochs, sampling two Main-on and one Short-on state of the well-known super-orbital period of the source. We find that the 2-7 keV polarization fraction increases significantly from ~ 7-9 % in the Main-on state to ~ 15-19 % in the Short-on state, while the polarization angle remains more or less constant or changes slightly, ~ 47-59 degrees, in all three Epochs. The polarization degree and polarization angle are consistent with being energy-independent for all three Epochs. We propose that in the Short-on state, when the neutron star is partially blocked by the disk warp, the increase in the polarization fraction can be explained as a result of the preferential obstruction of one of the magnetic poles of the neutron star.Comment: 7 pages, 4 Figures, 1 Table, accepted for publication in ApJ

NOTES2: Networks-of-Traces for Epidemic Spread Simulations

Decision making and intervention against infectious diseases require analysis of large volumes of data, including demographic data, contact networks, agespecific contact rates, mobility networks, and healthcare and control intervention data and models. In this paper, we present our Networks-Of-Traces for Epidemic Spread Simulations (NOTES2) model and system which aim at assisting experts and helping them explore existing simulation trace data sets. NOTES2 supports analysis and indexing of simulation data sets as well as parameter and feature analysis, including identification of unknown dependencies across the input parameters and output variables spanning the different layers of the observation and simulation data

Factorized Tensor Networks for Multi-Task and Multi-Domain Learning

Multi-task and multi-domain learning methods seek to learn multiple tasks/domains, jointly or one after another, using a single unified network. The key challenge and opportunity is to exploit shared information across tasks and domains to improve the efficiency of the unified network. The efficiency can be in terms of accuracy, storage cost, computation, or sample complexity. In this paper, we propose a factorized tensor network (FTN) that can achieve accuracy comparable to independent single-task/domain networks with a small number of additional parameters. FTN uses a frozen backbone network from a source model and incrementally adds task/domain-specific low-rank tensor factors to the shared frozen network. This approach can adapt to a large number of target domains and tasks without catastrophic forgetting. Furthermore, FTN requires a significantly smaller number of task-specific parameters compared to existing methods. We performed experiments on widely used multi-domain and multi-task datasets. We show the experiments on convolutional-based architecture with different backbones and on transformer-based architecture. We observed that FTN achieves similar accuracy as single-task/domain methods while using only a fraction of additional parameters per task

Comparative Study between Mobile Operating Systems and Android Application Development

Android operating system is a broadened source versatile application which relies upon Linux Kernel working framework. It is most popular application till now and has a low cost which makes it growing much faster than any other operating system. In today’s world of rapidly growing technology there are many operating system but android is the most efficient and user friendly operating system. The main reason towards its growing popularity is various functionalities, ease of use and utility. This can perform numerous tasks such as making call, sending or receiving Messages, music, online shopping, playing games, web browsing, many social media apps etc. As we all know Android OS is developed by Google and provides a huge variety of applications. This paper will show the increase of Android OS and the development of Android operating system

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Exploring Kyrö Gin's Market Entry into Australia

This thesis explores the potential of Kyrö Distillery Company to enter the gin industry in Australia and identifies the most appropriate market entry strategy. The research focuses on the appeal of the Australian market for Kyrö Gin, the best method of entry, and the potential success of a small craft distillery like Kyrö in Australia. The study relies on secondary data, including information from government websites, professional research and studies, company websites, and articles. The thesis utilizes various analytical frameworks, such as PESTEL Analysis, Porter's Five Forces, SWOT Analysis, Marketing Mix, STP Model, Risk Management, and Porter's Four Corners Model, to comprehend the market and strategic positioning. The study presents an overview of the alcohol industry in Australia, a comprehensive analysis of the business environment, and a thorough examination of the gin market, including consumer behavior and market trends. The thesis concludes by synthesizing the findings, pointing out the limitations and providing recommendations for Kyrö's strategic approach to entering the Australian market