22 research outputs found
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
STRIDE: Single-video based Temporally Continuous Occlusion Robust 3D Pose Estimation
The capability to accurately estimate 3D human poses is crucial for diverse
fields such as action recognition, gait recognition, and virtual/augmented
reality. However, a persistent and significant challenge within this field is
the accurate prediction of human poses under conditions of severe occlusion.
Traditional image-based estimators struggle with heavy occlusions due to a lack
of temporal context, resulting in inconsistent predictions. While video-based
models benefit from processing temporal data, they encounter limitations when
faced with prolonged occlusions that extend over multiple frames. This
challenge arises because these models struggle to generalize beyond their
training datasets, and the variety of occlusions is hard to capture in the
training data. Addressing these challenges, we propose STRIDE (Single-video
based TempoRally contInuous occlusion Robust 3D Pose Estimation), a novel
Test-Time Training (TTT) approach to fit a human motion prior for each video.
This approach specifically handles occlusions that were not encountered during
the model's training. By employing STRIDE, we can refine a sequence of noisy
initial pose estimates into accurate, temporally coherent poses during test
time, effectively overcoming the limitations of prior methods. Our framework
demonstrates flexibility by being model-agnostic, allowing us to use any
off-the-shelf 3D pose estimation method for improving robustness and temporal
consistency. We validate STRIDE's efficacy through comprehensive experiments on
challenging datasets like Occluded Human3.6M, Human3.6M, and OCMotion, where it
not only outperforms existing single-image and video-based pose estimation
models but also showcases superior handling of substantial occlusions,
achieving fast, robust, accurate, and temporally consistent 3D pose estimates
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