3,267 research outputs found
Shifting Into Gear: A Comprehensive Guide to Creating a Car Ownership Program
Offers detailed strategies for organizations pursuing car ownership programs to help low-income residents obtain vehicles for employment access and family economic improvement
Optimal periodic dividend strategies for spectrally positive L\'evy risk processes with fixed transaction costs
We consider the general class of spectrally positive L\'evy risk processes,
which are appropriate for businesses with continuous expenses and lump sum
gains whose timing and sizes are stochastic. Motivated by the fact that
dividends cannot be paid at any time in real life, we study
dividend strategies whereby dividend decisions are made according to a separate
arrival process.
In this paper, we investigate the impact of fixed transaction costs on the
optimal periodic dividend strategy, and show that a periodic
strategy is optimal when decision times arrive according to an independent
Poisson process. Such a strategy leads to lump sum dividends that bring the
surplus back to as long as it is no less than at a dividend
decision time. The expected present value of dividends (net of transaction
costs) is provided explicitly with the help of scale functions. Results are
illustrated.Comment: Accepted for publication in Insurance: Mathematics and Economic
Increasing the voluntary and community sector’s involvement in Integrated Offender Management(IOM)
As part of an undertaking to increase voluntary and community sector (VCS) involvement in service delivery, the Home Office set up an initiative to provide small grants to VCS organisations to work with IOM partnerships.
The Home Office commissioned an evaluation of the initiative which aimed to: explore the strengths and weaknesses of the funding model; identify perceived barriers and facilitators to voluntary and community sector involvement in IOM; explore how the Home Office might best work with the VCS to encourage and support their capacity to work in partnership with statutory agencies; and identify any implications for the delivery of future similar projects
Process evaluation of Derbyshire Intensive Alternatives to Custody Pilot
The aim of this study was to critically assess the implementation and development of the Intensive Alternatives to Custody (IAC) pilot in Derbyshire. The Ministry of Justice (MoJ) Penal Policy paper (May 2007) outlined the government’s intention to develop higher intensity community orders as an alternative to short-term custody. The IAC Order was subsequently developed and piloted, first in Derbyshire and then in six other areas.* The pilots were centrally funded until March 2011
On the optimality of joint periodic and extraordinary dividend strategies
In this paper, we model the cash surplus (or equity) of a risky business with
a Brownian motion. Owners can take cash out of the surplus in the form of
"dividends", subject to transaction costs. However, if the surplus hits 0 then
ruin occurs and the business cannot operate any more.
We consider two types of dividend distributions: (i) periodic, regular ones
(that is, dividends can be paid only at countable many points in time,
according to a specific arrival process); and (ii) extraordinary dividend
payments that can be made immediately at any time (that is, the dividend
decision time space is continuous and matches that of the surplus process).
Both types of dividends attract proportional transaction costs, and
extraordinary distributions also attracts fixed transaction costs, a realistic
feature. A dividend strategy that involves both types of distributions
(periodic and extraordinary) is qualified as "hybrid".
We determine which strategies (either periodic, immediate, or hybrid) are
optimal, that is, we show which are the strategies that maximise the expected
present value of dividends paid until ruin, net of transaction costs.
Sometimes, a liquidation strategy (which pays out all monies and stops the
process) is optimal. Which strategy is optimal depends on the profitability of
the business, and the level of (proportional and fixed) transaction costs.
Results are illustrated
COVIDNet-CT: Detection of COVID-19 from Chest CT Images using a Tailored Deep Convolutional Neural Network Architecture
The COVID-19 pandemic continues to have a tremendous impact on patients and healthcare systems around the world. To combat this disease, there is a need for effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as a key screening method to complement RT-PCR testing. Early studies have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, we introduce COVIDNet-CT, a deep convolutional neural network architecture tailored for detection of COVID-19 cases from chest CT images. We also introduce COVIDx-CT, a CT image dataset comprising 104,009 images across 1,489 patient cases. Finally, we leverage explainability to investigate the decision-making behaviour of COVIDNet-CT and ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images
Cancer-Net PCa-Data: An Open-Source Benchmark Dataset for Prostate Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data
The recent introduction of synthetic correlated diffusion (CDI) imaging
has demonstrated significant potential in the realm of clinical decision
support for prostate cancer (PCa). CDI is a new form of magnetic resonance
imaging (MRI) designed to characterize tissue characteristics through the joint
correlation of diffusion signal attenuation across different Brownian motion
sensitivities. Despite the performance improvement, the CDI data for PCa
has not been previously made publicly available. In our commitment to advance
research efforts for PCa, we introduce Cancer-Net PCa-Data, an open-source
benchmark dataset of volumetric CDI imaging data of PCa patients.
Cancer-Net PCa-Data consists of CDI volumetric images from a patient cohort
of 200 patient cases, along with full annotations (gland masks, tumor masks,
and PCa diagnosis for each tumor). We also analyze the demographic and label
region diversity of Cancer-Net PCa-Data for potential biases. Cancer-Net
PCa-Data is the first-ever public dataset of CDI imaging data for PCa, and
is a part of the global open-source initiative dedicated to advancement in
machine learning and imaging research to aid clinicians in the global fight
against cancer
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning
The COVID-19 pandemic continues to rage on, with multiple waves causing
substantial harm to health and economies around the world. Motivated by the use
of CT imaging at clinical institutes around the world as an effective
complementary screening method to RT-PCR testing, we introduced COVID-Net CT, a
neural network tailored for detection of COVID-19 cases from chest CT images as
part of the open source COVID-Net initiative. However, one potential limiting
factor is restricted quantity and diversity given the single nation patient
cohort used. In this study, we introduce COVID-Net CT-2, enhanced deep neural
networks for COVID-19 detection from chest CT images trained on the largest
quantity and diversity of multinational patient cases in research literature.
We introduce two new CT benchmark datasets, the largest comprising a
multinational cohort of 4,501 patients from at least 15 countries. We leverage
explainability to investigate the decision-making behaviour of COVID-Net CT-2,
with the results for select cases reviewed and reported on by two
board-certified radiologists with over 10 and 30 years of experience,
respectively. The COVID-Net CT-2 neural networks achieved accuracy, COVID-19
sensitivity, PPV, specificity, and NPV of 98.1%/96.2%/96.7%/99%/98.8% and
97.9%/95.7%/96.4%/98.9%/98.7%, respectively. Explainability-driven performance
validation shows that COVID-Net CT-2's decision-making behaviour is consistent
with radiologist interpretation by leveraging correct, clinically relevant
critical factors. The results are promising and suggest the strong potential of
deep neural networks as an effective tool for computer-aided COVID-19
assessment. While not a production-ready solution, we hope the open-source,
open-access release of COVID-Net CT-2 and benchmark datasets will continue to
enable researchers, clinicians, and citizen data scientists alike to build upon
them.Comment: 15 page
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