428 research outputs found
A novel NMF-based DWI CAD framework for prostate cancer.
In this thesis, a computer aided diagnostic (CAD) framework for detecting prostate cancer in DWI data is proposed. The proposed CAD method consists of two frameworks that use nonnegative matrix factorization (NMF) to learn meaningful features from sets of high-dimensional data. The first technique, is a three dimensional (3D) level-set DWI prostate segmentation algorithm guided by a novel probabilistic speed function. This speed function is driven by the features learned by NMF from 3D appearance, shape, and spatial data. The second technique, is a probabilistic classifier that seeks to label a prostate segmented from DWI data as either alignat, contain cancer, or benign, containing no cancer. This approach uses a NMF-based feature fusion to create a feature space where data classes are clustered. In addition, using DWI data acquired at a wide range of b-values (i.e. magnetic field strengths) is investigated. Experimental analysis indicates that for both of these frameworks, using NMF producing more accurate segmentation and classification results, respectively, and that combining the information from DWI data at several b-values can assist in detecting prostate cancer
A new system for better employment and social outcomes: interim report
This report argues that fundamental reform of the architecture of Australia\u27s welfare system is needed to better capture evolving labour market and social changes, and proposes four pillars of reform.
In December 2013, the Minister for Social Services, the Hon. Kevin Andrews MP, appointed an independent Reference Group to review Australia\u27s welfare system. This is the Reference Group’s Interim Report.
Mr Patrick McClure AO chairs the Reference Group. The other members are Mr Wesley Aird and Ms Sally Sinclair. The Reference Group was supported by a Taskforce in the Department of Social Services in preparing this report.
Executive summary
Government cash transfer payments to individuals and families represent the most significant component of Australia’s social support system in expenditure terms. The Department of Social Services has policy responsibility for income support payments and supplements worth around $100 billion in 2012–13. This is a significant investment with a wide reach across the community.
Changes to Australia’s income support system over time have resulted in unintended complexities, inconsistencies and disincentives for some people to work. The system is also out of step with today’s labour market realities and community expectations. The income support system is in need of major reform to deliver better outcomes for all Australians now and into the future.
Long-term reliance on income support increases the risks of poor health, low self-esteem and social isolation. It can also have intergenerational effects. Children who grow up in households with long periods on income support are more likely to have poor education, employment and social outcomes. In contrast, employment generates clear financial, health and social benefits for individuals, families and communities.
To maximise employment and social outcomes, and to remain sustainable over the longer term, Australia’s income support system needs to have a stronger employment focus. It should provide adequate support while encouraging more people to work to their capacity. It should also be simpler and more coherent.
While reforms in recent decades have increased participation expectations for income support recipients, a more fundamental reform of the architecture of the system is needed to better capture evolving labour market and social changes such as the growth in part-time work and the increased labour force participation of women.
The broader social support system should work in tandem with the income support system to assist those most in need. This includes well-functioning employment services, housing assistance, child care, and early intervention and integrated services for people and families with complex needs, such as homelessness, mental health conditions and drug or alcohol addiction.
Reform needs to take account of recent developments such as the system of lifelong care and support for people with disability being introduced through the National Disability Insurance Scheme, the expansion of paid parental leave and the opportunities offered by new technology. It should also take account of effective interventions to support people who are vulnerable in the labour market, such as people with mental health conditions and people with disability.
This report proposes four pillars of reform:
Simpler and sustainable income support system
Strengthening individual and family capability
Engaging with employers
Building community capacit
A new system for better employment and social outcomes: report of the Reference Group on Welfare Reform to the Minister for Social Services
This review’s purpose was to identify how to make Australia’s welfare system fairer, more effective, coherent and sustainable and encourage people to work.
Overview
The review, which was led by Patrick McClure AO, provides a comprehensive analysis and set of recommendations on simplification of Australia’s welfare system.
It recommends an integrated approach which builds on four pillars of reform:
Simpler and sustainable income support system
Strengthening individual and family capability
Engaging with employers
Building community capacity
The Government will consider the Report’s recommendations and will make further decisions on these as part of a longer term vision of Australia’s welfare system
Optimization of bitterness in chocolate through roasting with analysis of related changes in important bitter compounds
Chocolate is made from the fermented, dried, and roasted seeds of the Theobroma cacao tree, an important agricultural food crop which contains bioactive flavonoid polyphenols with beneficial health effects. Such effects include improvement of antioxidant status, positive impacts on cardiovascular health and endocrine system function, association with cancer prevention, LDL cholesterol reduction, and reduction of obesity and related conditions. However, products which have the highest levels of cacao flavonoids of all eating-chocolate, such as high-cacao-percentage dark chocolate, are known to be quite bitter, a taste modality that is not readily appreciated by humans. Though the complex causes of bitterness in cacao are still not completely understood, it has long been known that the methylxanthines theobromine and caffeine impart bitterness, as do certain flavan-3-ols, sometimes called catechins, which are a class of the aforementioned healthy bioactive polyphenolic flavonoids, also found in tea. Yet, what else is known of bitterness in cacao is sparse and even contradictory. Work on cacao bitterness has described the importance of cyclic dipeptides called 2,5-diketopiperazines (DKPs), while suggesting some form of interaction between theobromine and DKPs as well. Yet these earlier assertions have only been confirmed with mixed results by others, in part due to the incredible complexity of bitterness in roasted cacao, which has been said to require further sensory evaluation. More recent work on bitterness in cacao suggested for the first time that a DKP called cyclo(Pro-Val) is the most important bitter compound. However, even while seeming to confirm the importance of previously known important bitter compound classes, this research was based upon only a single cacao sample from a single origin of cacao, and with an undefined roasting treatment, even though previous work had noted that differences in DKP formation are dependent upon roast profile. Additionally, sensory work was based in part on recombinants of bitter compounds in aqueous solution, allowing for potentially biased estimation of the contribution of the different compounds to finished chocolate bitterness, since the varying kinetics of dissolution of the diverse bitter compounds from low-moisture, high-fat cacao matrix into saliva were not considered, nor were interactions with aroma compounds present in chocolate. Therefore, much was still to be learned about the variation in bitter-compound composition in cacao and related sensory characteristics, within and between different cacao origins and across different roast profiles. This fact, combined with a growing desire for healthy, functional versions of foods such as chocolate makes research into the impact of cacao roasting on consumer perceptions of bitterness and overall liking in chocolate, and the underlying chemical changes, all the more timely. This research project has resulted in findings covering a significant range of chocolate topics. First of all, a new efficient method for extraction and analysis of important bitter compounds in cacao and chocolate was developed. A custom response-surface methodology (RSM)-based design for the roasting treatments, with emphasis on I-optimality for minimizing prediction variance,was created. Chemical and sensory analysis of the roasted chocolate treatments were carried out, followed by in-depth data analysis and interpretation in the context of current chocolate science. Specifically, the aqueous 70% N,N-Dimethylformamide solvent system and HPLC method developed for fast and efficient extraction, followed by analysis, of important bitter compounds from three different chemical classes (i.e., methylxanthines, flavan-3-ols, and 2,5-diketopiperazines) simultaneously, functioned successfully, resulting in acceptable standard curves,% RSD values, and% recovery values. As for quantitative chemical findings, our work generally supports previous studies as regards changes in chemical concentrations during roasting. However, even with the large number of roasting treatments (i.e. 24, or 8 for each of 3 origins) across a reasonably large experimental region, we did not confirm the presence of concentrations of cyclo(Pro-Val) similar to that of previous research. As for sensory evaluation findings, we discovered that reduction of bitterness, sourness, and astringency are all correlated with increased liking in our chocolates. We also noted that consumers appear to have a preference for increased cocoa intensity. Roast profiles that minimize and maximize these characteristics respectively can vary by origin, but temperature and time combinations such as 20 minutes/171[degrees]C, 80 minutes/135[degrees]C, and 54 minutes/151[degrees]C were generally effective, whereas, raw and lightly roasted treatments (i.e., 0 minutes at 24[degrees]C, 11 minutes at 105[degrees]C, or 55 minutes at 64[degrees]C) were not, resulting in the lowest liking ratings. As with any complex food system, caveats do exist. Additions of sugar, salt, and other ingredients would likely introduce significant effects relevant to overall sensory characteristics and consumer liking, and intensity of various other aroma profiles not yet analyzed could do the same (e.g., floral, fruity, nutty). One additional sensory finding is that we can now say that perception of chocolate aroma is likely to play a large role in the perception of taste modalities (i.e., bitterness, sourness, sweetness), and astringency, as well as liking in chocolate. Finally, regarding the relationship of bitter chemical concentrations in the treatments, and consumer bitterness perception thereof, while the analysis is somewhat complicated by the stability of theobromine and caffeine during roasting, we can say that we have little evidence to suggest that theobromine concentration is strongly correlated to bitterness in chocolate. There is far more evidence that caffeine may play a role in the increase of bitterness in cacao, though the magnitude of its importance is not yet known, and to better understand the impact of both theobromine and caffeine, study of many more origins will be required. As for epicatechin and procyanidin B2, as already known, they are quite well correlated, and of all the chemicals we studied, they were, as a pair, the most correlated with changes in bitterness in our data across all treatments, including all three origins. Given that epicatechin has previously been shown to be a more important contributor to bitterness than higher molecular weight procyanidins (e.g., procyanidin B2), the overall importance of epicatechin could be the greatest of all the compounds that we studied. In contrast, catechin and cyclo(Pro-Val), do not appear to be particularly important for changes in bitterness. More specifically, we have found no evidence supporting the claim that the DKP cyclo(Pro-Val) is the most important bitter compound in cacao or chocolate. This does raise additional questions about the importance of diketopiperazines (DKPs) as a class as they relate to bitterness in chocolate.Includes bibliographical references (pages 353-374)
Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.
Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance
Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models
Federated learning (FL) is an approach to training machine learning models
that takes advantage of multiple distributed datasets while maintaining data
privacy and reducing communication costs associated with sharing local
datasets. Aggregation strategies have been developed to pool or fuse the
weights and biases of distributed deterministic models; however, modern
deterministic deep learning (DL) models are often poorly calibrated and lack
the ability to communicate a measure of epistemic uncertainty in prediction,
which is desirable for remote sensing platforms and safety-critical
applications. Conversely, Bayesian DL models are often well calibrated and
capable of quantifying and communicating a measure of epistemic uncertainty
along with a competitive prediction accuracy. Unfortunately, because the
weights and biases in Bayesian DL models are defined by a probability
distribution, simple application of the aggregation methods associated with FL
schemes for deterministic models is either impossible or results in sub-optimal
performance. In this work, we use independent and identically distributed (IID)
and non-IID partitions of the CIFAR-10 dataset and a fully variational
ResNet-20 architecture to analyze six different aggregation strategies for
Bayesian DL models. Additionally, we analyze the traditional federated
averaging approach applied to an approximate Bayesian Monte Carlo dropout model
as a lightweight alternative to more complex variational inference methods in
FL. We show that aggregation strategy is a key hyperparameter in the design of
a Bayesian FL system with downstream effects on accuracy, calibration,
uncertainty quantification, training stability, and client compute
requirements.Comment: 22 pages, 9 figure
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