4,414 research outputs found
On information captured by neural networks: connections with memorization and generalization
Despite the popularity and success of deep learning, there is limited
understanding of when, how, and why neural networks generalize to unseen
examples. Since learning can be seen as extracting information from data, we
formally study information captured by neural networks during training.
Specifically, we start with viewing learning in presence of noisy labels from
an information-theoretic perspective and derive a learning algorithm that
limits label noise information in weights. We then define a notion of unique
information that an individual sample provides to the training of a deep
network, shedding some light on the behavior of neural networks on examples
that are atypical, ambiguous, or belong to underrepresented subpopulations. We
relate example informativeness to generalization by deriving nonvacuous
generalization gap bounds. Finally, by studying knowledge distillation, we
highlight the important role of data and label complexity in generalization.
Overall, our findings contribute to a deeper understanding of the mechanisms
underlying neural network generalization.Comment: PhD thesi
A view of colonial life in South Australia: An osteological investigation of the health status among 19th-century migrant settlers
Studies of human skeletal remains contribute to understanding the extent to which conditions
prevailing in various past communities were detrimental to health. Few of these studies have
evaluated the situation in which the first European colonists of South Australia lived.
Colonial Australian skeletal collections are scarce, especially for research purposes. This
makes the 19th-century skeletal remains of individuals, excavated from St Mary’s Cemetery,
South Australia, a rare and valuable collection.
The overarching aim of this thesis was to investigate the general and oral health of this
specific group of 19th-century settlers, through the examination of their skeletons and
dentitions. Four research papers in this thesis address this overarching aim. The first two
papers determine the general skeletal health of the settlers, with a focus on pathological
manifestations on bones associated with metabolic deficiencies and the demands of
establishing an industrial society. Paper 3 investigated whether Large Volume Micro-
Computed Tomography (LV Micro-CT) could be used as a single technique for the analysis
of the in situ dentoalveolar complex of individuals from St Mary’s. This led to a detailed
investigation of the dentitions of the St Mary’s sample, in paper 4, with the aims of
determining the oral health status of these individuals, and understanding how oral conditions
may have influenced their general health.
The skeletal remains of 65 individuals (20 adults and 45 subadults) from St Mary’s sample
were available for the four component investigations using non-destructive techniques -
macroscopic, radiographic and micro-CT methods.
Signs of nutritional deficiencies (vitamin C and iron) were identified in Paper 1. The findings
of paper 2 showed joint diseases and traumatic fractures were seen and that gastrointestinal and pulmonary conditions were the leading causes of death in subadults and adults
respectively. Paper 3 found that the LV Micro-CT technique was the only method able to
generate images that allowed the full range of detailed measurements across all the oral
health categories studied. A combination of macroscopic and radiographic techniques
covered a number of these categories, but was more time-consuming, and did not provide the
same level of accuracy or include all measurements. Results for paper 4 confirmed that
extensive carious lesions, antemortem tooth loss and evidence of periodontal disease were
present in the St Mary’s sample. Developmental defects of enamel (EH) and areas of
interglobular dentine (IGD) were identified. Many individuals with dental defects also had
skeletal signs of co-morbidities. St Mary’s individuals had a similar percentage of carious
lesions as the British sample, which was more than other historic Australian samples, but less
than a contemporary New Zealand sample.
The 19th-century migrants to the colony of South Australia were faced with multiple
challenges such as adapting to local environmental conditions as well as participating in the
development of settlements, infrastructure and new industries. Evidence of joint diseases,
traumatic injuries and health insults, seen as pathological changes and/ or abnormalities on
the bone and/or teeth, confirmed that the settlers' health had been affected. The number of
burials in the ‘free ground’ area between the 1840s -1870s was greater than the number in the
leased plots, reflecting the economic problems of the colony during these early years.
Validation of the reliability and accuracy of the LV Micro-CT system for the analysis of the
dentoalveolar complex, in situ within archaeological human skull samples, provided a
microanalytical approach for the in-depth investigations of the St Mary’s dentition. Extensive
carious lesions, antemortem tooth loss and periodontal disease seen in this group would have
affected their general health status. The presence of developmental defects (EH and IGD)
indicated that many of the settlers had suffered health insults in childhood to young adulthood. Contemporaneous Australian, New Zealand and British samples had comparable
findings suggesting that little improvement had occurred in their oral health since arriving in
South Australia.
In conclusion, the findings of this investigation largely fulfilled the initial aims. Our
understanding of the extent to which conditions prevailing in the new colony were
detrimental to human health has increased, as has our knowledge of why pathological
manifestations and/or abnormalities were seen on the bones and teeth of individuals from the
St Mary’s sample. A multiple-method approach, to derive enhanced information has been
shown to be effective, whilst establishing a new methodology (LV Micro-CT) for the analysis
of dentition in situ in human archaeological skulls. Further, this investigation has digitally
preserved data relating to this historical group of individuals for future comparisons.Thesis (Ph.D.) -- University of Adelaide, School of Biomedicine, 202
Modified Theories of Gravity and Cosmological Applications
This reprint focuses on recent aspects of gravitational theory and cosmology. It contains subjects of particular interest for modified gravity theories and applications to cosmology, special attention is given to Einstein–Gauss–Bonnet, f(R)-gravity, anisotropic inflation, extra dimension theories of gravity, black holes, dark energy, Palatini gravity, anisotropic spacetime, Einstein–Finsler gravity, off-diagonal cosmological solutions, Hawking-temperature and scalar-tensor-vector theories
Computational Geometry Contributions Applied to Additive Manufacturing
This Doctoral Thesis develops novel articulations of Computation Geometry for applications on Additive Manufacturing, as follows:
(1) Shape Optimization in Lattice Structures. Implementation and sensitivity analysis of the SIMP (Solid Isotropic Material with Penalization) topology optimization strategy. Implementation of a method to transform density maps, resulting from topology optimization, into surface lattice structures. Procedure to integrate material homogenization and Design of Experiments (DOE) to estimate the stress/strain response of large surface lattice domains.
(2) Simulation of Laser Metal Deposition. Finite Element Method implementation of a 2D nonlinear thermal model of the Laser Metal Deposition (LMD) process considering temperaturedependent material properties, phase change and radiation. Finite Element Method implementation of a 2D linear transient thermal model for a metal substrate that is heated by the action of a laser.
(3) Process Planning for Laser Metal Deposition. Implementation of a 2.5D path planning method for Laser Metal Deposition. Conceptualization of a workflow for the synthesis of the Reeb Graph for a solid region in ℝ" denoted by its Boundary Representation (B-Rep). Implementation of a voxel-based geometric simulator for LMD process. Conceptualization, implementation, and validation of a tool for the minimization of the material over-deposition at corners in LMD. Implementation of a 3D (non-planar) slicing and path planning method for the LMD-manufacturing of overhanging features in revolute workpieces.
The aforementioned contributions have been screened by the international scientific community via Journal and Conference submissions and publications
Modelling, Monitoring, Control and Optimization for Complex Industrial Processes
This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors
Design and Performance Analysis of Dry Gas Fishbone Wells for Lower Carbon Footprint
Multilateral well drilling technology has recently assisted the drilling industry in improving borehole contact area and reducing operation time, while maintaining a competitive cost. The most advanced multilateral well drilling method is Fishbone drilling (FbD). This method has been utilized in several hydrocarbon fields worldwide, resulting in high recovery enhancement and reduced carbon emissions from drilling. FbD involves drilling several branches from laterals and can be considered as an alternative method to hydraulic fracturing to increase the stimulated reservoir volume. However, the expected productivity of applying a Fishbone well from one field to another can vary due to various challenges such as Fishbone well design, reservoir lithology, and accessibility. Another challenge is the lack of existing analytical models and the effect of each Fishbone parameter on the cumulative production, as well as the interaction between them. In this paper, analytical and empirical productivity models were modified for FbD in a dry gas reservoir. The modified analytical model showed a higher accuracy with respect to the existing model. It was also compared with the modified empirical model, which proved its higher accuracy. Finally, machine learning algorithms were developed to predict FbD productivity, which showed close results with both analytical and empirical models
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A Systematic Review of Data-Driven Attack Detection Trends in IoT
The Internet of Things is perhaps a concept that the world cannot be imagined without today, having become intertwined in our everyday lives in the domestic, corporate and industrial spheres. However, irrespective of the convenience, ease and connectivity provided by the Internet of Things, the security issues and attacks faced by this technological framework are equally alarming and undeniable. In order to address these various security issues, researchers race against evolving technology, trends and attacker expertise. Though much work has been carried out on network security to date, it is still seen to be lagging in the field of Internet of Things networks. This study surveys the latest trends used in security measures for threat detection, primarily focusing on the machine learning and deep learning techniques applied to Internet of Things datasets. It aims to provide an overview of the IoT datasets available today, trends in machine learning and deep learning usage, and the efficiencies of these algorithms on a variety of relevant datasets. The results of this comprehensive survey can serve as a guide and resource for identifying the various datasets, experiments carried out and future research directions in this field
Distributed Spatial Data Sharing: a new era in sharing spatial data
The advancements in information and communications technology, including the widespread adoption of GPS-based sensors, improvements in computational data processing, and satellite imagery, have resulted in new data sources, stakeholders, and methods of producing, using, and sharing spatial data. Daily, vast amounts of data are produced by individuals interacting with digital content and through automated and semi-automated sensors deployed across the environment. A growing portion of this information contains geographic information directly or indirectly embedded within it. The widespread use of automated smart sensors and an increased variety of georeferenced media resulted in new individual data collectors. This raises a new set of social concerns around individual geopricacy and data ownership. These changes require new approaches to managing, sharing, and processing geographic data. With the appearance of distributed data-sharing technologies, some of these challenges may be addressed. This can be achieved by moving from centralized control and ownership of the data to a more distributed system. In such a system, the individuals are responsible for gathering and controlling access and storing data. Stepping into the new area of distributed spatial data sharing needs preparations, including developing tools and algorithms to work with spatial data in this new environment efficiently. Peer-to-peer (P2P) networks have become very popular for storing and sharing information in a decentralized approach. However, these networks lack the methods to process spatio-temporal queries. During the first chapter of this research, we propose a new spatio-temporal multi-level tree structure, Distributed Spatio-Temporal Tree (DSTree), which aims to address this problem. DSTree is capable of performing a range of spatio-temporal queries. We also propose a framework that uses blockchain to share a DSTree on the distributed network, and each user can replicate, query, or update it. Next, we proposed a dynamic k-anonymity algorithm to address geoprivacy concerns in distributed platforms. Individual dynamic control of geoprivacy is one of the primary purposes of the proposed framework introduced in this research. Sharing data within and between organizations can be enhanced by greater trust and transparency offered by distributed or decentralized technologies. Rather than depending on a central authority to manage geographic data, a decentralized framework would provide a fine-grained and transparent sharing capability. Users can also control the precision of shared spatial data with others. They are not limited to third-party algorithms to decide their privacy level and are also not limited to the binary levels of location sharing. As mentioned earlier, individuals and communities can benefit from distributed spatial data sharing. During the last chapter of this work, we develop an image-sharing platform, aka harvester safety application, for the Kakisa indigenous community in northern Canada. During this project, we investigate the potential of using a Distributed Spatial Data sharing (DSDS) infrastructure for small-scale data-sharing needs in indigenous communities. We explored the potential use case and challenges and proposed a DSDS architecture to allow users in small communities to share and query their data using DSDS. Looking at the current availability of distributed tools, the sustainable development of such applications needs accessible technology. We need easy-to-use tools to use distributed technologies on community-scale SDS. In conclusion, distributed technology is in its early stages and requires easy-to-use tools/methods and algorithms to handle, share and query geographic information. Once developed, it will be possible to contrast DSDS against other data systems and thereby evaluate the practical benefit of such systems. A distributed data-sharing platform needs a standard framework to share data between different entities. Just like the first decades of the appearance of the web, these tools need regulations and standards. Such can benefit individuals and small communities in the current chaotic spatial data-sharing environment controlled by the central bodies
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