17,356 research outputs found
Rank-based linkage I: triplet comparisons and oriented simplicial complexes
Rank-based linkage is a new tool for summarizing a collection of objects
according to their relationships. These objects are not mapped to vectors, and
``similarity'' between objects need be neither numerical nor symmetrical. All
an object needs to do is rank nearby objects by similarity to itself, using a
Comparator which is transitive, but need not be consistent with any metric on
the whole set. Call this a ranking system on . Rank-based linkage is applied
to the -nearest neighbor digraph derived from a ranking system. Computations
occur on a 2-dimensional abstract oriented simplicial complex whose faces are
among the points, edges, and triangles of the line graph of the undirected
-nearest neighbor graph on . In steps it builds an
edge-weighted linkage graph where
is called the in-sway between objects and . Take to be
the links whose in-sway is at least , and partition into components of
the graph , for varying . Rank-based linkage is a
functor from a category of out-ordered digraphs to a category of partitioned
sets, with the practical consequence that augmenting the set of objects in a
rank-respectful way gives a fresh clustering which does not ``rip apart`` the
previous one. The same holds for single linkage clustering in the metric space
context, but not for typical optimization-based methods. Open combinatorial
problems are presented in the last section.Comment: 37 pages, 12 figure
In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning
Cracks and keyhole pores are detrimental defects in alloys produced by laser
directed energy deposition (LDED). Laser-material interaction sound may hold
information about underlying complex physical events such as crack propagation
and pores formation. However, due to the noisy environment and intricate signal
content, acoustic-based monitoring in LDED has received little attention. This
paper proposes a novel acoustic-based in-situ defect detection strategy in
LDED. The key contribution of this study is to develop an in-situ acoustic
signal denoising, feature extraction, and sound classification pipeline that
incorporates convolutional neural networks (CNN) for online defect prediction.
Microscope images are used to identify locations of the cracks and keyhole
pores within a part. The defect locations are spatiotemporally registered with
acoustic signal. Various acoustic features corresponding to defect-free
regions, cracks, and keyhole pores are extracted and analysed in time-domain,
frequency-domain, and time-frequency representations. The CNN model is trained
to predict defect occurrences using the Mel-Frequency Cepstral Coefficients
(MFCCs) of the lasermaterial interaction sound. The CNN model is compared to
various classic machine learning models trained on the denoised acoustic
dataset and raw acoustic dataset. The validation results shows that the CNN
model trained on the denoised dataset outperforms others with the highest
overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC
score (98%). Furthermore, the trained CNN model can be deployed into an
in-house developed software platform for online quality monitoring. The
proposed strategy is the first study to use acoustic signals with deep learning
for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
Full trajectory optimizing operator inference for reduced-order modeling using differentiable programming
Accurate and inexpensive Reduced Order Models (ROMs) for forecasting
turbulent flows can facilitate rapid design iterations and thus prove critical
for predictive control in engineering problems. Galerkin projection based
Reduced Order Models (GP-ROMs), derived by projecting the Navier-Stokes
equations on a truncated Proper Orthogonal Decomposition (POD) basis, are
popular because of their low computational costs and theoretical foundations.
However, the accuracy of traditional GP-ROMs degrades over long time prediction
horizons. To address this issue, we extend the recently proposed Neural
Galerkin Projection (NeuralGP) data driven framework to
compressibility-dominated transonic flow, considering a prototypical problem of
a buffeting NACA0012 airfoil governed by the full Navier-Stokes equations. The
algorithm maintains the form of the ROM-ODE obtained from the Galerkin
projection; however coefficients are learned directly from the data using
gradient descent facilitated by differentiable programming. This blends the
strengths of the physics driven GP-ROM and purely data driven neural
network-based techniques, resulting in a computationally cheaper model that is
easier to interpret. We show that the NeuralGP method minimizes a more rigorous
full trajectory error norm compared to a linearized error definition optimized
by the calibration procedure. We also find that while both procedures stabilize
the ROM by displacing the eigenvalues of the linear dynamics matrix of the
ROM-ODE to the complex left half-plane, the NeuralGP algorithm adds more
dissipation to the trailing POD modes resulting in its better long-term
performance. The results presented highlight the superior accuracy of the
NeuralGP technique compared to the traditional calibrated GP-ROM method
Chiral active fluids: Odd viscosity, active turbulence, and directed flows of hydrodynamic microrotors
While the number of publications on rotating active matter has rapidly increased in recent years, studies on purely hydrodynamically interacting rotors on the microscale are still rare, especially from the perspective of particle based hydrodynamic simulations. The work presented here targets to fill this gap. By means of high-performance computer simulations, performed in a highly parallelised fashion on graphics processing units, the dynamics of ensembles of up to 70,000 rotating colloids immersed in an explicit mesoscopic solvent consisting out of up to 30 million fluid particles, are investigated. Some of the results presented in this thesis have been worked out in collaboration with experimentalists, such that the theoretical considerations developed in this thesis are supported by experiments, and vice versa. The studied system, modelled in order to resemble the essential physics of the experimentally realisable system, consists out of rotating magnetic colloidal particles, i.e., (micro-)rotors, rotating in sync to an externally applied magnetic field, where the rotors solely interact via hydrodynamic and steric interactions. Overall, the agreement between simulations and experiments is very good, proving that hydrodynamic interactions play a key role in this and related systems.
While already an isolated rotating colloid is driven out of equilibrium, only collections of two or more rotors have experimentally shown to be able to convert the rotational energy input into translational dynamics in an orbital rotating fashion. The rotating colloids inject circular flows into the fluid, such that detailed balance is broken, and it is not a priori known whether equilibrium properties of colloids can be extended to isolated rotating colloids. A joint theoretical and experimental analysis of isolated, pairs, and small groups of hydrodynamically interacting rotors is given in chapter 2. While the translational dynamics of isolated rotors effectively resemble the dynamics of non-rotating colloids, the orbital rotation of pairs of rotors can be described with leading order hydrodynamics and a two-dimensional analogy of Faxén’s law is derived.
In chapter 3, a homogeneously distributed ensemble of rotors (bulk) as a realisation of a chiral active fluid is studied and it is explicitly shown computationally and experimentally that it carries odd viscosity. The mutual orbital translation of rotors and an increase of the effective solvent viscosity with rotor density lead to a non-monotonous behaviour of the average translational velocity. Meanwhile, the rotor suspension bears a finite osmotic compressibility resulting from the long-ranged nature of hydrody- namic interactions such that rotational and odd stresses are transmitted through the solvent also at small and intermediate rotor densities. Consequently, density inhomogeneities predicted for chiral active fluids with odd viscosity can be found and allow for an explicit measurement of odd viscosity in simulations and experiments. At intermediate densities, the collective dynamics shows the emergence of multi-scale vortices and chaotic motion which is identified as active turbulence with a self-similar power-law decay in the energy spectrum, showing that the injected energy on the rotor scale is transported to larger scales, similar to the inverse energy cascade of clas- sical two-dimensional turbulence. While either odd viscosity or active turbulence have been reported in chiral active matter previously, the system studied here shows that the emergence of both simultaneously is possible resulting from the osmotic compressibility and hydrodynamic mediation of odd and active stresses. The collective dynamics of colloids rotating out of phase, i.e., where a constant torque instead of a constant angular velocity is applied, is shown to be qualitatively very similar. However, at smaller densities, local density inhomogeneities imply position dependent angular velocities of the rotors resulting from inter-rotor friction.
While the friction of a quasi-2D layer of active colloids with the substrate is often not easily modifiable in experiments, the incorporation of substrate friction into the simulation models typically implies a considerable increase in computational effort. In chapter 4, a very efficient way of incorporating the friction with a substrate into a two-dimensional multiparticle collision dynamics solvent is introduced, allowing for an explicit investigation of the influences of substrate on active dynamics. For the rotor fluid, it is explicitly shown that the influence of the substrate friction results in a cutoff of the hydrodynamic interaction length, such that the maximum size of the formed vortices is controlled by the substrate friction, also resulting in a cutoff in the energy spectrum, because energy is taken out of the system at the respective length. These findings are in agreement with the experiments.
Since active particles in confinement are known to organise in states of collective dynamics, ensembles of rotationally actuated colloids are studied in circular confinement and in the presence of periodic obstacle lattices in chapters 5 and 6, respectively. The results show that the chaotic active turbulent transport of rotors in suspension can be enhanced and guided resulting from edge flows generated at the boundaries, as has recently been reported for a related chiral active system. The consequent collective rotor dynamics can be regarded as a superposition of active turbulent and imposed flows, leading to on average stationary flows. In contrast to the bulk dynamics, the imposed flows inject additional energy into the system on the long length scales, and the same scaling behaviour of the energy spectrum as in bulk is only obtained if the energy injection scales, due to the mutual generation of rotor translational dynamics throughout the system and the edge flows, are well separated. The combination of edge flow and entropic layering at the boundaries leads to oscillating hydrodynamic stresses and consequently to an oscillating vorticity profile. In the presence of odd viscosity, this consequently leads to non-trivial steady-state density modulations at the boundary, resulting from a balance of osmotic pressure and odd stresses.
Relevant for the efficient dispersion and mixing of inert particles on the mesoscale by means of active turbulent mixing powered by rotors, a study of the dynamics of a binary mixture consisting out of rotors and passive particles is presented in chapter 7. Because the rotors are not self-propelled, but the translational dynamics is induced by the surrounding rotors, the passive particles, which do not inject further energy into the system, are transported according to the same mechanism as the rotors. The collective dynamics thus resembles the pure rotor bulk dynamics at the respective density of only rotors. However, since no odd stresses act between the passive particles, only mutual rotor interactions lead to odd stresses leading to the accumulation of rotors in the regions of positive vorticity. This density increase is associated with a pressure increase, which balances the odd stresses acting on the rotors. However, the passive particles are only subject to the accumulation induced pressure increase such that these particles are transported into the areas of low rotor concentration, i.e., the regions of negative vorticity. Under conditions of sustained vortex flow, this results in segregation of both particle types.
Since local symmetry breaking can convert injected rotational into translational energy, microswimmers can be constructed out of rotor materials when a suitable breaking of symmetry is kept in the vicinity of a rotor. One hypothetical realisation, i.e., a coupled rotor pair consisting out of two rotors of opposite angular velocity and of fixed distance, termed a birotor, are studied in chapter 8. The birotor pumps the fluid into one direction and consequently translates into the opposite direction, and creates a flow field reminiscent of a source doublet, or sliplet flow field. Fixed in space the birotor might be an interesting realisation of a microfluidic pump. The trans- lational dynamics of a birotor can be mapped onto the active Brownian particle model for single swimmers. However, due to the hydrodynamic interactions among the rotors, the birotor ensemble dynamics do not show the emergence of stable motility induced clustering. The reason for this is the flow created by birotor in small aggregates which effectively pushes further arriving birotors away from small aggregates, which eventually are all dispersed by thermal fluctuations
Subsidiary Entrepreneurial Alertness: Antecedents and Outcomes
This thesis brings together concepts from both international business and entrepreneurship to develop a framework of the facilitators of subsidiary innovation and performance. This study proposes that Subsidiary Entrepreneurial Alertness (SEA) facilitates the recognition of opportunities (the origin of subsidiary initiatives). First introduced by Kirzner (1979) in the context of the individual, entrepreneurial alertness (EA) is the ability to notice an opportunity without actively searching. Similarly, to entrepreneurial alertness at the individual level, this study argues that SEA enables the subsidiary to best select opportunities based on resources available. The research further develops our conceptualisation of SEA by drawing on work by Tang et al. (2012) identifying three distinct activities of EA: scanning and search (identifying opportunities unseen by others due to their awareness gaps), association and connection of information, and evaluation and judgement to interpret or anticipate future viability of opportunities. This study then hypothesises that SEA leads to opportunity recognition at the subsidiary level and further hypothesises innovation and performance as outcomes of opportunity recognition. This research brings these arguments together to develop and test a comprehensive theoretical model.
The theoretical model is tested through a mail survey of the CEOs/MDs of foreign subsidiaries within the Republic of Ireland (an innovative hub for foreign subsidiaries). This method was selected as the best method to reach the targeted respondent, and due to the depth of knowledge the target respondent holds, the survey can answer the desired question more substantially. The results were examined using partial least squares structural equation modelling (PLS-SEM). The study’s findings confirm two critical aspects of subsidiary context, subsidiary brokerage and subsidiary credibility are positively related to SEA. The study establishes a positive link between SEA and both the generation of innovation and the subsidiary’s performance. This thesis makes three significant contributions to the subsidiary literature as it 1) introduces and develops the concept of SEA, 2) identifies the antecedents of SEA, and 3) demonstrates the impact of SEA on subsidiary opportunity recognition. Implications for subsidiaries, headquarters and policy makers are discussed along with the limitations of the study
A scoping review of natural language processing of radiology reports in breast cancer
Various natural language processing (NLP) algorithms have been applied in the literature to analyze radiology reports pertaining to the diagnosis and subsequent care of cancer patients. Applications of this technology include cohort selection for clinical trials, population of large-scale data registries, and quality improvement in radiology workflows including mammography screening. This scoping review is the first to examine such applications in the specific context of breast cancer. Out of 210 identified articles initially, 44 met our inclusion criteria for this review. Extracted data elements included both clinical and technical details of studies that developed or evaluated NLP algorithms applied to free-text radiology reports of breast cancer. Our review illustrates an emphasis on applications in diagnostic and screening processes over treatment or therapeutic applications and describes growth in deep learning and transfer learning approaches in recent years, although rule-based approaches continue to be useful. Furthermore, we observe increased efforts in code and software sharing but not with data sharing
Approaches to Improving the Accuracy of Machine Learning Models in Requirements Elicitation Techniques Selection
Selecting techniques is a crucial element of the business analysis approach
planning in IT projects. Particular attention is paid to the choice of
techniques for requirements elicitation. One of the promising methods for
selecting techniques is using machine learning algorithms trained on the
practitioners' experience considering different projects' contexts. The
effectiveness of ML models is significantly affected by the balance of the
training dataset, which is violated in the case of popular techniques. The
paper aims to analyze the efficiency of the Synthetic Minority Over-sampling
Technique usage in Machine Learning models for elicitation technique selection
in case of the imbalanced training dataset and possible ways for positive
feature importance selection. The computational experiment results confirmed
the effectiveness of using the proposed approaches to improve the accuracy of
machine learning models for selecting requirements elicitation techniques.
Proposed approaches can be used to build Machine Learning models for business
analysis activities planning in IT projects
A Visual Modeling Method for Spatiotemporal and Multidimensional Features in Epidemiological Analysis: Applied COVID-19 Aggregated Datasets
The visual modeling method enables flexible interactions with rich graphical
depictions of data and supports the exploration of the complexities of
epidemiological analysis. However, most epidemiology visualizations do not
support the combined analysis of objective factors that might influence the
transmission situation, resulting in a lack of quantitative and qualitative
evidence. To address this issue, we have developed a portrait-based visual
modeling method called +msRNAer. This method considers the spatiotemporal
features of virus transmission patterns and the multidimensional features of
objective risk factors in communities, enabling portrait-based exploration and
comparison in epidemiological analysis. We applied +msRNAer to aggregate
COVID-19-related datasets in New South Wales, Australia, which combined
COVID-19 case number trends, geo-information, intervention events, and
expert-supervised risk factors extracted from LGA-based censuses. We perfected
the +msRNAer workflow with collaborative views and evaluated its feasibility,
effectiveness, and usefulness through one user study and three subject-driven
case studies. Positive feedback from experts indicates that +msRNAer provides a
general understanding of analyzing comprehension that not only compares
relationships between cases in time-varying and risk factors through portraits
but also supports navigation in fundamental geographical, timeline, and other
factor comparisons. By adopting interactions, experts discovered functional and
practical implications for potential patterns of long-standing community
factors against the vulnerability faced by the pandemic. Experts confirmed that
+msRNAer is expected to deliver visual modeling benefits with spatiotemporal
and multidimensional features in other epidemiological analysis scenarios
MuRAL: Multi-Scale Region-based Active Learning for Object Detection
Obtaining large-scale labeled object detection dataset can be costly and
time-consuming, as it involves annotating images with bounding boxes and class
labels. Thus, some specialized active learning methods have been proposed to
reduce the cost by selecting either coarse-grained samples or fine-grained
instances from unlabeled data for labeling. However, the former approaches
suffer from redundant labeling, while the latter methods generally lead to
training instability and sampling bias. To address these challenges, we propose
a novel approach called Multi-scale Region-based Active Learning (MuRAL) for
object detection. MuRAL identifies informative regions of various scales to
reduce annotation costs for well-learned objects and improve training
performance. The informative region score is designed to consider both the
predicted confidence of instances and the distribution of each object category,
enabling our method to focus more on difficult-to-detect classes. Moreover,
MuRAL employs a scale-aware selection strategy that ensures diverse regions are
selected from different scales for labeling and downstream finetuning, which
enhances training stability. Our proposed method surpasses all existing
coarse-grained and fine-grained baselines on Cityscapes and MS COCO datasets,
and demonstrates significant improvement in difficult category performance
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