Time Waits for No One: Using Time as a Lens in Information Systems Research

Despite considerable research interest, IT projects still fail at a higher rate than other projects. Primary causes for these failures are relational, motivational, and scheduling issues on the team. Using the concept of time as a lens, the four essays in this dissertation examine how the ways that individuals and teams structure time can help explain these failures. The essays formulate the concept of temporal dissonance at the individual and team level, and explore how temporal dissonance causes negative consequences for IT workers and IT teams. Essay one synthesizes temporal dissonance from concepts of temporal congruity and cognitive dissonance. It proposes a model in which an interaction between salience and temporal congruity creates an affective reaction of discomfort, called temporal dissonance. Temporal dissonance provides a partial explanation for the mixed results for time management interventions. Essay two extends the model and tests it empirically. The essay proposes that IT workers differ in several temporal characteristics from managers, resulting in IT workers feeling more temporal dissonance than managers. This difference results in greater stress and cynicism among IT workers, and results in reduced willingness to meet deadlines. Essay three extends the theory of temporal dissonance to the team level, using group development processes, shared mental models, and cognitive dissonance as a framework. Conflicting temporal structures salient to the team create tension, called team temporal dissonance. Teams reduce temporal dissonance by engaging in affect and process conflict, which reduces the performance of the team. Essay four empirically confirms team temporal dissonance in IT project teams. The study finds that the consequences of team temporal dissonance can vary. When internally generated, temporal dissonance causes the team to engage in process conflict, reducing its performance. Conversely, generated temporal dissonance causes a team to engage in affect conflict as a dissonance reduction measure. The reduction in dissonance improves team performance. The four essays together triangulate on the concept of temporal dissonance, eliciting its existence from differing starting points. Together, they provide strong evidence of the existence and importance of temporal dissonance

Machine learning for efficient recognition of anatomical structures and abnormalities in biomedical images

Three studies have been carried out to investigate new approaches to efficient image segmentation and anomaly detection. The first study investigates the use of deep learning in patch based segmentation. Current approaches to patch based segmentation use low level features such as the sum of squared differences between patches. We argue that better segmentation can be achieved by harnessing the power of deep neural networks. Currently these networks make extensive use of convolutional layers. However, we argue that in the context of patch based segmentation, convolutional layers have little advantage over the canonical artificial neural network architecture. This is because a patch is small, and does not need decomposition and thus will not benefit from convolution. Instead, we make use of the canonical architecture in which neurons only compute dot products, but also incorporate modern techniques of deep learning. The resulting classifier is much faster and less memory-hungry than convolution based networks. In a test application to the segmentation of hippocampus in human brain MR images, we significantly outperformed prior art with a median Dice score up to 90.98% at a near real-time speed (<1s). The second study is an investigation into mouse phenotyping, and develops a high-throughput framework to detect morphological abnormality in mouse embryo micro-CT images. Existing work in this line is centred on, either the detection of phenotype-specific features or comparative analytics. The former approach lacks generality and the latter can often fail, for example, when the abnormality is not associated with severe volume variation. Both these approaches often require image segmentation as a pre-requisite, which is very challenging when applied to embryo phenotyping. A new approach to this problem in which non-rigid registration is combined with robust principal component analysis (RPCA), is proposed. The new framework is able to efficiently perform abnormality detection in a batch of images. It is sensitive to both volumetric and non-volumetric variations, and does not require image segmentation. In a validation study, it successfully distinguished the abnormal VSD and polydactyly phenotypes from the normal, respectively, at 85.19% and 88.89% specificities, with 100% sensitivity in both cases. The third study investigates the RPCA technique in more depth. RPCA is an extension of PCA that tolerates certain levels of data distortion during feature extraction, and is able to decompose images into regular and singular components. It has previously been applied to many computer vision problems (e.g. video surveillance), attaining excellent performance. However these applications commonly rest on a critical condition: in the majority of images being processed, there is a background with very little variation. By contrast in biomedical imaging there is significant natural variation across different images, resulting from inter-subject variability and physiological movements. Non-rigid registration can go some way towards reducing this variance, but cannot eliminate it entirely. To address this problem we propose a modified framework (RPCA-P) that is able to incorporate natural variation priors and adjust outlier tolerance locally, so that voxels associated with structures of higher variability are compensated with a higher tolerance in regularity estimation. An experimental study was applied to the same mouse embryo micro-CT data, and notably improved the detection specificity to 94.12% for the VSD and 90.97% for the polydactyly, while maintaining the sensitivity at 100%.Open Acces

Using phenomics to identify and integrate traits of interest for better-performing common beans: A validation study on an interspecific hybrid and its Acutifolii parents

Evaluations of interspecific hybrids are limited, as classical genebank accession descriptors are semi-subjective, have qualitative traits and show complications when evaluating intermediate accessions. However, descriptors can be quantified using recognized phenomic traits. This digitalization can identify phenomic traits which correspond to the percentage of parental descriptors remaining expressed/visible/measurable in the particular interspecific hybrid. In this study, a line of P. vulgaris, P. acutifolius and P. parvifolius accessions and their crosses were sown in the mesh house according to CIAT seed regeneration procedures

Contemporary Research on Management and Business

This book contains 74 selected papers presented at the 5th International Seminar of Contemporary Research on Business and Management (ISCRBM 2021), which was organized by the Alliance of Indonesian Master of Management Program (APMMI) and held in Jakarta, Indonesia on 18 December 2021. This online conference was hosted by the Master of Management Program of Indonesia University. This year, ISCRBM focused on research related to driving sustainable business through innovation. Business has had to deal with the Covid-19 pandemic, so a new approach towards managing business to survive competition is indispensable. Innovation is the key for all organizations in surviving in the new normal and beyond. The Seminar aimed to provide a forum for leading scholars, academics, researchers, and practitioners in the business and management area to reflect on the issues, challenges and opportunities, and to share the latest innovative research and best practices. This seminar brought together participants to exchange ideas on the future development of management disciplines: human resource, marketing, operation, finance, strategic management and entrepreneurship

Neural models of language use:Studies of language comprehension and production in context

Artificial neural network models of language are mostly known and appreciated today for providing a backbone for formidable AI technologies. This thesis takes a different perspective. Through a series of studies on language comprehension and production, it investigates whether artificial neural networks—beyond being useful in countless AI applications—can serve as accurate computational simulations of human language use, and thus as a new core methodology for the language sciences

A machine learning-guided data integration framework to measure multidimensional poverty

As developing nations like South Africa chart a path of socio-economic development, the spatialisation of progress, opportunity, and neglect is a critical antecedent to policy-making and regional interventionism. Efforts to capture meaningful data using household surveys and censuses face a diluted accuracy due to sampling, surveying, and quantification errors. The reliability and regularity of these traditional methods is also constrained since the processes are costly and time consuming. Recent investigations in the field of machine learning and satellite imaging have presented a viable proof-of-concept technique to exploit specific economic indicators to demonstrate economic development patterns across regional areas. The current study adopts several interrelated approaches encompassed within the field of remote sensing in order to evaluate and model poverty in the South African landscape. By adopting publicly accessible information for classification to indicate the intensity of poverty, this study proposed an inexpensive solution to poverty estimation. Concretely, the solution combined satellite imagery and geospatial data with regional poverty data exploiting an ensemble approach to poverty diagnosis. The solution is based upon multidimensional indicators and multi-layered insights that can be extrapolated from overlapping models to bolster them and help with socio-economic well-being estimations. Through machine learning techniques and object-oriented training of a convolutional neural network, this study revealed that a naïve combination of distinct data sources shows patterns of socio-economic well-being in South Africa by achieving an R2 of 0.56 wealth estimation compared to 0.54 from satellite imagery. This outlined variability and incongruity within landscapes that not only reflect the persistent subdivisions of apartheid-era enclavisation, but indicate critical gaps in domestic social services, infrastructure, and developmental pathways. This study is applicable to policy makers in low- and middle-income countries that lack accurate and timely data on economic development as an important precursor to public support, policy making, and planned expenditures.School of ComputingM. Tech. (Information Technology

Magnetoencephalography for the investigation and diagnosis of Mild Traumatic Brain Injury

Mild Traumatic Brain Injury (mTBI), (or concussion), is the most common type of brain injury. Despite this, it often goes undiagnosed and can cause long term disability—most likely caused by the disruption of axonal connections in the brain. Objective methods for diagnosis and prognosis are needed but clinically available neuroimaging modalities rarely show structural abnormalities, even when patients suffer persisting functional deficits. In the past three decades, new powerful techniques to image brain structure and function have shown promise in detecting mTBI related changes. Magnetoencephalography (MEG), which measures electrical brain activity by detecting magnetic fields outside the head generated by neural currents, is particularly sensitive and has therefore gained interest from researchers. Numerous studies are proposing abnormal low-frequency neural oscillations and functional connectivity—the statistical interdependency of signals from separate brain regions—as potential biomarkers for mTBI. However, typically small sample sizes, the lack of replication between groups, the heterogeneity of the cohorts studied, and the lack of longitudinal studies impedes the adoption of MEG as a clinical tool in mTBI management. In particular, little is known about the acute phase of mTBI. In this thesis, some of these gaps will be addressed by analysing MEG data from individuals with mTBI, using novel as well as conventional methods. The potential future of MEG in mTBI research will also be addressed by testing the capabilities of a wearable MEG system based on optically pumped magnetometers (OPMs). The thesis contains three main experimental studies. In study 1, we investigated the signal dynamics underlying MEG abnormalities, found in a cohort of subjects scanned within three months of an mTBI, using a Hidden Markov Model (HMM), as growing evidence suggests that neural dynamics are (in part) driven by transient bursting events. Applying the HMM to resting-state data, we show that previously reported findings of diminished intrinsic beta amplitude and connectivity in individuals with mTBI (compared to healthy controls) can be explained by a reduction in the beta-band content of pan-spectral bursts and a loss in the temporal coincidence of bursts respectively. Using machine learning, we find the functional connections driving group differences and achieve classification accuracies of 98%. In a motor task, mTBI resulted in reduced burst amplitude, altered modulation of burst probability during movement and decreased connectivity in the motor network. In study 2, we further test our HMM-based method in a cohort of subjects with mTBI and non-head trauma—scanned within two weeks of injury—to ensure specificity of any observed effects to mTBI and replicate our previous finding of reduced connectivity and high classification accuracy, although not the reduction in burst amplitude. Burst statistics were stable over both studies—despite data being acquired at different sites, using different scanners. In the same cohort, we applied a more conventional analysis of delta-band power. Although excess low-frequency power appears to be a promising candidate marker for persistently symptomatic mTBI, insufficient data exist to confirm this pattern in acute mTBI. We found abnormally high delta power to be a sensitive measure for discriminating mTBI subjects from healthy controls, however, similarly elevated delta amplitude was found in the cohort with non-head trauma, suggesting that excess delta may not be specific to mTBI, at least in the acute stage of injury. Our work highlights the need for longitudinal assessment of mTBI. In addition, there appears to be a need to investigate naturalistic paradigms which can be tailored to induce activity in symptom-relevant brain networks and consequently are likely to be more sensitive biomarkers than the resting state scans used to date. Wearable OPM-MEG makes naturalistic scanning possible and may offer a cheaper and more accessible alternative to cryogenic MEG, however, before deploying OPMs clinically, or in pitch-side assessment for athletes, for example, the reliability of OPM-derived measures needs to be verified. In the third and final study, we performed a repeatability study using a novel motor task, estimating a series of common MEG measures and quantifying the reliability of both activity and connectivity derived from OPM-MEG data. These initial findings—presently limited to a small sample of healthy controls—demonstrate the utility of OPM-MEG and pave the way for this technology to be deployed on patients with mTBI

Contributions of Graph Theory and Algorithms to Animal Behaviour and Neuroscience

Η θεωρία γραφημάτων και οι αλγόριθμοι προσφέρουν πολύτιμες εργαλειοθήκες για τη μοντε- λοποίηση καθώς και την ανάλυση πολυάριθμων φαινομένων στις φυσικές επιστήμες. Εδώ παρουσιάζεται μια ανασκόπηση της σύγχρονης βιβλιογραφίας, χωρισμένη σε τέσσερα κύρια κεφάλαια, δίνοντας κάποιες ενδείξεις για το πώς οι έννοιες αυτών των δύο κλάδων μπορούν να χρησιμοποιηθούν για τη μελέτη της συμπεριφοράς των ζώων και της νευροεπιστήμης. Κατ ’εξαίρεση, το πρώτο μέρος του πρώτου κεφαλαίου παρέχει μια σύντομη συζήτηση σχετικά με τις εφαρμογές της θεωρίας γραφημάτων στη μοριακή βιολογία. Η επιλογή αυτή έγινε προκειμένου να καταστεί η εργασία αυτή πληρέστερη και να δοθεί στους αναγνώστες με διαφορετικό υπόβαθρο, όσο το δυνατόν περισσότερο, συνολική άποψη για τη δυνητική χρησι- μότητα τέτοιων διεπιστημονικών προσεγγίσεων. Τα υπόλοιπα δύο τμήματα του πρώτου κεφα- λαίου εστιάζουν σε δίκτυα του εγκεφάλου και σε κεντρικές έννοιες της θεωρίας γραφημάτων, όπως η κεντρικότητα, στη μελέτη τους. Το δεύτερο κεφάλαιο εισάγει μερικές έννοιες της κοινωνικότητας των ζώων και αναφέρεται σε μελέτες της συνεργασίας στο ζωικό βασίλειο, εστιάζοντας στην εξελικτική θεωρία γραφημάτων και παιγνίων. Επιπλέον, στη τελευταία ενότητα αυτού του κεφαλαίου συζητείται η συλλογική κίνηση ομάδων ζώων, παρέχοντας εκτός των άλλων, εισαγωγή βασικών όρων για το επόμενο τρίτο κεφάλαιο. Η διεπιστημονική έρευνα, με στόχο την ενοποίηση μεθόδων από διαφορετικούς τομείς, λαμβάνει χώρα ευρέως για να απαντήσει βιολογικά ερωτήματα. Εντούτοις, όπως παρουσιάζεται παρακάτω, η έρευνα στους αλγορίθμους και στη βιολογία μπορούν να συμβάλλουν στην ανάπτυξη η μια της άλλης. Ως εκ τούτου, το τρίτο κεφάλαιο παρέχει πληροφορίες σχετικά με αλγόριθμους των οποίων ο σχεδιασμός έχει εμπνευστεί από τη (συλλογική) συμπεριφορά των ζώων στο φυσικό περιβάλλον. Τέλος, το τέταρτο κεφάλαιο αποκλίνει εκ νέου από το επίκεντρο των προηγούμε- νων κεφαλαίων και κάνει μια σύντομη εισαγωγή στο σημαντικό, αλλά και αμφιλεγόμενο, υπολογιστικό χαρακτήρα της νόησης και κατ’ επέκταση της συμπεριφοράς. Συνολικά, μπορεί κανείς να παρατηρήσει ότι η συνεργασία των προαναφερθέντων πεδίων είναι εκτεταμένη ενώ η πραγματοποιημένη έρευνα ανοίγει νέα ερωτήματα που μπορούν να μελετηθούν μόνο υπό το φως τέτοιων διεπιστημονικών συνεργασιών.Graph theory and algorithms offer precious toolboxes for the modelling as well as the analysis of numerous phenomena in natural sciences. Here a review of the modern bibliography is pre- sented, divided in four main chapters, giving some indications on how the concepts of these two disciplines can be used for the study of animal behaviour and neuroscience. As an exception the premier part of the first chapter provides a short discussion on the applications of graph theory on molecular biology. This choice made in order to make this work more complete and give to the readers from various backgrounds an, as much as possible, overall view of the future potential of such interdisciplinary approaches. The rest two sections of the first chapter deals with brain networks and central terms of graph theory, such as centrality, in their study. The second chapter introduces some concepts of animal sociality and refers to studies of animal cooperation, focusing on evolutionary graph and game theory. Moreover, in the last section of this chapter the collective motion of animal groups is discussed providing, into the bargain, an introduction of basic terms for the subsequent third chapter. Interdisciplinary research, aiming to unite methods from different fields, is vastly used in order to answer biological questions. Although, as it is presented below, both the fields of algorithms and biology can contribute to the elaboration of each other. Hence, the third chapter provides information about algorithms whose design has been inspired by the (collective) behaviour of animals in the nature. Finally, the fourth chapter deviates anew from the central focus of the previous chapters and makes a short introduction in the substantial controversial computational nature of cognition and by extension behaviour. Overall, one can observe that the cooperation of the above mentioned fields is extensive while the accomplished research opens new questions which can be studied only in the light of such collaborations

Thermal Emission of Strontium Products for Scalar Diagnostics in Internal Combustion Engines

Developments in optical diagnostics for combustion systems have been essential to the recent improvements in efficiency and abatement of emissions that internal combustion engines have undergone recently. Great emphasis has been placed in the measurement of quantities with high temporal and spatial resolution, which has enabled the understanding of key physical and chemical processes, but there remains a need for obtaining spatially integrated measurements to understand how local events affect the overall behavior of the gases in a turbulent combustion chamber. Strontium offers a potential avenue to provide these measurements. When present in combustion it produces strontium monohydroxide, which spontaneously emits radiation in several bands of the visible spectrum, and thus enables the determination of temperature independently of species concentration through the Boltzmann distribution. Further, chemical equilibrium calculations can relate equivalence ratio to the relative concentration strontium and strontium monohydroxide, which could also be measured optically. The potential of this technique was explored in this work. An optical engine was operated under different conditions with a strontium-containing fuel and spectral measurements of the radiation emitted from the chamber were performed. The temperature in the cylinder was predicted by a one-dimensional thermodynamic model that used a two-zone model for flame propagation. The relative spectrally resolved emission intensity of atomic strontium and strontium monohydroxide was measured using a spectrometer coupled with camera, and the collected signals were related to the conditions in the chamber. From the results the mathematical formulation for the relationship of spectral intensity with temperature was found to be adequate, and important insights for the application of the diagnostic in imaging experiments were obtained. A universally applicable calibration was not attained due to experimental limitations, however, but the key barriers to overcome were identified.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153368/1/ivantib_1.pd