Miracle-dz/UniFAR-FTUs huggingface dataset:
Action Learning in Practice

Contents: Introduction The state of the art, Mike Pedler Part 1 Origins: Introduction to part 1 Action learning: its origins and nature, Reg Revans The enterprise as a learning system, Reg Revans The power of action learning, Bob Garratt Minding our Ps and Qs, John Morris Continuity in action learning, Jean Lawrence David Casey on the role of the set adviser, David Casey Digging deeper: foundations of Revans' gold standard of action learning, Verna J. Willis Ad fontes a " Reg Revans: some early sources of his personal growth and values, Yury Boshyk Getting started: an action manual, David Pearce. Part 2 Varieties: Introduction to part 2 Self-managed action learning, Tom Bourner Action reflection learning, Lennart Rohlin Business-driven action learning today, Yury Boshyk Virtual action learning, Mollie Goodman and Jean-Anne Stewart Critical action learning, Kiran Trehan The practice and politics of living enquiry, Judi Marshall The varieties of action learning in practice: a rose by any other name, Judy O'Neil and Victoria J. Marsick. Part 3 Applications: Introduction to part 3 Leadership, Richard Thorpe Developing facilitative leaders: action learning facilitator training as leadership development, Katie Venner Action learning in SME development, Lisa Anderson, Jeff Gold and Allan Gibb Addressing systemic issues in public services, Clare Rigg Action learning for organization development in South Korea, Yonjoo Cho and Hyeon-Cheol Bong Facilitation and the affective domain, Ian McGill and Anne Brockbank Learning to be an action learning facilitator: three approaches, Christine Abbott and Tom Boydell Action learning and organization development, John Edmonstone Network learning in an Austrian hospital a " revisited, Otmar Donnenberg Action learning and social capital, Mike Pedler and Margaret Attwood Action learning round the world, Michael J. Marquardt. Part 4 Questions: Introduction to part 4 Action learning: a pragmatic and moral philosophy, John Burgoyne Practical knowing: the philosophy and methodology of action learning research, David Coghlan The action modalities: action learning's good company, Joe Raelin Action in action learning, Roland Yeo and Jeff Gold Learning in action or learning inaction? Emotion and politics in action learning, Russ Vince Reflective practice: origins and interpretations, Michael Reynolds Adult learning theories and the practice of action learning, Deborah Waddill and Michael Marquardt Evaluating action learning: a perspective informed by critical realism, network and complex adaptive systems theory, John Burgoyne Index.
Achieving Efficient Routing in Reconfigurable DCNs

Heavy and highly dynamic traffic demands in today's data center networks (DCNs) pose great challenges to efficient traffic engineering. With gigabit bandwidth, wireless communication technologies, such as free space optics and 60GHz wireless, are promising to augment DCNs and enable efficient traffic engineering. Complementary to the emerging reconfigurable architectures, we aim to achieve efficient routing and effectively balance the load with the performance guarantee. We derive a general interference model and propose a decomposition technique with proven performance guarantee and solve the load balancing problem in reconfigurable DCNs. In addition, we propose two solutions, WiRo and OFS, to flexibly reconfigure network topology and enable hybrid-routing with paths consisting of both stable wired links and flexible wireless links with different methods. Our measurement-facilitated and trace-driven simulations demonstrate that our solutions outperform existing flow scheduling algorithms with the average throughput of large flows increased by up to 190% and the average completion time reduced by up to 72.6%. Meanwhile, the average completion time of small flows is reduced by up to 64.5%.
On Query Optimization in a Temporal SPC Algebra

Tuples of a temporal relation are equipped with a valid time period. A simple extension of the SPC (SelectionProjection-Cross product) algebra for temporal relations is defined, which conforms to primitives in existing temporal query languages. In particular, temporal projection involves coalescing of time intervals, which results in non-monotonic queries. Also the “select-from-where” normal form is no longer available in this temporal extension. In view of these temporal peculiarities, it is natural and significant to ask whether query optimization techniques for the SPC algebra still apply in the temporal case. To this extent, we provide a temporal extension of the classical tableau formalism, and show its use and limits for temporal query optimization.
Digital Commons @ Michigan Tech

[ 1 ] Geophysical granular flows display complex nonlinear, nonuniform, and unsteady rheologies, depending on the volumetric grain concentration within the flow: kinetic, kinetic-collisional, and frictional. To account for the whole spectrum of granular rheologies (and hence concentrations), we have used and further developed for geophysical-atmospheric applications a multiphase computer model initially developed by U.S. Department of Energy laboratories: (Geophysical) Multiphase Flow with Interphase Exchange. As demonstrated in this manuscript, (G)MFIX can successfully simulate a large span of pyroclastic phenomena and related processes: plinian clouds, pyroclastic flows and surges, flow transformations, and depositional processes. Plinian cloud simulations agree well with the classical plume theory and historical eruptions in the upper altitude of the cloud (H T ) versus mass flux diagram. At high mass flux (>10 7 kg/s), plinian clouds pulsate periodically with time because of the vertical propagations of acoustic-gravity waves within the clouds. The lowest undercooled temperature anomalies measured within the upper part of the column can be as low as (cid:1) 18 K, which agrees well with El Chicho´n and Mt. St. Helens eruptions. Vertical and horizontal speed profiles within the plinian cloud compare well with those inferred from simple plume models and from umbrella experiments. Pyroclastic flow and surge simulations show that both end-members are closely tight together; e.g., an initially diluted flow may generate a denser basal underflow, which will eventually outrun the expanded head of the flow. We further illustrate evidence of vertical and lateral flow transformation processes between diluted and concentrated flows, particularly laterally from a turbulent ‘‘maintained over time fluidized zone’’ near source. Our comprehensive granular rheological model and our simulations demonstrate that the main depositional process is mainly a progressive vertical aggradation.
Fast computation of generalized Voronoi diagrams using graphics hardware

We present a new approach for computing generalized Voronoi diagrams in two and three dimensions using interpolation-based polygon rasterization hardware. The input primitives may be points, lines, polygons, curves, or surfaces. The algorithm computes a discrete Voronoi diagram by rendering a three dimensional distance mesh corresponding to each primitive. The polygonal mesh is a bounded-error approximation of a nonlinear distance function. The algorithm divides the space into regular cells. For each cell it computes the closest primitive and the distance to that primitive using polygon scan-conversion and Z-buffer depth comparison. We present efficient techniques to detect Voronoi boundaries and compute Voronoi neighbors. The algorithm has been implemented on SGI workstations and PCs using OpenGL and applied to complex 2D and 3D datasets. We also demonstrate the applications of our algorithm to fast motion planning in static and dynamic environments, and improving the performance of continuous Voronoi diagram computation.
Scholarship @ Claremont Scholarship @ Claremont

The 2015 article Creating Diversiﬁed Portfolios Using Cluster Analysis [18] proposes an algo-rithm that uses the Sharpe ratio and results from K-means clustering conducted on companies’ historical ﬁnancial ratios to generate stock market portfolios. This project seeks to evaluate the performance of the portfolio-building algorithm during the beginning period of the COVID-19 recession. S&P 500 companies’ historical stock price movement and their historical return on assets and asset turnover ratios are used as dissimilarity metrics for K-means clustering. After clustering, stock with the highest Sharpe ratio from each cluster is picked to become a part of the portfolio. The economic and ﬁnancial implications of the clustering results are also discussed. In the end, portfolios constructed with clustering results of stocks’ historical price movements perform poorly, but portfolios constructed with clustering results of companies’ ﬁnancial ratios consistently exceed market average. Using an alternative portfolio construction method that represents each cluster proportionally with regards to their sizes, portfolios constructed with historical stock price movements gain an increase in performance, while the returns of portfolios constructed using companies’ ﬁnancial ratios decrease. Further studies should be done with a diﬀerent portfolio performance index and a larger dataset.
Research Online Research

In this research study, the methodology of action research dynamics and a case study was employed in constructing a visual data mining framework for the processing and analysis of geographic land-use data in an agricultural context. The geographic data was made up of a digital elevation model (DEM), soil and land use profiles that were juxtaposed with previously captured climatic data from fixed weather stations in Australia. In this pilot study, monthly rainfall profiles for a selected study area were used to identify areas of soil variability. The rainfall was sampled for the beginning (April) of the rainy season for the known ‘drought’ year 2002 for the South West of Western Australia. The components of the processing framework were a set of software tools such as ArcGis, QuantumGIS and the Microsoft Access database as part of the pre-processing layer. In addition, the GRASS software package was used for producing the map overlays. Evaluation was carried out using techniques of visual data mining to detect the patterns of soil types found for the cropping land use. This was supported by analysis using WEKA and Microsoft Excel for validation. The results suggest that agriculture in these areas of high soil variability need to be managed differently to the more consistent cropping areas. Although this processing framework was used to analyse soil and rainfall climate data pertaining to agriculture in Western Australia; it is easily applicable to other datasets of a similar attribution in different areas.
DEEP LEARNING AND IMAGE PROCESSING FOR THE AUTOMATED ANALYSIS OF THERMAL EVENTS ON THE FIRST WALL AND DIVERTOR OF FUSION REACTORS

The analysis of thermal events on the components of fusion reactors is of major importance, both from a machine protection and from a science standpoints. This analysis, which can be conducted using infrared cameras placed inside the reactor [1], ought to be transferred from human operators to automatized processes because of the quantity of data involved and the need for real-time analysis. Hence, there is a need for an automated pipeline able to detect and classify thermal events, either for feedback control of the injected power during plasma operation, or for later analysis. The paper presents a multi-stage algorithm which aims at detecting, tracking and classifying thermal events automatically using thermal imaging of the inside of the fusion reactor as well as other relevant signals. The first stage of the pipeline is the detection of the hot spots in infrared images: it is carried out, one frame at a time, by the Cascade R-CNN algorithm [2], implemented using PyTorch by Facebook AI in Detectron2 [3]. The inference speed reaches 30fps on a high-end laptop equipped with a GPU, making this approach compatible with real-time monitoring. Once the hot spots detection has been performed in each image, thermal events are inferred by matching and tracking the hot spots in time, using the real-time tracking algorithm SORT [4]. The second stage of the pipeline is the classification of the detected thermal events according to a defined ontology. This step is crucial since it will dictate the response of the feedback system, that will react based on the criticality of the classified event. The classifier relies on the detected thermal events (from the first stage of the pipeline) as well as on signals from other diagnostics, such as the plasma current or the power injected in the reactor. The model is trained, validated and tested using a database of 1325 manually annotated thermal events from a divertor-facing
Work Package 2: Change of Behavior

In this paper, we use data from a new household-level panel survey to estimate shortand long-run price elasticities of residential electricity demand in Switzerland. We exploit Switzerland’s unique local variation in topography-related grid maintenance costs and electricity taxation, to address endogeneity of average prices in our models. Using first difference and gradual adjustment models, we find shortrun elasticities of -0.3 and long-run elasticities in excess of negative unity. Results thus suggest that a tax on electricity, as initially foreseen as a part of Switzerland’s Energy Strategy 2050, is likely to have a moderate effect in the short run, but an important one in the long run. JEL classification: C23, D12, Q41, Q48,
Designing for Rightful Presence in STEM: The Role of Making Present Practices

Opportunities to learn in consequential ways are shaped by the historicized injustices students encounter in relation to participation in STEM and schooling. In this article, it is argued that the construct of rightful presence, and the coconstructed “making present” practices that give rise to moments of rightful presence, is 1 way to consider how to make sense of the historicized and relational nature of consequential learning. Drawing on theories of consequential learning and critical justice, we analyze ethnographic data from 3 urban middle school classrooms in 2 states during a STEM unit focused on engineering for sustainable communities. Findings describe 2 making present practices students enacted as they engaged in engineering design: modeling ethnographic data and reperforming injustices toward solidarity building. We discuss how these practices supported moments of rightful presence in the STEM classrooms by inscribing youths’ marginalizing school experiences as a part of classroom science discourse and co-opting school science tasks as tools for exposing, critiquing, and addressing these unjust experiences. That which was silent and previously concealed from school authority figures gained a rightful place through the voices and scientific actions of the youth and their allies.
Deepwater Turbidite Lobe Deposits: A Review of the Research Frontiers

Deepwater/deep‐marine turbidite lobes are the most distal part of a siliciclastic depositional system and hold the largest sediment accumulation on the seafloor. As many giant hydrocarbon provinces have been discovered within deepwater lobe deposits, they represent one of the most promising exploration targets for hydrocarbon industry. Deepwater exploration is characterized by high cost, high risk but insufficient data because of the deep/ultra–deepwater depth. A thorough understanding of the deepwater turbidite lobe architecture, hierarchy, stacking pattern and internal facies distribution is thus vital. Recently, detailed outcrop characterizations and high–resolution seismic studies have both revealed that the deepwater lobe deposits are characterized into four–fold hierarchical arrangements from “beds”, to “lobe elements”, to “lobes” and to “lobe complex”. Quantitative compilations have shown that hierarchical components of lobe deposits have similar length to width ratios but different width to thickness ratios depending on different turbidite systems. At all hierarchical scales, sand–prone hierarchical lobe units are always separated by mud–prone bounding units except when the bounding units are eroded by their overlying lobe units thus giving rise to vertical amalgamation and connectivity. Amalgamations often occur at more proximal regions suggesting high flow energy. A mixed flow behavior may occur towards more distal regions, resulting in deposition of “hybrid event beds”. These synthesized findings could (1) help understand the lobe reservoir distribution and compartmentalization therefore benefit the exploration and development of turbidite lobes within the deep marine basins (e.g. South China Sea) and (2) provide rules and quantitative constraints on reservoir modeling. In addition, the findings associated with deepwater turbidite lobes might be a good starting point to understand the sedimentology, architecture and hierarchy of turbidites in deep lacustrine environment.
Fuzzy clustering analysis to study geomagnetic coastal effects

The utility of fuzzy set theory in cluster analysis and pattern recognition has been evolving since the mid 1960s, in conjunction with the emergence and evolution of computer technology. The classification of objects into categories is the subject of cluster analysis. The aim of this paper is to employ Fuzzy-clustering technique to examine the interrelationship of geomagnetic coastal and other effects at Indian observatories. Data from the observatories used for the present studies are from Alibag on the West Coast, Visakhapatnam and Pondicherry on the East Coast, Hyderabad and Nagpur as central inland stations which are located far from either of the coasts; all the above stations are free from the influence of the daytime equatorial electrojet. It has been found that Alibag and Pondicherry Observatories form a separate cluster showing anomalous variations in the vertical (Z)-component. H- and D-components form different clusters. The results are compared with the graphical method. Analytical technique and the results of Fuzzy-clustering analysis are discussed here.
MPR + SP : Towards a Unified MPR-based MANET Extension of OSPF

Heterogeneous networks combining both wired and wireless components – fixed routers as well as mobile routers – emerge as wireless mesh networks are being deployed. Such heterogeneity is bound to become more and more present in the near future as mobile ad hoc networking becomes a reality. While it is possible to cope with heterogeneity by employing different routing protocols for the fixed / wired part and for the wireless / ad hoc part of the network, this may lead to sub-optimal performance, e.g. by way of longer routing paths due to these routing protocols sharing prefixes and ”connecting” the network only at distinct gateways between the two routing domains. Thus, the establishment of a single unified routing domain, and the use of a single routing protocol, for such heterogeneous networks is desired. OSPF is a natural candidate for this task, due to its wide deployment, its modularity and its similarity with the popular ad hoc routing protocol OLSR. Multiple OSPF extensions for MANETs have therefore been specified by the IETF. This memorandum introduces a novel OSPF extension for operation on ad hoc networks, MPRSP, and compares it with the existing OSPF extensions via simulations, which show that MPR+SP outperforms prior art. Key-words: OSPF, MANET, MPR, Routing ∗ LIX Ecole Polytechnique, cordero@lix.polytechnique. fr † LIX Ecole Polytechnique, Thomas@ThomasClausen.org ‡ LIX Ecole Polytechnique, Emmanuel.Baccelli@inria.fr MPR+SP: Towards a Unified MPR-based MANET Extension for OSPF Résumé : Les réseaux hétérogènes combinant des parties filaires et des parties sans-fils – des routeurs mobiles ainsi que des routeurs fixes – sont déployés de nos jours sous forme de réseaux mesh. Dans le futur, les réseaux hétérogènes sont amenés à devenir de plus en plus présents, au fur et à mesure que les réseaux ad hoc deviendront réalité. Il est possible de gérer l’hétérogénéité de tels réseaux en utilisant plusieurs protocoles de routages à la fois, un pour la partie fixe / filaire, et un autre pour la partie mobile / sans-fil. Cependant, l’utilisation simultanée de plusieurs protocoles dans ce contexte mène souvent à des performances sous-optimales (par exemple en fora̧nt les routes à passer par certaines passerelles). C’est pourquoi il est préférable d’utiliser un protocole de routage unifié, couvrant un réseau hétérogène dans sa globalité. OSPF est le candidat naturel pour accomplir cette tche, dû à son usage répandu, à la modularité de son design et à sa ressemblance avec le protocole de routage ad hoc OLSR. Plusieurs extensions d’OSPF pour MANET ont donc été récemment normalisées par l’IETF. Ce rapport présente une nouvelle extension d’OSPF pour les réseaux ad hoc, nommée MPR+SP, et la compare aux extensions existantes au moyen de simulations, qui montrent que MPR+SP offre une meilleure performance que l’état de l’art. Mots-clés : OSPF, MANET, MPR, Routing MPR+SP: Towards a Unified MPR-based MANET Extension for OSPF 3
Subordinate class recognition using relational object models

We address the problem of sub-ordinate class recognition, l ike the distinction between different types of motorcycles. Our approach is motiv ated by observations from cognitive psychology, which identify parts as the defin ing component of basic level categories (like motorcycles), while sub-ordi nate categories are more often defined by part properties (like ’jagged wheels’). Acc ordingly, we suggest a two-stage algorithm: First, a relational part based objec t model is learnt using unsegmented object images from the inclusive class (e.g., m otorcycles in general). The model is then used to build a class-specific vector repres ntation for images, where each entry corresponds to a model’s part. In the second stage we train a standard discriminative classifier to classify subclass in ta ces (e.g., cross motorcycles) based on the class-specific vector representation. We describe extensive experimental results with several subclasses. The propose d algorithm typically gives better results than a competing one-step algorithm, o r a two stage algorithm where classification is based on a model of the sub-ordinate c l ss.
CellexalVR: A virtual reality platform for the exploration and analysis of single-cell gene expression data

Single-cell RNAseq is a powerful tool for the dissection of cell populations at the transcriptome level, and a myriad of techniques are available to project cells on to 3-dimensional space to construct cellular maps that aid the visualisation of heterogeneity and any sub-populations formed. Current visualisation methods for 3-dimensional data on conventional computer displays are poor, and coupled with a lack of intuitive point/cell selection methods often hinders a rapid exploration of finer details contained in the data. Here we present CellexalVR (www.cellexalvr.med.lu.se), a feature-rich, fully interactive, and immersive virtual reality environment for the analysis of single-cell RNAseq experiments that allows researchers to quickly and intuitively gain an understanding of their data.