The study of landscape structure and configuration is firmly established as integral to the continued advancement of ecology. The configuration of resource patches can have far-reaching implications for biodiversity, metapopulation dynamics, community structure, and habitat quality. Human activities, such as forestry, agriculture, and residential construction alter patch configuration by breaking larger patches into smaller fragments. This frequently results in pronounced, unforeseen consequences for species. The fragmentation and shrinking of habitat patches can lead to changes in the environmental conditions within the remaining patches (e.g., degradation), prompting responses from local populations. These responses can, in turn, cause changes to the metapopulation structure on large spatial scale.<br>I examined the relationship between the degree of habitat fragmentation (edge density), and forewing lengths of the ebony jewelwing damselfly (Calopteryx maculata Beauvois, Odonata: Calopterygidae). I used correlated random walks to determine the biologically relevant landscape area over which forest fragmentation was calculated. Then, I used Moran’s I to determine the spatial scale of wing length response to fragmentation. I found that wing lengths increased with edge density. I also found that wing lengths were spatially autocorrelated at distances below 5 Km. These findings suggest that damselflies adapt to changes in forest fragmentation at a relatively small spatial scale.<br>Next, I assessed the slime mold Physarum polycephalum’s usefulness as a microcosm of dispersal in fragmented landscapes. Slime mold plasmodia were placed in dishes with oat patches of varying sizes and distances. The probability of each patch type being colonized first was compared to predictions of patch occupancy based on C. maculata. Patches that were nearer or larger were likely to be colonized before patches that were more distant, or smaller. Observed patch occupancy matched model predictions when only patch distance was varied, but not when patch size was varied. These results suggest that P. polycephalum has the potential to serve as a useful microcosm of dispersal in patchy landscapes. However, more testing is needed to develop the microcosm system. <br>Finally, a lesson plan was developed to teach high school students about the concepts of landscape ecology and connectivity. An emphasis was placed on using active learning techniques, which have been demonstrated to result in greater understanding than traditional lecture formats. The lesson plan incorporates an education boardgame, Humans & Habitats, that I developed to illustrate how the conflicting goals of resource managers impact habitat connectivity. It also incorporates a scientific inquiry activity that uses P. polycephalum to test predictions about the effect of altered connectivity. The lesson plan and materials will be available to members of the public, free of charge.<br><br>

In the past few years, complex neural networks have achieved state of the art results in image classification. However, training these models requires large amounts of labelled data. Whereas unlabelled images are often readily available in large quantities, obtaining l abels takes considerable human effort. Active learning reduces the required labelling effort by selecting the most informative instances to label. The most popular active learning query strategy framework, uncertainty sampling, uses uncertainty estimates of the model being trained to select instances for labelling. However, modern classification neural networks often do not provide good uncertainty estimates. Baye sian neural networks model uncertainties over model parameters, which can be used to obtain uncertainties over model predictions. Exact Bayesian inference is intractable for neural networks, however several approximate methods have been proposed. We experiment with three such methods using various uncertainty sampling active learning query strategies.

The study investigated the Impact of Activity-Based Teaching Method (ABTM) on students’ academic performance in basic science at Junior Secondary Schools in Katsina Metropolis, Nigeria. Three research questions and three research hypotheses were formulated to guide the researcher in the conduct of the research. The study randomly sampled three hundred and thirty (330) out of nine thousand and six (9,006) Junior III Basic Science Students. Three of the randomly selected schools were placed as experimental control groups. A total of one hundred and sixty five (165) students were randomly sorted out, each way, to constitute the experimental and control groups. A quasi-experimental pre-test-post-test research design was used for the study. A pre-test was administered to ascertain the equivalence of the two groups. The study subjects in the experimental group were taught a number of concepts enshrined in environmental management for sustainability using the assets in activity-based teaching method; the control group was taught the same content using the lecture method for eight weeks. The students were subjected to “Basic Science Achievement Test” (BSAT); this instrument provided data for addressing the research questions and hypotheses raised in the study; the hypotheses were tested using SPSS version 20.0 packaged at 0.05 level of significance; t-test for independent samples was used to test the hypotheses. The study revealed that basic science students taught using activity-based teaching strategy performed significantly higher than their counterparts who were only taught using lecture method; similarly, there was significant difference in the academic performance of males, as compared to female students; similarly the students exposed to activity-based teaching strategy demonstrated a higher retention ability indices in the learning of basic science concepts, as compared to their colleagues who were exposed only to the lecture method. The study recommended that teachers should employ activity-based teaching methodology (ABTM) in teaching concepts in basic science at Junior Secondary Schools in order to enhance academic performance and retention of the content that was taught. The study further recommended that there should be provisions in schools of facilities, provisions and equipment which are vital for effective implementation of activity-based teaching method (ABTM). / Curriculum and Instructional Studies / D. Ed. (Didactics)

Många studier har genom åren visat på effektiviteten av aktiva lärmetoder hos studenter på universitet och senare även i undervisning av små barn. Effekten av dessa metoder på högstadieungdomar är däremot inte väl kända. Genom nyckelordssökningar fanns tio studier som mötte kriterierna a) en experimentbaserad studie, b) inom naturvetenskapsundervisning, c) för högstadieåldern eller motsvarande och d) en av metoderna; Problembaserat lärande, Peer Instruction, Just-in-time Teaching eller Flippat klassrum. Studiernas resultat undersöktes för att identifiera metodernas effekter och dessa analyserades sedan med bakgrund i sociokulturell utvecklingsteori. Tre huvudsakliga effekter av aktiva lärmetoder identifierades: förbättrade akademiska resultat, utveckling av kognitiva förmågor och ökat engagemang. Dessa effekters implikationer för svensk skola diskuterades, och förslag har getts på fortsatta forskningsmöjligheter.

<p>Ni-based superalloys continue to exert themselves as the industry standards in high stress and highly corrosive/oxidizing environments, such as are present in a gas turbine engine, due to their excellent high temperature strengths, thermal and microstructural stabilities, and oxidation and creep resistances. Gas turbine engines are essential components for energy generation and propulsion in the modern age. However, Ni-based superalloys are reaching their limits in the operating conditions of these engines due to their melting onset temperatures, which is approximately 1300 °C. Therefore, a new class of materials must be formulated to surpass the capabilities Ni-based superalloys, as increasing the operating temperature leads to increased efficiency and reductions in fuel consumption and greenhouse gas emissions. One of the proposed classes of materials is termed refractory complex concentrated alloys, or RCCAs, which consist of 4 or more refractory elements (in this study, selected from: Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, and W) in equimolar or near-equimolar proportions. So far, there have been highly promising results with these alloys, including far higher melting points than Ni-based superalloys and outstanding high-temperature strengths in non-oxidizing environments. However, improvements in room temperature ductility and high-temperature oxidation resistance are still needed for RCCAs. Also, given the millions of possible alloy compositions spanning various combinations and concentrations of refractory elements, more efficient methods than just serial experimental trials are needed for identifying RCCAs with desired properties. A coupled computational and experimental approach for exploring a wide range of alloy systems and compositions is crucial for accelerating the discovery of RCCAs that may be capable of replacing Ni-based superalloys. </p> <p>In this thesis, the CALPHAD method was utilized to generate basic thermodynamic properties of approximately 67,000 Al-bearing RCCAs. The alloys were then down-selected on the basis of certain criteria, including solidus temperature, volume percent BCC phase, and aluminum activity. Machine learning models with physics-based descriptors were used to select several BCC-based alloys for fabrication and characterization, and an active learning loop was employed to aid in rapid alloy discovery for high hardness and strength. This method resulted in rapid identification of 15 BCC-based, four component, Al-bearing RCCAs exhibiting room-temperature Vickers hardness from 1% to 35% above previously reported alloys. This work exemplifies the advantages of utilizing Integrated Computational Materials Engineering- and Materials Genome Initiative-driven approaches for the discovery and design of new materials with attractive properties.</p> <p> </p> <p><br></p>

Les applications futures de la robotique, en particulier pour des robots de service à la personne, exigeront des capacités d’adaptation continue à l'environnement, et notamment la capacité à reconnaître des nouveaux objets et apprendre des nouveaux mots via l'interaction avec les humains. Bien qu'ayant fait d'énormes progrès en utilisant l'apprentissage automatique, les méthodes actuelles de vision par ordinateur pour la détection et la représentation des objets reposent fortement sur de très bonnes bases de données d’entrainement et des supervisions d'apprentissage idéales. En revanche, les enfants de deux ans ont une capacité impressionnante à apprendre à reconnaître des nouveaux objets et en même temps d'apprendre les noms des objets lors de l'interaction avec les adultes et sans supervision précise. Par conséquent, suivant l'approche de le robotique développementale, nous développons dans la thèse des approches d'apprentissage pour les objets, en associant leurs noms et leurs caractéristiques correspondantes, inspirées par les capacités des enfants, en particulier l'interaction ambiguë avec l’homme en s’inspirant de l'interaction qui a lieu entre les enfants et les parents.L'idée générale est d’utiliser l'apprentissage cross-situationnel (cherchant les points communs entre différentes présentations d’un objet ou d’une caractéristique) et la découverte de concepts multi-modaux basée sur deux approches de découverte de thèmes latents: la Factorisation en Natrices Non-Négatives (NMF) et l'Allocation de Dirichlet latente (LDA). Sur la base de descripteurs de vision et des entrées audio / vocale, les approches proposées vont découvrir les régularités sous-jacentes dans le flux de données brutes afin de parvenir à produire des ensembles de mots et leur signification visuelle associée (p.ex le nom d’un objet et sa forme, ou un adjectif de couleur et sa correspondance dans les images). Nous avons développé une approche complète basée sur ces algorithmes et comparé leur comportements face à deux sources d'incertitudes: ambiguïtés de références, dans des situations où plusieurs mots sont donnés qui décrivent des caractéristiques d'objets multiples; et les ambiguïtés linguistiques, dans des situations où les mots-clés que nous avons l'intention d'apprendre sont intégrés dans des phrases complètes. Cette thèse souligne les solutions algorithmiques requises pour pouvoir effectuer un apprentissage efficace de ces associations de mot-référent à partir de données acquises dans une configuration d'acquisition simplifiée mais réaliste qui a permis d'effectuer des simulations étendues et des expériences préliminaires dans des vraies interactions homme-robot. Nous avons également apporté des solutions pour l'estimation automatique du nombre de thèmes pour les NMF et LDA.Nous avons finalement proposé deux stratégies d'apprentissage actives: la Sélection par l'Erreur de Reconstruction Maximale (MRES) et l'Exploration Basée sur la Confiance (CBE), afin d'améliorer la qualité et la vitesse de l'apprentissage incrémental en laissant les algorithmes choisir les échantillons d'apprentissage suivants. Nous avons comparé les comportements produits par ces algorithmes et montré leurs points communs et leurs différences avec ceux des humains dans des situations d'apprentissage similaires. / Future applications of robotics, especially personal service robots, will require continuous adaptability to the environment, and particularly the ability to recognize new objects and learn new words through interaction with humans. Though having made tremendous progress by using machine learning, current computational models for object detection and representation still rely heavily on good training data and ideal learning supervision. In contrast, two year old children have an impressive ability to learn to recognize new objects and at the same time to learn the object names during interaction with adults and without precise supervision. Therefore, following the developmental robotics approach, we develop in the thesis learning approaches for objects, associating their names and corresponding features, inspired by the infants' capabilities, in particular, the ambiguous interaction with humans, inspired by the interaction that occurs between children and parents.The general idea is to use cross-situational learning (finding the common points between different presentations of an object or a feature) and to implement multi-modal concept discovery based on two latent topic discovery approaches : Non Negative Matrix Factorization (NMF) and Latent Dirichlet Association (LDA). Based on vision descriptors and sound/voice inputs, the proposed approaches will find the underlying regularities in the raw dataflow to produce sets of words and their associated visual meanings (eg. the name of an object and its shape, or a color adjective and its correspondence in images). We developed a complete approach based on these algorithms and compared their behavior in front of two sources of uncertainties: referential ambiguities, in situations where multiple words are given that describe multiple objects features; and linguistic ambiguities, in situations where keywords we intend to learn are merged in complete sentences. This thesis highlights the algorithmic solutions required to be able to perform efficient learning of these word-referent associations from data acquired in a simplified but realistic acquisition setup that made it possible to perform extensive simulations and preliminary experiments in real human-robot interactions. We also gave solutions for the automatic estimation of the number of topics for both NMF and LDA.We finally proposed two active learning strategies, Maximum Reconstruction Error Based Selection (MRES) and Confidence Based Exploration (CBE), to improve the quality and speed of incremental learning by letting the algorithms choose the next learning samples. We compared the behaviors produced by these algorithms and show their common points and differences with those of humans in similar learning situations.

No description available.

A high quality and large scale training data-set is an important guarantee to teach an ideal classifier for image classification. Manually constructing a training data- set  with  appropriate  labels  is  an  expensive  and  time  consuming  task.    Active learning techniques have been used to improved the existing models by reducing the  number  of  required  annotations.    The  present  work  aims  to  investigate the  way  to  build  a  model  for  identifying  and  utilizing  potential  informative and  representativeness  unlabeled  samples.    To  this  end,  two  approaches  for deep image classification using active learning are proposed, implemented and evaluated.  The two versions of active leaning for deep image classification differ in  the  input  space  exploration  so  as  to  investigate  how  classifier  performance varies  when  automatic  labelization  on  the  high  confidence  unlabeled  samples is  performed.    Active  learning  heuristics  based  on  uncertainty  measurements on low confidence predicted samples,  a pseudo-labelization technique to boost active  learning  by  reducing  the  number  of  human  interactions  and  knowledge transferring  form  pre-trained  models,  are  proposed  and  combined  into  our methodology.  The experimental results on two benchmark image classification data-sets  verify  the  effectiveness  of  the  proposed  methodology.    In  addition, a  new  pool-based  active  learning  query  strategy  is  proposed.     Dealing  with retraining-based algorithms we define a ”forgetting event” to have occurred when an  individual  training  example  transitions  the  maximum  predicted  probability class over the course of retraining. We integrated the new approach with the semi- supervised learning method in order to tackle the above challenges and observedgood performance against existing methods. / En  högkvalitativ  och  storskalig  träningsdataset  är  en  viktig  garanti  för  att  bli en  idealisk  klassificerare  för  bildklassificering.     Att  manuellt  konstruera  en träningsdatasats  med  lämpliga  etiketter  är  en  dyr  och  tidskrävande  uppgift. Aktiv  inlärningstekniker  har  använts  för  att  förbättra  de  befintliga  modellerna genom att minska antalet nödvändiga annoteringar. Det nuvarande arbetet syftar till  att  undersöka  sättet  att  bygga  en  modell  för  att  identifiera  och  använda potentiella informativa och representativa omärkta prover.   För detta ändamål föreslås, genomförs och genomförs två metoder för djup bildklassificering med aktivt  lärande  utvärderas.      De  två  versionerna  av  aktivt  lärande  för  djup bildklassificering  skiljer  sig  åt  i  undersökningen  av  ingångsutrymmet  för  att undersöka hur klassificeringsprestanda varierar när automatisk märkning på de omärkta  proverna  med  hög  konfidens  utförs.   Aktiv  lärande  heuristik  baserad på  osäkerhetsmätningar  på  förutsagda  prover  med  låg  konfidens,  en  pseudo- märkningsteknik för att öka aktivt lärande genom att minska antalet mänskliga interaktioner  och  kunskapsöverföring  av  förutbildade  modeller,  föreslås  och kombineras   i   vår   metod.      Experimentella   resultat   på   två   riktmärken   för bildklassificering datauppsättningar verifierar effektiviteten hos den föreslagna metodiken.   Dessutom föreslås en ny poolbaserad aktiv inlärningsfrågestrategi. När  vi  använder  omskolningsbaserade  algoritmer  definierar  vi  en  ”glömmer händelse” som skulle ha inträffat när ett individuellt träningsexempel överskrider den maximala förutsagda sannolikhetsklassen under omskolningsprocessen.  Vi integrerade den nya metoden med den semi-övervakad inlärning för att hanteraovanstående utmaningar och observeras bra prestanda mot befintliga metoder.

Mémoire en recherche-création / Cette recherche a permis de développer un micromonde portant sur la conscience phonémique destiné aux apprenants de moins de six ans, afin de vérifier si ce type de logiciel d’apprentissage par découverte pouvait être utilisé dans un nouveau domaine, la linguistique, et afin de voir les réactions que son utilisation suscitait. Après avoir établi les spécifications du logiciel, quatre expertes ont confirmé que celui-ci venait combler un besoin. La conception du logiciel a donc été poursuivie en alternant programmation et observation des réactions des apprenants lors de l’utilisation du prototype. Il a été constaté que les apprenants étaient curieux et excités d’explorer le micromonde et qu’ils faisaient preuve d’autonomie en effectuant plusieurs tâches de conscience phonémique via son interface. De plus, il a été observé que les apprenants n’avaient pas de difficultés à être attentifs pendant trente minutes, qu’ils étaient plus concentrés le matin, et qu’ils maitrisaient davantage l’utilisation du tactile que de la souris. Des interventions d’étayage offrant un soutien moral se sont avérées essentielles pour maintenir la motivation des apprenants. En effet, lorsque l’interface n’était pas assez intuitive, les apprenants se décourageaient lorsque leurs tentatives ne portaient pas fruit après plusieurs essais. Les résultats de cette recherche ne sont pas généralisables, car elle a été effectuée sur un petit échantillon de convenance. Aussi, il n’a pas été possible de vérifier si une progression d’apprentissage avait lieu. Plusieurs aspects demeurent donc à explorer avant de pouvoir affirmer que les micromondes sont des outils adaptés à la linguistique. / A phonemic awareness microworld for six years old or less learners was developed in this research to verify if this type of discovery learning software can be use in a new field: linguistic. Four experts have confirmed that the specifications of the software seem to create something needed in the field. Therefore the creation of the software was continued by alternating programming and observing learner’s reactions when they used the prototype. It was found that learners were curious and excited to explore the microworld and were autonomous when performing several phonemic awareness tasks via its interface. In addition, it was observed that learners had no difficulty being attentive for thirty minutes, that they were more concentrated in the morning, and that they mastered the use of the touch function more than the use of the mouse. Supportive interventions that provided moral support have been essential to keep learners motivated. Indeed, when the interface was not intuitive enough, learners became discouraged after several unsuccessful attempts. It is impossible to generalise the results of this research as it was carried out on a small sample of convenience. Also, the presence of a learning progression was not verified. As such several aspects remain to be explored before we can affirm that microworlds are tools adapted to linguistics.

The purpose of the research reported here was to determine the impact learning the engineering design process (EDP) would have on the critical thinking skills of high school physics students. An EDP unit was conducted with 5 classes of high school physics students in grades 10-12 over 1 month. The EDP unit's curriculum allowed for the gradual release of responsibility as students became more familiar with the EDP and more consistent in using it. The six steps used in this EDP unit were Ask, Imagine, Plan, Create, Test, and Improve. The Watson-Glaser Critical Thinking Appraisal was given as a pre- and post-test to measure the growth in critical thinking skills. By measured standards, qualitative analysis and observation, students showed an increase in critical thinking skills and in confidence to use them.