Relevant bibliographies by topics / Adaptive Task Selection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Adaptive Task Selection'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Adaptive Task Selection"

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Suarez-Gonzalez, Adriana, Christian Lexer, and Quentin C. B. Cronk. "Adaptive introgression: a plant perspective." Biology Letters 14, no. 3 (March 2018): 20170688. http://dx.doi.org/10.1098/rsbl.2017.0688.

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Introgression is emerging as an important source of novel genetic variation, alongside standing variation and mutation. It is adaptive when such introgressed alleles are maintained by natural selection. Recently, there has been an explosion in the number of studies on adaptive introgression. In this review, we take a plant perspective centred on four lines of evidence: (i) introgression, (ii) selection, (iii) phenotype and (iv) fitness. While advances in genomics have contributed to our understanding of introgression and porous species boundaries (task 1), and the detection of signatures of selection in introgression (task 2), the investigation of adaptive introgression critically requires links to phenotypic variation and fitness (tasks 3 and 4). We also discuss the conservation implications of adaptive introgression in the face of climate change. Adaptive introgression is particularly important in rapidly changing environments, when standing genetic variation and mutation alone may only offer limited potential for adaptation. We conclude that clarifying the magnitude and fitness effects of introgression with improved statistical techniques, coupled with phenotypic evidence, has great potential for conservation and management efforts.
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Amirizadeh, Khosrow, and Rajeswari Mandava. "Fast Iterative model for Sequential-Selection-Based Applications." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 7 (February 14, 2014): 3689–96. http://dx.doi.org/10.24297/ijct.v12i7.3092.

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Accelerated multi-armed bandit (MAB) model in Reinforcement-Learning for on-line sequential selection problems is presented. This iterative model utilizes an automatic step size calculation that improves the performance of MAB algorithm under different conditions such as, variable variance of reward and larger set of usable actions. As result of these modifications, number of optimal selections will be maximized and stability of the algorithm under mentioned conditions may be amplified. This adaptive model with automatic step size computation may attractive for on-line applications in which, variance of observations vary with time and re-tuning their step size are unavoidable where, this re-tuning is not a simple task. The proposed model governed by upper confidence bound (UCB) approach in iterative form with automatic step size computation. It called adaptive UCB (AUCB) that may use in industrial robotics, autonomous control and intelligent selection or prediction tasks in the economical engineering applications under lack of information.
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Chen, Yi Mei. "Nonlinear Adaptive Tracking of the Steward Platform in Task Space." Applied Mechanics and Materials 511-512 (February 2014): 1017–21. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.1017.

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This paper presents an adaptive nonlinear controller equipped with a friction estimator for a 6 degree of freedom (DOF) parallel manipulator-steward platform. With the aid of direct adaptive technique and control Lyapunov function method, an adaptive controller is designed to complete the globally adaptive stability of the closed-loop system. The stabilized conditions and corresponding proof are also presented, and the selection method of the controller parameters is proposed. Simulation results are demonstrated in support of the proposed control scheme.
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Hattori, Masasi. "A quantitative model of optimal data selection in Wason's selection task." Quarterly Journal of Experimental Psychology Section A 55, no. 4 (October 2002): 1241–72. http://dx.doi.org/10.1080/02724980244000053.

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The optimal data selection model proposed by Oaksford and Chater (1994) successfully formalized Wason's selection task (Wason, 1966). The model, however, involved some questionable assumptions and was also not sufficient as a model of the task because it could not provide quantitative predictions of the card selection frequencies. In this paper, the model was revised to provide quantitative fits to the data. The model can predict the selection frequencies of cards based on a selection tendency function (STF), or conversely, it enables the estimation of subjective probabilities from data. Past experimental data were first re-analysed based on the model. In Experiment 1, the superiority of the revised model was shown. However, when the relationship between antecedent and consequent was forced to deviate from the biconditional form, the model was not supported. In Experiment 2, it was shown that sufficient emphasis on probabilistic information can affect participants’ performance. A detailed experimental method to sort participants by probabilistic strategies was introduced. Here, the model was supported by a subgroup of participants who used the probabilistic strategy. Finally, the results were discussed from the viewpoint of adaptive rationality.
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tiberghien, Kerensa, Wim Notebaert, Bert De Smedt, and Wim Fias. "Reactive and proactive control in arithmetical strategy selection." Journal of Numerical Cognition 3, no. 3 (January 30, 2018): 598–619. http://dx.doi.org/10.5964/jnc.v3i3.124.

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Individual differences in arithmetic have been explained by differences in cognitive processes and by arithmetic strategy use and selection. In the present study, we investigated the involvement of reactive and proactive control processes. We explored how variation in proactive and reactive control was related to individual differences in strategy selection. We correlated proactive and reactive measures obtained from the AX-CPT and an adjusted N-back task with a measure of strategy adaptiveness during a numerosity judgment task. The results showed that both measures of reactive control (of the AX-CPT and N-back task) correlated positively with strategy adaptiveness, while proactive control was not. This suggests that both cognitive control modes might have a different effect on adaptive strategy selection, where adaptive strategy selection seems to benefit from a transient (late) control mode, reactive control. We discuss these results in the light of the Dual Mechanisms Framework.
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Ma, Tengfei, Patrick Ferber, Siyu Huo, Jie Chen, and Michael Katz. "Online Planner Selection with Graph Neural Networks and Adaptive Scheduling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5077–84. http://dx.doi.org/10.1609/aaai.v34i04.5949.

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Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference.Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based.The code is available at https://github.com/matenure/GNN_planner.
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Morgan, T. J. H., L. E. Rendell, M. Ehn, W. Hoppitt, and K. N. Laland. "The evolutionary basis of human social learning." Proceedings of the Royal Society B: Biological Sciences 279, no. 1729 (July 27, 2011): 653–62. http://dx.doi.org/10.1098/rspb.2011.1172.

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Humans are characterized by an extreme dependence on culturally transmitted information. Such dependence requires the complex integration of social and asocial information to generate effective learning and decision making. Recent formal theory predicts that natural selection should favour adaptive learning strategies, but relevant empirical work is scarce and rarely examines multiple strategies or tasks. We tested nine hypotheses derived from theoretical models, running a series of experiments investigating factors affecting when and how humans use social information, and whether such behaviour is adaptive, across several computer-based tasks. The number of demonstrators, consensus among demonstrators, confidence of subjects, task difficulty, number of sessions, cost of asocial learning, subject performance and demonstrator performance all influenced subjects' use of social information, and did so adaptively. Our analysis provides strong support for the hypothesis that human social learning is regulated by adaptive learning rules.
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Guo, Wenhua, Jiabao Gao, Yanbin Tian, Fan Yu, and Zuren Feng. "SAFS: Object Tracking Algorithm Based on Self-Adaptive Feature Selection." Sensors 21, no. 12 (June 11, 2021): 4030. http://dx.doi.org/10.3390/s21124030.

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Object tracking is one of the most challenging problems in the field of computer vision. In challenging object tracking scenarios such as illumination variation, occlusion, motion blur and fast motion, existing algorithms can present decreased performances. To make better use of the various features of the image, we propose an object tracking method based on the self-adaptive feature selection (SAFS) algorithm, which can select the most distinguishable feature sub-template to guide the tracking task. The similarity of each feature sub-template can be calculated by the histogram of the features. Then, the distinguishability of the feature sub-template can be measured by their similarity matrix based on the maximum a posteriori (MAP). The selection task of the feature sub-template is transformed into the classification task between feature vectors by the above process and adopt modified Jeffreys’ entropy as the discriminant metric for classification, which can complete the update of the sub-template. Experiments with the eight video sequences in the Visual Tracker Benchmark dataset evaluate the comprehensive performance of SAFS and compare them with five baselines. Experimental results demonstrate that SAFS can overcome the difficulties caused by scene changes and achieve robust object tracking.
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Schumacher, Eric H., Erick J. Lauber, Jennifer M. Glass, Eileen L. Zurbriggen, Leon Gmeindl, David E. Kieras, and David E. Meyer. "Concurrent response-selection processes in dual-task performance: Evidence for adaptive executive control of task scheduling." Journal of Experimental Psychology: Human Perception and Performance 25, no. 3 (1999): 791–814. http://dx.doi.org/10.1037/0096-1523.25.3.791.

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Forstmann, Birte U., Marcel Brass, Iring Koch, and D. Yves von Cramon. "Voluntary Selection of Task Sets Revealed by Functional Magnetic Resonance Imaging." Journal of Cognitive Neuroscience 18, no. 3 (March 1, 2006): 388–98. http://dx.doi.org/10.1162/jocn.2006.18.3.388.

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In everyday life, we have to selectively adapt our behavior to different situations and tasks. In cognitive psychology, such adaptive behavior can be investigated with the task-switching paradigm. However, in contrast to everyday life, in experiments participants are unequivocally told which task to perform. The present functional magnetic resonance imaging (fMRI) study was set out to investigate processes that are relevant when participants can decide by their own which task to perform. The number of tasks to choose from was varied between a forced condition (no choice) and two voluntary selection conditions (two or three choices). We expected to find prolonged reaction times as well as higher activations within the midcingulate cortex for the choice conditions compared to the no-choice condition. The fMRI results revealed a significant activation difference for the choice conditions versus the no-choice condition. For the choice contrast, activation was found in the rostral cingulate zone (RCZ) as well as the superior parietal lobule and the posterior part of the intraparietal sulcus. These activations revealed no selection-specific difference between three and two choices. Finally, a post hoc analysis showed that the activation in the RCZ is not associated with higher task-dependent response conflict when participants can select a task set. Taken together, these findings indicate that distinct brain areas are involved in the voluntary selection of abstract task set information.
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Dissertations / Theses on the topic "Adaptive Task Selection"

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Haboush, W. S. "Adaptive task selection using threshold-based techniques in dynamic sensor networks." Thesis, University of Kent, 2008. https://kar.kent.ac.uk/24027/.

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"Developing a Neural Network Based Adaptive Task Selection System for anUndergraduate Level Organic Chemistry Course." Doctoral diss., 2020. http://hdl.handle.net/2286/R.I.57022.

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abstract: In the last decade, the immense growth of computational power, enhanced data storage capabilities, and the increasing popularity of online learning systems has led to adaptive learning systems becoming more widely available. Parallel to infrastructure enhancements, more researchers have started to study the adaptive task selection systems, concluding that suggesting tasks appropriate to students' needs may increase students' learning gains. This work built an adaptive task selection system for undergraduate organic chemistry students using a deep learning algorithm. The proposed model is based on a recursive neural network (RNN) architecture built with Long-Short Term Memory (LSTM) cells that recommends organic chemistry practice questions to students depending on their previous question selections. For this study, educational data were collected from the Organic Chemistry Practice Environment (OPE) that is used in the Organic Chemistry course at Arizona State University. The OPE has more than three thousand questions. Each question is linked to one or more knowledge components (KCs) to enable recommendations that precisely address the knowledge that students need. Subject matter experts made the connection between questions and related KCs. A linear model derived from students' exam results was used to identify skilled students. The neural network based recommendation system was trained using those skilled students' problem solving attempt sequences so that the trained system recommends questions that will likely improve learning gains the most. The model was evaluated by measuring the predicted questions' accuracy against learners' actual task selections. The proposed model not only accurately predicted the learners' actual task selection but also the correctness of their answers.
Dissertation/Thesis
Doctoral Dissertation Computer Science 2020
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Books on the topic "Adaptive Task Selection"

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Tran, Thanh V., Tam Nguyen, and Keith Chan. Adopting or Adapting Existing Instruments. Oxford University Press, 2018. http://dx.doi.org/10.1093/acprof:oso/9780190496470.003.0003.

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Cross-cultural translation is one of the major tasks in cross-cultural research. The task of translation becomes more challenging when an instrument is translated into two or more target languages simultaneously, especially with the translation of special constructs. This chapter (1) reviews existing cross-cultural translation approaches and offers the reader with practical guidelines; (2) presents a multilevel translation process encompassing back translation, expert evaluation, cognitive interviews, focus group evaluation, and field evaluation; and (3) offers a guide for best practices in selecting translators to perform cross-cultural translation.
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Okasha, Samir. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198815082.003.0001.

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There is a familiar story about the place of teleology in biology that goes as follows. Since Aristotle, biologists have used a teleological idiom to describe living organisms, but the justification for doing so only became apparent with Darwin. Though the process of evolution by natural selection is mechanical and lacks foresight, Darwinism nonetheless licenses talk of function and purpose in nature. In statements such as ‘the polar bear’s white coat is for camouflage’ and ‘the cactus has spines in order to deter herbivores’, the teleological terms (‘for’, ‘in order to’) are really a way of talking about adaptive significance. Natural selection led polar bears to evolve white coats and cacti to grow spines because these traits helped to camouflage bears and protect cacti, so were adaptive. Thus Darwinism supplies a naturalistic basis for at least some of the teleological idioms that biologists had long used....
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Kearney, Julia A., and Jennifer S. Ford. Adapting Meaning-Centered Psychotherapy for Adolescents and Young Adults with Cancer. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199837229.003.0008.

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There is a lack of validated psychotherapeutic interventions for the adolescent and young adult (AYA) cancer population, despite years of evidence of significant need. AYAs with cancer experience distress, anxiety, grief, life disruption, and loss of meaning. Meaning-making is a core developmental task of adolescence and contributes to identity development. This chapter reviews narrative and structural theories of identity development, viewed through the lens of a disruptive life event such as cancer. Clinical therapeutic issues are discussed, including the selection of AYA patients for participation in meaning-centered work, the therapeutic approach to difficult subjects such as prognosis or end of life, working with parents and caregivers, and dealing with grief and suicidality in a meaning-centered framework. Formal development of a manualized meaning-centered psychotherapy for AYAs is also discussed. A clinical vignette is presented to illustrate the main themes of a meaning-centered psychotherapeutic approach.
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Allen, Peg M., Linda J. Ahrendt, Kiley A. Hump, and Ross C. Brownson. Cancer Prevention Through Scaling-Up the Process of Evidence-Based Decision-Making in a State Health Department. Edited by David A. Chambers, Wynne E. Norton, and Cynthia A. Vinson. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190647421.003.0010.

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This case study provides an example of a collaboration between a university and a public health agency to build organizational capacity to spread data-driven decision-making, implementation, and evaluation of evidence-based cancer prevention strategies. The Office of Chronic Disease Prevention and Health Promotion at the South Dakota Department of Health provided the key management practices for scale-up of evidence-based decision-making (EBDM): leadership support, training, a supportive organizational climate and culture, inclusion of partners, and outcomes-based contracting with partnering organizations. A pre–post survey showed increased use of research evidence for several job tasks, including selection of interventions and evaluation. Perceived work unit access to skills in prioritization and adapting interventions also increased. The 16 staff and partners interviewed perceived leadership support, federal funding requirements, and an initial multi-day training as the key facilitators for spreading EBDM.
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LeFevre, Jo-Anne, Emma Wells, and Carla Sowinski. Individual Differences in Basic Arithmetical Processes in Children and Adults. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.005.

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This chapter describes the four main sources of individual differences in arithmetic that have been identified through research with children and adults. Numerical quantitative knowledge invokes basic cognitive processes that are either numerically specific or are recruited to be used in quantitative tasks (e.g. subitizing, discrimination acuity for approximate quantities). Attentional skills, including executive attention and various aspects of working memory are important, especially for more complex procedures. Linguistic knowledge is used within arithmetic to learn number system rules and structures, specific number words, and in developing and executing counting processes. Strategic abilities, which may reflect general planning and awareness skills, are involved in selecting procedures and solving problems adaptively. Other important sources of individual differences include automaticity of knowledge related to practice, experiences outside school, and the specific language spoken. Suggestions are made for further research that would be helpful in establishing a full picture of individual differences in arithmetic.
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Book chapters on the topic "Adaptive Task Selection"

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Banerjee, Ansuman, Himadri Sekhar Paul, Arijit Mukherjee, Swarnava Dey, and Pubali Datta. "A Framework for Speculative Scheduling and Device Selection for Task Execution on a Mobile Cloud." In Adaptive Resource Management and Scheduling for Cloud Computing, 36–51. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13464-2_4.

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Toubman, Armon, Maxim van Oldenbeek, and Olivier Claessen. "Staying Ahead of the Curve: Selecting Students for Newly Arising Tasks." In Adaptive Instructional Systems. Adaptation Strategies and Methods, 185–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77873-6_13.

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Kalyuga, Slava. "Assessment of Task-Specific Expertise." In Managing Cognitive Load in Adaptive Multimedia Learning, 81–100. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-048-6.ch004.

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Main implication of the expertise reversal effect is the need to tailor instructional techniques and procedures to changing levels of learner expertise in a specific task domain. In order to design adaptive procedures capable of tailoring instruction in real-time, it is necessary to have online measures of learner expertise. Such measures should be rapid enough to be used in real time. At the same time, they need to have sufficient diagnostic power to detect different levels of task-specific expertise. One of the previously mentioned reasons for low practical applicability of the results of studies in Aptitude-Treatment Interactions were inadequate aptitude measures. Most of the assessment methods used in those studies were psychometric instruments designed for selection purposes (e.g., large batteries of aptitude tests based on artificially simplified tasks administered mostly in laboratory conditions). Another suggested reason was unsuitability of those methods for dynamic, real-time applications while learners proceeded through a single learning session. This chapter describes a rapid diagnostic approach to the assessment of learner task-specific expertise that has been intentionally designed for rapid online application in adaptive learning environments. The method was developed using an analogy to experimental procedures applied in classical studies of chess expertise mentioned in Chapter I. In those studies, realistic board configurations were briefly presented for subsequent replications. With the described diagnostic approach, learners are briefly presented with a problem situation and required to indicate their first solution step in this problem situation or to rapidly verify suggested steps at various stages of a problem solution procedure.
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Azoulay, Rina, Esther David, Mireille Avigal, and Dorit Hutzler. "Adaptive task selection in automated educational software: a comparative study." In Intelligent Systems and Learning Data Analytics in Online Education, 179–204. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-823410-5.00008-5.

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Kalyuga, Slava. "Adaptive Procedures for Efficient Learning." In Managing Cognitive Load in Adaptive Multimedia Learning, 272–90. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-048-6.ch012.

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The rapid diagnostic approach to evaluating levels of learner task-specific expertise was introduced in Chapter IV and used in several studies that were subsequently described throughout this book. The rapid diagnostic techniques (first-step method and rapid verification technique) were instrumental in investigating some instances of the expertise reversal effect and in optimizing levels of cognitive load in faded worked example procedures (Section II and Chapter XI). This chapter describes some specific adaptive procedures based on rapid diagnostic methods for evaluating ongoing levels of learner task specific expertise. Two specific approaches to the design of adaptive instruction are considered, adaptive procedures based on rapid measures of performance and adaptive procedures based on combined measures of performance and cognitive load (efficiency measures). The expertise reversal effect established interactions between learner levels of task-specific expertise and effectiveness of different instructional methods. The major instructional implication of this effect is the need to tailor instructional methods and procedures to dynamically changing levels of learner expertise in a specific class of tasks within a domain. The rapid diagnostic approach was successfully used for real-time evaluation of levels of learner task-specific expertise in adaptive online tutorials in the domains of linear algebra equations (Kalyuga & Sweller, 2004; 2005) and vector addition motion problems in kinematics (Kalyuga, 2006) for high school students. Both first step diagnostic method and rapid verification technique were applied in adaptive procedures. According to the rapid assessment-based tailoring approach, these tutorials provided dynamic selection of levels of instructional guidance that were optimal for learners with different levels of expertise based on real-time online measures of these levels. The general designs of those studies were similar. In learner-adapted groups, at the beginning of training sessions, each student was provided with an appropriate level of instructional guidance according to the outcome of the initial rapid pretest. Then during the session, depending on the outcomes of the ongoing rapid tests, the student was allowed to proceed to the next learning stage or was required to repeat the same stage and then take the rapid test again. At each subsequent stage, a lower level of guidance was provided to learners (e.g., worked-out components of solution procedures were gradually omitted and progressively replaced with problem solving steps), and a higher level of the rapid diagnostic tasks was used at the end of the stage. In control non-adapted groups, learners either studied all tasks that were included in the corresponding stages of the training session of their yoked participants, or were required to study the whole set of tasks available in the tutorial.
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Morrison, Evan, Aditya K. Ghose, Hoa Dam, Alex Menzies, and Katayoun Khodaei. "Declarative Service Modeling through Adaptive Case Management." In Advances in Web Technologies and Engineering, 152–74. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5884-4.ch007.

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Adaptive case management addresses the shift away from the prescriptive process-centric view of operations towards a declarative framework for operational descriptions that promotes dynamic task selection in knowledge-intensive operations. A key difference between prescriptive services and declarative services is the way by which control flow is defined. Repeatable and straight-thru processes have been successfully used to model and optimise simple activity-based value chains. Increasingly, traditional process modeling techniques are being applied to knowledge intensive activities with often poor outcomes. By taking an adaptive case management approach to knowledge-intensive services, it is possible to model and execute workflows such as medical protocols that have previously been too difficult to describe with typical BPM frameworks. In this chapter, the authors describe an approach to design-level adaptive case management leveraging off existing repositories' semantically annotated business process models.
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El-aal, Shereen A., Rabie A. Ramadan, and Neveen Ghali. "Classification of EEG Signals for Motor Imagery Based on Mutual Information and Adaptive Neuro Fuzzy Inference System." In Fuzzy Systems, 347–66. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1908-9.ch016.

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Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.
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West-Eberhard, Mary Jane. "Development." In Developmental Plasticity and Evolution. Oxford University Press, 2003. http://dx.doi.org/10.1093/oso/9780195122343.003.0010.

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Any comprehensive theory of adaptive evolution has to feature development. Development produces the phenotypic variation that is screened by selection. For a mutation to affect evolution, it must first affect development. In order to understand phenotypic change during evolution, one has to understand phenotypic change during development, as well as how to relate that change to selection and gene-frequency change (evolution). The evolution of the phenotype is synonymous with the evolution of development. The genotype-phenotype problem addressed by the metaphors of chapter 2 is fundamentally a problem of development. Genetic programming, the canalized epigenetic landscape, and the recipes and blueprints contained in the genes—all are metaphors for development. Development is the missing link between genotype and phenotype, a place too often occupied by metaphors in the past. The task of this chapter is to outline a concept of development that connects it to mechanisms, on the one hand, and natural selection and evolution on the other, without a potentially misleading metaphorical crutch. The portrait of development provided by developmental biology is not adequate to this task. Evolutionary developmental biology extensively treats the genomic correlates of gross morphological variation across phyla, with little or no discussion of behavior, physiology, life histories, and the kind of variation within populations that is required for natural selection to work. Some progress toward a population approach has been made in plant developmental biology (e.g., see Lawton-Rauh et al., 2000). But a strong emphasis on the genome means that environmental influence is systematically ignored. If you begin with DNA and view development as “hard-wired” (e.g., Davidson, 2000), you overlook the flexible phenotype and the causes of its variation that are the mainsprings of adaptive evolution. I begin instead with the observation that DNA activity—gene expression—is universally condition sensitive and dependent upon materials from the environment. This implies connections between a DNA-centered approach and conventional insights about adaptive evolution in variable environments. The genome affects development at nearly every turn, so genes obviously play an important role in any theory of development and evolution.
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Bonneaud, Camille. "Host–Pathogen Evolution and Coevolution in Avian Systems." In Infectious Disease Ecology of Wild Birds, 77–98. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198746249.003.0005.

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The significance of studying birds and their pathogens goes far beyond the applied conservation or epidemiological implications of their interactions. Evidence suggests that avian host–pathogen systems can be used to test fundamental theoretical predictions about adaptive evolution and coevolution in natural populations. This chapter highlights recent advances in the field of bird–pathogen evolution and coevolution, how these advances have come about, and future directions of research. Further, it shows that, while there is a growing body of work that provides support for both avian host and pathogen evolution, evidence for their antagonistic coevolution, the process of adaptation and counter-adaptation in response to the reciprocal selection pressures that they impose on each other, remains rare. Rigorously demonstrating the processes of evolution and coevolution is complex in natural populations and doing so necessarily requires borrowing methodological approaches from a range of disciplines to fully characterize phenotypic change, its genetic and mechanistic basis, as well as its adaptive benefits. Overcoming the challenge of such a task will, however, generate important insights into a range of processes, from disease transmission dynamics and pathogenesis to the maintenance of biodiversity.
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Khan, Latifur, Dennis McLeod, and Cyrus Shahabi. "An Adaptive Probe-Based Technique to Optimize Join Queries in Distributed Internet Databases." In Human Computer Interaction Development & Management, 93–116. IGI Global, 2002. http://dx.doi.org/10.4018/978-1-931777-13-1.ch006.

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An adaptive probe-based optimization technique is developed and demonstrated in the context of an Internet-based distributed database environment. More and more common are database systems, which are distributed across servers communicating via the Internet where a query at a given site might require data from remote sites. Optimizing the response time of such queries is a challenging task due to the unpredictability of server performance and network traffic at the time of data shipment; this may result in the selection of an expensive query plan using a static query optimizer. We constructed an experimental setup consisting of two servers running the same DBMS connected via the Internet. Concentrating on join queries, we demonstrate how a static query optimizer might choose an expensive plan by mistake. This is due to the lack of a priori knowledge of the run-time environment, inaccurate statistical assumptions in size estimation, and neglecting the cost of remote method invocation. These shortcomings are addressed collectively by proposing a probing mechanism. Furthermore, we extend our mechanism with an adaptive technique that detects sub-optimality of a plan during query execution and attempts to switch to the cheapest plan while avoiding redundant work and imposing little overhead. An implementation of our run-time optimization technique for join queries was constructed in the Java language and incorporated into an experimental setup. The results demonstrate the superiority of our probe-based optimization over a static optimization.
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Conference papers on the topic "Adaptive Task Selection"

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Xi, Li, Hu Zhigang, and Yan Chaokun. "Adaptive Site Selection and Task Scheduling for Grid Workflow." In 2011 International Conference on Network Computing and Information Security (NCIS). IEEE, 2011. http://dx.doi.org/10.1109/ncis.2011.75.

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Yang, Peng, and Ping Li. "Efficient Online Multi-Task Learning via Adaptive Kernel Selection." In WWW '20: The Web Conference 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3366423.3379993.

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Dasgupta, Prithviraj, and Matthew Hoeing. "Task Selection in Multi-Agent Swarms using Adaptive Bid Auctions." In First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007). IEEE, 2007. http://dx.doi.org/10.1109/saso.2007.58.

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Rahmanl, Raziur, Chamila Perera, Souparno Ghosh, and Ranadip Pall. "Adaptive Multi-task Elastic Net based feature selection from Pharmacogenomics Databases." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8512229.

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Shang, Q., L. Zhang, L. Feng, Y. Hou, J. Zhong, A. Gupta, K. C. Tan, and H. L. Liu. "A Preliminary Study of Adaptive Task Selection in Explicit Evolutionary Many-Tasking." In 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. http://dx.doi.org/10.1109/cec.2019.8789909.

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Balicki, Jerzy. "Task Assignments in Logistics by Adaptive Multi-Criterion Evolutionary Algorithm with Elitist Selection." In 2014 Federated Conference on Computer Science and Information Systems. IEEE, 2014. http://dx.doi.org/10.15439/2014f309.

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Mazurok, Tetiana, and Volodymyr Chernykh. "KNOWLEDGE-BASED APPROACH TO ADAPTIVE SELECTION OF EQUIPMENT FOR TEACHING ROBOTICS." In 3rd International Baltic Symposium on Science and Technology Education (BalticSTE2019). Scientia Socialis Ltd., 2019. http://dx.doi.org/10.33225/balticste/2019.134.

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Innovations and progress in teaching, introduction of new academic disciplines in the curricula, changes in the paradigm of school education in Ukraine, the search for innovative tools, techniques and teaching methods, especially teaching STEAM create a good basis for teaching robotics. However, the organization of classes in robotics requires the creation of a special educational ecosystem, which important element is the technical base (equipment). It is not a secret, that administrative staff who sometimes even do not have required experience in technology are often engaged in the procurement of equipment and its selection. The current study was conducted to solve this problem, as well as to create a universal recommendation for creating an appropriate ecosystem for teaching robotics As part of the study, the task was to develop a prototype of an expert decision-making system for selecting of an appropriate equipment and zoning of a classroom (ICR) for conducting classes in robotics. Knowledge-oriented approach was used to create the prototype. Keywords: knowledge-based approach, robotics, adaptive selection, teaching robotics.
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Sarkar, Rituparna, Suvadip Mukherjee, Elisabeth Labruyere, and Jean-Christophe Olivo-Marin. "Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412641.

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Jalan, Ankit, and Purushottam Kar. "Accelerating Extreme Classification via Adaptive Feature Agglomeration." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/361.

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Extreme classification seeks to assign each data point, the most relevant labels from a universe of a million or more labels. This task is faced with the dual challenge of high precision and scalability, with millisecond level prediction times being a benchmark. We propose DEFRAG, an adaptive feature agglomeration technique to accelerate extreme classification algorithms. Despite past works on feature clustering and selection, DEFRAG distinguishes itself in being able to scale to millions of features, and is especially beneficial when feature sets are sparse, which is typical of recommendation and multi-label datasets. The method comes with provable performance guarantees and performs efficient task-driven agglomeration to reduce feature dimensionalities by an order of magnitude or more. Experiments show that DEFRAG can not only reduce training and prediction times of several leading extreme classification algorithms by as much as 40%, but also be used for feature reconstruction to address the problem of missing features, as well as offer superior coverage on rare labels.
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Yufereva, Nadezhda, and Maria Shchannikova. "SPECIES AND VARIETIES OF PERENNIAL GRASSES FOR MAKING OF STABLE LAWN COMMUNITIES." In Multifunctional adaptive feed production. ru: Federal Williams Research Center of Forage Production and Agroecology, 2020. http://dx.doi.org/10.33814/mak-2020-22-70-59-64.

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The correct selection of species and varieties of perennial grasses for making of stable lawn herbages is an important task for the researchers. Research of lawn herbages was conducted for six years period on the experimental field of the Vyatka State Agricultural Academy. As a result, were specified domestic varieties of lawn grasses which suit for the conditions of the Kirov region and show resistance to adverse weather conditions observed during the years of research. Red fescue Sigma and meadow grass Vagant and Dar forming lawns from good to excellent quality. The number of shoots per unit area for the sixth year for these species and varieties is 12.6–15.9 thousand pieces per square meter, 6.5–6.8 thousand pieces per square meter and 4.1–5.0 thousand pieces per square meter respectively. These species and varieties can be recommended for making of stable single–species lawn herbages. The seeding rate can be reduced in comparison to the recommended one. Perennial ryegrass in the conditions of the Kirov region shows low winter hardiness, the number of shoots declines since the third year of life and the number of undesirable plant increases. However, the perennial ryegrass has superior lawns in the year of sowing. Therefore, this species can be used for the quick making of lawns with a usable life no more than two years.
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