Literatura académica sobre el tema "Learning from Constraints"
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Artículos de revistas sobre el tema "Learning from Constraints"
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Cropper, Andrew y Rolf Morel. "Learning programs by learning from failures". Machine Learning 110, n.º 4 (19 de febrero de 2021): 801–56. http://dx.doi.org/10.1007/s10994-020-05934-z.
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AbstractWe describe an inductive logic programming (ILP) approach called learning from failures. In this approach, an ILP system (the learner) decomposes the learning problem into three separate stages: generate, test, and constrain. In the generate stage, the learner generates a hypothesis (a logic program) that satisfies a set of hypothesis constraints (constraints on the syntactic form of hypotheses). In the test stage, the learner tests the hypothesis against training examples. A hypothesis fails when it does not entail all the positive examples or entails a negative example. If a hypothesis fails, then, in the constrain stage, the learner learns constraints from the failed hypothesis to prune the hypothesis space, i.e. to constrain subsequent hypothesis generation. For instance, if a hypothesis is too general (entails a negative example), the constraints prune generalisations of the hypothesis. If a hypothesis is too specific (does not entail all the positive examples), the constraints prune specialisations of the hypothesis. This loop repeats until either (i) the learner finds a hypothesis that entails all the positive and none of the negative examples, or (ii) there are no more hypotheses to test. We introduce Popper, an ILP system that implements this approach by combining answer set programming and Prolog. Popper supports infinite problem domains, reasoning about lists and numbers, learning textually minimal programs, and learning recursive programs. Our experimental results on three domains (toy game problems, robot strategies, and list transformations) show that (i) constraints drastically improve learning performance, and (ii) Popper can outperform existing ILP systems, both in terms of predictive accuracies and learning times.
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Chou, Glen, Dmitry Berenson y Necmiye Ozay. "Learning constraints from demonstrations with grid and parametric representations". International Journal of Robotics Research 40, n.º 10-11 (13 de agosto de 2021): 1255–83. http://dx.doi.org/10.1177/02783649211035177.
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We extend the learning from demonstration paradigm by providing a method for learning unknown constraints shared across tasks, using demonstrations of the tasks, their cost functions, and knowledge of the system dynamics and control constraints. Given safe demonstrations, our method uses hit-and-run sampling to obtain lower cost, and thus unsafe, trajectories. Both safe and unsafe trajectories are used to obtain a consistent representation of the unsafe set via solving an integer program. Our method generalizes across system dynamics and learns a guaranteed subset of the constraint. In addition, by leveraging a known parameterization of the constraint, we modify our method to learn parametric constraints in high dimensions. We also provide theoretical analysis on what subset of the constraint and safe set can be learnable from safe demonstrations. We demonstrate our method on linear and nonlinear system dynamics, show that it can be modified to work with suboptimal demonstrations, and that it can also be used to learn constraints in a feature space.
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Okabe, Masayuki y Seiji Yamada. "Learning Similarity Matrix from Constraints of Relational Neighbors". Journal of Advanced Computational Intelligence and Intelligent Informatics 14, n.º 4 (20 de mayo de 2010): 402–7. http://dx.doi.org/10.20965/jaciii.2010.p0402.
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This paper describes a method of learning similarity matrix from pairwise constraints assumed used under the situation such as interactive clustering, where we can expect little user feedback. With the small number of pairwise constraints used, our method attempts to use additional constraints induced by the affinity relationship between constrained data and their neighbors. The similarity matrix is learned by solving an optimization problem formalized as semidefinite programming. Additional constraints are used as complementary in the optimization problem. Results of experiments confirmed the effectiveness of our proposed method in several clustering tasks and that our method is a promising approach.
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Mueller, Carl L. "Abstract Constraints for Safe and Robust Robot Learning from Demonstration". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 10 (3 de abril de 2020): 13728–29. http://dx.doi.org/10.1609/aaai.v34i10.7136.
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My thesis research incorporates high-level abstract behavioral requirements, called ‘conceptual constraints’, into the modeling processes of robot Learning from Demonstration (LfD) techniques. My most recent work introduces an LfD algorithm called Concept Constrained Learning from Demonstration. This algorithm encodes motion planning constraints as temporal Boolean operators that enforce high-level constraints over portions of the robot's motion plan during learned skill execution. This results in more easily trained, more robust, and safer learned skills. Future work will incorporate conceptual constraints into human-aware motion planning algorithms. Additionally, my research will investigate how these concept constrained algorithms and models are best incorporated into effective interfaces for end-users.
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Kato, Tsuyoshi, Wataru Fujibuchi y Kiyoshi Asai. "Learning Kernels from Distance Constraints". IPSJ Digital Courier 2 (2006): 441–51. http://dx.doi.org/10.2197/ipsjdc.2.441.
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Farina, Francesco, Stefano Melacci, Andrea Garulli y Antonio Giannitrapani. "Asynchronous Distributed Learning From Constraints". IEEE Transactions on Neural Networks and Learning Systems 31, n.º 10 (octubre de 2020): 4367–73. http://dx.doi.org/10.1109/tnnls.2019.2947740.
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Hammer, Rubi, Tomer Hertz, Shaul Hochstein y Daphna Weinshall. "Category learning from equivalence constraints". Cognitive Processing 10, n.º 3 (3 de diciembre de 2008): 211–32. http://dx.doi.org/10.1007/s10339-008-0243-x.
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Armesto, Leopoldo, João Moura, Vladimir Ivan, Mustafa Suphi Erden, Antonio Sala y Sethu Vijayakumar. "Constraint-aware learning of policies by demonstration". International Journal of Robotics Research 37, n.º 13-14 (26 de julio de 2018): 1673–89. http://dx.doi.org/10.1177/0278364918784354.
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Many practical tasks in robotic systems, such as cleaning windows, writing, or grasping, are inherently constrained. Learning policies subject to constraints is a challenging problem. In this paper, we propose a method of constraint-aware learning that solves the policy learning problem using redundant robots that execute a policy that is acting in the null space of a constraint. In particular, we are interested in generalizing learned null-space policies across constraints that were not known during the training. We split the combined problem of learning constraints and policies into two: first estimating the constraint, and then estimating a null-space policy using the remaining degrees of freedom. For a linear parametrization, we provide a closed-form solution of the problem. We also define a metric for comparing the similarity of estimated constraints, which is useful to pre-process the trajectories recorded in the demonstrations. We have validated our method by learning a wiping task from human demonstration on flat surfaces and reproducing it on an unknown curved surface using a force- or torque-based controller to achieve tool alignment. We show that, despite the differences between the training and validation scenarios, we learn a policy that still provides the desired wiping motion.
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Hewing, Lukas, Kim P. Wabersich, Marcel Menner y Melanie N. Zeilinger. "Learning-Based Model Predictive Control: Toward Safe Learning in Control". Annual Review of Control, Robotics, and Autonomous Systems 3, n.º 1 (3 de mayo de 2020): 269–96. http://dx.doi.org/10.1146/annurev-control-090419-075625.
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Recent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. Model predictive control (MPC), as the prime methodology for constrained control, offers a significant opportunity to exploit the abundance of data in a reliable manner, particularly while taking safety constraints into account. This review aims at summarizing and categorizing previous research on learning-based MPC, i.e., the integration or combination of MPC with learning methods, for which we consider three main categories. Most of the research addresses learning for automatic improvement of the prediction model from recorded data. There is, however, also an increasing interest in techniques to infer the parameterization of the MPC controller, i.e., the cost and constraints, that lead to the best closed-loop performance. Finally, we discuss concepts that leverage MPC to augment learning-based controllers with constraint satisfaction properties.
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Wu, Xintao y Daniel Barbará. "Learning missing values from summary constraints". ACM SIGKDD Explorations Newsletter 4, n.º 1 (junio de 2002): 21–30. http://dx.doi.org/10.1145/568574.568579.
Texto completoTesis sobre el tema "Learning from Constraints"
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Giannini, Francesco. "On the Integration of Logic and Learning". Doctoral thesis, Università di Siena, 2019. http://hdl.handle.net/11365/1072603.
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A key point in the success of machine learning, and in particular deep learning, has been the availability of high-performance computing architectures allowing to process a large amount of data. However, this potentially prevents a wider application of machine learning in real world applications, where the
collection of training data is often a slow and expensive process, requiring an extensive human intervention. This suggests to look at possible ways to overcome this limitation, for instance injecting prior knowledge into a learning problem to express some desired behaviors for the functions to be learned.
In this thesis, we consider the case of prior knowledge expressed by means of first-order logic formulas to be integrated into a learning problem. In particular, at first the formulas are converted into real-valued functions by means of t-norm fuzzy logic operators. Thereafter, a loss component (a constraint) is assigned to any function representing a formula and all these components are aggregated (e.g. summed) together with other possible loss components, e.g. a regularization term or some loss components associated to supervisions, if they are available for the functions to be learned. Both the functional representation of a formula and the mapping into a loss component have been investigated, and some theoretical results are discussed to get an insight on how to bring some benefits for different learning schemata. In particular we
define a fragment of Lukasiewicz logic that guarantees to yield convex functional constraints given any knowledge base made of first-order logic formulas. The convexity of these constraints is exploited to formulate collective classification as a quadratic optimization problem and some experimental results are discussed. In addition, we extend classic Support Vector Machines with logical constraints, still preserving quadratic programming resolution. Since formulas may be logically depending on each other, some of the constraints may turn out to be unnecessary with respect to the learning process. This suggests to generalize the notion of support vector to support constraint, and we provide both logical and algebraic criteria to determine the constraints that are unnecessary. Finally we present LYRICS, a general interface implemented in TensorFlow to integrate both deep learning architectures and a first-order logic representation of knowledge for a learning problem. In particular, we show several learning tasks that may be addressed in LYRICS, with a special discussion for the case of visual generation.
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Gritsenko, Artem. "Learning From Demonstrations in Changing Environments: Learning Cost Functions and Constraints for Motion Planning". Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/1246.
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"We address the problem of performing complex tasks for a robot operating in changing environments. We propose two approaches to the following problem: 1) define task-specific cost functions for motion planning that represent path quality by learning from an expert's preferences and 2) using constraint-based representation of the task inside learning from demonstration paradigm. In the first approach, we generate a set of paths for a given task using a motion planner and collect data about their features (path length, distance from obstacles, etc.). We provide these paths to an expert as a set of pairwise comparisons. We then form a ranking of the paths from the expert's comparisons. This ranking is used as training data for learning algorithms, which attempt to produce a cost function that maps path feature values to a cost that is consistent with the expert's ranking. We test our method on two simulated car-maintenance tasks with the PR2 robot: removing a tire and extracting an oil filter. We found that learning methods which produce non-linear combinations of the features are better able to capture expert preferences for the tasks than methods which produce linear combinations. This result suggests that the linear combinations used in previous work on this topic may be too simple to capture the preferences of experts for complex tasks. In the second approach, we propose to introduce a constraint-based description of the task that can be used together with the motion planner to produce the trajectories. The description is automatically created from the demonstration by performing segmentation and extracting constraints from the motion. The constraints are represented with the Task Space Regions (TSR) that are extracted from the demonstration and used to produce a desired motion. To account for the parts of the motion where constraints are different a segmentation of the demonstrated motion is performed using TSRs. The proposed approach allows performing tasks on robot from human demonstration in changing environments, where obstacle distribution or poses of the objects could change between demonstration and execution. The experimental evaluation on two example motions was performed to estimate the ability of our approach to produce the desired motion and recover a demonstrated trajectory."
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MAIETTINI, ELISA. "From Constraints to Opportunities: Efficient Object Detection Learning for Humanoid Robots". Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1005891.
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Reliable perception and efficient adaptation to novel conditions are priority skills for robots that function in ever-changing environments. Indeed, autonomously operating in real world scenarios raises the need of identifying different context’s states and act accordingly. Moreover, the requested tasks might not be known a-priori, requiring the system to update on-line. Robotic platforms allow to gather various types of perceptual information due to the multiple sensory modalities they are provided with. Nonetheless, latest results in computer vision motivate a particular interest in visual perception. Specifically, in this thesis, I mainly focused on the object detection task since it can be at the basis of more sophisticated capabilities.
The vast advancements in latest computer vision research, brought by deep learning methods, are appealing in a robotic setting. However, their adoption in applied domains is not straightforward since adapting them to new tasks is strongly demanding in terms of annotated data, optimization time and computational resources. These requirements do not generally meet current robotics constraints. Nevertheless, robotic platforms and especially
humanoids present opportunities that can be exploited. The sensors they are provided with represent precious sources of additional information. Moreover, their embodiment in the workspace and their motion capabilities allow for a natural interaction with the environment.
Motivated by these considerations, in this Ph.D project, I mainly aimed at devising and developing solutions able to integrate the worlds of computer vision and robotics, by focusing on the task of object detection. Specifically, I dedicated a large amount of effort in alleviating state-of-the-art methods requirements in terms of annotated data and training time, preserving their accuracy by exploiting robotics opportunity.
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Howard, Matthew. "Learning control policies from constrained motion". Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3972.
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Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the task or the environment. Constraints are usually unobservable and frequently change between contexts. In this thesis, we explore the problem of learning control policies from data containing variable, dynamic and non-linear constraints on motion. We show that an effective approach for doing this is to learn the unconstrained policy in a way that is consistent with the constraints. We propose several novel algorithms for extracting these policies from movement data, where observations are recorded under different constraints. Furthermore, we show that, by doing so, we are able to learn representations of movement that generalise over constraints and can predict behaviour under new constraints. In our experiments, we test the algorithms on systems of varying size and complexity, and show that the novel approaches give significant improvements in performance compared with standard policy learning approaches that are naive to the effect of constraints. Finally, we illustrate the utility of the approaches for learning from human motion capture data and transferring behaviour to several robotic platforms.
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Abdukalikova, Anara. "Machine Learning assisted system for the resource-constrained atrial fibrillation detection from short single-lead ECG signals". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71457.
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An integration of ICT advances into a conventional healthcare system is spreading extensively nowadays. This trend is known as Electronic health or E-Health. E-Health solutions help to achieve the sustainability goal of increasing the expected lifetime while improving the quality of life by providing a constant healthcare monitoring. Cardiovascular diseases are one of the main killers yearly causing approximately 17.7 million deaths worldwide. The focus of this work is on studying the detection of one of the cardiovascular diseases – Atrial Fibrillation (AF) arrhythmia. This type of arrhythmia has a severe influence on the heart health conditions and could cause congestive heart failure (CHF), stroke, and even increase the risk of death. Therefore, it is important to detect AF as early as possible. In this thesis we focused on studying various machine learning techniques for AF detection using only short single lead Electrocardiography recordings. A web-based solution was built as a final prototype, which first simulates the reception of a recorded signal, conducts the preprocessing, makes a prediction of the AF presence, and visualizes the result. For the AF detection the relatively high accuracy score was achieved comparable to the one of the state-of-the-art. The work was based on the investigation of the proposed architectures and the usage of the database of signals from the 2017 PhysioNet/CinC Challenge. However, an additional constraint was introduced to the original problem formulation, since the idea of a future deployment on the resource-limited devices places the restrictions on the complexity of the computations being performed for achieving the prediction. Therefore, this constraint was considered during the development phase of the project.
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Boyd, Adriane Amelia. "Detecting and Diagnosing Grammatical Errors for Beginning Learners of German: From Learner Corpus Annotation to Constraint Satisfaction Problems". The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1325170396.
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Graziani, Lisa. "Constrained Affective Computing". Doctoral thesis, 2021. http://hdl.handle.net/2158/1238365.
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Emotions have an important role in daily life, influence decision-making, human interaction, perception, attention, self-regulation. They have been studied since ancient times, philosophers have been always interested in analyzing human nature and bodily sensations, psychologists in studying the physical and psychological changes that influence thought and behavior. In the early 1970s, the psychologist Paul Ekman defined six universal emotions, namely anger, disgust, fear, happiness, sadness, and surprise. This categorization has been taken into account for several studies. In the late 1990s, Affective Computing was born, a new discipline spanning between computer science, psychology, and cognitive science. Affective Computing aims at developing intelligent systems able to recognize, interpret, process, and simulate human emotions. It has a wide range of applications, as healthcare, education, games, entertainment, marketing, automated driver assistance, robotics, and many others. Emotions can be detected from different channels, such as facial expressions, body gestures, speech, text, physiological signals. In order to enrich human-machine interaction, the machine should be able to perform tasks similar to humans, such as recognizing facial expressions, detecting emotions from what it is said (text) and from how it said (audio), and it should be able also to express its own emotions.
With the great success of deep learning, deep architectures have been employed also for many Affective Computing tasks. In this thesis, thinking about an emotional and intelligent agent, a detailed study of emotions has been carried out using deep learning techniques for various tasks, such as facial expression recognition, text and speech emotion recognition, and facial expression generation.
Nevertheless, deep learning methods to properly perform in general require a great computing power and large collections of labeled data. To overcome these limitations we exploit the framework of Learning from Constraints, which needs few supervised data and enables to exploit a great quantity of unsupervised data, which are easier to collect. Furthermore, such approach integrates low-level tasks processing sensorial data and reasoning using higher-level semantic knowledge, so allowing machines to behave in an intelligent way in real complex environments. These conditions are reached requiring the satisfaction of a set of constraints during the learning process. In this way a task is translated into a constrained satisfaction problem. In our case, considering that knowledge could not be always perfect, the constraints are softly injected into the learning problem, so allowing some slight violations for some inputs.
In this work different constraints have been employed in order to exploit knowledge that we have on the problem. In facial expression recognition, a predictor that detects emotions from the full face is enforced by three coherence constraints. One exploits the temporal sequence of the expression, another relates different face sub-parts (eyes, nose, mouth, eyebrows, jaw), and the last relates two feature representations. In text emotion recognition First Order Logic (FOL)-based constraints are used to exploit a great quantity of unlabeled data and data labeled with Facebook reactions. In facial expression generation cyclic-consistency FOL constraints are employed to translate a neutral face into a specific expression, and other logical rules are used to decide what emotion to generate putting together inputs coming from different channels. Finally, some logical constraints are proposed to develop a system that recognizes emotion from speech, and we built an Italian dataset that might be helpful to implement such model.
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"Deep Learning Approaches for Inferring Collective Macrostates from Individual Observations in Natural and Artificial Multi-Agent Systems Under Realistic Constraints". Doctoral diss., 2020. http://hdl.handle.net/2286/R.I.63087.
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abstract: A complex social system, whether artificial or natural, can possess its macroscopic properties as a collective, which may change in real time as a result of local behavioral interactions among a number of agents in it. If a reliable indicator is available to abstract the macrolevel states, decision makers could use it to take a proactive action, whenever needed, in order for the entire system to avoid unacceptable states or con-verge to desired ones. In realistic scenarios, however, there can be many challenges in learning a model of dynamic global states from interactions of agents, such as 1) high complexity of the system itself, 2) absence of holistic perception, 3) variability of group size, 4) biased observations on state space, and 5) identification of salient behavioral cues. In this dissertation, I introduce useful applications of macrostate estimation in complex multi-agent systems and explore effective deep learning frameworks to ad-dress the inherited challenges. First of all, Remote Teammate Localization (ReTLo)is developed in multi-robot teams, in which an individual robot can use its local interactions with a nearby robot as an information channel to estimate the holistic view of the group. Within the problem, I will show (a) learning a model of a modular team can generalize to all others to gain the global awareness of the team of variable sizes, and (b) active interactions are necessary to diversify training data and speed up the overall learning process. The complexity of the next focal system escalates to a colony of over 50 individual ants undergoing 18-day social stabilization since a chaotic event. I will utilize this natural platform to demonstrate, in contrast to (b), (c)monotonic samples only from “before chaos” can be sufficient to model the panicked society, and (d) the model can also be used to discover salient behaviors to precisely predict macrostates.Dissertation/Thesis
Doctoral Dissertation Computer Science 2020
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Koo, Hahn. "Change in the adult phonological processing system by learning non-adjacent phonotactic constraints from brief experience : an experimental and computational study /". 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3301171.
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Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.Source: Dissertation Abstracts International, Volume: 69-02, Section: A, page: 0591. Advisers: Richard W. Sproat; Jennifer S. Cole. Includes bibliographical references (leaves 132-143) Available on microfilm from Pro Quest Information and Learning.
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Zembrzuski, Dariusz. "Reduction Processes in Phonetics-Phonology Interface in Polish: An Analysis from the Perspective of Current Phonological American Theories". Doctoral thesis, 2018. https://depotuw.ceon.pl/handle/item/2687.
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This dissertation provides an analysis of reduction processes targeting Polish
homorganic sequences of affricates + fricatives, in particular the alveolar sequence [ʦs] and the post-alveolar sequence [t͡šš]. These clusters have a long fricative phase, which consists of the fricative portion of an affricate and a full fricative phoneme. This long phase is dispreferred pre-consonantally. For example, the cluster [ʦs] is reduced in grójecki /grujɛʦ + ski/ → [grujɛʦki] (adj. from [grujɛʦ], a Polish place name), whereas the cluster [t͡šš] is reduced in the casual pronunciation of trzcina [t͡ššʨina] → [t͡šʨina] ‘cane’. Consequently, the clusters of affricates + fricatives are reduced to affricates. However, such reduction presents a challenge to categorical phonology because there is no mechanism to simplify the long fricative phase. Potentially degemination could eliminate the pre-consonantal length distinction, but the rule cannot target the long fricative phase due to the fact that the phase does not constitute a geminate in phonological sense. Reduction is motivated on phonetic grounds, but it displays features of a phonological rule. Consequently, the dissertation analyses reduction processes at the interface between phonetics and phonology.
This dissertation is structured as follows. Chapter 1 provides a theoretical background for the discussion of reduction at the phonetics-phonology interface. Because the expectation is that [ʦs] and [t͡šš] reduction processes exhibit similar phonological and phonetic behaviour to other deletion/reduction processes in Polish, the chapter provides a discussion of two phonological deletion rules: Degemination and Strident Deletion with their phonostylistic equivalents. Moreover, the chapter argues for the monosegmental representation of affricates in order to exclude degemination as a viable solution to reduction. Chapter 2 presents the results of a study, which investigated the behaviour of [t͡šš] clusters in a variety of contexts, in two dialects of Polish with different intensity of reduction (high reduction in Silesian Polish and low reduction in Standard Polish). The motivation for the study was the lack of consistent and complete data in the existing literature. Chapter 3 provides theoretical approaches to the reduction of [t͡šš] clusters and argues that reduction in Silesian Polish is of phonological character. Consequently, intensive reduction is analysed within the framework of Standard Optimality Theory, whereas phonetic reduction in Standard Polish, due to its gradience, receives an analysis within Articulatory Phonology. Chapter 4 discusses the reduction of [ʦs] clusters. Unlike [t͡šš] reduction in Silesian Polish, [ʦs] reduction does not take place post-consonantally. Consequently, a different solution is offered which combines the models of Optimality Theory and Articulatory Phonology by making crucial use of gestural constraints. The dissertation also addresses the issue of free variation in reduction processes by employing the Gradual Learning Algorithm. Chapter 5 recapitulates this dissertation in the form of conclusions, highlighting the importance of the phonetics-phonology interface in reduction processes.
Niniejsza rozprawa przedstawia analizę procesów redukcji spółgłoskowej w polskich zbitkach homorganicznych spółgłosek zwarto-szczelinowych i szczelinowych, w szczególności dziąsłowej zbitki [ʦs] oraz zadziąsłowej zbitki [t͡šš]. Te zbitki zawierają długą porcję głoski szczelinowej, która składa się z części głoski zwarto-szczelinowej oraz z pełnej głoski szczelinowej. Ta długa porcja jest skracana w kontekście przed spółgłoską. Na przykład, zbitka [ʦs] jest skracana w grójecki /grujɛʦ + ski/ → [grujɛʦki] (od Grójec [grujɛʦ]), a zbitka [t͡šš] jest skracana w potocznej wymowie słowa trzcina [t͡ššʨina] → [t͡šʨina]. Zatem zbitki głosek zwarto-szczelinowych i szczelinowych są upraszczane do głosek zwarto-szczelinowych. Jednakże taka redukcja stanowi problem dla kategorialnej fonologii, w obrębie której nie ma mechanizmu upraszczającego długą porcję głoski szczelinowej. Potencjalnie degeminacja mogłaby usunąć rozróżnienie długości dźwięków w kontekście przed spółgłoską, ale ta reguła nie dotyczy długiej porcji głoski szczelinowej, ponieważ ów porcja nie stanowi fonologicznej geminaty. Redukcja jest motywowana fonetycznie, jednakowoż prezentując cechy reguły fonologicznej. W związku z powyższym, niniejsza rozprawa analizuje procesy redukcji w interfejsie pomiędzy fonetyką a fonologią. Rozprawa ma następującą strukturę. Rozdział 1 przedstawia podłoże teoretyczne do dyskusji nad redukcją w interfejsie fonetyczno-fonologicznym. Ze względu na przypuszczenie, że redukcje zbitek [ʦs] i [t͡šš] przejawiają podobne zachowanie fonologiczne i fonetyczne względem innych procesów redukcji / elizji w polskim, rozdział przedstawia dyskusję na temat dwóch procesów elizji: degeminacji i elizji spirantów wraz z ich fonostylistycznymi odpowiednikami. Ponadto, rozdział przyjmuje monosegmentalną interpretację głosek zwarto-szczelinowych, aby wykluczyć degeminację jako potencjalne rozwiązanie problemu redukcji. Rozdział 2 przedstawia wyniki eksperymentu, który miał na celu zbadanie zachowania zbitek [t͡šš] w różnych fonetycznych kontekstach, w dwóch dialektach języka polskiego zróżnicowanych pod względem stopnia redukcji (intensywna redukcja w śląskim dialekcie i słaba redukcja w standardowej polszczyźnie). Badanie motywowane było deficytem danych o redukcji oraz brakiem spójnej i całkowitej analizy redukcji w dotychczasowej literaturze. Rozdział 3 przedstawia rozwiązania teoretyczne dla redukcji zbitek [t͡šš] i argumentuje za fonologiczną naturą redukcji w śląskim dialekcie. Zatem intensywna redukcja jest analizowana w Teorii Optymalności, podczas gdy fonetyczna słaba redukcja w standardowej polszczyźnie jest analizowana w Fonologii Artykulacyjnej. Rozdział 4 przedstawia zagadnienie redukcji zbitek [ʦs], które nie występują w kontekście po spółgłosce, w przeciwieństwie do redukcji zbitek [t͡šš] w dialekcie śląskim. Zatem przedstawione jest inne rozwiązanie, które łączy elementy Teorii Optymalności i Fonologii Artykulacyjnej poprzez zastosowanie warunków struktury gestów (gestural constraints). Rozprawa omawia również zagadnienie wariancji w procesach redukcji poprzez zastosowanie Algorytmu Stopniowego Uczenia się (Gradual Learning Algorithm). Rozdział 5 podsumowuje rozprawę, podkreślając znaczenie interfejsu fonetyczno-fonologicznego w procesach redukcji.
Libros sobre el tema "Learning from Constraints"
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Wiltshire, Caroline R. Emergence of the Unmarked in Indian Englishes with Different Substrates. Editado por Markku Filppula, Juhani Klemola y Devyani Sharma. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199777716.013.007.
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This study uses data from Indian English as a second language, spoken by speakers of five first languages, to illustrate and evaluate the role of the emergence of the unmarked (TETU) in phonological theory. The analysis focusses on word-final consonant devoicing and cluster reduction, for which the five Indian first languages have various constraints, while Indian English is relatively unrestricted. Variation in L2 Indian Englishes results from both transfer of L1 phonotactics and the emergence of the unmarked, accounted for within Optimality Theory. The use of a learning algorithm also allows us to test the relative importance of markedness and frequency and to evaluate the relative markedness of various clusters. Thus, data from Indian Englishes provides insight into the form and function of markedness constraints, as well as the mechanisms of Second Language Acquisition (SLA).
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Olfert, C. M. M. Practical Truth and Learning from Pleasure. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190281007.003.0005.
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In Chapter 5, I argue that my account of practical truth illuminates the way practical reason learns about what is good for us. I begin with a constraint on rational learning based on my account of practical truth: because the proper function of practical reason is to be concerned with practical truth, practical reason will only learn from what it takes to be possible sources of practical truth. What, then, might be an appropriate starting point for practical reason’s learning? I argue that experiences of pleasure fit the bill. For Aristotle, experiences of pleasure have a special sort of content—roughly the content “X is good” (without qualifications). This means that the contents of our experiences of pleasure can be practically true or practically false. So, precisely because experiences of pleasure are possible sources of practical truth, they can be suitable, informative foundations for our rational learning about the practical good.
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Button, Chris, Ludovic Seifert, Jia Yi Chow, Duarte Araújo y Keith Davids. Dynamics of Skill Acquisition. 2a ed. Human Kinetics, 2021. http://dx.doi.org/10.5040/9781718214125.
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Dynamics of Skill Acquisition, Second Edition, provides an analysis of the processes underlying human skill acquisition. As the first text to outline the multidisciplinary ecological dynamics framework for understanding movement behavior, this heavily updated edition stays on the cutting edge, with principles of nonlinear pedagogy and methodologies from the constraints-led approach. Students and practitioners across a variety of professions—including coaches, physical educators, trainers, and rehabilitation specialists—will appreciate the applied focus of this second edition. Movement models throughout the text provide examples for visualizing task constraints and enhancing the study and understanding of movement behavior. Athletes and sports teams are presented as specific complex adaptive systems, with information on designing learning environments and adapting programs to foster skill development. Readers will learn the historical evolution of dynamical systems theory and the ecological dynamics framework. These foundational concepts illustrate the integration between intentional action, cognition, and decision making and their effects on performance and behavior. Complex theoretical concepts are explained in simple terms and related to practice, focusing on the implications of the work of pioneering researchers such as Nikolai Bernstein, Egon Brunswik, James Gibson, Scott Kelso, and Karl Newell. Case studies written by practitioners contain specific examples of the ecological dynamics framework in action, bringing theory to life. By learning how to identify and manipulate key constraints that influence learning skilled behavior, readers will gain insight into practice designs for creating positive learning experiences that enable individuals to develop and learn functional movements. Throughout the book, learning features guide readers through material with clear direction and focus to improve understanding. Spotlight on Research sidebars provide detailed descriptions of important studies to connect theory, research, and application. Lab activities teach application skills beyond the content, ensuring reader understanding. In addition, chapter objectives, self-test questions, and Key Concept sidebars highlight important concepts in each chapter. With the study of human movement now bridging many disciplines, including motor development, psychology, biology, and physical therapy, Dynamics of Skill Acquisition, Second Edition, provides a timely analysis of the ecological dynamics framework and presents a comprehensive model for understanding how coordination patterns are assembled, controlled, and acquired. The theoretical roots and development of the ecological dynamics framework provide application strategies for all people with an interest in movement coordination and control. AUDIENCE An upper-level undergraduate or graduate textbook for courses in human movement and skill acquisition. A professional reference for movement practitioners and scientists, including teachers, coaches, trainers, physical educators, physical therapists, rehabilitation specialists, sport scientists, psychologists, biomechanists, sport analysts and physiologists.
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Houdé, Olivier y Grégoire Borst, eds. The Cambridge Handbook of Cognitive Development. Cambridge University Press, 2022. http://dx.doi.org/10.1017/9781108399838.
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How does cognition develop in infants, children and adolescents? This handbook presents a cutting-edge overview of the field of cognitive development, spanning basic methodology, key domain-based findings and applications. Part One covers the neurobiological constraints and laws of brain development, while Part Two covers the fundamentals of cognitive development from birth to adulthood: object, number, categorization, reasoning, decision-making and socioemotional cognition. The final Part Three covers educational and school-learning domains, including numeracy, literacy, scientific reasoning skills, working memory and executive skills, metacognition, curiosity-driven active learning and more. Featuring chapters written by the world's leading scholars in experimental and developmental psychology, as well as in basic neurobiology, cognitive neuroscience, computational modelling and developmental robotics, this collection is the most comprehensive reference work to date on cognitive development of the twenty-first century. It will be a vital resource for scholars and graduate students in developmental psychology, neuroeducation and the cognitive sciences.
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Mills, Caitlin, Arianne Herrera-Bennett, Myrthe Faber y Kalina Christoff. Why the Mind Wanders. Editado por Kalina Christoff y Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.42.
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This chapter offers a functional account of why the mind—when free from the demands of a task or the constraints of heightened emotions—tends to wander from one topic to another, in a ceaseless and seemingly random fashion. We propose the default variability hypothesis, which builds on William James’s phenomenological account of thought as a form of mental locomotion, as well as on recent advances in cognitive neuroscience and computational modeling. Specifically, the default variability hypothesis proposes that the default mode of mental content production yields the frequent arising of new mental states that have heightened variability of content over time. This heightened variability in the default mode of mental content production may be an adaptive mechanism that (1) enhances episodic memory efficiency through de-correlating individual episodic memories from one another via temporally spaced reactivations, and (2) facilitates semantic knowledge optimization by providing optimal conditions for interleaved learning.
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Yu, Angela J. Bayesian Models of Attention. Editado por Anna C. (Kia) Nobre y Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.025.
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Traditionally, attentional selection has been thought of as arising naturally from resource limitations, with a focus on what might be the most apt metaphor, e.g. whether it is a ‘bottleneck’ or ‘spotlight’. However, these simple metaphors cannot account for the specificity, flexibility, and heterogeneity of the way attentional selection manifests itself in different behavioural contexts. A recent body of theoretical work has taken a different approach, focusing on the computational needs of selective processing, relative to environmental constraints and behavioural goals. They typically adopt a normative computational framework, incorporating Bayes-optimal algorithms for information processing and action selection. This chapter reviews some of this recent modelling work, specifically in the context of attention for learning, covert spatial attention, and overt spatial attention.
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Ali, Saleem H. Earthly Order. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780197640272.001.0001.
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How does natural order in the universe connect to social and political order on Earth? Pragmatically, answering such a fundamental question is key for society to confront global environmental and economic challenges. Earthly Order tackles this grand question of human inquiry for a broad audience through coverage of foundational knowledge for scientific literacy of the general public. The book argues that the complexity of current planetary processes requires us to embrace a hybrid form of order in which natural emergence from chaos and human technological innovations are intertwined. As a systems scientist and geographer, the author brings together his personal journey of intellectual growth from his childhood years in Pakistan to his studies in the natural sciences and environmental planning in the United States to weave a rich tapestry of learning that informs the parameters of global sustainability conversations. While resisting the temptation of environmental determinism, the narrative sets forth natural constraints and hierarchies under which trajectories of social, economic, and political systems must be charted to maintain an “earthly order” wherein humanity can thrive.
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Hardt, Heidi. Dilemmas in Design. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190672171.003.0003.
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Chapter 3 constitutes the first of four empirical chapters. The chapter describes NATO’s learning infrastructure, explains how the design of such infrastructure constrains knowledge-sharing of strategic errors and reveals which types of actions NATO elites consider to be errors. The chapter begins by explaining the research design of conducting structured interviews with 120 NATO elites across North America and Europe. The sections that follow present findings from interviews about what elites consider to be strategic errors, how they categorize them and what are examples. The chapter explores how consensus in IOs, such as NATO, constrains elite behaviour in knowledge-sharing. Moreover, NATO’s formal learning processes present disincentives for elites to employ them. Drawing on quotes from NATO civilian and military elites, the chapter identifies these disincentives, describes a lack of incentives to report strategic errors, as well as a lack of incentives for reading the formal lessons learned documents that are produced.
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Tessier, Anne-Michelle. Morpho-phonological Acquisition. Editado por Jeffrey L. Lidz, William Snyder y Joe Pater. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199601264.013.7.
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The chapter reports a current, basic understanding of the nature and facts of acquiring morpho-phonology—what needs to be learned, how it is observed to be learned, and how these observations might be explained by theories of learning. It begins with adult typological data that address the question of how much of a language’s alternations can or cannot be predicted from its phonology alone and summarizes how this typology is treated, especially in rule-based and constraint-based phonological grammars. The second section presents some empirical observations about morpho-phonological acquisition and considers several important views of how alternations are acquired.
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Wilson, Robyn S., Sarah M. McCaffrey y Eric Toman. Wildfire Communication and Climate Risk Mitigation. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.570.
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Throughout the late 19th century and most of the 20th century, risks associated with wildfire were addressed by suppressing fires as quickly as possible. However, by the 1960s, it became clear that fire exclusion policies were having adverse effects on ecological health, as well as contributing to larger and more damaging wildfires over time. Although federal fire policy has changed to allow fire to be used as a management tool on the landscape, this change has been slow to take place, while the number of people living in high-risk wildland–urban interface communities continues to increase. Under a variety of climate scenarios, in particular for states in the western United States, it is expected that the frequency and severity of fires will continue to increase, posing even greater risks to local communities and regional economies.Resource managers and public safety officials are increasingly aware of the need for strategic communication to both encourage appropriate risk mitigation behavior at the household level, as well as build continued public support for the use of fire as a management tool aimed at reducing future wildfire risk. Household decision making encompasses both proactively engaging in risk mitigation activities on private property, as well as taking appropriate action during a wildfire event to protect personal safety. Very little research has directly explored the connection between climate-related beliefs, wildfire risk perception, and action; however, the limited existing research suggests that climate-related beliefs have little direct effect on wildfire-related action. Instead, action appears to depend on understanding the benefits of different mitigation actions and in engaging the public in interactive, participatory communication programs that build trust between the public and natural resource managers. A relatively new line of research focuses on resource managers as critical decision makers in the risk management process, pointing to the need to thoughtfully engage audiences other than the lay public to improve risk management.Ultimately, improving the decision making of both the public and managers charged with mitigating the risks associated with wildfire can be achieved by carefully addressing several common themes from the literature. These themes are to (1) promote increased efficacy through interactive learning, (2) build trust and capacity through social interaction, (3) account for behavioral constraints and barriers to action, and (4) facilitate thoughtful consideration of risk-benefit tradeoffs. Careful attention to these challenges will improve the likelihood of successfully managing the increasing risks that wildfire poses to the public and ecosystems alike in a changing climate.
Capítulos de libros sobre el tema "Learning from Constraints"
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Gori, Marco. "Learning from Constraints". En Machine Learning and Knowledge Discovery in Databases, 6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23780-5_5.
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Chou, Glen, Dmitry Berenson y Necmiye Ozay. "Learning Constraints from Demonstrations". En Springer Proceedings in Advanced Robotics, 228–45. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44051-0_14.
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De Bie, Tijl, Johan Suykens y Bart De Moor. "Learning from General Label Constraints". En Lecture Notes in Computer Science, 671–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27868-9_73.
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Shcherbatyi, Iaroslav y Bjoern Andres. "Convexification of Learning from Constraints". En Lecture Notes in Computer Science, 79–90. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45886-1_7.
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Bortolussi, Luca y Guido Sanguinetti. "Learning and Designing Stochastic Processes from Logical Constraints". En Quantitative Evaluation of Systems, 89–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40196-1_7.
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Deligne, Sabine, François Yvon y Frédéric Bimbot. "Introducing statistical dependencies and structural constraints in variable-length sequence models". En Grammatical Interference: Learning Syntax from Sentences, 156–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0033351.
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Li, Changshuo y Dmitry Berenson. "Learning Object Orientation Constraints and Guiding Constraints for Narrow Passages from One Demonstration". En Springer Proceedings in Advanced Robotics, 197–210. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50115-4_18.
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Furtak, Erin Marie y Kelsey Tayne. "Affordances and Constraints of Learning Progression Designs in Supporting Formative Assessment". En Contributions from Science Education Research, 241–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17219-0_15.
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Jezdimirovic Ranito, Jovana. "Letting Go of Neoliberal Constraints: Learning from the Regulatory Process". En Regulating US Private Security Contractors, 65–95. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11241-7_3.
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Fumagalli, Mattia, Tiago Prince Sales y Giancarlo Guizzardi. "Mind the Gap!: Learning Missing Constraints from Annotated Conceptual Model Simulations". En Lecture Notes in Business Information Processing, 64–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91279-6_5.
Texto completoActas de conferencias sobre el tema "Learning from Constraints"
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Kostinger, M., M. Hirzer, P. Wohlhart, P. M. Roth y H. Bischof. "Large scale metric learning from equivalence constraints". En 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6247939.
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Moon, In-Ho y Kevin Harer. "Learning from constraints for formal property checking". En 2009 IEEE International High Level Design Validation and Test Workshop (HLDVT). IEEE, 2009. http://dx.doi.org/10.1109/hldvt.2009.5340176.
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Hoi, Steven C. H., Rong Jin y Michael R. Lyu. "Learning nonparametric kernel matrices from pairwise constraints". En the 24th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1273496.1273542.
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Yang, Tianbao, Rong Jin y Anil K. Jain. "Learning kernel combination from noisy pairwise constraints". En 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319813.
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Baghshah, Mahdieh Soleymani y Saeed Bagheri Shouraki. "Efficient Kernel Learning from Constraints and Unlabeled Data". En 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.821.
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Owens, Trevor, Kate Saenko, Ayan Chakrabarti, Ying Xiong, Todd Zickler y Trevor Darrell. "Learning object color models from multi-view constraints". En 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995705.
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Masui, Toshiyuki. "Evolutionary learning of graph layout constraints from examples". En the 7th annual ACM symposium. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/192426.192468.
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Mohseni-Kabir, Anahita, Charles Rich y Sonia Chernova. "Learning partial ordering constraints from a single demonstration". En HRI'14: ACM/IEEE International Conference on Human-Robot Interaction. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2559636.2559809.
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Kyriakis, Panagiotis, Jyotirmoy V. Deshmukh y Paul Bogdan. "Learning from Demonstrations under Stochastic Temporal Logic Constraints". En 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867861.
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Sacca, C., M. Diligenti, M. Gori y M. Maggini. "Learning to Tag from Logic Constraints in Hyperlinked Environments". En 2011 Tenth International Conference on Machine Learning and Applications (ICMLA 2011). IEEE, 2011. http://dx.doi.org/10.1109/icmla.2011.156.
Texto completoInformes sobre el tema "Learning from Constraints"
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Groh, Micah. Constraints on Neutrino Oscillation Parameters from Neutrinos and Antineutrinos with Machine Learning. Office of Scientific and Technical Information (OSTI), enero de 2021. http://dx.doi.org/10.2172/1774291.
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Levy, Brian. How Political Contexts Influence Education Systems: Patterns, Constraints, Entry Points. Research on Improving Systems of Education (RISE), diciembre de 2022. http://dx.doi.org/10.35489/bsg-rise-2022/pe04.
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This paper synthesises the findings of a set of country studies commissioned by the RISE Programme to explore the influence of politics and power on education sector policymaking and implementation. The synthesis groups the countries into three political-institutional contexts: Dominant contexts, where power is centred around a political leader and a hierarchical governance structure. As the Vietnam case details, top-down leadership potentially can provide a robust platform for improving learning outcomes. However, as the case studies of Ethiopia, Indonesia, Nigeria, and Tanzania illustrate, all-too-often dominant leaders’ goals vis-à-vis the education sector can veer in other directions. In impersonal competitive contexts, a combination of strong formal institutions and effective processes of resolving disagreements can, on occasion, result in a shared commitment among powerful interests to improve learning outcomes—but in none of the case studies is this outcome evident. In Peru, substantial learning gains have been achieved despite messy top-level politics. But the Chilean, Indian, and South African case studies suggest that the all-too-common result of rule-boundedness plus unresolved political contestation over the education sector’s goals is some combination of exaggerated rule compliance and/or performative isomorphic mimicry. Personalised competitive contexts (Bangladesh, Ghana, and Kenya for example) lack the seeming strengths of either their dominant or their impersonal competitive contexts; there are multiple politically-influential groups and multiple, competing goals—but no credible framework of rules to bring coherence either to political competition or to the education bureaucracy. The case studies show that political and institutional constraints can render ineffective many specialised sectoral interventions intended to improve learning outcomes. But they also point to the possibility that ‘soft governance’ entry points might open up some context-aligned opportunities for improving learning outcomes. In dominant contexts, the focus might usefully be on trying to influence the goals and strategies of top-level leadership. In impersonal competitive contexts, it might be on strengthening alliances between mission-oriented public officials and other developmentally-oriented stakeholders. In personalised competitive contexts, gains are more likely to come from the bottom-up—via a combination of local-level initiatives plus a broader effort to inculcate a shared sense among a country’s citizenry of ‘all for education’.
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Levy, Brian. How Political Contexts Influence Education Systems: Patterns, Constraints, Entry Points. Research on Improving Systems of Education (RISE), diciembre de 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/122.
Texto completoResumen
This paper synthesises the findings of a set of country studies commissioned by the RISE Programme to explore the influence of politics and power on education sector policymaking and implementation. The synthesis groups the countries into three political-institutional contexts: Dominant contexts, where power is centred around a political leader and a hierarchical governance structure. As the Vietnam case details, top-down leadership potentially can provide a robust platform for improving learning outcomes. However, as the case studies of Ethiopia, Indonesia, Nigeria, and Tanzania illustrate, all-too-often dominant leaders’ goals vis-à-vis the education sector can veer in other directions. In impersonal competitive contexts, a combination of strong formal institutions and effective processes of resolving disagreements can, on occasion, result in a shared commitment among powerful interests to improve learning outcomes—but in none of the case studies is this outcome evident. In Peru, substantial learning gains have been achieved despite messy top-level politics. But the Chilean, Indian, and South African case studies suggest that the all-too-common result of rule-boundedness plus unresolved political contestation over the education sector’s goals is some combination of exaggerated rule compliance and/or performative isomorphic mimicry. Personalised competitive contexts (Bangladesh, Ghana, and Kenya for example) lack the seeming strengths of either their dominant or their impersonal competitive contexts; there are multiple politically-influential groups and multiple, competing goals—but no credible framework of rules to bring coherence either to political competition or to the education bureaucracy. The case studies show that political and institutional constraints can render ineffective many specialised sectoral interventions intended to improve learning outcomes. But they also point to the possibility that ‘soft governance’ entry points might open up some context-aligned opportunities for improving learning outcomes. In dominant contexts, the focus might usefully be on trying to influence the goals and strategies of top-level leadership. In impersonal competitive contexts, it might be on strengthening alliances between mission-oriented public officials and other developmentally-oriented stakeholders. In personalised competitive contexts, gains are more likely to come from the bottom-up—via a combination of local-level initiatives plus a broader effort to inculcate a shared sense among a country’s citizenry of ‘all for education’.
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Dedeken, Chiara y Kevin Osborne. Repatriating FTFs from Syria: Learning from the Western Balkans. RESOLVE Network, octubre de 2021. http://dx.doi.org/10.37805/pn2021.23.wb.
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Four countries in the Western Balkan region (Kosovo, Bosnia and Herzegovina, North Macedonia, and Montenegro) are in the top ten countries with the most foreign terrorist fighters (FTFs) per capita. The political will to repatriate FTFs remains strong, at least in the Western Balkans, despite delays in 2020 due to COVID-19. In other parts of the world, especially high-income countries, political will to repatriate is considerably lower. COVID-19 has further constrained nations in their efforts to repatriate law-abiding citizens, which is less controversial than FTF families. Based on discussions with government officials and security officers in the Western Balkans as well as international experts and donors, this policy note provides operational recommendations to move forward with repatriation, rehabilitation, and reintegration of returnees building on lessons from repatriations in Albania, Kosovo, and North Macedonia. It urges governments globally to double down on repatriation efforts and to call on experience from governments in the Balkans to bring back their FTFs now. The recommendations in this policy note are relevant to any country where political will to repatriate FTFs can be generated.
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Carpenter, Marie y William Lazonick. The Pursuit of Shareholder Value: Cisco’s Transformation from Innovation to Financialization. Institute for New Economic Thinking Working Paper Series, febrero de 2023. http://dx.doi.org/10.36687/inetwp202.
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Once the global leader in telecommunication systems and the Internet, over the past two decades the United States has fallen behind global competitors, and in particular China, in mobile communication infrastructure—specifically 5G and Internet of Things (IoT). This national failure, with the socioeconomic and geopolitical tensions that it creates, is not due to a lack of US government investment in the knowledge required for the mobility revolution. Nor is it because of a dearth of domestic demand for the equipment, devices, and applications that can make use of this infrastructure. Rather, the problem is the dereliction of key US-based business corporations to take the lead in making the investments in organizational learning required to generate cutting edge communication-infrastructure products. No company in the United States exemplifies this deficiency more than Cisco Systems, the business corporation founded in Silicon Valley in 1984 that had explosive growth in the 1990s to become the foremost global enterprise-networking equipment producer in the Internet revolution. This paper provides in-depth analysis of Cisco’s organizational failure, attributing it ultimately to the company’s turn from innovation in the last decades of 20th century to financialization in the early decades of the 21st century. Since 2001, Cisco’s top management has chosen to allocate corporate cash to open-market share repurchases— aka stock buybacks—for the purpose of giving manipulative boosts to the company stock price rather than make the investments in organizational learning required to become a world leader in communication-infrastructure equipment for the era of 5G and IoT. From October 2001 through October 2022, Cisco spent $152.3 billion—95 percent of its net income over the period—on stock buybacks for the purpose of propping up its stock price. These funds wasted in pursuit of “maximizing shareholder value” were on top of the $55.5 billion that Cisco paid out to shareholders in dividends, representing an additional 35 percent of net income. In this paper, we trace how Cisco grew from a Silicon Valley startup in 1984 to become, through its innovative products, the world leader in enterprise-networking equipment over the next decade and a half. As the company entered the 21st century, building on its dominance of enterprise-networking, Cisco was positioned to upgrade its technological capabilities to become a major infrastructureequipment vendor to service providers. We analyze how and why, when the Internet boom turned to bust in 2001, the organizational structure that enabled Cisco to dominate enterprise networking posed constraints related to manufacturing and marketing on the company’s growth in the more sophisticated infrastructure-equipment segment. We then document how from 2002 Cisco turned from innovation to financialization, as it used its ample profits to do stock buybacks to prop up its stock price. Finally, we ponder the larger policy implications of Cisco’s turn from innovation to financialization for the competitive position of the US information-and-communication technology (ICT) industry in the global economy.
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Varina, Hanna B., Viacheslav V. Osadchyi, Kateryna P. Osadcha, Svetlana V. Shevchenko y Svitlana H. Lytvynova. Peculiarities of cloud computing use in the process of the first-year students' adaptive potential development. [б. в.], junio de 2021. http://dx.doi.org/10.31812/123456789/4453.
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Technologies based on cloud computing is one of the demanded and actively developing areas of the modern information world. Cloud computing refers to an innovative technology that allows you to combine IT resources of various hardware platforms into a single whole and provide the user with access to them via a local network or the global Internet. Cloud services from various providers offer users access to their resources via the Internet via free or shareware cloud applications, the hardware and software requirements of which do not imply that the user has high-performance and resource-consuming computers. Cloud technologies represent a new way of organizing the educational process and offers an alternative to traditional methods of organizing the educational process, creates an opportunity for personal learning, collective teaching, interactive classes, and the organization of psychological support. The scientific article is devoted to the problem of integrating cloud technologies not only in the process of training highly qualified specialists, but also in the formation of professionally important personality traits. The article describes the experience of introducing cloud technologies into the process of forming the adaptive potential of students in conditions of social constraints caused by the COVID-19 pandemic.
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Hwa, Yue-Yi, Sharon Kanthy Lumbanraja, Usha Adelina Riyanto y Dewi Susanti. The Role of Coherence in Strengthening CommunityAccountability for Remote Schools in Indonesia. Research on Improving Systems of Education (RISE), febrero de 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/090.
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Incoherence in accountability relationships can hamper the quality of education. Such incoherence can be a particular challenge in resource-constrained, remote villages where teachers tend to have higher educational capital and social status than the parents and communities that they serve. We analyze quantitative and qualitative data from a randomized controlled trial of a social accountability mechanism (SAM) for schools in remote Indonesian villages. The intervention had three treatment arms, all of which included the SAM, which engaged village-level stakeholders in a consensus-building process that led to joint service agreements for supporting the learning process. Prior analyses have found that all three treatment arms significantly improved student learning, but the treatment arm combining the SAM with performance pay based on camera-monitored teacher attendance led to much larger gains than the SAM-only treatment or the treatment arm combining the SAM with teacher performance pay based on a community-evaluated scorecard. Drawing on a range of quantitative data sources across all treatment schools (process monitoring, survey, and service agreement indicators) and qualitative data from nine case study schools (interviews and focus group discussions), we show firstly that the student learning gains across all three treatment arms were accompanied by increases in the coherence of the accountability relationships between village-level stakeholders, and in the degree to which these relationships were oriented toward the purpose of cultivating learning. We further show that the treatment combining SAM with camera-monitored teacher performance pay led to greater improvements in the coherence of accountability relationships than the other treatment arms, because the cameras improved both the technical capacity and the social legitimacy of community members to hold teachers accountable. This coherence-focused, relational explanation for the relative effectiveness of the treatment arms has more explanatory power than alternative explanations that focus narrowly on information quality or incentive structure. Our analysis reinforces arguments for ensuring that accountability structures are coherent with the local context, including local social structures and power dynamics.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak y Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, julio de 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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de Caritat, Patrice, Brent McInnes y Stephen Rowins. Towards a heavy mineral map of the Australian continent: a feasibility study. Geoscience Australia, 2020. http://dx.doi.org/10.11636/record.2020.031.
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Heavy minerals (HMs) are minerals with a specific gravity greater than 2.9 g/cm3. They are commonly highly resistant to physical and chemical weathering, and therefore persist in sediments as lasting indicators of the (former) presence of the rocks they formed in. The presence/absence of certain HMs, their associations with other HMs, their concentration levels, and the geochemical patterns they form in maps or 3D models can be indicative of geological processes that contributed to their formation. Furthermore trace element and isotopic analyses of HMs have been used to vector to mineralisation or constrain timing of geological processes. The positive role of HMs in mineral exploration is well established in other countries, but comparatively little understood in Australia. Here we present the results of a pilot project that was designed to establish, test and assess a workflow to produce a HM map (or atlas of maps) and dataset for Australia. This would represent a critical step in the ability to detect anomalous HM patterns as it would establish the background HM characteristics (i.e., unrelated to mineralisation). Further the extremely rich dataset produced would be a valuable input into any future machine learning/big data-based prospectivity analysis. The pilot project consisted in selecting ten sites from the National Geochemical Survey of Australia (NGSA) and separating and analysing the HM contents from the 75-430 µm grain-size fraction of the top (0-10 cm depth) sediment samples. A workflow was established and tested based on the density separation of the HM-rich phase by combining a shake table and the use of dense liquids. The automated mineralogy quantification was performed on a TESCAN® Integrated Mineral Analyser (TIMA) that identified and mapped thousands of grains in a matter of minutes for each sample. The results indicated that: (1) the NGSA samples are appropriate for HM analysis; (2) over 40 HMs were effectively identified and quantified using TIMA automated quantitative mineralogy; (3) the resultant HMs’ mineralogy is consistent with the samples’ bulk geochemistry and regional geological setting; and (4) the HM makeup of the NGSA samples varied across the country, as shown by the mineral mounts and preliminary maps. Based on these observations, HM mapping of the continent using NGSA samples will likely result in coherent and interpretable geological patterns relating to bedrock lithology, metamorphic grade, degree of alteration and mineralisation. It could assist in geological investigations especially where outcrop is minimal, challenging to correctly attribute due to extensive weathering, or simply difficult to access. It is believed that a continental-scale HM atlas for Australia could assist in derisking mineral exploration and lead to investment, e.g., via tenement uptake, exploration, discovery and ultimately exploitation. As some HMs are hosts for technology critical elements such as rare earth elements, their systematic and internally consistent quantification and mapping could lead to resource discovery essential for a more sustainable, lower-carbon economy.
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TEACHING-LEARNING BASED OPTIMIZATION METHOD CONSIDERING BUCKLING AND SLENDERNESS RESTRICTION FOR SPACE TRUSSES. The Hong Kong Institute of Steel Construction, marzo de 2022. http://dx.doi.org/10.18057/ijasc.2022.18.1.3.
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The structural performance of a building is a function of several parameters and constraints whose association may offer non unique solutions which, however, meet the design requirements. Therefore, an optimization routine is needed to determine the best solution within the set of available alternatives. In this study, the TLBO method was implemented for weight-based optimization of space trusses. The algorithm applies restrictions related to the critical buckling load as well as the slenderness ratio, which are the basis to obtain reliable and realistic results. To assess the capability of the TLBO method, two reference cases and a transmission tower are subjected to the optimization analysis. In the transmission tower analysis, however, a more realistic approach is adopted as it also considers, through a safety factor, the plastic behavior in the critical buckling load constraint. With no optimization, the ideal weight increases by 101.36% when the critical buckling load is considered in the first two cases, which is consistent with the expected behavior. If the slenderness of the elements is also restricted, the ideal weight now rises by 300.78% from the original case and by 99.04% from the case where only the critical buckling load restriction is applied. Now, considering the critical buckling load and slenderness restriction with the TLBO method applied, a 18.28% reduction in the ideal weight is verified. In fact, the proposed optimization procedure converged to a better solution than that of the reference study, which is based on the genetic algorithms method.