A heterosynaptic learning rule for neural networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre-and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean leaxning time increases with the number of patterns to be learned polynomially, indicating efficient learning.

Incremental learning from stream data

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method.

Science Literacy and media Literacy: a missing link?

The study of science in the media is increasingly highlighted within science programmes and represents an authentic context for interdisciplinary collaboration. Yet the literature on ‘media across the curriculum’ makes surprisingly little mention of links to science and cross-curricular approaches to teaching about and with science-based media resources is an area that is under-explored. This research study focuses on science in the news. The project involved 28 teachers from seven schools and brought together science and English teachers to explore collaborative working with the aim of promoting critical engagement with media reports with a science component. Teachers planned, developed and implemented a school-based activity with an emphasis on ‘connected learning’ rather than the compartmentalised learning that tends to accompany the discrete treatment of science matters in science class and media matters in English class. Not only did the project raise teachers’ awareness of science in the media as a potential, purposeful and profitable area for collaborative working, but it demonstrated how the synergy of the different experiences and expertise of science and English teachers produced very varied approaches to a programme of activities with an enhanced capacity to promote criticality in relation to science literacy and media literacy.

Developing students’ ability to engage critically with science in the news: identifying elements of the ‘media awareness’ dimension

Worldwide, science education reform movements are stressing the need to promote ‘scienti?c literacy’ among young people. Increasingly, this is taken to include empowering students to engage critically with science-related news reporting. Despite this requirement now featuring in statutory curricula throughout the UK, there has, to date, been a dearth of research-informed advice to assist science teachers as they identify appropriate instructional objectives in this regard and design relevant learning activities through which these might be achieved. In this study, prominent science communication
scholars, science journalists, science educators and media educators were interviewed to determine what knowledge, skills and habits of mind they judged valuable for individuals reading science-related news stories. Teachers of science and of English from nine secondary schools in Northern Ireland addressed the same issue. A striking – and signi?cant – ?nding of the study was the very substantial number of statements of knowledge, skill and disposition o?ered by participants that relate to ‘media awareness’, an issue largely overlooked in the science education literature. The school-focused phase of the research suggests that cross-curricular approaches involving teachers of science collaborating with those of English/media education or media studies may best serve to address this important curricular goal.

The Critical Reading of the Images Associated with Science-Related News Reports: Establishing a knowledge, skills, and attitudes framework

For the majority of adults, the media constitute their main source of information about science and science-related matters impacting on society. To help prepare young people to engage with science in the media, teachers are being exhorted to equip their students with the knowledge, skills, and attitudes to respond critically to science-related news reports. Typically, such reports comprise not only text, but also visual elements. These images are not simply adjuncts to the written word; they are integral to meaning-making. Though science teachers make considerable use of newspaper images, they tend to view these representations unproblematically, underestimating their potential ambiguity, complexity, and role in framing media messages. They rarely aim to develop students’ ability to ‘read’, critically, such graphics. Moreover, research into how this might be achieved is limited and, consequently, research-informed guidance which could support this instruction is lacking. This paper describes a study designed to formulate a framework for such teaching. Science communication scholars, science journalists and media educators with acknowledged relevant expertise were surveyed to ascertain what knowledge, skills, and attitudes they deemed useful to engagement with science related news images. Their proposals were recast as learning intentions (instructional objectives), and science and English teachers collaborated to suggest which could be addressed with secondary school students and the age group best suited to their introduction. The outcome is an inventory of learning intentions on which teachers could draw to support their planning of instructional sequences aimed at developing students’ criticality in respect of the totality of science news reports.

Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions

This paper presents a new algorithm for learning the structure of a special type of Bayesian network. The conditional phase-type (C-Ph) distribution is a Bayesian network that models the probabilistic causal relationships between a skewed continuous variable, modelled by the Coxian phase-type distribution, a special type of Markov model, and a set of interacting discrete variables. The algorithm takes a dataset as input and produces the structure, parameters and graphical representations of the fit of the C-Ph distribution as output.The algorithm, which uses a greedy-search technique and has been implemented in MATLAB, is evaluated using a simulated data set consisting of 20,000 cases. The results show that the original C-Ph distribution is recaptured and the fit of the network to the data is discussed.

Unexpected allies: advancing literacy in a ‘Science-English’ cross-curricular context

Critical reading of science-based media reports is an authentic context in which to explore the mutual interests of teachers of science and English, who want to use science in the media to promote their subject discipline while encouraging cross-curricular learning. This empirical study focused on 90 teachers of science and English to explore their aptitude and capability for critical reading of science-based news reports. The influences of specialist subject culture and the extent of classroom experience contributed to the distinctive nature of the responses. The study revealed features in critical reading that were characteristic of the subject background of the participants. It suggested approaches to initial teacher education and ongoing professional development that would be mutually beneficial to teachers from different disciplines in promoting among pupils a critical approach to science-related news media.

Robust visual tracking based on online learning sparse representation

Handling appearance variations is a very challenging problem for visual tracking. Existing methods usually solve this problem by relying on an effective appearance model with two features: (1) being capable of discriminating the tracked target from its background, (2) being robust to the target's appearance variations during tracking. Instead of integrating the two requirements into the appearance model, in this paper, we propose a tracking method that deals with these problems separately based on sparse representation in a particle filter framework. Each target candidate defined by a particle is linearly represented by the target and background templates with an additive representation error. Discriminating the target from its background is achieved by activating the target templates or the background templates in the linear system in a competitive manner. The target's appearance variations are directly modeled as the representation error. An online algorithm is used to learn the basis functions that sparsely span the representation error. The linear system is solved via ℓ1 minimization. The candidate with the smallest reconstruction error using the target templates is selected as the tracking result. We test the proposed approach using four sequences with heavy occlusions, large pose variations, drastic illumination changes and low foreground-background contrast. The proposed approach shows excellent performance in comparison with two latest state-of-the-art trackers.

Episodic memory and learning in patients with chronic schizophrenia

This study explored the pattern of memory functioning in 58 patients with chronic schizophrenia and compared their performance with 53 normal controls. Multiple domains of memory were assessed, including verbal and nonverbal memory span, verbal and non-verbal paired associate learning, verbal and visual long-term memory, spatial and non-spatial conditional associative learning, recognition memory and memory for temporal order. Consistent with previous studies, substantial deficits in long-term memory were observed, with relative preservation of memory span. Memory for temporal order and recognition memory was intact, although significant deficits were observed on the conditional associative learning tasks. There was no evidence of lateralized memory impairment. In these respects, the pattern of memory impairment in schizophrenia is more similar in nature to that found in patients with memory dysfunction following mesiotemporal lobe lesions, rather than that associated with focal frontal lobe damage. (C) 1999 Elsevier Science B.V. All rights reserved.

Learning curves, taking instructions, and patient safety:using a theoretical domains framework in an interview study to investigate prescribing errors among trainee doctors

ABSTRACT:

‘Reforming Social Work Education: Some Reflections on the Contribution of Practice Learning’

Social work in the UK is currently undergoing a process of major reform and a wide range of recommendations have been made aimed at improving practice and education. This paper focuses on the Social Work Reform Board's proposals for improving practice learning in qualifying level social work education. It examines how recommendations for better partnership working between Higher Education Institutions and employers and developing critical reflection in agencies are likely to impact on student learning. Drawing on experience of social work education in Northern Ireland it considers the potential of the Reform Board's proposals for improving the quality of practice learning and enhancing students' preparedness for employment. The paper concludes that differences in educational aims and priorities, resistant practice cultures and cut-backs in resourcing could present major obstacles that must be overcome if this potential is to be realised.

Evaluation of sampling methods for learning from imbalanced data

The problem of learning from imbalanced data is of critical importance in a large number of application domains and can be a bottleneck in the performance of various conventional learning methods that assume the data distribution to be balanced. The class imbalance problem corresponds to dealing with the situation where one class massively outnumbers the other. The imbalance between majority and minority would lead machine learning to be biased and produce unreliable outcomes if the imbalanced data is used directly. There has been increasing interest in this research area and a number of algorithms have been developed. However, independent evaluation of the algorithms is limited. This paper aims at evaluating the performance of five representative data sampling methods namely SMOTE, ADASYN, BorderlineSMOTE, SMOTETomek and RUSBoost that deal with class imbalance problems. A comparative study is conducted and the performance of each method is critically analysed in terms of assessment metrics. © 2013 Springer-Verlag.

COD and NH4-N Estimation in the Inflow of Wastewater Treatment Plants using Machine Learning Techniques

The in-line measurement of COD and NH4-N in the WWTP inflow is crucial for the timely monitoring of biological wastewater treatment processes and for the development of advanced control strategies for optimized WWTP operation. As a direct measurement of COD and NH4-N requires expensive and high maintenance in-line probes or analyzers, an approach estimating COD and NH4-N based on standard and spectroscopic in-line inflow measurement systems using Machine Learning Techniques is presented in this paper. The results show that COD estimation using Radom Forest Regression with a normalized MSE of 0.3, which is sufficiently accurate for practical applications, can be achieved using only standard in-line measurements. In the case of NH4-N, a good estimation using Partial Least Squares Regression with a normalized MSE of 0.16 is only possible based on a combination of standard and spectroscopic in-line measurements. Furthermore, the comparison of regression and classification methods shows that both methods perform equally well in most cases.

An Adaptive Machine Learning Decision System for Flexible Predictive Maintenance

Process monitoring and Predictive Maintenance (PdM) are gaining increasing attention in most manufacturing environments as a means of reducing maintenance related costs and downtime. This is especially true in industries that are data intensive such as semiconductor manufacturing. In this paper an adaptive PdM based flexible maintenance scheduling decision support system, which pays particular attention to associated opportunity and risk costs, is presented. The proposed system, which employs Machine Learning and regularized regression methods, exploits new information as it becomes available from newly processed components to refine remaining useful life estimates and associated costs and risks. The system has been validated on a real industrial dataset related to an Ion Beam Etching process for semiconductor manufacturing.