“The contributions of working memory and executive functioning to problem representation and solution generation in algebraic word problems”: Correction.

Reports an error in "The contributions of working memory and executive functioning to problem representation and solution generation in algebraic word problems" by Kerry Lee, Ee Lynn Ng and Swee Fong Ng (Journal of Educational Psychology, 2009[May], Vol 101[2], 373-387). In this article, the URL published for the supplemental material was incorrect. The correct URL is http://dx.doi.org/10.1037/a0013843.supp. (The following abstract of the original article appeared in record 2009-04640-010.) Solving algebraic word problems involves multiple cognitive phases. The authors used a multitask approach to examine the extent to which working memory and executive functioning are associated with generating problem models and producing solutions. They tested 255 11-year-olds on working memory (Counting Recall, Letter Memory, and Keep Track), ability to inhibit inappropriate responses (inhibition: numeric Stroop, Stop Signal), mental flexibility (switching: Number–Letter and Plus–Minus), English literacy, and algebraic problem-solving skills (problem representation, solution generation, and other subcomponents). Working memory explained about a quarter of the variance in both representation and solution formation. Literacy explained an additional 20% of the variance in representation formation. Ability to discern quantitative relationships explained an additional 10%. The findings go beyond a demonstration of an association between working memory and problem-solving accuracy. They show that success in word problems is particularly reliant on ability to decode and assign mathematical operators to quantitative relationships, 2 phases of problem solving that also draw heavily on working memory resources. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A general stochastic clustering method for automatic cluster discovery

Finding clusters in data is a challenging problem. Given a dataset, we usually do not know the number of natural clusters hidden in the dataset. The problem is exacerbated when there is little or no additional information except the data itself. This paper proposes a general stochastic clustering method that is a simplification of nature-inspired ant-based clustering approach. It begins with a basic solution and then performs stochastic search to incrementally improve the solution until the underlying clusters emerge, resulting in automatic cluster discovery in datasets. This method differs from several recent methods in that it does not require users to input the number of clusters and it makes no explicit assumption about the underlying distribution of a dataset. Our experimental results show that the proposed method performs better than several existing methods in terms of clustering accuracy and efficiency in majority of the datasets used in this study. Our theoretical analysis shows that the proposed method has linear time and space complexities, and our empirical study shows that it can accurately and efficiently discover clusters in large datasets in which many existing methods fail to run.     

Crosstalk between Schizophrenia and Metabolic Syndrome: The Role of Oxytocinergic Dysfunction

The high prevalence of metabolic syndrome in persons with schizophrenia has spurred investigational efforts to study the mechanism beneath its pathophysiology. Early psychosis dysfunction is present across multiple organ systems. On this account, schizophrenia may be a multisystem disorder in which one organ system is predominantly affected and where other organ systems are also concurrently involved. Growing evidence of the overlapping neurobiological profiles of metabolic risk factors and psychiatric symptoms, such as an association with cognitive dysfunction, altered autonomic nervous system regulation, desynchrony in the resting-state default mode network, and shared genetic liability, suggest that metabolic syndrome and schizophrenia are connected via common pathways that are central to schizophrenia pathogenesis, which may be underpinned by oxytocin system dysfunction. Oxytocin, a hormone that involves in the mechanisms of food intake and metabolic homeostasis, may partly explain this piece of the puzzle in the mechanism underlying this association. Given its prosocial and anorexigenic properties, oxytocin has been administered intranasally to investigate its therapeutic potential in schizophrenia and obesity. Although the pathophysiology and mechanisms of oxytocinergic dysfunction in metabolic syndrome and schizophrenia are both complex and it is still too early to draw a conclusion upon, oxytocinergic dysfunction may yield a new mechanistic insight into schizophrenia pathogenesis and treatment.     

Coupling studies and shielding techniques for electromagnetic penetration through apertures on complex cavities and vehicular platforms

The multilevel fast multipole moment method is employed to compute the electromagnetic coupling and shielding of various aperture-coupled metallic enclosures. A phenomenological study of electromagnetic coupling, due to various slot shapes and sizes, with or without the presence of wire penetration is conducted. These slots are situated on over-moded cavities and two methods are proposed to mitigate slot coupling into the cavity's interior. As part of this work, we also investigate the coupling through similar slots and apertures within a complex platform such as an automobile. The proposed methods have shown to increase shielding by as much as 5-35 dB within the frequency range of interest.     

Multiwavelength tunable fiber ring laser based on sampled chirp fiber Bragg grating

We propose and experimentally demonstrate a novel configuration of a multiwavelength tunable fiber ring laser with equally increased or decreased wavelength spacing. Wavelength tuning is achieved by tuning the strain gradient of a sampled chirp fiber Bragg grating (SCFBG), which is integrated in the laser cavity. To achieve equally increased or decreased wavelength spacing, the SCFBG is surface-mounted at a slant onto one lateral side of a plate so that each wavelength-selective element of the SCFBG will experience an equal strain increment when an external force is applied to the plate. We experimentally obtain seven lasing wavelengths with equally increased or decreased wavelength spacing.     

Extraction of rules from artificial neural networks for nonlinearregression

Neural networks (NNs) have been successfully applied to solve a variety of application problems including classification and function approximation. They are especially useful as function approximators because they do not require prior knowledge of the input data distribution and they have been shown to be universal approximators. In many applications, it is desirable to extract knowledge that can explain how Me problems are solved by the networks. Most existing approaches have focused on extracting symbolic rules for classification. Few methods have been devised to extract rules from trained NNs for regression. This article presents an approach for extracting rules from trained NNs for regression. Each rule in the extracted rule set corresponds to a subregion of the input space and a linear function involving the relevant input attributes of the data approximates the network output for all data samples in this subregion. Extensive experimental results on 32 benchmark data sets demonstrate the effectiveness of the proposed approach in generating accurate regression rules.     

Neuroadaptive quantized PID sliding‐mode control for heterogeneous vehicular platoon with unknown actuator deadzone

This paper focuses on the problem of neuroadaptive quantized control for heterogeneous vehicular platoon when the follower vehicles suffer from external disturbances, mismatch input quantization, and unknown actuator deadzone. The PID‐based sliding‐mode (PIDSM) control technique is used due to its superior capability to reduce spacing errors and to eliminate the steady‐state spacing errors. Then, a neuroadaptive quantized PIDSM control scheme with minimal learning parameters is designed not only to guarantee the string stability of the whole vehicular platoon and ultimate uniform boundedness of all adaptive law signals but also to attenuate the negative effects caused by external disturbance, mismatch input quantization, and unknown actuator deadzone. Furthermore, optimizing the interspacing between consecutive vehicles is very important to reduce traffic congestion on highways, and a new modified constant time headway policy is proposed to not only increase traffic density but also address the negative effect of nonzero initial spacing, velocity, and acceleration errors. Compared with most existing methods, the proposed method does not linearize the system model and neither requires precise knowledge of the system model. Finally, the effectiveness and advantage of the proposed method are demonstrated by comparative simulation studies.     

Artificial Synapses Based on Multiterminal Memtransistors for Neuromorphic Application

Neuromorphic computing, which emulates the biological neural systems could overcome the high‐power consumption issue of conventional von‐Neumann computing. State‐of‐the‐art artificial synapses made of two‐terminal memristors, however, show variability in filament formation and limited capacity due to their inherent single presynaptic input design. Here, a memtransistor‐based arti?cial synapse is realized by integrating a memristor and selector transistor into a multiterminal device using monolayer polycrys‐talline‐MoS₂ grown by a scalable chemical vapor deposition (CVD) process. Notably, the memtransistor offers both drain‐ and gate‐tunable nonvolatile memory functions, which efficiently emulates the long‐term potentiation/depression, spike‐amplitude, and spike‐timing‐dependent plasticity of biological synapses. Moreover, the gate tunability function that is not achievable in two‐terminal memristors, enables significant bipolar resistive states switching up to four orders‐of‐magnitude and high cycling endurance. First‐principles calculations reveal a new resistive switching mechanism driven by the diffusion of double sulfur vacancy perpendicular to the MoS₂ grain boundary, leading to a conducting switching path without the need for a filament forming process. The seamless integration of multiterminal memtransistors may offer another degree‐of‐freedom to tune the synaptic plasticity by a third gate terminal for enabling complex neuromorphic learning.