What's the subject? Brexit and politics as articulation

This paper focuses on the moment of Brexit and its political aftermath in order to challenge dominant academic and popular conceptions of the political subject as singular and coherent. Instead, we suggest that there is an urgent political and analytical need for a view of the subject as multiple, contradictory, and dialogic. As interdisciplinary scholars working on the borders of policy studies and cultural studies, we think this is a critical site for transdisciplinary conversations about such conceptions of the subject. In the political field, such subjects are selectively and unevenly addressed and mobilized by political projects—such as Vote Leave—that invite them to recognize themselves as part of an imagined collective identity. In the twin disturbances of the European Union referendum and the 2017 general election, we suggest that it is possible to see that other voicings, other identifications, and other projects remain possible. Specific political mobilizations are neither singular nor stable.     

Body representations and cognitive ontology: Drawing the boundaries of the body image

The distinction between body image and body schema has been incredibly influential in cognitive neuroscience. Recently, researchers have begun to speculate about the relationship between these representations (Gadsby, 2017, 2018; Pitron & de Vignemont, 2017; Pitron et al., 2018). Within this emerging literature, Pitron et al. (2018) proposed that the long-term body image and long-term body schema co-construct one another, through a process of reciprocal interaction. In proposing this model, they make two assumptions: that the long-term body image incorporates the spatial characteristics of tools, and that it is distorted in the case of Alice in wonderland syndrome. Here, I challenge these assumptions, with a closer examination of what the term “long-term body image” refers to. In doing so, I draw out some important taxonomic principles for research into body representation.     

Metric error monitoring in the numerical estimates

Recent studies have shown that participants can keep track of the magnitude and direction of their errors while reproducing target intervals (Akdoğan & Balcı, 2017) and producing numerosities with sequentially presented auditory stimuli (Duyan & Balcı, 2018). Although the latter work demonstrated that error judgments were driven by the number rather than the total duration of sequential stimulus presentations, the number and duration of stimuli are inevitably correlated in sequential presentations. This correlation empirically limits the purity of the characterization of “numerical error monitoring”. The current work expanded the scope of numerical error monitoring as a form of “metric error monitoring” to numerical estimation based on simultaneously presented array of stimuli to control for temporal correlates. Our results show that numerical error monitoring ability applies to magnitude estimation in these more controlled experimental scenarios underlining its ubiquitous nature.     

Children's scale errors: A by‐product of lexical development?

Scale errors occur when young children seriously attempt to perform an action on an object which is impossible due to its size. Children vary substantially in the incidence of scale errors with many factors potentially contributing to these differences, such as age and the type of scale errors. In particular, the evidence for an inverted U‐shaped curve of scale errors involving the child's body (i.e., body scale errors), which would point to a developmental stage, is mixed. Here we re‐examine how body scale errors vary with age and explore the possibility that these errors would be related to the size and properties of children's lexicon. A large sample of children aged 18–30 months (N = 125) was tested in a scale error elicitation situation. Additionally, parental questionnaires were collected to assess children's receptive and expressive lexicon. Our key findings are that scale errors linearly decrease with age in childhood, and are more likely to be found in early talkers rather than in less advanced ones. This suggests that scale errors do not correspond to a developmental stage, and that one determinant of these errors is the speed of development of the linguistic and conceptual system, as a potential explanation for the individual variability in prevalence.     

A discriminated similarity matrix construction based on sparse subspace clustering algorithm for hyperspectral imagery

The clustering of hyperspectral images is a challenging task because of the high dimensionality of the data. Sparse subspace clustering (SSC) algorithm is one of the popularly used clustering algorithm for high dimensionality data. However, SSC has not fully used the spectral and spatial information during similarity matrix construction based on single sparse representation coefficient for hyperspectral Imagery (HSI) clustering. In this paper, two novel similarity matrix construction methods named as Cosine-Euclidean similarity matrix (abbreviated as CE) and Cosine-Euclidean dynamic weighting similarity matrix (abbreviated as CEDW) are proposed for HSI clustering. They can combine the high spectral information and rich spatial information. Firstly, CE utilizes the cosine similarity of spectral information based on overall sparse representation vectors and classical Euclidean distance of spatial information to construct a novel similarity matrix. Secondly, inheriting CE merits, dynamic weighting adjustment method is introduced to CEDW for some external influence factors to the HSI information. Several experiments on HSI demonstrated that the proposed algorithms are effective for HSI clustering.     

Diagnosis of Parkinson’s disease using modified grey wolf optimization

This paper presents the Modified Grey Wolf Optimization (MGWO) algorithm which helps with the identification of the symptoms of Parkinson’s disease at a premature stage. Parkinson disease is kind of a movement malady, which if not cured timely can prove to be fatal.Thus it becomes significant to identify Parkinson’s disease at its premature phase so proper medications can provide longevity to patient by controlling the symptoms. In this work, a new model named Modified Grey Wolf Optimization (MGWO) has been proposed grounded on the traditional Grey Wolf Optimizer (GWO), which acts as a search strategy for feature selection. GWO is a meta-heuristic algorithm which is enthused by hunt down behavior of wolves. Random forest, k-nearest neighbor classifier and decision tree espy on selected features. The proposed model is evaluated using various types of datasets of voice, handwriting (spiral and meander) and speech. The put forward algorithm helps in the prediction of Parkinson disease with an estimated accuracy of 94.83%, detection rate of 98.28%, false alarm rate of 16.03% and further aid the individuals to receive a functional treatment at an early stage. The proposed bio-inspired algorithm is stable enough to find out the optimal subset of features. At last the results derived from the evaluation of proposed algorithm on datasets are compared with the results of Optimized Cuttlefish Algorithm (OCFA). The experimental results depict that the proposed algorithm helps in maximizing the accurateness and minimizing the number of features selected.     

An efficient cervical disease diagnosis approach using segmented images and cytology reporting

Cervical cancer is the second most common cancer in women globally. A computer aided cervical disease diagnosis system that can relieve pressure on medical experts and save the cost is proposed. To implement our approach in the reality of cervical diseases diagnosis, a multi-modal framework is designed for three kinds of cervical diseases diagnosis that integrates uterine cervix images, Thinprep Cytology Test, human papillomavirus test, and patients’ age. However, too many features increase memory storage costs and computational costs, and it affects the spread of this system in poor areas. Feature selection not only eliminates redundant or irrelevant features but also finds the factors that influence the disease most first is performed in multi-modal frameworks for cervical diseases diagnosis. The detailed process of the method is as follows: first, according the representative color, an efficient image segmentation algorithm is developed; then from three different types of segmented images, we extract color features and texture features for interpreting uterine cervix images; next, Boruta algorithm is applied to feature selection; finally, the performance of Random Forests that utilizes selected features for cervical disease diagnosis is investigated. In the experiment, the proposed multi-modal diagnostic approach gives the final diagnosis for three different kinds of cervical diseases with 83.1% accuracy, which significantly outperforms methods using any single source of information alone. The validation cohort is applied to validate the efficiency of our method, and the performance of random forest obtained by using only 1.2% of features is like or even better than using 100% of features.     

The man who lost his body: Suboptimal multisensory integration yields body awareness problems after a right temporoparietal brain tumour

Reports on patients who lack ownership over their entire body are extremely rare. Here, we present patient SA who suffered from complete body disownership after a tumour resection in the right temporoparietal cortex. Neuropsychological assessment disclosed selective bilateral ownership problems, despite intact primary visual and somatosensory senses. SA's disownership seems to stem from a suboptimal multimodal integration, as shown by the rubber hand illusion and the beneficial effect during and after simple exercises aiming at multisensory recalibration.     

Modes of spatial coding in the Simon task*

Many models of the Simon effect assume that categorical spatial representations underlie the phenomenon. The present study tested this assumption explicitly in two experiments, both of which involved eight possible spatial positions of imperative stimuli arranged horizontally on the screen. In Experiment 1, the eight stimulus locations were marked with eight square boxes that appeared at the same time during a trial. Results showed gradually increasing Simon effects from the central locations to the outer locations. In Experiment 2, the eight stimulus locations consisted of a combination of three frames of spatial reference (hemispace, hemifield, and position relative to the fixation), with each frame appearing in different timings. In contrast to Experiment 1, results showed an oscillating pattern of the Simon effect across the horizontal positions. These findings are discussed in terms of grouping factors involved in the Simon task. The locations seem to be coded as a single continuous dimension when all are visible at once as in Experiment 1, but they are represented as a combination of the lateral categories (“left” vs. “right”) with multiple frames of reference when the reference frames are presented successively as in Experiment 2.     

外部环境资源短缺对员工薪酬奖励偏好的影响与机制

以往关于时间与金钱奖励选择问题的探讨主要从员工工龄视角着手。但这一视角不能直接解释外部环境资源短缺时, 员工对时间与金钱奖励选择偏好的差异问题。本文依据生命史理论, 探讨外部环境资源(如就业资源和自然资源)短缺与否对员工选择时间和金钱奖励偏好的影响及内在机制。本文以即将步入职场的大学生和企业员工为样本, 发现不管是外在就业资源短缺还是外在自然资源短缺都会促使员工更加偏好金钱而非时间奖励, 内在机制是外部环境资源短缺时人们的心理表征更具体, 并且这一现象在高物质主义者身上更为明显(实验1和2)。本文结尾讨论了对资源短缺、时间和金钱等研究领域的理论突破与推进。