Consistent Set Of Features Research Articles

Single unit electrophysiology recordings and computational modeling can predict octopus arm movement.

The octopus simplified nervous system holds the potential to reveal principles of motor circuits and improve brain-machine interface devices through computational modeling with machine learning and statistical analysis. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100ms after stimulation were predictive of the resultant movement response. Computational models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. Deep learning models and unsupervised dimension reduction identified a consistent set of features that could be used to distinguish different types of arm movements. These models generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit.

Urban Space Insights Extraction Using Acoustic Histogram Information

Urban data mining can be identified as a highly potential area that can enhance the smart city services toward better sustainable development especially in the urban residential activity tracking. While existing human activity tracking systems have demonstrated the capability to unveil the hidden aspects of citizens' behavior, they often come with a high implementation cost and require a large communication bandwidth. In this article, we study the implementation of low-cost analog sound sensors to detect outdoor activities and estimate the raining period in an urban residential area. The analog sound sensors are transmitted to the cloud every 5 min in histogram format, which consists of sound data sampled every 100 ms (10 Hz). We then use wavelet transformation and principal component analysis to generate a more robust and consistent feature set from the histogram. After that, we performed unsupervised clustering and attempt to understand the individual characteristics of each cluster to identify outdoor residential activities. In addition, on-site validation has been conducted to show the effectiveness of our approach.

Optical Remote Sensing Image Registration Using Spatial-Consistency and Average Regional Information Divergence Minimization via Quantum-Behaved Particle Swarm Optimization

Due to invariance to significant intensity differences, similarity metrics have been widely used as criteria for an area-based method for registering optical remote sensing image. However, for images with large scale and rotation difference, the robustness of similarity metrics can greatly determine the registration accuracy. In addition, area-based methods usually require appropriately selected initial values for registration parameters. This paper presents a registration approach using spatial consistency (SC) and average regional information divergence (ARID), called spatial-consistency and average regional information divergence minimization via quantum-behaved particle swarm optimization (SC-ARID-QPSO) for optical remote sensing images registration. Its key idea minimizes ARID with SC to select an ARID-minimized spatial consistent feature point set. Then, the selected consistent feature set is tuned randomly to generate a set of M registration parameters, which provide initial particle warms to implement QPSO to obtain final optimal registration parameters. The proposed ARID is used as a criterion for the selection of consistent feature set, the generation of initial parameter sets, and fitness functions used by QPSO. The iterative process of QPSO is terminated based on a custom-designed automatic stopping rule. To evaluate the performance of SC-ARID-QPSO, both simulated and real images are used for experiments for validation. In addition, two data sets are particularly designed to conduct a comparative study and analysis with existing state-of-the-art methods. The experimental results demonstrate that SC-ARID-QPSO produces better registration accuracy and robustness than compared methods.

Polarimetric SAR Image Semantic Segmentation With 3D Discrete Wavelet Transform and Markov Random Field

Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper.With a newly defined channelwise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels.

Disillusionment: a prototype analysis

ABSTRACT Disillusionment is acknowledged to be a painful process with important personal and social consequences. However, scientific conceptualisations of the experience are inconsistent. Across four studies, we examined whether lay conceptions of disillusionment produce a consistent pattern of features. In Study 1 (N = 204), we extracted 19 features of disillusionment from open-ended participant definitions. In Study 2 (N = 131), participants rated the centrality of these features and indicated that features such as discovery, disappointment, and loss, were highly representative, while features such as hopelessness, orientation, and truth, were more peripheral. In two further studies, we used experimental designs to test the diagnosticity of these features. In Study 3 (N = 155), participants rated vignettes descriptions as more disillusioning when they were based on more, rather than less, prototypical disillusionment features. Given that disappointment is a feature of disillusionment, we conducted Study 4 (N = 60) to test whether the extracted features effectively distinguish disillusionment from disappointment. Overall, we found evidence to suggest that disillusionment contains a consistent set of features, and represents a state of negative epistemic affect associated with the violation of core assumptions. These results create avenues for research on disillusionment, its antecedents and its consequences.

Recognition of emotion in music based on deep convolutional neural network

In the domain of music information retrieval, emotion based classification is an active area of research. Emotion being a perceptual and subjective concept, the task is quite challenging. It is very difficult to design signal based descriptors to represent emotions. In this work deep leaning network is proposed and experiment is done with benchmark datasets namely, Soundtracks, Bi-Modal and MER_taffc. Experiment has also been done with hand crafted descriptor consisting of different time domain and spectral features, linear predictive coding and MFCC based features. Different classifiers like, neural network, support vector machine and random forest are tried. Although the combined feature set with neural network provides an optimal result for the datasets, but in general the performance of such approaches is limited. It is difficult to obtain a consistent feature set that works across the classifier and datasets. To get rid of the issue of feature design, deep learning based approach is followed. A convolutional neural network built around VGGNet and a novel post-processing technique are proposed. Proposed methodology provides substantial improvement of performance for the datasets. Comparison with other reported works on three different datasets also establishes the superiority of the proposed methodology. The improvement in performance has been substantiated by Z test.

Ad hoc categories and false memories: Memory illusions for categories created on-the-spot.

Three experiments were designed to test whether experimentally created ad hoc associative networks evoke false memories. We used the DRM (Deese, Roediger, McDermott) paradigm with lists of ad hoc categories composed of exemplars aggregated toward specific goals (e.g., going for a picnic) that do not share any consistent set of features. Experiment 1 revealed considerable levels of false recognitions of critical words from ad hoc categories. False recognitions occurred even when the lists were presented without an organizing theme (i.e., the category's label). Experiments 1 and 2 tested whether (a) the ease of identifying the categories' themes, and (b) the lists' backward associative strength could be driving the effect. List identifiability did not correlate with false recognition, and the effect remained even when backward associative strength was controlled for. Experiment 3 manipulated the distractor items in the recognition task to address the hypothesis that the salience of unrelated items could be facilitating the occurrence of the phenomenon. The effect remained when controlling for this source of facilitation. These results have implications for assumptions made by theories of false memories, namely the preexistence of associations in the activation-monitoring framework and the central role of gist extraction in fuzzy-trace theory, while providing evidence of the occurrence of false memories for more dynamic and context-dependent knowledge structures. (PsycINFO Database Record

A Fast and Accurate Feature Selection Algorithm Based on Binary Consistency Measure

Consistency‐based feature selection is an important category of feature selection research, and its advantage over other categories is due to consistency measures used to include the effect of interaction among features into evaluation of relevance of features. Even if features individually appear irrelevant to class labels, they can collectively show strong relevance. In such cases, we say that the features interact with each other. Consistency measures, in this regard, evaluate the collective relevance of a set of features and has been intuitively understood as a metric to measure a distance of an arbitrary feature set from the state of being consistent: A set of features is said to be consistent if, and only if, they as a whole determine class labels. In history, the binary consistency measure, which returns the value 1 if the feature set is consistent and 0 otherwise, was the first consistency measure introduced, and many advanced measures followed. The problem of the binary measure consists in the fact that it always returns 1 if a data set includes no consistent feature set. The measures that followed have solved this problem but sacrificed time efficiency of evaluation. Therefore, feature selection leveraging these measures are not fast enough to apply to large data sets. In this article, we aim to improve time efficiency of consistency‐based feature selection. To achieve the goal, we propose a new idea, which we call data set denoising: We eliminate examples which are viewed as noises from a data set until the data set becomes to include consistent feature sets and then apply the binary measure to find an appropriate feature set that is consistent. In our evaluation through intensive experiments, CWC, a new algorithm that implements data set denoising outperformed in both time efficiency and accuracy the benchmark consistency‐based algorithms. Specifically, CWC was about 31 times faster than the LCC that had been known as the fastest in the literature. Furthermore, in a comparison including feature selection algorithms that are not consistency‐based, CWC has turned out to be one of the fastest and the most accurate feature selection algorithms.

Text Categorization of Biomedical Data Sets Using Graph Kernels and a Controlled Vocabulary

Recently, graph representations of text have been showing improved performance over conventional bag-of-words representations in text categorization applications. In this paper, we present a graph-based representation for biomedical articles and use graph kernels to classify those articles into high-level categories. In our representation, common biomedical concepts and semantic relationships are identified with the help of an existing ontology and are used to build a rich graph structure that provides a consistent feature set and preserves additional semantic information that could improve a classifier's performance. We attempt to classify the graphs using both a set-based graph kernel that is capable of dealing with the disconnected nature of the graphs and a simple linear kernel. Finally, we report the results comparing the classification performance of the kernel classifiers to common text-based classifiers.

Structured interview data on 35 cases of dissociative identity disorder in Turkey

The aim of this study was to describe the clinical features of dissociative identity disorder in a group of Turkish patients as assessed with a structured interview. Thirty-five consecutive patients at the dissociative disorders program of a university psychiatric clinic who met the DSM-IV criteria for dissociative identity disorder were included in this study. The subjects were assessed with the Turkish versions of the Dissociative Disorders Interview Schedule and the Dissociative Experiences Scale. A descriptive analysis of the clinical features was carried out. Most (88.6%) of the patients were women. The mean age of the group was 22.8 years. Childhood physical or sexual abuse was reported by 77.1% of the patients. The mean Dissociative Experiences Scale score was 49.1. The patients reported an average of 12.5 somatic symptoms, 6.2 Schneiderian symptoms, 10.0 secondary features of the disorder, 3.8 borderline personality disorder criteria, and 4.1 extrasensory experiences. Dissociative identity disorder has a stable, consistent set of features throughout North America, in the Netherlands, and in Turkey. Cross-cultural research using standardized assessment measures will be invaluable in further illumination of the validity of this widely neglected psychiatric category.

Structured interview data on 102 cases of multiple personality disorder from four centers

Patients with multiple personality disorder (N = 102) at four different centers were interviewed with the Dissociative Disorders Interview Schedule. The presenting characteristics of the patients at all four centers were very similar. The clinical profile that emerged included a history of childhood physical and/or sexual abuse in 97 (95.1%) of the cases. The subjects reported an average of 15.2 somatic symptoms, 6.4 Schneiderian symptoms, 10.2 secondary features of the disorder, 5.2 borderline personality disorder criteria, and 5.6 extrasensory experiences; their average score on the Dissociative Experiences Scale was 41.4. The results indicate that multiple personality disorder has a stable, consistent set of features.

Effects of gender and topic on speech style

Male and female subjects described nine photographs that had been selected to be of interest to males, females, or both. The resulting speech samples were analyzed for the incidence of five features of word production and five features associated with a “women's language” (WL) speech style. Results showed that topic (photograph type) determined aspects of speech production. However, there were no gender differences in production or style. Correlations between dependent variables suggested that WL is a consistent set of features. Its use by both female and male speakers may reflect social uncertainty.

Duns Scotus's Rejection of 'Necessarily Exists' as a Predicate

Duns Scotus's Rejection of 'Necessarily Exists' as a Predicate CHARLES F. KIELKOPF THIS ISA RECONSTRUCTIONof an argument for the sixth conclusion of chapter three (3.23) of Duns Scotus's Treatise About the First Principle: ~it is the characteristic of but one nature to have necessary being of itself. The argument in 3.24 for this conclusion is reconstructed and shown to provide an argument that 'necessarily exists' is not a predicate because necessary existence cannot be a feature occurring in different natures. Slight changes in the reconstructed argument convert it into an argument that 'exists' is not a predicate because existence cannot be a feature occurring in different natures. Since neither necessary existence nor existence can be common features, problems are raised about the claim in 3.25 that "nothing is more perfect than a being having necessary existence of itself," and about Scotus's acceptance in 4.65 of a slightly retouched version of Anselm's Ontological Argument. By dismissing these problems I show that the fact that 'exists' and 'necessarily exists' are not predicates is irrelevant to ontological arguments. It is suggested that a necessary being necessarily has more perfections than any other being, although its existence and mode of existence are not features of it, let alone perfections of it. Similarly, it is suggested that the nature of an existing being has more perfections than any nature that is not the nature of an existing being but that, still, existence is not one of the features--let alone anything that can be called a perfection--making up the nature. Let us turn to a reconstruction of the 3.24 argument. Presentation of the second premise will be followed by some terminological stipulations. For reductio assume (1) N, and N2 are two different natures of necessary beings. (2) If N1 and N2 are two different natures of necessary beings, then there is a feature NE of existing necessarily that is common to both NI and N~. The second premise comes from the first four lines. We are not going to worry about the truth of the second premise. Indeed, Scotus's subsequent argument seems to show that existing necessarily cannot be a feature of a nature. Thus the subsequent argument may be strong enough to show that the whole of (2) is unacceptable because it is based on a mistaken analysis of what it would be for two distinct The basicideasof this paper werepresentedto the John CarrollSecondOhioConferenceon Medieval Studies, Cleveland, Ohio, October, 1975. 1Trans. A. Wolter,O.F.M. (Chicago:FranciscanHeraldPress, 1966).Thisis a translationof a revised Latin text of De primo principio and two related questionsfroman earlycommentaryon the Sentences. Hereafter, all passages from Scotusare from this book. Sections3.23and 3.24are appendedto this paper. Referencesto lineswillbe to the translationof 3.24, unless otherwisespecified. [13] 14 HISTORY OF PHILOSOPHY natures to be natures of necessary beings. However, as we shall see, the argument reconstructed below can be readily converted into a reductio argument against the consequent of (2). This reductio argument is of most interest because it is so readily transformed into an argument that 'necessarily exists' is not a predicate. For appreciation of the reconstructed argument, however, the terminology of (2) needs explanation (which I hope to provide by reference to 3.24). Let us use 'nature' very broadly to mean any consistent set of features. A feature in a nature shows what, among other things, an individual would be if it were to have the nature containing the feature. A feature is an entity that shows what an object is: a feature is an entitas quidditativa, as in line 3 of the Latin. A justification for using 'nature' so broadly is the fact that Scotus himself goes on in the next few lines to classify the alleged common feature of line 3 as a common nature. However, I am not classing all natures as features or entitates quidditativae. For instance, a set of features could be so large or complex that we could not know what is in it. Let us develop a notion of distinctive nature from "distinguuntur formalitatibus suis actualibus ultimis" of line 5. A distinctive nature DN, formalitas actualis ultima, is...