The Effect of Different Scale on Object to the Approximation of the First Order Polarization Tensor of Sphere, Ellipsoid, and Cube

Polarization tensor (PT) is a classical terminology in fluid mechanics and theory of electricity that can describe geometry in a specific boundary domain with different conductivity contrasts. In this regard, the geometry may appear in a different size, and for easy characterizing, the usage of PT to identify particular objects is crucial. Hence, in this paper, the first order polarization tensor for different types of object with a diverse range of sizes are presented. Here, we used three different geometries: sphere, ellipsoid, and cube, with fixed conductivity for each object. The software Matlab and Netgen Mesh Generator are the essential mathematical tools to aid the computation of the polarization tensor. From the analytical results obtained, the first order PT for sphere and ellipsoid depends on the size of both geometries. On the other hand, the numerical investigation is conducted for the first order PT for cube, since there is no analytical solution for the first order PT related to this geometry, to further verify the scaling property of the first order PT due to the scaling on the size of the original related object. Our results agree with the previous theoretical result that the first order polarization tensor of any geometry will be scaled at a fixed scaling factor according to the scaling on the size of the original geometry.

Authors Identification within the Subject Area in the Semantic Library

The peculiarities of the task of authors identifying and determining author's contribution to publications in digital bibliographic codes are considered. The features of the problem of insufficient identification are manifested in the repetition of information, doubling, the presence of authors with completely coincidental names, self-quotation, autoplagiate and plagiarism itself. It is proposed to use publication information that has already been accumulated in the digital library in the form of related object area data and a variety of target thesaurus data, as the author and user of the library. This information contains links whereby keyword contexts, multiple co-authors, and term associations in dictionaries and thesauruses can be used to identify authorship. It is important that an array of scientific publications is considered, since they have an established traditional structure, which allows comparing fixed text elements (annotations, keywords, classifier codes, etc.). Thus, even if the names in the publications are fully matched, the question of authorship can be raised if the publications in the digital library correspond to different subject areas. Resolution of such contradictions is accomplished by evaluating a plurality of links of all elements of secondary publication information. The result of the comparison could be the addition of the author to a specific area, i.e. the extension of the addressee's thesaurus and the author's personal thesaurus, or the appearance of full namesakes in the library, but from different areas of knowledge. It has been shown that modern data analysis tools allow you to evaluate the author's contribution to publication, despite the fact that of course, only the scientific community can evaluate the real contribution to scientific research.

Inferring object properties from human interaction and transferring them to new motions

Humans regularly interact with their surrounding objects. Such interactions often result in strongly correlated motions between humans and the interacting objects. We thus ask: “Is it possible to infer object properties from skeletal motion alone, even without seeing the interacting object itself?” In this paper, we present a fine-grained action recognition method that learns to infer such latent object properties from human interaction motion alone. This inference allows us to disentangle the motion from the object property and transfer object properties to a given motion. We collected a large number of videos and 3D skeletal motions of performing actors using an inertial motion capture device. We analyzed similar actions and learned subtle differences between them to reveal latent properties of the interacting objects. In particular, we learned to identify the interacting object, by estimating its weight, or its spillability. Our results clearly demonstrate that motions and interacting objects are highly correlated and that related object latent properties can be inferred from 3D skeleton sequences alone, leading to new synthesis possibilities for motions involving human interaction. Our dataset is available at http://vcc.szu.edu.cn/research/2020/IT.html.

Processing of subjectively and objectively invisible stimuli in human visual cortex

The study of unconscious processing requires a measure of conscious awareness. Awareness measures can be either subjective (based on participant’s report) or objective (based on perceptual performance). The preferred awareness measure depends on the theoretical position about consciousness, and may influence conclusions about the extent of unconscious processing and about the neural correlates of consciousness. We obtained fMRI measurements from 43 subjects while they viewed masked faces and houses that were either subjectively or objectively invisible. We show that neural representations of objectively invisible faces and houses are limited to visual (shape-related) object properties, while subjectively invisible stimuli are processed up to more abstract, categorical levels of representation. These results demonstrate that the hypothesized extent of unconscious information processing is determined by the measurement approach. Furthermore, our data show that subjective and objective approaches are associated with different neural correlates of consciousness and thus have implications for neural theories of consciousness.

Some verbs related to the object and prepositional clauses: بعض متعلقات الفعل في المفعول به والجار والمجرور

This study aims to give knowledge about some related verbs as there are few studies about this subject particularly in the object and prepositional clauses, the researcher studied some belonging or related (object and prepositional clause), and then the research explained that the object and prepositional clause do not come in advance in the Holy Qur’an unless there is a rhetorical reason required by the anecdotal context, and then if it is mentioned or deleted or brought in advance, it comes as a natural result for the anecdotal context which is found in the Holy Qur’an. If it is mentioned or deleted or advanced in a particular verse, so the scholar must take into consideration the previous verses to conclude that the advancement has a relation with those verses. If it is seen that in deleting or advancing the object has an expansion to the poet or the writer, for that is truly applied in the Holy Qur’an at all, so it is better in studying the advancement of the object or prepositional clause with the atmosphere of the event and the story, and not to adhere to the one-sentence system, which is better to explain the rhetorical images that are contemplated from this advancement.

Autism-Spectrum Traits Predict the Embodiment of Manipulation Knowledge about Object Concepts: Evidence from Eyetracking

Sensorimotor-based theories of cognition predict that even subtle developmental motor differences, such as those found in individuals on the autism spectrum, affect how we represent the meaning of manipulable objects (e.g., faucet). Here, we test 85 participants, who completed the Adult Autism Spectrum Quotient (to measure autism-spectrum characteristics), on a visual world experiment designed to assess conceptual representations of manipulable objects. Participants heard words referring to manually manipulable objects (e.g., faucet) while we recorded their eye movements to arrays of four objects: the named object, a related object typically manipulated similarly (e.g., jar), and two unrelated objects. Consistent with prior work, we observed more looks to the related object than to the unrelated ones (i.e., a manipulation-relatedness effect). This effect has been taken to reflect overlapping conceptual representations of objects sharing manipulation characteristics (e.g., faucet and jar) due to embodied sensorimotor properties being part of their representations. Critically, we observed that as participant-level autism-spectrum characteristics increased, manipulation-relatedness effects became smaller, whereas in control trials that included a shape (instead of manipulation) related object, relatedness effects increased. The results support the hypothesis that differences in object-concept representations on the autism spectrum emerge at least in part via differences in sensorimotor experience.

Dough-Stage Maize (Zea mays L.) Ear Recognition Based on Multiscale Hierarchical Features and Multifeature Fusion

Crop-related object recognition is of great importance in realizing intelligent agricultural machinery. Maize (Zea mays. L.) ear recognition could be a representative of crop-related object recognition, which is a critical technological premise for realizing automatic maize ear picking and maize yield prediction. In order to recognize maize ears in dough stage, this study combined deep learning and image processing, which have advantages of feature extraction and hardware flexibility, respectively. LabelImage was applied to mark and label maize plants, based on the deep learning framework TensorFlow, and this study developed multiscale hierarchical feature extraction together with quadruple-expanded convolutional kernels. To recognize the whole maize plant, 1250 images were acquired for training the recognition model, and its performance in a test set showed that the recognition accuracy was 99.47%. Subsequently, multifeatures of maize ear were determined, and the optimum binary threshold was obtained by fitting Gaussian distribution in the subblock image. Consequently, the maize ear was recognized by morphological process which was conducted by Python and OpenCV. Experiment was conducted in August 2018, and 10800 images were acquired for testing this algorithm. Experimental results showed that the average recognition accuracy was 97.02% and time consumption was 0.39 s for each image, which could meet a forward speed of 4.61 km/h for combine harvesters.

Digital detox: The effect of smartphone abstinence on mood, anxiety, and craving

Whether behavioural addictions should be conceptualised using a similar framework to substance-related addictions remains a topic of considerable debate. Previous literature has developed criteria, which allows any new behavioural addiction to be considered analogous to substance-related addictions. These imply that abstinence from a related object (e.g., smartphones for heavy smartphone users) would lead to mood fluctuations alongside increased levels of anxiety and craving. In a sample of smartphone users, we measured three variables (mood, anxiety, and craving) on four occasions, which included a 24-hour period of smartphone abstinence. Only craving was affected following a short period of abstinence. The results suggest that heavy smartphone usage does not fulfil the criteria required to be considered an addiction. This may have implications for other behavioural addictions.