Recruitment of a long-term memory supporting neural network during repeated maintenance of a multi-item abstract visual image in working memory

Humans can flexibly transfer information between different memory systems. Information in visual working memory (VWM) can for instance be stored in long-term memory (LTM). Conversely, information can be retrieved from LTM and temporarily held in WM when needed. It has previously been suggested that a neural transition from parietal- to midfrontal activity during repeated visual search reflects transfer of information from WM to LTM. Whether this neural transition indeed reflects consolidation and is also observed when memorizing a rich visual scene (rather than responding to a single target), is not known. To investigate this, we employed an EEG paradigm, in which abstract six-item colour-arrays were repeatedly memorized and explicitly visualized, or merely attended to. Importantly, we tested the functional significance of a potential neural shift for longer-term consolidation in a subsequent recognition task. Our results show a gradually enhanced- and sustained modulation of the midfrontal P170 component and a decline in parietal CDA, during repeated WM maintenance. Improved recollection/visualization of memoranda upon WM-cueing, was associated with contralateral parietal- and right temporal activity. Importantly, only colour-arrays previously held in WM, induced a greater midfrontal P170-response, together with left temporal- and late centro-parietal activity, upon re-exposure. These findings provide evidence for recruitment of an LTM-supporting neural network which facilitates visual WM maintenance.

Front vehicle detection based on improved fusion method for lidar and visual image

In order to ensure the detection accuracy, an improved adaptive weighted (IAW) method is proposed in this paper to fuse the data of images and lidar sensors for the vehicle object’s detection. Firstly, the IAW method is proposed in this paper and the first simulation is conducted. The unification of two sensors’ time and space should be completed at first. The traditional adaptive weighted average method (AWA) will amplify the noise in the fusion process, so the data filtered with Kalman Filter (KF) algorithm instead of with the AWA method. The proposed IAW method is compared with the AWA method and the Distributed Weighted fusion KF algorithm in the data fusion simulation to verify the superiority of the proposed algorithm. Secondly, the second simulation is conducted to verify the robustness and accuracy of the IAW algorithm. In the two experimental scenarios of sparse and dense vehicles, the vehicle detection based on image and lidar is completed, respectively. The detection data is correlated and merged through the IAW method, and the results show that the IAW method can correctly associate and fuse the data of the two sensors. Finally, the real vehicle test of object vehicle detection in different environments is carried out. The IAW method, the KF algorithm, and the Distributed Weighted fusion KF algorithm are used to complete the target vehicle detection in the real vehicle, respectively. The advantages of the two sensors can give full play, and the misdetection of the target objects can be reduced with proposed method. It has great potential in the application of object acquisition.

Inter-individual deep image reconstruction

The sensory cortex is characterized by general organizational principles such as topography and hierarchy. However, measured brain activity given identical input exhibits substantially different patterns across individuals. While anatomical and functional alignment methods have been proposed in functional magnetic resonance imaging (fMRI) studies, it remains unclear whether and how hierarchical and fine-grained representations can be converted between individuals while preserving the encoded perceptual contents. In this study, we evaluated machine learning models called neural code converters that predict one's brain activity pattern (target) from another's (source) given the same stimulus by the decoding of hierarchical visual features and the reconstruction of perceived images. The training data for converters consisted of fMRI data obtained with identical sets of natural images presented to pairs of individuals. Converters were trained using the whole visual cortical voxels from V1 through the ventral object areas, without explicit labels of visual areas. We decoded the converted brain activity patterns into hierarchical visual features of a deep neural network (DNN) using decoders pre-trained on the target brain and then reconstructed images via the decoded features. Without explicit information about visual cortical hierarchy, the converters automatically learned the correspondence between the visual areas of the same levels. DNN feature decoding at each layer showed higher decoding accuracies from corresponding levels of visual areas, indicating that hierarchical representations were preserved after conversion. The viewed images were faithfully reconstructed with recognizable silhouettes of objects even with relatively small amounts of data for converter training. The conversion also allows pooling data across multiple individuals, leading to stably high reconstruction accuracy compared to those converted between individuals. These results demonstrate that the conversion learns hierarchical correspondence and preserves the fine-grained representations of visual features, enabling visual image reconstruction using decoders trained on other individuals.

Obstacle Avoidance and Environmental Adaptability Analysis of Snake-like Robot Based on Deep Learning

Aiming at the problems of high complexity and low accuracy of visual depth map feature recognition, a graph recognition algorithm based on principal component direction depth gradient histogram (pca-hodg) is designed in this study. In order to obtain high-quality depth map, it is necessary to calculate the parallax of the visual image. At the same time, in order to obtain the quantized regional shape histogram, it is necessary to carry out edge detection and gradient calculation on the depth map, then reduce the dimension of the depth map combined with the principal component, and use the sliding window detection method to reduce the dimension again to realize the feature extraction of the depth map. The results show that compared with other algorithms, the pca-hodg algorithm designed in this study improves the average classification accuracy and significantly reduces the average running time. This shows that the algorithm can reduce the running time by reducing the dimension, extract the depth map features more accurately, and has good robustness.

Methodology of applying structural and logical schemes in the study of general physics in a technical university

A structural and logical scheme is a visual image of the logical connection of the main elements of knowledge within the framework of a training course, section or topic. When studying physics as an applied discipline in a technical university, its professional orientation and applied knowledge corresponding to this function come out in the first place. But applied knowledge as a consequence of physical theories is not enough for the development of a modern quantum-relativistic worldview. The idea of our research is to precede the systematic study of general physics with systematic ideas about the place and meaning of each physical theory, namely: before studying classical physics, to show its connection with quantum and relativistic physics. To do this, it is necessary to apply a preliminary and final generalization at different stages of the study of physics with the help of appropriate structural and logical schemes. When implementing this idea, the following methods were used: the method of structural and logical analysis of the course of general physics with the allocation of knowledge elements, the method of systematization based on clarifying the connection between physical theories and the method of generalization, leading to the construction of new generalized schemes of this course. In the proposed schemes "Connection of mechanical theories" and "Scales of the Universe-Velocities", we identify structural elements that reveal the specifics of the methodological representations of the theory in accordance with its place in the Universe and the velocities of its objects. The proposed methodology is based on two types of generalization: preliminary and final. The preliminary generalization shows the place of physical theory in the system of physical knowledge in the course of general physics, the final generalization is used to make students aware of the specifics of the entire range of methodological concepts used in this physical theory. The methodology is aimed at forming students ' systematic knowledge of general physics and at developing their modern quantum-relativistic worldview.

‘Can We See It?’: Contextualizing ‘Deforestation’ from an English-Medium Science Textbook for a Primary School Level

Understanding an English-medium science textbook is possibly challenging for some students. It is, for example, due to the language used. To deal with this issue, construing the use of the other mode, such as visual images, along with the verbal text is regarded useful. Thereby, the construal of multimodality in an English-medium science textbook becomes crucial. Albeit a myriad of inspections on multimodality exists, but to the best of the writer’s knowledge, such investigation with respect to an English-medium science textbook, particularly at a primary school level, was found to be limited. Therefore, this study aimed to scrutinize the verbal text and visual image presented in a science textbook used for a primary school level which is presented in English. To that end, a descriptive research design was employed. In this regard, a systemic functional multimodal discourse analysis (SF-MDA) within the trinocular metafunctions encompassing ideational, interpersonal, and textual metafunctions was utilized. The systemic functional linguistics theory, the grammar of visual design, intersemiotic complementarity, and logico-semantics were the frameworks employed to analyze the artefact, the English-medium science textbook. The findings revealed that the visual image and verbal text interact with one another within the three metafunctions. Given the interaction between the two modes, the present study suggests that both teachers and students are required to take into considerations and be aware of the potential or roles of images along with the verbal text, i.e. the images are not merely accessories, but instead, these are able to assist the comprehension of the science materials learned.