The nature of posttraumatic nightmares and school functioning in war-affected youth

Children and adolescents who have experienced traumatic events demonstrate a variety of posttraumatic symptoms, including recurrent nightmares, as well as adverse reactions in the school setting. The current study examined nightmare symptoms, posttraumatic stress, sleep disturbance, and self- and teacher-reported school functioning of 64 youths in the Gaza Strip, ages 12 to 16, who have lived through three wars and experience ongoing conflict and political insecurity. Students were treatment-seeking for sleep-problems and reported, on average, five nightmares per week for an average of three years, with concomitant disrupted sleep, fear of going to sleep, and not feeling rested in the morning. Both teachers and students reported that participants exhibited impaired academic functioning and daytime sleepiness. The content of the students’ nightmares demonstrated frightening themes of being under attack and loss of self-efficacy/control; threat levels were high, and almost 60% included the threat of death. Approximately half of the nightmares included surreal elements in addition to more realistic scenes of violence. Participants in the study demonstrated substantial posttraumatic sleep problems with intensely distressing, frequent and chronic nightmares, andnightmare symptoms were associated with impairment in school functioning. Given the disruptive and distressing nature of these students’ nightmare disturbance, we suggest that increasing self-efficacy in relation to the experience of recurrent nightmares may be a good point of intervention with these recurrently traumatized youth. Thus, increasing the understanding of students’ nightmare symptoms may lead to ameliorating the suffering of youths in war zones and may have positive effects on their school functioning.

Procedural interior generation for artificial intelligence training and computer graphics

Since the creation of computers, there has been a lingering problem of data storing and creation for various tasks. In terms of computer graphics and video games, there has been a constant need in assets. Although nowadays the issue of space is not one of the developers' prime concerns, the need in being able to automate asset creation is still relevant. The graphical fidelity, that the modern audiences and applications demand requires a lot of work on the artists' and designers' front, which costs a lot. The automatic generation of 3D scenes is of critical importance in the tasks of Artificial Intelligent (AI) robotics training, where the amount of generated data during training cannot even be viewed by a single person due to the large amount of data needed for machine learning algorithms. A completely separate, but nevertheless necessary task for an integrated solution, is furniture generation and placement, material and lighting randomisation. In this paper we propose interior generator for computer graphics and robotics learning applications. The suggested framework is able to generate and render interiors with furniture at photo-realistic quality. We combined the existing algorithms for generating plans and arranging interiors and then finally add material and lighting randomization. Our solution contains semantic database of 3D models and materials, which allows generator to get realistic scenes with randomization and per-pixel mask for training detection and segmentation algorithms.

Scene wheels: Measuring perception and memory of real-world scenes with a continuous stimulus space

Precisely characterizing mental representations of visual experiences requires careful control of experimental stimuli. Recent work leveraging such stimulus control in continuous report paradigms have led to important insights; however, these findings are constrained to simple visual properties like colour and line orientation. There remains a critical methodological barrier to characterizing perceptual and mnemonic representations of realistic visual experiences. Here, we introduce a novel method to systematically control visual properties of natural scene stimuli. Using generative adversarial networks (GAN), a state-of-art deep learning technique for creating highly realistic synthetic images, we generated scene wheels in which continuously changing visual properties smoothly transition between meaningful realistic scenes. To validate the efficacy of scene wheels, we conducted a memory experiment in which participants reconstructed to-be-remembered scenes from the scene wheels. Reconstruction errors for these scenes resemble error distributions observed in prior studies using simple stimulus properties. Importantly, memory precision varied systematically with scene wheel radius. These findings suggest our novel approach offers a window into the mental representations of naturalistic visual experiences.

Recognition of Point Sets Objects in Realistic Scenes

With the emergence of new intelligent sensing technologies such as 3D scanners and stereo vision, high-quality point clouds have become very convenient and lower cost. The research of 3D object recognition based on point clouds has also received widespread attention. Point clouds are an important type of geometric data structure. Because of its irregular format, many researchers convert this data into regular three-dimensional voxel grids or image collections. However, this can lead to unnecessary bulk of data and cause problems. In this paper, we consider the problem of recognizing objects in realistic senses. We first use Euclidean distance clustering method to segment objects in realistic scenes. Then we use a deep learning network structure to directly extract features of the point cloud data to recognize the objects. Theoretically, this network structure shows strong performance. In experiment, there is an accuracy rate of 98.8% on the training set, and the accuracy rate in the experimental test set can reach 89.7%. The experimental results show that the network structure in this paper can accurately identify and classify point cloud objects in realistic scenes and maintain a certain accuracy when the number of point clouds is small, which is very robust.