The Physics Laboratory Works – Individualized Computer Simulations

The physics laboratory-works creating and operating computer simulations experience is described. A significant amount of laboratory works can be classified as a “black box”. The studied physical phenomenon is hidden from direct observation, the control is carried out by means of electrical measuring devices. It is difficult to distinguish physical reality from its imitation when performing such work, so the virtualization of this one does not require realistic images. The schematic representation of the laboratory installation greatly simplifies the process of creating a simulator. A unique set of installation parameters is formed for each student performing laboratory work on the simulator, which contributes to the independence of the student's work. These parameters are stored in Google Sheets. Their transfer to the laboratory work’s html-template is carried out in encrypted form through the Google Apps Script service. Virtual laboratory work is implemented as a cross-platform web application.

Biologically Feasible Generative Neural Network Architecture with Effective Concept Learning Capacity

Representations play an essential role in the learning of artificial and biological systems due to their capacity to identify characteristic patterns in the sensory environment. In this work we examined latent representations of several sets of images, such as basic geometric shapes and handwritten digits, produced by generative models in the process of unsupervised generative learning. A biologically feasible neural network architecture based on bi-directional synaptic connection equivalent in training and processing to a symmetrical autoencoder was proposed and defined. It was demonstrated that conceptual representations with good decoupling of concept regions can be produced with generative models of limited complexity; and that incremental evolution of architecture can result in improved ability to learn data of increasing conceptual complexity, including realistic images such as handwritten digits. The results demonstrate potential of conceptual representations produced as a natural platform for conceptual modeling of sensory environments and other intelligent behaviors.

ANIMATED ADDITIVE MANUFACTURING Designing Dynamic Exhibits with 3D Printing & Pixel Displays

<p>Physical moving parts are prone to wear and tear. A pixel display can manifest complex motion and realistic images in full colour offering a form of tangibly while being less likely to suffer from wear and tear however, it remains restricted to 2D surfaces. The recent development in voxel-based printing (voxel = 3D pixel) allows multi-material and multi-colour 3D printing to transform images into physical objects. However, during the printing process the capacity to change the pixels colour and position in the future are lost, effectively fusing the digital information. The high demand for immersive experiences in video games, films, museums and interactive products are omnipresent. The combination of pixel display technology and multi-material 3D printing is a potential avenue to create immersive experiences to feed this high demand.</p>

ANIMATED ADDITIVE MANUFACTURING Designing Dynamic Exhibits with 3D Printing & Pixel Displays

<p>Physical moving parts are prone to wear and tear. A pixel display can manifest complex motion and realistic images in full colour offering a form of tangibly while being less likely to suffer from wear and tear however, it remains restricted to 2D surfaces. The recent development in voxel-based printing (voxel = 3D pixel) allows multi-material and multi-colour 3D printing to transform images into physical objects. However, during the printing process the capacity to change the pixels colour and position in the future are lost, effectively fusing the digital information. The high demand for immersive experiences in video games, films, museums and interactive products are omnipresent. The combination of pixel display technology and multi-material 3D printing is a potential avenue to create immersive experiences to feed this high demand.</p>

Demonstration of light reflection concepts for rendering realistic 3D tree images

With advancements in computer graphics, creating natural images has always been the main purpose, image rendering is all based on principles of physics. So, understanding the physics of image rendering will enable us to create the most realistic images. A ray of light hit a surface with different orientation and reflects as per the rules of physics. It is difficult to calculate the light reflection of complex foliage, such as trees, so, the reflection of this natural complexity needs to be adapted to rendering situations. In this research, the researchers provide demonstrations to enable students to understand the light reflection in nature, light calculation in computer graphics and methods to apply them to render realistic tree images. The researchers assign students to render 3D realistic tree images to assess the students’ understanding by applying the diffuse reflection value, specular reflection value and surface normal direction to render realistic tree images. The researchers find that most students understand of diffuse reflection, specular reflection, and surface normal direction causes the rendering results to be most realistic.

Unbalanced Feature Transport for Exemplar-based Image Translation

Despite the great success of GANs in images translation with different conditioned inputs such as semantic segmentation and edge maps, generating high-fidelity realistic images with reference styles remains a grand challenge in conditional image-to-image translation. This paper presents a general image translation framework that incorporates optimal transport for feature alignment between conditional inputs and style exemplars in image translation. The introduction of optimal transport mitigates the constraint of many-to-one feature matching significantly while building up accurate semantic correspondences between conditional inputs and exemplars. We design a novel unbalanced optimaltransport to address the transport between features with deviational distributions which exists widely between conditional inputs and exemplars. In addition, we design a semantic-activation normalization scheme that injects stylefeatures of exemplars into the image translation process successfully. Extensive experiments over multiple image translation tasks show that our method achieves superior image translation qualitatively and quantitatively as comparedwith the state-of-the-art.

Online Learning of Oil Leak Anomalies in Wind Turbines with Block-Based Binary Reservoir

The focus of this work is to design a deeply quantized anomaly detector of oil leaks that may happen at the junction between the wind turbine high-speed shaft and the external bracket of the power generator. We propose a block-based binary shallow echo state network (BBS-ESN) architecture belonging to the reservoir computing (RC) category and, as we believe, it also extends the extreme learning machines (ELM) domain. Furthermore, BBS-ESN performs binary block-based online training using fixed and minimal computational complexity to achieve low power consumption and deployability on an off-the-shelf micro-controller (MCU). This has been achieved through binarization of the images and 1-bit quantization of the network weights and activations. 3D rendering has been used to generate a novel publicly available dataset of photo-realistic images similar to those potentially acquired by image sensors on the field while monitoring the junction, without and with oil leaks. Extensive experimentation has been conducted using a STM32H743ZI2 MCU running at 480 MHz and the results achieved show an accurate identification of anomalies, with a reduced computational cost per image and memory occupancy. Based on the obtained results, we conclude that BBS-ESN is feasible on off-the-shelf 32 bit MCUs. Moreover, the solution is also scalable in the number of image cameras to be deployed and to achieve accurate and fast oil leak detections from different viewpoints.

Sex/Gender Attribution: When the Penis Makes the Difference

The present study aimed to replicate Kessler and McKenna’s (1978) ethnomethodological study that investigated how an individual attributes gender to a person. By administering figures depicted on overlays (Overlay Study), Kessler and McKenna found that the penis more than the vulva and the male sexual characteristics more than the female ones were significantly more salient in the gender attribution process. From all this, their adage is: “See someone as female only when you cannot see them as male.” Taking as a model Kessler and McKenna’s Overlay Study, we administered to 592 adults 120 new digital stimuli elaborated on realistic frontal images of human nudes to verify if the previously obtained results would be confirmed by using more realistic images. We found that the participants attributed male gender 86% of the time when the penis was shown, but only attributed female gender 67% of the time when the vulva was shown. All findings had strong statistical significance, confirming the findings of the Overlay Study that the penis makes the difference in gender recognition. Beyond an ethnomethodological approach, we have interpreted and discussed our results from the outlook of evolutionary and cognitive psychology and cognitive neuroscience, concluding that the cultural stereotypes and prejudices that affect gender attribution might not just be a mere cultural product, but rather the consequence of evolved cognitive biases.

Deep image synthesis from intuitive user input: A review and perspectives

In many applications of computer graphics, art, and design, it is desirable for a user to provide intuitive non-image input, such as text, sketch, stroke, graph, or layout, and have a computer system automatically generate photo-realistic images according to that input. While classically, works that allow such automatic image content generation have followed a framework of image retrieval and composition, recent advances in deep generative models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and flow-based methods have enabled more powerful and versatile image generation approaches. This paper reviews recent works for image synthesis given intuitive user input, covering advances in input versatility, image generation methodology, benchmark datasets, and evaluation metrics. This motivates new perspectives on input representation and interactivity, cross fertilization between major image generation paradigms, and evaluation and comparison of generation methods.

The Image of a Soviet Woman as Depicted by Mass Media in the 1950s–1960s and Engraved in Social Memory (regional aspects)

В статье раскрываются репрезентативные формы образа советской женщины на материалах нестоличного города. Возможность привлечения сведений радиопередач и эго-документов как малоизученных форм женской репрезентации позволяет автору выявить и сравнить идеализированный и реально существующий образ советской женщины 1950–1960-х годов. Средства массовой информации формировали идеологически одобренный женский портрет, являясь транслятором допустимых и запрещенных норм, которые жительницы города старались соблюдать. Превалирующий образ «женщины-работницы», активно вовлеченной в семейную и общественную жизнь, был недосягаем для «обычных» советских женщин. Несмотря на это, многие из них стремились занять лидирующие позиции хотя бы в одной из сфер. Несовпадение идеализированного и реально существующего женских образов влекло за собой критику со стороны власти и общественности. Опасение быть осужденной и желание обличать накладывали на женщин определенные каноны поведения, которые исподволь внедрялись массовой культурой. The article investigates the image of a Soviet woman as portrayed by provincial mass media. The article analyzes such underrated sources of reliable information as egodocuments and radio performances, which enables the author to compare the idealized image of a Soviet woman and the real image of a Soviet woman of the 1950s–1960s. Mass media created an ideologically “proper” image of a female worker who was actively involved in family and social life. Despite the fact that many “ordinary” Soviet women did their best to fully realize their potential in at least one sphere of life, they had no means to conform to the ideal image broadcast by the media. Due to the discrepancy between the ideal and realistic images, Soviet women often fell victim to social and political criticism. Gnawed by the fear of censure and the desire to condemn others, women were forced to acquire certain behavior patterns dictated by mass culture.