Comparison of flow and dispersion properties of free and wall turbulent jets for source dynamics characterisation

The objective of this paper is to provide an investigation, using large eddy simulations, into the dispersion of aircraft jets in co-flowing take-off conditions. Before carrying out such study, simple turbulent plane free and wall jet simulations are carried out to validate the computational models and to assess the impact of the presence of the solid boundary on the flow and dispersion properties. The current study represents a step towards a better understanding of the source dynamics behind an airplane jet engine during the take-off and landing phases. The information provided from these simulations can be used for future improvements of existing dispersion models

Fusing Classic Motion Energy Models and Deep Learning for Coarse-to-fine Moving Object Segmentation

Classic motion energy models are able to predict a wide range of physiological and behavioral aspects of motion perception in humans. Whether these models can be used as a basis for higher-level tasks, such as moving object segmentation, has however hardly been explored yet. Here, we present a model that combines a motion energy representation with recent computer vision approaches for figure-ground segmentation of naturalistic stimuli. We find that unlike established motion segmentation models but similar to humans, our model generalizes to random-dot stimuli when only trained on RGB videos

Beyond Core Object Recognition: DNNs as Models of Dynamic Scene Perception

Deep neural networks (DNNs) have become influential computational models of human vision, particularly in explaining neural responses in the ventral stream. However, they frequently diverge from well-established findings in psychophysics, especially with regard to human perceptual biases, robustness, and generalization behavior. Much of the progress in aligning DNNs with human perception has focused on the task of core object recognition—the rapid identification of objects in static images (DiCarlo et al., 2012). In contrast, other critical dimensions of visual perception, such as motion processing and multi-object scene understanding, remain comparatively underexplored. In this talk, I present our recent work on modeling how motion supports perceptual organization and discuss both the prospects and limitations of using DNNs as scientific models of dynamic scene perception. I argue that geometric aspects of visual experience—particularly motion and depth—offer a promising path forward for bridging human and machine vision

How motion promotes perceptual organization in humans and machines

Perceptual organization is a core process of human vision that transforms the raw visual input into a structured, object-centric scene representation. Motion information plays a central role in this process: On the one hand, perceiving motion requires a perceptual organization process to establish an identity behind percepts at different time points. On the other hand, motion information has been shown to be a dominant cue that humans use to infer scene structure, for example by the Gestalt principle of common fate. Moreover, motion has been shown to enable more efficient processing by guiding attention to relevant areas of scenes, and to contribute to learning perceptual organization during infancy. In this thesis, we combine insights from psychology and neuroscience with recent advances in machine learning in order study how motion motion promotes different aspects of dynamic scene perception. First, we study the role of motion in guiding eye movements as a basis for more efficient scene perception. Our analysis reveals several strong effects of temporal patterns on eye movements in a data-driven manner, but also identifies their scarcity in common benchmarks as a key limitation for modeling this process. We propose a new benchmark that combines the respective cases from several existing benchmarks to support future research on this topic. In our second project, we take inspiration from developmental psychology and study the role of motion for learning how to decompose a scene into objects. Trained this way, our model reflects central capabilities of scene perception in humans, such as the ability to complete partial objects and to generate novel scenes that systematically generalize beyond the training distribution. Finally, we study the neural basis of motion segmentation using a combination of computational modeling and experimental psychophysics. We find striking differences between state-of-the-art computer vision models and human perception in terms of appearance-independent segmentation of moving random dot patterns. Furthermore, we show that a neuroscience-inspired motion energy approach allows matching human perception and thus provide a compelling link between the neural mechanisms of motion perception and the Gestalt principle of common fate. In summary, the projects in this thesis contribute to our understanding how motion information promotes perceptual organization from an interdisciplinary NeuroAI perspective. DNNs allow building more capable scientific models of human vision, and thus enable novel insights into the perception of natural scenes. Conversely, we show that insights from human vision can be successfully transferred to a computer vision setting. Our work therefore contributes to a more holistic understanding of human vision and provides insights that may inspire more capable machine vision in the future

Sulfation of a High Endothelial Venule–Expressed Ligand for L-Selectin: Effects on Tethering and Rolling of Lymphocytes

During lymphocyte homing, L-selectin mediates the tethering and rolling of lymphocytes on high endothelial venules (HEVs) in secondary lymphoid organs. The L-selectin ligands on HEV are a set of mucin-like glycoproteins, for which glycosylation-dependent cell adhesion molecule 1 (GlyCAM-1) is a candidate. Optimal binding in equilibrium measurements requires sulfation, sialylation, and fucosylation of ligands. Analysis of GlyCAM-1 has revealed two sulfation modifications (galactose [Gal]-6-sulfate and N-acetylglucosamine [GlcNAc]-6-sulfate) of sialyl Lewis x. Recently, three related sulfotransferases (keratan sulfate galactose-6-sulfotransferase [KSGal6ST], high endothelial cell N-acetylglucosamine-6-sulfotransferase [GlcNAc6ST], and human GlcNAc6ST) were cloned, which can generate Gal-6-sulfate and GlcNAc-6-sulfate in GlyCAM-1. Imparting these modifications to GlyCAM-1, together with appropriate fucosylation, yields enhanced rolling ligands for both peripheral blood lymphocytes and Jurkat cells in flow chamber assays as compared with those generated with exogenous fucosyltransferase. Either sulfation modification results in an increased number of tethered and rolling lymphocytes, a reduction in overall rolling velocity associated with more frequent pausing of the cells, and an enhanced resistance of rolling cells to detachment by shear. All of these effects are predicted to promote the overall efficiency of lymphocyte homing. In contrast, the rolling interactions of E-selectin transfectants with the same ligands are not affected by sulfation

Integration models for SDC-capable medical devices into an existing OR network : a case study for a high-frequency surgical device

The interconnection of medical devices in an operating room (OR) represents a major step in optimizing clinical processes and increasing the quality of treatment. The IEEE 11073 Service-oriented Device Connectivity (SDC) standard family constitutes the foundation for manufacturerindependent information exchange and remote control of medical devices. However, integrating new SDC-capable devices into an existing OR network poses a major challenge for medical device manufacturers. Thus, suitable integration models are required. This work focuses on the definition of three possible integration models and their comparison according to architectural design patterns. Thereby, the use case of integrating a high-frequency (HF) surgical device to interconnect with existing SDC-capable devices is pursued. One of the models, which focuses on high expandability and low coupling, was successfully applied to interconnect an HF surgical device with an OR light in the research OR of Reutlingen University. The results indicate transferability to other integration scenarios and are intended to further promote manufacturer-independent integrated ORs

Ultra-high strained diamond spin register with coherent optical link

Solid-state spin defects, such as color centers in diamond, are among the most promising candidates for scalable and integrated quantum technologies. In particular, the good optical properties of silicon-vacancy centers in diamond combined with naturally occurring and exceptionally coherent nuclear spins serve as a building block for quantum networking applications. Here, we show that leveraging an ultra-high strained silicon-vacancy center inside a nanodiamond allows us to coherently and efficiently control its electron spin, while mitigating phonon-induced dephasing at liquid helium temperature. Moreover, we indirectly control and characterize a 13C nuclear spin and establish a quantum register. We overcome limited nuclear spin initialization by implementing single-shot nuclear spin readout. Lastly, we demonstrate coherent optical control with GHz rates, thus connecting the register to the optical domain. Our work paves the way for future integration of quantum network registers into conventional, well-established photonics and hybrid quantum communication systems

Ассоциативная музыкальная типология

Associative musical typology is aimed at deepening our knowledge about the psychology of personality and psycho-diagnostics from the perspective of associative socionics. According to the Butterfly Model of the psyche, the preferences in music and colour are not entirely random or influenced by culture but are linked with the personality typology. Personality type is inborn and does not change through an individual's lifetime. It is tied up with the psycho-physiological make-up and the core of a person’s identity. The individual preferences in music were studied between the four groups of personality types (Ego-, Id-, Superego-, Superid-types) from the perspective of the differences in the type of psychic energy we carry within us. In the article you will find the psychological profiles of music (Ego-, Id-, Superego-, Superid- music) which should be considered as a guideline only for the individual preferences in music for each of the four personality types.Ассоциативная музыкальная типология углубляет наше знание о психологии личности и психодиагностике. В соответствии с моделью человеческой психики «Бабочка» и ассоциативной теорией соционики, музыкальные и цветовые предпочтения не случайны по своей природе и зависят не только от культурных традиций, но соотносятся с типологическими характеристиками личности, т.е. с типологией. Психологический тип является врожденным, не меняется в течение жизни и имеет прямое отношение к концепции личности: к самоопределению и самооценке. Были изучены индивидуальные предпочтения в музыке людей 4 разных психотипов (Эго-, Ид-, СуперЭго- и СуперИд-типов). В статье Вы найдете описания музыки, характерной для этих групп типов. Музыкальную типологию ассоциативной соционики следует рассматривать как общее руководство применительно к психодиагностике индивидуальных музыкальных предпочтений. Этот инструмент психодиагностики можно использовать наряду с другими психологическими инструментами для достижения более объективных результатов типирования