Eye and face tracking in VR: avatar embodiment and enfacement with realistic and cartoon avatars. - In: MUM 2023, (2023), S. 270-278
Previous studies have explored the perception of various types of embodied avatars in immersive environments. However, the impact of eye and face tracking with personalized avatars is yet to be explored. In this paper, we investigate the impact of eye and face tracking on embodiment, enfacement, and the uncanny valley with four types of avatars using a VR-based mirroring task. We conducted a study (N=12) and created self-avatars with two rendering styles: a cartoon avatar (created in an avatar generator using a picture of the user’s face) and a photorealistic scanned avatar (created using a 3D scanner), each with and without eye and face tracking and respective adaptation of the mirror image. Our results indicate that adding eye and face tracking can be beneficial for certain enfacement scales (belonged), and we confirm that compared to a cartoon avatar, a scanned realistic avatar results in higher body ownership and increased enfacement (own face, belonging, mirror) - regardless of eye and face tracking. We critically discuss our experiences and outline the limitations of the applied hardware and software with respect to the provided level of control and the applicability for complex tasks such as displaying emotions. We synthesize these findings into a discussion about potential improvements for facial animation in VR and highlight the need for a better level of control, the integration of additional sensing and processing technologies, and an objective metric for comparing facial animation systems.
https://doi.org/10.1145/3626705.3627793
Ilm-NMR-P31: an open-access 31P nuclear magnetic resonance database and data-driven prediction of 31P NMR shifts. - In: Journal of cheminformatics, ISSN 1758-2946, Bd. 15 (2023), 122, S. 1-12
This publication introduces a novel open-access 31P Nuclear Magnetic Resonance (NMR) shift database. With 14,250 entries encompassing 13,730 distinct molecules from 3,648 references, this database offers a comprehensive repository of organic and inorganic compounds. Emphasizing single-phosphorus atom compounds, the database facilitates data mining and machine learning endeavors, particularly in signal prediction and Computer-Assisted Structure Elucidation (CASE) systems. Additionally, the article compares different models for 31P NMR shift prediction, showcasing the database’s potential utility. Hierarchically Ordered Spherical Environment (HOSE) code-based models and Graph Neural Networks (GNNs) perform exceptionally well with a mean squared error of 11.9 and 11.4 ppm respectively, achieving accuracy comparable to quantum chemical calculations.
https://doi.org/10.1186/s13321-023-00792-y
Computational modelling and mechanistic insight into light-driven CO dissociation of square-planar rhodium(I) complexes. - In: ChemPhotoChem, ISSN 2367-0932, Bd. n/a (2023), n/a, e202300219, S. 1-28
The activation step of Vaska-type Rh(I) complexes, such as the photocleavage of the Rh‑CO bond, plays an important role in the subsequent C-H activation. To elucidate the details of the photochemistry of Vaska-type Rh(I) complexes, such as trans-Rh(PMe3)2(CO)(Cl), we here present a computationally derived picture as obtained at the density functional level of theory (DFT) in combination with multireference wavefunction-based methods. We have identified that the photocleavage of CO proceeds via the metal-centered excited state, which is populated through intersystem crossing (ISC) from the dipole-allowed excited state S1. Moreover, the present study unraveled the reasons for the low C-H activation efficiency when using Rh featuring the bidentate ligand 1,2-bis(dimethylphosphino)ethane (dmpe), namely due to its unfavorable photochemical properties, i.e., the small driving force for light-induced CO loss and the fast deactivation of 3MC state back to the singlet ground state. In this study, we provide theoretical insight into mechanistic details underlying the light-induced CO dissociation process, for Rh complexes featuring PMe3 and dmpe ligands.
https://doi.org/10.1002/cptc.202300219
Influence of the processing on the properties of continuous fiber reinforced thermoplastic sheets prepared by extrusion. - In: AIP conference proceedings, ISSN 1551-7616, Bd. 2884 (2023), 1, 050005, S. 050005-1-050005-14
Continuous fiber reinforced thermoplastics (CFRT) are composite materials consisting of continuous fibers and a thermoplastic matrix and offer outstanding mechanical properties, low densities, short cycle times and recyclability. CFRT can be classified into unidirectional tapes and sheets utilizing various semi-finished textiles as reinforcement. CFRT sheets are of interest for area measured products or multiaxial loads. Various discontinuous and semi-continuous methods to prepare CFRP sheets are described in the literature. All these methods either feature high cycle times or high investment costs and require double melting of the polymer, e.g., first to produce a polymer film and second to produce the CFRT sheet. An energy efficient alternative to produce CFRT sheets is extrusion, which allows to spare one melting step. A twin-screw extruder melts the polymer, which is then conveyed by a melt pump to the film extrusion dies and applied to both sides of the semi-finished textile, which is wetted and consolidated using a calendar. Due to the high melt viscosity and the line load at the calendar the major challenge is to achieve full void-free impregnation of the semi-finished textile. The mechanical properties of a CFRT sheet are determined by fiber and void volume content. Hence, the influence of the processing conditions on the fiber and void volume content as well as the mechanical properties were examined applying a parametric study of the die temperature, the haul-off speed, and the gap between the calendar rolls. The properties of the extruded CFRT sheets were compared to compression molded sheets. The fiber volume content was directly adjusted by the haul-off speed and the extruder throughput. An increasing die temperature lowers the melt viscosity and results in an increased fiber volume content. Scanning electron microscopy shows complete macro impregnation between the fiber bundles but not completely wetted individual filaments within fiber bundles.
https://doi.org/10.1063/5.0168183
Interactive data cleaning for real-time streaming applications. - In: HILDA '23, (2023), 13, insges. 3 S.
The importance of data cleaning systems has continuously grown in recent years. Especially for real-time streaming applications, it is crucial, to identify and possibly remove anomalies in the data on the fly before further processing. The main challenge however lies in the construction of an appropriate data cleaning pipeline, which is complicated by the dynamic nature of streaming applications. To simplify this process and help data scientists to explore and understand the incoming data, we propose an interactive data cleaning system for streaming applications. In this paper, we list requirements for such a system and present our implementation to overcome the stated issues. Our demonstration shows, how a data cleaning pipeline can be interactively created, executed, and monitored at runtime. We also present several different tools, such as the automated advisor and the adaptive visualizer, that engage the user in the data cleaning process and help them understand the behavior of the pipeline.
https://doi.org/10.1145/3597465.3605229
Spatial prediction of the turbulent unsteady von Kármán vortex street using echo state networks. - In: Physics of fluids, ISSN 1089-7666, Bd. 35 (2023), 11, 115141, S. 115141-1-115141-15
The spatial prediction of the turbulent flow of the unsteady von Kármán vortex street behind a cylinder at Re = 1000 is studied. For this, an echo state network (ESN) with 6000 neurons was trained on the raw, low-spatial resolution data from particle image velocimetry. During prediction, the ESN is provided one half of the spatial domain of the fluid flow. The task is to infer the missing other half. Four different decompositions termed forward, backward, forward-backward, and vertical were examined to show whether there exists a favorable region of the flow for which the ESN performs best. Also, it was checked whether the flow direction has an influence on the network's performance. In order to measure the quality of the predictions, we choose the vertical velocity prediction of direction (VVPD). Furthermore, the ESN's two main hyperparameters, leaking rate (LR) and spectral radius (SR), were optimized according to the VVPD values of the corresponding network output. Moreover, each hyperparameter combination was run for 24 random reservoir realizations. Our results show that VVPD values are highest for LR ≈ 0.6, and quite independent of SR values for all four prediction approaches. Furthermore, maximum VVPD values of ≈ 0.83 were achieved for backward, forward-backward, and vertical predictions while for the forward case VVPDmax = 0.74 was achieved. We found that the predicted vertical velocity fields predominantly align with their respective ground truth. The best overall accordance was found for backward and forward-backward scenarios. In summary, we conclude that the stable quality of the reconstructed fields over a long period of time, along with the simplicity of the machine learning algorithm (ESN), which relied on coarse experimental data only, demonstrates the viability of spatial prediction as a suitable method for machine learning application in turbulence.
https://doi.org/10.1063/5.0172722
An ecological study protocol for the multimodal investigation of the neurophysiological underpinnings of dyadic joint action. - In: Frontiers in human neuroscience, ISSN 1662-5161, Bd. 17 (2023), 1305331, S. 1-19
A novel multimodal experimental setup and dyadic study protocol were designed to investigate the neurophysiological underpinnings of joint action through the synchronous acquisition of EEG, ECG, EMG, respiration and kinematic data from two individuals engaged in ecologic and naturalistic cooperative and competitive joint actions involving face-to-face real-time and real-space coordinated full body movements. Such studies are still missing because of difficulties encountered in recording reliable neurophysiological signals during gross body movements, in synchronizing multiple devices, and in defining suitable study protocols. The multimodal experimental setup includes the synchronous recording of EEG, ECG, EMG, respiration and kinematic signals of both individuals via two EEG amplifiers and a motion capture system that are synchronized via a single-board microcomputer and custom Python scripts. EEG is recorded using new dry sports electrode caps. The novel study protocol is designed to best exploit the multimodal data acquisitions. Table tennis is the dyadic motor task: it allows naturalistic and face-to-face interpersonal interactions, free in-time and in-space full body movement coordination, cooperative and competitive joint actions, and two task difficulty levels to mimic changing external conditions. Recording conditions - including minimum table tennis rally duration, sampling rate of kinematic data, total duration of neurophysiological recordings - were defined according to the requirements of a multilevel analytical approach including a neural level (hyperbrain functional connectivity, Graph Theoretical measures and Microstate analysis), a cognitive-behavioral level (integrated analysis of neural and kinematic data), and a social level (extending Network Physiology to neurophysiological data recorded from two interacting individuals). Four practical tests for table tennis skills were defined to select the study population, permitting to skill-match the dyad members and to form two groups of higher and lower skilled dyads to explore the influence of skill level on joint action performance. Psychometric instruments are included to assess personality traits and support interpretation of results. Studying joint action with our proposed protocol can advance the understanding of the neurophysiological mechanisms sustaining daily life joint actions and could help defining systems to predict cooperative or competitive behaviors before being overtly expressed, particularly useful in real-life contexts where social behavior is a main feature.
https://doi.org/10.3389/fnhum.2023.1305331
Reduced-order modeling of two-dimensional turbulent Rayleigh-Bénard flow by hybrid quantum-classical reservoir computing. - In: Physical review research, ISSN 2643-1564, Bd. 5 (2023), 4, 043242, S. 043242-1-043242-13
Two hybrid quantum-classical reservoir computing models are presented to reproduce the low-order statistical properties of a two-dimensional turbulent Rayleigh-Bénard convection flow at a Rayleigh number Ra=105 and Prandtl number Pr=10. These properties comprise the mean vertical profiles of the root mean square velocity and temperature and the turbulent convective heat flux. The latter is composed of vertical velocity and temperature and measures the global turbulent heat transfer across the convection layer; it manifests locally in coherent hot and cold thermal plumes that rise from the bottom and fall from the top boundaries. Both quantum algorithms differ by the arrangement of the circuit layers of the quantum reservoir, in particular the entanglement layers. The second of the two quantum circuit architectures, denoted H2, enables a complete execution of the reservoir update inside the quantum circuit without the usage of external memory. Their performance is compared with that of a classical reservoir computing model. Therefore, all three models have to learn the nonlinear and chaotic dynamics of the turbulent flow at hand in a lower-dimensional latent data space which is spanned by the time-dependent expansion coefficients of the 16 most energetic proper orthogonal decomposition (POD) modes. These training data are generated by a POD snapshot analysis from direct numerical simulations of the original turbulent flow. All reservoir computing models are operated in the reconstruction or open-loop mode, i.e., they receive three POD modes as an input at each step and reconstruct the 13 missing modes. We analyze different measures of the reconstruction error in dependence on the hyperparameters which are specific for the quantum cases or shared with the classical counterpart, such as the reservoir size and the leaking rate. We show that both quantum algorithms are able to reconstruct the essential statistical properties of the turbulent convection flow successfully with similar performance compared with the classical reservoir network. Most importantly, the quantum reservoirs are by a factor of four to eight smaller in comparison with the classical case.
https://doi.org/10.1103/PhysRevResearch.5.043242
Effect of partial and global shielding on surface-driven phenomena in keyhole mode laser beam welding. - In: Welding in the world, ISSN 1878-6669, Bd. 0 (2023), 0, insges. 1-22 S.
Partial shielding by means of local gas supply has proven to be very effective in reducing spatter. Besides the effect of gas-induced dynamic pressure, the shielding of oxygen is also highly relevant for melt pool dynamics and spatter formation due to the growth of oxides and the influence on surface tension. Therefore, this paper addresses the effect of local supplied argon on oxide growth and seam topography during keyhole mode laser beam welding of high-alloy steel AISI 304. To determine the shielding quality, the results are compared to laser beam welding in a global argon atmosphere. The topography of the upper weld seams was analyzed by scanning electron microscopy (SEM). An X-ray microanalysis (EDX) in line scan modus was performed to determine and to locate the elements which are covering the specimen surface. The chemical state of the found elements was quantified by X-ray photoelectron spectroscopy (XPS). In a last step, high-speed synchrotron X-ray imaging was performed to separate the effect of the gas-induced pressure and the gas-induced shielding on keyhole geometry. The results show that a local supply of argon contributes to a significant difference in oxide growth, affecting melt pool convection and weld seam geometry. It was further shown that the effect of gas flows at low flow rates is primarily because of oxygen shielding, as no significant difference in keyhole geometry was found by high-speed synchrotron X-ray imaging.
https://doi.org/10.1007/s40194-023-01627-y
The AI-augmented crowd: how human crowdvoters adopt AI (or not). - In: The journal of product innovation management, ISSN 1540-5885, Bd. n/a (2023), n/a, S. 1-25
To date, innovation management research on idea evaluation has focused on human experts and crowd evaluators. With recent advances in artificial intelligence (AI), idea evaluation and selection processes need to keep up. As a result, the potential role of AI-enabled systems in idea evaluation has become an important topic in innovation management research and practice. While AI can help overcome human capacity constraints and biases, prior research has identified also aversive behaviors of humans toward AI. However, research has also shown lay people's appreciation of AI. This study focuses on human crowdvoters’ AI adoption behavior. More precisely, we focus on gig workers, who despite often lacking expert knowledge are frequently engaged in crowdvoting. To investigate crowdvoters' AI adoption behavior, we conducted a behavioral experimental study (n = 629) with incentive-compatible rewards in a human-AI augmentation scenario. The participants had to predict the success or failure of crowd-generated ideas. In multiple rounds, participants could opt to delegate their decisions to an AI-enabled system or to make their own evaluations. Our findings contribute to the innovation management literature on open innovation, more specifically crowdvoting, by observing how human crowdvoters engage with AI. In addition to showing that the lay status of gig workers does not lead to an appreciation of AI, we identify factors that foster AI adoption in this specific innovation context. We hereby find mixed support for influencing factors previously identified in other contexts, including financial incentives, social incentives, and the provision of information about AI-enabled system's functionality. A second novel contribution of our empirical study is, however, the fading of crowdvoters’ aversive behavior over time.
https://doi.org/10.1111/jpim.12708