搜索结果：HMD Praxis der Wirtschaftsinformatik

It is especially challenging to achieve real-time human motion tracking on a standalone VR Head-Mounted Display (HMD) such as Meta Quest and PICO. In this paper, we propose HMD-Poser, the first unified approach to recover full-body motions using scalable sparse observations from HMD and body-worn IMUs. In particular, it can support a variety of input scenarios, such as HMD, HMD+2IMUs, HMD+3IMUs, etc. The scalability of inputs may accommodate users' choices for both high tracking accuracy and easy-to-wear. A lightweight temporal-spatial feature learning network is proposed in HMD-Poser to guarantee that the model runs in real-time on HMDs. Furthermore, HMD-Poser presents online body shape estimation to improve the position accuracy of body joints. Extensive experimental results on the challenging AMASS dataset show that HMD-Poser achieves new state-of-the-art results in both accuracy and real-time performance. We also build a new free-dancing motion dataset to evaluate HMD-Poser's on-device performance and investigate the performance gap between synthetic data and real-captured sensor data. Finally, we demonstrate our HMD-Poser with a real-time Avatar-driving application on a commer

Standard machine learning pipelines often admit many near-optimal models. These "Rashomon sets" pose a range of challenges and opportunities for uncertainty-aware, robust decision making. They allow users to incorporate domain knowledge and preferences that would otherwise be difficult to specify directly in an objective, and they quantify diversity among valid models for a given training dataset and objective function. However, computation of Rashomon sets, even for simple, interpretable model classes such as sparse decision trees, continues to require immense memory and runtime resources. We present PRAXIS, an algorithm to approximate this Rashomon set with orders of magnitude improvement in runtime and memory usage. We validate that PRAXIS regularly recovers almost all of the full Rashomon set. PRAXIS allows researchers and practitioners to scalably model the Rashomon set for real-world datasets. Code for PRAXIS is available at https://github.com/zakk-h/PRAXIS

Magnomechanical systems provide a promising route for exploring coherent hybrid magnon-phonon interactions and hybrid information processing, but their realization has so far been limited by weak magnon-phonon coupling in conventional bulk platforms. We show that a suspended membrane of a two-dimensional van der Waals ferromagnet with in-plane magnetization and out-of-plane mechanical oscillations exhibits large magnomechanical coupling dominated by magnetoelastic interactions. The parametric single magnon-phonon coupling rate scales linearly with pre-strain and can reach hundreds of Hertz to low kiloHertz in suspended membranes of van der Waals ferromagnets such as CrGeTe_3 under experimentally realistic conditions. This rate exceeds typical values reported for YIG spheres by more than three orders of magnitude. Our results demonstrate that suspended membranes of van der Waals magnets provide a robust and highly tunable platform for magnomechanics.

Large language models are moving scientific research from text assistance toward agentic workflows, yet biological research requires strong object validation, methodological suitability, reproducibility, and auditability. Prompt engineering, general RAG, or tool use alone cannot reliably produce domain-specific scientific judgment. Here, we present PRAXIS, a verifiable biological research agent framework driven by literature learning and case distillation. PRAXIS converts research experience, failure boundaries, domain rules, and executable procedures into structured long-term memory. By coordinating successful cases, negative cases, rules, and skills, PRAXIS supports problem definition, object validation, method selection, workflow execution, result interpretation, and review feedback across diverse biocomputational tasks. We instantiated PRAXIS as an agent suite for biomedical computing and evaluated it through object validation, case retrieval, memory ablation, public benchmarks, and cross-agent workflows. The results show that case-based learning improves method selection, error suppression, and workflow organization in complex biological research tasks. Rather than replacing s

Virtual reality (VR) not only allows head-mounted display (HMD) users to immerse themselves in virtual worlds but also to share them with others. When designed correctly, this shared experience can be enjoyable. However, in typical scenarios, HMD users are isolated by their devices, and non-HMD observers lack connection with the virtual world. To address this, our research investigates visually representing observers on both HMD and 2D screens to enhance shared experiences. The study, including five representation conditions, reveals that incorporating observer representation positively impacts both HMD users and observers. For how to design and represent them, our work shows that HMD users prefer methods displaying real-world visuals, while observers exhibit diverse preferences regarding being represented with real or virtual images. We provide design guidelines tailored to both displays, offering valuable insights to enhance co-located shared VR experiences for HMD users and non-HMD observers.

A quantitative understanding of the microscopic mechanisms responsible for damping in van der Waals nanomechanical resonators remains elusive. In this work, we investigate van der Waals magnets, where the thermal expansion coefficient exhibits an anomaly at the magnetic phase transition due to magnetoelastic coupling. Thermal expansion mediates the coupling between mechanical strain and heat flow and determines the strength of thermoelastic damping (TED). Consequently, variations in the thermal expansion coefficient are reflected directly in TED, motivating our focus on this mechanism. We extend existing TED models to incorporate anisotropic thermal conduction, a critical property of van der Waals materials. By combining the thermodynamic properties of the resonator material with the anisotropic TED model, we examine dissipation as a function of temperature. Our findings reveal a pronounced impact of the phase transition on dissipation, along with transitions between distinct dissipation regimes controlled by geometry and the relative contributions of in-plane and out-of-plane thermal conductivity. These regimes are characterized by the resonant interplay between strain and in-plan

Unresolved production cloud incidents cost an average of over $2M per hour. This paper introduces PRAXIS, an orchestrator that manages and deploys an agentic workflow for diagnosing code- and configuration-caused cloud incidents. PRAXIS employs an LLM-driven structured traversal over two types of graph: (1) a service dependency graph (SDG) that captures microservice-level dependencies; and (2) a hammock-block program dependence graph (PDG) that captures code-level dependencies for each microservice. Compared to state-of-the-art ReAct baselines, PRAXIS improves RCA accuracy by up to 6.3x while reducing token consumption by 5.3x. PRAXIS is demonstrated on a set of 30 comprehensive real-world incidents that is being compiled into an RCA benchmark.

Achieving magnon-photon hybridization in the microwave regime is essential for integrating magnetic excitations with superconducting circuits. While this has been extensively demonstrated in bulk magnetic systems, realizing it in two-dimensional van der Waals materials remains challenging due to their reduced magnetic volume and increased dissipation. Here, magnon-photon hybridization is observed in exfoliated flakes of the van der Waals ferromagnet Cr$_2$Ge$_2$Te$_6$, with thicknesses down to 30 nm. The resulting magnon polaritons-hybrid excitations of cavity photons and magnons-are evidenced by reproducible avoided crossings across six devices, enabled by a low-impedance superconducting resonator design. The coupling strength follows the expected square-root dependence on thickness, and extrapolation of this scaling indicates that hybridization in the monolayer limit is within reach.

Generating both plausible and accurate full body avatar motion is the key to the quality of immersive experiences in mixed reality scenarios. Head-Mounted Devices (HMDs) typically only provide a few input signals, such as head and hands 6-DoF. Recently, different approaches achieved impressive performance in generating full body motion given only head and hands signal. However, to the best of our knowledge, all existing approaches rely on full hand visibility. While this is the case when, e.g., using motion controllers, a considerable proportion of mixed reality experiences do not involve motion controllers and instead rely on egocentric hand tracking. This introduces the challenge of partial hand visibility owing to the restricted field of view of the HMD. In this paper, we propose the first unified approach, HMD-NeMo, that addresses plausible and accurate full body motion generation even when the hands may be only partially visible. HMD-NeMo is a lightweight neural network that predicts the full body motion in an online and real-time fashion. At the heart of HMD-NeMo is the spatio-temporal encoder with novel temporally adaptable mask tokens that encourage plausible motion in the

We combine polarized infrared magneto-transmission and Faraday angle rotation measurements to map the collective excitations of the van der Waals antiferromagnet FePS$_3$. Below the Néel temperature ($T_\mathrm{N} \approx 118~\mathrm{K}$), the phonon spectrum becomes strongly anisotropic, reflecting the underlying zigzag antiferromagnetic order. In contrast, a prominent excitation at $122~\mathrm{cm}^{-1}$ ($15$~meV) is polarization-independent, hardens on cooling, and splits linearly with magnetic field, identifying its magnetic origin. From absolute transmission and Faraday rotation, we reconstruct the circular optical conductivities and reveal a pronounced dichroism of the field-split excitations. The upper branch near $129~\mathrm{cm}^{-1}$ exhibits a reduced dichroic response, consistent with hybridization with a nearby infrared phonon. Several phonon modes exhibit sizable Faraday rotation, providing evidence for spin-phonon coupling and demonstrating that lattice vibrations acquire magnetic-field-dependent optical activity. In addition, additional excitations appear in the infrared spectra and a broad mid-infrared feature near $900~\mathrm{cm}^{-1}$ emerges only below $T_\mat

Here, we use atomic resolution scanning transmission electron microscopy (STEM) and first principles calculations to study the atomic and electronic structure of strongly charged domain walls in $α$-In$_2$Se$_3$. STEM imaging and density functional theory (DFT) show that head-to-head (HH) domain walls contain a layer of nonpolar $β$-In$_2$Se$_3$, whereas tail-to-tail (TT) domain walls are atomically abrupt. We apply 4D STEM and multislice electron ptychography to map ferroelectric domains in 2D and 3D, showing that nearly $180^\circ$ domain walls exhibit complex, curved 3D structures that differ from ideal $180^\circ$ structures. Band structure calculations show localized conducting states within a $\sim$ 1 nm thick layer at both HH and TT domain walls, such as a midgap state at the $β$ layer of the HH domain wall. These properties make strongly charged domain walls in $α$-In$_2$Se$_3$ excellent candidates for realizing 2D electron or hole gases and domain wall engineering in van der Waals ferroelectrics.

In the everyday context, e.g., a household, HMD users remain a part of the social life for Non-HMD users being co-located with them. Due to the social context situations arise that demand interaction between the HMD and the Non-HMD user. We focus on the challenge that the Non-HMD user is not able to interpret the HMD user's state -- e.g., attentiveness; the need for assistance --, as the HMD covers the wearer's face. We propose a front facing display attached to the HMD that supports collaboration by showing the state. We explore the impact of abstract and realistic visualizations for such displays on collaborative performance and social presence in a within-subject user study (N=25). We present to the Non-HMD user (1) a blank screen (baseline), (2) textual representation of the user's state and (3) a representation that looks like the HMD is see-through. The results show positive effects for textual representation on collaborative performance and a positive effect of realistic representation on social presence. We conclude that when developing HMDs we need to take into account the social needs of everyday life to reduce the risk of social separation in a household context.

Charged domain walls (CDW) in ferroelectrics are emerging as functional interfaces with potential applications in nonvolatile memory, logic, and neuromorphic computing. However, CDWs in conventional ferroelectrics are vertical, buried, or electrically inaccessible interfaces that prevent their use in functional devices. Here, we overcome these challenges by stacking two opposite polar domains of van der Waals ferroelectric $α$-In$_2$Se$_3$ to generate artificial head-head (H-H) CDWs and use edge contact to fabricate charged domain wall-based field-effect transistors (CDW-FET). We relate the atomic structure to the temperature-dependent electrical and magneto-transport of the CDW-FET. CDW-FETs exhibit a metal-to-insulator transition with decreasing temperature and enhanced conductance and field-effect mobility compared to single domain $α$-In$_2$Se$_3$. We identify two regimes of transport: variable range hopping due to disorder in the band edge below 70 K and thermally activated interfacial trap-assisted transport above 70 K. The CDW-FETs show room-temperature resistance down to 3.1 k$Ω$ which is 2-9 orders of magnitude smaller than the single CDW in thin-film ferroelectrics. These

Head-mounted displays (HMDs) serve as indispensable devices for observing extended reality (XR) environments and virtual content. However, HMDs present an obstacle to external recording techniques as they block the upper face of the user. This limitation significantly affects social XR applications, specifically teleconferencing, where facial features and eye gaze information play a vital role in creating an immersive user experience. In this study, we propose a new network for expression-aware video inpainting for HMD removal (EVI-HRnet) based on generative adversarial networks (GANs). Our model effectively fills in missing information with regard to facial landmarks and a single occlusion-free reference image of the user. The framework and its components ensure the preservation of the user's identity across frames using the reference frame. To further improve the level of realism of the inpainted output, we introduce a novel facial expression recognition (FER) loss function for emotion preservation. Our results demonstrate the remarkable capability of the proposed framework to remove HMDs from facial videos while maintaining the subject's facial expression and identity. Moreover,

To date, the widely adopted way to perform fixation collection in panoptic video is based on a head-mounted display (HMD), where users' fixations are collected while wearing an HMD to explore the given panoptic scene freely. However, this widely-used data collection method is insufficient for training deep models to accurately predict which regions in a given panoptic are most important when it contains intermittent salient events. The main reason is that there always exist "blind zooms" when using HMD to collect fixations since the users cannot keep spinning their heads to explore the entire panoptic scene all the time. Consequently, the collected fixations tend to be trapped in some local views, leaving the remaining areas to be the "blind zooms". Therefore, fixation data collected using HMD-based methods that accumulate local views cannot accurately represent the overall global importance - the main purpose of fixations - of complex panoptic scenes. To conquer, this paper introduces the auxiliary window with a dynamic blurring (WinDB) fixation collection approach for panoptic video, which doesn't need HMD and is able to well reflect the regional-wise importance degree. Using our

The layered metamagnet CrSBr offers a rich interplay between magnetic, optical and electrical properties that can be extended down to the two-dimensional (2D) limit. Despite the extensive research regarding the long-range magnetic order in magnetic van der Waals materials, short-range correlations have been loosely investigated. By using Small-Angle Neutron Scattering (SANS) we show the formation of short-range magnetic regions in CrSBr with correlation lengths that increase upon cooling up to ca. 3 nm at the antiferromagnetic ordering temperature (TN ~ 140 K). Interestingly, these ferromagnetic correlations start developing below 200 K, i.e., well above TN. Below TN, these correlations rapidly decrease and are negligible at low-temperatures. The experimental results are well-reproduced by an effective spin Hamiltonian, which pinpoints that the short-range correlations in CrSBr are intrinsic to the monolayer limit, and discard the appearance of any frustrated phase in CrSBr at low-temperatures within our experimental window between 2 and 200 nm. Overall, our results are compatible with a spin freezing scenario of the magnetic fluctuations in CrSBr and highlight SANS as a powerful t

In this paper, we investigate hand gesture classifiers that rely upon the abstracted 'skeletal' data recorded using the RGB-Depth sensor. We focus on 'skeletal' data represented by the body joint coordinates, from the Praxis dataset. The PRAXIS dataset contains recordings of patients with cortical pathologies such as Alzheimer's disease, performing a Praxis test under the direction of a clinician. In this paper, we propose hand gesture classifiers that are more effective with the PRAXIS dataset than previously proposed models. Body joint data offers a compressed form of data that can be analyzed specifically for hand gesture recognition. Using a combination of windowing techniques with deep learning architecture such as a Recurrent Neural Network (RNN), we achieved an overall accuracy of 70.8% using only body joint data. In addition, we investigated a long-short-term-memory (LSTM) to extract and analyze the movement of the joints through time to recognize the hand gestures being performed and achieved a gesture recognition rate of 74.3% and 67.3% for static and dynamic gestures, respectively. The proposed approach contributed to the task of developing an automated, accurate, and in

Spintronics is concerned with replacing charge current with current of spin, the electron's intrinsic angular momentum. In magnetic insulators, spin currents are carried by magnons, the quanta of spin-wave excitations on top of the magnetically ordered state. Magnon spin currents are especially promising for information technology due to their low intrinsic damping, non-reciprocal transport, micrometer wavelengths at microwave frequencies, and strong interactions that enable signal transduction. In this perspective, we give our view on the progress and challenges towards realizing magnon spintronics based on atomically thin Van der Waals magnets, a recently discovered class of magnetic materials of which the tunability and versatility has attracted a great deal of ongoing research.

For the development of nanoscale electronics and photonics using atomically thin two-dimensional (2D) materials, it is important to realize van der Waals (vdW) interfaces with low thermal resistance, to minimize performance reduction caused by heat accumulation. However, characterizing the thermal interface resistance between vdW materials is still a challenge. Here, we introduce a novel optomechanical methodology to characterize the thermal transport across interfaces in 2D heterostructures. We first determine the specific heat and thermal conductivity as the function of temperature for the upper and lower material layers separately and then extract the thermal boundary conductance (TBC) of the heterostructure from its thermal time constant. We obtain a TBC of $2.41 \pm 1.03$ and $4.14 \pm 1.74$~\si{MW m^{2} K^{-1}} for FePS$_3$/WSe$_2$ and MoS$_2$/FePS$_3$ interfaces, respectively, which are comparable to values reported in the literature. Moreover, they agree with a Debye model including the acoustic impedance mismatch of flexural phonons. This work enables efficient thermal management down to the nanoscale and offers new insights into energy dissipation in vdW heterostructures.