R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement

Predicting the future motion of dynamic agents is of paramount importance to ensure safety or assess risks in motion planning for autonomous robots. In this paper, we propose a two-stage motion prediction method, referred to as R-Pred, that effectively utilizes both the scene and interaction context using a cascade of the initial trajectory proposal network and the trajectory refinement network. The initial trajectory proposal network produces M trajectory proposals corresponding to M modes of a future trajectory distribution. The trajectory refinement network enhances each of M proposals using 1) the tube-query scene attention (TQSA) and 2) the proposal-level interaction attention (PIA). TQSA uses tube-queries to aggregate the local scene context features pooled from proximity around the trajectory proposals of interest. PIA further enhances the trajectory proposals by modeling inter-agent interactions using a group of trajectory proposals selected based on their distances from neighboring agents. Our experiments conducted on the Argoverse and nuScenes datasets demonstrate that the proposed refinement network provides significant performance improvements compared to the single-stage baseline and that R-Pred achieves state-of-the-art performance in some categories of the benchmark

Comparing the Effects of Perceived Enjoyment and Perceived Risk on Hedonic/Utilitarian Smartphone Applications

Despite the widespread adoption of smartphone applications, empirical research that examines the user acceptance on different application types is still scare. This paper empirically compares the effects of perceived enjoyment and perceived risk on hedonic and utilitarian smartphone applications. Our analyses show that perceived enjoyment is a stronger determinant of intention to use a hedonic smartphone application than a utilitarian application. Perceived risk has a significant negative influence on intention to use utilitarian smartphone applications, while it does not have a significant impact on intention to use hedonic applications. Surprisingly, perceived risk has an insignificant effect on perceived usefulness both in utilitarian and hedonic smartphone applications

MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation

We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting. Because multi-view images provide a variety of information about the scene, multi-view images in object-level inverse rendering have been taken for granted. However, owing to the absence of multi-view HDR synthetic dataset, scene-level inverse rendering has mainly been studied using single-view image. We were able to successfully perform scene-level inverse rendering using multi-view images by expanding OpenRooms dataset and designing efficient pipelines to handle multi-view images, and splitting spatially-varying lighting. Our experiments show that the proposed method not only achieves better performance than single-view-based methods, but also achieves robust performance on unseen real-world scene. Also, our sophisticated 3D spatially-varying lighting volume allows for photorealistic object insertion in any 3D location.Comment: Accepted by CVPR 2023; Project Page is https://bring728.github.io/mair.project

Current clinical application of dantrolene sodium

Dantrolene sodium (DS) was first introduced as an oral antispasmodic drug. However, in 1975, DS was demonstrated to be effective for managing malignant hyperthermia (MH) and was adopted as the primary therapeutic drug after intravenous administration. However, it is difficult to administer DS intravenously to manage MH. MH is life-threatening, pharmacogenomically related, and induced by depolarizing neuromuscular blocking agents or inhalational anesthetics. All anesthesiologists should know the pharmacology of DS. DS suppresses Ca2+ release from ryanodine receptors (RyRs). RyRs are expressed in various tissues, although their distribution differs among subtypes. The anatomical and physiological functions of RyRs have also been demonstrated as effective therapeutic drugs for cardiac arrhythmias, Alzheimer’s disease, and other RyR-related diseases. Recently, a new formulation was introduced that enhanced the hydrophilicity of the lipophilic DS. The authors summarize the pharmacological properties of DS and comment on its indications, contraindications, adverse effects, and interactions with other drugs by reviewing reference articles

Chronic exposure to dexamethasone may not affect sugammadex reversal of rocuronium-induced neuromuscular blockade: an in vivo study on rats

Background Chronic glucocorticoid exposure is associated with resistance to nondepolarizing neuromuscular blocking agents. Therefore, we hypothesized that sugammadex-induced recovery would occur more rapidly in subjects exposed to chronic dexamethasone compared to those who were not exposed. This study evaluated the sugammadex-induced recovery profile after neuromuscular blockade (NMB) in rats exposed to chronic dexamethasone. Methods Sprague–Dawley rats were allocated to three groups (dexamethasone, control, and pair-fed group) for the in vivo study. The mice received daily intraperitoneal dexamethasone injections (500 μg/kg) or 0.9% saline for 15 days. To achieve complete NMB, 3.5 mg/kg rocuronium was administered on the sixteenth day. The recovery time to a train-of-four ratio ≥ 0.9 was measured to evaluate the complete recovery following the sugammadex injection. Results Among the groups, no significant differences were observed in the recovery time to a train-of-four ratio ≥ 0.9 following sugammadex administration (P = 0.531). The time to the second twitch of the train-of-four recovery following rocuronium administration indicated that the duration of NMB was significantly shorter in Group D than that in Groups C and P (P = 0.001). Conclusions Chronic exposure to dexamethasone did not shorten the recovery time of sugammadex-induced NMB reversal. However, the findings of this study indicated that no adjustments to sugammadex dosage or route of administration is required, even in patients undergoing long-term steroid treatment

Quality Assessment of In-the-Wild Videos

Quality assessment of in-the-wild videos is a challenging problem because of the absence of reference videos and shooting distortions. Knowledge of the human visual system can help establish methods for objective quality assessment of in-the-wild videos. In this work, we show two eminent effects of the human visual system, namely, content-dependency and temporal-memory effects, could be used for this purpose. We propose an objective no-reference video quality assessment method by integrating both effects into a deep neural network. For content-dependency, we extract features from a pre-trained image classification neural network for its inherent content-aware property. For temporal-memory effects, long-term dependencies, especially the temporal hysteresis, are integrated into the network with a gated recurrent unit and a subjectively-inspired temporal pooling layer. To validate the performance of our method, experiments are conducted on three publicly available in-the-wild video quality assessment databases: KoNViD-1k, CVD2014, and LIVE-Qualcomm, respectively. Experimental results demonstrate that our proposed method outperforms five state-of-the-art methods by a large margin, specifically, 12.39%, 15.71%, 15.45%, and 18.09% overall performance improvements over the second-best method VBLIINDS, in terms of SROCC, KROCC, PLCC and RMSE, respectively. Moreover, the ablation study verifies the crucial role of both the content-aware features and the modeling of temporal-memory effects. The PyTorch implementation of our method is released at https://github.com/lidq92/VSFA.Comment: 9 pages, 7 figures, 4 tables. ACM Multimedia 2019 camera ready. -> Update alignment formatting of Table

Hypocapnia Attenuates, and Nitrous Oxide Disturbs the Cerebral Oximetric Response to the Rapid Introduction of Desflurane

The aim of this study was to develop a nonlinear mixed-effects model for the increase in cerebral oximetry (rSO2) during the rapid introduction of desflurane, and to determine the effect of hypocapnia and N2O on the model. Twelve American Society of Anesthesiologist physical status class 1 and 2 subjects were allocated randomly into an Air and N2O group. After inducing anesthesia, desflurane was then increased abruptly from 4.0 to 12.0%. The PETCO2, PETDESF and rSO2 were recorded at 12 predetermined periods for the following 10 min. The maximum increase in rSO2 reached +24-25% during normocapnia. The increase in rSO2 could be fitted to a four parameter logistic equation as a function of the logarithm of PETDESF. Hypocapnia reduced the maximum response of rSO2, shifted the EC50 to the right, and increased the slope in the Air group. N2O shifted the EC50 to the right, and reduced the slope leaving the maximum rSO2 unchanged. The N2O-effects disappeared during hypocapnia. The cerebrovascular reactivity of rSO2 to CO2 is still preserved during the rapid introduction of desflurane. N2O slows the response of rSO2. Hypocapnia overwhelms all the effects of N2O

Public awareness of advance care planning and hospice palliative care: a nationwide cross-sectional study in Korea

Abstract Context Advance care planning (ACP) and hospice palliative care (HPC) have potential benefits for individuals and health systems. Public awareness of them might increase their acceptance. Objectives To examine public awareness of ACP and HPC and related factors including individuals’ experience of health care among Korean population. Methods A cross-sectional study based on a nationally representative sample was conducted. Data from participants aged 15 years or older were examined. Socio-demographic characteristics, health-related factors, health care experience in the past year, and awareness of ACP and HPC were analyzed. Subgroup analysis was conducted to determine associations between specific experiences during outpatient visit and awareness of ACP and HPC. Results Of a total of 13,546 subjects, 39.3% and 35.7% reported awareness of ACP and HPC, respectively. About half (48.6%) of participants reported that they were completely unaware of ACP or HPC. Recent outpatient visit was positively associated with HPC awareness. Participants were more likely to recognize ACP or HPC if they had experience in hospitalization and health checkup over the past year and had trust in the medical system. Conversely, participants who had inadequate health care access due to cost burden showed low awareness of ACP and HPC. Conclusion There was a lack of public awareness of ACP and HPC. There were significant differences depending on various factors, especially individual health care experiences. Appropriate interventions are needed to facilitate discussion of ACP and HPC, thereby increasing public awareness

itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detection

Recently, point-cloud based 3D object detectors have achieved remarkable progress. However, most studies are limited to the development of deep learning architectures for improving only their accuracy. In this paper, we propose an autoencoder-style framework comprising channel-wise compression and decompression via interchange transfer for knowledge distillation. To learn the map-view feature of a teacher network, the features from a teacher and student network are independently passed through the shared autoencoder; here, we use a compressed representation loss that binds the channel-wised compression knowledge from both the networks as a kind of regularization. The decompressed features are transferred in opposite directions to reduce the gap in the interchange reconstructions. Lastly, we present an attentive head loss for matching the pivotal detection information drawn by the multi-head self-attention mechanism. Through extensive experiments, we verify that our method can learn the lightweight model that is well-aligned with the 3D point cloud detection task and we demonstrate its superiority using the well-known public datasets Waymo and nuScenes.Comment: 12 pages, 2 figures, 8 table