HI-GAN: Hierarchical Inpainting GAN with Auxiliary Inputs for Combined RGB and Depth Inpainting

Inpainting involves filling in missing pixels or areas in an image, a crucial technique employed in Mixed Reality environments for various applications, particularly in Diminished Reality (DR) where content is removed from a user's visual environment. Existing methods rely on digital replacement techniques which necessitate multiple cameras and incur high costs. AR devices and smartphones use ToF depth sensors to capture scene depth maps aligned with RGB images. Despite speed and affordability, ToF cameras create imperfect depth maps with missing pixels. To address the above challenges, we propose Hierarchical Inpainting GAN (HI-GAN), a novel approach comprising three GANs in a hierarchical fashion for RGBD inpainting. EdgeGAN and LabelGAN inpaint masked edge and segmentation label images respectively, while CombinedRGBD-GAN combines their latent representation outputs and performs RGB and Depth inpainting. Edge images and particularly segmentation label images as auxiliary inputs significantly enhance inpainting performance by complementary context and hierarchical optimization. We believe we make the first attempt to incorporate label images into inpainting process.Unlike previous approaches requiring multiple sequential models and separate outputs, our work operates in an end-to-end manner, training all three models simultaneously and hierarchically. Specifically, EdgeGAN and LabelGAN are first optimized separately and further optimized inside CombinedRGBD-GAN to enhance inpainting quality. Experiments demonstrate that HI-GAN works seamlessly and achieves overall superior performance compared with existing approaches

RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction

Efforts to predict stock market outcomes have yielded limited success due to the inherently stochastic nature of the market, influenced by numerous unpredictable factors. Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. To bridge this gap, we propose a novel model, RAGIC, which introduces sequence generation for stock interval prediction to quantify uncertainty more effectively. Our approach leverages a Generative Adversarial Network (GAN) to produce future price sequences infused with randomness inherent in financial markets. RAGIC's generator includes a risk module, capturing the risk perception of informed investors, and a temporal module, accounting for historical price trends and seasonality. This multi-faceted generator informs the creation of risk-sensitive intervals through statistical inference, incorporating horizon-wise insights. The interval's width is carefully adjusted to reflect market volatility. Importantly, our approach relies solely on publicly available data and incurs only low computational overhead. RAGIC's evaluation across globally recognized broad-based indices demonstrates its balanced performance, offering both accuracy and informativeness. Achieving a consistent 95% coverage, RAGIC maintains a narrow interval width. This promising outcome suggests that our approach effectively addresses the challenges of stock market prediction while incorporating vital risk considerations

Focal surfaces of discrete geometry

The differential geometry of smooth three-dimensional surfaces can be interpreted from one of two perspectives: in terms of oriented frames located on the surface, or in terms of a pair of associated focal surfaces. These focal surfaces are swept by the loci of the principal curvatures' radii. In this article, we develop a focal-surface-based differential geometry interpretation for discrete mesh surfaces. Focal surfaces have many useful properties. For instance, the normal of each focal surface indicates a principal direction of the corresponding point on the original surface. We provide algorithms to robustly approximate the focal surfaces of a triangle mesh with known or estimated normals. Our approach locally parameterizes the surface normals about a point by their intersections with a pair of parallel planes. We show neighboring normal triplets are constrained to pass simultaneously through two slits, which are parallel to the specified parametrization planes and rule the focal surfaces. We develop both CPU and GPU-based algorithms to efficiently approximate these two slits and, hence, the focal meshes. Our focal mesh estimation also provides a novel discrete shape operator that simultaneously estimates the principal curvatures and principal directions.Engineering and Applied Science

Chrysanthemum species used as food and medicine: Understanding quality differences on the global market

Background: Chrysanthemum flowers [Ch. x morifolium (Ramat.) Hemsl. and Ch. indicum L.] are a globally used and pharmacologically interesting botanical drug, however, with variable product quality. Objective: We aim at understanding the chemical variability of primary material available commercially based on different origins and associated quality problems like contamination with heavy metals. This needs to be assessed in the context of the current regulations for this botanical drug and associated problems. Material and Methods: 15 C. indicum L. and 50 C. x morifolium (Ramat.) Hemsl., including a range of geographical cultivars recognized in China, samples from the USA, Europe and China were analyzed using High Performance Thin Layer Chromatography (HPTLC) to compare their general chemical profile. Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) was used to quantify heavy metal contamination. Results: The: HPTLC fingerprints of C. indicum samples are clearly distinguishable from C. x morifolium. Fingerprints of samples from the same cultivars collected from markets in different countries (USA and China) show different patterns. Large variance of fingerprints within each cultivar group was observed. The heavy metal analysis showed excessive amounts of some harmful heavy metal in some commercial products with excessive cadmium being the most frequent problem. Conclusions: The Chinese medicinal cultivars vary. Differences between samples sourced from the USA and China might be ascribable to geographical factors (e.g. soil composition), degradation during transport/storage or adulteration, but geographical differences should also be taken into account. Importantly, a much more detailed definition of the drug are needed for better quality control. In addition, with continuous contamination problem observed, a more widespread regulation is an essential requirement for better quality

SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.Comment: Accepted by AAAI 2023, appendix included. 9 pages + 5 pages appendix, 12 figures, in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23), Feb 202

Predicting coordination variability of selected lower extremity couplings during a cutting movement:an investigation of deep neural networks with the LSTM structure

There are still few portable methods for monitoring lower limb joint coordination during the cutting movements (CM). This study aims to obtain the relevant motion biomechanical parameters of the lower limb joints at 90°, 135°, and 180° CM by collecting IMU data of the human lower limbs, and utilizing the Long Short-Term Memory (LSTM) deep neural-network framework to predict the coordination variability of selected lower extremity couplings at the three CM directions. There was a significant (p < 0.001) difference between the three couplings during the swing, especially at 90° vs the other directions. At 135° and 180°, t13-he coordination variability of couplings was significantly greater than at 90° (p < 0.001). It is important to note that the coordination variability of Hip rotation/Knee flexion-extension was significantly higher at 90° than at 180° (p < 0.001). By the LSTM, the CM coordination variability for 90° (CMC = 0.99063, RMSE = 0.02358), 135° (CMC = 0.99018, RMSE = 0.02465) and 180° (CMC = 0.99485, RMSE = 0.01771) were accurately predicted. The predictive model could be used as a reliable tool for predicting the coordination variability of different CM directions in patients or athletes and real-world open scenarios using inertial sensors

Drosophila TRPA1 isoforms detect UV light via photochemical production of H2O2

The transient receptor potential A1 (TRPA1) channel is an evolutionarily conserved detector of temperature and irritant chemicals. Here, we show that two specific isoforms of TRPA1 in Drosophila are H2O2 sensitive and that they can detect strong UV light via sensing light-induced production of H2O2. We found that ectopic expression of these H2O2-sensitive Drosophila TRPA1 (dTRPA1) isoforms conferred UV sensitivity to light-insensitive HEK293 cells and Drosophila neurons, whereas expressing the H2O2-insensitive isoform did not. Curiously, when expressed in one specific group of motor neurons in adult flies, the H2O2-sensitive dTRPA1 isoforms were as competent as the blue light-gated channelrhodopsin-2 in triggering motor output in response to light. We found that the corpus cardiacum (CC) cells, a group of neuroendocrine cells that produce the adipokinetic hormone (AKH) in the larval ring gland endogenously express these H2O2-sensitive dTRPA1 isoforms and that they are UV sensitive. Sensitivity of CC cells required dTRPA1 and H2O2 production but not conventional phototransduction molecules. Our results suggest that specific isoforms of dTRPA1 can sense UV light via photochemical production of H2O2. We speculate that UV sensitivity conferred by these isoforms in CC cells may allow young larvae to activate stress response--a function of CC cells--when they encounter strong UV, an aversive stimulus for young larvae

Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications

Understanding how the surrounding environment changes is crucial for performing downstream tasks safely and reliably in autonomous driving applications. Recent occupancy estimation techniques using only camera images as input can provide dense occupancy representations of large-scale scenes based on the current observation. However, they are mostly limited to representing the current 3D space and do not consider the future state of surrounding objects along the time axis. To extend camera-only occupancy estimation into spatiotemporal prediction, we propose Cam4DOcc, a new benchmark for camera-only 4D occupancy forecasting, evaluating the surrounding scene changes in a near future. We build our benchmark based on multiple publicly available datasets, including nuScenes, nuScenes-Occupancy, and Lyft-Level5, which provides sequential occupancy states of general movable and static objects, as well as their 3D backward centripetal flow. To establish this benchmark for future research with comprehensive comparisons, we introduce four baseline types from diverse camera-based perception and prediction implementations, including a static-world occupancy model, voxelization of point cloud prediction, 2D-3D instance-based prediction, and our proposed novel end-to-end 4D occupancy forecasting network. Furthermore, the standardized evaluation protocol for preset multiple tasks is also provided to compare the performance of all the proposed baselines on present and future occupancy estimation with respect to objects of interest in autonomous driving scenarios. The dataset and our implementation of all four baselines in the proposed Cam4DOcc benchmark will be released here: https://github.com/haomo-ai/Cam4DOcc

Biomechanical effects of exercise fatigue on the lower limbs of men during the forward lunge

Background: During competition and training, exercises involving the lungs may occur throughout the sport, and fatigue is a major injury risk factor in sport, before and after fatigue studies of changes in the lungs are relatively sparse. This study is to investigate into how fatigue affects the lower limb’s biomechanics during a forward lunge.Methods: 15 healthy young men participate in this study before and after to exposed to a fatigue protocol then we tested the forward lunge to obtain kinematic, kinetic changing during the task, and to estimate the corresponding muscles’ strength changes in the hip, knee, and ankle joints. The measurement data before and after the fatigue protocol were compared with paired samples t-test.Results: In the sagittal and horizontal planes of the hip and knee joints, in both, the peak angles and joint range of motion (ROM) increased, whereas the moments in the sagittal plane of the knee joint smaller. The ankle joint’s maximum angle smaller after fatigue. Peak vertical ground reaction force (vGRF) and peak contact both significantly smaller after completing the fatigue protocol and the quadriceps mean and maximum muscular strength significantly increased.Conclusion: After completing a fatigue protocol during lunge the hip, knee, and ankle joints become less stable in both sagittal and horizontal planes, hip and knee range of motion becomes greater. The quadriceps muscles are more susceptible to fatigue and reduced muscle force. Trainers should focus more on the thigh muscle groups

Identification of a prognostic signature and ENTR1 as a prognostic biomarker for colorectal mucinous adenocarcinoma

BackgroundMucinous adenocarcinoma (MAC) is a unique clinicopathological colorectal cancer (CRC) type that has been recognized as a separate entity from non-mucinous adenocarcinoma (NMAC), with distinct clinical, pathologic, and molecular characteristics. We aimed to construct prognostic signatures and identifying candidate biomarkers for patients with MAC.MethodsDifferential expression analysis, weighted correlation network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO)-Cox regression model were used to identify hub genes and construct a prognostic signature based on RNA sequencing data from TCGA datasets. The Kaplan-Meier survival curve, gene set enrichment analysis (GSEA), cell stemness, and immune infiltration were analyzed. Biomarker expression in MAC and corresponding normal tissues from patients operated in 2020 was validated using immunohistochemistry.ResultsWe constructed a prognostic signature based on ten hub genes. Patients in the high-risk group had significantly worse overall survival (OS) than patients in the low-risk group (p &lt; 0.0001). We also found that ENTR1 was closely associated with OS (p = 0.016). ENTR1 expression was significantly positively correlated with cell stemness of MAC (p &lt; 0.0001) and CD8+ T cell infiltration (p = 0.01), whereas it was negatively associated with stromal scores (p = 0.03). Finally, the higher expression of ENTR1 in MAC tissues than in normal tissues was validated.ConclusionWe established the first MAC prognostic signature, and determined that ENTR1 could serve as a prognostic marker for MAC