Nanoparticle enhanced evaporation of liquids: A case study of silicone oil and water

Evaporation is a fundamental physical phenomenon, of which many challenging questions remain unanswered. Enhanced evaporation of liquids in some occasions is of enormous practical significance. Here we report the enhanced evaporation of the nearly permanently stable silicone oil by dispersing with nanopariticles including CaTiO3, anatase and rutile TiO2. The results can inspire the research of atomistic mechanism for nanoparticle enhanced evaporation and exploration of evaporation control techniques for treatment of oil pollution and restoration of dirty water

Existence of Solutions for Two-Point Boundary Value Problem of Fractional Differential Equations at Resonance

We establish the existence results for two-point boundary value problem of fractional differential equations at resonance by means of the coincidence degree theory. Furthermore, a result on the uniqueness of solution is obtained. We give an example to demonstrate our results

Learning distributions with Particle Mirror Descent

As an effective and provable primal method to estimate posterior distribution, Particle Mirror Descent is appealing for its simplicity and flexibility. In this thesis we explore the applications of Particle Mirror Descent in both supervised and unsupervised learning. In the general classification problem with a parametric discriminative function, we seek a posterior distribution over parameters that maximizes classification accuracy. Existing algorithms usually solve the dual problem and the number of variables to optimize depends on the number of examples in the dataset. Therefore such methods suffer from the poor scalability in large datasets. We propose Constrained Particle Mirror Descent to effectively estimate posterior distribution in primal space even with expectation constraint. By marrying Bayesian probabilistic inference and deep neural networks, deep generative networks have shown remarkable success in various kinds of generative tasks. However, such models usually make an assumption that posterior distribution can be simply characterized as a Gaussian distribution, which is not always true since real data like images and audios yield complex structures in latent space. Motivated by the recent wide application of multi-modal posterior, we introduce a variant of Variational Auto-encoder model that uses a mixture of customized kernels as posterior distribution in latent space. Our deep generative model produces visually plausible images as well as good clustering performance using latent representations

N-(2,3-Dimeth­oxy­benzyl­idene)naphthalen-1-amine

The title compound, C19H17NO2, represents a trans isomer with respect to the C=N bond. The dihedral angle between the planes of the naphthyl ring system and the benzene ring is 71.70 (3)°. In the crystal, weak C—H⋯O hydrogen bonding is present

Neural Interactive Keypoint Detection

This work proposes an end-to-end neural interactive keypoint detection framework named Click-Pose, which can significantly reduce more than 10 times labeling costs of 2D keypoint annotation compared with manual-only annotation. Click-Pose explores how user feedback can cooperate with a neural keypoint detector to correct the predicted keypoints in an interactive way for a faster and more effective annotation process. Specifically, we design the pose error modeling strategy that inputs the ground truth pose combined with four typical pose errors into the decoder and trains the model to reconstruct the correct poses, which enhances the self-correction ability of the model. Then, we attach an interactive human-feedback loop that allows receiving users' clicks to correct one or several predicted keypoints and iteratively utilizes the decoder to update all other keypoints with a minimum number of clicks (NoC) for efficient annotation. We validate Click-Pose in in-domain, out-of-domain scenes, and a new task of keypoint adaptation. For annotation, Click-Pose only needs 1.97 and 6.45 NoC@95 (at precision 95%) on COCO and Human-Art, reducing 31.4% and 36.3% efforts than the SOTA model (ViTPose) with manual correction, respectively. Besides, without user clicks, Click-Pose surpasses the previous end-to-end model by 1.4 AP on COCO and 3.0 AP on Human-Art. The code is available at https://github.com/IDEA-Research/Click-Pose.Comment: Accepted to ICCV 202

One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer

Whole-body mesh recovery aims to estimate the 3D human body, face, and hands parameters from a single image. It is challenging to perform this task with a single network due to resolution issues, i.e., the face and hands are usually located in extremely small regions. Existing works usually detect hands and faces, enlarge their resolution to feed in a specific network to predict the parameter, and finally fuse the results. While this copy-paste pipeline can capture the fine-grained details of the face and hands, the connections between different parts cannot be easily recovered in late fusion, leading to implausible 3D rotation and unnatural pose. In this work, we propose a one-stage pipeline for expressive whole-body mesh recovery, named OSX, without separate networks for each part. Specifically, we design a Component Aware Transformer (CAT) composed of a global body encoder and a local face/hand decoder. The encoder predicts the body parameters and provides a high-quality feature map for the decoder, which performs a feature-level upsample-crop scheme to extract high-resolution part-specific features and adopt keypoint-guided deformable attention to estimate hand and face precisely. The whole pipeline is simple yet effective without any manual post-processing and naturally avoids implausible prediction. Comprehensive experiments demonstrate the effectiveness of OSX. Lastly, we build a large-scale Upper-Body dataset (UBody) with high-quality 2D and 3D whole-body annotations. It contains persons with partially visible bodies in diverse real-life scenarios to bridge the gap between the basic task and downstream applications.Comment: Accepted to CVPR2023; Top-1 on AGORA SMPLX benchmark; Project Page: https://osx-ubody.github.io

Effects of Salts on Structural, Physicochemical and Rheological Properties of Low-Methoxyl Pectin/Sodium Caseinate Complex

The addition of salts is an effective way to improve the properties of polysaccharide/protein complexes for use in foods. However, there is no comparative study on the effects of different ions on the complex system of low methoxyl pectin (LMP)/ sodium caseinate (CAS) complex. The effects of different concentrations of three salt ions (Na+, K+, Ca2+) on the physicochemical and rheological properties of the LMP/CAS complex were determined in this study, and the structure of LMP/CAS complex was characterized. The results showed that the addition of these three salt ions affected zeta potential, particle size, and turbidity of the LMP/CAS complex, and lead the LMP/CAS complex to form a more regular and uniform network structure, which helped improve its stability, solubility, and rheological properties. The particle size and turbidity value of the complex achieved with Ca2+ were higher than those obtained using Na+ and K+. Moreover, the secondary structure of the proteins in the complex changed to adding high concentrations of Ca2+. Our study provides valuable information for the application of the LMP/CAS complex in the food industry

Synchronous and subsynchronous vibration under the combined effect of bearings and seals: numerical simulation and its experimental validation

A three-dimensional computational fluid dynamics (CFD) model of a labyrinth seal was established in order to investigate the influence mechanism of combined effects between bearings and labyrinth seals on the dynamic characteristics of the rotor-bearing-seal system. The dynamic coefficients of the labyrinth seal for various rotating speeds were calculated. Results show that the absolute values of cross-coupled coefficients increase with the increasing rotating speed, while the absolute values of direct coefficients decrease slightly. The positive preswirl at the inlet tends to intensify the increase of cross-coupled coefficients and the decrease of direct coefficients. The negative preswirl shows the opposite effect. A finite element model was further setup. Results show that the labyrinth seal has a large influence on the synchronous response of rotor in the resonant region due to its damping effect. For other speeds, it has a minor effect. The labyrinth seal may promote the instability of the rotor-bearing-seal system. The subsynchronous vibration increases significantly when the seal force is taken into account. The system stability can be generally enhanced by introducing the negative preswirl at the inlet. Results also show that the detrimental influence of the labyrinth seal can be compensated by using suitable bearings. A proper bearing configuration can be designed to reduce the risks of rotordynamic instabilities due to seals. An experimental test was finally performed, and it shows good agreements with the numerical simulation

Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes

Humans have long been recorded in a variety of forms since antiquity. For example, sculptures and paintings were the primary media for depicting human beings before the invention of cameras. However, most current human-centric computer vision tasks like human pose estimation and human image generation focus exclusively on natural images in the real world. Artificial humans, such as those in sculptures, paintings, and cartoons, are commonly neglected, making existing models fail in these scenarios. As an abstraction of life, art incorporates humans in both natural and artificial scenes. We take advantage of it and introduce the Human-Art dataset to bridge related tasks in natural and artificial scenarios. Specifically, Human-Art contains 50k high-quality images with over 123k person instances from 5 natural and 15 artificial scenarios, which are annotated with bounding boxes, keypoints, self-contact points, and text information for humans represented in both 2D and 3D. It is, therefore, comprehensive and versatile for various downstream tasks. We also provide a rich set of baseline results and detailed analyses for related tasks, including human detection, 2D and 3D human pose estimation, image generation, and motion transfer. As a challenging dataset, we hope Human-Art can provide insights for relevant research and open up new research questions.Comment: CVPR202

Introducing Depth into Transformer-based 3D Object Detection

In this paper, we present DAT, a Depth-Aware Transformer framework designed for camera-based 3D detection. Our model is based on observing two major issues in existing methods: large depth translation errors and duplicate predictions along depth axes. To mitigate these issues, we propose two key solutions within DAT. To address the first issue, we introduce a Depth-Aware Spatial Cross-Attention (DA-SCA) module that incorporates depth information into spatial cross-attention when lifting image features to 3D space. To address the second issue, we introduce an auxiliary learning task called Depth-aware Negative Suppression loss. First, based on their reference points, we organize features as a Bird's-Eye-View (BEV) feature map. Then, we sample positive and negative features along each object ray that connects an object and a camera and train the model to distinguish between them. The proposed DA-SCA and DNS methods effectively alleviate these two problems. We show that DAT is a versatile method that enhances the performance of all three popular models, BEVFormer, DETR3D, and PETR. Our evaluation on BEVFormer demonstrates that DAT achieves a significant improvement of +2.8 NDS on nuScenes val under the same settings. Moreover, when using pre-trained VoVNet-99 as the backbone, DAT achieves strong results of 60.0 NDS and 51.5 mAP on nuScenes test. Our code will be soon.Comment: revisio