Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data

Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields. However, most previous works assumed that each view is complete and aligned. This leads to an inevitable deterioration in their performance when encountering practical problems such as missing or unaligned views. To address the challenge of representation learning on partially aligned multi-view data, we propose a new cross-view graph contrastive learning framework, which integrates multi-view information to align data and learn latent representations. Compared with current approaches, the proposed method has the following merits: (1) our model is an end-to-end framework that simultaneously performs view-specific representation learning via view-specific autoencoders and cluster-level data aligning by combining multi-view information with the cross-view graph contrastive learning; (2) it is easy to apply our model to explore information from three or more modalities/sources as the cross-view graph contrastive learning is devised. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method on the clustering and classification tasks

Microfluidic Mixing: A Review

The aim of microfluidic mixing is to achieve a thorough and rapid mixing of multiple samples in microscale devices. In such devices, sample mixing is essentially achieved by enhancing the diffusion effect between the different species flows. Broadly speaking, microfluidic mixing schemes can be categorized as either “active”, where an external energy force is applied to perturb the sample species, or “passive”, where the contact area and contact time of the species samples are increased through specially-designed microchannel configurations. Many mixers have been proposed to facilitate this task over the past 10 years. Accordingly, this paper commences by providing a high level overview of the field of microfluidic mixing devices before describing some of the more significant proposals for active and passive mixers

Comparison of the Offspring Sex Ratio Between Cleavage Stage Embryo Transfer and Blastocyst Transfer

SummaryObjectiveTo compare the sex ratio of offspring born after cleavage stage embryo transfer and blastocyst transfer.Materials and MethodsIn this retrospective study of embryo transfer (ET), we included 473 offspring from 446 deliveries during the period January 2002 to December 2007. Statistical analysis was performed on the sex ratio of offspring resulting from day 3 cleavage stage embryo transfer and from sequential blastocyst culture transfer.ResultsIn total, 446 patient deliveries were included in this analysis. There were 251 singleton pregnancies, 109 twin pregnancies, and four triplet pregnancies. The total number of offspring was 473, of which 118 resulted from day 3 ETs, and 355 resulted from blastocyst ETs. At our center, the influence on the sex ratio of cleavage stage ET and blastocyst-stage ET showed a bias towards males in both cases. The overall female to male ratio for offspring resulting from day 3 ETs was not significantly higher than the same ratio for offspring resulting from blastocyst ETs (p = 0.24; odds ratio, 0.762). The female to male ratio for either singleton births or multiple deliveries was also not significantly different between day 3 ETs and blastocyst ETs.ConclusionThe sex ratio was influenced by cleavage stage ET and blastocyst-stage ET. In both cases, there was a bias towards males. In addition, when blastocyst ET was compared with day 3 ET, there was no further increase in the percentage of male offspring

The roles of edge-based and surface-based information in the dynamic neural representation of objects.

We combined multivariate pattern analysis (MVPA) and electroencephalogram (EEG) to investigate the role of edge, color, and other surface information in the neural representation of visual objects. Participants completed a one-back task in which they were presented with color photographs, grayscale images, and line drawings of animals, tools, and fruits. Our results provide the first neural evidence that line drawings elicit similar neural activities as color photographs and grayscale images during the 175-305 ms window after the stimulus onset. Furthermore, we found that other surface information, rather than color information, facilitates decoding accuracy in the early stages of object representations and affects the speed of this. These results provide new insights into the role of edge-based and surface-based information in the dynamic process of neural representations of visual objects

Visualization of Photonic Band Structures via Far-field Measurements in SiNx Photonic Crystal Slabs

The band structures of the photonic crystal slabs play a significant role in manipulating the flow of light and pre-dicting exotic physics in photonics. In this letter, we show that the key features of photonic band structures can be achieved experimentally by the polarization- and momentum-resolved photoluminescence spectroscopy utilizing the light emission properties of SiNx. The two-dimensional spectra clearly reveal the energy-momentum dispersion of band structures which is in perfect agreement with the simulation results. The isofrequency contours can be measured easily by adding a bandpass filter with a desired photon energy. Furthermore, it is convenient to observe clearly and directly the optical singularity -- the optical bound states in the continuum featured by dark point in three-dimensional photoluminescence spectra. The polarization-resolved isofrequency contours clearly show that this dark point is the center of an azimuthally polarized vortex. Finally, the helical topological edge states can be easily observed in photonic topological insulators with deformed hexagonal lattices. Our work provides a simple and effective approach for exploring topological photonics and other intriguing phenomena hidden in the photonic crystal slabs.Comment: 6 pages, 5 figure

Experimental and Numerical Analysis of High-Resolution Injection Technique for Capillary Electrophoresis Microchip

This study presents an experimental and numerical investigation on the use of high-resolution injection techniques to deliver sample plugs within a capillary electrophoresis (CE) microchip. The CE microfluidic device was integrated into a U-shaped injection system and an expansion chamber located at the inlet of the separation channel, which can miniize the sample leakage effect and deliver a high-quality sample plug into the separation channel so that the detection performance of the device is enhanced. The proposed 45° U-shaped injection system was investigated using a sample of Rhodamine B dye. Meanwhile, the analysis of the current CE microfluidic chip was studied by considering the separation of Hae III digested ϕx-174 DNA samples. The experimental and numerical results indicate that the included 45° U-shaped injector completely eliminates the sample leakage and an expansion separation channel with an expansion ratio of 2.5 delivers a sample plug with a perfect detection shape and highest concentration intensity, hence enabling an optimal injection and separation performance

Learning Structure-Guided Diffusion Model for 2D Human Pose Estimation

One of the mainstream schemes for 2D human pose estimation (HPE) is learning keypoints heatmaps by a neural network. Existing methods typically improve the quality of heatmaps by customized architectures, such as high-resolution representation and vision Transformers. In this paper, we propose \textbf{DiffusionPose}, a new scheme that formulates 2D HPE as a keypoints heatmaps generation problem from noised heatmaps. During training, the keypoints are diffused to random distribution by adding noises and the diffusion model learns to recover ground-truth heatmaps from noised heatmaps with respect to conditions constructed by image feature. During inference, the diffusion model generates heatmaps from initialized heatmaps in a progressive denoising way. Moreover, we further explore improving the performance of DiffusionPose with conditions from human structural information. Extensive experiments show the prowess of our DiffusionPose, with improvements of 1.6, 1.2, and 1.2 mAP on widely-used COCO, CrowdPose, and AI Challenge datasets, respectively