LSTM Pose Machines

We observed that recent state-of-the-art results on single image human pose estimation were achieved by multi-stage Convolution Neural Networks (CNN). Notwithstanding the superior performance on static images, the application of these models on videos is not only computationally intensive, it also suffers from performance degeneration and flicking. Such suboptimal results are mainly attributed to the inability of imposing sequential geometric consistency, handling severe image quality degradation (e.g. motion blur and occlusion) as well as the inability of capturing the temporal correlation among video frames. In this paper, we proposed a novel recurrent network to tackle these problems. We showed that if we were to impose the weight sharing scheme to the multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN). This property decouples the relationship among multiple network stages and results in significantly faster speed in invoking the network for videos. It also enables the adoption of Long Short-Term Memory (LSTM) units between video frames. We found such memory augmented RNN is very effective in imposing geometric consistency among frames. It also well handles input quality degradation in videos while successfully stabilizes the sequential outputs. The experiments showed that our approach significantly outperformed current state-of-the-art methods on two large-scale video pose estimation benchmarks. We also explored the memory cells inside the LSTM and provided insights on why such mechanism would benefit the prediction for video-based pose estimations.Comment: Poster in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

Fiber bundle imaging resolution enhancement using deep learning

We propose a deep learning based method to estimate high-resolution images from multiple fiber bundle images. Our approach first aligns raw fiber bundle image sequences with a motion estimation neural network and then applies a 3D convolution neural network to learn a mapping from aligned fiber bundle image sequences to their ground truth images. Evaluations on lens tissue samples and a 1951 USAF resolution target suggest that our proposed method can significantly improve spatial resolution for fiber bundle imaging systems.National Institute of Biomedical Imaging and Bioengineering (NIBIB) [R21EB022378]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Phase unwrapping in optical metrology via denoised and convolutional segmentation networks

The interferometry technique is corn commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 27 pi ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are time-consuming and noise sensitive. To address those issues, we propose a new approach, where we transfer the task of phase unwrapping into a multi-class classification problem and introduce an efficient segmentation network to identify classes. Moreover, a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase. We have demonstrated the proposed method with simulated data and in a real interferometric system.China Scholarship Council (CSC) [201704910730]; National Science Foundation (NSF) [1455630]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoning

How neural networks in the human brain represent commonsense knowledge, and complete related reasoning tasks is an important research topic in neuroscience, cognitive science, psychology, and artificial intelligence. Although the traditional artificial neural network using fixed-length vectors to represent symbols has gained good performance in some specific tasks, it is still a black box that lacks interpretability, far from how humans perceive the world. Inspired by the grandmother-cell hypothesis in neuroscience, this work investigates how population encoding and spiking timing-dependent plasticity (STDP) mechanisms can be integrated into the learning of spiking neural networks, and how a population of neurons can represent a symbol via guiding the completion of sequential firing between different neuron populations. The neuron populations of different communities together constitute the entire commonsense knowledge graph, forming a giant graph spiking neural network. Moreover, we introduced the Reward-modulated spiking timing-dependent plasticity (R-STDP) mechanism to simulate the biological reinforcement learning process and completed the related reasoning tasks accordingly, achieving comparable accuracy and faster convergence speed than the graph convolutional artificial neural networks. For the fields of neuroscience and cognitive science, the work in this paper provided the foundation of computational modeling for further exploration of the way the human brain represents commonsense knowledge. For the field of artificial intelligence, this paper indicated the exploration direction for realizing a more robust and interpretable neural network by constructing a commonsense knowledge representation and reasoning spiking neural networks with solid biological plausibility

Diffraction of Quantum Dots Reveals Nanoscale Ultrafast Energy Localization

Unlike in bulk materials, energy transport in low-dimensional and nanoscale systems may be governed by a coherent “ballistic” behavior of lattice vibrations, the phonons. If dominant, such behavior would determine the mechanism for transport and relaxation in various energy-conversion applications. In order to study this coherent limit, both the spatial and temporal resolutions must be sufficient for the length-time scales involved. Here, we report observation of the lattice dynamics in nanoscale quantum dots of gallium arsenide using ultrafast electron diffraction. By varying the dot size from h = 11 to 46 nm, the length scale effect was examined, together with the temporal change. When the dot size is smaller than the inelastic phonon mean-free path, the energy remains localized in high-energy acoustic modes that travel coherently within the dot. As the dot size increases, an energy dissipation toward low-energy phonons takes place, and the transport becomes diffusive. Because ultrafast diffraction provides the atomic-scale resolution and a sufficiently high time resolution, other nanostructured materials can be studied similarly to elucidate the nature of dynamical energy localization

Association of interleukin 17 / angiotensin II with refractory hypertension risk in hemodialysis patients.

Objective: The study was performed to investigate the association of interleukin 17 (IL 17) or angiotensin II (Ang II) with refractory hypertension risk in hemodialysis patients. Methods: Ninety hemodialysis patients were enrolled into this study, and those with hypertension were divided into two groups. The Easy-to-Control Hypertension group (ECHG) had fifty patients, while the refractory hypertension group (RHG) had forty patients. Twenty healthy individuals were recruited as the control group. IL17 and Ang II were determined using a human IL 17 / Ang II enzyme-linked immunosorbent assay kit. Serum IL 17 and Ang II concentrations in RHG patients were higher than those in ECHG patients. Results: Serum IL 17 and Ang II concentrations in both patient groups were higher than those in the control group. Linear regression analysis showed a positive correlation between IL 17 and Ang II. In multivariate regression analysis, we found that IL17 and Ang II were associated with refractory hypertension risk in hemodialysis patients. Conclusion: IL17 and Ang II were associated with refractory hypertension risk in hemodialysis patients. There was also a positive correlation between IL 17and Ang II

Ultrafast atomic-scale visualization of acoustic phonons generated by optically excited quantum dots

Understanding the dynamics of atomic vibrations confined in quasi-zero dimensional systems is crucial from both a fundamental point-of-view and a technological perspective. Using ultrafast electron diffraction, we monitored the lattice dynamics of GaAs quantum dots—grown by Droplet Epitaxy on AlGaAs—with sub-picosecond and sub-picometer resolutions. An ultrafast laser pulse nearly resonantly excites a confined exciton, which efficiently couples to high-energy acoustic phonons through the deformation potential mechanism. The transient behavior of the measured diffraction pattern reveals the nonequilibrium phonon dynamics both within the dots and in the region surrounding them. The experimental results are interpreted within the theoretical framework of a non-Markovian decoherence, according to which the optical excitation creates a localized polaron within the dot and a travelling phonon wavepacket that leaves the dot at the speed of sound. These findings indicate that integration of a phononic emitter in opto-electronic devices based on quantum dots for controlled communication processes can be fundamentally feasible

Allelic Interactions among Pto-MIR475b and Its Four Target Genes Potentially Affect Growth and Wood Properties in Populus

MicroRNAs (miRNAs) play crucial roles in plant growth and development, but few studies have illuminated the allelic interactions among miRNAs and their targets in perennial plants. Here, we combined analysis of expression patterns and single-nucleotide polymorphism (SNP)-based association studies to explore the interactions between Pto-MIR475b and its four target genes (Pto-PPR1, Pto-PPR2, Pto-PPR3, and Pto-PPR4) in 435 unrelated individuals of Populus tomentosa. Expression patterns showed a significant negative correlation (r = -0.447 to -0.411, P < 0.01) between Pto-MIR475b and its four targets in eight tissues of P. tomentosa, suggesting that Pto-miR475b may negatively regulate the four targets. Single SNP-based association studies identified 93 significant associations (P < 0.01, Q < 0.1) representing associations of 80 unique SNPs in Pto-MIR475b and its four targets with nine traits, revealing their potential roles in tree growth and wood formation. Moreover, one common SNP in the precursor region significantly altered the secondary structure of the pre-Pto-miR475b and changed the expression level of Pto-MIR475b. Analysis of epistatic interactions identified 115 significant SNP–SNP associations (P < 0.01) representing 45 unique SNPs from Pto-MIR475b and its four targets for 10 traits, revealing that genetic interactions between Pto-MIR475b and its targets influence quantitative traits of perennial plants. Our study provided a feasible strategy to study population genetics in forest trees and enhanced our understanding of miRNAs by dissecting the allelic interactions between this miRNA and its targets in P. tomentosa

Dai-Huang-Fu-Zi-Tang Alleviates Intestinal Injury Associated with Severe Acute Pancreatitis by Regulating Mitochondrial Permeability Transition Pore of Intestinal Mucosa Epithelial Cells

Objective. The aim of the present study was to examine whether Dai-Huang-Fu-Zi-Tang (DHFZT) could regulate mitochondrial permeability transition pore (MPTP) of intestinal mucosa epithelial cells for alleviating intestinal injury associated with severe acute pancreatitis (SAP). Methods. A total of 72 Sprague-Dawley rats were randomly divided into 3 groups (sham group, SAP group, and DHFZT group, n=24 per group). The rats in each group were divided into 4 subgroups (n=6 per subgroup) accordingly at 1, 3, 6, and 12 h after the operation. The contents of serum amylase, D-lactic acid, diamine oxidase activity, and degree of MPTP were measured by dry chemical method and enzyme-linked immunosorbent assay. The change of mitochondria of intestinal epithelial cells was observed by transmission electron microscopy. Results. The present study showed that DHFZT inhibited the openness of MPTP at 3, 6, and 12 h after the operation. Meanwhile, it reduced the contents of serum D-lactic acid and activity of diamine oxidase activity and also drastically relieved histopathological manifestations and epithelial cells injury of intestine. Conclusion. DHFZT alleviates intestinal injury associated SAP via reducing the openness of MPTP. In addition, DHFZT could also decrease the content of serum diamine oxidase activity and D-lactic acid after SAP