325 research outputs found
Graph Regularized Tensor Sparse Coding for Image Representation
Sparse coding (SC) is an unsupervised learning scheme that has received an
increasing amount of interests in recent years. However, conventional SC
vectorizes the input images, which destructs the intrinsic spatial structures
of the images. In this paper, we propose a novel graph regularized tensor
sparse coding (GTSC) for image representation. GTSC preserves the local
proximity of elementary structures in the image by adopting the newly proposed
tubal-tensor representation. Simultaneously, it considers the intrinsic
geometric properties by imposing graph regularization that has been
successfully applied to uncover the geometric distribution for the image data.
Moreover, the returned sparse representations by GTSC have better physical
explanations as the key operation (i.e., circular convolution) in the
tubal-tensor model preserves the shifting invariance property. Experimental
results on image clustering demonstrate the effectiveness of the proposed
scheme
Negative Magnetoresistance in Dirac Semimetal Cd3As2
A large negative magnetoresistance is anticipated in topological semimetals
in the parallel magnetic and electric field configuration as a consequence of
the nontrivial topological properties. The negative magnetoresistance is
believed to demonstrate the chiral anomaly, a long-sought high-energy physics
effect, in solid-state systems. Recent experiments reveal that Cd3As2, a Dirac
topological semimetal, has the record-high mobility and exhibits positive
linear magnetoresistance in the orthogonal magnetic and electric field
configuration. However, the negative magnetoresistance in the parallel magnetic
and electric field configuration remains unveiled. Here, we report the
observation of the negative magnetoresistance in Cd3As2 microribbons in the
parallel magnetic and electric field configuration as large as 66% at 50 K and
even visible at room temperatures. The observed negative magnetoresistance is
sensitive to the angle between magnetic and electrical field, robust against
temperature, and dependent on the carrier density. We have found that carrier
densities of our Cd3As2 samples obey an Arrhenius's law, decreasing from
3.0x10^17 cm^-3 at 300 K to 2.2x10^16 cm^-3 below 50 K. The low carrier
densities result in the large values of the negative magnetoresistance. We
therefore attribute the observed negative magnetoresistance to the chiral
anomaly. Furthermore, in the perpendicular magnetic and electric field
configuration a positive non-saturating linear magnetoresistance up to 1670% at
14 T and 2 K is also observed. This work demonstrates potential applications of
topological semimetals in magnetic devices
Determination of the downwelling diffuse attenuation coefficient of lakewater with the sentinel-3A OLCI
The Ocean and Land Color Imager (OLCI) on the Sentinel-3A satellite, which was launched by the European Space Agency in 2016, is a new-generation water color sensor with a spatial resolution of 300 m and 21 bands in the range of 400-1020 nm. The OLCI is important to the expansion of remote sensing monitoring of inland waters using water color satellite data. In this study, we developed a dual band ratio algorithm for the downwelling diffuse attenuation coefficient at 490 nm (Kd(490)) for the waters of Lake Taihu, a large shallow lake in China, based on data measured during seven surveys conducted between 2008 and 2017 in combination with Sentinel-3A-OLCI data. The results show that: (1) Compared to the available Kd(490) estimation algorithms, the dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm developed in this study had a higher estimation accuracy (N = 26, coefficient of determination (R2) = 0.81, root-mean-square error (RMSE) = 0.99m-1and mean absolute percentage error (MAPE) = 19.55%) and validation accuracy (N = 14, R2= 0.83, RMSE = 1.06 m-1and MAPE = 27.30%), making it more suitable for turbid inland waters; (2) A comparison of the OLCI Kd(490) product and a similar Moderate Resolution Imaging Spectroradiometer (MODIS) product reveals a high consistency between the OLCI and MODIS products in terms of the spatial distribution of Kd(490). However, the OLCI product has a smoother spatial distribution and finer textural characteristics than the MODIS product and contains notably higher-quality data; (3) The Kd(490) values for Lake Taihu exhibit notable spatial and temporal variations. Kd(490) is higher in seasons with relatively high wind speeds and in open waters that are prone to wind- and wave-induced sediment resuspension. Finally, the Sentinel-3A-OLCI has a higher spatial resolution and is equipped with a relatively wide dynamic range of spectral bands suitable for inland waters. The Sentinel-3B satellite will be launched soon and, together with the Sentinel-3A satellite, will form a two-satellite network with the ability to make observations twice every three days. This satellite network will have a wider range of application and play an important role in the monitoring of inland waters with complex optical properties
Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China\u27s three largest freshwater lakes
Inherent optical properties (IOPs) play an important role in underwater light field, and are difficult to estimate accurately using satellite data in optically complex waters. To study water quality in appropriate temporal and spatial scales, it is necessary to develop methods to obtain IOPs form space-based observation with quantified uncertainties. Field-measured IOP data (N = 405) were collected from 17 surveys between 2011 and 2017 in the three major largest freshwater lakes of China (Lake Chaohu, Lake Taihu, and Lake Hongze) in the lower reaches of the Yangtze River and Huai River (LYHR). Here we provide a case-study on how to use in-situ observation of IOPs to devise an improved algorithm for retrieval of IOPs. We then apply this algorithm to observation with Sentinel-3A OLCI (Ocean and Land Colour Instrument, corrected with our improved AC scheme), and use in-situ data to show that the algorithm performs better than the standard OLCI IOP product. We use the satellite derived products to study the spatial and seasonal distributions of IOPs and concentrations of optically active constituents in these three lakes, including chlorophyll-a (Chla) and suspended particulate matter (SPM), using all cloud-free OLCI images (115 scenes) over the lakes in the LYHR basin in 2017. Our study provides a strategy for using local and remote observations to obtain important water quality parameters necessary to manage resources such as reservoirs, lakes and coastal waters
Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models
Deep generative models (DGMs) are data-eager because learning a complex model
on limited data suffers from a large variance and easily overfits. Inspired by
the classical perspective of the bias-variance tradeoff, we propose regularized
deep generative model (Reg-DGM), which leverages a nontransferable pre-trained
model to reduce the variance of generative modeling with limited data.
Formally, Reg-DGM optimizes a weighted sum of a certain divergence and the
expectation of an energy function, where the divergence is between the data and
the model distributions, and the energy function is defined by the pre-trained
model w.r.t. the model distribution. We analyze a simple yet representative
Gaussian-fitting case to demonstrate how the weighting hyperparameter trades
off the bias and the variance. Theoretically, we characterize the existence and
the uniqueness of the global minimum of Reg-DGM in a non-parametric setting and
prove its convergence with neural networks trained by gradient-based methods.
Empirically, with various pre-trained feature extractors and a data-dependent
energy function, Reg-DGM consistently improves the generation performance of
strong DGMs with limited data and achieves competitive results to the
state-of-the-art methods
Feasibility of using Y<sub>2</sub>Ti<sub>2</sub>O<sub>7</sub> nanoparticles to fabricate high strength oxide dispersion strengthened Fe-Cr-Al steels
Addition of Al can improve the corrosion resistance of oxide dispersion
strengthened (ODS) steels. However, Al reacts with Y2O3 to form large Y-Al-O
particles in the steels and deteriorates their mechanical properties. Herein, we
successfully prepared Y2Ti2O7 nanoparticles (NPs) by the combination of hydrogen
plasma-metal reaction (HPMR) and annealing. Y2Ti2O7 NPs with contents of 0.2 or 0.6 wt.% were then added into the Fe-14Cr-3Al-2W-0.35Ti (wt.%) steel to substitute the conventional Y2O3 NPs by mechanical alloying (MA). The Y2Ti2O7 NPs transformed into amorphous-like structure after 96 h MA. They crystallized with a fine size of 7.4±3.7 nm and shared a semi-coherent interface with the matrix after hot isostatic pressing (HIP) of the ODS steel with 0.6 wt.% Y2Ti2O7. With the increasing Y2Ti2O7 content from 0.2 to 0.6 wt.%, the tensile strength of the ODS steel increased from 1238 to 1296 MPa, which was much higher than that (949 MPa) of the ODS steel added with Y2O3. The remarkably improved mechanical properties of the Al-containing ODS steels were attributed to the increasing number density of Y2Ti2O7 nanoprecipitates. Our work demonstrates a novel route to fabricate high performance
ODS steels with both high mechanical strength and good corrosion resistance
T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations
In this work, we investigate a simple and must-known conditional generative
framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and
Generative Pre-trained Transformer (GPT) for human motion generation from
textural descriptions. We show that a simple CNN-based VQ-VAE with commonly
used training recipes (EMA and Code Reset) allows us to obtain high-quality
discrete representations. For GPT, we incorporate a simple corruption strategy
during the training to alleviate training-testing discrepancy. Despite its
simplicity, our T2M-GPT shows better performance than competitive approaches,
including recent diffusion-based approaches. For example, on HumanML3D, which
is currently the largest dataset, we achieve comparable performance on the
consistency between text and generated motion (R-Precision), but with FID 0.116
largely outperforming MotionDiffuse of 0.630. Additionally, we conduct analyses
on HumanML3D and observe that the dataset size is a limitation of our approach.
Our work suggests that VQ-VAE still remains a competitive approach for human
motion generation.Comment: Accepted to CVPR 2023. Project page:
https://mael-zys.github.io/T2M-GPT
Effects of Litchi chinensis fruit isolates on prostaglandin E2 and nitric oxide production in J774 murine macrophage cells
<p>Abstract</p> <p>Background</p> <p><it>Litchi chinensis </it>is regarded as one of the 'heating' fruits in China, which causes serious inflammation symptoms to people.</p> <p>Methods</p> <p>In the current study, the effects of isolates of litchi on prostaglandin E<sub>2 </sub>(PGE<sub>2</sub>) and nitric oxide (NO) production in J774 murine macrophage cells were investigated.</p> <p>Results</p> <p>The AcOEt extract (EAE) of litchi was found effective on stimulating PGE<sub>2 </sub>production, and three compounds, benzyl alcohol, hydrobenzoin and 5-hydroxymethyl-2-furfurolaldehyde (5-HMF), were isolated and identified from the EAE. Benzyl alcohol caused markedly increase in PGE<sub>2 </sub>and NO production, compared with lipopolysaccharide (LPS) as positive control, and in a dose-dependent manner. Hydrobenzoin and 5-HMF were found in litchi for the first time, and both of them stimulated PGE<sub>2 </sub>and NO production moderately in a dose-dependent manner. Besides, regulation of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) mRNA expression and NF-κB (p50) activation might be involved in mechanism of the stimulative process.</p> <p>Conclusion</p> <p>The study showed, some short molecular compounds in litchi play inflammatory effects on human.</p
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