6 research outputs found
Anomalies of upper critical field in the spinel superconductor LiTiO
High-field electrical transport and point-contact tunneling spectroscopy were
used to investigate superconducting properties of the unique spinel oxide,
LiTiO films with various oxygen content. We find that the
upper critical field gradually increases as more oxygen
impurities are brought into the samples by carefully tuning the deposition
atmosphere. It is striking that although the superconducting transition
temperature and energy gap are almost unchanged, an astonishing isotropic
up to 26 Tesla is observed in oxygen-rich sample, which
is doubled compared to the anoxic sample and breaks the Pauli limit. Such
anomalies of were rarely reported in other three dimensional
superconductors. Combined with all the anomalies, three dimensional spin-orbit
interaction induced by tiny oxygen impurities is naturally proposed to account
for the remarkable enhancement of in oxygen-rich
LiTiO films. Such mechanism could be general and therefore
provides ideas for optimizing practical superconductors with higher
Anomalies of upper critical field in the spinel superconductor LiTi2 O4-δ
© 2019 American Physical Society. High-field electrical transport and point-contact tunneling spectroscopy are used to investigate superconducting properties of spinel oxide LiTi2O4-δ films with various oxygen contents. It is striking that although the superconducting transition temperature and energy gap are almost unchanged, an isotropic upper critical field Bc2 up to 26.0 T is observed in the oxygen-rich sample, which is more than twice the Bc2 of 11.3 T in the anoxic one. The change of the dominating pair-breaking mechanism from the orbital effect to the spin flip at Bc2 is achieved by tuning oxygen contents, which can be explained by the appearance of small Fermi pockets due to extra oxygen. Our paper provides deep understanding of the intrinsic relation between Bc2 and the complex Fermi surface, and contributes a promising way to enhance Bc2 for practical superconductors
Evaluation of Machine Learning Algorithms for Anomaly Detection
The cyber-physical security of Industrial Control Systems (ICSs) represents an actual and worthwhile research topic. In this paper, we compare and evaluate different Machine Learning (ML) algorithms for anomaly detection in industrial control networks. We analyze supervised and unsupervised ML-based anomaly detection approaches using datasets extracted from the Secure Water Treatment (SWaT), a testbed developed to emulate a scaled-down real industrial plant. Our experiments show strengths and limitations of the two ML-based anomaly detection approaches for industrial networks.</div
DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation
Successful implementation of oil spill segmentationin Synthetic Aperture Radar (SAR) images is vital for marineenvironmental protection. In this paper, we develop an effectivesegmentation framework named DGNet, which performs oilspill segmentation by incorporating the intrinsic distribution ofbackscatter values in SAR images. Specifically, our proposedsegmentation network is constructed with two deep neuralmodules running in an interactive manner, where one is theinference module to achieve latent feature variable inference fromSAR images, and the other is the generative module to produce oilspill segmentation maps by drawing the latent feature variablesas inputs. Thus, to yield accurate segmentation, we take intoaccount the intrinsic distribution of backscatter values in SARimages and embed it in our segmentation model. The intrinsicdistribution originates from SAR imagery, describing the physicalcharacteristics of oil spills. In the training process, the formulatedintrinsic distribution guides efficient learning of optimal latentfeature variable inference for oil spill segmentation. The efficientlearning enables the training of our proposed DGNet with asmall amount of image data. This is economically beneficial tooil spill segmentation where the availability of oil spill SAR imagedata is limited in practice. Additionally, benefiting from optimallatent feature variable inference, our proposed DGNet performsaccurate oil spill segmentation. We evaluate the segmentationperformance of our proposed DGNet with different metrics, andexperimental evaluations demonstrate its effective segmentations</p
SWIPENET: Object detection in noisy underwater scenes
Deep learning based object detection methods have achieved promising performance in controlled environments. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) images in the underwater datasets and real applications are blurry whilst accompanying severe noise that confuses the detectors and (2) objects in real applications are usually small. In this paper, we propose a Sample-WeIghted hyPEr Network (SWIPENET), and a novel training paradigm named Curriculum Multi-Class Adaboost (CMA), to address these two problems at the same time. Firstly, the backbone of SWIPENET produces multiple high resolution and semantic-rich Hyper Feature Maps, which significantly improve small object detection. Secondly, inspired by the human education process that drives the learning from easy to hard concepts, we propose the noise-robust CMA training paradigm that learns the clean data first and then move on to learns the diverse noisy data. Experiments on four underwater object detection datasets show that the proposed SWIPENET+CMA framework achieves better or competitive accuracy in object detection against several state-of-the-art approaches.</p
Application of curcumin-mediated antibacterial photodynamic technology for preservation of fresh Tremella Fuciformis
Tremella Fuciformis is an edible fungus with high water content and nutritional values. However, fresh T. Fuciformis can quickly lose its quality by physical damage, water loss and microbial degradation during storage. Herein, we evaluated the effects of curcumin-mediated photodynamic technology (PDT) using light-emitting diode (LED) light to preserve fresh T. Fuciformis. Changes in bacterial counts and community, physicochemical properties, and sensory attributes of curcumin-mediated PDT-treated fresh T. Fuciformis were assessed. The results indicated that treatment with 30 μmol/L curcumin and 30 min of LED light exposure could reduce bacterial counts by ~1.99 ± 0.06 log (CFU/g) in fresh T. Fuciformis upon 5 days storage. The bacterial microbiota in T. Fuciformis during storage was also altered upon PDT treatment. PDT treatment also retained the color, water content, hardness, tactility, and appearance of fresh T. Fuciformis. In conclusion, this study demonstrated that curcumin-mediated PDT could be a viable and promising non-thermal technology for preserving the quality of fresh T. Fuciformis