Anomalies of upper critical field in the spinel superconductor LiTi2_2O4−δ_{4-\delta}

High-field electrical transport and point-contact tunneling spectroscopy were used to investigate superconducting properties of the unique spinel oxide, LiTi$_2$O$_{4-\delta}$ films with various oxygen content. We find that the upper critical field $B_\mathrm{c2}$ 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 $B_\mathrm{c2}$ up to $\sim$ 26 Tesla is observed in oxygen-rich sample, which is doubled compared to the anoxic sample and breaks the Pauli limit. Such anomalies of $B_\mathrm{c2}$ 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 $B_\mathrm{c2}$ in oxygen-rich LiTi$_2$O$_{4-\delta}$ films. Such mechanism could be general and therefore provides ideas for optimizing practical superconductors with higher $B_\mathrm{c2}$

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