Large-scale Weakly Supervised Learning for Road Extraction from Satellite Imagery

Automatic road extraction from satellite imagery using deep learning is a viable alternative to traditional manual mapping. Therefore it has received considerable attention recently. However, most of the existing methods are supervised and require pixel-level labeling, which is tedious and error-prone. To make matters worse, the earth has a diverse range of terrain, vegetation, and man-made objects. It is well known that models trained in one area generalize poorly to other areas. Various shooting conditions such as light and angel, as well as different image processing techniques further complicate the issue. It is impractical to develop training data to cover all image styles. This paper proposes to leverage OpenStreetMap road data as weak labels and large scale satellite imagery to pre-train semantic segmentation models. Our extensive experimental results show that the prediction accuracy increases with the amount of the weakly labeled data, as well as the road density in the areas chosen for training. Using as much as 100 times more data than the widely used DeepGlobe road dataset, our model with the D-LinkNet architecture and the ResNet-50 backbone exceeds the top performer of the current DeepGlobe leaderboard. Furthermore, due to large-scale pre-training, our model generalizes much better than those trained with only the curated datasets, implying great application potential

Few-Shot Deep Adversarial Learning for Video-based Person Re-identification

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras. Also, it is noticed that learning effective video representations with view invariance is not explicitly addressed for which features exhibit different distributions otherwise. Thus, matching videos for person re-ID demands flexible models to capture the dynamics in time-series observations and learn view-invariant representations with access to limited labeled training samples. In this paper, we propose a novel few-shot deep learning approach to video-based person re-ID, to learn comparable representations that are discriminative and view-invariant. The proposed method is developed on the variational recurrent neural networks (VRNNs) and trained adversarially to produce latent variables with temporal dependencies that are highly discriminative yet view-invariant in matching persons. Through extensive experiments conducted on three benchmark datasets, we empirically show the capability of our method in creating view-invariant temporal features and state-of-the-art performance achieved by our method.Comment: Appearing at IEEE Transactions on Image Processin

Metali u medu iz provincije Henan, Kina: sustavna analiza metodom ICP-AES

In this study, the method for determining ten elements (including K, Na, Ca, Mg, Fe, Mn, Cu, Zn, Pb, and Cd) was designed. With this method, we evaluated 15 honey samples, including three kinds of honey collected from 11 different geographic sites in Henan province of China, with inductively coupled plasma atomic emission spectrometry (ICP-AES). The obtained detecting data were analysed with principal component analysis, correlation analysis, and cluster analysis techniques. The results showed that the recovery is in the range of 93.0−107.0 %, and the relative standard deviations (RSDs) were all below 5.89 %, which indicates that the current analytical method is dependable for the detection of metallic elements in honey. This work is licensed under a Creative Commons Attribution 4.0 International License.U ovom istraživanju osmišljena je metoda određivanja deset elemenata (K, Na, Ca, Mg, Fe, Mn, Cu, Zn, Pb i Cd). Tom metodom evaluirano je 15 uzoraka meda, uključujući tri vrste meda prikupljenih s 11 lokaliteta u provinciji Henan, Kina, atomskom emisijskom spektrometrijom s induktivno spregnutom plazmom (ICP-AES). Dobiveni podatci proučeni su analizom glavnih komponenti, korelacijskom analizom i tehnikama klasterskih analiza. Rezultati su pokazali da se oporaba kreće u rasponu od 93,0 do 107,0 %, a relativne standardne devijacije (RSD) bile su ispod 5,89 %, što ukazuje da je trenutačna analitička metoda pouzdana za otkrivanje metala u medu. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Electroacupuncture Inhibition of Hyperalgesia in Rats with Adjuvant Arthritis: Involvement of Cannabinoid Receptor 1 and Dopamine Receptor Subtypes in Striatum

Electroacupuncture (EA) has been regarded as an alternative treatment for inflammatory pain for several decades. However, the molecular mechanisms underlying the antinociceptive effect of EA have not been thoroughly clarified. Previous studies have shown that cannabinoid CB1 receptors are related to pain relief. Accumulating evidence has shown that the CB1 and dopamine systems sometimes interact and may operate synergistically in rat striatum. To our knowledge, dopamine D1/D2 receptors are involved in EA analgesia. In this study, we found that repeated EA at Zusanli (ST36) and Kunlun (BL60) acupoints resulted in marked improvements in thermal hyperalgesia. Both western blot assays and FQ-PCR analysis results showed that the levels of CB1 expression in the repeated-EA group were much higher than those in any other group (P=0.001). The CB1-selective antagonist AM251 inhibited the effects of repeated EA by attenuating the increases in CB1 expression. The two kinds of dopamine receptors imparted different actions on the EA-induced CB1 upregulation in AA rat model. These results suggested that the strong activation of the CB1 receptor after repeated EA resulted in the concomitant phenomenon of the upregulation of D1 and D2 levels of gene expression

Shenxiong Drop Pill exerts neuroprotective effect against focal cerebral ischemia partly via regulation of the expressions of ICAM-1 and caspase-3

Purpose: To investigate the effect of Shenxiong Drop Pill (SXDP) on cerebral infarction (CI) in rats, and the involvement of anti-inflammatory response in the process.Methods: Rats were sacrificed at three different time points, viz, 24, 48 and 72 h after establishment of CI model. Neurological deficit score (NDS) was determined using Bederson’s neurological behavioral scoring method, whereas triphenyltetrazolium chloride (TTC) staining was used to show brain injury. The integrated optical density (IOD) of Nissl bodies and caspase-3-positive nerve cells were measured with Nissl staining and SP kit, respectively. The mRNA expression of intercellular adhesion molecule 1(ICAM-1) was determined using reverse transcription-polymerase chain reaction (RT-PCR).Results: SXDP produced neuroprotective effect at high, medium, and low doses. The infarct volumes in the high-, medium- and low-dose SXDP, and cyclophosphamide groups were significantly reduced at each time point. Different doses of SXDP significantly reduced the mRNA expression of ICAM-1 and the IOD of caspase-3.Conclusion: These results indicate that SXDP exerts neuroprotective effects against ischemic injury by negatively regulating ICAM-1/caspase-3 downstream of inflammatory and apoptosis pathways

Inhibition of Glucose-6-Phosphate Dehydrogenase Could Enhance 1,4-Benzoquinone-Induced Oxidative Damage in K562 Cells

Benzene is a chemical contaminant widespread in industrial and living environments. The oxidative metabolites of benzene induce toxicity involving oxidative damage. Protecting cells and cell membranes from oxidative damage, glucose-6-phosphate dehydrogenase (G6PD) maintains the reduced state of glutathione (GSH). This study aims to investigate whether the downregulation of G6PD in K562 cell line can influence the oxidative toxicity induced by 1,4-benzoquinone (BQ). G6PD was inhibited in K562 cell line transfected with the specific siRNA of G6PD gene. An empty vector was transfected in the control group. Results revealed that G6PD was significantly upregulated in the control cells and in the cells with inhibited G6PD after they were exposed to BQ. The NADPH/NADP and GSH/GSSG ratio were significantly lower in the cells with inhibited G6PD than in the control cells at the same BQ concentration. The relative reactive oxygen species (ROS) level and DNA oxidative damage were significantly increased in the cell line with inhibited G6PD. The apoptotic rate and G2 phase arrest were also significantly higher in the cells with inhibited G6PD and exposed to BQ than in the control cells. Our results suggested that G6PD inhibition could reduce GSH activity and alleviate oxidative damage. G6PD deficiency is also a possible susceptible risk factor of benzene exposure

Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator

The transformer model is known to be computationally demanding, and prohibitively costly for long sequences, as the self-attention module uses a quadratic time and space complexity with respect to sequence length. Many researchers have focused on designing new forms of self-attention or introducing new parameters to overcome this limitation, however a large portion of them prohibits the model to inherit weights from large pretrained models. In this work, the transformer's inefficiency has been taken care of from another perspective. We propose Fourier Transformer, a simple yet effective approach by progressively removing redundancies in hidden sequence using the ready-made Fast Fourier Transform (FFT) operator to perform Discrete Cosine Transformation (DCT). Fourier Transformer is able to significantly reduce computational costs while retain the ability to inherit from various large pretrained models. Experiments show that our model achieves state-of-the-art performances among all transformer-based models on the long-range modeling benchmark LRA with significant improvement in both speed and space. For generative seq-to-seq tasks including CNN/DailyMail and ELI5, by inheriting the BART weights our model outperforms the standard BART and other efficient models. \footnote{Our code is publicly available at \url{https://github.com/LUMIA-Group/FourierTransformer}