7,310 research outputs found
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 93.0ā107.0 %, 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 osmiÅ”ljena je metoda odreÄivanja deset elemenata (K, Na, Ca, Mg, Fe, Mn, Cu, Zn, Pb i Cd). Tom metodom evaluirano je 15 uzoraka meda, ukljuÄujuÄi tri vrste meda prikupljenih s 11 lokaliteta u provinciji Henan, Kina, atomskom emisijskom spektrometrijom s induktivno spregnutom plazmom (ICP-AES). Dobiveni podatci prouÄeni su analizom glavnih komponenti, korelacijskom analizom i tehnikama klasterskih analiza. Rezultati su pokazali da se oporaba kreÄe u rasponu od 93,0 do 107,0 %, a relativne standardne devijacije (RSD) bile su ispod 5,89 %, Å”to ukazuje da je trenutaÄna analitiÄka metoda pouzdana za otkrivanje metala u medu.
Ovo djelo je dano na koriÅ”tenje pod licencom Creative Commons Imenovanje 4.0 meÄunarodna
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
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
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 Bedersonās 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
Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
As a step beyond traditional personalized recommendation, group
recommendation is the task of suggesting items that can satisfy a group of
users. In group recommendation, the core is to design preference aggregation
functions to obtain a quality summary of all group members' preferences. Such
user and group preferences are commonly represented as points in the vector
space (i.e., embeddings), where multiple user embeddings are compressed into
one to facilitate ranking for group-item pairs. However, the resulted group
representations, as points, lack adequate flexibility and capacity to account
for the multi-faceted user preferences. Also, the point embedding-based
preference aggregation is a less faithful reflection of a group's
decision-making process, where all users have to agree on a certain value in
each embedding dimension instead of a negotiable interval. In this paper, we
propose a novel representation of groups via the notion of hypercubes, which
are subspaces containing innumerable points in the vector space. Specifically,
we design the hypercube recommender (CubeRec) to adaptively learn group
hypercubes from user embeddings with minimal information loss during preference
aggregation, and to leverage a revamped distance metric to measure the affinity
between group hypercubes and item points. Moreover, to counteract the
long-standing issue of data sparsity in group recommendation, we make full use
of the geometric expressiveness of hypercubes and innovatively incorporate
self-supervision by intersecting two groups. Experiments on four real-world
datasets have validated the superiority of CubeRec over state-of-the-art
baselines.Comment: To appear in SIGIR'2
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