MiniMax Entropy Network: Learning Category-Invariant Features for Domain Adaptation

How to effectively learn from unlabeled data from the target domain is crucial for domain adaptation, as it helps reduce the large performance gap due to domain shift or distribution change. In this paper, we propose an easy-to-implement method dubbed MiniMax Entropy Networks (MMEN) based on adversarial learning. Unlike most existing approaches which employ a generator to deal with domain difference, MMEN focuses on learning the categorical information from unlabeled target samples with the help of labeled source samples. Specifically, we set an unfair multi-class classifier named categorical discriminator, which classifies source samples accurately but be confused about the categories of target samples. The generator learns a common subspace that aligns the unlabeled samples based on the target pseudo-labels. For MMEN, we also provide theoretical explanations to show that the learning of feature alignment reduces domain mismatch at the category level. Experimental results on various benchmark datasets demonstrate the effectiveness of our method over existing state-of-the-art baselines.Comment: 8 pages, 6 figure

Learning Cross-domain Semantic-Visual Relation for Transductive Zero-Shot Learning

Zero-Shot Learning (ZSL) aims to learn recognition models for recognizing new classes without labeled data. In this work, we propose a novel approach dubbed Transferrable Semantic-Visual Relation (TSVR) to facilitate the cross-category transfer in transductive ZSL. Our approach draws on an intriguing insight connecting two challenging problems, i.e. domain adaptation and zero-shot learning. Domain adaptation aims to transfer knowledge across two different domains (i.e., source domain and target domain) that share the identical task/label space. For ZSL, the source and target domains have different tasks/label spaces. Hence, ZSL is usually considered as a more difficult transfer setting compared with domain adaptation. Although the existing ZSL approaches use semantic attributes of categories to bridge the source and target domains, their performances are far from satisfactory due to the large domain gap between different categories. In contrast, our method directly transforms ZSL into a domain adaptation task through redrawing ZSL as predicting the similarity/dissimilarity labels for the pairs of semantic attributes and visual features. For this redrawn domain adaptation problem, we propose to use a domain-specific batch normalization component to reduce the domain discrepancy of semantic-visual pairs. Experimental results over diverse ZSL benchmarks clearly demonstrate the superiority of our method

The impact of external plant carbon sources on nitrogen removal and microbial community structure in vertical flow constructed wetlands

The present study was developed to explore nitrogen removal performance and associated microbial mechanisms of action in vertical flow constructed wetlands (VFCWs) when using external carbon sources. These analyses ultimately revealed that alkali-soaked Phragmites australis (P. australis) could serve as an effective plant carbon source, exhibiting the lower levels of total nitrogen (TN) release and the highest chemical oxygen demand (COD) of all tested carbon sources. Nitrogen removal efficiency improved following the addition of plant carbon sources, and under carbon/nitrogen (C/N) rations of 2, 4, 5, and 7, the VFCW system was able to remove 43.69%–75.76% TN, with the highest removal rate being observed at a C/N of 5. The abundance of denitrifying microorganisms such as Thiobaillus and Halomonas were also more enriched in VFCW1 than VFCW0, with stronger correlations in the microbial network community. A qPCR approach was used to analyze functional genes involved in denitrification, revealing that the addition of plant carbon sources was associated with increases in total gene abundance and the abundance of the denitrifying gene nirS, whereas no corresponding increase in amoA or nxrA abundance was observed. Higher total gene, amoA, and nxrA abundance were observed in the upper levels of these VFCW systems as compared to the lower layers, whereas nirS exhibited the opposite abundance pattern. Overall, these findings suggested that short-range denitrification is likely to be the primary denitrification process active in this VFCW system

Association between long-term exposure to fine particulate matter constituents and progression of cerebral blood flow velocity in Beijing: Modifying effect of greenness

Few studies have explored the effects of fine particulate matter (PM2.5) and its constituents on the progression of cerebral blood flow velocity (BFV) and the potential modifying role of greenness. In this study, we investigated the association of PM2.5 and its constituents, including sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), organic matter (OM), and black carbon (BC), with the progression of BFV in the middle cerebral artery. Participants from the Beijing Health Management Cohort who underwent at least two transcranial Doppler sonography examinations during 2015–2020 were recruited. BFV change and BFV change rate were used to define the progression of cerebral BFV. Linear mixed effects models were employed to analyze the data, and the weighted quantile sum regression assessed the contribution of PM2.5 constituents. Additionally, greenness was examined as a modifier. Among the examined constituents, OM exhibited the strongest association with BFV progression. An interquartile range increase in PM2.5 and OM exposure concentrations was associated with a decrease of −16.519 cm/s (95% CI: −17.837, −15.201) and −15.403 cm/s (95% CI: −16.681, −14.126) in BFV change, and −10.369 cm/s/year (95% CI: −11.387, −9.352) and −9.615 cm/s/year (95% CI: −10.599, −8.632) in BFV change rate, respectively. Furthermore, stronger associations between PM2.5 and BFV progression were observed in individuals working in areas with lower greenness, those aged under 45 years, and females. In conclusion, reducing PM2.5 levels in the air, particularly the OM constituent, and enhancing greenness could potentially contribute to the protection of cerebrovascular health

Prevalence of porcine circovirus-like agent P1 in Jiangsu, China

Recently, we identified a novel porcine circovirus type 2-like agent P1 isolate from swine. The present study represents the first survey of P1 prevalence in swine herds from Jiangsu, China, by using PCR targeting the complete genome of P1. Prevalences of 50% and 19% were found among 6 herds and 248 animals, respectively. The results indicate a high prevalence of P1 in China pig populations

Contourlet textual features: Improving the diagnosis of solitary pulmonary nodules in two dimensional ct images

Materials and Methods: A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data.Results: Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93.Objective: To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. Copyright:Conclusion: Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer