Spectral Compressive Sensing with Model Selection

The performance of existing approaches to the recovery of frequency-sparse signals from compressed measurements is limited by the coherence of required sparsity dictionaries and the discretization of frequency parameter space. In this paper, we adopt a parametric joint recovery-estimation method based on model selection in spectral compressive sensing. Numerical experiments show that our approach outperforms most state-of-the-art spectral CS recovery approaches in fidelity, tolerance to noise and computation efficiency.Comment: 5 pages, 2 figures, 1 table, published in ICASSP 201

Auditor Bargaining Power and Audit Fee Lowballing

Incoming auditors usually charge less audit fees to obtain the business (audit fee lowballing). Prior research shows that industry expert auditors have better expertise and resources to perform higher quality audit than the non-expert auditors. Consistent with this literature, we predict and find empirical evidence that the magnitude of lowballing will be significantly smaller for industry expert auditors comparing with non-experts auditors. This result adds new evidence of the impact of auditors’ barging power to the audit fee lowballing literature. 

Reactions of Chinese adults to warning labels on cigarette packages: A survey in Jiangsu Province

<p>Abstract</p> <p>Background</p> <p>To compare reactions to warning labels presented on cigarette packages with a specific focus on whether the new Chinese warning labels are better than the old labels and international labels.</p> <p>Methods</p> <p>Participants aged 18 and over were recruited in two cities of Jiangsu Province in 2008, and 876 face-to-face interviews were completed. Participants were shown six types of warning labels found on cigarette packages. They comprised one old Chinese label, one new label used within the Chinese market, and one Chinese overseas label and three foreign brand labels. Participants were asked about the impact of the warning labels on: their knowledge of harm from smoking, giving cigarettes as a gift, and quitting smoking.</p> <p>Results</p> <p>Compared with the old Chinese label, a higher proportion of participants said the new label provided clear information on harm caused by smoking (31.2% vs 18.3%). Participants were less likely to give cigarettes with the new label on the package compared with the old label (25.2% vs 20.8%). These proportions were higher when compared to the international labels. Overall, 26.8% of participants would quit smoking based on information from the old label and 31.5% from the new label. When comparing the Chinese overseas label and other foreign labels to the new Chinese label with regard to providing knowledge of harm warning, impact of quitting smoking and giving cigarettes as a gift, the overseas labels were more effective.</p> <p>Conclusion</p> <p>Both the old and the new Chinese warning label are not effective in this target population.</p

Can We Evaluate the Distinguishability of the OpenSARurban Dataset?

In Synthetic Aperture Radar (SAR) image classification tasks, the performance depends on both the classifier and the dataset itself. However, in comparison with plenty of SAR classification methods, there is little work aimed at analyzing the distinguishability of the dataset. In the classification dataset, some classes are semantically different but their distinguishability is low, the classes are hard to be classified especially in some more practical cases that there are unknown classes without supervision exist. Referring to open set recognition (OSR), in this paper, we proposed the SAR Distinguishability Analysor (SAR-DA) to evaluate the distinguishability of the OpenSARUrban dataset. By modeling each class as a multivariate Gaussian distribution in latent space, SAR-DA can not only classify the classes having been seen in training phase, but also can recognize unknown samples if a test sample is out of each known distribution. Each class in OpenSARUr-ban is set unknown in turn, then we apply the SAR-DA on the split dataset in OSR and supervised setting. The distinguishability can be reflected by the unknown recognition recall rate. The experimental results show that the unknown recognition recall rate in OSR setting significantly decreased compared with those in supervised setting, indicating that even though the classes in OpenSARUrban are semantically different from each other, the latent distributions of some classes are quite similar and hard to be classified, thus these classes are of low distinguishability

Radion and Higgs mixing at the LHC

We study the resonance production of radions and Higgs via gluon-gluon fusion in the Randall-Sundrum model with Higgs-curvature mixing at the LHC. We find that radion can be detected both in mixed (with Higgs boson) and unmixed case if the radion vacuum expectation value $\Lambda_\phi$ is around 1 TeV. The $\Lambda_\phi \sim 10$ TeV case is also promising for certain values of mixing parameters and radion masses. The mixing can affect the production and decay of Higgs boson in a significant way. Thus Higgs search strategies at the LHC may need refinements in case of radion-Higgs mixing in the Randall-Sundrum model.Comment: Version to appear in Physics Letters

OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation

Sentinel-1 mission provides a freely accessible opportunity for urban interpretation from synthetic aperture radar (SAR) images with specific resolution, which is of paramount importance for earth observation. In parallel, with the rapid development of advanced technologies, especially deep learning, it is urgently needed to construct a large-scale SAR dataset leading urban interpretation. This paper presents OpenSARUrban: a Sentinel-1 dataset dedicated to urban interpretation from SAR images, including a well-defined hierarchical annotation scheme, the data collection, the well-established procedures for dataset construction and organizations, the properties, visualizations, and applications of this dataset. Particularly, the OpenSARUrban provides 33358 image patches of SAR urban scene, covering 21 major cities of China, including 10 different categories, 4 kinds of formats, 2 kinds of polarization modes, and owning 5 essential properties: large-scale, diversity, specificity, reliability, and sustainability. These properties guarantee the achievable of several goals for OpenSARUrban. The first is to support urban target characterization. The second is to help develop applicable and advanced algorithms for Sentinel-1 urban target classification. The dataset visualization is implemented from the perspective of manifold to give an intuitive understanding. Besides a detailed description and visualization of the dataset, we present results of some benchmark algorithms, demonstrating that this dataset is practical and challenging. Notably, developing algorithms to enhance the classification performance on the whole dataset and considering the data imbalance are especially challenging

Measurement and temporal and spatial characteristics of agricultural eco-efficiency under climate change: a case study of Anhui, China

IntroductionAgricultural eco-efficiency is an important index to evaluate the agricultural sustainable development and ecological economics, while simultaneously providing a metric for improvements to the rural environment and the stability of agricultural ecosystems.MethodsThis study took Anhui province as a case, and applied unit survey and list analysis methodologies to quantify rural agricultural non-point source pollution (NPS). Input-oriented super-efficient DEA-SBM was used to measure agricultural eco-efficiency in the typical North-South Transition Zone, and evaluated spatial correlations and differences.ResultsThis study showed that NPS was relatively stable, with less than 5% local variation in Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP) pollutants. The environmental pressure caused by livestock breeding and the use of chemical fertilizers was very substantial, and the differences of rural agricultural NPS in Anhui Province had obvious north-south characteristics. The agricultural eco-efficiency exhibited an “inverted N” trend. Affected by the “Spatial proximity effect” and the “Matthew effect”, it presented spatial agglomeration and positive spatial correlation. The regional differences were significant, and the heterogeneity increased in our study areas. The southern region had the greatest variation, followed by the northern region, with the smallest variation in the central region, although inter-regional differences were consistent.DiscussionThough the rational allocation of resources, coordination between agricultural economic and environmental protection would be realized, and better conditions for the sustainable development of agricultural ecology and the long-term stability of agricultural ecosystem would be created

Upconversion NaYF 4

Upconversion nanoparticles (UCNPs) based on NaYF4 nanocrystals with strong upconversion luminescence are synthesized by the solvothermal method. The emission color of these NaYF4 upconversion nanoparticles can be easily modulated by the doping. These NaYF4 upconversion nanocrystals can be employed as fluorescence donors to pump fluorescent organic molecules. For example, the efficient luminescence resonant energy transfer (LRET) can be achieved by controlling the distance between NaYF4:Yb3+/Er3+ UCNPs and Rhodamine B (RB). NaYF4:Yb3+/Er3+ UCNPs can emit green light at the wavelength of ~540 nm while RB can efficiently absorb the green light of ~540 nm to emit red light of 610 nm. The LRET efficiency is highly dependent on the concentration of NaYF4 upconversion fluorescent donors. For the fixed concentration of 3.2 µg/mL RB, the optimal concentration of NaYF4:Yb3+/Er3+ UCNPs is equal to 4 mg/mL which generates the highest LRET signal ratio. In addition, it is addressed that the upconversion nanoparticles with diameter of 200 nm are suitable for imaging the cells larger than 10 µm with clear differentiation between cell walls and cytoplasm

Development and validation of a novel necroptosis-related gene signature for predicting prognosis and therapeutic response in Ewing sarcoma

Ewing sarcoma (ES) is the second most common malignant bone tumor in children and has a poor prognosis due to early metastasis and easy recurrence. Necroptosis is a newly discovered cell death method, and its critical role in tumor immunity and therapy has attracted widespread attention. Thus, the emergence of necroptosis may provide bright prospects for the treatment of ES and deserves our further study. Here, based on the random forest algorithm, we identified 6 key necroptosis-related genes (NRGs) and used them to construct an NRG signature with excellent predictive performance. Subsequent analysis showed that NRGs were closely associated with ES tumor immunity, and the signature was also good at predicting immunotherapy and chemotherapy response. Next, a comprehensive analysis of key genes showed that RIPK1, JAK1, and CHMP7 were potential therapeutic targets. The Cancer Dependency Map (DepMap) results showed that CHMP7 is associated with ES cell growth, and the Gene Set Cancer Analysis (GSCALite) results revealed that the JAK1 mutation frequency was the highest. The expression of 3 genes was all negatively correlated with methylation and positively with copy number variation (CNV). Finally, an accurate nomogram was constructed with this signature and clinical traits. In short, this study constructed an accurate prognostic signature and identified 3 novel therapeutic targets against ES