The influence of risk culture on the performance of international joint-venture securities

With the development of economic globalization, culture is a key factor supporting the sustainability of foreign direct investment (FDI), especially for multinational enterprises. This paper takes the Chinese capital market as a sample and, combined with interviews with managers of international joint-venture securities (IJVS), finds that the culture of participants formed in developed and emerging capital market has a significant impact on the performance of IJVS. Using the degree of price fluctuation to measure the risk culture of each capital market, this paper observes that the risk culture in the Chinese capital market is significantly stronger than that of developed countries. This paper also finds that the stronger the risk culture IJVS shareholders have, the better they can adapt to the environment of the Chinese capital market and the better the performance they can achieve. Furthermore, risk culture distance, calculated by the risk culture differences between foreign shareholders and Chinese capital market, are significantly negatively correlated with IJVS performance and efficiency

Matched Shrunken Cone Detector (MSCD): Bayesian Derivations and Case Studies for Hyperspectral Target Detection

Hyperspectral images (HSIs) possess non-negative properties for both hyperspectral signatures and abundance coefficients, which can be naturally modeled using cone-based representation. However, in hyperspectral target detection, cone-based methods are barely studied. In this paper, we propose a new regularized cone-based representation approach to hyperspectral target detection, as well as its two working models by incorporating into the cone representation l2-norm and l1-norm regularizations, respectively. We call the new approach the matched shrunken cone detector (MSCD). Also important, we provide principled derivations of the proposed MSCD from the Bayesian perspective: we show that MSCD can be derived by assuming a multivariate half-Gaussian distribution or a multivariate half-Laplace distribution as the prior distribution of the coefficients of the models. In the experimental studies, we compare the proposed MSCD with the subspace methods and the sparse representation-based methods for HSI target detection. Two real hyperspectral data sets are used for evaluating the detection performances on sub-pixel targets and full-pixel targets, respectively. Results show that the proposed MSCD can outperform other methods in both cases, demonstrating the competitiveness of the regularized cone-based representation

Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks

To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot semantic segmentation methods first pre-train the models on `seen' classes, and then evaluate their generalization performance on `unseen' classes. However, the prior pre-training stage not only introduces excessive time overhead, but also incurs a significant domain gap on `unseen' classes. To tackle these issues, we propose an efficient Training-free Few-shot 3D Segmentation netwrok, TFS3D, and a further training-based variant, TFS3D-T. Without any learnable parameters, TFS3D extracts dense representations by trigonometric positional encodings, and achieves comparable performance to previous training-based methods. Due to the elimination of pre-training, TFS3D can alleviate the domain gap issue and save a substantial amount of time. Building upon TFS3D, TFS3D-T only requires to train a lightweight query-support transferring attention (QUEST), which enhances the interaction between the few-shot query and support data. Experiments demonstrate TFS3D-T improves previous state-of-the-art methods by +6.93% and +17.96% mIoU respectively on S3DIS and ScanNet, while reducing the training time by -90%, indicating superior effectiveness and efficiency.Comment: Code is available at https://github.com/yangyangyang127/TFS3

Soft-bodied adaptive multimodal locomotion strategies in fluid-filled confined spaces

Soft-bodied locomotion in fluid-filled confined spaces is critical for future wireless medical robots operating inside vessels, tubes, channels, and cavities of the human body, which are filled with stagnant or flowing biological fluids. However, the active soft-bodied locomotion is challenging to achieve when the robot size is comparable with the cross-sectional dimension of these confined spaces. Here, we propose various control and performance enhancement strategies to let the sheet-shaped soft millirobots achieve multimodal locomotion, including rolling, undulatory crawling, undulatory swimming, and helical surface crawling depending on different fluid-filled confined environments. With these locomotion modes, the sheet-shaped soft robot can navigate through straight or bent gaps with varying sizes, tortuous channels, and tubes with a flowing fluid inside. Such soft robot design along with its control and performance enhancement strategies are promising to be applied in future wireless soft medical robots inside various fluid-filled tight regions of the human body

Comparison of the Filtration Culture and Multiple Real-Time PCR Examination for Campylobacter spp. From Stool Specimens in Diarrheal Patients

Campylobacter is one of the most common pathogens leading to the bacterial diarrheal illness. In order to set up one effective culture independent assay for the screen of the Campylobacter infection in the diarrheal patients, the quadruple real-time PCR method comparing to the culture based on the enriched filtration method which was recognized as the most effective isolation method was assessed for 190 stool samples from the diarrheal patients collected during the Foodborne Diseases Active Surveillance Network in Beijing. This multiple real-time PCR was designed to identify the Campylobacter genus, C. jejuni, C. coli, and C. lari simultaneously. With the enrichment culture method, 23 (12.1%, 23/190) Campylobacter isolates were obtained (20 C. jejuni and 3 C. coli), however, 31 samples (16.3%, 31/190) were detected positively with the real-time PCR (21 C. jejuni, 8 C. coli, and 2 Campylobacter genus only). With the comparison, the real-time-PCR method is more sensitive than the enrichment filtration method (16.3 vs. 12.1%, p = 0.021). Among the culture-positive samples, 95.7% (22/23) were detected positively by PCR which indicate the specificity of this method was higher. These two methods were consistent well (Kappa = 0.785, p < 0.05). Comparing to the culture methods, the result of the multiple real-time PCR method is sensitive, reliable and rapid. The present study indicated this multiple real-time PCR can be used both for the surveillance network and the preceding screen for bacteria isolation. This is first comparative study between the culture and multiple real-time PCR method for Campylobacter identification in stool specimens from the diarrheal patients

Data-augmented matched subspace detector for hyperspectral subpixel target detection

The performance of subspace-based methods such as matched subspace detector (MSD) and MSD with interaction effects (MSDinter) heavily depends on the background subspace and the target subspace. Nonetheless, constructing a representative target subspace is challenging due to the limited availability of target spectra in a collected hyperspectral image. In this paper, we propose two new hyperspectral target detection methods termed data-augmented MSD (DAMSD) and data-augmented MSDinter (DAMSDI) that can effectively solve the scarcity problem of target spectra and from which a representative target-background mixed subspace can be learned. We first synthesise target-background mixed spectra based on classical hyperspectral mixing models and then learn a target-background mixed subspace via principal component analysis. Compared with MSD and MSDinter, the learned mixed subspace is more representative as spectral variability of target spectra is explained to the largest extent and it leads to an improvement in computational speed and numerical stability. We demonstrate the efficacy of DAMSD and DAMSDI for subpixel target detection on two public hyperspectral image datasets