An FGO-based Unified Initial Alignment Method of Strapdown Inertial Navigation System

The initial alignment process can provide an accurate initial attitude of strapdown inertial navigation system. The conventional two-procedure method usually includes coarse and fine alignment processes. Coarse alignment converges fast because of its batch estimating characteristics and the initial attitude does not influence the results. But coarse alignment is low accuracy without considering the IMU's bias. The fine alignment is more accurate by applying a recursive Bayesian filter to estimate the IMU's bias, but the attitude converges slowly as the initial value influence the convergence speed of the recursive filter. Researchers have proposed the unified initial alignment to achieve initial alignment in one procedure, existing unified methods make improvements on the basics of recursive Bayesian filter and those methods are still slow to converge. In this paper, a unified method based on batch estimator FGO (factor graph optimization) is raised, which is converge fast like coarse alignment and accurate than the existing method. We redefine the state and rederivation the state dynamic model first. Then, the optimal attitude and the IMU's bias are estimated simultaneously through FGO. The fast convergence and high accuracy of this method are verified by simulation and physical experiments on a rotation SINS.Comment: 9 pages, Journal Paper

Do Insiders Trade on Government Subsidies?

We examine whether and how insiders trade on government subsidies, a major instrument through which the governments intervene in the economy. Using a novel dataset of government subsidies of Chinese listed firms, we find that net insider purchase increases significantly during the month of subsidy receipt. The effect of subsidies on insider trading is weaker in firms with a more transparent information environment and when subsidies are granted in a more predictable manner. In contrast, the effect is more pronounced for politically connected firms. Further analysis shows that the subsidy-trading relation may reflect both insiders’ informational advantage concerning subsidies and their superior ability to detect mispricing-related opportunities. Our findings provide new insights into the capital market consequences of government subsidies through the lens of insider trading

Stage-Aware Learning for Dynamic Treatments

Recent advances in dynamic treatment regimes (DTRs) provide powerful optimal treatment searching algorithms, which are tailored to individuals' specific needs and able to maximize their expected clinical benefits. However, existing algorithms could suffer from insufficient sample size under optimal treatments, especially for chronic diseases involving long stages of decision-making. To address these challenges, we propose a novel individualized learning method which estimates the DTR with a focus on prioritizing alignment between the observed treatment trajectory and the one obtained by the optimal regime across decision stages. By relaxing the restriction that the observed trajectory must be fully aligned with the optimal treatments, our approach substantially improves the sample efficiency and stability of inverse probability weighted based methods. In particular, the proposed learning scheme builds a more general framework which includes the popular outcome weighted learning framework as a special case of ours. Moreover, we introduce the notion of stage importance scores along with an attention mechanism to explicitly account for heterogeneity among decision stages. We establish the theoretical properties of the proposed approach, including the Fisher consistency and finite-sample performance bound. Empirically, we evaluate the proposed method in extensive simulated environments and a real case study for COVID-19 pandemic

Prevalence and spectrum of BRCA germline variants in mainland Chinese familial breast and ovarian cancer patients.

Germline mutations in BRCA1 and BRCA2 are the most penetrating genetic predispositions for breast and ovarian cancer, and their presence is largely ethnic-specific. Comprehensive information about the prevalence and spectrum of BRCA mutations has been collected in European and North American populations. However, similar information is lacking in other populations, including the mainland Chinese population despite its large size of 1.4 billion accounting for one fifth of the world\u27s population. Herein, we performed an extensive literature analysis to collect BRCA variants identified from mainland Chinese familial breast and ovarian cancer patients. We observed 137 distinct BRCA1 variants in 409 of 3,844 and 80 distinct BRCA2 variants in 157 of 3,024 mainland Chinese patients, with an estimated prevalence of 10.6% for BRCA1 and 5.2% for BRCA2. Of these variants, only 40.3% in BRCA1 and 42.5% in BRCA2 are listed in current Breast Cancer Information Core database. We observed higher frequent variation in BRCA1 exons 11A, 11C, 11D, and 24 and BRCA2 exon 10 in Chinese patients than in the patients of other populations. The most common pathogenic variant in BRCA1 wasc.981_982delAT in exon 11A, and in BRCA2 c.3195_3198delTAAT in exon 11B and c.5576_5579delTTAA in exon 11E; the most common novel variant in BRCA1 was c.919A\u3eG in exon 10A, and in BRCA2 c.7142delC in exon 14. None of the variants overlap with the founder mutations in other populations. Our analysis indicates that the prevalence of BRCA variation in mainland Chinese familial breast and ovarian cancer patients is at a level similar to but the spectrum is substantially different from the ones of other populations

Effective Quantization for Diffusion Models on CPUs

Diffusion models have gained popularity for generating images from textual descriptions. Nonetheless, the substantial need for computational resources continues to present a noteworthy challenge, contributing to time-consuming processes. Quantization, a technique employed to compress deep learning models for enhanced efficiency, presents challenges when applied to diffusion models. These models are notably more sensitive to quantization compared to other model types, potentially resulting in a degradation of image quality. In this paper, we introduce a novel approach to quantize the diffusion models by leveraging both quantization-aware training and distillation. Our results show the quantized models can maintain the high image quality while demonstrating the inference efficiency on CPUs. The code is publicly available at: https://github.com/intel/intel-extension-for-transformers

The Representation of Mosuo People and Mosuo Culture in Chinese Tourism Websites

Past research has shown that because tourism itself is a product of a gendered society, its processes are gendered in terms of construction, presentation, and consumption. This study examines how these websites shape the image of the Mosuo people and the Mosuo culture by analyzing texts in Chinese tourism websites. Ten representative Chinese tourism websites were selected for this study, and all relevant texts that could be retrieved were analyzed manually. All samples selected were officially published and represent only the attitudes of the tourism websites. The results of the study show that there are a large number of feminized or sexualized descriptions in the texts about the Mosuo people and the Mosuo culture provided by Chinese tourism websites. The language used on tourism websites is shaped by discourses of patriarchy and sexuality and is intended for heterosexual male tourists

A grazing Gomphotherium in Middle Miocene Central Asia, 10 million years prior to the origin of the Elephantidae

Feeding preference of fossil herbivorous mammals, concerning the coevolution of mammalian and floral ecosystems, has become of key research interest. In this paper, phytoliths in dental calculus from two gomphotheriid proboscideans of the middle Miocene Junggar Basin, Central Asia, have been identified, suggesting that Gomphotherium connexum was a mixed feeder, while the phytoliths from G. steinheimense indicates grazing preference. This is the earliest-known proboscidean with a predominantly grazing habit. These results are further confirmed by microwear and isotope analyses. Pollen record reveals an open steppic environment with few trees, indicating an early aridity phase in the Asian interior during the Mid-Miocene Climate Optimum, which might urge a diet remodeling of G. steinheimense. Morphological and cladistic analyses show that G. steinheimense comprises the sister taxon of tetralophodont gomphotheres, which were believed to be the general ancestral stock of derived “true elephantids”; whereas G. connexum represents a more conservative lineage in both feeding behavior and tooth morphology, which subsequently became completely extinct. Therefore, grazing by G. steinheimense may have acted as a behavior preadaptive for aridity, and allowing its lineage evolving new morphological features for surviving later in time. This study displays an interesting example of behavioral adaptation prior to morphological modification

Characterization of deep sub-wavelength nanowells by imaging the photon state scattering spectra

Optical-matter interactions and photon scattering in a sub-wavelength space are of great interest in many applications, such as nanopore-based gene sequencing and molecule characterization. Previous studies show that spatial distribution features of the scattering photon states are highly sensitive to the dielectric and structural properties of the nanopore array and matter contained on or within them, as a result of the complex optical-matter interaction in a confined system. In this paper, we report a method for shape characterization of subwavelength nanowells using photon state spatial distribution spectra in the scattering near field. Far-field parametric images of the near-field optical scattering from sub-wavelength nanowell arrays on a SiN substrate were obtained experimentally. Finite-difference time-domain simulations were used to interpret the experimental results. The rich features of the parametric images originating from the interaction of the photons and the nanowells were analyzed to recover the size of the nanowells. Experiments on nanoholes modified with Shp2 proteins were also performed. Results show that the scattering distribution of modified nanoholes exhibits significant differences compared to empty nanoholes. This work highlights the potential of utilizing the photon status scattering of nanowells for molecular characterization or other virus detection applications