The Discrimination Classification in the Listed Companies in Accordance with the Market Quality Indexes

All listed companies in the Shanghai Stock Exchange in 2008 were ranked according to the total market capitalization, the revenue and the net profit. The top 100 listed companies and the countdown listed companies were selected. Then the random 100 listed companies were extracted. The two discrimination classification functions of the listed companies in accordance with the market quality indexes were established by the three types companies as categorical dependent variables and the price impacting index, the liquidity index, the large transaction costs and the excess volatility ratio as the independent variables that were selected among the 11 market quality indexes by the forward stepwise method. The accuracy rate of the discrimination classification functions of the listed companies was 77.74% through verifying by the original back substitution. The classification results were significant. Key words: The price impacting index; The liquidity index; The large transaction costs; The excess volatility ratio; The discrimination classification functio

MOSFET Modulated Dual Conversion Gain CMOS Image Sensors

In recent years, vision systems based on CMOS image sensors have acquired significant ground over those based on charge-coupled devices (CCD). The main advantages of CMOS image sensors are their high level of integration, random accessibility, and low-voltage, low-power operation. Previously proposed high dynamic range enhancement schemes focused mainly on extending the sensor dynamic range at the high illumination end. Sensor dynamic range extension at the low illumination end has not been addressed. Since most applications require low-noise, high-sensitivity, characteristics for imaging of the dark region as well as dynamic range expansion to the bright region, the availability of a low-noise, high-sensitivity pixel device is particularly important. In this dissertation, a dual-conversion-gain (DCG) pixel architecture was proposed; this architecture increases the signal to noise ratio (SNR) and the dynamic range of CMOS image sensors at both the low and high illumination ends. The dual conversion gain pixel improves the dynamic range by changing the conversion gain based on the illumination level without increasing artifacts or increasing the imaging readout noise floor. A MOSFET is used to modulate the capacitance of the charge sensing node. Under high light illumination conditions, a low conversion gain is used to achieve higher full well capacity and wider dynamic range. Under low light conditions, a high conversion gain is enabled to lower the readout noise and achieve excellent low light performance. A sensor prototype using the new pixel architecture with 5.6μm pixel pitch was designed and fabricated using Micron Technology’s 130nm 3-metal and 2-poly silicon process. The periphery circuitries were designed to readout the pixel and support the pixel characterization needs. The pixel design, readout timing, and operation voltage were optimized. A detail sensor characterization was performed; a 127μV/e was achieved for the high conversion gain mode and 30.8μV/e for the low conversion gain mode. Characterization results confirm that a 42ke linear full well was achieved for the low conversion gain mode and 10.5ke for the high conversion gain mode. An average 2.1e readout noise was measured for the high conversion gain mode and 8.6e for the low conversion gain mode. The total sensor dynamic range was extended to 86dB by combining the two modes of operation with a 46.2dB maximum SNR. Several images were taken by the prototype sensor under different illumination levels. The simple processed color images show the clear advantage of the high conversion gain mode for the low light imaging

Functional evaluation of Asp76, 84, 102 and 150 in human arsenic(III) methyltransferase (hAS3MT) interacting with S-adenosylmethionine

AbstractWe prepared eight mutants (D76P, D76N, D84P, D84N, D102P, D102N, D150P and D150N) to investigate the functions of residues Asp76, 84, 102 and 150 in human arsenic(III) methyltransferase (hAS3MT) interacting with the S-adenosylmethionine (SAM)-binding. The affinity of all the mutants for SAM were weakened. All the mutants except for D150N completely lost their methylation activities. Residues Asp76, 84, 102 and 150 greatly influenced hAS3MT catalytic activity via affecting SAM-binding or methyl transfer. Asp76 and 84 were located in the SAM-binding pocket, and Asp102 significantly affected SAM-binding via forming hydrogen bonds with SAM

MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond

Neural radiance fields (NeRF) and its subsequent variants have led to remarkable progress in neural rendering. While most of recent neural rendering works focus on objects and small-scale scenes, developing neural rendering methods for city-scale scenes is of great potential in many real-world applications. However, this line of research is impeded by the absence of a comprehensive and high-quality dataset, yet collecting such a dataset over real city-scale scenes is costly, sensitive, and technically difficult. To this end, we build a large-scale, comprehensive, and high-quality synthetic dataset for city-scale neural rendering researches. Leveraging the Unreal Engine 5 City Sample project, we develop a pipeline to easily collect aerial and street city views, accompanied by ground-truth camera poses and a range of additional data modalities. Flexible controls over environmental factors like light, weather, human and car crowd are also available in our pipeline, supporting the need of various tasks covering city-scale neural rendering and beyond. The resulting pilot dataset, MatrixCity, contains 67k aerial images and 452k street images from two city maps of total size $28km^2$. On top of MatrixCity, a thorough benchmark is also conducted, which not only reveals unique challenges of the task of city-scale neural rendering, but also highlights potential improvements for future works. The dataset and code will be publicly available at our project page: https://city-super.github.io/matrixcity/.Comment: Accepted to ICCV 2023. Project page: $\href{https://city-super.github.io/matrixcity/}{this\, https\, URL}

OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images

This paper presents OmniCity, a new dataset for omnipotent city understanding from multi-level and multi-view images. More precisely, the OmniCity contains multi-view satellite images as well as street-level panorama and mono-view images, constituting over 100K pixel-wise annotated images that are well-aligned and collected from 25K geo-locations in New York City. To alleviate the substantial pixel-wise annotation efforts, we propose an efficient street-view image annotation pipeline that leverages the existing label maps of satellite view and the transformation relations between different views (satellite, panorama, and mono-view). With the new OmniCity dataset, we provide benchmarks for a variety of tasks including building footprint extraction, height estimation, and building plane/instance/fine-grained segmentation. Compared with the existing multi-level and multi-view benchmarks, OmniCity contains a larger number of images with richer annotation types and more views, provides more benchmark results of state-of-the-art models, and introduces a novel task for fine-grained building instance segmentation on street-level panorama images. Moreover, OmniCity provides new problem settings for existing tasks, such as cross-view image matching, synthesis, segmentation, detection, etc., and facilitates the developing of new methods for large-scale city understanding, reconstruction, and simulation. The OmniCity dataset as well as the benchmarks will be available at https://city-super.github.io/omnicity

COMICS: a community property-based triangle motif clustering scheme

With the development of science and technology, network scales of various fields have experienced an amazing growth. Networks in the fields of biology, economics and society contain rich hidden information of human beings in the form of connectivity structures. Network analysis is generally modeled as network partition and community detection problems. In this paper, we construct a community property-based triangle motif clustering scheme (COMICS) containing a series of high efficient graph partition procedures and triangle motif-based clustering techniques. In COMICS, four network cutting conditions are considered based on the network connectivity. We first divide the large-scale networks into many dense subgraphs under the cutting conditions before leveraging triangle motifs to refine and specify the partition results. To demonstrate the superiority of our method, we implement the experiments on three large-scale networks, including two co-authorship networks (the American Physical Society (APS) and the Microsoft Academic Graph (MAG)), and two social networks (Facebook and gemsec-Deezer networks). We then use two clustering metrics, compactness and separation, to illustrate the accuracy and runtime of clustering results. A case study is further carried out on APS and MAG data sets, in which we construct a connection between network structures and statistical data with triangle motifs. Results show that our method outperforms others in both runtime and accuracy, and the triangle motif structures can bridge network structures and statistical data in the academic collaboration area

CD14 mediates the innate immune responses to arthritopathogenic peptidoglycan–polysaccharide complexes of Gram-positive bacterial cell walls

Bacterial infections play an important role in the multifactorial etiology of rheumatoid arthritis. The arthropathic properties of Gram-positive bacteria have been associated with peptidoglycan–polysaccharide complexes (PG-PS), which are major structural components of bacterial cell walls. There is little agreement as to the identity of cellular receptors that mediate innate immune responses to PG-PS. A glycosylphosphatidylinositol-linked cell surface protein, CD14, the lipopolysaccharide receptor, has been proposed as a PG-PS receptor, but contradictory data have been reported. Here, we examined the inflammatory and pathogenic responses to PG-PS in CD14 knockout mice in order to examine the role for CD14 in PG-PS-induced signaling. We found that PG-PS-induced responses in vitro, including transient increase in intracellular calcium, activation of nuclear factor-κB, and secretion of the cytokines tumor necrosis factor-α and interleukin-6, were all strongly inhibited in CD14 knockout macrophages. In vivo, the incidence and severity of PG-PS induced acute polyarthritis were significantly reduced in CD14 knockout mice as compared with their wild-type counterparts. Consistent with these findings, CD14 knockout mice had significantly inhibited inflammatory cell infiltration and synovial hyperplasia, and reduced expression of inflammatory cytokines in PG-PS arthritic joints. These results support an essential role for CD14 in the innate immune responses to PG-PS and indicate an important role for CD14 in PG-PS induced arthropathy