679 research outputs found
Dynamic structure of stock communities: A comparative study between stock returns and turnover rates
The detection of community structure in stock market is of theoretical and
practical significance for the study of financial dynamics and portfolio risk
estimation. We here study the community structures in Chinese stock markets
from the aspects of both price returns and turnover rates, by using a
combination of the PMFG and infomap methods based on a distance matrix. We find
that a few of the largest communities are composed of certain specific industry
or conceptional sectors and the correlation inside a sector is generally larger
than the correlation between different sectors. In comparison with returns, the
community structure for turnover rates is more complex and the sector effect is
relatively weaker. The financial dynamics is further studied by analyzing the
community structures over five sub-periods. Sectors like banks, real estate,
health care and New Shanghai take turns to compose a few of the largest
communities for both returns and turnover rates in different sub-periods.
Several specific sectors appear in the communities with different rank orders
for the two time series even in the same sub-period. A comparison between the
evolution of prices and turnover rates of stocks from these sectors is
conducted to better understand their differences. We find that stock prices
only had large changes around some important events while turnover rates surged
after each of these events relevant to specific sectors, which may offer a
possible explanation for the complexity of stock communities for turnover
rates
How volatilities nonlocal in time affect the price dynamics in complex financial systems
What is the dominating mechanism of the price dynamics in financial systems
is of great interest to scientists. The problem whether and how volatilities
affect the price movement draws much attention. Although many efforts have been
made, it remains challenging. Physicists usually apply the concepts and methods
in statistical physics, such as temporal correlation functions, to study
financial dynamics. However, the usual volatility-return correlation function,
which is local in time, typically fluctuates around zero. Here we construct
dynamic observables nonlocal in time to explore the volatility-return
correlation, based on the empirical data of hundreds of individual stocks and
25 stock market indices in different countries. Strikingly, the correlation is
discovered to be non-zero, with an amplitude of a few percent and a duration of
over two weeks. This result provides compelling evidence that past volatilities
nonlocal in time affect future returns. Further, we introduce an agent-based
model with a novel mechanism, that is, the asymmetric trading preference in
volatile and stable markets, to understand the microscopic origin of the
volatility-return correlation nonlocal in time.Comment: 16 pages, 7 figure
StuArt: Individualized Classroom Observation of Students with Automatic Behavior Recognition and Tracking
Each student matters, but it is hardly for instructors to observe all the
students during the courses and provide helps to the needed ones immediately.
In this paper, we present StuArt, a novel automatic system designed for the
individualized classroom observation, which empowers instructors to concern the
learning status of each student. StuArt can recognize five representative
student behaviors (hand-raising, standing, sleeping, yawning, and smiling) that
are highly related to the engagement and track their variation trends during
the course. To protect the privacy of students, all the variation trends are
indexed by the seat numbers without any personal identification information.
Furthermore, StuArt adopts various user-friendly visualization designs to help
instructors quickly understand the individual and whole learning status.
Experimental results on real classroom videos have demonstrated the superiority
and robustness of the embedded algorithms. We expect our system promoting the
development of large-scale individualized guidance of students.Comment: Novel pedagogical approaches in signal processing for K-12 educatio
JIN Formula Inhibits Tumorigenesis Pathways in Human Lung Carcinoma Cells and Tumor Growth in Athymic Nude Mice
Origin and Evolution of the Ore-Forming Fluids in the Liyuan Gold Deposit, Central North China Craton: Constraints from Fluid Inclusions and H-O-C Isotopic Compositions
The Liyuan gold deposit is hosted within Archean basement metamorphic rocks and controlled by the NNE-trending faults in the central North China Craton. The ore-forming processes can be divided into three stages (early, middle, and late). Three types of primary fluid inclusions (FIs) are identified in the Liyuan, including pure carbonic, carbonic-aqueous, and aqueous inclusions. The primary FIs of three stages are mainly homogenized at temperatures of 318–408°C, 201–329°C, and 136–229°C, with salinities of 2.1–8.9, 0.5–12.4, and 0.4–6.3 wt.% NaCl equivalent, respectively. The main Au mineralization is related to the middle stage, and water-rock interaction caused rapid precipitation of gold in this stage. The initial ore-forming fluids were likely magmatic water or metamorphic fluid and mixed with meteoric water at later stages. Due to the lack of granite body at the present mining levels, we speculate that it was magmatic water that might have been exsolved from a concealed granite body at greater depth or it was metamorphic fluid that was directly transported from depth via deep faults. Based on all the available geological and geochemical evidence, we suggest that the Liyuan deposit belongs to orogenic gold deposit that located in the interior North China Craton
Some Rare Earth Elements Analysis by Microwave Plasma Torch Coupled with the Linear Ion Trap Mass Spectrometry
A sensitive mass spectrometric analysis method based on the microwave plasma technique is developed for the fast detection of trace rare earth elements (REEs) in aqueous solution. The plasma was produced from a microwave plasma torch (MPT) under atmospheric pressure and was used as ambient ion source of a linear ion trap mass spectrometer (LTQ). Water samples were directly pneumatically nebulized to flow into the plasma through the central tube of MPT. For some REEs, the generated composite ions were detected in both positive and negative ion modes and further characterized in tandem mass spectrometry. Under the optimized conditions, the limit of detection (LOD) was at the level 0.1 ng/mL using MS2 procedure in negative mode. A single REE analysis can be completed within 2~3 minutes with the relative standard deviation ranging between 2.4% and 21.2% (six repeated measurements) for the 5 experimental runs. Moreover, the recovery rates of these REEs are between the range of 97.6%–122.1%. Two real samples have also been analyzed, including well and orange juice. These experimental data demonstrated that this method is a useful tool for the field analysis of REEs in water and can be used as an alternative supplement of ICP-MS
Genesis of the Zhijiadi Ag-Pb-Zn Deposit, Central North China Craton: Constraints from Fluid Inclusions and Stable Isotope Data
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