Network-Based Trajectory Search over Time Intervals

2021 ◽  
Vol 25 ◽  
pp. 100221
Author(s):  
Mingming Chen ◽  
Ning Wang ◽  
Guofeng Lin ◽  
Jedi S. Shang
Keyword(s):  
Time Intervals ◽  
Over Time ◽  
Trajectory Search

scholarly journals Hubungan Perilaku Perdagangan Investor Dengan Volume Perdagangan Di Bursa Efek Indonesia

2009 ◽  
Vol 2 (1) ◽  
Author(s):  
Ghazali Syamni
Keyword(s):  
Trading Volume ◽  
Stock Exchange ◽  
Time Interval ◽  
Time Intervals ◽  
Using Data ◽  
Changes Over Time ◽  
Relationship Of ◽  
The Relationship ◽  
Order Pattern ◽  
Over Time

This paper examines the relationship of behavior trading investor using data detailed transaction history-corporate edition demand and order history in Indonesia Stock Exchange during period of March, April and May 2005. Peculiarly, behavior placing of investor order at trading volume. The result of this paper indicates that trading volume order pattern to have pattern U shape. The pattern happened that investors have strong desires to places order at the opening and close of compared to in trading periods. While the largest orders are of market at the opening indicates that investor is more conservatively when opening, where many orders when opening has not happened transaction to match. In placing order both of investor does similar strategy. By definition, informed investors’ orders more large than uninformed investors. If comparison of order examined hence both investors behavior relatively changes over time. But, statistically shows there is not ratio significant. This implies behavior trading of informed investors and uninformed investors stable relative over time. The result from regression analysis indicates that informed investors to correlate at trading volume in all time intervals, but not all uninformed investors correlates in every time interval. This imply investor order inform is more can explain trading volume pattern compared to uninformed investor order in Indonesia Stock Exchange. Finally, result of regression also finds that order status match has greater role determines trading volume pattern intraday especially informed buy match and informed sale match. While amend, open and withdraw unable to have role to determine intraday trading volume pattern.

Active Optimum Control of Orthotropic Plates Using Piezoelectric Stiffeners

1993 ◽  
Author(s):  
Victor Birman ◽  
Sarp Adali
Keyword(s):  
Time Interval ◽  
Short Time Interval ◽  
Bending Moments ◽  
Optimum Control ◽  
Orthotropic Plates ◽  
Time Intervals ◽  
Control Forces ◽  
Short Time ◽  
Alternating Voltage ◽  
Over Time

Abstract Active control of orthotropic plates subjected to an impulse loading is considered. The dynamic response is minimized using in-plane forces or bending moments induced by piezoelectric stiffeners bonded to the opposite surfaces of the plate and placed symmetrically with respect to the middle plane. The control forces and moments are activated by a piece-wise constant alternating voltage with varying switch-over time intervals. The magnitude of voltage is bounded while the switch-over time intervals are constantly adjusted to achieve an optimum control. Numerical examples presented in the paper demonstrate the effectiveness of the method and the possibility of reducing the vibrations to very small amplitudes within a short time interval which is in the order of a second.

Finding Minimal Infrequent Elements in Multi-Dimensional Data Defined over Partially Ordered Sets and its Applications

2010 ◽  
pp. 98-116
Author(s):  
Khaled M. Elbassioni
Keyword(s):  
Association Rules ◽  
Partially Ordered Set ◽  
Quantitative Data ◽  
Partially Ordered Sets ◽  
Threshold Value ◽  
Ordered Sets ◽  
Time Intervals ◽  
Partially Ordered ◽  
The Given ◽  
Over Time

The authors consider databases in which each attribute takes values from a partially ordered set (poset). This allows one to model a number of interesting scenarios arising in different applications, including quantitative databases, taxonomies, and databases in which each attribute is an interval representing the duration of a certain event occurring over time. A natural problem that arises in such circumstances is the following: given a database D and a threshold value t, find all collections of “generalizations” of attributes which are “supported” by less than t transactions from D. They call such collections infrequent elements. Due to monotonicity, they can reduce the output size by considering only minimal infrequent elements. We study the complexity of finding all minimal infrequent elements for some interesting classes of posets. The authors show how this problem can be applied to mining association rules in different types of databases, and to finding “sparse regions” or “holes” in quantitative data or in databases recording the time intervals during which a re-occurring event appears over time. Their main focus will be on these applications rather than on the correctness or analysis of the given algorithms.

scholarly journals Usage of the Term Big Data in Biomedical Publications: A Text Mining Approach

2019 ◽  
Vol 3 (1) ◽  
pp. 13
Author(s):  
Allard van Altena ◽  
Perry Moerland ◽  
Aeilko Zwinderman ◽  
Sílvia Olabarriaga
Keyword(s):  
Big Data ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Distinct Type ◽  
Research Field ◽  
Time Intervals ◽  
Learning Classifier ◽  
Biomedical Field ◽  
Biomedical Publications ◽  
Over Time

In this study, we attempt to assess the value of the term Big Data when used by researchers in their publications. For this purpose, we systematically collected a corpus of biomedical publications that use and do not use the term Big Data. These documents were used as input to a machine learning classifier to determine how well they can be separated into two groups and to determine the most distinguishing classification features. We generated 100 classifiers that could correctly distinguish between Big Data and non-Big Data documents with an area under the Receiver Operating Characteristic (ROC) curve of 0.96. The differences between the two groups were characterized by terms specific to Big Data themes—such as `computational’, `mining’, and `challenges’—and also by terms that indicate the research field, such as `genomics’. The ROC curves when plotted for various time intervals showed no difference over time. We conclude that there is a detectable and stable difference between publications that use the term Big Data and those that do not. Furthermore, the use of the term Big Data within a publication seems to indicate a distinct type of research in the biomedical field. Therefore, we conclude that value can be attributed to the term Big Data when used in a publication and this value has not changed over time.

scholarly journals Correction to “On a Class of Hermite-Obreshkov One-Step Methods with Continuous Spline Extension” [Axioms 7(3), 58, 2018]

Axioms ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 59
Author(s):  
Francesca Mazzia ◽  
Alessandra Sestini
Keyword(s):  
Kepler Problem ◽  
Hamiltonian Function ◽  
First Integrals ◽  
The Other ◽  
Time Intervals ◽  
One Step ◽  
Over Time

The authors of the above mentioned paper specify that the considered class of one-step symmetric Hermite-Obreshkov methods satisfies the property of conjugate-symplecticity up to order p + r , where r = 2 and p is the order of the method. This generalization of conjugate-symplecticity states that the methods conserve quadratic first integrals and the Hamiltonian function over time intervals of length O ( h − r ) . Theorem 1 of the above mentioned paper is then replaced by a new one. All the other results in the paper do not change. Two new figures related to the already considered Kepler problem are also added.

scholarly journals A simple metric to quantify seismicity clustering

2010 ◽  
Vol 17 (4) ◽  
pp. 293-302 ◽  
Author(s):  
N. F. Cho ◽  
K. F. Tiampo ◽  
S. D. Mckinnon ◽  
J. A. Vallejos ◽  
W. Klein ◽  
...  
Keyword(s):  
Physical Meaning ◽  
Finite Time ◽  
Metastable Equilibrium ◽  
Lower Sensitivity ◽  
Time Interval ◽  
Finite Time Interval ◽  
Time Intervals ◽  
The Past ◽  
Local Clustering ◽  
Over Time

Abstract. The Thirulamai-Mountain (TM) metric was first developed to study ergodicity in fluids and glasses (Thirumalai and Mountain, 1993) using the concept of effective ergodicity, where a large but finite time interval is considered. Tiampo et al. (2007) employed the TM metric to earthquake systems to search for effective ergodic periods, which are considered to be metastable equilibrium states that are disrupted by large events. The physical meaning of the TM metric for seismicity is addressed here in terms of the clustering of earthquakes in both time and space for different sets of data. It is shown that the TM metric is highly dependent not only on spatial/temporal seismicity clustering, but on the past seismic activity of the region and the time intervals considered as well, and that saturation occurs over time, resulting in a lower sensitivity to local clustering. These results confirm that the TM metric can be used to quantify seismicity clustering from both spatial and temporal perspectives, in which the disruption of effective ergodic periods are caused by the agglomeration of events.

Acceleration of hemoglobin C crystallization by hemoglobin S

Blood ◽  
1989 ◽  
Vol 74 (5) ◽  
pp. 1823-1825
Author(s):  
MJ Lin ◽  
RL Nagel ◽  
RE Hirsch
Keyword(s):  
Gel Electrophoresis ◽  
Phosphate Buffer ◽  
Potassium Phosphate ◽  
Potassium Phosphate Buffer ◽  
Time Intervals ◽  
Agar Gel ◽  
Hemoglobin C ◽  
Agar Gel Electrophoresis ◽  
Over Time

We previously reported that circulating hemoglobin (Hb) CC erythrocytes contain oxygenated HbC crystals with little or no HbF and that HbF inhibits in vitro crystallization of HbC. We now report that HbS accelerates in vitro crystallization of HbC. Crystals were formed in 1.8 mol/L potassium phosphate buffer, pH 7.4, at 30 degrees C and were counted in several time intervals with a hematocytometer. The hemoglobin composition of Millipore-isolated crystals and supernatant were also analyzed. Under the conditions selected, 100% HbS formed needle-shaped crystals only after two hours. Pure HbC does not form crystals within 15 minutes, whereas a ratio of 10% HbS:90% HbC forms 1,100 crystals/mm3, 20% HbS:80% HbC forms 370 crystals/mm3, and 30% HbS:70% HbC forms 5 crystals/mm3. Crystals formed in the presence of HbS are tetragonal, as are pure HbC crystals. As compared with 100% HbC, HbA or albumin mixed with HbC showed a decreased number of crystals as a result of dilution. Analysis of the Hb content of isolated crystals by citrate agar gel electrophoresis showed that HbS was rapidly incorporated into the crystal in the same ratio over time. These results demonstrate that HbS accelerates crystallization of HbC with respect to the rates of crystallization of any of these two Hbs separately, through a mechanism that involves cocrystallization. These results may be significant in understanding SC disease.

scholarly journals Hydrometeor classification from two-dimensional video disdrometer data

2014 ◽  
Vol 7 (9) ◽  
pp. 2869-2882 ◽  
Author(s):  
J. Grazioli ◽  
D. Tuia ◽  
S. Monhart ◽  
M. Schneebeli ◽  
T. Raupach ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Visual Inspection ◽  
Particle Image ◽  
Support Vector ◽  
Two Dimensional ◽  
Time Intervals ◽  
Classification Technique ◽  
Statistical Behavior ◽  
Over Time

Abstract. The first hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data is presented. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using the statistical behavior of a set of particle descriptors as input, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over 60 s precipitation time steps labeled by visual inspection. In this way, eight dominant hydrometeor classes can be discriminated. The algorithm achieved high classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.

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