scholarly journals Dynamic Graph Learning: A Structure-Driven Approach

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 168
Author(s):  
Bo Jiang ◽  
Yuming Huang ◽  
Ashkan Panahi ◽  
Yiyi Yu ◽  
Hamid Krim ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Time Varying ◽  
Dynamic Graph ◽  
Time Intervals ◽  
Data Set ◽  
Network Nodes ◽  
Continuous Relaxation ◽  
Short Time ◽  
The Brain ◽  
Objective Functional

The purpose of this paper is to infer a dynamic graph as a global (collective) model of time-varying measurements at a set of network nodes. This model captures both pairwise as well as higher order interactions (i.e., more than two nodes) among the nodes. The motivation of this work lies in the search for a connectome model which properly captures brain functionality across all regions of the brain, and possibly at individual neurons. We formulate it as an optimization problem, a quadratic objective functional and tensor information of observed node signals over short time intervals. The proper regularization constraints reflect the graph smoothness and other dynamics involving the underlying graph’s Laplacian, as well as the time evolution smoothness of the underlying graph. The resulting joint optimization is solved by a continuous relaxation of the weight parameters and an introduced novel gradient-projection scheme. While the work may be applicable to any time-evolving data set (e.g., fMRI), we apply our algorithm to a real-world dataset comprising recorded activities of individual brain cells. The resulting model is shown to be not only viable but also efficiently computable.

scholarly journals A Hybrid Metaheuristic Algorithm for the Efficient Placement of UAVs

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 323
Author(s):  
Stephanie Alvarez Fernandez ◽  
Marcelo M. Carvalho ◽  
Daniel G. Silva
Keyword(s):  
Optimization Problem ◽  
Data Transfer ◽  
Fixed Number ◽  
Metaheuristic Algorithm ◽  
Network Coverage ◽  
Time Intervals ◽  
Hybrid Metaheuristic ◽  
Relay Communications ◽  
Real World Application ◽  
Short Time

This work addresses the problem of using Unmanned Aerial Vehicles (UAV) to deploy a wireless aerial relay communications infrastructure for stations scattered on the ground. In our problem, every station in the network must be assigned to a single UAV, which is responsible for handling all data transfer on behalf of the stations that are assigned to it. Consequently, the placement of UAVs is key to achieving both network coverage and the maximization of the aggregate link capacities between UAVs and stations, and among the UAVs themselves. Because the complexity of this problem increases significantly with the number of stations to cover, for a given fixed number p of available UAVs, we model it as a single allocation p-hub median optimization problem, and we propose a hybrid metaheuristic algorithm to solve it. A series of numerical experiments illustrate the efficiency of the proposed algorithm against traditional optimization tools, which achieves high-quality results in very short time intervals, thus making it an attractive solution for real-world application scenarios.

Randomly Connected Networks Have Short Temporal Memory

2013 ◽  
Vol 25 (6) ◽  
pp. 1408-1439 ◽  
Author(s):  
Edward Wallace ◽  
Hamid Reza Maei ◽  
Peter E. Latham
Keyword(s):  
Temporal Processing ◽  
Mammalian Brain ◽  
Time Varying ◽  
Time Constants ◽  
Complex Time ◽  
Temporal Memory ◽  
Current State ◽  
Time Of Year ◽  
Short Time ◽  
The Brain

The brain is easily able to process and categorize complex time-varying signals. For example, the two sentences, “It is cold in London this time of year” and “It is hot in London this time of year,” have different meanings, even though the words hot and cold appear several seconds before the ends of the two sentences. Any network that can tell these sentences apart must therefore have a long temporal memory. In other words, the current state of the network must depend on events that happened several seconds ago. This is a difficult task, as neurons are dominated by relatively short time constants—tens to hundreds of milliseconds. Nevertheless, it was recently proposed that randomly connected networks could exhibit the long memories necessary for complex temporal processing. This is an attractive idea, both for its simplicity and because little tuning of recurrent synaptic weights is required. However, we show that when connectivity is high, as it is in the mammalian brain, randomly connected networks cannot exhibit temporal memory much longer than the time constants of their constituent neurons.

scholarly journals Selective inhibition of glucose oxidation by triethyltin in rat brain in vivo

1970 ◽  
Vol 119 (1) ◽  
pp. 95-102 ◽  
Author(s):  
J. E. Cremer
Keyword(s):  
Experimental Data ◽  
Amino Acids ◽  
Glucose Metabolism ◽  
Glucose Oxidation ◽  
Selective Inhibition ◽  
Time Intervals ◽  
Main Action ◽  
Short Time ◽  
The Brain

Results are reported of a comparative study in vivo of the metabolism of [2-14C]-glucose and [1-14C]acetate in brains of rats intoxicated with triethyltin sulphate. The incorporation of 14C from glucose into glutamate, glutamine, γ-aminobutyrate and aspartate was greatly decreased. The incorporation of 14C from acetate into these amino acids was unaffected. The experimental data indicated that the main action of triethyltin was to decrease the rate at which pyruvate formed from glucose is oxidized. Glycolysis was not inhibited. Changes in glucose metabolism in the brain are shown not to be directly due to hypothermia. Some of the advantages of measuring the labelling of intermediates at very short time intervals after the injection of the labelled glucose are demonstrated.

Secular Form to Affiliation Rule Mining Employing P-tree and T-tree

2017 ◽  
Vol 7 (8) ◽  
pp. 155
Author(s):  
Aparna Agarwal ◽  
Deevyankar Agarwal
Keyword(s):  
Association Rule ◽  
Synthetic Data ◽  
Dependent Data ◽  
Time Varying ◽  
Time Dimension ◽  
Rule Mining ◽  
Time Intervals ◽  
Data Set ◽  
Wide Range ◽  
Tree Data

The real commercialisminformation often demonstrates temporal feature and time varying behavior. Temporal affiliation rule has thus got an active area of explore. A calendar part such as months and days, clock parts such as hours and seconds and differentiated units such as business days and academic years, act a major role in a wide range of information system applications. The calendar-based form has already been proposed by explorers to restrict the time-based association ships. This paper advises a novel algorithmic program to determine association rule on time dependent data employingeffective T tree and P-tree data structures. The algorithm complicates the significant advantage in terms of time and memory while comprising time dimension. Our approach path of scanning based on time-intervals yields littlerinformation set for a given valid interval thus cutting down the processing time. This approach is enforced on a synthetic data-set and result shows that temporal TFP tree collapses better performance over a TFP tree access.

Rotational Characteristics of the Green Solar Corona: 1947-1991

1994 ◽  
Vol 144 ◽  
pp. 139-141 ◽  
Author(s):  
J. Rybák ◽  
V. Rušin ◽  
M. Rybanský
Keyword(s):  
Solar Corona ◽  
World Wide ◽  
Rotation Period ◽  
Original Data ◽  
Coronal Emission ◽  
Time Intervals ◽  
Data Set ◽  
The World ◽  
Coronal Emission Line ◽  
Homogeneous Data

AbstractFe XIV 530.3 nm coronal emission line observations have been used for the estimation of the green solar corona rotation. A homogeneous data set, created from measurements of the world-wide coronagraphic network, has been examined with a help of correlation analysis to reveal the averaged synodic rotation period as a function of latitude and time over the epoch from 1947 to 1991.The values of the synodic rotation period obtained for this epoch for the whole range of latitudes and a latitude band ±30° are 27.52±0.12 days and 26.95±0.21 days, resp. A differential rotation of green solar corona, with local period maxima around ±60° and minimum of the rotation period at the equator, was confirmed. No clear cyclic variation of the rotation has been found for examinated epoch but some monotonic trends for some time intervals are presented.A detailed investigation of the original data and their correlation functions has shown that an existence of sufficiently reliable tracers is not evident for the whole set of examinated data. This should be taken into account in future more precise estimations of the green corona rotation period.

Proposal of PV Output Estimate Method in Short Time Intervals

2016 ◽  
Vol 136 (12) ◽  
pp. 891-897 ◽  
Author(s):  
Katsuhiro Matsuda ◽  
Kazuhiro Misawa ◽  
Hirotaka Takahashi ◽  
Kenta Furukawa ◽  
Satoshi Uemura
Keyword(s):  
Time Intervals ◽  
Estimate Method ◽  
Short Time

EVALUATION OF SHORT TIME INTERVALS IN NORMAL AGING AND LATE-LIFE DEPRESSIONS

2020 ◽  
pp. 54-69
Author(s):  
Elena Yu. Balashova ◽  
◽  
Lika I. Mikeladze ◽  
Elena K. Kozlova ◽  
◽  
...  
Keyword(s):  
Late Life ◽  
Normal Aging ◽  
Time Intervals ◽  
Short Time

High Frequency Vibration of Worst Ear Lobule induced short period of Rapid Inhibition of any Cancer Activity including Advanced Terminal Cancers, Most Malignant Brain Tumor "Glioblastoma" as well as Cancer of Lung, Breast & Gastrointestinal Cancers particularly Pancreatic Cancer.

2020 ◽  
Vol 44 (3) ◽  
pp. 241-249
Author(s):  
Yoshiaki Omura
Keyword(s):  
Pancreatic Cancer ◽  
Vitamin D3 ◽  
High Frequency ◽  
Optimal Dose ◽  
High Frequency Stimulation ◽  
Frequency Stimulation ◽  
Short Time ◽  
The Brain ◽  
Cancer Activity ◽  
Stimulation Of

While a visiting Professor at the University of Paris, VI (formerly Sorvonne) more than 40 years ago, the Author became very good friends with Dr. Paul Nogier who periodically gave seminars and workshops in Paris. After the author diagnosed his cervical problem & offered him simple help, Dr. Nogier asked the Author to present lectures and demonstrations on the effects of ear stimulation, namely the effects of acupuncture & electrical stimulation of the ear lobules. It is only now, in 2019 that we have discovered 2–5 minute high frequency stimulation of the ear lobule inhibits cancer activity for 1– 4 hours post stimulation. Although the procedure is extremely simple. First take optimal dose of Vitamin D3, which has the most essential 10 unique beneficial factors required for every human cell activity. Next, apply high frequency stimulation to ear lobule while the worst ear lobule is held by all fingers with vibrator directly touching the surface of the worst ear lobule, preferably after patient repeatedly takes optimal dose of Vitamin D3. When the worst ear lobule is held between thumb & index fingers and applying mechanical stimulation of 250 ~ 500 mechanical vibration/second for 2 ~ 5 minutes using an electrical vibrator, there is rapid disappearance of cancer activity in both the brain and rest of the body for short time duration 1 ~ 4 hours. The effect often increases by additional pressure by holding fingers. As of May 2019, the Author found that many people from various regions of the world developed early stages of multiple cancers. For evaluation of this study, U. S. patented Bi-Digital O-Ring Test (BDORT) was used which was developed by the Author while doing his Graduate experimental physics research at Colombia University. BDORT was found to be most essential for determining the beneficial effects as well as harmful effects of any substance or treatment. Using BDORT, Author was the first to recognize severe increasing mid-backache was an early sign of pancreatic cancer of President of New York State Board of Medicine after top pain specialists failed to detect the cause after 3 years of effort, while the BDORT showed early stages of cancer whereas conventional X-Ray of the pancreas did not show any cancer image until 2 months after Author detected with BDORT. For example, the optimal dose of the banana is usually about 2.0 - 2.5 millimeters cross section of the banana. A whole banana is more than 50 ~ 100 times the optimal dose. Any substance eaten in more than 25 times of its optimal dose becomes highly toxic and creates DNA mutations which can cause multiple malignancies in the presence of strong electro-magnetic field. With standard medication given by doctor, patients often become sick and they are unable to reduce body weight, unless medication is reduced or completely stopped. When the amount of zinc is very high, DNA often becomes unstable and multiple cancers can grow rapidly in the presence of strong electromagnetic field. Large amount of Vitamin C from regular orange or orange juice inhibit the most important Vitamin D3 effects. At least 3 kinds of low Vitamin C oranges will not inhibit Vitamin D3. Since B12 particularly methyl cobalamin which is a red small tablet is known to improve brain circulation very significantly we examined its effect within 20 seconds of oral intake we found the following very significant changes. Acetylcholine in both sides of the brain often increases over 4,500 ng. Longevity gene Sirtuin 1 level increases significantly for short time of few hours. Thymosin α1 and Thymosinβ4 both increase to over 1500 ng from 20 ng or less.

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

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