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INSTITUTE OF MATHEMATICAL SCIENCES

UNIVERSITY OF MALAYA

SIRI SEMINAR KUMPULAN PENYELIDIKAN

Title : COMMUNITY STRUCTURE AND DETECTION IN A NETWORK

Speaker : Chin Jia Hou

Institute of Mathematical Sciences,

University of Malaya.

Date : 3 May 2017

Time : 3 – 4 pm

Venue : MM3, ISM

ABSTRACT

In this thesis, new community detection methods called the constrained label propagation

algorithm with grouped nodes reallocation (CLPA-GNR), semi-synchronous constrained label

propagation algorithm (SSCLPA) and SSCLPA in the directed and weighted networks

(SSCLPA-DW) are proposed. The CLPA-GNR is a variant of the label propagation algorithm

(LPA). It addresses the randomness issues in the original LPA by introducing fixed update

sequences and rules to break ties between multiple labels. Thus, the CLPA-GNR can obtain

deterministic detection in contrast to the LPA that has no unique solution. An improved

version of the CLPA-GNR called the SSCLPA is proposed to handle the trivial detection

issue. In the SSCLPA, communities that exceed certain strength threshold are exempted from

the label propagation processes in order to prevent them from overgrowing into huge size

communities that eventually lead to trivial detection. Finally, the SSCLPA is extended into

directed and weighted networks (SSCLPA-DW). Aside from this extension in the

SSCLPADW, nodes that fulfil certain criteria, such as nodes with one and two degree, will

only be assigned into the detected communities at the end of the algorithm. By doing so, the

speed of the algorithm can be improved. All the proposed detection algorithms are compared

with the other algorithms in both the benchmark and real-world networks.

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SIRI SEMINAR KUMPULAN PENYELIDIKAN

Title : SOLVING NON-LINEAR SYSTEMS IN GAME THEORY

Speaker : TEY SIEW KIAN

Date : 24- 05- 2017 (Wednesday)

Time : 3.00-4.00 PM

Venue : MM3, Institute of Mathematical Sciences

ABSTRACT

Game theory is a branch of mathematics involving the study of cooperation and conflicts in

the society. Given the importance of cooperation in the fight for common good, the

emergence of cooperation amongst selfish individuals is a fundamental and important issue

in the economics and behavioural sciences. The aim of this thesis is to further our

understanding of the roles of various factors such as incentives and network on the

enhancement of cooperation in different economic models involving non-linear systems. In

particular, two models, one involving social dilemma with N-players and the other involving

economic behaviours with two players are analysed and solved. These two models are used

to develop a third model which retains the main features of the second model, but modified

to include N-players with an evolutionary trait as in the first model. This is to give some

insights on the effects of the various features in the first model on the frequency of

cooperation and magnitude of incentives such as technological leapfrogging in the second

model.

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COLLOQUIUM SERIES

Title : Mean Latin hypercube finite difference solutions for cocaine consumption in

Spain Speaker : MAHA ABDULJABBAR MOHAMMED


Time : 3.00-4.00 PM


ABSTRACT

Abstract: In this seminar, Mean Latin Hypercube Finite Difference (MLHFD) method is proposed for solving a

nonlinear initial value problem (IVP) of ordinary differential equations with dependent variables on time t. Finite

difference (FD) method, which is merged with a simulation technique in each of the numerical iteration is used to

solve this problem. This technique is achieved to simulate random variable coefficients by employing Latin

hypercube sampling for the deterministic mathematical model of the cocaine consumed in Spain. The current

improved numerical technique makes use of randomization properties from the Latin hypercube sampling

simulation process to introduce alternative results with large number of simulation in the real model solution and

to predict the future behavior of the epidemic system. The obtained numerical simulation results are compared

with the classic FD and homotopy analysis solutions. The MLHFD results are also tabulated, graphed and

compared for validation with previous statistical estimations from 1995 into 2015. The MLHFD results are found

to be in good agreement with previous statistical estimations with small errors. The expected behavior of cocaine

consumption in Spain is computed and discussed numerically until 2045. The MLHFD results which are closer to

statistical solutions than the existing classic numerical and analytical solutions for the model, also provide

predicted range for random distribution of the model solutions.

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COLLOQUIUM SERIES

Title : Mathematical Modeling of the Tumor Cells Population Dynamics in

Breast Cancer

Speaker : Amin Oroji (SHB140005), ISM, UM

Date : 16- 08- 2017

Time : 03 -04 PM


ABSTRACT

The role of mathematics in cancer research has steadily increased over time.

Multidisciplinary collaboration in cancer research is essential and mathematical applications

can significantly contribute to many areas of cancer research. For example, mathematical

models can provide deeper insight and establish a framework for understanding properties

of cancer cells.

Modeling the effects of radiation on cancer cells is one of the most interesting areas in

mathematical biology and a variety of models by using the Target theory and DNA

fragmentations have been applied to describe how radiation influence tumor cells.

In this study, two new mathematical frameworks are proposed to model the population

dynamics of heterogeneous tumor cells after the treatment with external beam radiation.

Moreover, a number of experiments have been done on MCF-7 breast cancer cells. The cell

cycle analysis assay has been used to analyze the obtained experimental data. Then the

obtained data was applied to calibrate and verify the models.

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COLLOQUIUM SERIES

Title : Recent Advances in Data Science and Machine Intelligence – Industrial

Applications

Speaker : Dr. Bernard Montaron, Cenozai Sdn. Bhd, Malaysia

Date : 06 - 09 - 2017 (Wednesday)

Time : 2:30 – 4:30 PM


ABSTRACT

A review of the progress during the last 6 years will be made in the field of data science and

machine intelligence and how this has been applied to "big data" analytics. The presentation will

cover some of the most spectacular results obtained recently by deep neural networks, particularly

for image recognition, and discuss some industrial applications. Finally, the presentation will

conclude on applications of deep learning to solve problems specific to oil and gas exploration and

production.

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COLLOQUIUM SERIES Title : Modified finite difference method using random sampling for nonlinear epidemic

models

Speaker : MAHA ABDUL JABBAR MOHAMMED


Time : 3.00-4.00 PM


ABSTRACT

Abstract: In this seminar, new modified numerical simulation schemes are proposed to solve social epidemic models in the form of nonlinear initial value problems (IVP) of ordinary differential equations with multiple random variable coefficients. The variables of the systems are dependent on time 𝑡. The utilization of Monte Carlo (MC) simulation with central divided difference formula is repeated 𝑛 times to simulate values of the variable coefficients of the Spain weight reduction model as random sampling instead being limited as real values with respect to time. The mean of the 𝑛 final solutions via this integrated technique, named in short as mean Monte Carlo Finite Difference (MMCFD) method, represents the final solution of the system. The numerical outputs are tabulated, graphed and compared with previous statistical estimations for 2013, 2015 and 2030 respectively. The solutions of FD and MMCFD are found to be in good agreement with small standard deviation of the means, and small measure of difference. In the social epidemic of cocaine abuse, the FD numerical method is integrated with Latin hypercube sampling (LHS) technique in every simulation to simulate random variable coefficients for the stochastic-deterministic model. The mean of final solutions of the FD iterations is known as Mean Latin Hypercube Finite Difference (MLHFD) solutions. The results obtained are compared with deterministic solutions of classical FD and homotopy analysis methods as relative to the previous statistical estimations from 1995 to 2015. Good agreement between the two is perceived with small errors. The MLHFD results are tabulated and graphed, discussed pertaining to the model expected behavior until 2045. MMCFD and MLHFD are proposed for the first time in this thesis to calculate and to predict future behavior. The results shows the range for random distribution for the numerical solutions of these epidemiology models with better approximation and agreement compared with the existing randomized statistical estimations.

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