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CALSCALE:GREGORIAN
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BEGIN:VEVENT
SUMMARY:Chimere Anabanti (University of Technology (TU Graz) Austria)
DTSTART;VALUE=DATE-TIME:20200611T130000Z
DTEND;VALUE=DATE-TIME:20200611T140000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/1
DESCRIPTION:Title: On the classification of finite filled groups\nby Chime
re Anabanti (University of Technology (TU Graz) Austria) as part of MESS (
Mathematics Essex Seminar Series)\n\n\nAbstract\nWe give an introduction t
o product-free sets in finite groups\, discuss an application to Combinato
rics\, and conclude with what is known about filled groups.\n
END:VEVENT
BEGIN:VEVENT
SUMMARY:Dmitry Savostyanov (University of Essex)
DTSTART;VALUE=DATE-TIME:20200618T130000Z
DTEND;VALUE=DATE-TIME:20200618T140000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/2
DESCRIPTION:Title: Epidemiological models on networks: Numerical approache
s and challenges (Work in progress)\nby Dmitry Savostyanov (University of
Essex) as part of MESS (Mathematics Essex Seminar Series)\n\n\nAbstract\nM
athematical modelling of infectious disease is an important area of applie
d mathematics. The Kermack--McKendrick compartmental SIR model is quite si
mple but also quite powerful --- it describes the epidemics with a system
of ordinary differential equations (ODEs)\, which can be easily solved usi
ng a suitable numerical method\, and predicts the behaviour of outbreaks v
ery similar to that observed in many recorded epidemics. Even though compa
rtmental models are almost hundred years old now\, they are still widely u
sed not only in a classroom\, but also to predict the development of dange
rous diseases and to inform Government strategies in case of emergency. Th
e quality of a mathematical model\, and our understanding of its assumptio
ns and applicability in a particular scenario\, is therefore crucial to ma
ke correct decisions to protect public health and respond to epidemics eff
ectively when they occur. The fundamental assumption of a compartmental mo
del is that the population is well-mixed: there is no firm boundary betwee
n susceptible\, infected and recovered individuals. Everyone interacts wit
h everyone at once\, similar to chemical molecules in a mixture. Although
this assumption may be appropriate on a later stages of epidemic\, it clea
rly limits the model's capability to accurately describe and predict the e
arly stages\, when the infection is largely localised in one location and
is carried to other locations through a network of transport and/or social
and community links. If we consider how a disease progresses through a ne
twork\, only neighbouring nodes can participate in transmission --- the ne
twork is not well-mixed. Hence\, the compartmental model is no longer fit
for purpose\, and has to be replaced with a probabilistic model\, where we
estimate the probability for each node to be in susceptible\, infected or
recovered state at a given time. Importantly\, the states of the neighb
ours are not independent --- quite the opposite! --- a susceptible person
in direct contact with an infected person is likely to become infected soo
n. This means that instead of considering individual probabilities\, we ha
ve to describe the evolution of the joint probability distribution\, accou
nting for the states of all nodes at once. This high--dimensional problem
struggles from the curse of dimensionality --- the number of unknowns grow
s exponentially with the number of nodes\, and traditional ODE solvers can
't cope with he growing complexity when the number of nodes exceeds severa
l tens. For this reason\, the problem is typically solved using Stochastic
Simulation Algorithms (SSA)\, such as Monte Carlo and its variants. Using
our experience with high--dimensional problems\, such as Fokker--Planck\,
Chemical Master Equation and Quantum Spin Dynamics\, we consider applying
tensor product algorithms to solve this high--dimensional ODE with high a
ccuracy\, and hence obtain a full probabilistic picture of the disease tra
nsfer through the network. In preliminary experiments we find tensor produ
ct approach to be successful in principle. In particular\, it can accurate
ly estimate the probabilities of rare events\, as well as higher moments o
f the observed quantities\, where SSA often struggles. This is a work in p
rogress! The presented results are in preparation for publication. We will
appreciate all feedback and suggestions regarding this work.\n
END:VEVENT
BEGIN:VEVENT
SUMMARY:George Kinnear (University of Edinburgh)
DTSTART;VALUE=DATE-TIME:20200626T130000Z
DTEND;VALUE=DATE-TIME:20200626T140000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/3
DESCRIPTION:Title: Teaching mathematics online with STACK\nby George Kinne
ar (University of Edinburgh) as part of MESS (Mathematics Essex Seminar Se
ries)\n\n\nAbstract\nAt the University of Edinburgh\, we have been increas
ing our use of the STACK computer-aided assessment system to provide pract
ice and homework for students. I will give an overview of the features of
STACK\, and describe different ways it is being used across all years of o
ur programme. In particular\, I will show how STACK was a key part of the
design of a new optional course for incoming students\, "Fundamentals of A
lgebra and Calculus"\, which covers key topics from Advanced Higher and A-
Level syllabuses. The course is delivered almost entirely online\, as a se
ries of STACK quizzes which interleave textbook-style exposition with vide
os of worked examples\, interactive applets\, and practice questions. I wi
ll describe how ideas from education research and cognitive science (such
as spacing and retrieval practice) informed the course design\, from its o
verall structure to the content of individual questions. I will also show
some results from our evaluation of the course\, including measures of the
students' learning gains.\n
END:VEVENT
BEGIN:VEVENT
SUMMARY:Chris Antonopoulos (University of Essex)
DTSTART;VALUE=DATE-TIME:20201015T140000Z
DTEND;VALUE=DATE-TIME:20201015T150000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/4
DESCRIPTION:Title: An extended SIR model for the spread of COVID-19 in dif
ferent communities\nby Chris Antonopoulos (University of Essex) as part of
MESS (Mathematics Essex Seminar Series)\n\nInteractive livestream: https:
//essex-university.zoom.us/j/96737423878\nPassword hint: Write to the orga
nisers to obtain the password.\n\nAbstract\nIn this paper\, we study the e
ffectiveness of the modelling approach on the pandemic due to the spreadin
g of the novel COVID-19 disease and develop an extended-susceptible-infect
ed-removed (eSIR) model that provides a theoretical framework to investiga
te its spread within a community. The eSIR model is based upon the well-kn
own susceptible-infected-removed (SIR) model with the difference that a to
tal population is not defined or kept constant per se and the number of su
sceptible individuals does not decline monotonically. To the contrary\, as
we show herein\, it can be increased in surge periods! In particular\, we
investigate the time evolution of different populations and monitor diver
se significant parameters for the spread of the disease in various communi
ties\, represented by countries and the state of Texas in the USA. The eSI
R model can provide us with insights and predictions of the spread of the
virus in communities that recorded data alone cannot. Our work shows the i
mportance of modelling the spread of COVID-19 by the eSIR model that we pr
opose here\, as it can help to assess the impact of the disease by offerin
g valuable predictions. Our analysis takes into account data from January
to June\, 2020\, the period that contains the data before and during the i
mplementation of strict and control measures. We propose predictions on va
rious parameters related to the spread of COVID-19 and on the number of su
sceptible\, infected and removed populations until September 2020. By comp
aring the recorded data with the data from our modelling approaches\, we d
educe that the spread of COVID-19 can be under control in all communities
considered\, if proper restrictions and strong policies are implemented to
control the infection rates early from the spread of the disease.\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Anne-Sophie Kaloghiros (Brunel University)
DTSTART;VALUE=DATE-TIME:20201112T150000Z
DTEND;VALUE=DATE-TIME:20201112T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/5
DESCRIPTION:by Anne-Sophie Kaloghiros (Brunel University) as part of MESS
(Mathematics Essex Seminar Series)\n\nInteractive livestream: https://esse
x-university.zoom.us/j/96737423878\nPassword hint: Write to the organisers
to obtain the password.\nAbstract: TBA\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Marina Iliopoulou (University of Kent)
DTSTART;VALUE=DATE-TIME:20201119T150000Z
DTEND;VALUE=DATE-TIME:20201119T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/6
DESCRIPTION:by Marina Iliopoulou (University of Kent) as part of MESS (Mat
hematics Essex Seminar Series)\n\nInteractive livestream: https://essex-un
iversity.zoom.us/j/96737423878\nPassword hint: Write to the organisers to
obtain the password.\nAbstract: TBA\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jessica Claridge (University of Essex)
DTSTART;VALUE=DATE-TIME:20201126T150000Z
DTEND;VALUE=DATE-TIME:20201126T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/7
DESCRIPTION:Title: Matrix Channels for Network Coding\nby Jessica Claridge
(University of Essex) as part of MESS (Mathematics Essex Seminar Series)\
n\nInteractive livestream: https://essex-university.zoom.us/j/96737423878\
nPassword hint: Write to the organisers to obtain the password.\n\nAbstrac
t\nNetwork coding allows intermediate nodes of a network to compute with a
nd modify data\, as opposed to the traditional view of nodes as ‘on/off
’ switches. This can significantly increase the rate of information flow
through a network. Linear network coding is sufficient to maximize inform
ation flow in multicast problems\, and remarkably\, random linear network
coding (where intermediate nodes simply compute random linear combinations
of incoming packets) achieves capacity with probability exponentially app
roaching 1 with the code length. In this talk we will first discuss the ba
sics of network coding and then describe a finite-field matrix channel to
model random linear network coding. A major challenge is finding optimal i
nput schemes with simple decoding to use in such models. We will discuss r
esults on the channel capacity and input distributions that can achieve th
is.\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Nicola Walker (Centre for Environment\, Fisheries and Aquaculture
Science)
DTSTART;VALUE=DATE-TIME:20201203T150000Z
DTEND;VALUE=DATE-TIME:20201203T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/8
DESCRIPTION:Title: Cod on the menu? Using mathematical modelling to provid
e fisheries management advice\nby Nicola Walker (Centre for Environment\,
Fisheries and Aquaculture Science) as part of MESS (Mathematics Essex Semi
nar Series)\n\nInteractive livestream: https://essex-university.zoom.us/j/
96737423878\nPassword hint: Write to the organisers to obtain the password
.\n\nAbstract\nScientific advice on the management of fish stocks is often
informed by mathematical assessment models that fit to information from c
atches\, research surveys and life history. North Sea cod is a high-profil
e and commercially important stock with a long history of highs and lows.
In particular\, the latest assessment estimates that the stock is below sa
fe biological limits\, which comes just two years after the fishery was ce
rtified sustainable. Using North Sea cod as a case study\, Dr Walker will
present the state-space assessment model (SAM) and detail the process of t
urning model outputs into scientific advice for fisheries management. She
will discuss diagnostics for assessing the quality of input data and model
fits and highlight some of the problems facing the assessment of this sto
ck.\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Murat Akman (University of Essex)
DTSTART;VALUE=DATE-TIME:20210204T150000Z
DTEND;VALUE=DATE-TIME:20210204T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/9
DESCRIPTION:by Murat Akman (University of Essex) as part of MESS (Mathemat
ics Essex Seminar Series)\n\nInteractive livestream: https://essex-univers
ity.zoom.us/j/96737423878\nPassword hint: Write to the organisers to obtai
n the password.\nAbstract: TBA\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alastair Litterick (University of Essex)
DTSTART;VALUE=DATE-TIME:20210211T150000Z
DTEND;VALUE=DATE-TIME:20210211T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/10
DESCRIPTION:by Alastair Litterick (University of Essex) as part of MESS (M
athematics Essex Seminar Series)\n\nInteractive livestream: https://essex-
university.zoom.us/j/96737423878\nPassword hint: Write to the organisers t
o obtain the password.\nAbstract: TBA\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Christina de Filippis (University of Oxford)
DTSTART;VALUE=DATE-TIME:20210225T150000Z
DTEND;VALUE=DATE-TIME:20210225T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/11
DESCRIPTION:by Christina de Filippis (University of Oxford) as part of MES
S (Mathematics Essex Seminar Series)\n\nInteractive livestream: https://es
sex-university.zoom.us/j/96737423878\nPassword hint: Write to the organise
rs to obtain the password.\nAbstract: TBA\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alexei Vernitski (University of Essex)
DTSTART;VALUE=DATE-TIME:20210304T150000Z
DTEND;VALUE=DATE-TIME:20210304T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/12
DESCRIPTION:by Alexei Vernitski (University of Essex) as part of MESS (Mat
hematics Essex Seminar Series)\n\nInteractive livestream: https://essex-un
iversity.zoom.us/j/96737423878\nPassword hint: Write to the organisers to
obtain the password.\nAbstract: TBA\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Nivedita Viswanathan (University of Edinburgh)
DTSTART;VALUE=DATE-TIME:20210318T150000Z
DTEND;VALUE=DATE-TIME:20210318T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/13
DESCRIPTION:by Nivedita Viswanathan (University of Edinburgh) as part of M
ESS (Mathematics Essex Seminar Series)\n\nInteractive livestream: https://
essex-university.zoom.us/j/96737423878\nPassword hint: Write to the organi
sers to obtain the password.\nAbstract: TBA\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mehmet Cihan (University of Essex)
DTSTART;VALUE=DATE-TIME:20210325T150000Z
DTEND;VALUE=DATE-TIME:20210325T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/14
DESCRIPTION:by Mehmet Cihan (University of Essex) as part of MESS (Mathema
tics Essex Seminar Series)\n\nInteractive livestream: https://essex-univer
sity.zoom.us/j/96737423878\nPassword hint: Write to the organisers to obta
in the password.\nAbstract: TBA\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Federica Armani (University of Essex)
DTSTART;VALUE=DATE-TIME:20210429T140000Z
DTEND;VALUE=DATE-TIME:20210429T150000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/15
DESCRIPTION:by Federica Armani (University of Essex) as part of MESS (Math
ematics Essex Seminar Series)\n\nInteractive livestream: https://essex-uni
versity.zoom.us/j/96737423878\nPassword hint: Write to the organisers to o
btain the password.\nAbstract: TBA\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Marina Logares (Universidad Complutense de Madrid)
DTSTART;VALUE=DATE-TIME:20210520T140000Z
DTEND;VALUE=DATE-TIME:20210520T150000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/16
DESCRIPTION:by Marina Logares (Universidad Complutense de Madrid) as part
of MESS (Mathematics Essex Seminar Series)\n\nInteractive livestream: http
s://essex-university.zoom.us/j/96737423878\nPassword hint: Write to the or
ganisers to obtain the password.\nAbstract: TBA\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sergey Dolgov (University of Bath)
DTSTART;VALUE=DATE-TIME:20210617T140000Z
DTEND;VALUE=DATE-TIME:20210617T150000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/17
DESCRIPTION:by Sergey Dolgov (University of Bath) as part of MESS (Mathema
tics Essex Seminar Series)\n\nInteractive livestream: https://essex-univer
sity.zoom.us/j/96737423878\nPassword hint: Write to the organisers to obta
in the password.\nAbstract: TBA\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Georgios Amanatidis (University of Essex)
DTSTART;VALUE=DATE-TIME:20201105T150000Z
DTEND;VALUE=DATE-TIME:20201105T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/18
DESCRIPTION:Title: Rapid mixing of the switch Markov chain for strongly st
able degree sequences\nby Georgios Amanatidis (University of Essex) as par
t of MESS (Mathematics Essex Seminar Series)\n\nInteractive livestream: ht
tps://essex-university.zoom.us/j/96737423878\nPassword hint: Write to the
organisers to obtain the password.\n\nAbstract\nThe switch Markov chain ha
s been extensively studied as the most natural Markov Chain Monte Carlo ap
proach for sampling graphs with prescribed degree sequences. We show that
the switch chain for sampling simple undirected graphs with a given degree
sequence is rapidly mixing when the degree sequence is so-called strongly
stable. Strong stability is satisfied by all degree sequences for which t
he switch chain was known to be rapidly mixing based on Sinclair's multico
mmodity flow method up until a recent manuscript of Erd\\H{o}s et al. (201
9). Our approach relies on an embedding argument\, involving a Markov chai
n defined by Jerrum and Sinclair (1990). This results in a much shorter pr
oof that unifies (almost) all the rapid mixing results for the switch chai
n in the literature\, and extends them up to sharp characterizations of P-
stable degree sequences. In particular\, our work resolves an open problem
posed by Greenhill and Sfragara (2017).\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Anouchah Latifi (University of Qom)
DTSTART;VALUE=DATE-TIME:20201029T150000Z
DTEND;VALUE=DATE-TIME:20201029T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/20
DESCRIPTION:by Anouchah Latifi (University of Qom) as part of MESS (Mathem
atics Essex Seminar Series)\n\nInteractive livestream: https://essex-unive
rsity.zoom.us/j/96737423878\nPassword hint: Write to the organisers to obt
ain the password.\nAbstract: TBA\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Simon Blackburn (Royal Holloway\, University of London)
DTSTART;VALUE=DATE-TIME:20201210T150000Z
DTEND;VALUE=DATE-TIME:20201210T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/21
DESCRIPTION:by Simon Blackburn (Royal Holloway\, University of London) as
part of MESS (Mathematics Essex Seminar Series)\n\nInteractive livestream:
https://essex-university.zoom.us/j/96737423878\nPassword hint: Write to t
he organisers to obtain the password.\n\nAbstract\nFor a positive integer
$n$\, write $f(n)$ for the number of isomorphism classes of rings of order
$n$. What can we say about $f(n)$? Determining $f(n)$ exactly for all $n$
looks unrealistic\, but in 1970 Kruse and Price (J LMS) stated an asympto
tic result that gives the growth rate of $f(n)$ as $n\\rightarrow\\infty$.
Sadly\, there are problems with their proof. I will talk about recent jo
int work with K. Robin McLean (University of Liverpool) in which we fix th
e problems\, and improve the error terms\, of the Kruse--Price result. No
knowledge of ring theory above a first undergraduate course will be assume
d!\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
BEGIN:VEVENT
SUMMARY:Melissa Lee (University of Auckland)
DTSTART;VALUE=DATE-TIME:20201217T150000Z
DTEND;VALUE=DATE-TIME:20201217T160000Z
DTSTAMP;VALUE=DATE-TIME:20200812T064844Z
UID:EssexMaths/22
DESCRIPTION:by Melissa Lee (University of Auckland) as part of MESS (Mathe
matics Essex Seminar Series)\n\nInteractive livestream: https://essex-univ
ersity.zoom.us/j/96737423878\nPassword hint: Write to the organisers to ob
tain the password.\nAbstract: TBA\n
URL:https://essex-university.zoom.us/j/96737423878
END:VEVENT
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