8,774 research outputs found
Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics
Cryptocurrencies have recently experienced a new wave of price volatility and
interest; activity within social media communities relating to cryptocurrencies
has increased significantly. There is currently limited documented knowledge of
factors which could indicate future price movements. This paper aims to
decipher relationships between cryptocurrency price changes and topic
discussion on social media to provide, among other things, an understanding of
which topics are indicative of future price movements. To achieve this a
well-known dynamic topic modelling approach is applied to social media
communication to retrieve information about the temporal occurrence of various
topics. A Hawkes model is then applied to find interactions between topics and
cryptocurrency prices. The results show particular topics tend to precede
certain types of price movements, for example the discussion of 'risk and
investment vs trading' being indicative of price falls, the discussion of
'substantial price movements' being indicative of volatility, and the
discussion of 'fundamental cryptocurrency value' by technical communities being
indicative of price rises. The knowledge of topic relationships gained here
could be built into a real-time system, providing trading or alerting signals.Comment: 3rd International Conference on Knowledge Engineering and
Applications (ICKEA 2018) - Moscow, Russia (June 25-27 2018
A multiscale model for collagen alignment in wound healing
It is thought that collagen alignment plays a significant part in scar tissue formation during dermal wound healing. We present a multiscale model for collagen deposition and alignment during this process. We consider fibroblasts as discrete units moving within an extracellular matrix of collagen and fibrin modelled as continua. Our model includes flux induced alignment of collagen by fibroblasts, and contact guidance of fibroblasts by collagen fibres. We can use the model to predict the effects of certain manipulations, such as varying fibroblast speed, or placing an aligned piece of tissue in the wound. We also simulate experiments which alter the TGF-β concentrations in a healing dermal wound and use the model to offer an explanation of the observed influence of this growth factor on scarring
Cancer modelling: Getting to the heart of the problem
Paradoxically, improvements in healthcare that have enhanced the life expectancy of humans in the Western world have, indirectly, increased the prevalence of certain types of cancer such as prostate and breast. It remains unclear whether this phenomenon should be attributed to the ageing process itself or the cumulative effect of prolonged exposure to harmful environmental stimuli such as ultraviolet light, radiation and carcinogens (Franks and Teich, 1988). Equally, there is also compelling evidence that certain genetic abnormalities can predispose individuals to specific cancers (Ilyas et al., 1999). The variety of factors that have been implicated in the development of solid tumours stems, to a large extent, from the fact that ‘cancer’ is a generic term, often used to characterize a series of disorders that share common features. At this generic level of description, cancer may be viewed as a cellular disease in which controls that usually regulate growth and maintain homeostasis are disrupted. Cancer is typically initiated by genetic mutations that lead to enhanced mitosis of a cell lineage and the formation of an avascular tumour. Since it receives nutrients by diffusion from the surrounding tissue, the size of an avascular tumour is limited to several millimeters in diameter. Further growth relies on the tumour acquiring the ability to stimulate the ingrowth of a new, circulating blood supply from the host vasculature via a process termed angiogenesis (Folkman, 1974). Once vascularised, the tumour has access to a vast nutrient source and rapid growth ensues. Further, tumour fragments that break away from the primary tumour, on entering the vasculature, may be transported to other organs in which they may establish secondary tumours or metastases that further compromise the host. Invasion is another key feature of solid tumours whereby contact with the tissue stimulates the production of enzymes that digest the tissue, liberating space into which the tumour cells migrate. Thus, cancer is a complex, multiscale process. The spatial scales of interest range from the subcellular level, to the cellular and macroscopic (or tissue) levels while the timescales may vary from seconds (or less) for signal transduction pathways to months for tumour doubling times The variety of phenomena involved, the range of spatial and temporal scales over which they act and the complex way in which they are inter-related mean that the development of realistic theoretical models of solid tumour growth is extremely challenging. While there is now a large literature focused on modelling solid tumour growth (for a review, see, for example, Preziosi, 2003), existing models typically focus on a single spatial scale and, as a result, are unable to address the fundamental problem of how phenomena at different scales are coupled or to combine, in a systematic manner, data from the various scales. In this article, a theoretical framework will be presented that is capable of integrating a hierarchy of processes occurring at different scales into a detailed model of solid tumour growth (Alarcon et al., 2004). The model is formulated as a hybrid cellular automaton and contains interlinked elements that describe processes at each spatial scale: progress through the cell cycle and the production of proteins that stimulate angiogenesis are accounted for at the subcellular level; cell-cell interactions are treated at the cellular level; and, at the tissue scale, attention focuses on the vascular network whose structure adapts in response to blood flow and angiogenic factors produced at the subcellular level. Further coupling between the different spatial scales arises from the transport of blood-borne oxygen into the tissue and its uptake at the cellular level. Model simulations will be presented to illustrate the effect that spatial heterogeneity induced by blood flow through the vascular network has on the tumour’s growth dynamics and explain how the model may be used to compare the efficacy of different anti-cancer treatment protocols
Modeling Adoption and Usage of Competing Products
The emergence and wide-spread use of online social networks has led to a
dramatic increase on the availability of social activity data. Importantly,
this data can be exploited to investigate, at a microscopic level, some of the
problems that have captured the attention of economists, marketers and
sociologists for decades, such as, e.g., product adoption, usage and
competition.
In this paper, we propose a continuous-time probabilistic model, based on
temporal point processes, for the adoption and frequency of use of competing
products, where the frequency of use of one product can be modulated by those
of others. This model allows us to efficiently simulate the adoption and
recurrent usages of competing products, and generate traces in which we can
easily recognize the effect of social influence, recency and competition. We
then develop an inference method to efficiently fit the model parameters by
solving a convex program. The problem decouples into a collection of smaller
subproblems, thus scaling easily to networks with hundred of thousands of
nodes. We validate our model over synthetic and real diffusion data gathered
from Twitter, and show that the proposed model does not only provides a good
fit to the data and more accurate predictions than alternatives but also
provides interpretable model parameters, which allow us to gain insights into
some of the factors driving product adoption and frequency of use
Long-range forces in controlled systems
This thesis investigates new phenomena due to long-range forces and their effects
on different multi-DOFs systems. In particular the systems considered are metamaterials,
i.e. materials with long-range connections. The long-range connections
characterizing metamaterials are part of the more general framework of non-local
elasticity.
In the theory of non-local elasticity, the connections between non-adjacent particles
can assume different configurations, namely one-to-all, all-to-all, all-to-all-limited,
random-sparse and all-to-all-twin. In this study three aspects of the long-range
interactions are investigated, and two models of non-local elasticity are considered:
all-to-all and random-sparse.
The first topic considers an all-to-all connections topology and formalizes the mathematical
models to study wave propagation in long-range 1D metamaterials. Closed
forms of the dispersion equation are disclosed, and a propagation map synthesizes
the properties of these materials which unveil wave-stopping, negative group velocity,
instability and non-local effects. This investigation defines how long-range
interactions in elastic metamaterials can produce a variety of new effects in wave
propagation.
The second one considers an all-to-all connections topology and aims to define an
optimal design of the long-range actions in terms of spatial and intensity distribution
to obtain a passive control of the propagation behavior which may produces
exotic effects. A phenomenon of frequency filtering in a confined region of a 1D
metamaterial is obtained and the optimization process guarantees this is the best
obtainable result for a specific set of control parameters.
The third one considers a random-sparse connections topology and provides a new
definition of long-range force, based on the concept of small-world network. The
small-world model, born in the field of social networks, is suitably applied to a
regular lattice by the introduction of additional, randomly selected, elastic connections
between different points. These connections modify the waves propagation
within the structure and the system exhibits a much higher propagation speed and
synchronization. This result is one of the remarkable characteristics of the defined
long-range connections topology that can be applied to metamaterials as well as
other multi-DOFs systems. Qualitative experimental results are presented, and a
preliminary set-up is illustrated.
To summarize, this thesis highlights non-local elastic structures which display unusual
propagation behaviors; moreover, it proposes a control approach that produces
a frequency filtering material and shows the fast propagation of energy within a
random-sparse connected material
Multiscale Computations on Neural Networks: From the Individual Neuron Interactions to the Macroscopic-Level Analysis
We show how the Equation-Free approach for multi-scale computations can be
exploited to systematically study the dynamics of neural interactions on a
random regular connected graph under a pairwise representation perspective.
Using an individual-based microscopic simulator as a black box coarse-grained
timestepper and with the aid of simulated annealing we compute the
coarse-grained equilibrium bifurcation diagram and analyze the stability of the
stationary states sidestepping the necessity of obtaining explicit closures at
the macroscopic level. We also exploit the scheme to perform a rare-events
analysis by estimating an effective Fokker-Planck describing the evolving
probability density function of the corresponding coarse-grained observables
Modelling hair follicle growth dynamics as an excitable medium
The hair follicle system represents a tractable model for the study of stem cell behaviour in regenerative adult epithelial tissue. However, although there are numerous spatial scales of observation (molecular, cellular, follicle and multi follicle), it is not yet clear what mechanisms underpin the follicle growth cycle. In this study we seek to address this problem by describing how the growth dynamics of a large population of follicles can be treated as a classical excitable medium. Defining caricature interactions at the molecular scale and treating a single follicle as a functional unit, a minimal model is proposed in which the follicle growth cycle is an emergent phenomenon. Expressions are derived, in terms of parameters representing molecular regulation, for the time spent in the different functional phases of the cycle, a formalism that allows the model to be directly compared with a previous cellular automaton model and experimental measurements made at the single follicle scale. A multi follicle model is constructed and numerical simulations are used to demonstrate excellent qualitative agreement with a range of experimental observations. Notably, the excitable medium equations exhibit a wider family of solutions than the previous work and we demonstrate how parameter changes representing altered molecular regulation can explain perturbed patterns in Wnt over-expression and BMP down-regulation mouse models. Further experimental scenarios that could be used to test the fundamental premise of the model are suggested. The key conclusion from our work is that positive and negative regulatory interactions between activators and inhibitors can give rise to a range of experimentally observed phenomena at the follicle and multi follicle spatial scales and, as such, could represent a core mechanism underlying hair follicle growth
Artificial neural networks as models of stimulus control
We evaluate the ability of artificial neural network models (multi-layer perceptrons) to predict stimulus-Âresponse relationships. A variety of empirical results are considered, such as generalization, peak-shift (supernormality) and stimulus intensity effects. The networks were trained on the same tasks as the animals in the considered experiments. The subsequent generalization tests on the networks showed that the model replicates correctly the empirical results. It is concluded that these models are valuable tools in the study of animal behaviour
Disorder and fluctuations in nonlinear excitations in DNA
We study the effects of the sequence on the propagation of nonlinear
excitations in simple models of DNA, and how those effects are modified by
noise. Starting from previous results on soliton dynamics on lattices defined
by aperiodic potentials, [F. Dom\'\i nguez-Adame {\em et al.}, Phys. Rev. E
{\bf 52}, 2183 (1995)], we analyze the behavior of lattices built from real DNA
sequences obtained from human genome data. We confirm the existence of
threshold forces, already found in Fibonacci sequences, and of stop positions
highly dependent on the specific sequence. Another relevant conclusion is that
the effective potential, a collective coordinate formalism introduced by
Salerno and Kivshar [Phys. Lett. A {\bf 193}, 263 (1994)] is a useful tool to
identify key regions that control the behaviour of a larger sequence. We then
study how the fluctuations can assist the propagation process by helping the
excitations to escape the stop positions. Our conclusions point out to
improvements of the model which look promising to describe mechanical
denaturation of DNA. Finally, we also consider how randomly distributed energy
focus on the chain as a function of the sequence.Comment: 14 pages, final version, accepted in Fluctuation and Noise Letters,
scheduled to apper in vol. 4, issue 3 (2004
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