1,108 research outputs found
Anomalous transport in the crowded world of biological cells
A ubiquitous observation in cell biology is that diffusion of macromolecules
and organelles is anomalous, and a description simply based on the conventional
diffusion equation with diffusion constants measured in dilute solution fails.
This is commonly attributed to macromolecular crowding in the interior of cells
and in cellular membranes, summarising their densely packed and heterogeneous
structures. The most familiar phenomenon is a power-law increase of the MSD,
but there are other manifestations like strongly reduced and time-dependent
diffusion coefficients, persistent correlations, non-gaussian distributions of
the displacements, heterogeneous diffusion, and immobile particles. After a
general introduction to the statistical description of slow, anomalous
transport, we summarise some widely used theoretical models: gaussian models
like FBM and Langevin equations for visco-elastic media, the CTRW model, and
the Lorentz model describing obstructed transport in a heterogeneous
environment. Emphasis is put on the spatio-temporal properties of the transport
in terms of 2-point correlation functions, dynamic scaling behaviour, and how
the models are distinguished by their propagators even for identical MSDs.
Then, we review the theory underlying common experimental techniques in the
presence of anomalous transport: single-particle tracking, FCS, and FRAP. We
report on the large body of recent experimental evidence for anomalous
transport in crowded biological media: in cyto- and nucleoplasm as well as in
cellular membranes, complemented by in vitro experiments where model systems
mimic physiological crowding conditions. Finally, computer simulations play an
important role in testing the theoretical models and corroborating the
experimental findings. The review is completed by a synthesis of the
theoretical and experimental progress identifying open questions for future
investigation.Comment: review article, to appear in Rep. Prog. Phy
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Single atom imaging with time-resolved electron microscopy
Developments in scanning transmission electron microscopy (STEM) have opened
up new possibilities for time-resolved imaging at the atomic scale. However, rapid
imaging of single atom dynamics brings with it a new set of challenges, particularly
regarding noise and the interaction between the electron beam and the specimen. This
thesis develops a set of analytical tools for capturing atomic motion and analyzing the
dynamic behaviour of materials at the atomic scale.
Machine learning is increasingly playing an important role in the analysis of electron
microscopy data. In this light, new unsupervised learning tools are developed here for
noise removal under low-dose imaging conditions and for identifying the motion of
surface atoms. The scope for real-time processing and analysis is also explored, which is
of rising importance as electron microscopy datasets grow in size and complexity.
These advances in image processing and analysis are combined with computational
modelling to uncover new chemical and physical insights into the motion of atoms
adsorbed onto surfaces. Of particular interest are systems for heterogeneous catalysis,
where the catalytic activity can depend intimately on the atomic environment. The
study of Cu atoms on a graphene oxide support reveals that the atoms undergo
anomalous diffusion as a result of spatial and energetic disorder present in the substrate.
The investigation is extended to examine the structure and stability of small Cu clusters
on graphene oxide, with atomistic modelling used to understand the significant role
played by the substrate. Finally, the analytical methods are used to study the surface
reconstruction of silicon alongside the electron beam-induced motion of adatoms on
the surface.
Taken together, these studies demonstrate the materials insights that can be obtained
with time-resolved STEM imaging, and highlight the importance of combining state-ofthe-
art imaging with computational analysis and atomistic modelling to quantitatively
characterize the behaviour of materials with atomic resolution.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007â2013)/ERC grant agreement 291522â3DIMAGE, as well as from the European Union Seventh Framework Programme under Grant Agreement 312483-ESTEEM2 (Integrated Infrastructure Initiative -I3)
Learning Human Behaviour Patterns by Trajectory and Activity Recognition
The worldâs population is ageing, increasing the awareness of neurological and behavioural
impairments that may arise from the human ageing. These impairments can be manifested
by cognitive conditions or mobility reduction. These conditions are difficult to be
detected on time, relying only on the periodic medical appointments. Therefore, there is
a lack of routine screening which demands the development of solutions to better assist
and monitor human behaviour. The available technologies to monitor human behaviour
are limited to indoors and require the installation of sensors around the userâs homes
presenting high maintenance and installation costs. With the widespread use of smartphones,
it is possible to take advantage of their sensing information to better assist the
elderly population. This study investigates the question of what we can learn about human
pattern behaviour from this rich and pervasive mobile sensing data. A deployment
of a data collection over a period of 6 months was designed to measure three different
human routines through human trajectory analysis and activity recognition comprising
indoor and outdoor environment. A framework for modelling human behaviour was
developed using human motion features, extracted in an unsupervised and supervised
manner. The unsupervised feature extraction is able to measure mobility properties such
as step length estimation, user points of interest or even locomotion activities inferred
from an user-independent trained classifier. The supervised feature extraction was design
to be user-dependent as each user may have specific behaviours that are common to
his/her routine. The human patterns were modelled through probability density functions
and clustering approaches. Using the human learned patterns, inferences about
the current human behaviour were continuously quantified by an anomaly detection
algorithm, where distance measurements were used to detect significant changes in behaviour.
Experimental results demonstrate the effectiveness of the proposed framework
that revealed an increase potential to learn behaviour patterns and detect anomalies
Graph based Anomaly Detection and Description: A Survey
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised vs. (semi-)supervised approaches, for static vs. dynamic graphs, for attributed vs. plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the âwhyâ, of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field
Signatures of LeÂŽvy ïŹights with annealed disorder
We present theoretical and experimental results of LeÂŽvy ïŹights of light originating from a random walk of
photons in a hot atomic vapor. In contrast to systems with quenched disorder, this system does not present any
correlations between the position and the step length of the random walk. In an analytical model based on micro-
scopic ïŹrst principles including Doppler broadening we ïŹnd anomalous LeÂŽvy-type superdiffusion corresponding
to a single-step size distribution P (x) â xâ(1+α), with α â 1. We show that this step size distribution leads to a
violation of Ohmâs law [Tdiff â Lâα/2 ┏= Lâ1], as expected for a LeÂŽvy walk of independent steps. Furthermore,
the spatial proïŹle of the transmitted light develops power-law tails [Tdiff(r) â râ3âα]. In an experiment using a
slab geometry with hot Rb vapor, we measured the total diffuse transmission Tdiff and the spatial proïŹle of the
transmitted light Tdiff(r). We obtained the microscopic LeŽvy parameter α under macroscopic multiple scattering
conditions paving the way to investigation of LeÂŽvy ïŹights in different atomic physics and astrophysics systems.We thank Dominique Delande for fruitful discussions and we acknowledge funding for N.M. and Q.B. by the french Direction Generale de l'Armement. R.P acknowledges the support of LABEX WIFI (Laboratory of Excellence ANR-10-LABX-24) within the French Program "Investments for the Future" under reference ANR-10-IDEX-0001-02 PSL*. E.J.N. and R.K. acknowledge the FCT/CNRS exchange program (441.00 CNRS)
Using Gaze for Behavioural Biometrics
A principled approach to the analysis of eye movements for behavioural biometrics is laid
down. The approach grounds in foraging theory, which provides a sound basis to capture the unique-
ness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for
quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The rel-
evant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are
shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished
via a classical classification technique. A proof of concept of the method is provided by measuring
its identification performance on a publicly available dataset. Data and code for reproducing the
analyses are made available. Overall, we argue that the approach offers a fresh view on either the
analyses of eye-tracking data and prospective applications in this field
Multiple Particle Positron Emission Particle Tracking and its Application to Flows in Porous Media
Positron emission particle tracking (PEPT) is a method for flow interrogation capable of measurement in opaque systems. In this work a novel method for PEPT is introduced that allows for simultaneous tracking of multiple tracers. This method (M-PEPT) is adapted from optical particle tracking techniques and is designed to track an arbitrary number of positron-emitting tracer-particles entering and leaving the field of view of a detector array. M-PEPT is described, and its applicability is demonstrated for a number of measurements ranging from turbulent shear flow interrogation to cell migration. It is found that this method can locate over 80 particles simultaneously with spatial resolution of order 0.2 mm at tracking frequency of 10 Hz and, at lower particle number densities, can achieve similar spatial resolution at tracking frequency 1000 Hz. The method is limited in its ability to resolve particles approaching close to one another, and suggestions for future improvements are made.M-PEPT is used to study flow in porous media constructed from packing of glass beads of different diameters. Anomalous (i.e. non-Fickian) dispersion of tracers is studied in these systems under the continuous time random walk (CTRW) paradigm. Pore-length transition time distributions are measured, and it is found that in all cases, these distributions indicate the presence of long waiting times between transitions, confirming the central assumption of the CTRW model. All systems demonstrate non-Fickian spreading of tracers at early and intermediate times with a late time recovery of Fickian dispersion, but a clear link between transition time distributions and tracer spreading is not made. Velocity increment statistics are examined, and it is found that temporal velocity increments in the mean-flow direction show a universal scaling. Spatial velocity increments also appear to collapse to a similar form, but there is insufficient data to determine the presence of universal scaling
Towards trustworthy social computing systems
The rising popularity of social computing systems has managed to attract rampant forms of service abuse that negatively affects the sustainability of these systems and degrades the quality of service experienced by their users. The main factor that enables service abuse is the weak identity infrastructure used by most sites, where identities are easy to create with no verification by a trusted authority. Attackers are exploiting this infrastructure to launch Sybil attacks, where they create multiple fake (Sybil) identities to take advantage of the combined privileges associated with the identities to abuse the system.
In this thesis, we present techniques to mitigate service abuse by designing and building defense schemes that are robust and practical. We use two broad defense strategies: (1) Leveraging the social network: We first analyze existing social network-based Sybil detection schemes and present their practical limitations when applied on real world social networks. Next, we present an approach called Sybil Tolerance that bounds the impact an attacker can gain from using multiple identities; (2) Leveraging activity history of identities: We present two approaches, one that applies anomaly detection on user social behavior to detect individual misbehaving identities, and a second approach called Stamper that focuses on detecting a group of Sybil identities. We show that both approaches in this category raise the bar for defense against adaptive attackers.Die steigende PopularitĂ€t sozialer Medien fĂŒhrt zu umfangreichen Missbrauch mit negativen Folgen fĂŒr die nachhaltige FunktionalitĂ€t und verringerter QualitĂ€t des Services. Der Missbrauch wird maĂgeblich durch die Nutzung schwacher Identifikationsverfahren, die eine einfache Anmeldung ohne Verifikation durch eine vertrauenswĂŒrdige Behörde erlaubt, ermöglicht. Angreifer nutzen diese Umgebung aus und attackieren den Service mit sogenannten Sybil Angriffen, bei denen mehrere gefĂ€lschte (Sybil) IdentitĂ€ten erstellt werden, um einen Vorteil durch die gemeinsamen Privilegien der IdentitĂ€ten zu erhalten und den Service zu missbrauchen.
Diese Doktorarbeit zeigt Techniken zur Verhinderung von Missbrauch sozialer Medien, in dem Verteidigungsmechanismen konstruiert und implementiert werden, die sowohl robust als auch praktikabel sind. Zwei Verteidigungsstrategien werden vorgestellt: (1) Unter Ausnutzung des sozialen Netzwerks: Wir analysieren zuerst existierende soziale Netzwerk-basierende Sybil Erkennungsmechanismen und zeigen deren praktische Anwendungsgrenzen auf bei der Anwendung auf soziale Netzwerke aus der echten Welt. Im Anschluss zeigen wir den Ansatz der sogenannten Sybil Toleranz, welcher die Folgen eines Angriffs mit mehreren IdentitĂ€ten einschrĂ€nkt. (2) Unter Ausnutzung des AktivitĂ€tsverlaufs von IdentitĂ€ten: Wir prĂ€sentieren zwei AnsĂ€tze, einen anwendbar fĂŒr die Erkennung von UnregelmĂ€Ăigkeiten in dem sozialen Verhalten eines Benutzers zur Erkennung unanstĂ€ndiger Benutzer und ein weiterer Ansatz namens Stamper, dessen Fokus die Erkennung von Gruppen bestehend aus Sybil IdentitĂ€ten ist. Beide gezeigten AnsĂ€tze erschweren adaptive Angriffe und verbessern existierende Verteidigungsmechanismen
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