2,229 research outputs found
On the benefits of saturating information in consensus networks with noise
In a consensus network subject to non-zero mean noise, the system state may be driven away even when the disagreement exhibits a bounded response. This is unfavourable in applications since the nodes may not work properly and even be faulty outside their operating region. In this paper, we propose a new control algorithm to mitigate this issue by assigning each node a favourite interval that characterizes the nodes desired convergence region. The algorithm is implemented in a self-triggered fashion. If the nodes do not share a global clock, the network operates in a fully asynchronous mode. By this algorithm, we show that the state evolution is confined around the favourite interval and the node disagreement is bounded by a simple linear function of the noise magnitude, without requiring any priori information on the noise. We also show that if the nodes share some global information, then the algorithm can be adjusted to make the nodes evolve into the favourite interval, improve on the disagreement bound and achieve asymptotic consensus in the noiseless case
Adaptive Network Dynamics and Evolution of Leadership in Collective Migration
The evolution of leadership in migratory populations depends not only on
costs and benefits of leadership investments but also on the opportunities for
individuals to rely on cues from others through social interactions. We derive
an analytically tractable adaptive dynamic network model of collective
migration with fast timescale migration dynamics and slow timescale adaptive
dynamics of individual leadership investment and social interaction. For large
populations, our analysis of bifurcations with respect to investment cost
explains the observed hysteretic effect associated with recovery of migration
in fragmented environments. Further, we show a minimum connectivity threshold
above which there is evolutionary branching into leader and follower
populations. For small populations, we show how the topology of the underlying
social interaction network influences the emergence and location of leaders in
the adaptive system. Our model and analysis can describe other adaptive network
dynamics involving collective tracking or collective learning of a noisy,
unknown signal, and likewise can inform the design of robotic networks where
agents use decentralized strategies that balance direct environmental
measurements with agent interactions.Comment: Submitted to Physica D: Nonlinear Phenomen
Black-Box Data-efficient Policy Search for Robotics
The most data-efficient algorithms for reinforcement learning (RL) in
robotics are based on uncertain dynamical models: after each episode, they
first learn a dynamical model of the robot, then they use an optimization
algorithm to find a policy that maximizes the expected return given the model
and its uncertainties. It is often believed that this optimization can be
tractable only if analytical, gradient-based algorithms are used; however,
these algorithms require using specific families of reward functions and
policies, which greatly limits the flexibility of the overall approach. In this
paper, we introduce a novel model-based RL algorithm, called Black-DROPS
(Black-box Data-efficient RObot Policy Search) that: (1) does not impose any
constraint on the reward function or the policy (they are treated as
black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for
data-efficient RL in robotics, and (3) is as fast (or faster) than analytical
approaches when several cores are available. The key idea is to replace the
gradient-based optimization algorithm with a parallel, black-box algorithm that
takes into account the model uncertainties. We demonstrate the performance of
our new algorithm on two standard control benchmark problems (in simulation)
and a low-cost robotic manipulator (with a real robot).Comment: Accepted at the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS) 2017; Code at
http://github.com/resibots/blackdrops; Video at http://youtu.be/kTEyYiIFGP
Real-Time Structural Damage Assessment Using Artificial Neural Networks and Antiresonant Frequencies
Coordination networks under noisy measurements and sensor biases
Large scale network systems have been constructed and utilized to provide services ranging from energy acquisition and water distribution to health monitoring and transportation. The operation of these complex systems relies on sensors and actuators to acquire and control the system states, which are commonly exchanged among the sub-parts of the systems via communication channels, due to the spatial separation of the systems. Considering the pervasiveness of these man-made complex systems and the importance of the data extraction and exchange, attention should be paid in understanding how large scale systems behave when there are uncertainties in the measurements and communications.Aside from transmission delays and information missing, noise is also a major issue in data exchange. In addition, when sensors are used to measure variables, the problem that arises commonly is that the read-out may not be exactly equal to real value. In both cases, the data error prevents the systems to get accurate state information. As the current emergence of Internet of Thing, Industry 4.0, smart city and 5G, sensors and communication mediums are playing more and more important roles in network systems. Considering these facts, this thesis focuses on analysing and addressing the issues in networksystems caused by the error in state measurement and exchange.We first consider two algorithms to deal with the data exchange error, with a particular interest in designing robust network coordination algorithms againstunknown but bounded communication noise. In chapter 3, we propose a self-triggered consensus algorithm to tackle the state drift problem of consensus dynamics caused by the communication noise. In chapter 4, we refine the resultby proposing a different algorithm. Although these two algorithms both can achieve practical consensus and guarantee boundedness of system state, the mechanisms of them are different. The first algorithm relies on an adaptive threshold, which is adjusted based on the node state, to zero the control inputs of the nodes when their disagreements are sufficiently small. The second algorithm imposes the bound on the state of each node by saturating the state received from the node neighbours.Lastly, we consider the state measurement error, and focus on estimating the sensor bias from the incorrect measurement. The sensors in the network measure the relative states of their neighbours, and the measurements may contain biases. We discuss the conditions of the measurement graphs and the number of biased sensors that allow the biases to be reconstructed from the measurements. Furthermore, we provide distributed algorithms to compute the value of the biases
Intrinsic limits to gene regulation by global crosstalk
Gene regulation relies on the specificity of transcription factor (TF) - DNA
interactions. In equilibrium, limited specificity may lead to crosstalk: a
regulatory state in which a gene is either incorrectly activated due to
noncognate TF-DNA interactions or remains erroneously inactive. We present a
tractable biophysical model of global crosstalk, where many genes are
simultaneously regulated by many TFs. We show that in the simplest regulatory
scenario, a lower bound on crosstalk severity can be analytically derived
solely from the number of (co)regulated genes and a suitable parameter that
describes binding site similarity. Estimates show that crosstalk could present
a significant challenge for organisms with low-specificity TFs, such as
metazoans, unless they use appropriate regulation schemes. Strong cooperativity
substantially decreases crosstalk, while joint regulation by activators and
repressors, surprisingly, does not; moreover, certain microscopic details about
promoter architecture emerge as globally important determinants of crosstalk
strength. Our results suggest that crosstalk imposes a new type of global
constraint on the functioning and evolution of regulatory networks, which is
qualitatively distinct from the known constraints acting at the level of
individual gene regulatory elements
Science 3.0: Corrections to the Science 2.0 paradigm
The concept of Science 2.0 was introduced almost a decade ago to describe the
new generation of online-based tools for researchers allowing easier data
sharing, collaboration and publishing. Although technically sound, the concept
still does not work as expected. Here we provide a systematic line of arguments
to modify the concept of Science 2.0, making it more consistent with the spirit
and traditions of science and Internet. Our first correction to the Science 2.0
paradigm concerns the open-access publication models charging fees to the
authors. As discussed elsewhere, we show that the monopoly of such publishing
models increases biases and inequalities in the representation of scientific
ideas based on the author's income. Our second correction concerns
post-publication comments online, which are all essentially non-anonymous in
the current Science 2.0 paradigm. We conclude that scientific post-publication
discussions require special anonymization systems. We further analyze the
reasons of the failure of the current post-publication peer-review models and
suggest what needs to be changed in Science 3.0 to convert Internet into a
large journal club.Comment: 7 figure
On discretisation drift and smoothness regularisation in neural network training
The deep learning recipe of casting real-world problems as mathematical optimisation and tackling the optimisation by training deep neural networks using gradient-based optimisation has undoubtedly proven to be a fruitful one. The understanding behind why deep learning works, however, has lagged behind its practical significance. We aim to make steps towards an improved understanding of deep learning with a focus on optimisation and model regularisation. We start by investigating gradient descent (GD), a discrete-time algorithm at the basis of most popular deep learning optimisation algorithms. Understanding the dynamics of GD has been hindered by the presence of discretisation drift, the numerical integration error between GD and its often studied continuous-time counterpart, the negative gradient flow (NGF). To add to the toolkit available to study GD, we derive novel continuous-time flows that account for discretisation drift. Unlike the NGF, these new flows can be used to describe learning rate specific behaviours of GD, such as training instabilities observed in supervised learning and two-player games. We then translate insights from continuous time into mitigation strategies for unstable GD dynamics, by constructing novel learning rate schedules and regularisers that do not require additional hyperparameters. Like optimisation, smoothness regularisation is another pillar of deep learning's success with wide use in supervised learning and generative modelling. Despite their individual significance, the interactions between smoothness regularisation and optimisation have yet to be explored. We find that smoothness regularisation affects optimisation across multiple deep learning domains, and that incorporating smoothness regularisation in reinforcement learning leads to a performance boost that can be recovered using adaptions to optimisation methods
On discretisation drift and smoothness regularisation in neural network training
The deep learning recipe of casting real-world problems as mathematical
optimisation and tackling the optimisation by training deep neural networks
using gradient-based optimisation has undoubtedly proven to be a fruitful one.
The understanding behind why deep learning works, however, has lagged behind
its practical significance. We aim to make steps towards an improved
understanding of deep learning with a focus on optimisation and model
regularisation. We start by investigating gradient descent (GD), a
discrete-time algorithm at the basis of most popular deep learning optimisation
algorithms. Understanding the dynamics of GD has been hindered by the presence
of discretisation drift, the numerical integration error between GD and its
often studied continuous-time counterpart, the negative gradient flow (NGF). To
add to the toolkit available to study GD, we derive novel continuous-time flows
that account for discretisation drift. Unlike the NGF, these new flows can be
used to describe learning rate specific behaviours of GD, such as training
instabilities observed in supervised learning and two-player games. We then
translate insights from continuous time into mitigation strategies for unstable
GD dynamics, by constructing novel learning rate schedules and regularisers
that do not require additional hyperparameters. Like optimisation, smoothness
regularisation is another pillar of deep learning's success with wide use in
supervised learning and generative modelling. Despite their individual
significance, the interactions between smoothness regularisation and
optimisation have yet to be explored. We find that smoothness regularisation
affects optimisation across multiple deep learning domains, and that
incorporating smoothness regularisation in reinforcement learning leads to a
performance boost that can be recovered using adaptions to optimisation
methods.Comment: PhD thesis. arXiv admin note: text overlap with arXiv:2302.0195
- …