RA Symposium 2016

Welcome all to the 2016 Department of Computing Research Associate Symposium!

• Date: Tuesday 14th June 2016
• Venue: Huxley Building LT145
• Keynote: 2pm Deep learning in complex environments by Raia Hadsell (Google DeepMind)

The RA Symposium is a one-day event for research associates in the Department of Computing to present their work to other research associates, staff and PhD students. The aim of the symposium is to showcase the wide range of interesting research projects in the department to all staff members.

Organisers

• Nicholas Ng (nickng@imperial.ac.uk)
• Luigi Nardi (l.nardi@imperial.ac.uk)
• Sarah Willis (s.willis@imperial.ac.uk)

Call for abstracts

If you would like to present your work in this symposium, please submit your abstract to Nicholas Ng (nickng@imperial.ac.uk) by Tuesday 24th May 2016. The format of the talks is semi-formal and there will not be formal proceedings, and work-in-progress talks are especially welcome. All submissions will be accepted and you will receive feedback from us on your submission before the symposium.

Important dates

• Abstract submission deadline: Tuesday 7th June 2016
• RA Symposium 2016: Tuesday 14th June 2016

Lunch and coffee will be provided.

There will be prizes (£500, £250, £100) to be awarded to top presenters based on votes by attendees of the symposium.

To attend, register on Eventbrite.
We look forward to your participation!

Best talk winners

Winner Edward Johns – Deep Learning via Simulation for Robot Grasping
Runner-up Charence Wong – Deep Learning for Human Activity Recognition: A Resource Efficient Implementation on Low-Power Devices
2nd Runner-up Daniele Ravì – Semantic Segmentation on Embedded Hyperspectral Images for Brain Cancer Detection

Keynote

Deep learning in complex environments

Tuesday 14th June 2016, 2pm @ LT145 by Raia Hadsell (Google DeepMind)

Abstract

Learning to solve complex sequences of tasks—while both leveraging transfer and avoiding catastrophic forgetting—remains a key obstacle to achieving human-level intelligence. Interactive environments such as complex outdoor scenes or changing video games present a challenge for agents, since they must demonstrate robustness and adaptability. In this talk, I will discuss the role of deep neural architectures in supporting and structuring continual learning.

Speaker Bio

Raia Hadsell, a senior research scientist at Google DeepMind, has worked on deep learning and robotics problems for over 10 years. Her thesis on Vision for Mobile Robots won the Best Dissertation award from New York University, and was followed by a post-doc at Carnegie Mellon’s Robotics Institute. Raia then worked as a senior scientist and tech manager at SRI International. Raia joined DeepMind in 2014, where she leads a research team studying robot navigation and lifelong learning.

Schedule

Abstracts

Deep Learning for Human Activity Recognition: A Resource Efficient
Implementation on Low-Power Devices

Charence Wong

Human Activity Recognition provides valuable contextual information for
wellbeing, healthcare, and sport applications. Over the past decades, many
machine learning approaches have been proposed to identify activities from
inertial sensor data for specific applications. Most methods, however, are
designed for offline processing rather than processing on the sensor node. In
this paper, a human activity recognition technique based on a deep learning
methodology is designed to enable accurate and real-time classification for
low-power wearable devices. To obtain invariance against changes in sensor
orientation, sensor placement, and in sensor acquisition rates, we design a
feature generation process that is applied to the spectral domain of the
inertial data. Specifically, the proposed method uses sums of temporal
convolutions of the transformed input. Accuracy of the proposed approach is
evaluated against the current state-of-the-art methods using both laboratory and
real world activity datasets. A systematic analysis of the feature generation
parameters and a comparison of activity recognition computation times on mobile
devices and sensor nodes are also presented.

Computational imaging for digitizing shape and appearance of “hard-to-scan” materials

Ilya Reshetouski

Scanning geometry and appearance of real objects is an important
part of computer graphics. Unfortunately, not every object is easy to digitize.
One reason is the complicated nature of some objects (for example glass
objects). Another reason is that most of the outdoors objects are impossible to
bring to the laboratory environment, where they can be reliably scanned.

In my talk I will speak in more details about our recent results for each of
these cases. One part of my talk will be dedicated to our transmission imaging
based approach to solve the problem of reconstruction of symmetric glass objects
with liquids inside. Second part of my talk will cover our polarization imaging
method for on-site acquisition of surface reflectance for planar materials in
uncontrolled outdoor environments.

DropNeuron: An Approach for Simplifying the Structure of Deep Neural
Networks

Wei Pan

Deep learning using multi-layer neural networks (NNs) architecture manifests
superb power in modern machine learning systems. The trained NNs are typically
large. The question we would like to address is whether it is possible to
simplify the NN during training process to achieve a reasonable performance
within an acceptable computational time We presented a novel approach of
optimising a deep neural network through regularisation of network architecture.
We proposed regularisers which support a simple mechanism of dropping neurons
during a network training process. The method supports the construction of a
simpler deep neural networks with compatible performance with its simplified
version. We evaluate the proposed method with a few examples including sparse
linear regression and deep autoencoder. The valuations demonstrate excellent
performance.

Transfer Learning for Optimal Configuration of Big Data Software

Pooyan Jamshidi

Big Data software systems typically consist of an extensible execution engine
(e.g., MapReduce), pluggable distributed storage engines (e.g., Apache
Cassandra), and a range of data sources (e.g., Apache Kafka). Each of these
frameworks is highly configurable. While configurability has several benefits,
it challenges performance tuning. Often, the influence of a configuration option
on software performance are difficult to understand, making performance tuning a
daunting task. This problem is further complicated by the fact that different
parameters may interact with each other and by the exponential size of the
configuration space. To tackle this issue, we propose a method for transferring
the performance tuning knowledge gained from previous versions of the software
system in order to ease the search for an optimal configuration in the current
version under test. The method is called Transfer Learning for Configuration
Optimization (TL4CO) and leverages Multi-Task Gaussian Processes (MTGPs) to
iteratively capture posterior distributions of the configuration spaces. Past
experimental measurements are treated as transferable knowledge to bootstrap the
configuration tuning process. Validation based on three stream processing
systems and a NoSQL benchmark system shows that TL4CO significantly speeds up
the optimization process compared to state-of-the-art software configuration
approaches.

Collective Perception

Sajad Saeedi

We live in a world where we collaborate and interact with each other to be more
efficient. Ants, bees, wolves, and many other animals are masters of
collaborating with each other. Similarly, robots and future man-made machines
must work together to achieve the desired goals. Understanding the animals’
collective perception, which enables them to work together, is a complex
problem. The underlying mechanism for collective perception in most animals has
not yet been fully discovered, but with the current technology, we can make the
collective perception for the robots to become a reality. We are even able to go
beyond the norm. Humans, for instance, learn and exchange knowledge and
experience, but are not able to share the core of their perception. Unlike
humans, robots can share every single bit that they learn and experience. With
this extraordinary ability, the future robots, devices, and sensors will be able
to achieve more than what we expect.

In this talk, major challenges and our recent advancements in multi-robot
perception is presented. It is shown that the robots are able to perform
cooperative simultaneous localization and mapping in large-scale urban
environments, without seeing each other. It is demonstrated that how
heterogeneous robots, such as quadrotors and ground robots, are able to
cooperatively map an unknown environment, without having any knowledge about
there relative positions. At the end, current ongoing works, such as multi-robot
dense SLAM applied to virtual reality, is presented. Finally the future
directions and challenges, such as semantic SLAM and multi-session multi-robot
SLAM, are presented.

Semantic Segmentation on Embedded Hyperspectral Images for Brain Cancer
Detection

Daniele Ravi

This work proposes a method to delineate the exact boundaries of
tumours during a brain cancer resection. The novel aspect of the proposed
solution is the use of hyperspectral imaging that is a non-contact, non-ionizing
and minimally-invasive sensing technique. Previous work demonstrates that
hyperspectral imaging can be used for certain cancer detection in animals, but
no application for real-time cancer detection in the human brain has been
proposed so far. However, working with hyperspectral images is not
straightforward since they present a high dimensionality that makes real-time
processing hard to achieve. Therefore, in order to handle hyperspectral images
adequately we decided to apply a dimensional reduction algorithm with the
purpose to cut down its dimensionality. Many algorithms for dimensionality
reduction have been developed in the past, but their assessment is not easy to
formalize. For this reason, we propose also a novel formula to judge the quality
of a given mapping and the suitability of a specific technique. Moreover,
existing state-of-the-art approaches for dimensional reduction can be time
consuming and may not guarantee a consistent embedding. This is due to the lack
of a fixed coordinate system which prohibits the tissue characterization across
different images. Consequently, we have designed a novel manifold embedding
method based on the T-distributed stochastic neighbour (t-SNE) to overcome these
issues. A database containing 33 images collected from 18 different operations
and the relative ground truth maps have been created through a semi-automatic
process. To evaluate the proposed manifold embedding, we have compared our
solution with 22 other different state-of-the-art methods. According to the
experiments, our proposed algorithm provides the best results. To finally obtain
the tumour classification map, a semantic segmentation approach is performed on
the embedded images using an existing approach called DCT-STF (Discrete Cosine
Transform – Semantic Texton Forest). Our experiments show that, on the current
dataset, the proposed solution obtains good quality results. It also suggests
that, having a bigger dataset in the future will allow us to obtain a classifier
that can recognize and characterise universally all the different tumours in the
brain. To conclude, while current diagnoses of brain tumours are invasive with
many potential side effects and not available in real-time since they require
off-line histopathology sample analysis, with the proposed system, these issues
can be overcome and tumour resection can be greatly improved during surgery
procedures.

Deep Learning via Simulation for Robot Grasping

Edward Johns

Consider a robot which is asked to clear away objects from a messy table. This
requires the robot to observe its local environment with a camera, make
decisions about where to send its hand and fingers, and then control their
movement for grasping and manipulating these objects. But whilst this seems a
trivial task for humans, robots do not benefit from millions of years of
evolution of the brain’s visual cortex; they simply see a list of numbers
representing the pixels in an image. Therefore, a newly-built robot has to learn
these abilities, and how to interpret these pixels, from scratch. Recently, it
has been shown that robots which learn visual perception by themselves with
minimal supervision, are more effective than those which are hand-engineered by
humans. In this talk, I will discuss ways in which neural networks and “deep
learning” can encourage robots to autonomously learn how to grasp objects,
directly mapping pixels in an image to a higher-level, meaningful understanding
of the environment. In particular, I will introduce a method to generate
large-scale training data using a physics simulator and synthetic rendering of
3D objects, such that a robot can be taught to robustly grasp objects without
ever having done so in real life.

Learning for 3D Scene Understanding

Ankur Handa

Much of the success story of computer vision has evolved around the ability to
reconstruct accurate and real-time 3D maps of the scenes using a video stream of
RGB and recently RGB-D frames. However, only geometric understanding of the
scenes is not sufficient to carry out various high level tasks that require
interaction, manipulation and searching. Using recent advances in deep learning
and leveraging large quantity of training data from labelled synthetic 3D
scenes, we show how such semantic understanding can be achieved to allow
reasoning and interaction at the level of objects, often needed to carry out
those high level tasks.

Static Deadlock Detection for Go based on Session Graph Synthesis

Nicholas Ng

I will present a static deadlock detector based on session types for the Go
programming language developed by Google. The multicore programming language
provides channel-based concurrency features based on CSP/process calculi, from
which our analysis tool extract the communication operations as Session Types.
The session types are then converted to Communicating Finite State Machines
(CFSMs), which we apply a recent theoretical result on choreography synthesis to
generate a global graph representing the overall communication pattern of a
concurrent program. If the synthesis is successful, then the program is free
from communication errors and deadlocks.

Minimizing application deployment cost using spot cloud resources

Daniel J. Dubois

Performance assessment of cloud-based applications requires new methodologies to
deal with the complexity of software systems and the variability of spot cloud
resources. The spot instance model is a virtual machine pricing scheme in which
some resources of cloud providers are offered to the highest bidder. This leads
to the formation of a spot price, whose fluctuations can determine customers to
be overbid by other users and lose the virtual machine they rented. In this
presentation, we address the problem of reducing the total costs for running
cloud-based applications while fulfilling service-level objectives (SLOs). To
this end, we propose (i) a heuristic that, given the model of an application, it
determines an optimal set of cloud resources to deploy it, and an optimal
bidding strategy; (ii) a model-driven application refactoring strategy to
further optimise the result. The performance of our method is compared to that
of nonlinear programming and shown to markedly accelerate the finding of
low-cost optimal solutions.

Dense 3D reconstruction and modelling of faces: addressing the
real-world challenges

Anastasios (Tassos) Roussos

Human face is one of the most commonly considered object in
Computer Vision and Computer Graphics. Modelling and reconstructing the detailed
3D shape and dynamics of the human face has numerous applications, including
facial expression recognition, facial recognition/verification, human-computer
interaction, augmented reality, performance capture, computer games, visual
effects and craniofacial surgery.

Despite the important advances in this field, the existing methods have several
limitations, since they can only work reliably under restrictive acquisition
conditions and for certain demographic groups. In this talk, I will present our
recent and ongoing work on overcoming these limitations, by developing novel
methodologies of dense 3D reconstruction and modelling of human faces that deal
with challenging real-life image data.

In more detail, I will first discuss about our research on dense 3D
reconstruction of faces from monocular videos. I will show how, adopting novel
variational formulations and appropriate face models, we achieve accuracy and
robustness to especially challenging scenarios that often arise in real-world
face videos, such as low-resolution images, severe occlusions and strong
illumination changes.

Furthermore, I will demonstrate the 3D face model (3D Morphable Model – 3DMM)
that we have recently built from a database of high-quality 3D scans of around
10,000 distinct facial identities. This is the largest-scale 3DMM of faces ever
constructed, containing statistical information from a huge range of gender,
age, and ethnicity combinations. I will finally present our robust and fully
automatic pipeline for constructing 3DMMs of this scale.