Bidirectional-Curious? – Dr. Conor McBride

Event details

  • When: 22nd November 2017 13:00 - 14:30
  • Where: Cole 1.33b
  • Format: Talk

Type systems are often presented in a declarative style, but with an emphasis on ensuring that there is some sort of type synthesis algorithm. Since Pierce and Turner’s “Local Type Inference” system, however, there has been a small but growing alternative: bidirectional typing, where types are synthesized for variables and elimination forms, but must always be proposed in advance for introduction forms and checked. You can still get away without any type annotations, as long as you write only normal forms. But where’s the fun in that? If you want to write terms that compute, you need to write type annotations at exactly the point where an introduction form collides with its elimination form, showing exactly the type at which computation is happening.

For type systems with some sorts of value dependency, the bidirectional approach seriously cuts down on the amount of annotation required in terms, needed only to achieve type synthesis. We have a real opportunity to reduce clutter and also to give a clearer account of the connections between types and computation.

But it doesn’t stop there. A disciplined approach to the construction of bidirectional type systems makes it easier to get their metatheory right. I’ll show this by reconstructing Martin-Löf’s 1971 type theory (the inconsistent one) in a bidirectional style and show why it has type
preservation, even without normalization.

Computational Approaches for Accurate, Automated and Safe Cancer Care – HIG Seminar

Event details

  • When: 22nd November 2017 14:00 - 15:00
  • Where: Cole 1.33a
  • Series: HIG Seminar Series
  • Format: Seminar

Modern external beam radiation therapy techniques allow the design of highly conformal radiation treatment plans that permit high doses of ionsing radition to be delivered to the tumour in order to eradicate cancer cells while sparing surrounding normal tissue. However, since it is difficult to avoid irradiation of normal tissue altogether and ionising radiation also damages normal cells, patients may develop radiation-induced toxicity following treatment. Furthermore, the highly conformal nature of the radiation treatment plans makes them particularly susceptible to geometric or targeting uncertainties in treatment delivery. Geometric uncertainties may result in under-dosage of the tumour leading to local tumour recurrence or unacceptable morbidity from over-dosage of neighbouring healthy tissue.

I will present work in three areas that bear directly on treatment accuracy and safety in radiation oncology. The first area addresses the development of automated image registration algorithms for image-guided radiation therapy with the aim of improving the accuracy and precision of treatment delivery. The registration methods I will present are based on statistical and spectral models of signal and noise in CT and x-ray images. The second part of my talk addresses the identification of predictors of normal tissue toxicity after radiation therapy and the study of the spatial sensitivity of normal tissue to dose. I will address the development of innovative methods to accurately model the spatial characteristics of radiation dose distributions in 3D and results of the analysis of this important, but heretofore lacking, information as a contributing factor in the development of radiation-induced toxicity. Finally, given the increasing complexity of modern radiation treatment plans and a trend towards an escalation in prescribed doses, it is important to implement a safety system to reduce the risk of adverse events arising during treatment and improve clinical efficiency. I will describe ongoing efforts to formalise and automate quality assurance processes in radiation oncology.

Biography
Reshma Munbodh is currently an Assistant Professor in the Department of Diagnostic Imaging and Therapeutics at UConn Health. She received her undergraduate degree in Computer Science and Electronics from the University of Edinburgh and her PhD in medical image processing and analysis applied to cancer from Yale University. Following her PhD, she performed research and underwent clinical training in Therapeutic Medical Physics at the Memorial Sloan-Kettering Cancer Center. She is interested in the development and application of powerful analytical and computational approaches towards improving the diagnosis, understanding and treatment of cancer. Her current projects include the development of image registration algorithms for image-guided radiation therapy, the study of normal tissue toxicity following radiation therapy, longitudinal studies of brain gliomas to monitor tumour progression and treatment response using quantitative MRI analysis and the formalisation and automation of quality assurance processes in radiation oncology.

SRG Seminar: “Interactional Justice vs. The Paradox of Self-Amendment and the Iron Law of Oligarchy” by Jeremy Pitt

Event details

  • When: 15th November 2017 13:00 - 14:00
  • Where: Cole 1.33a
  • Series: Systems Seminars Series
  • Format: Seminar

Self-organisation and self-governance offer an effective approach to resolving collective action problems in multi-agent systems, such as fair and sustainable resource allocation. Nevertheless, self-governing systems which allow unrestricted and unsupervised self-modification expose themselves to several risks, including the Suber’s paradox of self-amendment (rules specify their own amendment) and Michel’s iron law of oligarchy (that the system will inevitably be taken over by a small clique and be run for its own benefit, rather than in the collective interest). This talk will present an algorithmic approach to resisting both the paradox and the iron law, based on the idea of interactional justice derived from sociology, and legal and organizational theory. The process of interactional justice operationalised in this talk uses opinion formation over a social network with respect to a shared set of congruent values, to transform a set of individual, subjective self-assessments into a collective, relative, aggregated assessment.

Using multi-agent simulation, we present some experimental results about detecting and resisting cliques. We conclude with a discussion of some implications concerning institutional reformation and stability, ownership of the means of coordination, and knowledge management processes in ‘democratic’ systems.

Biography
Photograph of Professor Jeremy Pitt
Jeremy Pitt is Professor of Intelligent and Self-Organising Systems in the Department of Electrical & Electronic Engineering at Imperial College London, where he is also Deputy Head of the Intelligent Systems & Networks Group. His research interests focus on developing formal models of social processes using computational logic, and their application in self-organising multi-agent systems, for example fair and sustainable common-pool resource management in ad hoc and sensor network. He also has strong interests in human-computer interaction, socio-technical systems, and the social impact of technology; with regard to the latter he has edited two books, This Pervasive Day (IC Press, 2012) and The Computer After Me (IC Press, 2014). He has been an investigator on more than 30 national and European research projects and has published more than 150 articles in journals and conferences. He is a Senior Member of the ACM, a Fellow of the BCS, and a Fellow of the IET; he is also an Associate Editor of ACM Transactions on Autonomous and Adaptive Systems and an Associate Editor of IEEE Technology and Society Magazine.

PhD viva success: Adam Barwell

Congratulations to Adam Barwell, who successfully defended his thesis yesterday. Adam’s thesis was supervised by Professor Kevin Hammond. He is pictured with second supervisor Dr Christopher Brown, Internal examiner Dr Susmit Sarkar and external examiner Professor Susan Eisenbach from Imperial College, London.

“Ambient intelligence with sensor networks” by Lucas Amos and “Location, Location, Location: Exploring Amazon EC2 Spot Instance Pricing Across Geographical Regions” by Nnamdi Ekwe-Ekwe

Event details

  • When: 9th November 2017 13:00 - 14:00
  • Where: Cole 1.33a
  • Series: Systems Seminars Series
  • Format: Seminar

Lucas’s abstract

“Indoor environment quality has a significant effect on worker productivity through a complex interplay of factors such as temperature, humidity and levels of Volatile Organic Compounds (VOCs).

In this talk I will discuss my Masters project which used off the shelf sensors and Raspberry Pis to collect environmental readings at one minute intervals throughout the Computer Science buildings. The prevalence of erroneous readings due to sensor failure and the strategy used for the identification and correction of such faults will be presented. Identifiable correlations between environmental variables and attempts to model these relationships will be discussed

Past studies identifying the ideal environmental conditions for human comfort and productivity allow for the objective assessment of indoor environmental conditions. An adaptation of Frešer’s environment rating system will be presented, showing how VOC levels can be incorporated into assessments of environment quality and how this can be communicated to building users.”

Nnamdi’s abstract

“Cloud computing is becoming an almost ubiquitous part of the computing landscape. For many companies today, moving their entire infrastructure and workloads to the cloud reduces complexity, time to deployment, and saves money. Spot Instances, a subset of Amazon’s cloud computing infrastructure (EC2), expands on this. They allow a user to bid on spare compute capacity in Amazon’s data centres at heavily discounted prices. If demand was ever to increase such that the user’s maximum bid is exceeded, their compute instance is terminated.

In this work, we conduct one of the first detailed analyses of how location affects the overall cost of deployment of a spot instance. We simultaneously examine the reliability of pricing data of a spot instance, and whether a user can be confident that their instance has a low risk of termination.

We analyse spot pricing data across all available Amazon Web Services regions for 60 days on a variety of instance types. We find that location does play a critical role in spot instance pricing and also that pricing differs depending on the granularity of the location – from a more coarse-grained AWS region to a more fine-grained Availability Zone within a region. We relate the pricing differences we find to the price’s stability, confirming whether we can be confident in the bid prices we make.

We conclude by showing that it is very possible to run workloads on Spot Instances achieving
both a very low risk of termination as well as paying very low amounts per hour.”

Towards Refinement by Resolution in Dependent Type Theory – František Farka

Event details

  • When: 9th November 2017 12:00 - 13:00
  • Where: Cole 1.33b
  • Format: Talk

Abstract
Dependent types are increasingly used in functional programming languages. The surface syntax of dependent types, as seen by a programmer, is elaborated by a compiler into an internal, type-theoretic representation. In order to perform this step, the compiler needs to infer a nontrivial amount of information to successfully type-check the internal representation. This process—type refinement—is complex, implementation dependent, and very few formal developments currently exist. We discuss a novel and simpler formalisation of type refinement in first order type theory with dependent types. We propose a translation of type-refinement problems to Horn-Clause logic with explicit proof-terms, using proof-relevant resolution as the type inference mechanism.

iVoLVER receives Best Demo Jury Award at ACM ISS

The iVoLVER system, created by Gonzalo Méndez and Miguel Nacenta from the SACHI group at the School of Computer Science, University of St Andrews, received Best Demo Jury Award at the ACM Interactive Surfaces and Spaces (ACM ISS) conference last week.

ACM ISS 2017, took place in Brighton, UK and selects a different location each year, with Tokyo, Japan selected as next year’s destination. The conference is a premier venue for research that studies how people interact in smart spaces and surfaces and how to design and engineer solutions for novel interfaces.

iVoLVER is a web-based visual programming environment that enables anyone to transform visualizations that they find in-the-wild (e.g., in a poster or a newspaper) into new visualizations that are more useful for them. Congratulations to the iVoLVER team. You can try out the open source iVoLVER prototype using a browser.

An example iVoLVER interface

Best Demo Jury Award

“A Decentralised Multimodal Integration of Social Signals: A Bio-Inspired Approach” by Esma Benssassi and “Plug and Play Bench: Simplifying Big Data Benchmarking Using Containers” by Sheriffo Ceesay

Event details

  • When: 26th October 2017 13:00 - 14:00
  • Where: Cole 1.33a
  • Series: Systems Seminars Series
  • Format: Seminar

Esma’s abstract

The ability to integrate information from different sensory modalities in a social context is crucial for achieving an understanding of social cues and gaining useful social interaction and experience. Recent research has focused on multi-modal integration of social signals from visual, auditory, haptic or physiological data. Different data fusion techniques have been designed and developed; however, the majority have not achieved significant accuracy improvement in recognising social cues compared to uni-modal social signal recognition. One of the possible limitations is that these existing approaches have no sufficient capacity to model various types of interactions between different modalities and have not been able to leverage the advantages of multi-modal signals by considering each of them as complementary to the others. We introduce ideas for creating a decentralised model for social signals integration inspired by computational models of multi-sensory integration in neuroscience and the perception of social signals in the human brain.

Sheriffo’s abstract

The recent boom of big data, coupled with the challenges of its processing and storage gave rise to the development of distributed data processing and storage paradigms like MapReduce, Spark, and NoSQL databases. With the advent of cloud computing, processing and storing such massive datasets on clusters of machines is now feasible with ease. However, there are limited tools and approaches, which users can rely on to gauge and comprehend the performance of their big data applications deployed locally on clusters, or in the cloud. Researchers have started exploring this area by providing benchmarking suites suitable for big data applications. However, many of these tools are fragmented, complex to deploy and manage, and do not provide transparency with respect to the monetary cost of benchmarking an application.

In this talk, I will present Plug And Play Bench PAPB (https://github.com/sneceesay77/papb): an infrastructure aware abstraction built to integrate and simplify the process of big data benchmarking. PAPB automates the tedious process of installing, configuring and executing common big data benchmark workloads by containerising the tools and settings based on the underlying cluster deployment framework. Our proof of concept implementation utilises HiBench as the benchmark suite, HDP as the cluster deployment framework and Azure as the cloud platform. The talk will further illustrate the inclusion of cost metrics based on the underlying Microsoft Azure cloud platform.

SRG Seminar: “Adaptive Multisite Computation Offloading in Mobile Clouds” by Dawand Sulaiman and “Topological Ranking-Based Resource Scheduling for Multi-Accelerator Systems” by Teng Yu

Event details

  • When: 12th October 2017 13:00 - 14:00
  • Where: Cole 1.33b
  • Series: Systems Seminars Series
  • Format: Seminar

Dawand’s abstract

The concept of using cloud hosted infrastructure as a means to overcome the resource-constraints of mobile devices is known as Mobile Cloud Computing (MCC), and allows applications to run partially on the device, and partially on a remote cloud instance, thereby overcoming any device-specific resource constraints. However, as smart phones and tablets gain more CPU power and longer battery life, the meaning of MCC gradually changes. Instead of being fully dependent on the cloud, a number of nearby devices can be used to coordinate and distribute content and resources in a decentralised manner; this is known as Mobile Ad hoc Cloud Computing. Mobile devices with less computational power and lower battery life can be leveraged by the nearby mobile devices to run resource-intensive applications. Therefore, more efficient and reliable methodologies need to be explored for resource hungry and real time applications such as face recognition, data-intensive, and augmented reality mobile applications.
We present a unified framework which allows each mobile device within the shared environment to intelligently offload its computation to other external platforms. For the individual mobile devices, it is important to make the offloading decision based on network conditions, load of other machines, and mobile device’s own constraints (e.g., mobility and battery). Moreover, to achieve a global optimal task completion time for tasks from all the mobile devices, it is necessary to devise a task scheduling solution that schedules offloaded tasks in real time. The offloading decision engine needs to adapt to the dynamic changes in both the host device and connected nearby and remote devices.

Teng’s abstract

Accelerators are becoming increasingly prevalent in distributed computation. FPGAs have been shown to be fast and power efficient for particular tasks, yet scheduling on multi-accelerator systems is challenging when workloads vary significantly in granularity in terms of task size and/or number of computational unit required.
We present a novel approach for dynamically scheduling tasks on networked multi-accelerator systems which maintains high performance, even in the presence of irregular jobs. Our topological ranking-based scheduling allows realistic irregular workloads to be processed while maintaining a significantly higher level of performance than existing schedulers.