'It'll get worse before it gets better': Local experiences of living in a regeneration area

The negative consequences of living in deprived neighbourhoods for residents’ quality of life are well documented. Area-based regeneration initiatives are invariably concerned with improving local quality of life over the long term. The process of regeneration, however, can itself directly result in immediate and potentially lasting negative effects for local communities. This paper discusses some of the ways in which living in an area undergoing regeneration can adversely affect inhabitants’ quality of life, including problems associated with voids, relocation, demolitions, environmental quality, complexity, funding issues, uncertainty, frustration, fear for the future and consultation fatigue. A case study approach draws examples from a deprived neighbourhood in the North East of England. The conclusion discusses some of the possible implications for future regeneration policy, including: the importance of ongoing communication between professionals and communities; the need to value local people’s experience, judgement and the contribution they can make to local decision-making processes; recognition that successful regeneration can take many years; and the implications of current UK government policy

A study on communication optimization of distributed gradient descent algorithms based on large-scale machine learning

In recent years, the rapid development of new generation information technology has resulted in an unprecedented expansion of information capacity. Machine learning algorithms are also increasingly used to compute information sets and build information systems to solve problems whose complexity makes algorithmic solutions infeasible. Examples include autonomous vehicles, speech recognition or user determination (recommendation systems). The complexity of the machine learning model, combined with the larger amount of data collected, makes it much more expensive to use the model on a single machine, or even impossible to train. Using the computing power of distributed systems is a straightforward, simple solution to the problem. Today, powerful computer clusters are used to train complex deep neural networks on large data sets. However, in large-scale clustered environments, the commonly used distributed synchronous stochastic gradient descent algorithms require frequent node communication to ensure consistency of the gradients (parameters). This has led to the communication bandwidth being a key constraint for distributed machine learning systems

Information Cost Tradeoffs for Augmented Index and Streaming Language Recognition

This paper makes three main contributions to the theory of communication complexity and stream computation. First, we present new bounds on the information complexity of AUGMENTED-INDEX. In contrast to analogous results for INDEX by Jain, Radhakrishnan and Sen [J. ACM, 2009], we have to overcome the significant technical challenge that protocols for AUGMENTED-INDEX may violate the "rectangle property" due to the inherent input sharing. Second, we use these bounds to resolve an open problem of Magniez, Mathieu and Nayak [STOC, 2010] that asked about the multi-pass complexity of recognizing Dyck languages. This results in a natural separation between the standard multi-pass model and the multi-pass model that permits reverse passes. Third, we present the first passive memory checkers that verify the interaction transcripts of priority queues, stacks, and double-ended queues. We obtain tight upper and lower bounds for these problems, thereby addressing an important sub-class of the memory checking framework of Blum et al. [Algorithmica, 1994]

Privacy-Aware Processing of Biometric Templates by Means of Secure Two-Party Computation

The use of biometric data for person identification and access control is gaining more and more popularity. Handling biometric data, however, requires particular care, since biometric data is indissolubly tied to the identity of the owner hence raising important security and privacy issues. This chapter focuses on the latter, presenting an innovative approach that, by relying on tools borrowed from Secure Two Party Computation (STPC) theory, permits to process the biometric data in encrypted form, thus eliminating any risk that private biometric information is leaked during an identification process. The basic concepts behind STPC are reviewed together with the basic cryptographic primitives needed to achieve privacy-aware processing of biometric data in a STPC context. The two main approaches proposed so far, namely homomorphic encryption and garbled circuits, are discussed and the way such techniques can be used to develop a full biometric matching protocol described. Some general guidelines to be used in the design of a privacy-aware biometric system are given, so as to allow the reader to choose the most appropriate tools depending on the application at hand

Communication Theoretic Data Analytics

Widespread use of the Internet and social networks invokes the generation of big data, which is proving to be useful in a number of applications. To deal with explosively growing amounts of data, data analytics has emerged as a critical technology related to computing, signal processing, and information networking. In this paper, a formalism is considered in which data is modeled as a generalized social network and communication theory and information theory are thereby extended to data analytics. First, the creation of an equalizer to optimize information transfer between two data variables is considered, and financial data is used to demonstrate the advantages. Then, an information coupling approach based on information geometry is applied for dimensionality reduction, with a pattern recognition example to illustrate the effectiveness. These initial trials suggest the potential of communication theoretic data analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan. 201

Streaming algorithms for language recognition problems

We study the complexity of the following problems in the streaming model. Membership testing for \DLIN We show that every language in \DLIN\ can be recognised by a randomized one-pass $O(\log n)$ space algorithm with inverse polynomial one-sided error, and by a deterministic p-pass $O(n/p)$ space algorithm. We show that these algorithms are optimal. Membership testing for \LL$(k)$ For languages generated by \LL$(k)$ grammars with a bound of $r$ on the number of nonterminals at any stage in the left-most derivation, we show that membership can be tested by a randomized one-pass $O(r\log n)$ space algorithm with inverse polynomial (in $n$) one-sided error. Membership testing for \DCFL We show that randomized algorithms as efficient as the ones described above for \DLIN\ and \LL(k) (which are subclasses of \DCFL) cannot exist for all of \DCFL: there is a language in \VPL\ (a subclass of \DCFL) for which any randomized p-pass algorithm with error bounded by $\epsilon < 1/2$ must use $\Omega(n/p)$ space. Degree sequence problem We study the problem of determining, given a sequence $d_1, d_2,..., d_n$ and a graph $G$, whether the degree sequence of $G$ is precisely $d_1, d_2,..., d_n$. We give a randomized one-pass $O(\log n)$ space algorithm with inverse polynomial one-sided error probability. We show that our algorithms are optimal. Our randomized algorithms are based on the recent work of Magniez et al. \cite{MMN09}; our lower bounds are obtained by considering related communication complexity problems