The functional organisation of the fronto-temporal language system:evidence from syntactic and semantic ambiguity

Spoken language comprehension is known to involve a large left-dominant network of fronto-temporal brain regions, but there is still little consensus about how the syntactic and semantic aspects of language are processed within this network. In an fMRI study, volunteers heard spoken sentences that contained either syntactic or semantic ambiguities as well as carefully matched low-ambiguity sentences. Results showed ambiguity-related responses in the posterior left inferior frontal gyrus (pLIFG) and posterior left middle temporal regions. The pLIFG activations were present for both syntactic and semantic ambiguities suggesting that this region is not specialised for processing either semantic or syntactic information, but instead performs cognitive operations that are required to resolve different types of ambiguity irrespective of their linguistic nature, for example by selecting between possible interpretations or reinterpreting misparsed sentences. Syntactic ambiguities also produced activation in the posterior middle temporal gyrus. These data confirm the functional relationship between these two brain regions and their importance in constructing grammatical representations of spoken language.

Correcting errors in optical data transmission using neural networks

Optical data communication systems are prone to a variety of processes that modify the transmitted signal, and contribute errors in the determination of 1s from 0s. This is a difficult, and commercially important, problem to solve. Errors must be detected and corrected at high speed, and the classifier must be very accurate; ideally it should also be tunable to the characteristics of individual communication links. We show that simple single layer neural networks may be used to address these problems, and examine how different input representations affect the accuracy of bit error correction. Our results lead us to conclude that a system based on these principles can perform at least as well as an existing non-trainable error correction system, whilst being tunable to suit the individual characteristics of different communication links.

Suitability of dysphonia measurements for telemonitoring of Parkinson's disease

We present an assessment of the practical value of existing traditional and non-standard measures for discriminating healthy people from people with Parkinson's disease (PD) by detecting dysphonia. We introduce a new measure of dysphonia, Pitch Period Entropy (PPE), which is robust to many uncontrollable confounding effects including noisy acoustic environments and normal, healthy variations in voice frequency. We collected sustained phonations from 31 people, 23 with PD. We then selected 10 highly uncorrelated measures, and an exhaustive search of all possible combinations of these measures finds four that in combination lead to overall correct classification performance of 91.4%, using a kernel support vector machine. In conclusion, we find that non-standard methods in combination with traditional harmonics-to-noise ratios are best able to separate healthy from PD subjects. The selected non-standard methods are robust to many uncontrollable variations in acoustic environment and individual subjects, and are thus well-suited to telemonitoring applications.

Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.

Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity

The standard reference clinical score quantifying average Parkinson's disease (PD) symptom severity is the Unified Parkinson's Disease Rating Scale (UPDRS). At present, UPDRS is determined by the subjective clinical evaluation of the patient's ability to adequately cope with a range of tasks. In this study, we extend recent findings that UPDRS can be objectively assessed to clinically useful accuracy using simple, self-administered speech tests, without requiring the patient's physical presence in the clinic. We apply a wide range of known speech signal processing algorithms to a large database (approx. 6000 recordings from 42 PD patients, recruited to a six-month, multi-centre trial) and propose a number of novel, nonlinear signal processing algorithms which reveal pathological characteristics in PD more accurately than existing approaches. Robust feature selection algorithms select the optimal subset of these algorithms, which is fed into non-parametric regression and classification algorithms, mapping the signal processing algorithm outputs to UPDRS. We demonstrate rapid, accurate replication of the UPDRS assessment with clinically useful accuracy (about 2 UPDRS points difference from the clinicians' estimates, p < 0.001). This study supports the viability of frequent, remote, cost-effective, objective, accurate UPDRS telemonitoring based on self-administered speech tests. This technology could facilitate large-scale clinical trials into novel PD treatments.

Creative credits:a randomized controlled industrial policy experiment

This report examines the results of a pilot study, which used a method of evaluation called randomised control trials (RCTs) to see if a popular business support scheme called Creative Credits worked effectively. The pilot study, which began in Manchester in 2009, was structured so that vouchers, or 'Creative Credits', would be randomly allocated to small and medium-sized businesses applying to invest in creative projects such as developing websites, video production and creative marketing campaigns, to see if they had a real effect on innovation. The research found that the firms who were awarded Creative Credits enjoyed a short-term boost in their innovation and sales growth in the six months following completion of their creative projects. However, the positive effects were not sustained, and after 12 months there was no longer a statistically significant difference between the groups that received the credits and those that didn’t. The report argues that these results would have remained hidden using the normal evaluation methods used by government, and calls for RCTs to be used more widely when evaluating policies to support business growth.

Performance-based vs socially supportive culture:a cross-national study of descriptive norms and entrepreneurship

This paper is a cross-national study testing a framework relating cultural descriptive norms to entrepreneurship in a sample of 40 nations. Based on data from the Global Leadership and Organizational Behavior Effectiveness project, we identify two higher-order dimensions of culture – socially supportive culture (SSC) and performance-based culture (PBC) – and relate them to entrepreneurship rates and associated supply-side and demand-side variables available from the Global Entrepreneurship Monitor. Findings provide strong support for a social capital/SSC and supply-side variable explanation of entrepreneurship rate. PBC predicts demand-side variables, such as opportunity existence and the quality of formal institutions to support entrepreneurship.

Correcting errors in optical data transmission using neural networks

Optical data communication systems are prone to a variety of processes that modify the transmitted signal, and contribute errors in the determination of 1s from 0s. This is a difficult, and commercially important, problem to solve. Errors must be detected and corrected at high speed, and the classifier must be very accurate; ideally it should also be tunable to the characteristics of individual communication links. We show that simple single layer neural networks may be used to address these problems, and examine how different input representations affect the accuracy of bit error correction. Our results lead us to conclude that a system based on these principles can perform at least as well as an existing non-trainable error correction system, whilst being tunable to suit the individual characteristics of different communication links.

Institutions and social entrepreneurship:the role of institutional voids, institutional support, and institutional configurations

We develop the institutional configuration perspective to understand which national contexts facilitate social entrepreneurship (SE). We confirm joint effects on SE of formal regulatory (government activism), informal cognitive (postmaterialist cultural values), and informal normative (socially supportive cultural norms, or weak-tie social capital) institutions in a multilevel study of 106,484 individuals in 26 nations. We test opposing propositions from the institutional void and institutional support perspectives. Our results underscore the importance of resource support from both formal and informal institutions, and highlight motivational supply side influences on SE. They advocate greater consideration of institutional configurations in institutional theory and comparative entrepreneurship research.

On the persuadability of memory:is changing people's memories no more than changing their minds?

The observation of parallels between the memory distortion and persuasion literatures leads, quite logically, to the appealing notion that people can be 'persuaded' to change their memories. Indeed, numerous studies show that memory can be influenced and distorted by a variety of persuasive tactics, and the theoretical accounts commonly used by researchers to explain episodic and autobiographical memory distortion phenomena can generally predict and explain these persuasion effects. Yet, despite these empirical and theoretical overlaps, explicit reference to persuasion and attitude-change research in the memory distortion literature is surprisingly rare. In this paper, we argue that stronger theoretical foundations are needed to draw the memory distortion and persuasion literatures together in a productive direction. We reason that theoretical approaches to remembering that distinguish (false) beliefs in the occurrence of events from (false) memories of those events - compatible with a source monitoring approach - would be beneficial to this end. Such approaches, we argue, would provide a stronger platform to use persuasion findings to enhance the psychological understanding of memory distortion.

Assessing an experimental approach to industrial policy evaluation:Applying RCT+ to the case of Creative Credits

Experimental methods of policy evaluation are well-established in social policy and development eco-nomics but are rare in industrial and innovation policy. In this paper, we consider the arguments forapplying experimental methods to industrial policy measures, and propose an experimental policy eval-uation approach (which we call RCT+). This approach combines the randomised assignment of firmsto treatment and control groups with a longitudinal data collection strategy incorporating quantitativeand qualitative data (so-called mixed methods). The RCT+ approach is designed to provide a causativerather than purely summative evaluation, i.e. to assess both ‘whether’ and ‘how’ programme outcomesare achieved. In this paper, we assess the RCT+ approach through an evaluation of Creative Credits – aUK business-to-business innovation voucher initiative intended to promote new innovation partnershipsbetween SMEs and creative service providers. The results suggest the potential value of the RCT+ approachto industrial policy evaluation, and the benefits of mixed methods and longitudinal data collection.

Increasing the electrolyte capacity of alkaline Zn-air fuel cells by scavenging zincate with Ca(OH)2

The use of calcium hydroxide for scavenging zincate species is demonstrated to be a highly effective approach for increasing the electrolyte capacity and improving the performance of the zinc-air fuel cell system. A fundamental approach is established in this study to quantify the formation of calcium zincate as the product of scavenging and the amount of water compensation necessary for optimal performance. The good agreement between predicted and experimental results proves the validity of the proposed theoretical approach. By applying the results of theoretical predictions, both the electrolyte capacity and the cell longevity have been increased by more than 40%. It is also found that, using Ca(OH)2 to scavenge zincate species in concentrated KOH solutions, affects mostly the removal of zincate, rather than ZnO, from the electrolyte, whereas the presence of excess, free, mobile H2O plays a key role in dissolving ZnO and forming zincate. The results obtained in this study demonstrate that the proposed approach can widely and effectively be applied to all zinc-air cell systems during their discharge cycle.