Birds of a Feather - Better Together? Exploring the Optimal Spatial Distribution of Ethnic Inventors

We examine how the spatial and social proximity of inventors affects knowledge flows, focusing especially on how the two forms of proximity interact. We develop a knowledge flow production function (KFPF) as a flexible tool for modeling access to knowledge and show that the optimal spatial concentration of socially proximate inventors in a city or nation depends on whether spatial and social proximity are complements or substitutes in facilitating knowledge flows. We employ patent citation data, using same-MSA and co-ethnicity as proxies for spatial and social proximity, respectively, to estimate the key KFPF parameters. Although co-location and co-ethnicity both predict knowledge flows, the marginal benefit of co-location is significantly less for co-ethnic inventors. These results imply that dispersion of socially proximate individuals is optimal from the perspectives of the city and the economy. In contrast, for socially proximate individuals themselves, spatial concentration is preferred - and the only stable equilibrium.

Little Drummer Bot: Building, Testing, and Interfacing With a New Expressive Mechatronic Drum System

(Abstract to follow

Brain Drain or Brain Bank? The Impact of Skilled Emigration on Poor-Country Innovation

The development prospects of a poor country depend in part on its capacity for innovation. The productivity of its innovators depends in turn on their access to technological knowledge. The emigration of highly skilled individuals weakens local knowledge networks (brain drain), but may also help remaining innovators access valuable knowledge accumulated abroad (brain bank). We develop a model in which the size of the optimal innovator diaspora depends on the competing strengths of co-location and diaspora effects for accessing knowledge. Then, using patent citation data associated with inventions from India, we estimate the key co-location and diaspora parameters; the net effect of innovator emigration is to harm domestic knowledge access, on average. However, knowledge access conferred by the diaspora is particularly valuable in the production of India's most important inventions as measured by citations received. Thus, our findings imply that the optimal emigration level may depend, at least partly, on the relative value resulting from the most cited compared to average inventions.

Collaborative composition for musical robots

The goal of this research is to collaborate with a number of different artists to explore the capabilities of robotic musical instruments to cultivate new music. This paper describes the challenges faced in using musical robotics in rehearsals and on the performance stage. It also describes the design of custom software frameworks and tools for the variety of composers and performers interacting with the new instruments. Details of how laboratory experiments and rehearsals moved to the concert hall in a variety of diverse performance scenarios are described. Finally, a paradigm for how to teach musical robotics as a multimedia composition course is discussed

Multiple forms of LTP in hippocampal CA3 neurons use a common postsynaptic mechanism

nature neuroscience • volume 2 no 7 • july 1999 625 Dynamic changes in synaptic strength are thought to provide a cellular basis for information storage in the nervous system. For example, long-term potentiation (LTP) of synaptic transmission is a long-lasting, activity-dependent form of synaptic plasticity that is expressed by all principal neurons in the hippocampus-a brain structure implicated in certain forms of long-term memory 1 . In the hippocampus (and elsewhere), multiple forms of LTP have been described: those that require activation of the N-methyl-D-aspartate (NMDA) subclass of glutamate receptors for induction, and those that are independent of NMDA receptor activation 2 . At nearly all glutamatergic synapses in the hippocampus, including commissural/associational (C/A) synapses in CA3, LTP induction has been firmly established to depend on an initial postsynaptic rise in cytosolic Ca 2+ concentration The one hippocampal synapse that seems to be an exception to this rule is the mossy fiber input to CA3 pyramidal neurons from dentate granule cells. This synapse has several unusual structural features, including large terminals, multiple release sites and a proximal termination zone along the apical dendrites of CA3 neurons We have used high-speed fluorescence imaging and improved methods for stimulating and recording from the CA3 region to re-examine the possible contribution of Ca 2+ to the induction of these hypothesized multiple forms of LTP, and to determine the specific conditions for their induction. LTP induced with either stimulation protocol similarly required a rise in postsynaptic [Ca 2+ ]. LTP induced with B-HFS or L-HFS was prevented by chelation of postsynaptic [Ca 2+ ], although the concentration of Ca 2+ buffer required for blocking LTP was substantially different for the two protocols. Additionally, a rise in postsynaptic [Ca 2+ ], sufficient to induce mossy fiber LTP, occurred even when fast synaptic transmission was blocked by the ionotropic glutamate receptor antagonist kynurenate. These Ca 2+ rises resulted from release by internal Ca 2+ stores and depended on metabotropic glutamate receptor (mGluR) activation. Finally, LTP was significantly suppressed by postsynaptic inhibition of cAMP-dependent protein kinase. Taken together, these results suggest that mossy fiber LTP shares with other hippocampal synapses the common induction mechanism of an initial rise in postsynaptic [Ca 2+ ]. RESULTS Mossy fiber responses to stratum lucidum stimulatio

SmartEAR: Smartwatch-based Unsupervised Learning for Multi-modal Signal Analysis in Opportunistic Sensing Framework

Wrist-bands such as smartwatches have become an unobtrusive interface for collecting physiological and contextual data from users. Smartwatches are being used for smart healthcare, telecare, and wellness monitoring. In this paper, we used data collected from the AnEAR framework leveraging smartwatches to gather and store physiological data from patients in naturalistic settings. This data included temperature, galvanic skin response (GSR), acceleration, and heart rate (HR). In particular, we focused on HR and acceleration, as these two modalities are often correlated. Since the data was unlabeled we relied on unsupervised learning for multi-modal signal analysis. We propose using k-means clustering, GMM clustering, and Self-Organizing maps based on Neural Networks for group the multi-modal data into homogeneous clusters. This strategy helped in discovering latent structures in our data

Addressing diabetes mellitus as part of the strategy for ending TB.

As we enter the new era of Sustainable Development Goals, the international community has committed to ending the TB epidemic by 2030 through implementation of an ambitious strategy to reduce TB-incidence and TB-related mortality and avoiding catastrophic costs for TB-affected families. Diabetes mellitus (DM) triples the risk of TB and increases the probability of adverse TB treatment outcomes such as failure, death and recurrent TB. The rapidly escalating global epidemic of DM means that DM needs to be addressed if TB-related milestones and targets are to be achieved. WHO and the International Union Against Tuberculosis and Lung Disease's Collaborative Framework for Care and Control of Tuberculosis and Diabetes, launched in 2011, provides a template to guide policy makers and implementers to combat the epidemics of both diseases. However, more evidence is required to answer important questions about bi-directional screening, optimal ways of delivering treatment, integration of DM and TB services, and infection control. This should in turn contribute to better and earlier TB case detection, and improved TB treatment outcomes and prevention. DM and TB collaborative care can also help guide the development of a more effective and integrated public health approach for managing non-communicable diseases

A New Dataset for Amateur Vocal Percussion Analysis

The imitation of percussive instruments via the human voice is a natural way for us to communicate rhythmic ideas and, for this reason, it attracts the interest of music makers. Specifically, the automatic mapping of these vocal imitations to their emulated instruments would allow creators to realistically prototype rhythms in a faster way. The contribution of this study is two-fold. Firstly, a new Amateur Vocal Percussion (AVP) dataset is introduced to investigate how people with little or no experience in beatboxing approach the task of vocal percussion. The end-goal of this analysis is that of helping mapping algorithms to better generalise between subjects and achieve higher performances. The dataset comprises a total of 9780 utterances recorded by 28 participants with fully annotated onsets and labels (kick drum, snare drum, closed hi-hat and opened hi-hat). Lastly, we conducted baseline experiments on audio onset detection with the recorded dataset, comparing the performance of four state-of-the-art algorithms in a vocal percussion context