Leveraging Social Networks in Direct Services: Are Foundations Doing All They Can?

· Social networks are critical to physical and mental health, and they shape how people see themselves and their possible futures. · Social networks represent an under-leveraged resource in social services’ efforts to alleviate poverty and other social challenges. · Foundations may be unintentionally creating barriers to practice that leverages social networks by incentivizing individually-focused, highly specific services delivered in standardized, replicable ways. · “Network-oriented” practice can help craft a new way forward that threads the needle between everything-is-different-for-everyone and everything- is-the-same-for-everyone. · By focusing funding on efforts that build and support social networks, foundations can deepen and sustain the impact of their funding

More evidence on the puzzle of interindustry wage differentials : the case of West Germany.

Lohn; Deutschland;

Towards Automatic Speech Identification from Vocal Tract Shape Dynamics in Real-time MRI

Vocal tract configurations play a vital role in generating distinguishable speech sounds, by modulating the airflow and creating different resonant cavities in speech production. They contain abundant information that can be utilized to better understand the underlying speech production mechanism. As a step towards automatic mapping of vocal tract shape geometry to acoustics, this paper employs effective video action recognition techniques, like Long-term Recurrent Convolutional Networks (LRCN) models, to identify different vowel-consonant-vowel (VCV) sequences from dynamic shaping of the vocal tract. Such a model typically combines a CNN based deep hierarchical visual feature extractor with Recurrent Networks, that ideally makes the network spatio-temporally deep enough to learn the sequential dynamics of a short video clip for video classification tasks. We use a database consisting of 2D real-time MRI of vocal tract shaping during VCV utterances by 17 speakers. The comparative performances of this class of algorithms under various parameter settings and for various classification tasks are discussed. Interestingly, the results show a marked difference in the model performance in the context of speech classification with respect to generic sequence or video classification tasks.Comment: To appear in the INTERSPEECH 2018 Proceeding