1,547 research outputs found
Article: Pedagogy of Master Clarinet and Saxophone Teacher, Joe Allard: A Panel Discussion
This is an edited transcription of a panel discussion held at the International Clarinet Association\u27s ClarinetFest 2008 in Kansas City, Missouri about the pedagogy of clarinet/saxophone master teacher, Joe Allard. I participated in the panel, along with another former student of Joe Allard, as well as with an author of a dissertation written about Allard
Practicar / Practicing Tips
A number of approaches to practicing technical passages that utilize varied rhythms, meters, and poly rhythms. Originally a blog post on Clariperu website in Spanish, English translation appears here
Book Reviews
Book reviews by John Cipolla of: Mandat, Eric. Chips Off the Ol\u27 Block for Solo Bass Clarinet Rossini, Gioachino & Carlos Pässler. Divertimento for Clarinet & Piano del Aguila, Miguel. Pacific Serenade, Op. 59 for Clarinet & String Quartet Wilson, Jeffrey. Colour Studies for Clarine
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Convolutional CRFs for semantic segmentation
For the challenging semantic image segmentation task the best performing models
have traditionally combined the structured modelling capabilities of Conditional Random
Fields (CRFs) with the feature extraction power of CNNs. In more recent works however,
CRF post-processing has fallen out of favour. We argue that this is mainly due to the slow
training and inference speeds of CRFs, as well as the difficulty of learning the internal
CRF parameters. To overcome both issues we propose to add the assumption of conditional
independence to the framework of fully-connected CRFs. This allows us to reformulate the
inference in terms of convolutions, which can be implemented highly efficiently on GPUs.
Doing so speeds up inference and training by two orders of magnitude. All parameters of
the convolutional CRFs can easily be optimized using backpropagation. Towards the goal
of facilitating further CRF research we have made our implementations publicly available
Nuclear burst plasma injection into the magnetosphere and resulting spacecraft charging
The passage of debris from a high altitude ( 400 km) nuclear burst over the ionospheric plasma is found to be capable of exciting large amplitude whistler waves which can act to structure a collisionless shock. This instability will occur in the loss cone exits of the nuclear debris bubble, and the accelerated ambient ions will freestream along the magnetic field lines into the magnetosphere. Using Starfish-like parameters and accounting for plasma diffusion and thermalization of the propagating plasma mass, it is found that synchronous orbit plasma fluxes of high temperature electrons (near 10 keV) will be significantly greater than those encountered during magnetospheric substorms. These fluxes will last for sufficiently long periods of time so as to charge immersed bodies to high potentials and arc discharges to take place
Combined application of real-time control and green technologies to urban drainage systems
The increase in waterproof surfaces, a typical phenomenon of urbanization, on the one hand, reduces the volume of rainwater that naturally infiltrates the subsoil and, on the other, it determines the increase in speeds, flow rates, and outflow volume surface; at the same time, it causes a qualitative deterioration of the water. This study researched the optimal management of urban drainage systems via the combined application of real-time control and green technologies. A hydraulic model of the sewer system of the suburbs of Bologna (Italy) was set up using the Environmental Protection Agency (EPA) Storm Water Management Model (SWMM) to evaluate the reduction in water volume and the masses of pollutants discharged in water bodies. The combined application of these technologies allows significantly reducing both the pollutants released into the receiving water bodies and the overflow volumes, while optimizing the operation of the treatment plants. Green technologies cause an average reduction equal to 45% in volume and 53% of total suspended solids (TSS) sent to the receiver. The modeled cases represent only some of the possible configurations achievable on urban drainage systems; the combined use of different solutions could lead to further improvements in the overall functioning of the drainage system
PoseNet: A convolutional network for real-time 6-dof camera relocalization
© 2015 IEEE. We present a robust and real-time monocular six degree of freedom relocalization system. Our system trains a convolutional neural network to regress the 6-DOF camera pose from a single RGB image in an end-to-end manner with no need of additional engineering or graph optimisation. The algorithm can operate indoors and outdoors in real time, taking 5ms per frame to compute. It obtains approximately 2m and 3 degrees accuracy for large scale outdoor scenes and 0.5m and 5 degrees accuracy indoors. This is achieved using an efficient 23 layer deep convnet, demonstrating that convnets can be used to solve complicated out of image plane regression problems. This was made possible by leveraging transfer learning from large scale classification data. We show that the PoseNet localizes from high level features and is robust to difficult lighting, motion blur and different camera intrinsics where point based SIFT registration fails. Furthermore we show how the pose feature that is produced generalizes to other scenes allowing us to regress pose with only a few dozen training examples
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