15,137 research outputs found
One Table to Count Them All: Parallel Frequency Estimation on Single-Board Computers
Sketches are probabilistic data structures that can provide approximate
results within mathematically proven error bounds while using orders of
magnitude less memory than traditional approaches. They are tailored for
streaming data analysis on architectures even with limited memory such as
single-board computers that are widely exploited for IoT and edge computing.
Since these devices offer multiple cores, with efficient parallel sketching
schemes, they are able to manage high volumes of data streams. However, since
their caches are relatively small, a careful parallelization is required. In
this work, we focus on the frequency estimation problem and evaluate the
performance of a high-end server, a 4-core Raspberry Pi and an 8-core Odroid.
As a sketch, we employed the widely used Count-Min Sketch. To hash the stream
in parallel and in a cache-friendly way, we applied a novel tabulation approach
and rearranged the auxiliary tables into a single one. To parallelize the
process with performance, we modified the workflow and applied a form of
buffering between hash computations and sketch updates. Today, many
single-board computers have heterogeneous processors in which slow and fast
cores are equipped together. To utilize all these cores to their full
potential, we proposed a dynamic load-balancing mechanism which significantly
increased the performance of frequency estimation.Comment: 12 pages, 4 figures, 3 algorithms, 1 table, submitted to EuroPar'1
Visual Object Networks: Image Generation with Disentangled 3D Representation
Recent progress in deep generative models has led to tremendous breakthroughs
in image generation. However, while existing models can synthesize
photorealistic images, they lack an understanding of our underlying 3D world.
We present a new generative model, Visual Object Networks (VON), synthesizing
natural images of objects with a disentangled 3D representation. Inspired by
classic graphics rendering pipelines, we unravel our image formation process
into three conditionally independent factors---shape, viewpoint, and
texture---and present an end-to-end adversarial learning framework that jointly
models 3D shapes and 2D images. Our model first learns to synthesize 3D shapes
that are indistinguishable from real shapes. It then renders the object's 2.5D
sketches (i.e., silhouette and depth map) from its shape under a sampled
viewpoint. Finally, it learns to add realistic texture to these 2.5D sketches
to generate natural images. The VON not only generates images that are more
realistic than state-of-the-art 2D image synthesis methods, but also enables
many 3D operations such as changing the viewpoint of a generated image, editing
of shape and texture, linear interpolation in texture and shape space, and
transferring appearance across different objects and viewpoints.Comment: NeurIPS 2018. Code: https://github.com/junyanz/VON Website:
http://von.csail.mit.edu
Sketching sonic interactions by imitation-driven sound synthesis
Sketching is at the core of every design activity. In visual design, pencil and paper are the preferred tools to produce sketches for their simplicity and immediacy. Analogue tools for sonic sketching do not exist yet, although voice and gesture are embodied abilities commonly exploited to communicate sound concepts. The EU project SkAT-VG aims to support vocal sketching with computeraided technologies that can be easily accessed, understood and controlled through vocal and gestural imitations. This imitation-driven sound synthesis approach is meant to overcome the ephemerality and timbral limitations of human voice and gesture, allowing to produce more refined sonic sketches and to think about sound in a more designerly way. This paper presents two main outcomes of the project: The Sound Design Toolkit, a palette of basic sound synthesis models grounded on ecological perception and physical description of sound-producing phenomena, and SkAT-Studio, a visual framework based on sound design workflows organized in stages of input, analysis, mapping, synthesis, and output. The integration of these two software packages provides an environment in which sound designers can go from concepts, through exploration and mocking-up, to prototyping in sonic interaction design, taking advantage of all the possibilities of- fered by vocal and gestural imitations in every step of the process
Speculative Approximations for Terascale Analytics
Model calibration is a major challenge faced by the plethora of statistical
analytics packages that are increasingly used in Big Data applications.
Identifying the optimal model parameters is a time-consuming process that has
to be executed from scratch for every dataset/model combination even by
experienced data scientists. We argue that the incapacity to evaluate multiple
parameter configurations simultaneously and the lack of support to quickly
identify sub-optimal configurations are the principal causes. In this paper, we
develop two database-inspired techniques for efficient model calibration.
Speculative parameter testing applies advanced parallel multi-query processing
methods to evaluate several configurations concurrently. The number of
configurations is determined adaptively at runtime, while the configurations
themselves are extracted from a distribution that is continuously learned
following a Bayesian process. Online aggregation is applied to identify
sub-optimal configurations early in the processing by incrementally sampling
the training dataset and estimating the objective function corresponding to
each configuration. We design concurrent online aggregation estimators and
define halting conditions to accurately and timely stop the execution. We apply
the proposed techniques to distributed gradient descent optimization -- batch
and incremental -- for support vector machines and logistic regression models.
We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big
Data analytics system -- and evaluate their performance over terascale-size
synthetic and real datasets. The results confirm that as many as 32
configurations can be evaluated concurrently almost as fast as one, while
sub-optimal configurations are detected accurately in as little as a
fraction of the time
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