110,471 research outputs found
Progressive Simplification of Polygonal Curves
Simplifying polygonal curves at different levels of detail is an important
problem with many applications. Existing geometric optimization algorithms are
only capable of minimizing the complexity of a simplified curve for a single
level of detail. We present an -time algorithm that takes a polygonal
curve of n vertices and produces a set of consistent simplifications for m
scales while minimizing the cumulative simplification complexity. This
algorithm is compatible with distance measures such as the Hausdorff, the
Fr\'echet and area-based distances, and enables simplification for continuous
scaling in time. To speed up this algorithm in practice, we present
new techniques for constructing and representing so-called shortcut graphs.
Experimental evaluation of these techniques on trajectory data reveals a
significant improvement of using shortcut graphs for progressive and
non-progressive curve simplification, both in terms of running time and memory
usage.Comment: 20 pages, 20 figure
Proving Distributed Algorithms by Combining Refinement and Local Computations
Distributed algorithms are considered to be very complex to design and to prove; our paper contributes to the design of correct-by-construction distributed algorithms. The main idea relies upon the development of distributed algorithms following a top/down approach, which is clearly well known in earlier works of Dijkstra, and to use refinement for proving the correctness of the resulting algorithms. However, the link between the problem and the first model remains to be expressed and the refinement is a real help to justify in a very progressive way the choices of design. We propose in this work a framework combining local computations models
and refinement to prove the correctness of a large class of distributed algorithms. Local computations models define abstract computing processes for solving problems by distributed algorithms and can be integrated into a the Event-B modelling language
to define proof-based patterns for the design of distributed algorithms. We illustrate our approach by examples like the leader election protocol or the distributed coloring algorithm. Our proposal is integrated into an environment called ViSiDiA
A Review and Characterization of Progressive Visual Analytics
Progressive Visual Analytics (PVA) has gained increasing attention over the past years.
It brings the user into the loop during otherwise long-running and non-transparent computations
by producing intermediate partial results. These partial results can be shown to the user
for early and continuous interaction with the emerging end result even while it is still being
computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth
various interpretations and instantiations that have created a research domain of competing terms,
various definitions, as well as long lists of practical requirements and design guidelines spread across
different scientific communities. This makes it more and more difficult to get a succinct understanding
of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and
discussion of PVA presented in this paper address these issues and provide (1) a literature collection
on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical
recommendations for implementing and using PVA-based visual analytics solutions
RLFC: Random Access Light Field Compression using Key Views and Bounded Integer Encoding
We present a new hierarchical compression scheme for encoding light field
images (LFI) that is suitable for interactive rendering. Our method (RLFC)
exploits redundancies in the light field images by constructing a tree
structure. The top level (root) of the tree captures the common high-level
details across the LFI, and other levels (children) of the tree capture
specific low-level details of the LFI. Our decompressing algorithm corresponds
to tree traversal operations and gathers the values stored at different levels
of the tree. Furthermore, we use bounded integer sequence encoding which
provides random access and fast hardware decoding for compressing the blocks of
children of the tree. We have evaluated our method for 4D two-plane
parameterized light fields. The compression rates vary from 0.08 - 2.5 bits per
pixel (bpp), resulting in compression ratios of around 200:1 to 20:1 for a PSNR
quality of 40 to 50 dB. The decompression times for decoding the blocks of LFI
are 1 - 3 microseconds per channel on an NVIDIA GTX-960 and we can render new
views with a resolution of 512X512 at 200 fps. Our overall scheme is simple to
implement and involves only bit manipulations and integer arithmetic
operations.Comment: Accepted for publication at Symposium on Interactive 3D Graphics and
Games (I3D '19
Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach
Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy
Algorithm/Architecture Co-Design for Low-Power Neuromorphic Computing
The development of computing systems based on the conventional von Neumann architecture has slowed down in the past decade as complementary metal-oxide-semiconductor (CMOS) technology scaling becomes more and more difficult. To satisfy the ever-increasing demands in computing power, neuromorphic computing has emerged as an attractive alternative. This dissertation focuses on developing learning algorithm, hardware architecture, circuit components, and design methodologies for low-power neuromorphic computing that can be employed in various energy-constrained applications.
A top-down approach is adopted in this research. Starting from the algorithm-architecture co-design, a hardware-friendly learning algorithm is developed for spiking neural networks (SNNs). The possibility of estimating gradients from spike timings is explored. The learning algorithm is developed for the ease of hardware implementation, as well as the compatibility with many well-established learning techniques developed for classic artificial neural networks (ANNs). An SNN hardware equipped with the proposed on-chip learning algorithm is implemented in CMOS technology. In this design, two unique features of SNNs, the event-driven computation and the inferring with a progressive precision, are leveraged to reduce the energy consumption. In addition to low-power SNN hardware, accelerators for ANNs are also presented to accelerate the adaptive dynamic programing algorithm. An efficient and flexible single-instruction-multiple-data architecture is proposed to exploit the inherent data-level parallelism in the inference and learning of ANNs. In addition, the accelerator is augmented with a virtual update technique, which helps improve the throughput and energy efficiency remarkably. Lastly, two techniques in the architecture-circuit level are introduced to mitigate the degraded reliability of the memory system in a neuromorphic hardware owing to the aggressively-scaled supply voltage and integration density. The first method uses on-chip feedback to compensate for the process variation and the second technique improves the throughput and energy efficiency of a conventional error-correction method.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144149/1/zhengn_1.pd
DROP: Dimensionality Reduction Optimization for Time Series
Dimensionality reduction is a critical step in scaling machine learning
pipelines. Principal component analysis (PCA) is a standard tool for
dimensionality reduction, but performing PCA over a full dataset can be
prohibitively expensive. As a result, theoretical work has studied the
effectiveness of iterative, stochastic PCA methods that operate over data
samples. However, termination conditions for stochastic PCA either execute for
a predetermined number of iterations, or until convergence of the solution,
frequently sampling too many or too few datapoints for end-to-end runtime
improvements. We show how accounting for downstream analytics operations during
DR via PCA allows stochastic methods to efficiently terminate after operating
over small (e.g., 1%) subsamples of input data, reducing whole workload
runtime. Leveraging this, we propose DROP, a DR optimizer that enables speedups
of up to 5x over Singular-Value-Decomposition-based PCA techniques, and exceeds
conventional approaches like FFT and PAA by up to 16x in end-to-end workloads
Progressive refinement rendering of implicit surfaces
The visualisation of implicit surfaces can be an inefficient task when such surfaces are complex and highly detailed. Visualising a surface by first converting it to a
polygon mesh may lead to an excessive polygon count. Visualising a surface by direct ray casting is often a slow procedure. In this paper we present a progressive refinement renderer for implicit surfaces that are Lipschitz continuous. The renderer first displays a low resolution estimate of what the final image is going to be and, as the computation progresses, increases the quality of this estimate at an interactive frame rate. This renderer provides a quick previewing facility that significantly reduces the design cycle of a new and complex implicit surface. The renderer is also capable of completing an image faster than a conventional implicit surface rendering algorithm based on ray casting
Progressive Transient Photon Beams
In this work we introduce a novel algorithm for transient rendering in
participating media. Our method is consistent, robust, and is able to generate
animations of time-resolved light transport featuring complex caustic light
paths in media. We base our method on the observation that the spatial
continuity provides an increased coverage of the temporal domain, and
generalize photon beams to transient-state. We extend the beam steady-state
radiance estimates to include the temporal domain. Then, we develop a
progressive version of spatio-temporal density estimations, that converges to
the correct solution with finite memory requirements by iteratively averaging
several realizations of independent renders with a progressively reduced kernel
bandwidth. We derive the optimal convergence rates accounting for space and
time kernels, and demonstrate our method against previous consistent transient
rendering methods for participating media
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