3,484 research outputs found
An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model
In this work, we propose a novel procedure for video super-resolution, that
is the recovery of a sequence of high-resolution images from its low-resolution
counterpart. Our approach is based on a "sequential" model (i.e., each
high-resolution frame is supposed to be a displaced version of the preceding
one) and considers the use of sparsity-enforcing priors. Both the recovery of
the high-resolution images and the motion fields relating them is tackled. This
leads to a large-dimensional, non-convex and non-smooth problem. We propose an
algorithmic framework to address the latter. Our approach relies on fast
gradient evaluation methods and modern optimization techniques for
non-differentiable/non-convex problems. Unlike some other previous works, we
show that there exists a provably-convergent method with a complexity linear in
the problem dimensions. We assess the proposed optimization method on {several
video benchmarks and emphasize its good performance with respect to the state
of the art.}Comment: 37 pages, SIAM Journal on Imaging Sciences, 201
Hydra: An Accelerator for Real-Time Edge-Aware Permeability Filtering in 65nm CMOS
Many modern video processing pipelines rely on edge-aware (EA) filtering
methods. However, recent high-quality methods are challenging to run in
real-time on embedded hardware due to their computational load. To this end, we
propose an area-efficient and real-time capable hardware implementation of a
high quality EA method. In particular, we focus on the recently proposed
permeability filter (PF) that delivers promising quality and performance in the
domains of HDR tone mapping, disparity and optical flow estimation. We present
an efficient hardware accelerator that implements a tiled variant of the PF
with low on-chip memory requirements and a significantly reduced external
memory bandwidth (6.4x w.r.t. the non-tiled PF). The design has been taped out
in 65 nm CMOS technology, is able to filter 720p grayscale video at 24.8 Hz and
achieves a high compute density of 6.7 GFLOPS/mm2 (12x higher than embedded
GPUs when scaled to the same technology node). The low area and bandwidth
requirements make the accelerator highly suitable for integration into SoCs
where silicon area budget is constrained and external memory is typically a
heavily contended resource
Berends-Giele recursion for double-color-ordered amplitudes
Tree-level double-color-ordered amplitudes are computed using Berends--Giele
recursion relations applied to the bi-adjoint cubic scalar theory. The standard
notion of Berends--Giele currents is generalized to double-currents and their
recursions are derived from a perturbiner expansion of linearized fields that
solve the non-linear field equations. Two applications are given. Firstly, we
prove that the entries of the inverse KLT matrix are equal to Berends--Giele
double-currents (and are therefore easy to compute). And secondly, a simple
formula to generate tree-level BCJ-satisfying numerators for arbitrary
multiplicity is proposed by evaluating the field-theory limit of tree-level
string amplitudes for various color orderings using double-color-ordered
amplitudes.Comment: 15 pages, harvmac TeX, v2: published versio
Thermodynamic Analysis of Interacting Nucleic Acid Strands
Motivated by the analysis of natural and engineered DNA and RNA systems, we present the first algorithm for calculating the partition function of an unpseudoknotted complex of multiple interacting nucleic acid strands. This dynamic program is based on a rigorous extension of secondary structure models to the multistranded case, addressing representation and distinguishability issues that do not arise for single-stranded structures. We then derive the form of the partition function for a fixed volume containing a dilute solution of nucleic acid complexes. This expression can be evaluated explicitly for small numbers of strands, allowing the calculation of the equilibrium population distribution for each species of complex. Alternatively, for large systems (e.g., a test tube), we show that the unique complex concentrations corresponding to thermodynamic equilibrium can be obtained by solving a convex programming problem. Partition function and concentration information can then be used to calculate equilibrium base-pairing observables. The underlying physics and mathematical formulation of these problems lead to an interesting blend of approaches, including ideas from graph theory, group theory, dynamic programming, combinatorics, convex optimization, and Lagrange duality
BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition
Despite the remarkable progress achieved on automatic speech recognition,
recognizing far-field speeches mixed with various noise sources is still a
challenging task. In this paper, we introduce novel student-teacher transfer
learning, BridgeNet which can provide a solution to improve distant speech
recognition. There are two key features in BridgeNet. First, BridgeNet extends
traditional student-teacher frameworks by providing multiple hints from a
teacher network. Hints are not limited to the soft labels from a teacher
network. Teacher's intermediate feature representations can better guide a
student network to learn how to denoise or dereverberate noisy input. Second,
the proposed recursive architecture in the BridgeNet can iteratively improve
denoising and recognition performance. The experimental results of BridgeNet
showed significant improvements in tackling the distant speech recognition
problem, where it achieved up to 13.24% relative WER reductions on AMI corpus
compared to a baseline neural network without teacher's hints.Comment: Accepted to 2018 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP 2018
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