2,129 research outputs found
Determine the strength of soft bonds
The strength of a simple soft bond under constant loading rate is studied
theoretically. We find a scaling regime where rebinding is negligible and the
rupture force of the bond scales as , where is
the loading rate. The scaling regime is smaller for weaker bonds and broader
for stronger bonds. For loading rate beyond the upper limit of the scaling
regime, bond rupture is deterministic and thermal effects are negligible. For
loading rate below the lower limit of the scaling regime, contribution from
rebinding becomes important, and there is no simple scaling relation between
rupture force and loading rate. When we extend the theory to include the effect
of rebinding we find good agreement between theory and simulation even below
the scaling regime.Comment: 9 pages, 3 figure
Surface dynamics of a freely standing smectic-A film
A theoretical analysis of surface fluctuations of a freely standing
thermotropic smectic-A liquid crystal film is provided, including the effects
of viscous hydrodynamics. We find two surface dynamic modes (undulation and
peristaltic). For long wavelengths and small frequencies in a thin film, the
undulation mode is the dominant mode. Permeation enters the theory only through
the boundary conditions. The resulting power spectrum is compared with existing
experiments. It is also shown that feasible light scattering experiments on a
freely standing smectic-A film can reveal viscosity and elastic coefficients
through the structure of the power spectrum of both the undulation and
peristaltic modes.Comment: 11 pages; 3 ps figures; latex, revte
Hardware Acceleration for Boolean Satisfiability Solver by Applying Belief Propagation Algorithm
Boolean satisfiability (SAT) has an extensive application domain in computer
science, especially in electronic design automation applications. Circuit
synthesis, optimization, and verification problems can be solved by
transforming original problems to SAT problems. However, the SAT problem is
known as NP-complete, which means there is no efficient method to solve it.
Therefore, an efficient SAT solver to enhance the performance is always
desired. We propose a hardware acceleration method for SAT problems. By
surveying the properties of SAT problems and the decoding of low-density
parity-check (LDPC) codes, a special class of error-correcting codes, we
discover that both of them are constraint satisfaction problems. The belief
propagation algorithm has been successfully applied to the decoding of LDPC,
and the corresponding decoder hardware designs are extensively studied.
Therefore, we proposed a belief propagation based algorithm to solve SAT
problems. With this algorithm, the SAT solver can be accelerated by hardware. A
software simulator is implemented to verify the proposed algorithm and the
performance improvement is estimated. Our experiment results show that time
complexity does not increase with the size of SAT problems and the proposed
method can achieve at least 30x speedup compared to MiniSat
Hydrodynamics of stratified epithelium: steady state and linearized dynamics
A theoretical model for stratified epithelium is presented. The viscoelastic
properties of the tissue is assumed to be dependent on the spatial distribution
of proliferative and differentiated cells. Based on this assumption, a
hydrodynamic description for tissue dynamics at long-wavelength, long-time
limit is developed, and the analysis reveals important insight for the dynamics
of an epithelium close to its steady state. When the proliferative cells occupy
a thin region close to the basal membrane, the relaxation rate towards the
steady state is enhanced by cell division and cell apoptosis. On the other
hand, when the region where proliferative cells reside becomes sufficiently
thick, a flow induced by cell apoptosis close to the apical surface could
enhance small perturbations. This destabilizing mechanism is general for
continuous self-renewal multi-layered tissues, it could be related to the
origin of certain tissue morphology and developing pattern.Comment: 33pages, 8 figure
Learning Disentangled Representations for Timber and Pitch in Music Audio
Timbre and pitch are the two main perceptual properties of musical sounds.
Depending on the target applications, we sometimes prefer to focus on one of
them, while reducing the effect of the other. Researchers have managed to
hand-craft such timbre-invariant or pitch-invariant features using domain
knowledge and signal processing techniques, but it remains difficult to
disentangle them in the resulting feature representations. Drawing upon
state-of-the-art techniques in representation learning, we propose in this
paper two deep convolutional neural network models for learning disentangled
representation of musical timbre and pitch. Both models use encoders/decoders
and adversarial training to learn music representations, but the second model
additionally uses skip connections to deal with the pitch information. As music
is an art of time, the two models are supervised by frame-level instrument and
pitch labels using a new dataset collected from MuseScore. We compare the
result of the two disentangling models with a new evaluation protocol called
"timbre crossover", which leads to interesting applications in audio-domain
music editing. Via various objective evaluations, we show that the second model
can better change the instrumentation of a multi-instrument music piece without
much affecting the pitch structure. By disentangling timbre and pitch, we
envision that the model can contribute to generating more realistic music audio
as well
Multitask learning for frame-level instrument recognition
For many music analysis problems, we need to know the presence of instruments
for each time frame in a multi-instrument musical piece. However, such a
frame-level instrument recognition task remains difficult, mainly due to the
lack of labeled datasets. To address this issue, we present in this paper a
large-scale dataset that contains synthetic polyphonic music with frame-level
pitch and instrument labels. Moreover, we propose a simple yet novel network
architecture to jointly predict the pitch and instrument for each frame. With
this multitask learning method, the pitch information can be leveraged to
predict the instruments, and also the other way around. And, by using the
so-called pianoroll representation of music as the main target output of the
model, our model also predicts the instruments that play each individual note
event. We validate the effectiveness of the proposed method for framelevel
instrument recognition by comparing it with its singletask ablated versions and
three state-of-the-art methods. We also demonstrate the result of the proposed
method for multipitch streaming with real-world music. For reproducibility, we
will share the code to crawl the data and to implement the proposed model at:
https://github.com/biboamy/ instrument-streaming.Comment: This is a pre-print version of an ICASSP 2019 pape
Unseen Object Segmentation in Videos via Transferable Representations
In order to learn object segmentation models in videos, conventional methods
require a large amount of pixel-wise ground truth annotations. However,
collecting such supervised data is time-consuming and labor-intensive. In this
paper, we exploit existing annotations in source images and transfer such
visual information to segment videos with unseen object categories. Without
using any annotations in the target video, we propose a method to jointly mine
useful segments and learn feature representations that better adapt to the
target frames. The entire process is decomposed into two tasks: 1) solving a
submodular function for selecting object-like segments, and 2) learning a CNN
model with a transferable module for adapting seen categories in the source
domain to the unseen target video. We present an iterative update scheme
between two tasks to self-learn the final solution for object segmentation.
Experimental results on numerous benchmark datasets show that the proposed
method performs favorably against the state-of-the-art algorithms.Comment: Accepted in ACCV'18 (oral). Code is available at
https://github.com/wenz116/TransferSe
Vertex-Context Sampling for Weighted Network Embedding
In recent years, network embedding methods have garnered increasing attention
because of their effectiveness in various information retrieval tasks. The goal
is to learn low-dimensional representations of vertexes in an information
network and simultaneously capture and preserve the network structure. Critical
to the performance of a network embedding method is how the edges/vertexes of
the network is sampled for the learning process. Many existing methods adopt a
uniform sampling method to reduce learning complexity, but when the network is
non-uniform (i.e. a weighted network) such uniform sampling incurs information
loss. The goal of this paper is to present a generalized vertex sampling
framework that works seamlessly with most existing network embedding methods to
support weighted instead of uniform vertex/edge sampling. For efficiency, we
propose a delicate sequential vertex-to-context graph data structure, such that
sampling a training pair for learning takes only constant time. For scalability
and memory efficiency, we design the graph data structure in a way that keeps
space consumption low without requiring additional space. In addition to
implementing existing network embedding methods, the proposed framework can be
used to implement extensions that feature high-order proximity modeling and
weighted relation modeling. Experiments conducted on three datasets, including
a commercial large-scale one, verify the effectiveness and efficiency of the
proposed weighted network embedding methods on a variety of tasks, including
word similarity search, multi-label classification, and item recommendation.Comment: 10 page
Hit Song Prediction for Pop Music by Siamese CNN with Ranking Loss
A model for hit song prediction can be used in the pop music industry to
identify emerging trends and potential artists or songs before they are
marketed to the public. While most previous work formulates hit song prediction
as a regression or classification problem, we present in this paper a
convolutional neural network (CNN) model that treats it as a ranking problem.
Specifically, we use a commercial dataset with daily play-counts to train a
multi-objective Siamese CNN model with Euclidean loss and pairwise ranking loss
to learn from audio the relative ranking relations among songs. Besides, we
devise a number of pair sampling methods according to some empirical
observation of the data. Our experiment shows that the proposed model with a
sampling method called A/B sampling leads to much higher accuracy in hit song
prediction than the baseline regression model. Moreover, we can further improve
the accuracy by using a neural attention mechanism to extract the highlights of
songs and by using a separate CNN model to offer high-level features of songs
Mitigating the Impact of Speech Recognition Errors on Spoken Question Answering by Adversarial Domain Adaptation
Spoken question answering (SQA) is challenging due to complex reasoning on
top of the spoken documents. The recent studies have also shown the
catastrophic impact of automatic speech recognition (ASR) errors on SQA.
Therefore, this work proposes to mitigate the ASR errors by aligning the
mismatch between ASR hypotheses and their corresponding reference
transcriptions. An adversarial model is applied to this domain adaptation task,
which forces the model to learn domain-invariant features the QA model can
effectively utilize in order to improve the SQA results. The experiments
successfully demonstrate the effectiveness of our proposed model, and the
results are better than the previous best model by 2% EM score.Comment: Accepted by ICASSP 201
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