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 $const. + (\ln (kv))^{2/3}$, where $kv$ 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

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

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

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