arXiv subjects

Weihong Xu

Publications and source records attributed to Weihong Xu.

42 records · Page 3Linked to original sources

arXiv2022

Massively Parallel Open Modification Spectral Library Searching with Hyperdimensional Computing

Mass spectrometry, commonly used for protein identification, generates a massive number of spectra that need to be matched against a large database. In reality, most of them remain unidentified or mismatched due to unexpected post-translational modifications. Open modification search (OMS) has been proposed as a strategy to improve the identification rate by considering every possible change in spectra, but it expands the search space exponentially. In this work, we propose HyperOMS, which redesigns OMS based on hyperdimensional computing to cope with such challenges. Unlike existing algorithms that represent spectral data with floating point numbers, HyperOMS encodes them with high dimensional binary vectors and performs the efficient OMS in high-dimensional space. With the massive parallelism and simple boolean operations, HyperOMS can be efficiently handled on parallel computing platforms. Experimental results show that HyperOMS on GPU is up to $17\times$ faster and $6.4\times$ more energy efficient than the state-of-the-art GPU-based OMS tool while providing comparable search quality to competing search tools.

cs.DC↗

arXiv2021

On an equivalence of divisors on $\bar{M}_{0,n}$ from Gromov-Witten theory and conformal blocks

We consider a conjecture that identifies two types of base point free divisors on $\bar{M}_{0,n}$. The first arises from Gromov-Witten theory of a Grassmannian. The second comes from first Chern classes of vector bundles associated to simple Lie algebras in type A. Here we reduce this conjecture on $\bar{M}_{0,n}$ to the same statement for $n=4$. A reinterpretation leads to a proof of the conjecture on $\bar{M}_{0,n}$ for a large class, and we give sufficient conditions for the non-vanishing of these divisors.

math.AG↗

arXiv2018

Polar Decoding on Sparse Graphs with Deep Learning

In this paper, we present a sparse neural network decoder (SNND) of polar codes based on belief propagation (BP) and deep learning. At first, the conventional factor graph of polar BP decoding is converted to the bipartite Tanner graph similar to low-density parity-check (LDPC) codes. Then the Tanner graph is unfolded and translated into the graphical representation of deep neural network (DNN). The complex sum-product algorithm (SPA) is modified to min-sum (MS) approximation with low complexity. We dramatically reduce the number of weight by using single weight to parameterize the networks. Optimized by the training techniques of deep learning, proposed SNND achieves comparative decoding performance of SPA and obtains about $0.5$ dB gain over MS decoding on ($128,64$) and ($256,128$) codes. Moreover, $60 \%$ complexity reduction is achieved and the decoding latency is significantly lower than the conventional polar BP.

eess.SP↗

arXiv2018

Joint Neural Network Equalizer and Decoder

Recently, deep learning methods have shown significant improvements in communication systems. In this paper, we study the equalization problem over the nonlinear channel using neural networks. The joint equalizer and decoder based on neural networks are proposed to realize blind equalization and decoding process without the knowledge of channel state information (CSI). Different from previous methods, we use two neural networks instead of one. First, convolutional neural network (CNN) is used to adaptively recover the transmitted signal from channel impairment and nonlinear distortions. Then the deep neural network decoder (NND) decodes the detected signal from CNN equalizer. Under various channel conditions, the experiment results demonstrate that the proposed CNN equalizer achieves better performance than other solutions based on machine learning methods. The proposed model reduces about $2/3$ of the parameters compared to state-of-the-art counterparts. Besides, our model can be easily applied to long sequence with $\mathcal{O}(n)$ complexity.

eess.SP↗

arXiv2018

Improving Massive MIMO Belief Propagation Detector with Deep Neural Network

In this paper, deep neural network (DNN) is utilized to improve the belief propagation (BP) detection for massive multiple-input multiple-output (MIMO) systems. A neural network architecture suitable for detection task is firstly introduced by unfolding BP algorithms. DNN MIMO detectors are then proposed based on two modified BP detectors, damped BP and max-sum BP. The correction factors in these algorithms are optimized through deep learning techniques, aiming at improved detection performance. Numerical results are presented to demonstrate the performance of the DNN detectors in comparison with various BP modifications. The neural network is trained once and can be used for multiple online detections. The results show that, compared to other state-of-the-art detectors, the DNN detectors can achieve lower bit error rate (BER) with improved robustness against various antenna configurations and channel conditions at the same level of complexity.

eess.SP↗

arXiv2013

Environment-dependent payoffs in finite populations

In constant-payoff finite population games, when selection is weak and population size is large, the one-third law serves as the condition for a strategy to be advantageous. We generalize the result to the case where payoff matrices are environment-dependent and provide a more general law. In this way we model feedback from the environment and show its impact on the dynamics.

q-bio.PE↗