arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 919 records · Page 51Linked to original sources

arXiv2021

Dancing Synchronization in Coupled Spin-Torque Nano-Oscillators

We are reporting a new type of synchronization, termed dancing synchronization, between two spin-torque nano-oscillators (STNOs) coupled through spin waves. Different from the known synchronizations in which two STNOs are locked with various fixed relative phases, in this new synchronized state two STNOs have the same frequency, but their relative phase varies periodically within the common period, resulting in a dynamic waving pattern. The amplitude of the oscillating relative phase depends on the coupling strength of two STNOs, as well as the driven currents. The dancing synchronization turns out to be universal, and can exist in two nonlinear Van der Pol oscillators coupled both reactively and dissipativly. Our findings open doors for new functional STNO-based devices.

cond-mat.mes-hall↗

arXiv2021

Pruning and Quantization for Deep Neural Network Acceleration: A Survey

Deep neural networks have been applied in many applications exhibiting extraordinary abilities in the field of computer vision. However, complex network architectures challenge efficient real-time deployment and require significant computation resources and energy costs. These challenges can be overcome through optimizations such as network compression. Network compression can often be realized with little loss of accuracy. In some cases accuracy may even improve. This paper provides a survey on two types of network compression: pruning and quantization. Pruning can be categorized as static if it is performed offline or dynamic if it is performed at run-time. We compare pruning techniques and describe criteria used to remove redundant computations. We discuss trade-offs in element-wise, channel-wise, shape-wise, filter-wise, layer-wise and even network-wise pruning. Quantization reduces computations by reducing the precision of the datatype. Weights, biases, and activations may be quantized typically to 8-bit integers although lower bit width implementations are also discussed including binary neural networks. Both pruning and quantization can be used independently or combined. We compare current techniques, analyze their strengths and weaknesses, present compressed network accuracy results on a number of frameworks, and provide practical guidance for compressing networks.

cs.CV↗

arXiv2021

When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control

Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and the requirement of high model performance, many applications in industry call for building a secure and efficient LR model for multiple parties. Most existing work uses either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they incur potential security risks. SS based methods have provable security, but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security. We then present the distributed implementation of CAESAR for scalability requirement. We have deployed CAESAR in a risk control task and conducted comprehensive experiments. Our experimental results show that CAESAR improves the state-of-the-art model by around 130 times.

cs.CR↗

arXiv2021

Signal Acquisition of Luojia-1A Low Earth Orbit Navigation Augmentation System with Software Defined Receiver

Low earth orbit (LEO) satellite navigation signal can be used as an opportunity signal in case of a Global navigation satellite system (GNSS) outage, or as an enhancement means of traditional GNSS positioning algorithms. No matter which service mode is used, signal acquisition is the prerequisite of providing enhanced LEO navigation service. Compared with the medium orbit satellite, the transit time of the LEO satellite is shorter. Thus, it is of great significance to expand the successful acquisition time range of the LEO signal. Previous studies on LEO signal acquisition are based on simulation data. However, signal acquisition research based on real data is very important. In this work, the signal characteristics of LEO satellite: power space density in free space and the Doppler shift of LEO satellite are individually studied. The unified symbol definitions of several integration algorithms based on the parallel search signal acquisition algorithm are given. To verify these algorithms for LEO signal acquisition, a software-defined receiver (SDR) is developed. The performance of those integration algorithms on expanding the successful acquisition time range is verified by the real data collected from the Luojia-1A satellite. The experimental results show that the integration strategy can expand the successful acquisition time range, and it will not expand indefinitely with the integration duration.

eess.SP↗

arXiv2021

RFCBF: enhance the performance and stability of Fast Correlation-Based Filter

Feature selection is a preprocessing step which plays a crucial role in the domain of machine learning and data mining. Feature selection methods have been shown to be effctive in removing redundant and irrelevant features, improving the learning algorithm's prediction performance. Among the various methods of feature selection based on redundancy, the fast correlation-based filter (FCBF) is one of the most effective. In this paper, we proposed a novel extension of FCBF, called RFCBF, which combines resampling technique to improve classification accuracy. We performed comprehensive experiments to compare the RFCBF with other state-of-the-art feature selection methods using the KNN classifier on 12 publicly available data sets. The experimental results show that the RFCBF algorithm yields significantly better results than previous state-of-the-art methods in terms of classification accuracy and runtime.

cs.LG↗

arXiv2021

Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification

Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in signigicant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gradient boosting (XGBoost) and a multi-objective optimization genetic algorithm (XGBoost-MOGA) for cancer classification in microarray datasets. In the first stage, the genes are ranked use an ensemble-based feature selection using XGBoost. This stage can effectively remove irrelevant genes and yield a group comprising the most relevant genes related to the class. In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes's group using a multi-objective optimization genetic algorithm. We performed comprehensive experiments to compare XGBoost-MOGA with other state-of-the-art feature selection methods using two well-known learning classifiers on 13 publicly available microarray expression datasets. The experimental results show that XGBoost-MOGA yields significantly better results than previous state-of-the-art algorithms in terms of various evaluation criteria, such as accuracy, F-score, precision, and recall.

cs.LG↗

arXiv2021

A Novel Framework Integrating AI Model and Enzymological Experiments Promotes Identification of SARS-CoV-2 3CL Protease Inhibitors and Activity-based Probe

The identification of protein-ligand interaction plays a key role in biochemical research and drug discovery. Although deep learning has recently shown great promise in discovering new drugs, there remains a gap between deep learning-based and experimental approaches. Here we propose a novel framework, named AIMEE, integrating AI Model and Enzymology Experiments, to identify inhibitors against 3CL protease of SARS-CoV-2, which has taken a significant toll on people across the globe. From a bioactive chemical library, we have conducted two rounds of experiments and identified six novel inhibitors with a hit rate of 29.41%, and four of them showed an IC50 value less than 3 μM. Moreover, we explored the interpretability of the central model in AIMEE, mapping the deep learning extracted features to domain knowledge of chemical properties. Based on this knowledge, a commercially available compound was selected and proven to be an activity-based probe of 3CLpro. This work highlights the great potential of combining deep learning models and biochemical experiments for intelligent iteration and expanding the boundaries of drug discovery.

q-bio.MN↗

arXiv2021

Nonlinear localized excitations in a topological ferromagnetic honeycomb lattice

We theoretically investigate nonlinear localized excitations in a Heisenberg honeycomb ferromagnet with a second neighbour Dzialozinskii-Moriya interaction, which has been proved to possess the topological band structure. Using the time-dependent variational principle, we obtain the equation of motion for the system in the Glauber coherent-state representation. By means of the semidiscrete multiple-scale method, different types of nonlinear localized excitations are gotten.Our results show that both two-dimensional discrete breathers and bulk gap solitons may occur in the Heisenberg honeycomb ferromagnet.

cond-mat.mes-hall↗

arXiv2021

Biomedical named entity recognition using BERT in the machine reading comprehension framework

Recognition of biomedical entities from literature is a challenging research focus, which is the foundation for extracting a large amount of biomedical knowledge existing in unstructured texts into structured formats. Using the sequence labeling framework to implement biomedical named entity recognition (BioNER) is currently a conventional method. This method, however, often cannot take full advantage of the semantic information in the dataset, and the performance is not always satisfactory. In this work, instead of treating the BioNER task as a sequence labeling problem, we formulate it as a machine reading comprehension (MRC) problem. This formulation can introduce more prior knowledge utilizing well-designed queries, and no longer need decoding processes such as conditional random fields (CRF). We conduct experiments on six BioNER datasets, and the experimental results demonstrate the effectiveness of our method. Our method achieves state-of-the-art (SOTA) performance on the BC4CHEMD, BC5CDR-Chem, BC5CDR-Disease, NCBI-Disease, BC2GM and JNLPBA datasets, achieving F1-scores of 92.92%, 94.19%, 87.83%, 90.04%, 85.48% and 78.93%, respectively.

cs.CL↗

arXiv2021

Pore-scale investigation on natural convection melting in a square cavity with gradient porous media

In this paper, a numerical study on the melting behavior of phase change material (PCM) with gradient porous media has been carried out at the pore scales. In order to solve the governing equations, a pore-scale lattice Boltzmann method with the double distribution functions is used, in which a volumetric LB scheme is employed to handle the boundary. The Monte Carlo random sampling is adopted to generate a microstructure of two-dimensional gradient foam metal which are then used to simulate the solid-liquid phase transition in the cavity. The effect of several factors, such as gradient porosity structure, gradient direction, Rayleigh number and particle diameters on the liquid fraction of PCM are systematically investigated. It is observed that the presence of gradient media affect significantly the melting rate and shortens full melting time compared to that for constant porosity by enhancing natural convection. The melting time of positive and negative gradients will change with Rayleigh number, and there is a critical value for Rayleigh number. Specifically, when Rayleigh number is below the critical value, the positive gradient is more advantageous, and when Rayleigh number exceeds the critical value, the negative gradient is more advantageous. Moreover, smaller particle diameters would lead to lower permeability and larger internal surfaces for heat transfer.

physics.flu-dyn↗

arXiv2021

Numerical study on electrohydrodynamic enhancement of PCM melting in cylindrical annulus under microgravity

Latent heat thermal energy storage (LHTES) has been recommended as an effective technology to the thermal management system of space exploration for its excellent ability of storing thermal energy. However, it is well known that the low thermal conductivity of phase change material (PCM) seriously weakens the heat charging and discharging rates of LHTES system. In present study, the electrohydrodynamic (EHD), which is a popular active heat transfer enhancement technology, is introduced to enhance the PCM melting in a shell-tube LHTES system under microgravity. In our numerical simulations, we mainly focus on the combined effects of the electric Rayleigh number $T$ and the eccentricity $Γ$ on the melting performance under microgravity. Based on the numerical results, it is found that in contrast to the case without the electric field, the presence of the electric field causes the heat transfer efficiency and melting behavior of LHTES system to be enhanced significantly. In addition, our results indicates that the EHD technique always shows a good performance in accelerating the melting process even under microgravity, and specially, the maximum time saving in some cases is more than $90\%$. Furthermore, we note that although the concentric annulus is always the optimal configuration under no-gravity condition, the optimal eccentric position of the internal tube strongly depends on the electric Rayleigh number if the gravity effects are taken into account.

physics.flu-dyn↗

arXiv2021

Giant Crystal Hall Effect in Collinear Antiferromagnetic $γ$-FeMn

The spontaneous Hall effect is usually governed by three conventional mechanisms, such as the Berry curvature, skew scattering and side jump, which widely exist in ferromagnetic or antiferromagnetic materials. However, in this work, based on first principle calculations, we predict a giant crystal Hall effect (CHE) in the antiferromagnetic $γ$-FeMn, which can not be understood by the previous three conventional mechanisms and the Hall angle therein can be as large as 18.4% at low temperature. Furthermore, with Boltzmann transport equation and a tight-binding model, we conclude that, the asymmetric group velocities on Fermi surface is the origin of this CHE in $γ$-FeMn. And with a systematic symmetry argument, we show that, this unusual effect is not dependent on specific materials but universal in any crystals with similar symmetry even without local magnetization.

cond-mat.mes-hall↗

arXiv2021

Lepton-specific inert two-Higgs-doublet model confronted with the new results for muon and electron g-2 anomalies and multi-lepton searches at the LHC

Combining the multi-lepton searches at the LHC, we study the possibilities of accommodating the new data of muon and electron $g-2$ anomalies in the lepton-specific inert two-Higgs-doublet model. We take the heavy CP-even Higgs as the 125 GeV Higgs, and find the muon and electron $g-2$ anomalies can be explained simultaneously in the region of 5 GeV $< m_h<60$ GeV, 200 GeV $<m_A< 620$ GeV, 190 GeV $<m_{H^\pm}< 620$ GeV for appropriate Yukawa couplings between leptons and inert Higgs. Meanwhile, the model can give a better fit to the data of lepton universality in $τ$ decays than the SM. Further, the multi-lepton event searches at the LHC impose a stringent upper bound on $m_h$, $m_h<$ 35 GeV.

hep-ph↗

arXiv2021

muon $g-2$ anomaly and $μ$-$τ$-philic Higgs doublet with a light CP-even component

We examine the possibilities of accommodating the muon $g-2$ anomaly reported by Fermilab in the 2HDM with a discrete $Z_4$ symmetry in which an inert Higgs doublet field ($H,~A,~H^\pm$) has the lepton flavor violation $μ$-$τ$ interactions. We study the case of light $H$ (5 GeV $<m_H<$ 115 GeV) and assume the Yukawa matrices to be real and symmetrical. Considering relevant theoretical and experimental constraints, especially for the multi-lepton searches at the LHC, we find the muon $g-2$ anomaly can be explained within $2σ$ range in the region of 5 GeV $<m_H<20$ GeV, 130 GeV $< m_A~(m_{H^\pm})<$ 610 GeV, and 0.005 $<ρ<$ 0.014. Meanwhile, the $χ^2_τ$ fitting the data of lepton flavour universality in the $τ$ decays approaches to the SM prediction.

hep-ph↗

arXiv2021

A $μ$-$τ$-philic Higgs doublet confronted with the muon g-2, $τ$ decays and LHC data

We propose a two-Higgs-doublet model in which one Higgs doublet has the same interactions with fermions as the SM, and another Higgs doublet only has the $μ$-$τ$ LFV interactions. Assuming that the Yukawa matrices are real and symmetric, we impose various relevant theoretical and experimental constraints, and find that the excesses of muon $g-2$ and lepton flavour universality in the $τ$ decays can be simultaneously explained in the region of small mass splittings between the heavy CP-even Higgs and the CP-odd Higgs ($m_A > m_H$). The multi-lepton event searches at the LHC can sizably reduce the mass ranges of extra Higgses, and $m_H$ is required to be larger than 560 GeV.

hep-ph↗

arXiv2021

Divergent operator with degeneracy and related sharp inequalities

In this paper we classify all positive extremal functions to a sharp weighted Sobolev inequality on the upper half space, which involves divergent operators with degeneracy on the boundary. As an application of the results, we can derive a sharp Sobolev type inequality involving Baouendi-Grushin operator, and classify certain extremal functions for all $τ>0$ and $m\ne2 $ or $ n\ne1$.

math.AP↗

arXiv2021

Exploring the Collective Phenomenon at the Electron-Ion Collider

Based on rare fluctuations in strong interactions, we argue that there is a strong physical resemblance between the high multiplicity events in photo-nuclear collisions and those in $pA$ collisions, in which interesting long range collective phenomena are discovered. This indicates that the collectivity can also be studied in certain kinematic region of the upcoming Electron-Ion Collider (EIC) where the incoming virtual photon has a sufficiently long lifetime. Using a model in the Color Glass Condensate formalism, we first show that the initial state interactions can explain the recent ATLAS azimuthal correlation results measured in the photo-nuclear collisions, and then we provide quantitative predictions for the long range correlations in $eA$ collisions in the EIC regime. With the unprecedented precision and the ability to change the size of the collisional system, the high luminosity EIC will open a new window to explore the physical mechanism responsible for the collective phenomenon.

hep-ph↗

arXiv2021

DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation

Neural Architecture Search (NAS) has shown great potentials in automatically designing scalable network architectures for dense image predictions. However, existing NAS algorithms usually compromise on restricted search space and search on proxy task to meet the achievable computational demands. To allow as wide as possible network architectures and avoid the gap between target and proxy dataset, we propose a Densely Connected NAS (DCNAS) framework, which directly searches the optimal network structures for the multi-scale representations of visual information, over a large-scale target dataset. Specifically, by connecting cells with each other using learnable weights, we introduce a densely connected search space to cover an abundance of mainstream network designs. Moreover, by combining both path-level and channel-level sampling strategies, we design a fusion module to reduce the memory consumption of ample search space. We demonstrate that the architecture obtained from our DCNAS algorithm achieves state-of-the-art performances on public semantic image segmentation benchmarks, including 84.3% on Cityscapes, and 86.9% on PASCAL VOC 2012. We also retain leading performances when evaluating the architecture on the more challenging ADE20K and Pascal Context dataset.

cs.CV↗