arXiv subjects

Wei Li

Publications and source records attributed to Wei Li.

At least 919 records · Page 51Linked to original sources

arXiv2022

A Creativity Survey of Parallel Sorting Algorithm

Sorting is one of the most fundamental problems in the field of computer science. With the rapid development of manycore processors, it shows great importance to design efficient parallel sort algorithm on manycore architecture. This paper studies the parallel memory sorting method on modern hardware, and summarizes its research status and progress. Classify the research problems, research methods and measurement methods of the target papers and references. In the end, we summarize all the researches and list the directions not researched and innovative places. Keywords: Sorting Algorithm, Parallel Algorithm, Parallel Optimization, CPU, GPU, Memory Hierarchy

cs.DC↗

arXiv2022

Toroidal and Elliptic Quiver BPS Algebras and Beyond

The quiver Yangian, an infinite-dimensional algebra introduced recently in arXiv:2003.08909, is the algebra underlying BPS state counting problems for toric Calabi-Yau three-folds. We introduce trigonometric and elliptic analogues of quiver Yangians, which we call toroidal quiver algebras and elliptic quiver algebras, respectively. We construct the representations of the shifted toroidal and elliptic algebras in terms of the statistical model of crystal melting. We also derive the algebras and their representations from equivariant localization of three-dimensional $\mathcal{N}=2$ supersymmetric quiver gauge theories, and their dimensionally-reduced counterparts. The analysis of supersymmetric gauge theories suggests that there exist even richer classes of algebras associated with higher-genus Riemann surfaces and generalized cohomology theories.

hep-th↗

arXiv2022

TONet: Tone-Octave Network for Singing Melody Extraction from Polyphonic Music

Singing melody extraction is an important problem in the field of music information retrieval. Existing methods typically rely on frequency-domain representations to estimate the sung frequencies. However, this design does not lead to human-level performance in the perception of melody information for both tone (pitch-class) and octave. In this paper, we propose TONet, a plug-and-play model that improves both tone and octave perceptions by leveraging a novel input representation and a novel network architecture. First, we present an improved input representation, the Tone-CFP, that explicitly groups harmonics via a rearrangement of frequency-bins. Second, we introduce an encoder-decoder architecture that is designed to obtain a salience feature map, a tone feature map, and an octave feature map. Third, we propose a tone-octave fusion mechanism to improve the final salience feature map. Experiments are done to verify the capability of TONet with various baseline backbone models. Our results show that tone-octave fusion with Tone-CFP can significantly improve the singing voice extraction performance across various datasets -- with substantial gains in octave and tone accuracy.

eess.AS↗

arXiv2022

A quasi steady-state measurement of exciton diffusion lengths in organic semiconductors

Understanding the role that exciton diffusion plays in organic solar cells is a crucial to understanding the recent rise in power conversion effciencies brought about by non-fullerene acceptors (NFA). Established methods for measuring exciton diffusion lengths in organic solar cells require specialized equipment designed for measuring high-resolution time-resolved photoluminescence (TRPL). Here we introduce a technique, coined pulsed-PLQY, to measure the diffusion length of organic solar cells without any temporal measurements. Using a Monte-Carlo model we simulate the dynamics within a thin film semiconductor and analyse the results using both pulsed-PLQY and TRPL methods. We find that pulsed-PLQY has a larger operational region and depends less on the excitation fuence than the TRPL approach. We validate these simulated results by preforming both measurements on organic thin films and reproduce the predicted trends. Pulsed-PLQY is then used to evaluate the diffusion length in a variety of technologically relevant organic semiconductors. It is found that the diffusion lengths in NFA's are much larger than in the benchmark fullerene and that this increase is driven by an increase in diffusivity.

physics.app-ph↗

arXiv2022

A Joint Morphological Profiles and Patch Tensor Change Detection for Hyperspectral Imagery

Multi-temporal hyperspectral images can be used to detect changed information, which has gradually attracted researchers' attention. However, traditional change detection algorithms have not deeply explored the relevance of spatial and spectral changed features, which leads to low detection accuracy. To better excavate both spectral and spatial information of changed features, a joint morphology and patch-tensor change detection (JMPT) method is proposed. Initially, a patch-based tensor strategy is adopted to exploit similar property of spatial structure, where the non-overlapping local patch image is reshaped into a new tensor cube, and then three-order Tucker decompositon and image reconstruction strategies are adopted to obtain more robust multi-temporal hyperspectral datasets. Meanwhile, multiple morphological profiles including max-tree and min-tree are applied to extract different attributes of multi-temporal images. Finally, these results are fused to general a final change detection map. Experiments conducted on two real hyperspectral datasets demonstrate that the proposed detector achieves better detection performance.

cs.CV↗

arXiv2022

Z$_2$ nontrivial topology of rare-earth binary oxide superconductor

Recently, superconductivity has been discovered in rock-salt structured binary lanthanum monoxide LaO through state-of-the-art oxide thin-film epitaxy. In this work, we reveal that the normal state of superconducting LaO is a $Z_2$ nontrivial topological metal, where the Dirac point protected by the crystal symmetry is located around the Fermi energy. By analysing the orbital characteristics, we show that the nature of the topological band structure of LaO originates from the intra-atomic transition from the outer shell La 5$d$ to the inner shell 4$f$ orbitals driven by the strong octahedral crystal-field. Furthermore, the appearance of novel surface states unambiguously demonstrates the topological signature of LaO superconductor. Our theoretical findings not only shed new light into the understanding of the exotic quantum behaviors in LaO superconductor with intimate correlation between 4$f$ and 5$d$ orbitals in La, but also provide an exciting platform to explore the interplay between nontrivial topology and superconductivity.

cond-mat.supr-con↗

arXiv2022

DDU-Net: Dual-Decoder-U-Net for Road Extraction Using High-Resolution Remote Sensing Images

Extracting roads from high-resolution remote sensing images (HRSIs) is vital in a wide variety of applications, such as autonomous driving, path planning, and road navigation. Due to the long and thin shape as well as the shades induced by vegetation and buildings, small-sized roads are more difficult to discern. In order to improve the reliability and accuracy of small-sized road extraction when roads of multiple sizes coexist in an HRSI, an enhanced deep neural network model termed Dual-Decoder-U-Net (DDU-Net) is proposed in this paper. Motivated by the U-Net model, a small decoder is added to form a dual-decoder structure for more detailed features. In addition, we introduce the dilated convolution attention module (DCAM) between the encoder and decoders to increase the receptive field as well as to distill multi-scale features through cascading dilated convolution and global average pooling. The convolutional block attention module (CBAM) is also embedded in the parallel dilated convolution and pooling branches to capture more attention-aware features. Extensive experiments are conducted on the Massachusetts Roads dataset with experimental results showing that the proposed model outperforms the state-of-the-art DenseUNet, DeepLabv3+ and D-LinkNet by 6.5%, 3.3%, and 2.1% in the mean Intersection over Union (mIoU), and by 4%, 4.8%, and 3.1% in the F1 score, respectively. Both ablation and heatmap analyses are presented to validate the effectiveness of the proposed model.

cs.CV↗

arXiv2022

Evolution from helical to collinear ferromagnetic order of the Eu$^{2+}$ spins in RbEu(Fe$_{1-x}$Ni$_{x}$)$_{4}$As$_{4}$

The ground-state magnetic structures of the Eu$^{2+}$ spins in recently discovered RbEu(Fe$_{1-x}$Ni$_{x}$)$_{4}$As$_{4}$ superconductors have been investigated by neutron powder diffraction measurements. It is found that as the superconductivity gets suppressed with the increase of Ni doping, the magnetic propagation vector of the Eu sublattice diminishes, corresponding to the decrease of the rotation angle between the moments in neighboring Eu layers. The ferromagnetic Eu layers are helically modulated along the $\mathit{c}$ axis with an incommensurate magnetic propagation vector in both the ferromagnetic superconductor RbEu(Fe$_{0.95}$Ni$_{0.05}$)$_{4}$As$_{4}$ and the superconducting ferromagnet RbEu(Fe$_{0.93}$Ni$_{0.07}$)$_{4}$As$_{4}$. Such a helical structure transforms into a purely collinear ferromagnetic structure for non-superconducting RbEu(Fe$_{0.91}$Ni$_{0.09}$)$_{4}$As$_{4}$, with all the Eu$^{2+}$ spins lying along the tetragonal (1 1 0) direction. The evolution from helical to collinear ferromagnetic order of the Eu$^{2+}$ spins with increasing Ni doping is supported by first-principles calculations. The variation of the rotation angle between adjacent Eu$^{2+}$ layers can be well explained by considering the change of magnetic exchange couplings mediated by the indirect Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction.

cond-mat.str-el↗

arXiv2022

Generative adversarial network for super-resolution imaging through a fiber

A multimode fiber represents the ultimate limit in miniaturization of imaging endoscopes. Here we propose a fiber imaging approach employing compressive sensing with a data-driven machine learning framework. We implement a generative adversarial network for image reconstruction without relying on a sample sparsity constraint. The proposed method outperforms the conventional compressive imaging algorithms in terms of image quality and noise robustness. We experimentally demonstrate speckle-based imaging below the diffraction limit at a sub-Nyquist speed through a multimode fiber.

eess.IV↗

arXiv2022

A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection

Recently, many arbitrary-oriented object detection (AOOD) methods have been proposed and attracted widespread attention in many fields. However, most of them are based on anchor-boxes or standard Gaussian heatmaps. Such label assignment strategy may not only fail to reflect the shape and direction characteristics of arbitrary-oriented objects, but also have high parameter-tuning efforts. In this paper, a novel AOOD method called General Gaussian Heatmap Label Assignment (GGHL) is proposed. Specifically, an anchor-free object-adaptation label assignment (OLA) strategy is presented to define the positive candidates based on two-dimensional (2-D) oriented Gaussian heatmaps, which reflect the shape and direction features of arbitrary-oriented objects. Based on OLA, an oriented-bounding-box (OBB) representation component (ORC) is developed to indicate OBBs and adjust the Gaussian center prior weights to fit the characteristics of different objects adaptively through neural network learning. Moreover, a joint-optimization loss (JOL) with area normalization and dynamic confidence weighting is designed to refine the misalign optimal results of different subtasks. Extensive experiments on public datasets demonstrate that the proposed GGHL improves the AOOD performance with low parameter-tuning and time costs. Furthermore, it is generally applicable to most AOOD methods to improve their performance including lightweight models on embedded platforms.

cs.CV↗

arXiv2021

An Intelligent Self-driving Truck System For Highway Transportation

Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing works mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this paper, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real-world deployment, 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which shows our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS

cs.RO↗

arXiv2021

A study on the effect of input data length on deep learning based magnitude classifier

The rapid characterisation of earthquake parameters such as its magnitude is at the heart of Earthquake Early Warning (EEW). In traditional EEW methods the robustness in the estimation of earthquake parameters have been observed to increase with the length of input data. Since time is a crucial factor in EEW applications, in this paper we propose a deep learning based magnitude classifier and, further we investigate the effect of using five different durations of seismic waveform data after first P wave arrival of length 1s, 3s, 10s, 20s and 30s. This is accomplished by testing the performance of the proposed model that combines Convolution and Bidirectional Long-Short Term Memory units to classify waveforms based on their magnitude into three classes "noise", "low magnitude events" and "high magnitude events". Herein, any earthquake signal with magnitude equal to or above 5.0 is labelled as high magnitude. We show that the variation in the results produced by changing the length of the data, is no more than the inherent randomness in the trained models, due to their initialisation.

physics.geo-ph↗

arXiv2021

Quantifying the Excitonic Static Disorder in Organic Semiconductors

Organic semiconductors are disordered molecular solids and as a result, their internal charge dynamics and ultimately, the performance of the optoelectronic devices they constitute, are governed by energetic disorder. To ascertain how energetic disorder impacts charge generation, exciton transport, charge transport, and the performance of organic semiconductor devices, an accurate approach is first required to measure this critical parameter. In this work, we show that the static disorder has no relation with the so-called Urbach energy in organic semiconductors. Instead, it can be obtained from photovoltaic external quantum efficiency spectra at wavelengths near the absorption onset. We then present a detailed methodology, alongside a computational framework, for quantifying the static energetic disorder associated with singlet excitons. Moreover, the role of optical interference in this analysis is considered to achieve a high-accuracy quantification. Finally, the excitonic static disorder was quantified in several technologically-relevant donor-acceptor blends, including high-efficiency PM6:Y6.

physics.app-ph↗

arXiv2021

Laser-induced periodic surface structured electrodes with a 45 % energy saving in electrochemical fuel generation through field localization

Electrochemical oxidation-reduction of radicals is a green and environmentally friendly approach to generating fuels. These reactions, however, suffer from sluggish kinetics due to a low local concentration of radicals around the electrocatalyst. A large electrode potential can enhance the fuel generation efficiency via enhancing the radical concentration around the electrocatalyst sites, but this comes at the cost of electricity. Here, we report about a 45 percent saving in energy to achieve an electrochemical hydrogen generation rate of 10 mA per cm2 through localized electric field-induced enhancement in the reagent concentration (LEFIRC) at laser-induced periodic surface structured (LIPSS) electrodes. The finite element model is used to simulate the spatial distribution of the electric field to understand the effects of LIPSS geometric parameters in field localization. When the LIPSS patterned electrodes are used as substrates to support Pt-C and RuO2 electrocatalysts, the overpotentials for HER and OER are decreased by 40 and 25 percent, respectively. Moreover, the capability of the LIPSS-patterned electrodes to operate at significantly reduced energy is also demonstrated in a range of electrolytes including alkaline, acidic, neutral, and seawater. Importantly, when two LIPSS patterned electrodes were assembled as the anode and cathode into a cell, it requires 330 mVs of lower electric potential with enhanced stability over a similar cell made of pristine electrodes to drive a current density of 10 mA/cm2. This work demonstrates a physical and versatile approach of electrode surface patterning to boost electrocatalytic fuel generation performance and can be applied to any metal and semiconductor catalysts for a range of electrochemical reactions.

physics.chem-ph↗

arXiv2021

Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting

We introduce a three stage pipeline: resized-diverse-inputs (RDIM), diversity-ensemble (DEM) and region fitting, that work together to generate transferable adversarial examples. We first explore the internal relationship between existing attacks, and propose RDIM that is capable of exploiting this relationship. Then we propose DEM, the multi-scale version of RDIM, to generate multi-scale gradients. After the first two steps we transform value fitting into region fitting across iterations. RDIM and region fitting do not require extra running time and these three steps can be well integrated into other attacks. Our best attack fools six black-box defenses with a 93% success rate on average, which is higher than the state-of-the-art gradient-based attacks. Besides, we rethink existing attacks rather than simply stacking new methods on the old ones to get better performance. It is expected that our findings will serve as the beginning of exploring the internal relationship between attack methods. Codes are available at https://github.com/278287847/DEM.

cs.CV↗

arXiv2021

In-orbit timing calibration of the Insight-Hard X-ray Modulation Telescope

We describe the timing system and the timing calibration results of the three payloads on-board the Insight-Hard X-ray Modulation Telescope (Insight-HXMT). These three payloads are the High Energy X-ray telescope (HE, 20-250 keV), the Medium Energy X-ray telescope (ME, 5-30 keV) and the low Energy X-ray telescope (LE, 1-10 keV). We present a method to correct the temperature-dependent period response and the long-term variation of the on-board crystal oscillator, especially for ME that does not carry a temperature-compensated crystal oscillator. The time of arrivals (ToAs) of the Crab pulsar are measured to evaluate the accuracy of the timing system. As the ephemeris of the Crab pulsar given by Jodrell Bank observatory has systematic errors around 40 μs (Rots et al. 2014), we use the quasi-simultaneous observations of the X-ray Timing Instrument (XTI) on-board the Neutron star Interior Composition Explorer (NICER) to produce the Crab ephemerides and to verify the timing system of Insight-HXMT. The energy-dependent ToAs' offsets relative to the NICER measurements including physical and instrumental origins are about 24.7μs, 10.1μs and 864.7μs, and the systematic errors of the timing system are determined as 12.1μs, 8.6μs, and 15.8μs, for HE, ME and LE respectively.

astro-ph.HE↗

arXiv2021

Polarized exciton emission enhancement of monolayer MoS2 coupled with plasmonic Salisbury-type absorber

The plasmon-mediated manipulation of light-matter interaction in two-dimensional atomically transition-metal dichalcogenides (TMDs) critically depends on the design of plasmonic nanostructures to achieve the maximum optical field in TMDs. Here, a metal-isolator-metal Salisbury-type perfect absorber was fabricated to serve as a generator of the localized surface plasmons. The significant photoluminescence (PL) enhancement up to 60-fold was observed experimentally in the monolayer (ML) MoS2 on the top of this gold plasmonic hybrid nanostructures. Furthermore, the PL linear polarization can approach ~60 % around the peak of exciton emission and is independent on the polarization of the excitation laser. This Salisbury-type plasmon-exciton hybrid system paves a new way to develop optoelectronic devices based on TMDs.

physics.optics↗

arXiv2021

Variational Autoencoder with CCA for Audio-Visual Cross-Modal Retrieval

Cross-modal retrieval is to utilize one modality as a query to retrieve data from another modality, which has become a popular topic in information retrieval, machine learning, and database. How to effectively measure the similarity between different modality data is the major challenge of cross-modal retrieval. Although several reasearch works have calculated the correlation between different modality data via learning a common subspace representation, the encoder's ability to extract features from multi-modal information is not satisfactory. In this paper, we present a novel variational autoencoder (VAE) architecture for audio-visual cross-modal retrieval, by learning paired audio-visual correlation embedding and category correlation embedding as constraints to reinforce the mutuality of audio-visual information. On the one hand, audio encoder and visual encoder separately encode audio data and visual data into two different latent spaces. Further, two mutual latent spaces are respectively constructed by canonical correlation analysis (CCA). On the other hand, probabilistic modeling methods is used to deal with possible noise and missing information in the data. Additionally, in this way, the cross-modal discrepancy from intra-modal and inter-modal information are simultaneously eliminated in the joint embedding subspace. We conduct extensive experiments over two benchmark datasets. The experimental outcomes exhibit that the proposed architecture is effective in learning audio-visual correlation and is appreciably better than the existing cross-modal retrieval methods.

cs.IR↗