arXiv subjects

Wei Li

Publications and source records attributed to Wei Li.

At least 955 records · Page 53Linked to original sources

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↗

arXiv2021

Ferroelectric materials and their applications in green catalysis

The demand for renewable and environmentally friendly energy source has attracted extensive research on high performance catalysts. Ferroelectrics which are a class of materials with a switchable polarization are the unique and promising catalyst candidates due to the significant effects of polarization on surfaces physical and chemical properties. The band bending at the ferroelectric/semiconductor interface induced by the polarization flip promotes the charge separation and transfer, there by enhancing the photocatalytic performance. More importantly, the reactants can be selectively adsorbed on the surface of the ferroelectric materials depending on the polarization direction, which can effectively lift the basic limitations as imposed by Sabatier principle on catalytic activity. This review summarizes the latest developments of ferroelectric materials, and introduces the ferroelectric-related catalytic application. The possible research directions of 2D ferroelectric materials in chemical catalysis is discussed at the end. The review is expected to inspire the extensive research interests from physical, chemical and material science communities.

cond-mat.mtrl-sci↗

arXiv2021

Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Most existing UDA approaches enable knowledge transfer via learning domain-invariant representation and sharing one classifier across two domains. However, ignoring the domain-specific information that are related to the task, and forcing a unified classifier to fit both domains will limit the feature expressiveness in each domain. In this paper, by observing that the Transformer architecture with comparable parameters can generate more transferable representations than CNN counterparts, we propose a Win-Win TRansformer framework (WinTR) that separately explores the domain-specific knowledge for each domain and meanwhile interchanges cross-domain knowledge. Specifically, we learn two different mappings using two individual classification tokens in the Transformer, and design for each one a domain-specific classifier. The cross-domain knowledge is transferred via source guided label refinement and single-sided feature alignment with respect to source or target, which keeps the integrity of domain-specific information. Extensive experiments on three benchmark datasets show that our method outperforms the state-of-the-art UDA methods, validating the effectiveness of exploiting both domain-specific and invariant

cs.CV↗

arXiv2021

Spin-Triplet Pairing Induced by Near-Neighbor Attraction in the Cuprate Chain

In quantum materials, the electronic interaction and the electron-phonon coupling are, in general, two essential ingredients, the combined impact of which may drive exotic phases. Recently, an anomalously strong electron-electron attraction, mediated by phonons, has been unveiled in one-dimensional copper-oxide chain Ba$_{2-x}$Sr$_x$CuO$_{3+δ}$. Yet, it is unclear how this strong near-neighbor attraction $V$ influences the superconductivity pairing in the compound. Here we perform accurate many-body calculations to study the extended Hubbard model with on-site Coulomb repulsion $U>0$ and attraction $V<0$ that well describes the cuprate chain and likely other similar transition-metal materials with both strong correlations and lattice effects. We find a rich quantum phase diagram containing an intriguing Tomonaga-Luttinger liquid phase -- besides the spin density wave and various phase separation phases -- that can host dominant spin-triplet pairing correlations and divergent superconductive susceptibility. Upon doping, the spin-triplet superconducting regime can be further broadened in the parameter space and extends to larger $U$, offering a feasible mechanism to realize $p$-wave superconductivity in realistic cuprate chains.

cond-mat.str-el↗

arXiv2021

FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Unsupervised anomaly detection and localization is crucial to the practical application when collecting and labeling sufficient anomaly data is infeasible. Most existing representation-based approaches extract normal image features with a deep convolutional neural network and characterize the corresponding distribution through non-parametric distribution estimation methods. The anomaly score is calculated by measuring the distance between the feature of the test image and the estimated distribution. However, current methods can not effectively map image features to a tractable base distribution and ignore the relationship between local and global features which are important to identify anomalies. To this end, we propose FastFlow implemented with 2D normalizing flows and use it as the probability distribution estimator. Our FastFlow can be used as a plug-in module with arbitrary deep feature extractors such as ResNet and vision transformer for unsupervised anomaly detection and localization. In training phase, FastFlow learns to transform the input visual feature into a tractable distribution and obtains the likelihood to recognize anomalies in inference phase. Extensive experimental results on the MVTec AD dataset show that FastFlow surpasses previous state-of-the-art methods in terms of accuracy and inference efficiency with various backbone networks. Our approach achieves 99.4% AUC in anomaly detection with high inference efficiency.

cs.CV↗

arXiv2021

AWESAM: A Python Module for Automated Volcanic Event Detection Applied to Stromboli

Many active volcanoes in the world exhibit Strombolian activity, which is typically characterized by relatively frequent mild events and also by rare and much more destructive major explosions and paroxysms. Detailed analyses of past major and minor events can help to understand the eruptive behavior of the volcano and the underlying physical and chemical processes. Catalogs of volcanic eruptions may be established using continuous seismic recordings at stations in the proximity of volcanoes. However, in many cases, the analysis of the recordings relies heavily on the manual picking of events by human experts. Recently developed Machine Learning-based approaches require large training data sets which may not be available a priori. Here, we propose an alternative automated approach: the Adaptive-Window Volcanic Event Selection Analysis Module (AWESAM). This process of creating event catalogs consists of three main steps: (i) identification of potential volcanic events based on squared ground-velocity amplitudes, an adaptive MaxFilter, and a prominence threshold. (ii) catalog consolidation by comparing and verification of the initial detections based on recordings from two different seismic stations. (iii) identification and exclusion of signals from regional tectonic earthquakes. The software package is applied to publicly accessible continuous seismic recordings from two almost equidistant stations at Stromboli volcano in Italy. We tested AWESAM by comparison with a hand-picked catalog and found that around 95 percent of the eruptions with a signal-to-noise ratio above three are detected. In a first application, we derive a new amplitude-frequency relationship from over 290.000 volcanic events at Stromboli during 2019-2020. The module allows for a straightforward generalization and application to other volcanoes worldwide.

physics.geo-ph↗

arXiv2021

Acceleration of DNA Replication of Klenow Fragment by Small Resisting Force

DNA polymerases are an essential class of enzymes or molecular motors that catalyze processive DNA syntheses during DNA replications. A critical issue for DNA polymerases is their molecular mechanism of processive DNA replication. We have previously proposed a model for chemomechanical coupling of DNA polymerases, based on which the predicted results have been provided about the dependence of DNA replication velocity upon the external force on Klenow fragment of DNA polymerase I. Here, we performed single molecule measurements of the replication velocity of Klenow fragment under the external force by using magnetic tweezers. The single molecule data verified quantitatively the previous theoretical predictions, which is critical to the chemomechanical coupling mechanism of DNA polymerases. A prominent characteristic for the Klenow fragment is that the replication velocity is independent of the assisting force whereas the velocity increases largely with the increase of the resisting force, attains the maximum velocity at about 3.8 pN and then decreases with the further increase of the resisting force.

cond-mat.soft↗

arXiv2021

SgSum: Transforming Multi-document Summarization into Sub-graph Selection

Most of existing extractive multi-document summarization (MDS) methods score each sentence individually and extract salient sentences one by one to compose a summary, which have two main drawbacks: (1) neglecting both the intra and cross-document relations between sentences; (2) neglecting the coherence and conciseness of the whole summary. In this paper, we propose a novel MDS framework (SgSum) to formulate the MDS task as a sub-graph selection problem, in which source documents are regarded as a relation graph of sentences (e.g., similarity graph or discourse graph) and the candidate summaries are its sub-graphs. Instead of selecting salient sentences, SgSum selects a salient sub-graph from the relation graph as the summary. Comparing with traditional methods, our method has two main advantages: (1) the relations between sentences are captured by modeling both the graph structure of the whole document set and the candidate sub-graphs; (2) directly outputs an integrate summary in the form of sub-graph which is more informative and coherent. Extensive experiments on MultiNews and DUC datasets show that our proposed method brings substantial improvements over several strong baselines. Human evaluation results also demonstrate that our model can produce significantly more coherent and informative summaries compared with traditional MDS methods. Moreover, the proposed architecture has strong transfer ability from single to multi-document input, which can reduce the resource bottleneck in MDS tasks. Our code and results are available at: \url{https://github.com/PaddlePaddle/Research/tree/master/NLP/EMNLP2021-SgSum}.

cs.CL↗

arXiv2021

Deep Learning for UAV-based Object Detection and Tracking: A Survey

Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, traffic management. This paper provides a comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods. More specifically, we first outline the challenges, statistics of existing methods, and provide solutions from the perspectives of DL-based models in three research topics: object detection from the image, object detection from the video, and object tracking from the video. Open datasets related to UAV-dominated object detection and tracking are exhausted, and four benchmark datasets are employed for performance evaluation using some state-of-the-art methods. Finally, prospects and considerations for the future work are discussed and summarized. It is expected that this survey can facilitate those researchers who come from remote sensing field with an overview of DL-based UAV object detection and tracking methods, along with some thoughts on their further developments.

cs.CV↗

arXiv2021

MST: Masked Self-Supervised Transformer for Visual Representation

Transformer has been widely used for self-supervised pre-training in Natural Language Processing (NLP) and achieved great success. However, it has not been fully explored in visual self-supervised learning. Meanwhile, previous methods only consider the high-level feature and learning representation from a global perspective, which may fail to transfer to the downstream dense prediction tasks focusing on local features. In this paper, we present a novel Masked Self-supervised Transformer approach named MST, which can explicitly capture the local context of an image while preserving the global semantic information. Specifically, inspired by the Masked Language Modeling (MLM) in NLP, we propose a masked token strategy based on the multi-head self-attention map, which dynamically masks some tokens of local patches without damaging the crucial structure for self-supervised learning. More importantly, the masked tokens together with the remaining tokens are further recovered by a global image decoder, which preserves the spatial information of the image and is more friendly to the downstream dense prediction tasks. The experiments on multiple datasets demonstrate the effectiveness and generality of the proposed method. For instance, MST achieves Top-1 accuracy of 76.9% with DeiT-S only using 300-epoch pre-training by linear evaluation, which outperforms supervised methods with the same epoch by 0.4% and its comparable variant DINO by 1.0\%. For dense prediction tasks, MST also achieves 42.7% mAP on MS COCO object detection and 74.04% mIoU on Cityscapes segmentation only with 100-epoch pre-training.

cs.CV↗

arXiv2021

Learning UI Navigation through Demonstrations composed of Macro Actions

We have developed a framework to reliably build agents capable of UI navigation. The state space is simplified from raw-pixels to a set of UI elements extracted from screen understanding, such as OCR and icon detection. The action space is restricted to the UI elements plus a few global actions. Actions can be customized for tasks and each action is a sequence of basic operations conditioned on status checks. With such a design, we are able to train DQfD and BC agents with a small number of demonstration episodes. We propose demo augmentation that significantly reduces the required number of human demonstrations. We made a customization of DQfD to allow demos collected on screenshots to facilitate the demo coverage of rare cases. Demos are only collected for the failed cases during the evaluation of the previous version of the agent. With 10s of iterations looping over evaluation, demo collection, and training, the agent reaches a 98.7\% success rate on the search task in an environment of 80+ apps and websites where initial states and viewing parameters are randomized.

cs.AI↗

arXiv2021

Detecting Confined and Deconfined Spinons in Dynamical Quantum Simulations

Dynamical spin-structure factor (DSF) contains fingerprint information of collective excitations in interacting quantum spin systems. In solid state experiments, DSF can be measured through neutron scatterings. However, it is in general challenging to compute the spectral properties accurately via many-body simulations. Currently, quantum simulation and computation constitute a thriving research field, which are believed to provide a very promising platform for simulating quantum many-body systems. In this work, we establish a link between the many-body dynamics and quantum simulations by studying the non-equilibrium DSF (nDSF) measured on direct product states, which are accessible in contemporary quantum simulators with Rydberg atoms, superconducting qubits, etc. Based on the many-body calculations of transverse field Ising chains, we find the nDSF can be used to sensitively probe the multi-spinon continua associated with the two-spinon creation and the spinon-antispinon process, etc. Moreover, we further demonstrate that the low-energy spinons can be confined -- forming spinon bound states -- under a finite longitudinal field. Our results pave the way of quantum simulation and manipulation of fractional excitations in highly-entangled quantum many-body systems.

quant-ph↗

arXiv2021

$SL(3,\mathbb{Z})$ Modularity and New Cardy Limits of the $\mathcal{N}=4$ Superconformal Index

The entropy of $1/16$-th BPS AdS$_5$ black holes can be microscopically accounted for by the superconformal index of the $\mathcal{N}=4$ super-Yang-Mills theory. One way to compute this is through a Cardy-like limit of a formula for the index obtained in [1] using the "$S$-transformation" of the elliptic $Γ$ function. In this paper, we derive more general $SL(3,\mathbb{Z})$ modular properties of the elliptic $Γ$ function. We then use these properties to obtain a three integer parameter family of generalized Cardy-like limits of the $\mathcal{N}=4$ superconformal index. From these limits, we obtain entropy formulae that have a similar form as that of the original AdS$_5$ black hole, up to an overall rescaling of the entropy. We interpret this both on the field theory and the gravitational side. Finally, we comment on how our work suggests a generalization of the Farey tail to four dimensions.

hep-th↗

arXiv2021

The optimal positive operator-valued measure for state discrimination

Evaluating the amount of information obtained from non-orthogonal quantum states is an important topic in the field of quantum information. The commonly used evaluation method is Holevo bound, which only provides a loose upper bound for quantum measurement. In this paper, we provide a theoretical study of the positive operator-valued measure (POVM) for discriminating nonorthogonal states. We construct a generalized POVM measurement operation, and derive the optimal one for state discrimination by Lagrange multiplier method. With simulation, we find that the optimal POVM measurement provides a tight upper bound for state discrimination, which is significantly lower than that predicted by Holevo bound. The derivation of optimal POVM measurement will play an important role in the security research of quantum key distribution.

quant-ph↗