arXiv subjects

Kai Wang

Publications and source records attributed to Kai Wang.

At least 685 records · Page 38Linked to original sources

arXiv2021

AU-Guided Unsupervised Domain Adaptive Facial Expression Recognition

The domain diversities including inconsistent annotation and varied image collection conditions inevitably exist among different facial expression recognition (FER) datasets, which pose an evident challenge for adapting the FER model trained on one dataset to another one. Recent works mainly focus on domain-invariant deep feature learning with adversarial learning mechanism, ignoring the sibling facial action unit (AU) detection task which has obtained great progress. Considering AUs objectively determine facial expressions, this paper proposes an AU-guided unsupervised Domain Adaptive FER (AdaFER) framework to relieve the annotation bias between different FER datasets. In AdaFER, we first leverage an advanced model for AU detection on both source and target domain. Then, we compare the AU results to perform AU-guided annotating, i.e., target faces that own the same AUs with source faces would inherit the labels from source domain. Meanwhile, to achieve domain-invariant compact features, we utilize an AU-guided triplet training which randomly collects anchor-positive-negative triplets on both domains with AUs. We conduct extensive experiments on several popular benchmarks and show that AdaFER achieves state-of-the-art results on all these benchmarks.

cs.CV↗

arXiv2021

TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domain

In this paper, we propose a transformer-based architecture, called two-stage transformer neural network (TSTNN) for end-to-end speech denoising in the time domain. The proposed model is composed of an encoder, a two-stage transformer module (TSTM), a masking module and a decoder. The encoder maps input noisy speech into feature representation. The TSTM exploits four stacked two-stage transformer blocks to efficiently extract local and global information from the encoder output stage by stage. The masking module creates a mask which will be multiplied with the encoder output. Finally, the decoder uses the masked encoder feature to reconstruct the enhanced speech. Experimental results on the benchmark dataset show that the TSTNN outperforms most state-of-the-art models in time or frequency domain while having significantly lower model complexity.

eess.AS↗

arXiv2021

In Operando magnetometry study on the charge storage mechanism of SnCo alloy lithium ion batteries

In view of the long-standing controversy over the reversibility of transition metals in Sn-based alloys as anode for Li-ion batteries, an in situ real-time magnetic monitoring method was used to investigate the evolution of Sn-Co intermetallic during the electrochemical cycling. Sn-Co alloy film anodes with different compositions were prepared via magnetron sputtering without using binders and conductive additives. The magnetic responses showed that the Co particles liberated by Li insertion recombine fully with Sn during the delithiation to reform Sn-Co intermetallic into stannum richer phases Sn7Co3. However, as the Co content increases, it can only recombine partially with Sn into cobalt richer phases Sn3Co7. The unconverted Co particles may form a dense barrier layer and prevent the full reaction of Li with all the Sn in the anode, leading to lower capacities. These critical results shed light on understanding the reaction mechanism of transition metals, and provide valuable insights toward the design of high-performance Sn alloy based anodes.

cond-mat.mtrl-sci↗

arXiv2021

Benchmarking calculations with spectroscopic accuracy of level energies and wavelengths in W LVII - W LXII tungsten ions

Atomic properties of $n=3$ states of the W$^{56+}$ $-$ W$^{61+}$ ions are systematically investigated through two state-of-the-art methods, namely, the second-order many-body perturbation theory, and the multi-configuration Dirac-Hartree-Fock method combined with the relativistic configuration interaction approach. The contributions of valence-valence and core-valence electron correlations, the Breit interaction, the higher-order retardation correction beyond the Breit interaction through the transverse photon interaction, and the quantum electrodynamical corrections to the excitation energies are studied in detail. The excitation energies and wavelengths obtained with the two methods agree with each other within \approx 0.01 %. The present results achieve spectroscopic accuracy and provide a benchmark test for various applications and other theoretical calculations of W$^{56+}$ $-$ W$^{61+}$ ions. They will assist spectroscopists in their assignment and direct identification of observed lines in complex spectra.

physics.atom-ph↗

arXiv2021

Round-robin differential phase-time-shifting protocol for quantum key distribution: theory and experiment

Quantum key distribution (QKD) allows the establishment of common cryptographic keys among distant parties. Many of the QKD protocols that were introduced in the past involve the challenge of monitoring the signal disturbance over the communication line, in order to evaluate the information leakage to a potential eavesdropper. Recently, a QKD protocol that circumvents the need for monitoring signal disturbance, has been proposed and demonstrated in initial experiments. Here, we propose a new version of this so-called round-robin differential phase-shifting (RRDPS) protocol, in which both time and phase degrees-of-freedom are utilized to enlarge the Hilbert space dimensionality, without increasing experimental complexity or relaxing security assumptions. We derive the security proofs of the round-robin differential phase-time-shifting (RRDPTS) protocol in the collective attack scenario and benchmark the new protocol against RRDPS for different experimental parameters. Furthermore, a proof-of-concept experiment of the RRDPTS protocol, using weak coherent pulses and decoy-state method, is demonstrated over 80 km of fiber link. Our results show that the RRDPTS protocol can achieve higher secret key rate in comparison with the RRDPS, in the condition of high quantum bit error rate.

quant-ph↗

arXiv2021

Simple and effective localized attribute representations for zero-shot learning

Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their semantic descriptions. Some recent papers have shown the importance of localized features together with fine-tuning the feature extractor to obtain discriminative and transferable features. However, these methods require complex attention or part detection modules to perform explicit localization in the visual space. In contrast, in this paper we propose localizing representations in the semantic/attribute space, with a simple but effective pipeline where localization is implicit. Focusing on attribute representations, we show that our method obtains state-of-the-art performance on CUB and SUN datasets, and also achieves competitive results on AWA2 dataset, outperforming generally more complex methods with explicit localization in the visual space. Our method can be implemented easily, which can be used as a new baseline for zero shot-learning. In addition, our localized representations are highly interpretable as attribute-specific heatmaps.

cs.CV↗

arXiv2021

On Implicit Attribute Localization for Generalized Zero-Shot Learning

Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their attribute-based descriptions. Since attributes are often related to specific parts of objects, many recent works focus on discovering discriminative regions. However, these methods usually require additional complex part detection modules or attention mechanisms. In this paper, 1) we show that common ZSL backbones (without explicit attention nor part detection) can implicitly localize attributes, yet this property is not exploited. 2) Exploiting it, we then propose SELAR, a simple method that further encourages attribute localization, surprisingly achieving very competitive generalized ZSL (GZSL) performance when compared with more complex state-of-the-art methods. Our findings provide useful insight for designing future GZSL methods, and SELAR provides an easy to implement yet strong baseline.

cs.CV↗

arXiv2021

How to empirically model star formation in dark matter halos: I. Inferences about central galaxies from numerical simulations

We use TNG and EAGLE hydrodynamic simulations to investigate the central galaxy - dark matter halo relations that are needed for a halo-based empirical model of star formation in galaxies. Using a linear dimension reduction algorithm and a model ensemble method, we find that for both star-forming and quenched galaxies, the star formation history (SFH) is tightly related to the halo mass assembly history (MAH). The quenching of a low-mass galaxy is mainly due to the infall-ejection process related to a nearby massive halo, while the quenching of a high-mass galaxy is closely related to the formation of a massive progenitor in its host halo. The classification of star-forming and quenched populations based solely on halo properties contains contamination produced by sample imbalance and overlapping distributions of the two populations. Guided by the results from hydrodynamic simulations, we build an empirical model to predict the SFH of central galaxies based on the MAH of their host halos, and we model the star-forming and quenched populations separately. Our model is based on the idea of adopting star formation templates from hydrodynamic simulations to reduce model complexity. We use various tests to demonstrate that the model can recover star formation histories of individual galaxies, and can statistically reproduce the galaxy bimodal distribution, stellar mass - halo mass and star formation rate - halo mass relations from low to high redshift, and assembly bias. Our study provides a framework of using hydrodynamic simulations to discover, and to motivate the use of, key ingredients to model galaxy formation using halo properties.

astro-ph.GA↗

arXiv2021

Ab initio MCDHF calculations of the In and Tl electron affinities and their isotope shifts

We report multiconfiguration Dirac-Hartree-Fock and relativistic configuration interaction calculations on the Thallium (Tl) electron affinity, as well as on the excited energy levels arising from the ground configuration of Tl$^-$. The results are compared with the available experimental values and further validated by extending the study to its homologous, lighter element, Indium (In), belonging to Group 13 (III.A) of the periodic table. The calculated electron affinities of In and Tl, 383.4 and 322.8 meV, agree with the latest measurements by within 1\%. Three bound states $^3P_{0,1,2}$ are confirmed in the $5s^25p^2$ configuration of In$^-$ while only the ground state $^3P_{0}$ is bound in the $6s^26p^2$ configuration of Tl$^-$. The isotope shifts on the In and Tl electron affinities are also estimated. The E2/M1 intraconfiguration radiative transition rates within $5s^25p^2 \; ^3P_{0,1,2}$ of In$^-$ are used to calculate the radiative lifetimes of the metastable $^3P_{1,2}$ levels.

physics.atom-ph↗

arXiv2021

A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection

In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of medical resources, manual diagnosis is time-consuming and may delay the condition. With the development of deep learning, some researches on ophthalmic diseases have achieved good results, however, most of them are just based on one disease. During fundus screening, ophthalmologists usually give diagnoses of multi-disease on binocular fundus image, so we release a dataset with 8 diseases to meet the real medical scene, which contains 10,000 fundus images from both eyes of 5,000 patients. We did some benchmark experiments on it through some state-of-the-art deep neural networks. We found simply increasing the scale of network cannot bring good results for multi-disease classification, and a well-structured feature fusion method combines characteristics of multi-disease is needed. Through this work, we hope to advance the research of related fields.

cs.CV↗

arXiv2021

Applications of Deep Learning in Fundus Images: A Review

The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmentation, disease diagnosis and image synthesis. Therefore, it is very necessary to summarize the recent developments in deep learning for fundus images with a review paper. In this review, we introduce 143 application papers with a carefully designed hierarchy. Moreover, 33 publicly available datasets are presented. Summaries and analyses are provided for each task. Finally, limitations common to all tasks are revealed and possible solutions are given. We will also release and regularly update the state-of-the-art results and newly-released datasets at https://github.com/nkicsl/Fundus Review to adapt to the rapid development of this field.

eess.IV↗

arXiv2021

Gaussian limit for determinantal point processes with $J$-Hermitian kernels

We show that the central limit theorem for linear statistics over determinantal point processes with $J$-Hermitian kernels holds under fairly general conditions. In particular, We establish Gaussian limit for linear statistics over determinantal point processes on union of two copies of $\mathbb{R}^d$ when the correlation kernels are $J$-Hermitian translation-invariant.

math.PR↗

arXiv2020

Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition

The audio-video based emotion recognition aims to classify a given video into basic emotions. In this paper, we describe our approaches in EmotiW 2019, which mainly explores emotion features and feature fusion strategies for audio and visual modality. For emotion features, we explore audio feature with both speech-spectrogram and Log Mel-spectrogram and evaluate several facial features with different CNN models and different emotion pretrained strategies. For fusion strategies, we explore intra-modal and cross-modal fusion methods, such as designing attention mechanisms to highlights important emotion feature, exploring feature concatenation and factorized bilinear pooling (FBP) for cross-modal feature fusion. With careful evaluation, we obtain 65.5% on the AFEW validation set and 62.48% on the test set and rank third in the challenge.

cs.CV↗

arXiv2020

Modulating nano-inhomogeneity at electrode-solid electrolyte interfaces for dendrite-proof solid-state batteries and long-life memristors

The penetration of dendrites in ceramic lithium conductors severely constrains the development of solid-state batteries (SSBs) while its nanoscopic origin remain unelucidated. We develop an in-situ nanoscale electrochemical characterization technique to reveal the nanoscopic lithium dendrite growth kinetics and use it as a guiding tool to unlock the design of interfaces for dendrite-proof SSBs. Using Li7La3Zr2O12 (LLZO) as a model system, in-situ nanoscopic dendrite triggering measurements, ex-situ electro-mechanical characterizations, and finite element simulations are carried out which reveal the dominating role of Li+ flux detouring and nano-mechanical inhomogeneity on dendrite penetration. To mitigate such nano-inhomogeneity, an ionic-conductive homogenizing layer based on poly(propylene carbonate) is designed which in-situ reacts with lithium to form a highly conformal interphase at mild conditions. A high critical current density of 1.8mA cm-2 and a low interfacial resistance of 14Ω cm2 is achieved. Practical SSBs based on LiFePO4 cathodes show great cyclic stability without capacity decay over 300 cycles. Beyond this, highly reversible electrochemical dendrite healing behavior in LLZO is discovered using the nano-electrode, based on which a model memristor with a high on/off ratio of ~10^5 is demonstrated for >200 cycles. This work not only provides a novel tool to investigate and design interfaces in SSBs but offers also new opportunities for solid electrolytes beyond energy applications.

physics.app-ph↗

arXiv2020

PA-Cache: Evolving Learning-Based Popularity-Aware Content Caching in Edge Networks

As ubiquitous and personalized services are growing boomingly, an increasingly large amount of traffic is generated over the network by massive mobile devices. As a result, content caching is gradually extending to network edges to provide low-latency services, improve quality of service, and reduce redundant data traffic. Compared to the conventional content delivery networks, caches in edge networks with smaller sizes usually have to accommodate more bursty requests. In this paper, we propose an evolving learning-based content caching policy, named PA-Cache in edge networks. It adaptively learns time-varying content popularity and determines which contents should be replaced when the cache is full. Unlike conventional deep neural networks (DNNs), which learn a fine-tuned but possibly outdated or biased prediction model using the entire training dataset with high computational complexity, PA-Cache weighs a large set of content features and trains the multi-layer recurrent neural network from shallow to deeper when more requests arrive over time. We extensively evaluate the performance of our proposed PA-Cache on real-world traces from a large online video-on-demand service provider. \rb{The results show that PA-Cache outperforms existing popular caching algorithms and approximates the optimal algorithm with only a 3.8\% performance gap when the cache percentage is 1.0\%}. PA-Cache also significantly reduces the computational cost compared to conventional DNN-based approaches.

cs.NI↗

arXiv2020

PiPoMonitor: Mitigating Cross-core Cache Attacks Using the Auto-Cuckoo Filter

Cache side channel attacks obtain victim cache line access footprint to infer security-critical information. Among them, cross-core attacks exploiting the shared last level cache are more threatening as their simplicity to set up and high capacity. Stateful approaches of detection-based mitigation observe precise cache behaviors and protect specific cache lines that are suspected of being attacked. However, their recording structures incur large storage overhead and are vulnerable to reverse engineering attacks. Exploring the intrinsic non-determinate layout of a traditional Cuckoo filter, this paper proposes a space efficient Auto-Cuckoo filter to record access footprints, which succeed to decrease storage overhead and resist reverse engineering attacks at the same time. With Auto-Cuckoo filter, we propose PiPoMonitor to detect \textit{Ping-Pong patterns} and prefetch specific cache line to interfere with adversaries' cache probes. Security analysis shows the PiPoMonitor can effectively mitigate cross-core attacks and the Auto-Cuckoo filter is immune to reverse engineering attacks. Evaluation results indicate PiPoMonitor has negligible impact on performance and the storage overhead is only 0.37$\%$, an order of magnitude lower than previous stateful approaches.

cs.CR↗

arXiv2020

Efficient and Effective Community Search on Large-scale Bipartite Graphs

Bipartite graphs are widely used to model relationships between two types of entities. Community search retrieves densely connected subgraphs containing a query vertex, which has been extensively studied on unipartite graphs. However, community search on bipartite graphs remains largely unexplored. Moreover, all existing cohesive subgraph models on bipartite graphs can only be applied to measure the structure cohesiveness between two sets of vertices while overlooking the edge weight in forming the community. In this paper, we study the significant (alpha, beta)-community search problem on weighted bipartite graphs. Given a query vertex q, we aim to find the significant (alpha, beta)-community R of q which adopts (alpha, beta)-core to characterize the engagement level of vertices, and maximizes the minimum edge weight (significance) within R. To support fast retrieval of R, we first retrieve the maximal connected subgraph of (alpha, beta)-core containing the query vertex (the (alpha, beta)-community), and the search space is limited to this subgraph with a much smaller size than the original graph. A novel index structure is presented which can be built in O(delta * m) time and takes O(delta * m) space where m is the number of edges in G, delta is bounded by the square root of m and is much smaller in practice. Utilizing the index, the (alpha, beta)-community can be retrieved in optimal time. To further obtain R, we develop peeling and expansion algorithms to conduct searches by shrinking from the (alpha, beta)-community and expanding from the query vertex, respectively. The experimental results on real graphs not only demonstrate the effectiveness of the significant (alpha, beta)-community model but also validate the efficiency of our query processing and indexing techniques.

cs.DB↗

arXiv2020

Monitoring AGNs with Hβ Asymmetry. II. Reverberation Mapping of Three Seyfert Galaxies Historically Displaying Hβ Profiles with Changing Asymmetry: Mrk 79, NGC 3227, and Mrk 841

We report the results of reverberation mapping three bright Seyfert galaxies, Mrk 79, NGC 3227, and Mrk 841, from a campaign conducted from December 2016 to May 2017 with the Wyoming Infrared Observatory (WIRO) 2.3-meter telescope. All three of these targets have shown asymmetric broad H$β$ emission lines in the past, although their emission lines were relatively symmetric during our observations. We measured Hβ time lags for all three targets and estimated masses of their black holes -- for the first time in the case of Mrk 841. For Mrk 79 and NGC 3227, the data are of sufficient quality to resolve distinct time lags as a function of velocity and to compute two-dimensional velocity-delay maps. Mrk 79 shows smaller time lags for high-velocity gas but the distribution is not symmetric, and its complex velocity-delay map could result from the combination of both inflowing and outflowing Hβ emitting disks that may be part of a single larger structure. NGC 3227 shows the largest time lags for blueshifted gas and the two-dimensional velocity-delay map suggests a disk with some inflow. We compare our results with previous work and find evidence for different time lags despite similar luminosities, as well as evolving broad line region structures.

astro-ph.GA↗