arXiv subjects

Kai Wang

Publications and source records attributed to Kai Wang.

At least 667 records · Page 37Linked to original sources

arXiv2021

A Received Power Model for Reconfigurable Intelligent Surface and Measurement-based Validations

The idea of using a Reconfigurable Intelligent Surface (RIS) consisting of a large array of passive scattering elements to assist wireless communication systems has recently attracted much attention from academia and industry. A central issue with RIS is how much power they can effectively convey to the target radio nodes. Regarding this question, several power level models exist in the literature but few have been validated through experiments. In this paper, we propose a radar cross section-based received power model for an RIS-aided wireless communication system that is rooted in the physical properties of RIS. Our proposed model follows the intuition that the received power is related to the distances from the transmitter/receiver to the RIS, the angles in the TX-RIS-RX triangle, the effective area of each element, and the reflection coefficient of each element. To the best of our knowledge, this paper is the first to model the angle-dependent phase shift of the reflection coefficient, which is typically ignored in existing literature. We further measure the received power with our experimental platform in different scenarios to validate our model. The measurement results show that our model is appropriate both in near field and far field and can characterize the impact of angles well.

cs.IT↗

arXiv2021

A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis

Pathology image analysis is an essential procedure for clinical diagnosis of many diseases. To boost the accuracy and objectivity of detection, nowadays, an increasing number of computer-aided diagnosis (CAD) system is proposed. Among these methods, random field models play an indispensable role in improving the analysis performance. In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models. Firstly, we introduce the background of two random fields and pathology images. Secondly, we summarize the basic mathematical knowledge of MRFs and CRFs from modelling to optimization. Then, a thorough review of the recent research on the MRFs and CRFs of pathology images analysis is presented. Finally, we investigate the popular methodologies in the related works and discuss the method migration among CAD field.

cs.CV↗

arXiv2021

Learning to Cluster Faces via Transformer

Face clustering is a useful tool for applications like automatic face annotation and retrieval. The main challenge is that it is difficult to cluster images from the same identity with different face poses, occlusions, and image quality. Traditional clustering methods usually ignore the relationship between individual images and their neighbors which may contain useful context information. In this paper, we repurpose the well-known Transformer and introduce a Face Transformer for supervised face clustering. In Face Transformer, we decompose the face clustering into two steps: relation encoding and linkage predicting. Specifically, given a face image, a \textbf{relation encoder} module aggregates local context information from its neighbors and a \textbf{linkage predictor} module judges whether a pair of images belong to the same cluster or not. In the local linkage graph view, Face Transformer can generate more robust node and edge representations compared to existing methods. Experiments on both MS-Celeb-1M and DeepFashion show that our method achieves state-of-the-art performance, e.g., 91.12\% in pairwise F-score on MS-Celeb-1M.

cs.CV↗

arXiv2021

Continual learning in cross-modal retrieval

Multimodal representations and continual learning are two areas closely related to human intelligence. The former considers the learning of shared representation spaces where information from different modalities can be compared and integrated (we focus on cross-modal retrieval between language and visual representations). The latter studies how to prevent forgetting a previously learned task when learning a new one. While humans excel in these two aspects, deep neural networks are still quite limited. In this paper, we propose a combination of both problems into a continual cross-modal retrieval setting, where we study how the catastrophic interference caused by new tasks impacts the embedding spaces and their cross-modal alignment required for effective retrieval. We propose a general framework that decouples the training, indexing and querying stages. We also identify and study different factors that may lead to forgetting, and propose tools to alleviate it. We found that the indexing stage pays an important role and that simply avoiding reindexing the database with updated embedding networks can lead to significant gains. We evaluated our methods in two image-text retrieval datasets, obtaining significant gains with respect to the fine tuning baseline.

cs.CV↗

arXiv2021

Personalized Bundle Recommendation in Online Games

In business domains, \textit{bundling} is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers. Existing recommender systems mostly focus on recommending individual items that users may be interested in. In this paper, we target at a practical but less explored recommendation problem named bundle recommendation, which aims to offer a combination of items to users. To tackle this specific recommendation problem in the context of the \emph{virtual mall} in online games, we formalize it as a link prediction problem on a user-item-bundle tripartite graph constructed from the historical interactions, and solve it with a neural network model that can learn directly on the graph-structure data. Extensive experiments on three public datasets and one industrial game dataset demonstrate the effectiveness of the proposed method. Further, the bundle recommendation model has been deployed in production for more than one year in a popular online game developed by Netease Games, and the launch of the model yields more than 60\% improvement on conversion rate of bundles, and a relative improvement of more than 15\% on gross merchandise volume (GMV).

cs.IR↗

arXiv2021

Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation

In recent years, there are great interests as well as challenges in applying reinforcement learning (RL) to recommendation systems (RS). In this paper, we summarize three key practical challenges of large-scale RL-based recommender systems: massive state and action spaces, high-variance environment, and the unspecific reward setting in recommendation. All these problems remain largely unexplored in the existing literature and make the application of RL challenging. We develop a model-based reinforcement learning framework, called GoalRec. Inspired by the ideas of world model (model-based), value function estimation (model-free), and goal-based RL, a novel disentangled universal value function designed for item recommendation is proposed. It can generalize to various goals that the recommender may have, and disentangle the stochastic environmental dynamics and high-variance reward signals accordingly. As a part of the value function, free from the sparse and high-variance reward signals, a high-capacity reward-independent world model is trained to simulate complex environmental dynamics under a certain goal. Based on the predicted environmental dynamics, the disentangled universal value function is related to the user's future trajectory instead of a monolithic state and a scalar reward. We demonstrate the superiority of GoalRec over previous approaches in terms of the above three practical challenges in a series of simulations and a real application.

cs.IR↗

arXiv2021

MulDE: Multi-teacher Knowledge Distillation for Low-dimensional Knowledge Graph Embeddings

Link prediction based on knowledge graph embeddings (KGE) aims to predict new triples to automatically construct knowledge graphs (KGs). However, recent KGE models achieve performance improvements by excessively increasing the embedding dimensions, which may cause enormous training costs and require more storage space. In this paper, instead of training high-dimensional models, we propose MulDE, a novel knowledge distillation framework, which includes multiple low-dimensional hyperbolic KGE models as teachers and two student components, namely Junior and Senior. Under a novel iterative distillation strategy, the Junior component, a low-dimensional KGE model, asks teachers actively based on its preliminary prediction results, and the Senior component integrates teachers' knowledge adaptively to train the Junior component based on two mechanisms: relation-specific scaling and contrast attention. The experimental results show that MulDE can effectively improve the performance and training speed of low-dimensional KGE models. The distilled 32-dimensional model is competitive compared to the state-of-the-art high-dimensional methods on several widely-used datasets.

cs.AI↗

arXiv2021

High Energy Neutrinos from Choked Gamma-Ray Bursts in AGN Accretion Disks

Both long-duration gamma-ray bursts (LGRBs) from core collapse of massive stars and short-duration GRBs (SGRBs) from mergers of binary neutron star (BNS) or neutron star--black hole (NSBH) are expected to occur in the accretion disk of active galactic nuclei (AGNs). We show that GRB jets embedded in the migration traps of AGN disks are promised to be choked by the dense disk material. Efficient shock acceleration of cosmic rays at the reverse shock is expected, and high-energy neutrinos would be produced. We find that these sources can effectively produce detectable TeV--PeV neutrinos through $pγ$ interactions. From a choked LGRB jet with isotropic equivalent energy of $10^{53}\,{\rm erg}$ at $100\,{\rm Mpc}$, one expects $\sim2\,(7)$ neutrino events detectable by IceCube (IceCube-Gen2). The contribution from choked LGRBs to the observed diffuse neutrino background depends on the unknown local event rate density of these GRBs in AGN disks. For example, if the local event rate density of choked LGRBs in AGN disk is $\sim5\%$ that of low-luminosity GRBs $(\sim10\,{\rm Gpc}^{-3}\,{\rm yr}^{-1})$, the neutrinos from these events would contribute to $\sim10\%$ of the observed diffuse neutrino background. Choked SGRBs in AGN disks are potential sources for future joint electromagnetic, neutrino, and gravitational wave multi-messenger observations.

astro-ph.HE↗

arXiv2021

Observation of arbitrary topological windings of a non-Hermitian band

The non-trivial topological features in the energy band of non-Hermitian systems provide promising pathways to achieve robust physical behaviors in classical or quantum open systems. A key topological feature, unique to non-Hermitian systems, is the non-trivial winding of the energy band in the complex energy plane. Here we provide direct experimental demonstrations of such non-trivial winding, by implementing non-Hermitian lattice Hamiltonians along a frequency synthetic dimension formed in a ring resonator undergoing simultaneous phase and amplitude modulations, and by directly characterizing the complex band structures. Moreover, we show that the topological winding can be straightforwardly controlled by changing the modulation waveform. Our results open a pathway for the experimental synthesis and characterization of topologically non-trivial phases in non-conservative systems.

physics.optics↗

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↗