arXiv subjects

Liang Liu

Publications and source records attributed to Liang Liu.

At least 235 records · Page 13Linked to original sources

arXiv2020

Towards an Astronomical Science Platform: Experiences and Lessons Learned from Chinese Virtual Observatory

In the era of big data astronomy, next generation telescopes and large sky surveys produce data sets at the TB or even PB level. Due to their large data volumes, these astronomical data sets are extremely difficult to transfer and analyze using personal computers or small clusters. In order to offer better access to data, data centers now generally provide online science platforms that enable analysis close to the data. The Chinese Virtual Observatory (China-VO) is one of the member projects in the International Virtual Observatory Alliance and it is dedicated to providing a research and education environment where globally distributed astronomy archives are simple to find, access, and interoperate. In this study, we summarize highlights of the work conducted at the China-VO, as well the experiences and lessons learned during the full life-cycle management of astronomical data. Finally, We discuss the challenges and future trends for astronomical science platforms.

astro-ph.IM↗

arXiv2020

Hierarchical and Efficient Learning for Person Re-Identification

Recent works in the person re-identification task mainly focus on the model accuracy while ignore factors related to the efficiency, e.g. model size and latency, which are critical for practical application. In this paper, we propose a novel Hierarchical and Efficient Network (HENet) that learns hierarchical global, partial, and recovery features ensemble under the supervision of multiple loss combinations. To further improve the robustness against the irregular occlusion, we propose a new dataset augmentation approach, dubbed Random Polygon Erasing (RPE), to random erase irregular area of the input image for imitating the body part missing. We also propose an Efficiency Score (ES) metric to evaluate the model efficiency. Extensive experiments on Market1501, DukeMTMC-ReID, and CUHK03 datasets shows the efficiency and superiority of our approach compared with epoch-making methods.

cs.CV↗

arXiv2020

FReeNet: Multi-Identity Face Reenactment

This paper presents a novel multi-identity face reenactment framework, named FReeNet, to transfer facial expressions from an arbitrary source face to a target face with a shared model. The proposed FReeNet consists of two parts: Unified Landmark Converter (ULC) and Geometry-aware Generator (GAG). The ULC adopts an encode-decoder architecture to efficiently convert expression in a latent landmark space, which significantly narrows the gap of the face contour between source and target identities. The GAG leverages the converted landmark to reenact the photorealistic image with a reference image of the target person. Moreover, a new triplet perceptual loss is proposed to force the GAG module to learn appearance and geometry information simultaneously, which also enriches facial details of the reenacted images. Further experiments demonstrate the superiority of our approach for generating photorealistic and expression-alike faces, as well as the flexibility for transferring facial expressions between identities.

cs.CV↗

arXiv2020

Intelligent Reflecting Surface Assisted Massive MIMO Communications

In a practical massive MIMO (multiple-input multiple-output) system, the number of antennas at a base station (BS) is constrained by the space and cost factors, which limits the throughput gain promised by theoretical analysis. This paper thus studies the feasibility of adopting the intelligent reflecting surface (IRS) to further improve the beamforming gain of the uplink communications in a massive MIMO system. Under such a novel system, the central question lies in whether the IRS is able to enhance the network throughput as expected, if the channel estimation overhead is taken into account. In this paper, we first show that the favorable propagation property for the conventional massive MIMO system without IRS, i.e., the channels of arbitrary two users are orthogonal, no longer holds for the IRS-assisted massive MIMO system, due to its special channel property that each IRS element reflects the signals from all the users to the BS via the same channel. As a result, the maximal-ratio combining (MRC) receive beamforming strategy leads to strong inter-user interference and thus even lower user rates than those of the massive MIMO system without IRS. To tackle this challenge, we propose a novel strategy for zero-forcing (ZF) beamforming design at the BS and reflection coefficients design at the IRS to efficiently null the inter-user interference. Under our proposed strategy, it is rigorously shown that even if the channel estimation overhead is considered, the IRS-assisted massive MIMO system can always achieve higher throughput compared to its counterpart without IRS, despite the fact that the favorable propagation property no longer holds.

cs.IT↗

arXiv2020

APB2Face: Audio-guided face reenactment with auxiliary pose and blink signals

Audio-guided face reenactment aims at generating photorealistic faces using audio information while maintaining the same facial movement as when speaking to a real person. However, existing methods can not generate vivid face images or only reenact low-resolution faces, which limits the application value. To solve those problems, we propose a novel deep neural network named APB2Face, which consists of GeometryPredictor and FaceReenactor modules. GeometryPredictor uses extra head pose and blink state signals as well as audio to predict the latent landmark geometry information, while FaceReenactor inputs the face landmark image to reenact the photorealistic face. A new dataset AnnVI collected from YouTube is presented to support the approach, and experimental results indicate the superiority of our method than state-of-the-arts, whether in authenticity or controllability.

cs.CV↗

arXiv2020

A Covariance-based User Activity Detection and Channel Estimation Approach with Novel Pilot Design

This paper studies the massive machine-type communications (mMTC) for the future Internet of Things (IoT) applications, where a large number of IoT devices exist in the network and a random subset of them become active at each time instant. Building upon the fact that the covariance matrix of the received signal can be accurately estimated in the spatial domain if the base station (BS) is equipped with a massive number of antennas, we propose a covariance-based device activity detection and channel estimation strategy in a massive MIMO (multiple-input multiple-output) aided mMTC system. For this strategy, a novel approach for the pilot sequence design is first provided, where the pilot of each device is merely determined by a unique phase parameter. Then, by estimating the phase parameters of the active pilot sequences that contribute to the received covariance matrix, an efficient algorithm is proposed to detect the active devices without the prior information about the total number of active devices. At last, given the estimation of active devices, channel estimation is conducted based on the conventional minimum mean-squared error (MMSE) approach. It is worth noting that our proposed strategy is able to obtain all the results in closed-forms, and is thus of much lower complexity compared to the existing strategies that are based on iterative algorithms for device detection and channel estimation.

eess.SP↗

arXiv2020

Extended Feature Pyramid Network for Small Object Detection

Small object detection remains an unsolved challenge because it is hard to extract information of small objects with only a few pixels. While scale-level corresponding detection in feature pyramid network alleviates this problem, we find feature coupling of various scales still impairs the performance of small objects. In this paper, we propose extended feature pyramid network (EFPN) with an extra high-resolution pyramid level specialized for small object detection. Specifically, we design a novel module, named feature texture transfer (FTT), which is used to super-resolve features and extract credible regional details simultaneously. Moreover, we design a foreground-background-balanced loss function to alleviate area imbalance of foreground and background. In our experiments, the proposed EFPN is efficient on both computation and memory, and yields state-of-the-art results on small traffic-sign dataset Tsinghua-Tencent 100K and small category of general object detection dataset MS COCO.

cs.CV↗

arXiv2020

A Learning Framework for n-bit Quantized Neural Networks toward FPGAs

The quantized neural network (QNN) is an efficient approach for network compression and can be widely used in the implementation of FPGAs. This paper proposes a novel learning framework for n-bit QNNs, whose weights are constrained to the power of two. To solve the gradient vanishing problem, we propose a reconstructed gradient function for QNNs in back-propagation algorithm that can directly get the real gradient rather than estimating an approximate gradient of the expected loss. We also propose a novel QNN structure named n-BQ-NN, which uses shift operation to replace the multiply operation and is more suitable for the inference on FPGAs. Furthermore, we also design a shift vector processing element (SVPE) array to replace all 16-bit multiplications with SHIFT operations in convolution operation on FPGAs. We also carry out comparable experiments to evaluate our framework. The experimental results show that the quantized models of ResNet, DenseNet and AlexNet through our learning framework can achieve almost the same accuracies with the original full-precision models. Moreover, when using our learning framework to train our n-BQ-NN from scratch, it can achieve state-of-the-art results compared with typical low-precision QNNs. Experiments on Xilinx ZCU102 platform show that our n-BQ-NN with our SVPE can execute 2.9 times faster than with the vector processing element (VPE) in inference. As the SHIFT operation in our SVPE array will not consume Digital Signal Processings (DSPs) resources on FPGAs, the experiments have shown that the use of SVPE array also reduces average energy consumption to 68.7% of the VPE array with 16-bit.

cs.LG↗

arXiv2020

Towards Reliable UAV Swarm Communication in D2D-Enhanced Cellular Network

In the existing cellular networks, it remains a challenging problem to communicate with and control an unmanned aerial vehicle (UAV) swarm with both high reliability and low latency. Due to the UAV swarm's high working altitude and strong ground-to-air channels, it is generally exposed to multiple ground base stations (GBSs), while the GBSs that are serving ground users (occupied GBSs) can generate strong interference to the UAV swarm. To tackle this issue, we propose a novel two-phase transmission protocol by exploiting cellular plus device-to-device (D2D) communication for the UAV swarm. In Phase I, one swarm head is chosen for ground-to-air channel estimation, and all the GBSs that are not serving ground users (available GBSs) transmit a common control message to the UAV swarm simultaneously, using the same cellular frequency band, to combat the strong interference from occupied GBSs. In Phase II, all the UAVs that have decoded the common control message in Phase I further relay it to the other UAVs in the swarm via D2D communication, by exploiting the less interfered D2D frequency band and the proximity among UAVs. In this paper, we aim to characterize the reliability performance of the above two-phase protocol, i.e., the expected percentage of UAVs in the swarm that can decode the common control message, which is a non-trivial problem due to the complex system setup and the intricate coupling between the two phases. Nevertheless, we manage to obtain an approximated expression of the reliability performance of interest, under reasonable assumptions and with the aid of the Pearson distributions. Numerical results validate the accuracy of our analytical results and show the effectiveness of our protocol over other benchmark protocols. We also study the effect of key system parameters on the reliability performance, to reveal useful insights on the practical system design.

cs.IT↗

arXiv2020

Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms

Algorithms for Massive MIMO uplink detection and downlink precoding typically rely on a centralized approach, by which baseband data from all antenna modules are routed to a central node in order to be processed. In the case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, said routing becomes a bottleneck since interconnection throughput is limited. This paper presents a fully decentralized architecture and an algorithm for Massive MIMO uplink detection and downlink precoding based on the Stochastic Gradient Descent (SGD) method, which does not require a central node for these tasks. Through a recursive approach and very low complexity operations, the proposed algorithm provides a good trade-off between performance, interconnection throughput and latency. Further, our proposed solution achieves significantly lower interconnection data-rate than other architectures, enabling future scalability.

eess.SP↗

arXiv2020

On the evolution of word usage of classical Chinese poetry

The hierarchy of classical Chinese poetry has been broadly acknowledged by a number of studies in Chinese literature. However, quantitative investigations about the evolutionary linkages of classical Chinese poetry are limited. The primary goal of this study is to provide quantitative evidence of the evolutionary linkages, with emphasis on character usage, among different period genres of classical Chinese poetry. Specifically, various statistical analyses are performed to find and compare the patterns of character usage in the poems of nine period genres, including shi jing, chu ci, Han shi , Jin shi, Tang shi, Song shi, Yuan shi, Ming shi, and Qing shi. The result of analysis indicates that each of nine period genres has unique patterns of character usage, with some Chinese characters that are preferably used in the poems of a particular period genre. The analysis on the general pattern of character preference implies a decreasing trend in the use of Chinese characters that rarely occur in modern Chinese literature along the timeline of dynastic types of classical Chinese poetry. The phylogenetic analysis based on the distance matrix suggests that the evolutionary linkages of different types of classical Chinese poetry are congruent with their chronological order, suggesting that character frequencies contain phylogenetic information that is useful for inferring evolutionary linkages among various types of classical Chinese poetry. The estimated phylogenetic tree identifies four groups (shi jing, chu ci), (Han shi, Jin shi), (Tang shi, Song shi, Yuan shi), and (Ming shi, Qing shi). The statistical analyses conducted in this study can be generalized to analyze the data sets of general Chinese literature. Such analyses can provide quantitative insights about the evolutionary linkages of general Chinese literature.

physics.soc-ph↗

arXiv2020

Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser Communications

In the intelligent reflecting surface (IRS) assisted communication systems, the acquisition of channel state information (CSI) is a crucial impediment for achieving the passive beamforming gain of IRS because of the considerable overhead required for channel estimation. Specifically, under the current beamforming design for IRS-assisted communications, $KMN+KM$ channel coefficients should be estimated if the passive IRS cannot estimate its channels with the base station (BS) and users due to its lack of radio frequency (RF) chains, where $K$, $N$ and $M$ denote the number of users, reflecting elements of the IRS, and antennas at the BS, respectively. This number can be extremely large in practice considering the current trend of massive MIMO (multiple-input multiple-output), i.e., a large $M$, and massive connectivity, i.e., a large $K$. To accurately estimate such a large number of channel coefficients within a short time interval, we devote our endeavour in this paper to investigating the efficient pilot-based channel estimation method in IRS-assisted uplink communications. Building upon the observation that the IRS reflects the signals from all the users to the BS via the same channels, we analytically verify that a time duration consisting of $K+N+\max(K-1,\lceil (K-1)N/M \rceil)$ pilot symbols is sufficient for the BS to perfectly recover all the $KMN+KM$ channel coefficients in the case without noise. In contrast to the conventional uplink communications without IRS in which the minimum pilot sequence length for channel estimation is independent with the number of receive antennas, our study reveals the significant role of massive MIMO in reducing the channel training time for IRS-assisted communication systems.

cs.IT↗

arXiv2019

Doppler-insensitive two-qubit controlled-PHASE gate protocol with dual-pulse off-resonant modulated driving for neutral atoms

For neutral atom qubits, the residual thermal motion of the cold atoms constitutes a major challenge that limits the accessible two-qubit gate fidelity. Recently, an interesting type of two-qubit controlled-PHASE quantum gate protocol has been introduced for neutral atom qubit platform, which relies upon off-resonant modulated driving and Rydberg blockade effect. Building upon this progress, we have further developed an upgrade in the form of dual-pulse off-resonant modulated driving. Besides the inherent advantages of avoiding shelving population in Rydberg levels, not necessarily requiring individual site addressing, not sensitive to the exact value of blockade shift while suppressing population leakage error and rotation error, the major new feature of this protocol is Doppler-insensitive. In principle, the gate fidelity remains reasonably high over a relatively significant velocity range of the qubit atoms. Moreover, we anticipate that this protocol will inspire future improvements in quantum gate protocols for other types of qubit platforms, and its strategies may find applications in the area of quantum optimal control.

quant-ph↗

arXiv2019

An Iterative Interference Cancellation Algorithm for Large Intelligent Surfaces

The Large Intelligent Surface (LIS) concept is a promising technology aiming to revolutionize wireless communication by exploiting spatial multiplexing at its fullest. Despite of its potential, due to the size of the LIS and the large number of antenna elements involved there is a need of decentralized architectures together with distributed algorithms which can reduce the inter-connection data-rate and computational requirement in the Central Processing Unit (CPU). In this article we address the uplink detection problem in the LIS system and propose a decentralize architecture based on panels, which perform local linear processing. We also provide the sum-rate capacity for such architecture and derive an algorithm to obtain the equalizer, which aims to maximize the sum-rate capacity. A performance analysis is also presented, including a comparison to a naive approach based on a reduced form of the matched filter (MF) method. The results shows the superiority of the proposed algorithm.

eess.SP↗

arXiv2019

From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

Visual counting, a task that predicts the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in $[0,+\infty)$ in theory. However, the collected images and labeled count values are limited in reality, which means only a small closed set is observed. Existing methods typically model this task in a regression manner, while they are likely to suffer from an unseen scene with counts out of the scope of the closed set. In fact, counting is decomposable. A dense region can always be divided until sub-region counts are within the previously observed closed set. Inspired by this idea, we propose a simple but effective approach, Spatial Divide-and- Conquer Network (S-DCNet). S-DCNet only learns from a closed set but can generalize well to open-set scenarios via S-DC. S-DCNet is also efficient. To avoid repeatedly computing sub-region convolutional features, S-DC is executed on the feature map instead of on the input image. S-DCNet achieves the state-of-the-art performance on three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QNRF), a vehicle counting dataset (TRANCOS) and a plant counting dataset (MTC). Compared to the previous best methods, S-DCNet brings a 20.2% relative improvement on the ShanghaiTech Part B, 20.9% on the UCF-QNRF, 22.5% on the TRANCOS and 15.1% on the MTC. Code has been made available at: https://github. com/xhp-hust-2018-2011/S-DCNet.

cs.CV↗

arXiv2019

Low Power Receiver Front Ends: Scaling Laws and Applications

In this paper, we combine communication-theoretic laws with known, practically verified results from circuit theory. As a result, we obtain closed-form theoretical expressions linking fundamental system design and environment parameters with the power consumption of analog front ends for communication receivers. This collection of scaling laws and bounds is meant to serve as a theoretical reference for practical low power front end design. In one set of results, we first find that the front end power consumption scales at least as SNDR^3/2 if environment parameters (fading and blocker levels) are static. The obtained scaling law is subsequently used to derive relations between front end power consumption and several other important communication system parameters, namely, digital modulation constellation size, symbol error probability, error control coding gain and coding rate. Such relations, in turn, can be used when deciding which system design strategies to adopt for low-power applications. For example, if error control coding is employed, the most energy-efficient strategy for the entire receiver is to use codes with moderate coding gain and simple decoding algorithms, such as convolutional codes. In another collection of results, we find how front end power scales with environment parameters if the performance is kept constant. This yields bounds on average power reduction of receivers that adapt to the communication environment. For instance, if a receiver front end adapts to fading fluctuations while keeping the performance above some given minimum requirement, power can theoretically be reduced at least 20x compared to a non-adaptive front end.

cs.IT↗

arXiv2019

PoseConvGRU: A Monocular Approach for Visual Ego-motion Estimation by Learning

While many visual ego-motion algorithm variants have been proposed in the past decade, learning based ego-motion estimation methods have seen an increasing attention because of its desirable properties of robustness to image noise and camera calibration independence. In this work, we propose a data-driven approach of fully trainable visual ego-motion estimation for a monocular camera. We use an end-to-end learning approach in allowing the model to map directly from input image pairs to an estimate of ego-motion (parameterized as 6-DoF transformation matrices). We introduce a novel two-module Long-term Recurrent Convolutional Neural Networks called PoseConvGRU, with an explicit sequence pose estimation loss to achieve this. The feature-encoding module encodes the short-term motion feature in an image pair, while the memory-propagating module captures the long-term motion feature in the consecutive image pairs. The visual memory is implemented with convolutional gated recurrent units, which allows propagating information over time. At each time step, two consecutive RGB images are stacked together to form a 6 channels tensor for module-1 to learn how to extract motion information and estimate poses. The sequence of output maps is then passed through a stacked ConvGRU module to generate the relative transformation pose of each image pair. We also augment the training data by randomly skipping frames to simulate the velocity variation which results in a better performance in turning and high-velocity situations. We evaluate the performance of our proposed approach on the KITTI Visual Odometry benchmark. The experiments show a competitive performance of the proposed method to the geometric method and encourage further exploration of learning based methods for the purpose of estimating camera ego-motion even though geometrical methods demonstrate promising results.

cs.CV↗

arXiv2019

Modulation of magnetism via electric field in MgO nanoribbons

We report on a theoretical study of electromagnetic properties of zigzag magnesium oxides nanoribbons (Z-MgONRs). We propose that the polar charges and the spin polarization are the two key factors for edge magnetism. Based on first-principle calculations, we demonstrate that both O- and Mg-edges are magnetic and their magnetic moments all can be efficiently modulated via external electric field.And the edge magnetism is further studied in the framework of effective tight-binding model, which provides a starting point for the calculation of one particle Green's function and the determination of exchange interactions. Utilizing the linear response model, we find that Z-MgONRs exhibit two kinds of exchange interaction with extremely different natures. The magnetism in O-edge is strongly localized with ferromagnetic order while the magnetism in Mg-edge is itinerant with Ruderman-Kittel-Kasuya-Yosida (RKKY) like interactions. And these two couplings can also be modulated by electric field, giving rise to the electrical modulations of spin-density-wave (SDW) and the modulation of Curie temperature in O-edge. All these suggest that Z-MgONRs are ideal platforms for ME coupling and appealing candidates for manifold applications.

cond-mat.mes-hall↗