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Liang Zhang

Publications and source records attributed to Liang Zhang.

At least 343 records · Page 19Linked to original sources
arXiv2022

Analysis and visualization of spatial transcriptomic data

Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation that measures transcriptomic information while preserving spatial information. Spatial transcriptomic data can be generated in several ways. RNA molecules are measured by in situ sequencing, in situ hybridization, or spatial barcoding to recover original spatial coordinates. The inclusion of spatial information expands the range of possibilities for analysis and visualization, and spurred the development of numerous novel methods. In this review, we summarize the core concepts of spatial genomics technology and provide a comprehensive review of current analysis and visualization methods for spatial transcriptomics.

q-bio.QM↗
arXiv2022

Finite-time enclosing control for multiple moving targets: a continuous estimator approach

This work addresses the finite-time enclosing control problem where a set of followers are deployed to encircle and rotate around multiple moving targets with a predefined spacing pattern in finite time. A novel distributed and continuous estimator is firstly proposed to track the geometric center of targets in finite time using only local information for every follower. Then a pair of decentralized control laws for both the relative distance and included angle, respectively, are designed to achieve the desired spacing pattern in finite time based on the output of the proposed estimator. Through both theoretical analysis and simulation validation, we show that the proposed estimator is continuous and therefore can avoid dithering control output while still inheriting the merit of finite-time convergence. The steady errors of the estimator and the enclosing controller are guaranteed to converge to some bounded and adjustable regions around zero.

math.OC↗
arXiv2022

Approaching the Fundamental Limit of Orbital Angular Momentum Multiplexing Through a Hologram Metasurface

Establishing and approaching the fundamental limit of orbital angular momentum (OAM) multiplexing are necessary and increasingly urgent for current multiple-input multiple-output research. In this work, we elaborate the fundamental limit in terms of independent scattering channels (or degrees of freedom of scattered fields) through angular-spectral analysis, in conjunction with a rigorous Green function method. The scattering channel limit is universal for arbitrary spatial mode multiplexing, which is launched by a planar electromagnetic device, such as antenna, metasurface, etc, with a predefined physical size. As a proof of concept, we demonstrate both theoretically and experimentally the limit by a metasurface hologram that transforms orthogonal OAM modes to plane-wave modes scattered at critically separated angular-spectral regions. Particularly, a minimax optimization algorithm is applied to suppress angular spectrum aliasing, achieving good performances in both full-wave simulation and experimental measurement at microwave frequencies. This work offers a theoretical upper bound and corresponding approach route for engineering designs of OAM multiplexing.

physics.app-ph↗
arXiv2021

Gram-SLD: Automatic Self-labeling and Detection for Instance Objects

Instance object detection plays an important role in intelligent monitoring, visual navigation, human-computer interaction, intelligent services and other fields. Inspired by the great success of Deep Convolutional Neural Network (DCNN), DCNN-based instance object detection has become a promising research topic. To address the problem that DCNN always requires a large-scale annotated dataset to supervise its training while manual annotation is exhausting and time-consuming, we propose a new framework based on co-training called Gram Self-Labeling and Detection (Gram-SLD). The proposed Gram-SLD can automatically annotate a large amount of data with very limited manually labeled key data and achieve competitive performance. In our framework, gram loss is defined and used to construct two fully redundant and independent views and a key sample selection strategy along with an automatic annotating strategy that comprehensively consider precision and recall are proposed to generate high quality pseudo-labels. Experiments on the public GMU Kitchen Dataset , Active Vision Dataset and the self-made BHID-ITEM Datasetdemonstrate that, with only 5% labeled training data, our Gram-SLD achieves competitive performance in object detection (less than 2% mAP loss), compared with the fully supervised methods. In practical applications with complex and changing environments, the proposed method can satisfy the real-time and accuracy requirements on instance object detection.

cs.CV↗
arXiv2021

Efficient Pressure: Improving efficiency for signalized intersections

Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC) problem. However, existing RL-based methods are rarely deployed considering that they are neither cost-effective in terms of computing resources nor more robust than traditional approaches, which raises a critical research question: how to construct an adaptive controller for TSC with less training and reduced complexity based on RL-based approach? To address this question, in this paper, we (1) innovatively specify the traffic movement representation as a simple but efficient pressure of vehicle queues in a traffic network, namely efficient pressure (EP); (2) build a traffic signal settings protocol, including phase duration, signal phase number and EP for TSC; (3) design a TSC approach based on the traditional max pressure (MP) approach, namely efficient max pressure (Efficient-MP) using the EP to capture the traffic state; and (4) develop a general RL-based TSC algorithm template: efficient Xlight (Efficient-XLight) under EP. Through comprehensive experiments on multiple real-world datasets in our traffic signal settings' protocol for TSC, we demonstrate that efficient pressure is complementary to traditional and RL-based modeling to design better TSC methods. Our code is released on Github.

cs.LG↗
arXiv2021

In situ control of integrated Kerr nonlinearity

Kerr nonlinearity in nanophotonic cavities provides a versatile platform to explore fundamental physical sciences and develop novel photonic technologies. This is driven by the precise dispersion control and significant field enhancement with nanoscale structures. Beyond dispersion and pump engineering, the direct control of Kerr nonlinearity can release the ultimate performance and functionality of photonic systems. Here, we report the in situ control of integrated Kerr nonlinearity through its interplay with the cascaded Pockels nonlinear process. Kerr nonlinearity is tuned over 10~dB dynamic range without modifying photonic structures. Fano resonance is observed with nonlinear spectrum, in contrast to standard linear transmission. This confirms the quantum interference between competing optical nonlinear pathways. Besides nonlinearity enhancement, we also demonstrate the novel capability to suppress the material intrinsic nonlinearity. Finally, we use the tunable nonlinearity to control the spectral brightness and coincidence-to-accidental ratio of single-photon generation. This work paves the way towards the complete nanoscale control of optical nonlinear processes at quantum level.

physics.optics↗
arXiv2021

On-chip parallel processing of quantum frequency combs for high-dimensional hyper-entanglement generation

High-dimensional encoding and hyper-entanglement are unique features that distinguish optical photons from other quantum information carriers, leading to improved system efficiency and novel quantum functions. However, the disparate requirements to control different optical degrees of freedom have prevent the development of complete integrated platforms that is capable of leveraging the complementary benefits of high-dimensional encoding and hyper-entanglement at the same time. Here we demonstrate the chip-scale solution to the generation and manipulation of high-dimensional hyper-entanglement. This is achieved by the parallel processing of multiple quantum frequency combs in the path domain. Cavity-enhanced parametric down-conversion with Sagnac configuration is implemented to ensure the spectral indistinguishability. Simultaneous entanglement in path and frequency is realized with high dimensions. On-chip reconfiguration of the entanglement structure is also demonstrated. We further present quantum interference in both entanglement degrees of freedom with high visibility. Our work provides the critical step for the efficient and parallel processing of quantum information with integrated photonics.

quant-ph↗
arXiv2021

Compound eruptions of twin flux ropes in a solar active region

Compound eruptions represent that multiple closely spaced magnetic structures erupt consecutively within a short interval, and then lead to a single flare and a single CME. However, it is still subtle for the links between multiple eruptions and the associated single flare or/and single CME. In this Letter, we report the compound eruptions of twin close flux ropes (FR1 and FR2) within a few minutes that resulted in a flare with a single soft X-ray peak and a CME with two cores. The successive groups of expanding loops and double peaks of intensity flux in AIA cool wavelengths indicate two episodes of internal magnetic reconnections during the compound eruptions. Following the eruption of FR2, the erupting FR1 was accelerated, and then the expanding loops overlying FR2 were deflected. Moreover, the eruption of FR2 likely involved the external magnetic reconnection between the bottom of the overlying stretching field lines and the rebounding loops that were previously pushed by the eruption of FR1, which was evidenced by a pair of groups of newly-formed loops. All results suggest that the compound eruptions involved both internal and external magnetic reconnections, and two erupting structures of twin FRs interacted at the initial stage. We propose that two episodes of internal magnetic reconnections were likely united within a few minutes to form the continuous impulsive phase of the single peaked flare, and two separated cores of the CME was possibly because that the latter core was too slow to merge with the former one.

astro-ph.SR↗
arXiv2021

A Comprehensive X-ray Report on AT2019wey

Here, we present MAXI, SWIFT, NICER, NuSTAR and Chandra observations of the X-ray transient AT2019wey (SRGA J043520.9+552226, SRGE J043523.3+552234). From spectral and timing analyses we classify it as a Galactic low-mass X-ray binary (LMXB) with a black hole (BH) or neutron star (NS) accretor. AT2019wey stayed in the low/hard state (LHS) from 2019 December to 2020 August 21, and the hard-intermediate state (HIMS) from 2020 August 21 to 2020 November. For the first six months of the LHS, AT2019wey had a flux of $\sim 1$ mCrab, and displayed a power-law X-ray spectrum with photon index $Γ= 1.8$. From 2020 June to August, it brightened to $\sim 20$ mCrab. Spectral features characteristic of relativistic reflection became prominent. On 2020 August 21, the source left the "hard line" on the rms--intensity diagram, and transitioned from LHS to HIMS. The thermal disk component became comparable to the power-law component. A low-frequency quasi-periodic oscillation (QPO) was observed. The QPO central frequency increased as the spectrum softened. No evidence of pulsation was detected. We are not able to decisively determine the nature of the accretor (BH or NS). However, the BH option is favored by the position of this source on the $Γ$--$L_{\rm X}$, $L_{\rm radio}$--$L_{\rm X}$, and $L_{\rm opt}$--$L_{\rm X}$ diagrams. We find the BH candidate XTE J1752-223 to be an analog of AT2019wey. Both systems display outbursts with long plateau phases in the hard states. We conclude by noting the potential of SRG in finding new members of this emerging class of low luminosity and long-duration LMXB outbursts.

astro-ph.HE↗
arXiv2021

Spatio-Temporal Interaction Graph Parsing Networks for Human-Object Interaction Recognition

For a given video-based Human-Object Interaction scene, modeling the spatio-temporal relationship between humans and objects are the important cue to understand the contextual information presented in the video. With the effective spatio-temporal relationship modeling, it is possible not only to uncover contextual information in each frame but also to directly capture inter-time dependencies. It is more critical to capture the position changes of human and objects over the spatio-temporal dimension when their appearance features may not show up significant changes over time. The full use of appearance features, the spatial location and the semantic information are also the key to improve the video-based Human-Object Interaction recognition performance. In this paper, Spatio-Temporal Interaction Graph Parsing Networks (STIGPN) are constructed, which encode the videos with a graph composed of human and object nodes. These nodes are connected by two types of relations: (i) spatial relations modeling the interactions between human and the interacted objects within each frame. (ii) inter-time relations capturing the long range dependencies between human and the interacted objects across frame. With the graph, STIGPN learn spatio-temporal features directly from the whole video-based Human-Object Interaction scenes. Multi-modal features and a multi-stream fusion strategy are used to enhance the reasoning capability of STIGPN. Two Human-Object Interaction video datasets, including CAD-120 and Something-Else, are used to evaluate the proposed architectures, and the state-of-the-art performance demonstrates the superiority of STIGPN.

cs.CV↗
arXiv2021

Deep Natural Language Processing for LinkedIn Search

Many search systems work with large amounts of natural language data, e.g., search queries, user profiles, and documents. Building a successful search system requires a thorough understanding of textual data semantics, where deep learning based natural language processing techniques (deep NLP) can be of great help. In this paper, we introduce a comprehensive study for applying deep NLP techniques to five representative tasks in search systems: query intent prediction (classification), query tagging (sequential tagging), document ranking (ranking), query auto completion (language modeling), and query suggestion (sequence to sequence). We also introduce BERT pre-training as a sixth task that can be applied to many of the other tasks. Through the model design and experiments of the six tasks, readers can find answers to four important questions: (1). When is deep NLP helpful/not helpful in search systems? (2). How to address latency challenges? (3). How to ensure model robustness? This work builds on existing efforts of LinkedIn search, and is tested at scale on LinkedIn's commercial search engines. We believe our experiences can provide useful insights for the industry and research communities.

cs.IR↗
arXiv2021

Pre-Trained Models: Past, Present and Future

Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophisticated pre-training objectives and huge model parameters, large-scale PTMs can effectively capture knowledge from massive labeled and unlabeled data. By storing knowledge into huge parameters and fine-tuning on specific tasks, the rich knowledge implicitly encoded in huge parameters can benefit a variety of downstream tasks, which has been extensively demonstrated via experimental verification and empirical analysis. It is now the consensus of the AI community to adopt PTMs as backbone for downstream tasks rather than learning models from scratch. In this paper, we take a deep look into the history of pre-training, especially its special relation with transfer learning and self-supervised learning, to reveal the crucial position of PTMs in the AI development spectrum. Further, we comprehensively review the latest breakthroughs of PTMs. These breakthroughs are driven by the surge of computational power and the increasing availability of data, towards four important directions: designing effective architectures, utilizing rich contexts, improving computational efficiency, and conducting interpretation and theoretical analysis. Finally, we discuss a series of open problems and research directions of PTMs, and hope our view can inspire and advance the future study of PTMs.

cs.AI↗
arXiv2021

Deep Natural Language Processing for LinkedIn Search Systems

Many search systems work with large amounts of natural language data, e.g., search queries, user profiles and documents, where deep learning based natural language processing techniques (deep NLP) can be of great help. In this paper, we introduce a comprehensive study of applying deep NLP techniques to five representative tasks in search engines. Through the model design and experiments of the five tasks, readers can find answers to three important questions: (1) When is deep NLP helpful/not helpful in search systems? (2) How to address latency challenges? (3) How to ensure model robustness? This work builds on existing efforts of LinkedIn search, and is tested at scale on a commercial search engine. We believe our experiences can provide useful insights for the industry and research communities.

cs.CL↗
arXiv2021

Kähler-Ricci flow for deformed complex structures

Let $(M,J_0)$ be a Fano manifold which admits a Kähler-Ricci soliton, we analyze the behavior of the Kähler-Ricci flow near this soliton as we deform the complex structure $J_0$. First, we will establish an inequality of Lojasiewicz's type for Perelman's entropy along the Kähler-Ricci flow. Then we prove the convergence of Kähler-Ricci flow when the complex structure associated to the initial value lies in the kernel $Z$ or negative part of the second variation operator of Perelman's entropy. As applications, we solve the Yau-Tian-Donaldson conjecture for the existence of Kähler-Ricci solitons in the moduli space of complex structures near $J_0$, and we show that the kernel $Z$ corresponds to the local moduli space of Fano manifolds which are modified $K$-semistable. We also prove an uniqueness theorem for Kähler-Ricci solitons.

math.DG↗
arXiv2021

Photonic analogue of Mollow triplet with on-chip photon pair generation in dressed modes

Making analogy with atomic physics is a powerful tool for photonic technology, witnessed by the recent development in topological photonics and non-Hermitian photonics based on parity-time symmetry. Mollow triplet is a prominent atomic effect with both fundamental and technological importance. Here we demonstrate the analogue of Mollow triplet with quantum photonic systems. Photonic entanglement is generated with spontaneous nonlinear processes in dressed photonic modes, which are introduced through coherent multimode coupling. We further demonstrate the flexibility of the photonic system to realize different configurations of dressed states, leading to the controlled modification of Mollow triplet. Our work would enable the investigation of complex atomic processes and the realization of unique quantum functionalities based on photonic systems.

physics.optics↗
arXiv2021

WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training

Multi-modal pre-training models have been intensively explored to bridge vision and language in recent years. However, most of them explicitly model the cross-modal interaction between image-text pairs, by assuming that there exists strong semantic correlation between the text and image modalities. Since this strong assumption is often invalid in real-world scenarios, we choose to implicitly model the cross-modal correlation for large-scale multi-modal pre-training, which is the focus of the Chinese project `WenLan' led by our team. Specifically, with the weak correlation assumption over image-text pairs, we propose a two-tower pre-training model called BriVL within the cross-modal contrastive learning framework. Unlike OpenAI CLIP that adopts a simple contrastive learning method, we devise a more advanced algorithm by adapting the latest method MoCo into the cross-modal scenario. By building a large queue-based dictionary, our BriVL can incorporate more negative samples in limited GPU resources. We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model. Extensive experiments demonstrate that the pre-trained BriVL model outperforms both UNITER and OpenAI CLIP on various downstream tasks.

cs.CV↗
arXiv2021

A Systematic Collection of Medical Image Datasets for Deep Learning

The astounding success made by artificial intelligence (AI) in healthcare and other fields proves that AI can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data-dependent and require large datasets for training. The lack of data in the medical imaging field creates a bottleneck for the application of deep learning to medical image analysis. Medical image acquisition, annotation, and analysis are costly, and their usage is constrained by ethical restrictions. They also require many resources, such as human expertise and funding. That makes it difficult for non-medical researchers to have access to useful and large medical data. Thus, as comprehensive as possible, this paper provides a collection of medical image datasets with their associated challenges for deep learning research. We have collected information of around three hundred datasets and challenges mainly reported between 2013 and 2020 and categorized them into four categories: head & neck, chest & abdomen, pathology & blood, and ``others''. Our paper has three purposes: 1) to provide a most up to date and complete list that can be used as a universal reference to easily find the datasets for clinical image analysis, 2) to guide researchers on the methodology to test and evaluate their methods' performance and robustness on relevant datasets, 3) to provide a ``route'' to relevant algorithms for the relevant medical topics, and challenge leaderboards.

eess.IV↗