arXiv subjects

Qi Zheng

Publications and source records attributed to Qi Zheng.

At least 91 records · Page 5Linked to original sources

arXiv2022

Tunable Sample-wide Electronic Kagome Lattice in Low-angle Twisted Bilayer Graphene

Overlaying two graphene layers with a small twist angle can create a moire superlattice to realize exotic phenomena that are entirely absent in graphene monolayer. A representative example is the predicted formation of localized pseudo-Landau levels (PLLs) with Kagome lattice in tiny-angle twisted bilayer graphene (TBG) with theta < 0.3 deg when the graphene layers are subjected to different electrostatic potentials. However, this was shown only for the model of rigidly rotated TBG which is not realized in reality due to an interfacial structural reconstruction. It is believed that the interfacial structural reconstruction strongly inhibits the formation of the PLLs. Here, we systematically study electronic properties of the TBG with 0.075 deg < theta < 1.2 deg and demonstrate, unexpectedly, that the PLLs are quite robust for all the studied TBG. The structural reconstruction suppresses the formation of the emergent Kagome lattice in the tiny-angle TBG. However, for the TBG around magic angle, the sample-wide electronic Kagome lattices with tunable lattice constants are directly imaged by using scanning tunneling microscope. Our observations open a new direction to explore exotic correlated phases in moire systems.

cond-mat.mes-hall↗

arXiv2022

Bypass Network for Semantics Driven Image Paragraph Captioning

Image paragraph captioning aims to describe a given image with a sequence of coherent sentences. Most existing methods model the coherence through the topic transition that dynamically infers a topic vector from preceding sentences. However, these methods still suffer from immediate or delayed repetitions in generated paragraphs because (i) the entanglement of syntax and semantics distracts the topic vector from attending pertinent visual regions; (ii) there are few constraints or rewards for learning long-range transitions. In this paper, we propose a bypass network that separately models semantics and linguistic syntax of preceding sentences. Specifically, the proposed model consists of two main modules, i.e. a topic transition module and a sentence generation module. The former takes previous semantic vectors as queries and applies attention mechanism on regional features to acquire the next topic vector, which reduces immediate repetition by eliminating linguistics. The latter decodes the topic vector and the preceding syntax state to produce the following sentence. To further reduce delayed repetition in generated paragraphs, we devise a replacement-based reward for the REINFORCE training. Comprehensive experiments on the widely used benchmark demonstrate the superiority of the proposed model over the state of the art for coherence while maintaining high accuracy.

cs.CV↗

arXiv2022

About 300 days optical quasi-periodic oscillations in the long-term light curves of the blazar PKS 2155-304

Based on the long-term light curves collected from the Catalina Sky Survey (CSS) (from 2005 to 2013) and the All-Sky Automated Survey for Supernovae (ASAS-SN) (from 2014 to 2018), optical quasi-periodic oscillations (QPOs) about 300 days can be well determined in the well-known blazar PKS~2155-304 through four different methods: the generalized Lomb-Scargle periodogram (GLSP) method, the weighted wavelet Z-transform (WWZ) technique, the epoch-folded method and redfit method. The GLSP determined significance level for the periodicity is higher than 99.9999\% based on a false alarm probability. The redfit provided confidence level for the periodicity is higher than 99\% in ASAS-SN light curve, after considering the effects of red noise. Based on continuous autoregressive (CAR) process created artificial light curves, the probability of detecting fake QPOs is lower than 0.8\%. The determined optical periodicity of 300 days from CSS and ASAS-SN light curves is well consistent with the reported optical periodicity in the literature. Moreover, three possible models are discussed to explain the optical QPOs in PKS 2155-304: the relativistic Frame-dragging effect, the binary black hole (BBH) model and the jet precession model.

astro-ph.HE↗

arXiv2022

Molecular Collapse States in Elliptical Graphene/WSe2 Heterostructure Quantum Dots

In relativistic physics, both atomic collapse in heavy nucleus and Hawking radiation in black hole are predicted to occur through Klein tunneling process that couples particles and antiparticles. Recently, atomic collapse states (ACSs) were explicitly realized in graphene because of its relativistic Dirac excitation with large fine structure constant. However, essential role of the Klein tunneling on the ACSs remains elusive in experiment. Here we systematically study the quasibound states in elliptical graphene quantum dots (GQDs). Bonding and antibonding molecular collapse states formed by two coupled ACSs are observed in the elliptical GQDs. Our experiments, supported by theoretical calculations, indicate that the antibonding state of the ACSs will change into a Klein-tunneling-induced quasibound state, revealing deep connection between the ACSs and the Klein tunneling.

cond-mat.mes-hall↗

arXiv2022

Visual Superordinate Abstraction for Robust Concept Learning

Concept learning constructs visual representations that are connected to linguistic semantics, which is fundamental to vision-language tasks. Although promising progress has been made, existing concept learners are still vulnerable to attribute perturbations and out-of-distribution compositions during inference. We ascribe the bottleneck to a failure of exploring the intrinsic semantic hierarchy of visual concepts, e.g. \{red, blue,...\} $\in$ `color' subspace yet cube $\in$ `shape'. In this paper, we propose a visual superordinate abstraction framework for explicitly modeling semantic-aware visual subspaces (i.e. visual superordinates). With only natural visual question answering data, our model first acquires the semantic hierarchy from a linguistic view, and then explores mutually exclusive visual superordinates under the guidance of linguistic hierarchy. In addition, a quasi-center visual concept clustering and a superordinate shortcut learning schemes are proposed to enhance the discrimination and independence of concepts within each visual superordinate. Experiments demonstrate the superiority of the proposed framework under diverse settings, which increases the overall answering accuracy relatively by 7.5\% on reasoning with perturbations and 15.6\% on compositional generalization tests.

cs.CV↗

arXiv2022

Progressive Localization Networks for Language-based Moment Localization

This paper targets the task of language-based video moment localization. The language-based setting of this task allows for an open set of target activities, resulting in a large variation of the temporal lengths of video moments. Most existing methods prefer to first sample sufficient candidate moments with various temporal lengths, and then match them with the given query to determine the target moment. However, candidate moments generated with a fixed temporal granularity may be suboptimal to handle the large variation in moment lengths. To this end, we propose a novel multi-stage Progressive Localization Network (PLN) which progressively localizes the target moment in a coarse-to-fine manner. Specifically, each stage of PLN has a localization branch, and focuses on candidate moments that are generated with a specific temporal granularity. The temporal granularities of candidate moments are different across the stages. Moreover, we devise a conditional feature manipulation module and an upsampling connection to bridge the multiple localization branches. In this fashion, the later stages are able to absorb the previously learned information, thus facilitating the more fine-grained localization. Extensive experiments on three public datasets demonstrate the effectiveness of our proposed PLN for language-based moment localization, especially for localizing short moments in long videos.

cs.CV↗

arXiv2022

FAVER: Blind Quality Prediction of Variable Frame Rate Videos

Video quality assessment (VQA) remains an important and challenging problem that affects many applications at the widest scales. Recent advances in mobile devices and cloud computing techniques have made it possible to capture, process, and share high resolution, high frame rate (HFR) videos across the Internet nearly instantaneously. Being able to monitor and control the quality of these streamed videos can enable the delivery of more enjoyable content and perceptually optimized rate control. Accordingly, there is a pressing need to develop VQA models that can be deployed at enormous scales. While some recent effects have been applied to full-reference (FR) analysis of variable frame rate and HFR video quality, the development of no-reference (NR) VQA algorithms targeting frame rate variations has been little studied. Here, we propose a first-of-a-kind blind VQA model for evaluating HFR videos, which we dub the Framerate-Aware Video Evaluator w/o Reference (FAVER). FAVER uses extended models of spatial natural scene statistics that encompass space-time wavelet-decomposed video signals, to conduct efficient frame rate sensitive quality prediction. Our extensive experiments on several HFR video quality datasets show that FAVER outperforms other blind VQA algorithms at a reasonable computational cost. To facilitate reproducible research and public evaluation, an implementation of FAVER is being made freely available online: \url{https://github.com/uniqzheng/HFR-BVQA}.

eess.IV↗

arXiv2021

Imaging field-tuned quantum Hall broken-symmetry orders and quantum Hall conducting channel in charge-neutral graphene WSe2 heterostructure

The zeroth Landau level (0LL) in graphene has emerged as a flat-band platform in which distinct many-body phases can be explored with unprecedented control by simply tuning the strength and/or direction of magnetic fields1-22. A rich set of quantum Hall ferromagnetic (QHFM) phases with different lattice-scale symmetry-breaking orders are predicted to be realized in high magnetic fields when the 0LL in graphene is half filled1-8,13-16. Here we report a field-tuned continuous quantum phase transition of different valley orderings in QHFM of charge-neutral graphene on insulating tungsten diselenide (WSe2). The phase transition is clearly revealed by anomalous field-dependent energy gap in the half-filled 0LL. Via atomic resolution imaging of electronic wavefunctions during the phase transition, we observe microscopic signatures of field-tuned continuous-varied valley polarization and valley inversion, which are unexpected and beyond current theory predictions. Moreover, the topological quantum Hall conducting channel of the graphene is directly imaged when the substrate (WSe2) introduces band bending of the 0LL.

cond-mat.mes-hall↗

arXiv2021

Decoupling Visual-Semantic Feature Learning for Robust Scene Text Recognition

Semantic information has been proved effective in scene text recognition. Most existing methods tend to couple both visual and semantic information in an attention-based decoder. As a result, the learning of semantic features is prone to have a bias on the limited vocabulary of the training set, which is called vocabulary reliance. In this paper, we propose a novel Visual-Semantic Decoupling Network (VSDN) to address the problem. Our VSDN contains a Visual Decoder (VD) and a Semantic Decoder (SD) to learn purer visual and semantic feature representation respectively. Besides, a Semantic Encoder (SE) is designed to match SD, which can be pre-trained together by additional inexpensive large vocabulary via a simple word correction task. Thus the semantic feature is more unbiased and precise to guide the visual feature alignment and enrich the final character representation. Experiments show that our method achieves state-of-the-art or competitive results on the standard benchmarks, and outperforms the popular baseline by a large margin under circumstances where the training set has a small size of vocabulary.

cs.CV↗

arXiv2021

Coexistence of electron whispering-gallery modes and atomic collapse states in graphene WSe2 heterostructure quantum dots

The relativistic massless charge carriers with a Fermi velocity of about c300 in graphene enable us to realize two distinct types of resonances (c, the speed of light in vacuum). One is electron whispering-gallery mode in graphene quantum dots arising from the Klein tunneling of the massless Dirac fermions. The other is atomic collapse state, which has never been observed in experiment with real atoms due to the difficulty of producing heavy nuclei with charge Z 170, however, can be realized near a Coulomb impurity in graphene with a charge Z 1 because of the small velocity of the Dirac excitations. Here, unexpectedly, we demonstrate that both the electron whispering-gallery modes and atomic collapse states coexist in grapheneWSe2 heterostructure quantum dots due to the Coulomb-like potential near their edges. By applying a perpendicular magnetic field, evolution from the atomic collapse states to unusual Landau levels in the collapse regime are explored for the first time.

cond-mat.mes-hall↗

arXiv2021

SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is an emerging fine-grained sentiment analysis task that aims to extract aspects, classify corresponding sentiment polarities and find opinions as the causes of sentiment. The latest research tends to solve the ABSA task in a unified way with end-to-end frameworks. Yet, these frameworks get fine-tuned from downstream tasks without any task-adaptive modification. Specifically, they do not use task-related knowledge well or explicitly model relations between aspect and opinion terms, hindering them from better performance. In this paper, we propose SentiPrompt to use sentiment knowledge enhanced prompts to tune the language model in the unified framework. We inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets. Experimental results demonstrate that our approach can outperform strong baselines on Triplet Extraction, Pair Extraction, and Aspect Term Extraction with Sentiment Classification by a notable margin.

cs.CL↗

arXiv2021

Inference for High Dimensional Censored Quantile Regression

With the availability of high dimensional genetic biomarkers, it is of interest to identify heterogeneous effects of these predictors on patients' survival, along with proper statistical inference. Censored quantile regression has emerged as a powerful tool for detecting heterogeneous effects of covariates on survival outcomes. To our knowledge, there is little work available to draw inference on the effects of high dimensional predictors for censored quantile regression. This paper proposes a novel procedure to draw inference on all predictors within the framework of global censored quantile regression, which investigates covariate-response associations over an interval of quantile levels, instead of a few discrete values. The proposed estimator combines a sequence of low dimensional model estimates that are based on multi-sample splittings and variable selection. We show that, under some regularity conditions, the estimator is consistent and asymptotically follows a Gaussian process indexed by the quantile level. Simulation studies indicate that our procedure can properly quantify the uncertainty of the estimates in high dimensional settings. We apply our method to analyze the heterogeneous effects of SNPs residing in lung cancer pathways on patients' survival, using the Boston Lung Cancer Survival Cohort, a cancer epidemiology study on the molecular mechanism of lung cancer.

stat.ME↗

arXiv2021

Pouring Dynamics Estimation Using Gated Recurrent Units

One of the most commonly performed manipulation in a human's daily life is pouring. Many factors have an effect on target accuracy, including pouring velocity, rotation angle, geometric of the source, and the receiving containers. This paper presents an approach to increase the repeatability and accuracy of the robotic manipulator by estimating the change in the amount of water of the pouring cup to a sequence of pouring actions using multiple layers of the deep recurrent neural network, especially gated recurrent units (GRU). The proposed GRU model achieved a validation mean squared error as low as 1e-4 (lbf) for the predicted value of weight f(t). This paper contains a comprehensive evaluation and analysis of numerous experiments with various designs of recurrent neural networks and hyperparameters fine-tuning.

cs.LG↗

arXiv2021

Classifying States of Cooking Objects Using Convolutional Neural Network

Automated cooking machine is a goal for the future. The main aim is to make the cooking process easier, safer, and create human welfare. To allow robots to accurately perform the cooking activities, it is important for them to understand the cooking environment and recognize the objects, especially correctly identifying the state of the cooking objects. This will significantly improve the correctness of the following cooking recipes. In this project, several parts of the experiment were conducted to design a robust deep convolutional neural network for classifying the state of the cooking objects from scratch. The model is evaluated by using various techniques, such as adjusting architecture layers, tuning key hyperparameters, and using different optimization techniques to maximize the accuracy of state classification.

cs.CV↗

arXiv2021

Two distinct superconducting states controlled by orientation of local wrinkles in LiFeAs

We observe two types of superconducting states controlled by orientations of local wrinkles on the surface of LiFeAs. Using scanning tunneling microscopy/spectroscopy, we find type-I wrinkles enlarge the superconducting gaps and enhance the transition temperature, whereas type-II wrinkles significantly suppress the superconducting gaps. The vortices on wrinkles show a C2 symmetry, indicating the strain effects on the wrinkles. A discontinuous switch of superconductivity occurs at the border between two different wrinkles. Our results demonstrate that the local strain effect could affect superconducting order parameter of LiFeAs with a possible Lifshitz transition, by alternating crystal structure in different directions.

cond-mat.supr-con↗

arXiv2020

Anticipative Tracking with the Short-Term Synaptic Plasticity of Spintronic Devices

Real-time tracking of high-speed objects in cognitive tasks is challenging in the present artificial intelligence techniques because the data processing and computation are time-consuming resulting in impeditive time delays. A brain-inspired continuous attractor neural network (CANN) can be used to track quickly moving targets, where the time delays are intrinsically compensated if the dynamical synapses in the network have the short-term plasticity. Here, we show that synapses with short-term depression can be realized by a magnetic tunnel junction, which perfectly reproduces the dynamics of the synaptic weight in a widely applied mathematical model. Then, these dynamical synapses are incorporated into one-dimensional and two-dimensional CANNs, which are demonstrated to have the ability to predict a moving object via micromagnetic simulations. This portable spintronics-based hardware for neuromorphic computing needs no training and is therefore very promising for the tracking technology for moving targets.

physics.app-ph↗

arXiv2020

Localized spin-orbit polaron in magnetic Weyl semimetal Co$_3$Sn$_2$S$_2$

The kagome lattice Co$_3$Sn$_2$S$_2$ exhibits the quintessential topological phenomena of a magnetic Weyl semimetal such as the chiral anomaly and Fermi-arc surface states. Probing its magnetic properties is crucial for understanding this correlated topological state. Here, using spin-polarized scanning tunneling microscopy/spectroscopy (STM/S), we report the discovery of localized spin-orbit polarons (SOPs) with three-fold rotation symmetry nucleated around single-S vacancies in Co$_3$Sn$_2$S$_2$. The SOPs carry a spin-polarized magnetic moment and a large orbital magnetization of a topological origin associated with the Berry phase and the persistent circulating current. Appreciable magneto-elastic coupling of the SOP is detected by atomic force microscope and STM. Our findings suggest that the SOPs can enhance magnetism and stability of the magnetic Weyl nodes for more robust time-reversal-symmetry-breaking topological phenomena. Controlled engineering of the SOPs may pave the way toward practical applications in functional quantum devices.

cond-mat.mtrl-sci↗

arXiv2020

Recurrent Neural Networks Made of Magnetic Tunnel Junctions

Artificial intelligence based on artificial neural networks, which are originally inspired by the biological architectures of human brain, has mostly been realized using software but executed on conventional von Neumann computers, where the so-called von Neumann bottleneck essentially limits the executive efficiency due to the separate computing and storage units. Therefore, a suitable hardware platform that can exploit all the advantages of brain-inspired computing is highly desirable. Based upon micromagnetic simulation of the magnetization dynamics, we demonstrate theoretically and numerically that recurrent neural networks consisting of as few as 40 magnetic tunnel junctions can generate and recognize periodic time series after they are trained with an efficient machine-learning algorithm. With ultrahigh operating speed, nonvolatile memory and high endurance and reproducibility, spintronic devices are promising hardware candidates for neuromorphic computing.

cond-mat.dis-nn↗