arXiv subjects

Tian Tian

Publications and source records attributed to Tian Tian.

71 records · Page 4Linked to original sources

arXiv2019

Observation of dynamical phase transitions in a topological nanomechanical system

Dynamical phase transitions (DPTs), characterized by non-analytic behaviors in time domain, extend the equilibrium phase transitions to far-from-equilibrium situations. It has been predicted that DPTs can be precisely identified by the discontinuities of the Pancharatnam geometric phase (PGP) during the time evolution. However, PGP always mixes with dynamical phase and the experimental observation of DPTs by PGP is still absent. Here, we theoretically present a novel scheme for eliminating the dynamical phase by taking advantage of chiral symmetry in the Su-Schrieffer-Heeger (SSH) model, and experimentally observe DPTs by directly measuring PGP in a quenched topological nanomechanical lattice. Time-dependent topological structures of the SSH model are configured by eight strong-coupled high-quality-factor nanomechanical oscillators. By measuring the vibration phase and the normalized amplitude of the edge oscillator, we show a direct classical analog of DPTs. Furthermore, we experimentally demonstrate the robustness of DPTs against weak structure disorders, and numerically explore the relation between DPTs and the equilibrium phase boundary. This work not only establishes the quantitative method to identify DPTs, but also opens the door for studying non-equilibrium topological dynamics with a well-controlled nanomechanical system.

quant-ph↗

arXiv2019

Quantum beats and metrology in a rapidly rotating Nitrogen-Vacancy center

In this paper, we study the dynamical behavior and quantum metrology in a rotating Nitrogen-Vacancy(NV) center system which is subject to an external magnetic field. Based on the recently realized rapid rotation of nano-rotor [J. Ahn, et. al., Phys. Rev. Lett. 121, 033603 (2018) and R. Reimann, et. al., Phys. Rev. Lett. 121, 033602 (2018)], the frequency of the rotation is close to that of the intrinsic frequency of the NV center system, we predict the quantum beats phenomenon in the time domain and show that the quantum metrology can be enhanced by the superposition effect in our system.

quant-ph↗

arXiv2019

MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

The Arcade Learning Environment (ALE) is a popular platform for evaluating reinforcement learning agents. Much of the appeal comes from the fact that Atari games demonstrate aspects of competency we expect from an intelligent agent and are not biased toward any particular solution approach. The challenge of the ALE includes (1) the representation learning problem of extracting pertinent information from raw pixels, and (2) the behavioural learning problem of leveraging complex, delayed associations between actions and rewards. Often, the research questions we are interested in pertain more to the latter, but the representation learning problem adds significant computational expense. We introduce MinAtar, short for miniature Atari, a new set of environments that capture the general mechanics of specific Atari games while simplifying the representational complexity to focus more on the behavioural challenges. MinAtar consists of analogues of five Atari games: Seaquest, Breakout, Asterix, Freeway and Space Invaders. Each MinAtar environment provides the agent with a 10x10xn binary state representation. Each game plays out on a 10x10 grid with n channels corresponding to game-specific objects, such as ball, paddle and brick in the game Breakout. To investigate the behavioural challenges posed by MinAtar, we evaluated a smaller version of the DQN architecture as well as online actor-critic with eligibility traces. With the representation learning problem simplified, we can perform experiments with significantly less computational expense. In our experiments, we use the saved compute time to perform step-size parameter sweeps and more runs than is typical for the ALE. Experiments like this improve reproducibility, and allow us to draw more confident conclusions. We hope that MinAtar can allow researchers to thoroughly investigate behavioural challenges similar to those inherent in the ALE.

cs.LG↗

arXiv2018

Multi-resolution Progressive Computational Ghost Imaging

Ghost imaging needs massive measurements to obtain an image with good visibility and the imaging speed is usually very low. In order to realize real-time high-resolution ghost imaging of a target which is located in a scenario with a large field of view (FOV), we propose a high-speed multi-resolution progressive computational ghost imaging approach. The target area is firstly locked by a low-resolution image with a small number of measurements, then high-resolution imaging of the target can be obtained by only modulating the light fields corresponding to the target area. The experiments verify the feasibility of the approach. The influence of detection signal-to-noise ratio on the quality of multi-resolution progressive computational ghost imaging is also investigated experimentally. This approach may be applied to some practical application scenarios such as ground-to-air or air-to-air imaging with a large FOV.

eess.IV↗

arXiv2018

Next-Generation Quantum Theory of Atoms in Molecules for the Ground and Excited State of the Ring-Opening of Cyclohexadiene (CHD)

The factors underlying the experimentally observed branching ratio (70:30) of the (1,3-cyclohexadiene) CHD$\rightarrow$HT (1,3,5-hexatriene) photochemical ring-opening reaction are investigated. The ring-opening reaction path is optimized by a high-level multi-reference DFT method and the density along the path is analyzed by the QTAIM and stress tensor methods. The performed density analysis suggests that, in both $S_{1}$ and $S_{0}$ electronic states, there exists an attractive interaction between the ends of the fissile $σ$ -bond of CHD that steers the ring-opening reaction predominantly in the direction of restoration of the ring. It is suggested that opening of the ring and formation of the reaction product (HT) can only be achieved when there is a sufficient persistent nuclear momentum in the direction of stretching of the fissile bond. As this orientation of the nuclear momentum vector can be expected to be relatively rare during the dynamics, this explains the observed low quantum yield of the ring-opening reaction.

physics.chem-ph↗

arXiv2018

Semi-crowdsourced Clustering with Deep Generative Models

We consider the semi-supervised clustering problem where crowdsourcing provides noisy information about the pairwise comparisons on a small subset of data, i.e., whether a sample pair is in the same cluster. We propose a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset. The two parts share the latent variables. To make the model automatically trade-off between its complexity and fitting data, we also develop its fully Bayesian variant. The challenge of inference is addressed by fast (natural-gradient) stochastic variational inference algorithms, where we effectively combine variational message passing for the relational part and amortized learning of the DGM under a unified framework. Empirical results on synthetic and real-world datasets show that our model outperforms previous crowdsourced clustering methods.

stat.ML↗

arXiv2018

Estimation of Spatial-Temporal Gait Parameters based on the Fusion of Inertial and Film-Pressure Signals

Gait analysis (GA) has been widely used in physical activity monitoring and clinical contexts, and the estimation of the spatial-temporal gait parameters is of primary importance for GA. With the quick development of smart tiny sensors, GA methods based on wearable devices have become more popular recently. However, most existing wearable GA methods focus on data analysis from inertial sensors. In this paper, we firstly present a two-foot-worn in-shoe system (Gaitboter) based on low-cost, wearable and multimodal sensors' fusion for GA, comprising an inertial sensor and eight film-pressure sensors with each foot for gait raw data collection while walking. Secondly, a GA algorithm for estimating the spatial-temporal parameters of gait is proposed. The algorithm fully uses the fusion of two kinds of sensors' signals: inertial sensor and film-pressure sensor, in order to estimate the spatial-temporal gait parameters, such as stance phase, swing phase, double stance phase, cadence, step time, stride time, stride length, velocity. Finally, to verify the effectiveness of this system and algorithm of the paper, an experiment is performed with 27 stoke patients from local hospital that the spatial-temporal gait parameters obtained with the system and the algorithm are compared with a GA tool used in medical laboratories. And the experimental results show that it achieves very good consistency and correlation between the proposed system and the compared GA tool.

eess.SP↗

arXiv2018

General Monogamy of Tsallis $q$-Entropy Entanglement in Multiqubit Systems

In this paper, we study the monogamy inequality of Tsallis-q entropy entanglement. We first provide an analytic formula of Tsallis-q entropy entanglement in two-qubit systems for $\frac{5-\sqrt{13}}{2}\leq q\leq\frac{5+\sqrt{13}}{2}.$ The analytic formula of Tsallis-q entropy entanglement in $2\otimes d$ system is also obtained and we show that Tsallis-q entropy entanglement satisfies a set of hierarchical monogamy equalities. Furthermore, we prove the squared Tsallis-q entropy entanglement follows a general inequality in the qubit systems. Based on the monogamy relations, a set of multipartite entanglement indicators is constructed, which can detect all genuine multiqubit entangled states even in the case of $N$-tangle vanishes. Moreover, we study some examples in multipartite higher-dimensional system for the monogamy inequalities.

quant-ph↗

arXiv2018

Adversarial Attack on Graph Structured Data

Deep learning on graph structures has shown exciting results in various applications. However, few attentions have been paid to the robustness of such models, in contrast to numerous research work for image or text adversarial attack and defense. In this paper, we focus on the adversarial attacks that fool the model by modifying the combinatorial structure of data. We first propose a reinforcement learning based attack method that learns the generalizable attack policy, while only requiring prediction labels from the target classifier. Also, variants of genetic algorithms and gradient methods are presented in the scenario where prediction confidence or gradients are available. We use both synthetic and real-world data to show that, a family of Graph Neural Network models are vulnerable to these attacks, in both graph-level and node-level classification tasks. We also show such attacks can be used to diagnose the learned classifiers.

cs.LG↗

arXiv2018

Spectroscopic size and thickness metrics for liquid-exfoliated h-BN

For many 2D materials, optical and Raman spectra are richly structured, and convey information on a range of parameters including nanosheet size and defect content. By contrast, the equivalent spectra for h-BN are relatively simple, with both the absorption and Raman spectra consisting of a single feature each, disclosing relatively little information. Here, the ability to size-select liquid-exfoliated h-BN nanosheets has allowed us to comprehensively study the dependence of h-BN optical spectra on nanosheet dimensions. We find the optical extinction coefficient spectrum to vary systematically with nanosheet lateral size due to the presence of light scattering. Conversely, once light scattering has been decoupled to give the optical absorbance spectra, we find the size dependence to be mostly removed save for a weak but well-defined variation in energy of peak absorbance with nanosheet thickness. This finding is corroborated by our ab initio GW and Bethe-Salpeter equation calculations, which include electron correlations and quasiparticle self-consistency (QSGW). In addition, while we find the position of the sole h-BN Raman line to be invariant with nanosheet dimensions, the linewidth appears to vary weakly with nanosheet thickness. These size-dependent spectroscopic properties can be used as metrics to estimate nanosheet thickness from spectroscopic data.

cond-mat.mes-hall↗

arXiv2018

Asymmetric Electric Field Screening in van der Waals Heterostructures

Electric field screening plays an important role in the physical and chemical properties of materials and their devices. Here, we use a compelling set of theoretical and experimental techniques involving van der Waals (vdW) ab initio density functional theory (DFT) simulations, quantum capacitance-based classical model and electric force microscopy (EFM) to elucidate the intrinsic dielectric screening properties of vdW heterostructures (vdWHs) formed by MoS2 and graphene layers. We experimentally observed an asymmetric electric response in the MoS2/Graphene vdWHs under different directions of the external electric field. That is, when the electric fields are shed towards graphene, a large amount of polarized charges screen the fields, but as the sign of the field was reversed, a strong depolarization field was present, and a partial screening was detected. This effect is thickness-dependent, in particular on the number of the MoS2 layers; whereas increased thickness of graphene showed a small effect on their electrical and screening behavior. Our results indicate that asymmetric dipolar contributions at the interface between graphene and MoS2 are the main cause to the unusual field-effect screening in the vdWHs. This work not only provides new insights on the screening properties of a vast amount of heterojunction fabricated so far, but also uncovers the great potential of controlling a fundamental property, such as screening, for device applications.

cond-mat.mes-hall↗

arXiv2016

Generalised monogamy relation of convex-roof extended negativity in multi-level systems

In this paper, we investigate the generalised monogamy inequalities of convex-roof extended negativity (CREN) in multi-level systems. The generalised monogamy inequalities provide the upper and lower bounds of bipartite entanglement, which are obtained by using CREN and the CREN of assistance (CRENOA). Furthermore, we show that the CREN of multi-qubit pure states satisfies some monogamy relations. Additionally, we test the generalised monogamy inequalities for qudits by considering the partially coherent superposition of a generalised W-class state in a vacuum and show that the generalised monogamy inequalities are satisfied in this case as well.

quant-ph↗

arXiv2016

Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification

Due to the lack of structured knowledge applied in learning distributed representation of cate- gories, existing work cannot incorporate category hierarchies into entity information. We propose a framework that embeds entities and categories into a semantic space by integrating structured knowledge and taxonomy hierarchy from large knowledge bases. The framework allows to com- pute meaningful semantic relatedness between entities and categories. Our framework can han- dle both single-word concepts and multiple-word concepts with superior performance on concept categorization and yield state of the art results on dataless hierarchical classification.

cs.CL↗

arXiv2016

Machine Comprehension Based on Learning to Rank

Machine comprehension plays an essential role in NLP and has been widely explored with dataset like MCTest. However, this dataset is too simple and too small for learning true reasoning abilities. \cite{hermann2015teaching} therefore release a large scale news article dataset and propose a deep LSTM reader system for machine comprehension. However, the training process is expensive. We therefore try feature-engineered approach with semantics on the new dataset to see how traditional machine learning technique and semantics can help with machine comprehension. Meanwhile, our proposed L2R reader system achieves good performance with efficiency and less training data.

cs.CL↗

arXiv2016

Joint Embeddings of Hierarchical Categories and Entities

Due to the lack of structured knowledge applied in learning distributed representation of categories, existing work cannot incorporate category hierarchies into entity information.~We propose a framework that embeds entities and categories into a semantic space by integrating structured knowledge and taxonomy hierarchy from large knowledge bases. The framework allows to compute meaningful semantic relatedness between entities and categories.~Compared with the previous state of the art, our framework can handle both single-word concepts and multiple-word concepts with superior performance in concept categorization and semantic relatedness.

cs.CL↗

arXiv2016

Non-reciprocal Radio Frequency Transduction in a Parametric Mechanical Artificial Lattice

Generating non-reciprocal radio frequency transduction plays important roles in a wide range of research and applications, and an aspiration is to integrate this functionality into micro-circuit without introducing magnetic field, which, however, remains challenging. By designing a 1D artificial lattice structure with neighbor-interaction engineered parametrically, we predicted a non-reciprocity transduction with giant unidirectionality. We then experimentally demonstrated the phenomenon on a nano-electromechanical chip fabricated by conventional complementary metal-silicon processing. A unidirectionality with isolation as high as 24dB is achieved and several different transduction schemes are realized by programming the control voltages topology. Apart from being used as a radio frequency isolator, the system provides a way to build practical on-chip programmable device for many researches and applications in radio frequency domain.

cond-mat.mes-hall↗

arXiv2015

Jointly Modeling Topics and Intents with Global Order Structure

Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words. While the topics are relatively unchanged through one document, the rhetorical functions of sentences usually change following certain orders in discourse. We propose GMM-LDA, a topic modeling based Bayesian unsupervised model, to analyze the document intent structure cooperated with order information. Our model is flexible that has the ability to combine the annotations and do supervised learning. Additionally, entropic regularization can be introduced to model the significant divergence between topics and intents. We perform experiments in both unsupervised and supervised settings, results show the superiority of our model over several state-of-the-art baselines.

cs.CL↗