arXiv subjects

Jian Liu

Publications and source records attributed to Jian Liu.

At least 397 records · Page 22Linked to original sources

arXiv2019

A Dual-gate MoS2 Photodetector Based on Interface Coupling Effect

Two-dimensional (2D) transition metal dichalcogenides (TMDs) based photodetectors have shown great potential for the next generation optoelectronics. However, most of the reported MoS2 photodetectors function under the photogating effect originated from the charge-trap mechanism, which is difficult for quantitative control. Such devices generally suffer from a poor compromise between response speed and responsivity (R) and large dark current. Here, a dual-gated (DG) MoS2 phototransistor operating based on the interface coupling effect (ICE) is demonstrated. By simultaneously applying a negative top-gate voltage (VTG) and positive back-gate voltage (VBG) to the MoS2 channel, the photo-generated holes can be effectively trapped in the depleted region under TG. An ultrahigh R of ~1E5 A/W and detectivity (D*) of ~1E14 Jones have been achieved in several devices with different thickness under Pin of 53 uW/cm2 at VTG=-5 V. Moreover, the response time of the DG phototransistor can also be modulated based on the ICE. Based on these systematic measurements of MoS2 DG phototransistors, the results show that the ICE plays an important role in the modulation of photoelectric performances. Our results also pave the way for the future optoelectrical application of 2D TMDs materials and prompt for further investigation in the DG structured phototransistors.

physics.app-ph↗

arXiv2019

Epitaxial growth and antiferromagnetism of Sn-substituted perovskite iridate SrIr$_{0.8}$Sn$_{0.2}$O$_3$

5d iridates have shown vast emergent phenomena due to a strong interplay among its lattice, charge and spin degrees of freedom, because of which the potential in spintronic application of the thin-film form is highly leveraged. Here we have epitaxially stabilized perovskite SrIr$_{0.8}$Sn$_{0.2}$O$_3$ on [001] SrTiO$_3$ substrates through pulsed laser deposition and systematically characterized the structural, electronic and magnetic properties. Physical properties measurements unravel an insulating ground state with a weak ferromagnetism in the compressively strained epitaxial film. The octahedral rotation pattern is identified by synchrotron x-ray diffraction, resolving a mix of $a^+b^-c^-$ and $a^-b^+c^-$ domains. X-ray magnetic resonant scattering directly demonstrates a G-type antiferromagnetic structure of the magnetic order and the spin canting nature of the weak ferromagnetism.

cond-mat.mtrl-sci↗

arXiv2019

Enhanced sensing of non-Newtonian effects at ultrashort range with exceptional points in optomechanical systems

We propose an optomechanical nano-gravimeter based on exceptional points. The system is a coupled cavity optomechanical system, in which the gain and loss are applied by driving the cavities with a blue detuned and red detuned electromagnetic field, respectively. When the gain and loss reach a balance, the system will show the degeneracy of exceptional points, and any perturbation will cause an eigenfrequencies split, which is proportional to the square root of the perturbation strength. Compared with the traditional optomechanical sensors, the sensitivity is greatly enhanced. This work paves the way for the design of optomechanical ultrasensitive force sensors that can be applied to detect non-Newtonian effects, high-order weak interactions, and so on.

quant-ph↗

arXiv2019

Faster super-resolution imaging with auto-correlation two-step deconvolution

Despite super-resolution fluorescence blinking microscopes break the diffraction limit, the intense phototoxic illumination and long-term image sequences thus far still pose to major challenges in visualizing live-organisms. Here, we proposed a super-resolution method based on auto-correlation two-step deconvolution (SACD) to enhance the temporal resolution at lower signal intensity levels. Unlike conventional techniques, such as super-resolution optical fluctuation imaging (SOFI) or stochastic optical reconstruction microscopy (STORM), our model allows 16 frames to generate super-resolution images, without noticeable degradation in recording quality. We demonstrate SACD both in simulated predictions and experimental validations, with the resulting spatial resolution of 64 nm and 2-10 fold speed improvements. The use of low signal-to-noise ratio acquisition of image sequences, our SACD enables fast, background-free, super-resolution imaging that maybe advance implemented as a suitable tool for rapid live-cells imaging.

physics.optics↗

arXiv2019

Ultrasensitive optomechanical detection of an axion-mediated force based on a sharp peak emerging in probe absorption spectrum

Axion remains the most convincing solution to the strong-CP problem and a well-motivated dark matter candidate, causing the search for axions and axion-like particles(ALPs) to attract attention continually. The exchange of such particles may cause anomalous spin-dependent forces, inspiring many laboratory ALP searching experiments based on the detection of macroscopic monopole-dipole interactions between polarized electrons/nucleons and unpolarized nucleons. Since there is no exact proof of the existence of these interactions, to detect them is still of great significance. In the present paper, we study the electron-neucleon monopole-dipole interaction with a new method, in which a hybrid spin-nanocantilever optomechanical system consisting of a nitrogen-vacancy(NV) center and a nanocantilever resonator is used. With a static magnetic field and a pump microwave beam and a probe microwave beam applied, a probe absorption spectrum could be obtained. Through specific peaks appearing in the spectrum, we can identify this monopole-dipole interaction. And we also provide a prospective constraint to constrain the interaction. Furthermore, because our method can also be applied to the detection of some other spin-dependent interactions, this work provides new ideas for the experimental searches of the anomalous spin-dependent interactions.

astro-ph.CO↗

arXiv2019

Free-riders in Federated Learning: Attacks and Defenses

Federated learning is a recently proposed paradigm that enables multiple clients to collaboratively train a joint model. It allows clients to train models locally, and leverages the parameter server to generate a global model by aggregating the locally submitted gradient updates at each round. Although the incentive model for federated learning has not been fully developed, it is supposed that participants are able to get rewards or the privilege to use the final global model, as a compensation for taking efforts to train the model. Therefore, a client who does not have any local data has the incentive to construct local gradient updates in order to deceive for rewards. In this paper, we are the first to propose the notion of free rider attacks, to explore possible ways that an attacker may construct gradient updates, without any local training data. Furthermore, we explore possible defenses that could detect the proposed attacks, and propose a new high dimensional detection method called STD-DAGMM, which particularly works well for anomaly detection of model parameters. We extend the attacks and defenses to consider more free riders as well as differential privacy, which sheds light on and calls for future research in this field.

cs.LG↗

arXiv2019

Possible Scale Invariant Linear Magnetoresistance in Pyrochlore Iridates Bi$_2$Ir$_2$O$_7$

We report the observation of a linear magnetoresistance in single crystals and epitaxial thin films of the pyrochlore iridate Bi$_2$Ir$_2$O$_7$. The linear magnetoresistance is positive and isotropic at low temperatures, without any sign of saturation up to 35 Tesla. As temperature increases, the linear field dependence gradually evolves to a quadratic field dependence. The temperature and field dependence of magnetoresistance of Bi$_2$Ir$_2$O$_7$ bears strikingly resemblance to the scale invariant magnetoresistance observed in the strange metal phase in high Tc cuprates. However, the residual resistivity of Bi$_2$Ir$_2$O$_7$ is more than two orders of magnitude higher than the curpates. Our results suggest that the correlation between linear magnetoresistance and quantum fluctuations may exist beyond high temperature superconductors.

cond-mat.str-el↗

arXiv2019

Drastic suppression of superconducting $T_{c}$ by anisotropic strain near a nematic quantum critical point

High temperature superconductivity emerges in the vicinity of competing strongly correlated phases. In the iron-based superconductor $Ba(Fe_{1-x}Co_{x})_{2}As_{2}$, the superconducting state shares the composition-temperature phase diagram with an electronic nematic phase and an antiferromagnetic phase that break the crystalline rotational symmetry. Symmetry considerations suggest that anisotropic strain can enhance these competing phases and thus suppress the superconductivity. Here we study the effect of anisotropic strain on the superconducting transition in single crystals of $Ba(Fe_{1-x}Co_{x})_{2}As_{2}$ through electrical transport, magnetic susceptibility, and x-ray diffraction measurements. We find that in the underdoped and near-optimally doped regions of the phase diagram, the superconducting critical temperature is rapidly suppressed by both compressive and tensile stress, and in the underdoped case this suppression is enough to induce a strain-tuned superconductor to metal quantum phase transition.

cond-mat.supr-con↗

arXiv2019

Latent Dirichlet Allocation in Generative Adversarial Networks

We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs). Despite the success of existing methods, they often ignore the underlying structure of vision data or its multimodal generation characteristics. To address this problem, we introduce the Dirichlet prior for multimodal image generation, which leads to a new Latent Dirichlet Allocation based GAN (LDAGAN). In detail, for the generative process modelling, LDAGAN defines a generative mode for each sample, determining which generative sub-process it belongs to. For the adversarial training, LDAGAN derives a variational expectation-maximization (VEM) algorithm to estimate model parameters. Experimental results on real-world datasets have demonstrated the outstanding performance of LDAGAN over other existing GANs.

cs.LG↗

arXiv2019

Anomalous Magnetoresistance due to Longitudinal Spin Fluctuations in a Jeff = 1/2 Mott Semiconductor

As a hallmark of electronic correlation, spin-charge interplay underlies many emergent phenomena in doped Mott insulators, such as high-temperature superconductivity, whereas the half-filled parent state is usually electronically frozen with an antiferromagnetic order that resists external control. We report on the observation of a new positive magnetoresistance that probes the staggered susceptibility of a pseudospin-half square-lattice Mott insulator built as an artificial SrIrO3/SrTiO3 superlattice. Its size is particularly large in the high-temperature insulating paramagnetic phase near the Néel transition. This novel magnetoresistance originates from a collective charge response to the large longitudinal spin fluctuations under a linear coupling between the external magnetic field and the staggered magnetization enabled by strong spin-orbit interaction. Our results demonstrate a magnetic control of the binding energy of the fluctuating particle-hole pairs in the Slater-Mott crossover regime analogous to the BCS-to-Bose-Einstein condensation crossover of ultracold-superfluids.

cond-mat.str-el↗

arXiv2019

A novel method of searching for an axial-vector mediated force

We present an atomic, molecular, and optical physics based method for the purpose of search for axial-vector mediated dipole-dipole interaction between electrons. In our scheme, applying a static magnetic field and a pump beam and a probe beam to a hybrid mechanical system composed of a nitrogen-vacancy center and a cantilever resonator, we could obtain a probe absorption spectrum. Based on the study of the relationship between this spectrum and the exotic dipole-dipole interaction, we put forward our detection principle and then provide a prospective constraint most stringent at a rough interaction range from $4\times{10}^{-8}$ to $2\times {10}^{-7}m$ . Our results indicate that this scheme could be put into consideration in relevant experimental searches .

quant-ph↗

arXiv2019

Making Speculative BFT Resilient with Trusted Monotonic Counters

Consensus mechanisms used by popular distributed ledgers are highly scalable but notoriously inefficient. Byzantine fault tolerance (BFT) protocols are efficient but far less scalable. Speculative BFT protocols such as Zyzzyva and Zyzzyva5 are efficient and scalable but require a trade-off: Zyzzyva requires only $3f + 1$ replicas to tolerate $f$ faults, but even a single slow replica will make Zyzzyva fall back to more expensive non-speculative operation. Zyzzyva5 does not require a non-speculative fallback, but requires $5f + 1$ replicas in order to tolerate $f$ faults. BFT variants using hardware-assisted trusted components can tolerate a greater proportion of faults, but require that every replica have this hardware. We present SACZyzzyva, addressing these concerns: resilience to slow replicas and requiring only $3f + 1$ replicas, with only one replica needing an active monotonic counter at any given time. We experimentally evaluate our protocols, demonstrating low latency and high scalability. We prove that SACZyzzyva is optimally robust and that trusted components cannot increase fault tolerance unless they are present in greater than two-thirds of replicas.

cs.CR↗

arXiv2019

Adversarial Attack on Skeleton-based Human Action Recognition

Deep learning models achieve impressive performance for skeleton-based human action recognition. However, the robustness of these models to adversarial attacks remains largely unexplored due to their complex spatio-temporal nature that must represent sparse and discrete skeleton joints. This work presents the first adversarial attack on skeleton-based action recognition with graph convolutional networks. The proposed targeted attack, termed Constrained Iterative Attack for Skeleton Actions (CIASA), perturbs joint locations in an action sequence such that the resulting adversarial sequence preserves the temporal coherence, spatial integrity, and the anthropomorphic plausibility of the skeletons. CIASA achieves this feat by satisfying multiple physical constraints, and employing spatial skeleton realignments for the perturbed skeletons along with regularization of the adversarial skeletons with Generative networks. We also explore the possibility of semantically imperceptible localized attacks with CIASA, and succeed in fooling the state-of-the-art skeleton action recognition models with high confidence. CIASA perturbations show high transferability for black-box attacks. We also show that the perturbed skeleton sequences are able to induce adversarial behavior in the RGB videos created with computer graphics. A comprehensive evaluation with NTU and Kinetics datasets ascertains the effectiveness of CIASA for graph-based skeleton action recognition and reveals the imminent threat to the spatio-temporal deep learning tasks in general.

cs.CV↗

arXiv2019

Impossibility of Full Decentralization in Permissionless Blockchains

Bitcoin uses blockchain technology and proof-of-work (PoW) mechanism where nodes spend computing resources and earn rewards in return for spending these resources. This incentive system has caused power to be significantly biased towards a few nodes, called mining pools. In fact, poor decentralization appears not only in PoW-based coins but also in coins adopting other mechanisms such as proof-of-stake (PoS) and delegated proof-of-stake (DPoS). In this paper, we target this centralization issue. To this end, we first define (m, \varepsilon, δ)-decentralization as a state that satisfies 1) there are at least m participants running a node and 2) the ratio between the total resource power of nodes run by the richest and δ-th percentile participants is less than or equal to 1+\varepsilon. To see if it is possible to achieve good decentralization, we introduce sufficient conditions for the incentive system of a blockchain to reach (m, \varepsilon, δ)-decentralization. When satisfying the conditions, a blockchain system can reach full decentralization with probability 1. However, to achieve this, the blockchain system should be able to assign a positive Sybil cost, where the Sybil cost is defined as the difference between the cost for one participant running multiple nodes and the total cost for multiple participants each running one node. On the other hand, we prove that when there is no Sybil cost, the probability of reaching (m, \varepsilon, δ)-decentralization is upper bounded by a value close to 0, considering a large rich-poor gap. To determine the conditions that each system cannot satisfy, we also analyze protocols of all PoW, PoS, and DPoS coins in the top 100 coins according to our conditions. Finally, we conduct data analysis of these coins to validate our theory.

cs.CR↗

arXiv2019

Temporally Coherent Full 3D Mesh Human Pose Recovery from Monocular Video

Advances in Deep Learning have recently made it possible to recover full 3D meshes of human poses from individual images. However, extension of this notion to videos for recovering temporally coherent poses still remains unexplored. A major challenge in this regard is the lack of appropriately annotated video data for learning the desired deep models. Existing human pose datasets only provide 2D or 3D skeleton joint annotations, whereas the datasets are also recorded in constrained environments. We first contribute a technique to synthesize monocular action videos with rich 3D annotations that are suitable for learning computational models for full mesh 3D human pose recovery. Compared to the existing methods which simply "texture-map" clothes onto the 3D human pose models, our approach incorporates Physics based realistic cloth deformations with the human body movements. The generated videos cover a large variety of human actions, poses, and visual appearances, whereas the annotations record accurate human pose dynamics and human body surface information. Our second major contribution is an end-to-end trainable Recurrent Neural Network for full pose mesh recovery from monocular video. Using the proposed video data and LSTM based recurrent structure, our network explicitly learns to model the temporal coherence in videos and imposes geometric consistency over the recovered meshes. We establish the effectiveness of the proposed model with quantitative and qualitative analysis using the proposed and benchmark datasets.

cs.CV↗

arXiv2019

Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation

When purchasing appearance-first products, e.g., clothes, product appearance aesthetics plays an important role in the decision process. Moreover, user's aesthetic preference, which can be regarded as a personality trait and a basic requirement, is domain independent and could be used as a bridge between domains for knowledge transfer. However, existing work has rarely considered the aesthetic information in product photos for cross-domain recommendation. To this end, in this paper, we propose a new deep Aesthetic preference Cross-Domain Network (ACDN), in which parameters characterizing personal aesthetic preferences are shared across networks to transfer knowledge between domains. Specifically, we first leverage an aesthetic network to extract relevant features. Then, we integrate the aesthetic features into a cross-domain network to transfer users' domain independent aesthetic preferences. Moreover, network cross-connections are introduced to enable dual knowledge transfer across domains. Finally, the experimental results on real-world data show that our proposed ACDN outperforms other benchmark methods in terms of recommendation accuracy. The results also show that users' aesthetic preferences are effective in alleviating the data sparsity issue on the cross-domain recommendation.

cs.IR↗

arXiv2019

Epitaxial stabilization of Sr3Ir2O7 thin films

Ruddlesden-popper type Srn+1IrnO3n+1 compound is a major focus of condensed matter physics where the subtle balance between electron-electron correlation, spin-orbit interaction and crystal field effect brings a host of emergent phenomena. While it is understandable that a canted antiferromagnetic (AFM) insulating state with an easy-plane anisotropy is developed in Sr2IrO4 as the 2D limit of the series, it is intriguing that bilayer Sr3Ir2O7, with slightly higher effective dimensionality, stabilizes c-axis collinear antiferromagnetism. This also renders Sr3Ir2O7 as a unique playground to study exotic physics near a critical spin transition point. However, the epitaxial growth of the Sr3Ir2O7 is still a challenging task because of the narrow growth window. In our research, we have studied the thermodynamic process during synthesis of Sr3Ir2O7 thin films. We successfully expanded the synthesis window by mapping out the relation between the thin film sample crystal structure and gas pressure. Our work thus provides a more accessible avenue to stabilize metastable materials.

cond-mat.str-el↗

arXiv2019

Ideal type-II Weyl phonons in wurtzite CuI

Weyl semimetals exhibitinging the topologically nontrivial touching points in electronic band dispersion of solids pave the wave way to novel electronic devices and functionalities. Here, we demonstrate the signature of topologically nontrivial Weyl points (WPs) in phonon dispersion of solids through a first-principles investigations. ofT noncentrosymmetric wurtzite CuI (cuprous iodide at high temperature). These type-II phononic WP in phonon dispersion of wurtzite CuI is manifested in noncentrosymmetric wurtzite CuI by six pairs of the nontrivial touching points in the k_z=0.0 plane. The ideal WPs in phonon dispersion are completely isolated from bulk phonon continuum, are different distinct from many type-II WPs in electronic band dispersion of solids associated with the overlapping band states. The opposite chirality of Weyl phonon nodes with quantized Berry curvature are utilized forproduces Weyl phonon Hall effect, in analogous analogy towith valley Hall effect of electrons. Such ideal type-II Weyl phonons are phase is readily observable in experiment, and could provideing a unique platform to study novel thermal transport properties different distinct from that in type-I Weyl phonons phase.

cond-mat.mes-hall↗