arXiv subjects

Hao Zhang

Publications and source records attributed to Hao Zhang.

At least 1,261 records · Page 70Linked to original sources

arXiv2016

Learning Concept Taxonomies from Multi-modal Data

We study the problem of automatically building hypernym taxonomies from textual and visual data. Previous works in taxonomy induction generally ignore the increasingly prominent visual data, which encode important perceptual semantics. Instead, we propose a probabilistic model for taxonomy induction by jointly leveraging text and images. To avoid hand-crafted feature engineering, we design end-to-end features based on distributed representations of images and words. The model is discriminatively trained given a small set of existing ontologies and is capable of building full taxonomies from scratch for a collection of unseen conceptual label items with associated images. We evaluate our model and features on the WordNet hierarchies, where our system outperforms previous approaches by a large gap.

cs.CL↗

arXiv2016

Observation of 2D fermionic Mott insulators of $^{40}$K with single-site resolution

We report on the site-resolved observation of characteristic states of the two-dimensional repulsive Fermi-Hubbard model, using ultracold $^{40}$K atoms in an optical lattice. By varying the tunneling, interaction strength, and external confinement, we realize metallic, Mott-insulating, and band-insulating states. We directly measure the local moment, which quantifies the degree of on-site magnetization, as a function of temperature and chemical potential. Entropies per particle as low as $0.99(6)\,k_B$ indicate that nearest-neighbor antiferromagnetic correlations should be detectable using spin-sensitive imaging.

cond-mat.quant-gas↗

arXiv2016

Observation of Spatial Charge and Spin Correlations in the 2D Fermi-Hubbard Model

Strong electron correlations lie at the origin of transformative phenomena such as colossal magneto-resistance and high-temperature superconductivity. Already near room temperature, doped copper oxide materials display remarkable features such as a pseudo-gap and a "strange metal" phase with unusual transport properties. The essence of this physics is believed to be captured by the Fermi-Hubbard model of repulsively interacting, itinerant fermions on a lattice. Here we report on the site-resolved observation of charge and spin correlations in the two-dimensional (2D) Fermi-Hubbard model realized with ultracold atoms. Antiferromagnetic spin correlations are maximal at half-filling and weaken monotonically upon doping. Correlations between singly charged sites are negative at large doping, revealing the Pauli and correlation hole\textemdash a suppressed probability of finding two fermions near each other. However, as the doping is reduced below a critical value, correlations between such local magnetic moments become positive, signaling strong bunching of doublons and holes. Excellent agreement with numerical linked-cluster expansion (NLCE) and determinantal quantum Monte Carlo (DQMC) calculations is found. Positive non-local moment correlations directly imply potential energy fluctuations due to doublon-hole pairs, which should play an important role for transport in the Fermi-Hubbard model.

cond-mat.quant-gas↗

arXiv2016

Self-Reflective Risk-Aware Artificial Cognitive Modeling for Robot Response to Human Behaviors

In order for cooperative robots ("co-robots") to respond to human behaviors accurately and efficiently in human-robot collaboration, interpretation of human actions, awareness of new situations, and appropriate decision making are all crucial abilities for co-robots. For this purpose, the human behaviors should be interpreted by co-robots in the same manner as human peers. To address this issue, a novel interpretability indicator is introduced so that robot actions are appropriate to the current human behaviors. In addition, the complete consideration of all potential situations of a robot's environment is nearly impossible in real-world applications, making it difficult for the co-robot to act appropriately and safely in new scenarios. This is true even when the pretrained model is highly accurate in a known situation. For effective and safe teaming with humans, we introduce a new generalizability indicator that allows a co-robot to self-reflect and reason about when an observation falls outside the co-robot's learned model. Based on topic modeling and two novel indicators, we propose a new Self-reflective Risk-aware Artificial Cognitive (SRAC) model. The co-robots are able to consider action risks and identify new situations so that better decisions can be made. Experiments both using real-world datasets and on physical robots suggest that our SRAC model significantly outperforms the traditional methodology and enables better decision making in response to human activities.

cs.RO↗

arXiv2016

Enforcing Template Representability and Temporal Consistency for Adaptive Sparse Tracking

Sparse representation has been widely studied in visual tracking, which has shown promising tracking performance. Despite a lot of progress, the visual tracking problem is still a challenging task due to appearance variations over time. In this paper, we propose a novel sparse tracking algorithm that well addresses temporal appearance changes, by enforcing template representability and temporal consistency (TRAC). By modeling temporal consistency, our algorithm addresses the issue of drifting away from a tracking target. By exploring the templates' long-term-short-term representability, the proposed method adaptively updates the dictionary using the most descriptive templates, which significantly improves the robustness to target appearance changes. We compare our TRAC algorithm against the state-of-the-art approaches on 12 challenging benchmark image sequences. Both qualitative and quantitative results demonstrate that our algorithm significantly outperforms previous state-of-the-art trackers.

cs.CV↗

arXiv2016

First-principle calculations of phononic, electronic and optical properties of monolayer arsenene and antimonene allotropes

Recently a stable monolayer of antimony in buckled honeycomb structure called antimonene was successfully grown on 3D topological insulator Bi$_2$Te$_3$ and Sb$_2$Te$_3$, which displays semiconducting properties. By first principle calculations, we systematically investigate the phononic, electronic and optical properties of $α-$ and $β-$ allotropes of monolayer arsenene/antimonene. We investigate the dynamical stabilities of these four materials by considering the phonon dispersions. The obtained electronic structures reveal the direct band gap of monolayer $α-$As/Sb and indirect band gap of $β-$As/Sb. Significant absorption is observed in $α-$Sb, which can be used as a broad saturable absorber.

cond-mat.mtrl-sci↗

arXiv2016

Electronic, optical, and thermodynamic properties of borophene from first-principle calculations

Borophene (two-dimensional boron sheet) is a new type of two-dimensional material, which was recently grown successfully on single crystal Ag substrates. In this paper, we investigate the electronic structure and bonding characteristics of borophene by first-principle calculations. The band structure of borophene shows highly anisotropic metallic behaviour. The obtained optical properties of borophene exhibit strong anisotropy as well. The combination of high optical transparency and high electrical conductivity in borophene makes it a promising candidate for future design of transparent conductors used in photovoltaics. Finally, the thermodynamic properties are investigated based on the phonon properties.

cond-mat.mes-hall↗

arXiv2016

DoraPicker: An Autonomous Picking System for General Objects

Robots that autonomously manipulate objects within warehouses have the potential to shorten the package delivery time and improve the efficiency of the e-commerce industry. In this paper, we present a robotic system that is capable of both picking and placing general objects in warehouse scenarios. Given a target object, the robot autonomously detects it from a shelf or a table and estimates its full 6D pose. With this pose information, the robot picks the object using its gripper, and then places it into a container or at a specified location. We describe our pick-and-place system in detail while highlighting our design principles for the warehouse settings, including the perception method that leverages knowledge about its workspace, three grippers designed to handle a large variety of different objects in terms of shape, weight and material, and grasp planning in cluttered scenarios. We also present extensive experiments to evaluate the performance of our picking system and demonstrate that the robot is competent to accomplish various tasks in warehouse settings, such as picking a target item from a tight space, grasping different objects from the shelf, and performing pick-and-place tasks on the table.

cs.RO↗

arXiv2016

Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation

State-of-the-art results of semantic segmentation are established by Fully Convolutional neural Networks (FCNs). FCNs rely on cascaded convolutional and pooling layers to gradually enlarge the receptive fields of neurons, resulting in an indirect way of modeling the distant contextual dependence. In this work, we advocate the use of spatially recurrent layers (i.e. ReNet layers) which directly capture global contexts and lead to improved feature representations. We demonstrate the effectiveness of ReNet layers by building a Naive deep ReNet (N-ReNet), which achieves competitive performance on Stanford Background dataset. Furthermore, we integrate ReNet layers with FCNs, and develop a novel Hybrid deep ReNet (H-ReNet). It enjoys a few remarkable properties, including full-image receptive fields, end-to-end training, and efficient network execution. On the PASCAL VOC 2012 benchmark, the H-ReNet improves the results of state-of-the-art approaches Piecewise, CRFasRNN and DeepParsing by 3.6%, 2.3% and 0.2%, respectively, and achieves the highest IoUs for 13 out of the 20 object classes.

cs.CV↗

arXiv2016

Conductance Quantization at zero magnetic field in InSb nanowires

Ballistic electron transport is a key requirement for existence of a topological phase transition in proximitized InSb nanowires. However, measurements of quantized conductance as direct evidence of ballistic transport have so far been obscured due to the increased chance of backscattering in one dimensional nanowires. We show that by improving the nanowire-metal interface as well as the dielectric environment we can consistently achieve conductance quantization at zero magnetic field. Additionally, studying the sub-band evolution in a rotating magnetic field reveals an orbital degeneracy between the second and third sub-bands for perpendicular fields above 1T.

cond-mat.mes-hall↗

arXiv2016

Low lattice thermal conductivity of stanene

A fundamental understanding of phonon transport in stanene is crucial to predict the thermal performance in potential stanene-based devices. By combining first-principle calculation and phonon Boltzmann transport equation, we obtain the lattice thermal conductivity of stanene. A much lower thermal conductivity (11.6 W/mK) is observed in stanene, which indicates higher thermoelectric efficiency over other 2D materials. The contributions of acoustic and optical phonons to the lattice thermal conductivity are evaluated. Detailed analysis of phase space for three-phonon processes shows that phonon scattering channels LA+LA/TA/ZA$\leftrightarrow$TA/ZA are restricted, leading to the dominant contributions of high-group-velocity LA phonons to the thermal conductivity. The size dependence of thermal conductivity is investigated as well for the purpose of the design of thermoelectric nanostructures.

cond-mat.mtrl-sci↗

arXiv2016

All-optical bandwidth-tailorable radar

Radar has been widely used in military, security, and rescue. Metamaterial cloak is employed in stealth targets to evade radar detection. Hence modern radar should be reconfigurable at multi-bands for detecting stealth targets, which might be realized based on microwave photonics. Here, we demonstrate an all-optical bandwidth-tailorable radar architecture. It is a coherent system utilizing one mode-locked laser for both signal generation and reception. Heterodyning of two individually filtered optical pulses that are pre-chirped via wavelength-to-time mapping generates wideband linearly-chirped radar signal. The working bands can be flexibly tailored with desired bandwidth at user-preferred carrier frequency. After modulated onto the pre-chirped optical pulse, radar echoes are time-stretched and frequency-compressed by several times. The digitization becomes much easier without loss of detection ability. We believe that the demonstration can innovate the radar's architecture with ultra-high range resolution.

physics.ins-det↗

arXiv2016

Quantum phase diagram of a frustrated antiferromagnet on the bilayer honeycomb lattice

We study the spin-1/2 Heisenberg antiferromagnet on a bilayer honeycomb lattice including interlayer frustration. Using a set of complementary approaches, namely Schwinger bosons, dimer series expansion, bond operators, and exact diagonalization, we map out the quantum phase diagram. Analyzing ground state energies and elementary excitation spectra, we find four distinct phases, corresponding to three collinear magnetic long range ordered states, and one quantum disordered interlayer dimer phase. We detail, that the latter phase is adiabatically connected to an "exact" singlet product ground state of the the bilayer which exists along a line of maximum interlayer frustration. The order within the remaining three phases will be clarified.

cond-mat.str-el↗

arXiv2016

Kaluza-Klein graviton phenomenology for warped compactifications, and the 750 GeV diphoton excess

A generic prediction of scenarios with extra dimensions accessible in TeV-scale collisions is the existence of Kaluza-Klein excitations of the graviton. For a broad class of strongly-warped scenarios one expects to initially find an isolated resonance, whose phenomenology in the simplest cases is described by a simplified model with two parameters, its mass, and a constant $Λ$ with units of mass parameterizing its coupling to the Standard Model stress tensor. These parameters are in turn determined by the geometrical configuration of the warped compactification. We explore the possibility that the 750 GeV excess recently seen in 13 TeV data at ATLAS and CMS could be such a warped Kaluza-Klein graviton, and find a best-fit value $Λ\approx 60$ TeV. We find that while there is some tension between this interpretation and data from 8 TeV and from the dilepton channel at 13 TeV, it is not strongly excluded. However, in the simplest scenarios of this kind, such a signal should soon become apparent in both diphoton and dilepton channels.

hep-ph↗

arXiv2016

Shortcuts to adiabatic holonomic quantum computation in decoherence-free subspace with transitionless quantum driving algorithm

By using transitionless quantum driving algorithm (TQDA), we present an efficient scheme for the shortcuts to the holonomic quantum computation (HQC). It works in decoherence-free subspace (DFS) and the adiabatic process can be speeded up in the shortest possible time. More interestingly, we give a physical implementation for our shortcuts to HQC with nitrogen-vacancy centers in diamonds dispersively coupled to a whispering-gallery mode microsphere cavity. It can be efficiently realized by controlling appropriately the frequencies of the external laser pulses. Also, our scheme has good scalability with more qubits. Different from previous works, we first use TQDA to realize a universal HQC in DFS, including not only two noncommuting accelerated single-qubit holonomic gates but also a accelerated two-qubit holonomic controlled-phase gate, which provides the necessary shortcuts for the complete set of gates required for universal quantum computation. Moreover, our experimentally realizable shortcuts require only two-body interactions, not four-body ones, and they work in the dispersive regime, which relax greatly the difficulty of their physical implementation in experiment. Our numerical calculations show that the present scheme is robust against decoherence with current experimental parameters.

quant-ph↗

arXiv2016

The 750GeV diphoton excess: who introduces it?

Recently, both ATLAS and CMS collaborations report an excess at 750GeV in the diphoton invariant mass spectrum at 13TeV LHC. If it is a new scalar produced via loop induced gluon-gluon fusion process, it is important to know what is the particle in the loop. In this work, we investigate the possibility of determine the fraction of the contribution from the standard model top-quark in the loop.

hep-ph↗

arXiv2016

Behaviour of light transmission channels in random media with inhomogeneous disorder

We present a numerical study on the light transport properties and statistics of transmission channels in random media with inhomogeneous disorder. For the case of longitudinal inhomogeneity of disorder we find that the statistics of the transmission channels is independent of the inhomogeneity and the system can be equivalent to a counterpart with homogeneous disorder strength, both of which have the same statistical distribution of the transmission channels. However, for the case of transverse inhomogeneity of disorder, such equivalence does not exist, moreover, the transmission eigenvalues are pushed to the two ends of the distribution and the distribution of the total transmission is broadened since the spatial structure gives rise to larger and smaller transmitted incident channels.

physics.optics↗

arXiv2016

On the Reducibility of Submodular Functions

The scalability of submodular optimization methods is critical for their usability in practice. In this paper, we study the reducibility of submodular functions, a property that enables us to reduce the solution space of submodular optimization problems without performance loss. We introduce the concept of reducibility using marginal gains. Then we show that by adding perturbation, we can endow irreducible functions with reducibility, based on which we propose the perturbation-reduction optimization framework. Our theoretical analysis proves that given the perturbation scales, the reducibility gain could be computed, and the performance loss has additive upper bounds. We further conduct empirical studies and the results demonstrate that our proposed framework significantly accelerates existing optimization methods for irreducible submodular functions with a cost of only small performance losses.

cs.LG↗