arXiv subjects

Hao Zhang

Publications and source records attributed to Hao Zhang.

At least 1,225 records · Page 68Linked to original sources

arXiv2017

Evaluating the accuracy of the dynamic mode decomposition

Dynamic mode decomposition (DMD) gives a practical means of extracting dynamic information from data, in the form of spatial modes and their associated frequencies and growth/decay rates. DMD can be considered as a numerical approximation to the Koopman operator, an infinite-dimensional linear operator defined for (nonlinear) dynamical systems. This work proposes a new criterion to estimate the accuracy of DMD on a mode-by-mode basis, by estimating how closely each individual DMD eigenfunction approximates the corresponding Koopman eigenfunction. This approach does not require any prior knowledge of the system dynamics or the true Koopman spectral decomposition. The method may be applied to extensions of DMD (i.e., extended/kernel DMD), which are applicable to a wider range of problems. The accuracy criterion is first validated against the true error with a synthetic system for which the true Koopman spectral decomposition is known. We next demonstrate how this proposed accuracy criterion can be used to assess the performance of various choices of kernel when using the kernel method for extended DMD. Finally, we show that our proposed method successfully identifies modes of high accuracy when applying DMD to data from experiments in fluids, in particular particle image velocimetry of a cylinder wake and a canonical separated boundary layer.

math.DS↗

arXiv2017

Efficient and Effective Single-Document Summarizations and A Word-Embedding Measurement of Quality

Our task is to generate an effective summary for a given document with specific realtime requirements. We use the softplus function to enhance keyword rankings to favor important sentences, based on which we present a number of summarization algorithms using various keyword extraction and topic clustering methods. We show that our algorithms meet the realtime requirements and yield the best ROUGE recall scores on DUC-02 over all previously-known algorithms. We show that our algorithms meet the realtime requirements and yield the best ROUGE recall scores on DUC-02 over all previously-known algorithms. To evaluate the quality of summaries without human-generated benchmarks, we define a measure called WESM based on word-embedding using Word Mover's Distance. We show that the orderings of the ROUGE and WESM scores of our algorithms are highly comparable, suggesting that WESM may serve as a viable alternative for measuring the quality of a summary.

cs.IR↗

arXiv2017

Coupling of evanescent waves into propagation channels within two-dimensional random waveguides

The transformation from evanescent waves to propagation waves is the key mechanism for the realization of some super-resolution imaging methods. By using the recursive Green function and scattering-matrix theory, we investigated in details on the transport of evanescent waves through a random medium and analyzed quantitatively the coupling of evanescent channels to propagation channels. By numerical calculations, we found that the transmission for the incident evanescent channel is determined by both the eigenvalues of the scattering matrix and the coupling strength to the corresponding propagation channels in random medium, and the disorder strength of the random medium influences both of them.

cond-mat.mes-hall↗

arXiv2017

Generative Semantic Manipulation with Contrasting GAN

Generative Adversarial Networks (GANs) have recently achieved significant improvement on paired/unpaired image-to-image translation, such as photo$\rightarrow$ sketch and artist painting style transfer. However, existing models can only be capable of transferring the low-level information (e.g. color or texture changes), but fail to edit high-level semantic meanings (e.g., geometric structure or content) of objects. On the other hand, while some researches can synthesize compelling real-world images given a class label or caption, they cannot condition on arbitrary shapes or structures, which largely limits their application scenarios and interpretive capability of model results. In this work, we focus on a more challenging semantic manipulation task, which aims to modify the semantic meaning of an object while preserving its own characteristics (e.g. viewpoints and shapes), such as cow$\rightarrow$sheep, motor$\rightarrow$ bicycle, cat$\rightarrow$dog. To tackle such large semantic changes, we introduce a contrasting GAN (contrast-GAN) with a novel adversarial contrasting objective. Instead of directly making the synthesized samples close to target data as previous GANs did, our adversarial contrasting objective optimizes over the distance comparisons between samples, that is, enforcing the manipulated data be semantically closer to the real data with target category than the input data. Equipped with the new contrasting objective, a novel mask-conditional contrast-GAN architecture is proposed to enable disentangle image background with object semantic changes. Experiments on several semantic manipulation tasks on ImageNet and MSCOCO dataset show considerable performance gain by our contrast-GAN over other conditional GANs. Quantitative results further demonstrate the superiority of our model on generating manipulated results with high visual fidelity and reasonable object semantics.

cs.CV↗

arXiv2017

High-precision Joint Time and Frequency Transfer over a Fiber-optic Telecom Testbed

It is highly desired to operate a joint time and frequency transfer over the exiting fiber-optic telecom networks with the commercial data transmission. Previously, we proposed a time transfer scheme based on bidirectional time division multiplexing transmission over a single fiber with the same wavelength (BTDM-SFSW), and an optical amplification scheme of single-fiber bidirectional-transmission unidirectional optical amplifier (SFBT-UOA). The effects of excess noises like Rayleigh backscattering etc. on the received time signals can be efficiently suppressed for an accepted timing jitter. Simultaneously, the bidirectional propagation delay symmetry can be guaranteed for an expected uncertainty without requiring complicated and expensive link calibration. In this paper, we propose a high-precision joint BTDM-SFSW based time and unidirectional frequency transfer over conventional telecom networks. A stable frequency signal is achieved by compensating the fiber temperature dependent phase variation according to the time transfer. The proposed joint transfer scheme is demonstrated over a 320 km fiber-optic telecom testbed with two 10 Gb/s data transmission channels. The time transfer stabilities (time deviation, TDEV) of 23.2 ps/s and 5.4 ps/10^5 s, respectively, and the frequency transfer stabilities (Allan deviation, ADEV) of 5.4X10^(-13)/s and 9.5X10^(-17)/10^5 s, respectively, are reached. The measured bit error rates (BER) of the 10 Gb/s communication data are well below 10^(-13).

physics.ins-det↗

arXiv2017

Preparation of CH3NH3PbI3 thin films with tens of micrometer scale at high temperature

The fabrication of high-quality organic-inorganic hybrid halide perovskite layers is the key prerequisite for the realization of high efficient photon energy harvest and electric energy conversion in their related solar cells. In this article, we report a novel fabrication technique of CH3NH3PbI3 layer based on high temperature chemical vapor reaction. CH3NH3PbI3 layers have been prepared by the reaction of PbI2 films which were deposited by pulsed laser deposition, with CH3NH3I vapor at various temperatures from 160 oC to 210 oC. X-ray diffraction patterns confirm the formation of pure phase, and photoluminescence spectra show the strong peak at around 760 nm. Scanning electron microscopy images confirm the significantly increased average grain size from nearly 1 μm at low reaction temperature of 160 oC to more than 10 μm at high reaction temperature of 200 oC. The solar cells were fabricated, and short-circuit current density of 15.75 mA/cm2, open-circuit voltage of 0.49 V and fill factor of 71.66% have been obtained.

cond-mat.mtrl-sci↗

arXiv2017

Ballistic superconductivity in semiconductor nanowires

Semiconductor nanowires have opened new research avenues in quantum transport owing to their confined geometry and electrostatic tunability. They have offered an exceptional testbed for superconductivity, leading to the realization of hybrid systems combining the macroscopic quantum properties of superconductors with the possibility to control charges down to a single electron. These advances brought semiconductor nanowires to the forefront of efforts to realize topological superconductivity and Majorana modes. A prime challenge to benefit from the topological properties of Majoranas is to reduce the disorder in hybrid nanowire devices. Here, we show ballistic superconductivity in InSb semiconductor nanowires. Our structural and chemical analyses demonstrate a high-quality interface between the nanowire and a NbTiN superconductor which enables ballistic transport. This is manifested by a quantized conductance for normal carriers, a strongly enhanced conductance for Andreev-reflecting carriers, and an induced hard gap with a significantly reduced density of states. These results pave the way for disorder-free Majorana devices.

cond-mat.mes-hall↗

arXiv2017

Online dynamic mode decomposition for time-varying systems

Dynamic mode decomposition (DMD) is a popular technique for modal decomposition, flow analysis, and reduced-order modeling. In situations where a system is time varying, one would like to update the system's description online as time evolves. This work provides an efficient method for computing DMD in real time, updating the approximation of a system's dynamics as new data becomes available. The algorithm does not require storage of past data, and computes the exact DMD matrix using rank-1 updates. A weighting factor that places less weight on older data can be incorporated in a straightforward manner, making the method particularly well suited to time-varying systems. A variant of the method may also be applied to online computation of "windowed DMD", in which only the most recent data are used. The efficiency of the method is compared against several existing DMD algorithms: for problems in which the state dimension is less than about~200, the proposed algorithm is the most efficient for real-time computation, and it can be orders of magnitude more efficient than the standard DMD algorithm. The method is demonstrated on several examples, including a time-varying linear system and a more complex example using data from a wind tunnel experiment. In particular, we show that the method is effective at capturing the dynamics of surface pressure measurements in the flow over a flat plate with an unsteady separation bubble.

math.OC↗

arXiv2017

Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters

Deep learning models can take weeks to train on a single GPU-equipped machine, necessitating scaling out DL training to a GPU-cluster. However, current distributed DL implementations can scale poorly due to substantial parameter synchronization over the network, because the high throughput of GPUs allows more data batches to be processed per unit time than CPUs, leading to more frequent network synchronization. We present Poseidon, an efficient communication architecture for distributed DL on GPUs. Poseidon exploits the layered model structures in DL programs to overlap communication and computation, reducing bursty network communication. Moreover, Poseidon uses a hybrid communication scheme that optimizes the number of bytes required to synchronize each layer, according to layer properties and the number of machines. We show that Poseidon is applicable to different DL frameworks by plugging Poseidon into Caffe and TensorFlow. We show that Poseidon enables Caffe and TensorFlow to achieve 15.5x speed-up on 16 single-GPU machines, even with limited bandwidth (10GbE) and the challenging VGG19-22K network for image classification. Moreover, Poseidon-enabled TensorFlow achieves 31.5x speed-up with 32 single-GPU machines on Inception-V3, a 50% improvement over the open-source TensorFlow (20x speed-up).

cs.LG↗

arXiv2017

Nonlinear Zeeman effect, line shapes and optical pumping in electromagnetically induced transparency

We perform Zeeman spectroscopy on a Rydberg electromagnetically induced transparency (EIT) system in a room-temperature Cs vapor cell, in magnetic fields up to 50~Gauss and for several polarization configurations. The magnetic interactions of the $\vert 6S_{1/2}, F_g=4 \rangle$ ground, $\vert 6P_{3/2}, F_e=5 \rangle$ intermediate, and $\vert 33S_{1/2} \rangle$ Rydberg states that form the ladder-type EIT system are in the linear Zeeman, quadratic Zeeman, and the deep hyperfine Paschen-Back regimes, respectively. Starting in magnetic fields of about 5~Gauss, the spectra develop an asymmetry that becomes paramount in fields $\gtrsim40$~Gauss. We use a quantum Monte Carlo wave-function approach to quantitatively model the spectra. Simulated spectra are in good agreement with experimental data. The asymmetry in the spectra is, in part, due to level shifts caused by the quadratic Zeeman effect, but it also reflects the complicated interplay between optical pumping and EIT in the magnetic field. Relevance to measurement applications is discussed. %The simulations are also used to study optical pumping in the magnetic field and to investigate the interplay between optical pumping and EIT, which reduces photon scattering and optical pumping.

physics.atom-ph↗

arXiv2017

Testing Einstein-dilaton-Gauss-Bonnet gravity from the reflection spectrum of accreting black holes

Einstein-dilaton-Gauss-Bonnet gravity is a theoretically well-motivated alternative theory of gravity emerging as a low-energy 4-dimensional model from heterotic string theory. Its rotating black hole solutions are known numerically and can have macroscopic deviations from the Kerr black holes of Einstein's gravity. Einstein-dilaton-Gauss-Bonnet gravity can thus be tested with observations of astrophysical black holes. In the present paper, we simulate observations of the reflection spectrum of thin accretion disks with present and future X-ray facilities to understand whether X-ray reflection spectroscopy can distinguish the black holes in Einstein-dilaton-Gauss-Bonnet gravity from those in Einstein's gravity. We find that this is definitively out of reach for present X-ray missions, but it may be achieved with the next generation of facilities.

gr-qc↗

arXiv2017

Testing Naturalness

Solutions to the electroweak hierarchy problem typically introduce a new symmetry to stabilize the quadratic ultraviolet sensitivity in the self-energy of the Higgs boson. The new symmetry is either broken softly or collectively, as for example in supersymmetric and little Higgs theories. At low energies such theories contain naturalness partners of the Standard Model fields which are responsible for canceling the quadratic divergence in the squared Higgs mass. Post the discovery of any partner-like particles, we propose to test the aforementioned cancellation by measuring relevant Higgs couplings. Using the fermionic top partners in little Higgs theories as an illustration, we construct a simplified model for naturalness and initiate a study on testing naturalness. After electroweak symmetry breaking, naturalness in the top sector requires $a_T = - λ_t^2$ at leading order, where $λ_t$ and $a_T$ are the Higgs couplings to a pair of top quarks and top partners, respectively. Using a multivariate method of Boosted Decision Tree to tag boosted particles in the Standard Model, we show that, with a luminosity of 30 $ab^{-1}$ at a 100 TeV $pp$-collider, naturalness could be tested with a precision of 10 % for a top partner mass up to 2.5 TeV.

hep-ph↗

arXiv2017

GRASS: Generative Recursive Autoencoders for Shape Structures

We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively characterized by their hierarchical organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.

cs.GR↗

arXiv2017

Multisensory Omni-directional Long-term Place Recognition: Benchmark Dataset and Analysis

Recognizing a previously visited place, also known as place recognition (or loop closure detection) is the key towards fully autonomous mobile robots and self-driving vehicle navigation. Augmented with various Simultaneous Localization and Mapping techniques (SLAM), loop closure detection allows for incremental pose correction and can bolster efficient and accurate map creation. However, repeated and similar scenes (perceptual aliasing) and long term appearance changes (e.g. weather variations) are major challenges for current place recognition algorithms. We introduce a new dataset Multisensory Omnidirectional Long-term Place recognition (MOLP) comprising omnidirectional intensity and disparity images. This dataset presents many of the challenges faced by outdoor mobile robots and current place recognition algorithms. Using MOLP dataset, we formulate the place recognition problem as a regularized sparse convex optimization problem. We conclude that information extracted from intensity image is superior to disparity image in isolating discriminative features for successful long term place recognition. Furthermore, when these discriminative features are extracted from an omnidirectional vision sensor, a robust bidirectional loop closure detection approach is established, allowing mobile robots to close the loop, regardless of the difference in the direction when revisiting a place.

cs.RO↗

arXiv2017

Electronic, transport and optical properties of monolayer $α$ and $β-$GeSe: A first-principles study

The extraordinary properties and the novel applications of black phosphorene induce the research interest on the monolayer group-IV monochalcogenides. Here using the first-principles calculations, we systematically investigate the electronic, transport and optical properties of monolayer $α-$ and $β-$GeSe, the latter of which was recently experimentally realized. We found that, monolayer $α-$GeSe is a semiconductor with direct band gap of 1.6 eV, and $β-$GeSe displays indirect semiconductor with the gap of 2.47 eV, respectively. For monolayer $β-$GeSe, the electronic/hole transport is anisotropic with an extremely high electron mobility of 7.84 $\times10^4$$cm^2/V\cdot {s}$ along the zigzag direction, comparable to that of black phosphorene. Furthermore, for $β-$GeSe, robust band gaps nearly disregarding the applied tensile strain along the zigzag direction is observed. Both monolayer $α-$ and $β-$GeSe exhibit anisotropic optical absorption in the visible spectrum.

cond-mat.mtrl-sci↗

arXiv2017

SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays

Chest X-ray (CXR) is one of the most commonly prescribed medical imaging procedures, often with over 2-10x more scans than other imaging modalities such as MRI, CT scan, and PET scans. These voluminous CXR scans place significant workloads on radiologists and medical practitioners. Organ segmentation is a crucial step to obtain effective computer-aided detection on CXR. In this work, we propose Structure Correcting Adversarial Network (SCAN) to segment lung fields and the heart in CXR images. SCAN incorporates a critic network to impose on the convolutional segmentation network the structural regularities emerging from human physiology. During training, the critic network learns to discriminate between the ground truth organ annotations from the masks synthesized by the segmentation network. Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes. Extensive experiments show that our method produces highly accurate and natural segmentation. Using only very limited training data available, our model reaches human-level performance without relying on any existing trained model or dataset. Our method also generalizes well to CXR images from a different patient population and disease profiles, surpassing the current state-of-the-art.

cs.CV↗

arXiv2017

Exotic decays of the 125 GeV Higgs boson at future $e^+e^-$ lepton colliders

Discovery of unexpected properties of the Higgs boson offers an intriguing opportunity of shedding light on some of the most profound puzzles in particle physics. The Beyond Standard Model (BSM) decays of the Higgs boson could reveal new physics in a direct manner. Future electron-positron lepton colliders operating as Higgs factories, including CEPC, FCC-ee and ILC, with the advantages of a clean collider environment and large statistics, could greatly enhance the sensitivity in searching for these BSM decays. In this work, we perform a general study of Higgs exotic decays at future $e^+e^-$ lepton colliders, focusing on the Higgs decays with hadronic final states and/or missing energy, which are very challenging for the High-Luminosity program of the Large Hadron Collider (HL-LHC). We show that with simple selection cuts, $O(10^{-3}\sim10^{-5})$ limits on the Higgs exotic decay branching fractions can be achieved using the leptonic decaying spectator $Z$ boson in the associated production mode $e^+e^-\rightarrow Z H$. We further discuss the interplay between the detector performance and Higgs exotic decay, and other possibilities of exotic decays. Our work is a first step in a comprehensive study of Higgs exotic decays at future lepton colliders, which is a key ingredient of Higgs physics that deserves further investigation.

hep-ph↗

arXiv2017

Recurrent Topic-Transition GAN for Visual Paragraph Generation

A natural image usually conveys rich semantic content and can be viewed from different angles. Existing image description methods are largely restricted by small sets of biased visual paragraph annotations, and fail to cover rich underlying semantics. In this paper, we investigate a semi-supervised paragraph generative framework that is able to synthesize diverse and semantically coherent paragraph descriptions by reasoning over local semantic regions and exploiting linguistic knowledge. The proposed Recurrent Topic-Transition Generative Adversarial Network (RTT-GAN) builds an adversarial framework between a structured paragraph generator and multi-level paragraph discriminators. The paragraph generator generates sentences recurrently by incorporating region-based visual and language attention mechanisms at each step. The quality of generated paragraph sentences is assessed by multi-level adversarial discriminators from two aspects, namely, plausibility at sentence level and topic-transition coherence at paragraph level. The joint adversarial training of RTT-GAN drives the model to generate realistic paragraphs with smooth logical transition between sentence topics. Extensive quantitative experiments on image and video paragraph datasets demonstrate the effectiveness of our RTT-GAN in both supervised and semi-supervised settings. Qualitative results on telling diverse stories for an image also verify the interpretability of RTT-GAN.

cs.CV↗