arXiv subjects

Hong Tang

Publications and source records attributed to Hong Tang.

48 records · Page 3Linked to original sources

arXiv2019

Active Quenching of Superconducting Nanowire Single Photon Detectors

Superconducting nanowire single photon detectors are typically biased using a constant current source and shunted in a conductance which is over an order of magnitude larger than the peak normal domain conductance of the detector. While this design choice is required to ensure quenching of the normal domain, the use of a small load resistor limits the pulse amplitude, rising-edge slew rate, and recovery time of the detector. Here, we explore the possibility of actively quenching the normal domain, thereby removing the need to shunt the detector in a small resistance. We first consider the theoretical performance of an actively quenched superconducting nanowire single photon detector and, in comparison to a passively quenched device, we predict roughly an order of magnitude improvement in the slew rate and peak voltage achieved in this configuration. The experimental performance of actively and passively quenched superconducting nanowire single photon detectors are then compared. It is shown that, in comparison to a passively quenched device, the actively quenched detectors simultaneously exhibited improved count rates, dark count rates, and timing jitter.

physics.ins-det↗

arXiv2019

Spin-wave Confinement and Coupling in Organic-Based Magnetic Nanostructures

Vanadium tetracyanoethylene (V[TCNE]$_\text{x}$) is an organic-based ferrimagnet that exhibits robust magnetic ordering (T$_\text{C}$ of over 600 K), high quality-factor (high-Q) microwave resonance (Q up to 3,500), and compatibility with a wide variety of substrates and encapsulation technologies. Here, we substantially expand the potential scope and impact of this emerging material by demonstrating the ability to produce engineered nanostructures with tailored magnetic anisotropy that serve as a platform for the exploration of cavity magnonics, revealing strongly coupled quantum confined standing wave modes that can be tuned into and out of resonance with an applied magnetic field. Specifically, time-domain micromagnetic simulations of these nanostructures faithfully reproduce the experimentally measured spectra, including the quasi-uniform mode and higher-order spin-wave (magnon) modes. Finally, when the two dominant magnon modes present in the spectra are brought into resonance by varying the orientation of the in-plane magnetic field, we observe anti-crossing behavior indicating strong coherent coupling between these two magnon modes at room temperature. These results position V[TCNE]$_\text{x}$ as a leading candidate for the development of coherent magnonics, with potential applications ranging from microwave electronics to quantum information.

cond-mat.mtrl-sci↗

arXiv2019

Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning

Controlling output length in neural language generation is valuable in many scenarios, especially for the tasks that have length constraints. A model with stronger length control capacity can produce sentences with more specific length, however, it usually sacrifices semantic accuracy of the generated sentences. Here, we denote a concept of Controllable Length Control (CLC) for the trade-off between length control capacity and semantic accuracy of the language generation model. More specifically, CLC is to alter length control capacity of the model so as to generate sentence with corresponding quality. This is meaningful in real applications when length control capacity and outputs quality are requested with different priorities, or to overcome unstability of length control during model training. In this paper, we propose two reinforcement learning (RL) methods to adjust the trade-off between length control capacity and semantic accuracy of length control models. Results show that our RL methods improve scores across a wide range of target lengths and achieve the goal of CLC. Additionally, two models LenMC and LenLInit modified on previous length-control models are proposed to obtain better performance in summarization task while still maintain the ability to control length.

cs.CL↗

arXiv2019

A Novel Modeling Approach for All-Dielectric Metasurfaces Using Deep Neural Networks

Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target electromagnetic (EM) response, an approach that demands significant efforts to investigate the enormous number of possible meta-atom structures. In this paper, a deep neural network approach is introduced that significantly improves on both speed and accuracy compared to techniques currently used to assemble metasurface-based devices. Our neural network approach overcomes three key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch, accurate EM-wave phase prediction, as well as adaptation to 3-D dielectric structures, and can be generically applied to a wide variety of metasurface device designs across the entire electromagnetic spectrum. Using this new methodology, examples of neural networks capable of producing on-demand designs for meta-atoms, metasurface filters, and phase-change reconfigurable metasurfaces are demonstrated.

physics.optics↗

arXiv2019

Molecule-surface interaction from van der Waals-corrected semilocal density functionals: the example of thiophene on transition-metal surfaces

Semi-local density functional approximations are widely used. None of them can capture the long-range van der Waals (vdW) attraction between separated subsystems, but they differ remarkably in the extent to which they capture intermediate-range vdW effects responsible for equilibrium bonds between neighboring small closed-shell subsystems. The local density approximation (LDA) often overestimates this effect, while the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) underestimates it. The strongly-constrained and appropriately normed (SCAN) meta-GGA often estimates it well. All of these semi-local functionals require an additive non-local correction such as the revised Vydrov-Van Voorhis 2010 (rVV10) to capture the long-range part. This work reports adsorption energies and the corresponding geometry of the aromatic thiophene (C$_4$H$_4$S) bound to transition metal surfaces. The adsorption process requires a genuine interplay of covalent and weak binding and requires a simultaneously accurate description of surface and adsorption energies with the correct prediction of the adsorption site. All these quantities must come from well balanced short and long-range correlation effects for a universally applicable method for weak interactions with chemical accuracy.

cond-mat.mtrl-sci↗

arXiv2019

Rational design principles of quantum anomalous Hall effect from superlattice-like magnetic topological insulators

As one of paradigmatic phenomena in condensed matter physics, the quantum anomalous Hall effect (QAHE) in stoichiometric Chern insulators has drawn great interest for years. By using model Hamiltonian analysis and first-principle calculations, we establish a topological phase diagram and map on it with different two-dimensional configurations, which is taken from the recently-grown magnetic topological insulators MnBi4Te7 and MnBi6Te10 with superlattice-like stacking patterns. These configurations manifest various topological phases, including quantum spin Hall effect with and without time-reversal symmetry, as well as QAHE. We then provide design principles to trigger QAHE by tuning experimentally accessible knobs, such as slab thickness and magnetization. Our work reveals that superlattice-like magnetic topological insulators with tunable exchange interaction serve as an ideal platform to realize the long-sought QAHE in pristine compounds, paving a new avenue within the area of topological materials.

cond-mat.mes-hall↗

arXiv2019

Key Technologies for the Wide Field Infrared Survey Telescope Coronagraph Instrument

The Wide Field Infrared Survey Telescope (WFIRST) Coronagraph Instrument (CGI) is a high-contrast imager and integral field spectrograph that will enable the study of exoplanets and circumstellar disks at visible wavelengths. Ground-based high-contrast instrumentation has fundamentally limited performance at small working angles, even under optimistic assumptions for 30m-class telescopes. There is a strong scientific driver for better performance, particularly at visible wavelengths. Future flagship mission concepts aim to image Earth analogues with visible light flux ratios of more than 10^10. CGI is a critical intermediate step toward that goal, with a predicted 10^8-9 flux ratio capability in the visible. CGI achieves this through improvements over current ground and space systems in several areas: (i) Hardware: space-qualified (TRL9) deformable mirrors, detectors, and coronagraphs, (ii) Algorithms: wavefront sensing and control; post-processing of integral field spectrograph, polarimetric, and extended object data, and (iii) Validation of telescope and instrument models at high accuracy and precision. This white paper, submitted to the 2018 NAS Exoplanet Science Strategy call, describes the status of key CGI technologies and presents ways in which performance is likely to evolve as the CGI design matures.

astro-ph.IM↗

arXiv2018

Classification of normal/abnormal heart sound recordings based on multi-domain features and back propagation neural network

This paper aims to classify a single PCG recording as normal or abnormal for computer-aided diagnosis. The proposed framework for this challenge has four steps: preprocessing, feature extraction, training and validation. In the preprocessing step, a recording is segmented into four states, i.e., the first heart sound, systolic interval, the second heart sound, and diastolic interval by the Springer Segmentation algorithm. In the feature extraction step, the authors extract 324 features from multi-domains to perform classification. A back propagation neural network is used as predication model. The optimal threshold for distinguishing normal and abnormal is determined by the statistics of model output for both normal and abnormal. The performance of the proposed predictor tested by the six training sets is sensitivity 0.812 and specificity 0.860 (overall accuracy is 0.836). However, the performance reduces to sensitivity 0.807 and specificity 0.829 (overall accuracy is 0.818) for the hidden test set.

eess.SP↗

arXiv2018

Blind Receive Beamforming for Autonomous Grant-Free High-Overloading Multiple Access

Massive number of internet of things (IoT) devices are expected to simultaneously connect to the mMTC and beyond future generations of wireless network, posing severe challenge to aspects such as RACH procedure, user equipment detection and channel estimation. Although spatial combining has provided significant gains in conventional grant-based transmission, this technique is stuck in dilemma when it comes to autonomous grant-free transmission tailored for IoT use cases. To address this, blind spatial combining and its incorporation in the data-only MUD are elaborated in this paper answering to both the academic and industry's concern in the overloading potential of autonomous grant-free (AGF) transmission. Blind spatial combining could be interpreted as blind receive beamforming heuristically. Simulation results show that the blind spatial combining enhanced data-only MUD performance for AGF transmission is rather impressive.

cs.IT↗

arXiv2018

Origin of the size-dependence of the equilibrium van der Waals binding between nanostructures

Nanostructures can be bound together at equilibrium by the van der Waals (vdW) effect, a small but ubiquitous many-body attraction that presents challenges to density functional theory. How does the binding energy depend upon the size or number of atoms in one of a pair of identical nanostructures? To answer this question, we treat each nanostructure properly as a whole object, not as a collection of atoms. Our calculations start from an accurate static dipole polarizability for each considered nanostructure, and an accurate equilibrium center-to-center distance for the pair (the latter from experiment, or from the vdW-DF-cx functional). We consider the competition in each term $-C_{2k}/d^{2k}$ ($k=3, 4, 5$) of the long-range vdW series for the interaction energy, between the size dependence of the vdW coefficient $C_{2k}$ and that of the $2k$-th power of the center-to-center distance $d$. The damping of these vdW terms can be negligible, but in any case it does not affect the size dependence for a given term in the absence of non-vdW binding. To our surprise, the vdW energy can be size-independent for quasi-spherical nanoclusters bound to one another by vdW interaction, even with strong nonadditivity of the vdW coefficient, as demonstrated for fullerenes. We also show that, for low-dimensional systems, the vdW interaction yields the strongest size-dependence, in stark contrast to that of fullerenes. We illustrate this with parallel planar polycyclic aromatic hydrocarbons. Other cases are between, as shown by sodium clusters.

physics.chem-ph↗

arXiv2013

Electric Field Conjugation with the Project 1640 coronagraph

The Project 1640 instrument on the 200-inch Hale telescope at Palomar Observatory is a coronagraphic instrument with an integral field spectrograph at the back end, designed to find young, self-luminous planets around nearby stars. To reach the necessary contrast for this, the PALM-3000 adaptive optics system corrects for fast atmospheric speckles, while CAL, a phase-shifting interferometer in a Mach-Zehnder configuration, measures the quasistatic components of the complex electric field in the pupil plane following the coronagraphic stop. Two additional sensors measure and control low-order modes. These field measurements may then be combined with a system model and data taken separately using a white-light source internal to the AO system to correct for both phase and amplitude aberrations. Here, we discuss and demonstrate the procedure to maintain a half-plane dark hole in the image plane while the spectrograph is taking data, including initial on-sky performance.

astro-ph.IM↗

arXiv2008

Modeling Loosely Annotated Images with Imagined Annotations

In this paper, we present an approach to learning latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: (1) ambiguous correspondences between visual features and annotated keywords; (2) incomplete lists of annotated keywords. The second reason motivates us to enrich the incomplete annotation in a simple way before learning topic models. In particular, some imagined keywords are poured into the incomplete annotation through measuring similarity between keywords. Then, both given and imagined annotations are used to learning probabilistic topic models for automatically annotating new images. We conduct experiments on a typical Corel dataset of images and loose annotations, and compare the proposed method with state-of-the-art discrete annotation methods (using a set of discrete blobs to represent an image). The proposed method improves word-driven probability Latent Semantic Analysis (PLSA-words) up to a comparable performance with the best discrete annotation method, while a merit of PLSA-words is still kept, i.e., a wider semantic range.

cs.IR↗