arXiv subjects

Wei Huang

Publications and source records attributed to Wei Huang.

At least 397 records · Page 22Linked to original sources

arXiv2021

DeFLOCNet: Deep Image Editing via Flexible Low-level Controls

User-intended visual content fills the hole regions of an input image in the image editing scenario. The coarse low-level inputs, which typically consist of sparse sketch lines and color dots, convey user intentions for content creation (\ie, free-form editing). While existing methods combine an input image and these low-level controls for CNN inputs, the corresponding feature representations are not sufficient to convey user intentions, leading to unfaithfully generated content. In this paper, we propose DeFLOCNet which relies on a deep encoder-decoder CNN to retain the guidance of these controls in the deep feature representations. In each skip-connection layer, we design a structure generation block. Instead of attaching low-level controls to an input image, we inject these controls directly into each structure generation block for sketch line refinement and color propagation in the CNN feature space. We then concatenate the modulated features with the original decoder features for structure generation. Meanwhile, DeFLOCNet involves another decoder branch for texture generation and detail enhancement. Both structures and textures are rendered in the decoder, leading to user-intended editing results. Experiments on benchmarks demonstrate that DeFLOCNet effectively transforms different user intentions to create visually pleasing content.

cs.CV↗

arXiv2021

Efficiency at maximum power of thermoelectric heat engines with the symmetric semiconductor superlattice

Efficiency at maximum power (EMP) is a very important specification for a heat engine to evaluate the capacity of outputting adequate power with high efficiency. It has been proved theoretically that the limit EMP of thermoelectric heat engine can be achieved with the hypothetical boxcar-shaped electron transmission, which is realized here by the resonant tunneling in the one-dimensional symmetric InP/InSe superlattice. It is found with the transfer matrix method that a symmetric mode is robust that regardless of the periodicity, and the obtained boxcar-like electron transmission stems from the strong coupling between symmetric mode and Fabry-Pérot modes inside the allowed band. High uniformity of the boxcar-like transmission and the sharp drop of the transmission edge are both beneficial to the maximum power and the EMP, which are optimized by the bias voltage and the thicknesses of barrier and well. The maximum power and EMP are extracted with the help of machine learning technique, and more than 95% of their theoretical limits can both be achieved for smaller temperature difference, smaller barrier width and larger well width. We hope the obtain results could provide some basic guidance for the future designs of high EMP thermoelectric heat engines.

cond-mat.mes-hall↗

arXiv2021

Machine learning topological invariants of non-Hermitian systems

The study of topological properties by machine learning approaches has attracted considerable interest recently. Here we propose machine learning the topological invariants that are unique in non-Hermitian systems. Specifically, we train neural networks to predict the winding of eigenvalues of four prototypical non-Hermitian Hamiltonians on the complex energy plane with nearly $100\%$ accuracy. Our demonstrations in the non-Hermitian Hatano-Nelson model, Su-Schrieffer-Heeger model and generalized Aubry-André-Harper model in one dimension, and two-dimensional Dirac fermion model with non-Hermitian terms show the capability of the neural networks in exploring topological invariants and the associated topological phase transitions and topological phase diagrams in non-Hermitian systems. Moreover, the neural networks trained by a small data set in the phase diagram can successfully predict topological invariants in untouched phase regions. Thus, our work paves the way to revealing non-Hermitian topology with the machine learning toolbox.

physics.comp-ph↗

arXiv2021

Broadband terahertz half-wave plate with multi-layers metamaterial via quantum engineering

In this paper, we employ the novel design of the metamaterial half-wave plate by using the multiple layers of the metamaterials with some specific rotation angles. The rotation angles are given by composite pulse control which is the well-known quantum control technique. A big advantage of this method can analytically calculate the rotation angles and can be easily extended to the multiple layers. The more layers of the metamaterials can present more bandwidth of half-wave plate. In this paper, we present 1, 3, 5, 7 configurations of layers of a metamaterial half-wave plate and we demonstrate that our device increasingly enhance the bandwidth of performance.

physics.optics↗

arXiv2021

Electronic controllable broadband and robust terahertz surface plasmon-polaritons switch based on hybrid ITO waveguide coupler

The surface plasmon-polaritons (SPPs) switch is the key element of the integrated devices in optical computation and terahertz (THz) communications. In this paper, we propose a novel design of THz SPPs switch based on quantum engineering. Due to the robustness of coherent quantum control technique, our switch is very robust against with perturbations of geometrical parameters and presents a good performance at on-state (and off-state) from 0.5 THz to 0.7 THz. The on-state and off-state of our device can be controlled by the external voltage. We believe this finding will be the great improvement for the integrated optical computing and THz communications.

physics.optics↗

arXiv2020

Fairness and Accuracy in Federated Learning

In the federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure privacy. Normally, inconsistent distribution of data across different devices in a federated network and limited communication bandwidth between end devices impose both statistical heterogeneity and expensive communication as major challenges for federated learning. This paper proposes an algorithm to achieve more fairness and accuracy in federated learning (FedFa). It introduces an optimization scheme that employs a double momentum gradient, thereby accelerating the convergence rate of the model. An appropriate weight selection algorithm that combines the information quantity of training accuracy and training frequency to measure the weights is proposed. This procedure assists in addressing the issue of unfairness in federated learning due to preferences for certain clients. Our results show that the proposed FedFa algorithm outperforms the baseline algorithm in terms of accuracy and fairness.

cs.LG↗

arXiv2020

Implicit bias of deep linear networks in the large learning rate phase

Most theoretical studies explaining the regularization effect in deep learning have only focused on gradient descent with a sufficient small learning rate or even gradient flow (infinitesimal learning rate). Such researches, however, have neglected a reasonably large learning rate applied in most practical applications. In this work, we characterize the implicit bias effect of deep linear networks for binary classification using the logistic loss in the large learning rate regime, inspired by the seminal work by Lewkowycz et al. [26] in a regression setting with squared loss. They found a learning rate regime with a large stepsize named the catapult phase, where the loss grows at the early stage of training and eventually converges to a minimum that is flatter than those found in the small learning rate regime. We claim that depending on the separation conditions of data, the gradient descent iterates will converge to a flatter minimum in the catapult phase. We rigorously prove this claim under the assumption of degenerate data by overcoming the difficulty of the non-constant Hessian of logistic loss and further characterize the behavior of loss and Hessian for non-separable data. Finally, we demonstrate that flatter minima in the space spanned by non-separable data along with the learning rate in the catapult phase can lead to better generalization empirically.

cs.LG↗

arXiv2020

Twin-field quantum key distribution with passive-decoy state

Twin-Field quantum key distribution (TF-QKD) and its variants, e.g. Phase-Matching QKD, Sending-or-not-sending QKD, and No Phase Post-Selection TFQKD promise high key rates at long distance to beat the rate distance limit without a repeater. The security proof of these protocols are based on decoy-state method, which is usually performed by actively modulating a variable optical attenuator together with a random number generator in practical experiments, however, active-decoy schemes like this may lead to side channel and could open a security loophole. To enhance the source security of TF-QKD, in this paper, we propose passive-decoy based TF-QKD, in which we combine TF-QKD with the passive-decoy method. And we present a simulation comparing the key generation rate with that in active-decoy, the result shows our scheme performs as good as active decoy TF-QKD, and our scheme could reach satisfactory secret key rates with just a few photon detectors. This shows our work is meaningful in practice.

quant-ph↗

arXiv2020

Optimized protocol for twin-field quantum key distribution

Twin-field quantum key distribution (TF-QKD) and its variant protocols are highly attractive due to the advantage of overcoming the rate-loss limit for secret key rates of point-to-point QKD protocols. For variations of TF-QKD, the key point to ensure security is switching randomly between a code mode and a test mode. Among all TF-QKD protocols, their code modes are very different, e.g. modulating continuous phases, modulating only two opposite phases, and sending or not sending signal pulses. Here we show that, by discretizing the number of global phases in the code mode, we can give a unified view on the first two types of TF-QKD protocols, and demonstrate that increasing the number of discrete phases extends the achievable distance, and as a trade-off, lowers the secret key rate at short distances due to the phase post-selection.

quant-ph↗

arXiv2020

Randomness Quantification for Quantum Random Number Generation Based on Detection of Amplified Spontaneous Emission Noise

The amplified spontaneous emission (ASE) noise has been extensively studied and employed to build quantum random number generators (QRNGs). While the previous relative works mainly focus on the realization and verification of the QRNG system, the comprehensive physical model and randomness quantification for the general detection of the ASE noise are still incomplete, which is essential for the quantitative security analysis. In this paper, a systematical physical model for the emission, detection and acquisition of the ASE noise with added electronic noise is developed and verified, based on which the numerical simulations are performed under various setups and the simulation results all significantly fit well with the corresponding experimental data. Then, a randomness quantification method and the corresponding experimentally verifiable approach are proposed and validated, which quantifies the randomness purely resulted from the quantum process and improves the security analysis for the QRNG based on the detection of the ASE noise. The physical model and the randomness quantification method proposed in this paper are of significant feasibility and applicable for the QRNG system with randomness originating from the detection of the photon number with arbitrary distributions.

quant-ph↗

arXiv2020

High-speed Gaussian modulated continuous-variable quantum key distribution with a local local oscillator based on pilot-tone-assisted phase compensation

A high-speed Gaussian modulated continuous-variable quantum key distribution (CVQKD) with a local local oscillator (LLO) is experimentally demonstrated based on pilot-tone-assisted phase compensation. In the proposed scheme, the frequency-multiplexing and polarization-multiplexing techniques are used for the separate transmission and heterodyne detection between quantum signal and pilot tone, guaranteeing no crosstalk from strong pilot tone to weak quantum signal and different detection requirements of low-noise for quantum signal and high-saturation limitation for pilot tone. Moreover, compared with the conventional CVQKD based on homodyne detection, the proposed LLO-CVQKD scheme can measure X and P quadrature simultaneously using heterodyne detection without need of extra random basis selection. Besides, the phase noise, which contains the fast-drift phase noise due to the relative phase of two independent lasers and the slow-drift phase noise introduced by quantum channel disturbance, has been compensated experimentally in real time, so that a low level of excess noise with a 25km optical fiber channel is obtained for the achievable secure key rate of 7.04 Mbps in the asymptotic regime and 1.85 Mbps under the finite-size block of 10^7.

quant-ph↗

arXiv2020

Bell-state tomography in a silicon many-electron artificial molecule

An error-corrected quantum processor will require millions of qubits, accentuating the advantage of nanoscale devices with small footprints, such as silicon quantum dots. However, as for every device with nanoscale dimensions, disorder at the atomic level is detrimental to qubit uniformity. Here we investigate two spin qubits confined in a silicon double-quantum-dot artificial molecule. Each quantum dot has a robust shell structure and, when operated at an occupancy of 5 or 13 electrons, has single spin-$\frac{1}{2}$ valence electron in its $p$- or $d$-orbital, respectively. These higher electron occupancies screen atomic-level disorder. The larger multielectron wavefunctions also enable significant overlap between neighbouring qubit electrons, while making space for an interstitial exchange-gate electrode. We implement a universal gate set using the magnetic field gradient of a micromagnet for electrically-driven single qubit gates, and a gate-voltage-controlled inter-dot barrier to perform two-qubit gates by pulsed exchange coupling. We use this gate set to demonstrate a Bell state preparation between multielectron qubits with fidelity 90.3%, confirmed by two-qubit state tomography using spin parity measurements.

cond-mat.mes-hall↗

arXiv2020

Finite-key analysis for twin-field quantum key distribution based on generalized operator dominance condition

Quantum key distribution (QKD) can help two distant peers to share secret key bits, whose security is guaranteed by the law of physics. In practice, the secret key rate of a QKD protocol is always lowered with the increasing of channel distance, which severely limits the applications of QKD. Recently, twin-field (TF) QKD has been proposed and intensively studied, since it can beat the rate-distance limit and greatly increase the achievable distance of QKD. Remarkalebly, K. Maeda et. al. proposed a simple finite-key analysis for TF-QKD based on operator dominance condition. Although they showed that their method is sufficient to beat the rate-distance limit, their operator dominance condition is not general, i.e. it can be only applied in three decoy states scenarios, which implies that its key rate cannot be increased by introducing more decoy states, and also cannot reach the asymptotic bound even in case of preparing infinite decoy states and optical pulses. Here, to bridge this gap, we propose an improved finite-key analysis of TF-QKD through devising new operator dominance condition. We show that by adding the number of decoy states, the secret key rate can be furtherly improved and approach the asymptotic bound. Our theory can be directly used in TF-QKD experiment to obtain higher secret key rate. Our results can be directly used in experiments to obtain higher key rates.

quant-ph↗

arXiv2020

Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations

Deep encoder-decoder based CNNs have advanced image inpainting methods for hole filling. While existing methods recover structures and textures step-by-step in the hole regions, they typically use two encoder-decoders for separate recovery. The CNN features of each encoder are learned to capture either missing structures or textures without considering them as a whole. The insufficient utilization of these encoder features limit the performance of recovering both structures and textures. In this paper, we propose a mutual encoder-decoder CNN for joint recovery of both. We use CNN features from the deep and shallow layers of the encoder to represent structures and textures of an input image, respectively. The deep layer features are sent to a structure branch and the shallow layer features are sent to a texture branch. In each branch, we fill holes in multiple scales of the CNN features. The filled CNN features from both branches are concatenated and then equalized. During feature equalization, we reweigh channel attentions first and propose a bilateral propagation activation function to enable spatial equalization. To this end, the filled CNN features of structure and texture mutually benefit each other to represent image content at all feature levels. We use the equalized feature to supplement decoder features for output image generation through skip connections. Experiments on the benchmark datasets show the proposed method is effective to recover structures and textures and performs favorably against state-of-the-art approaches.

cs.CV↗

arXiv2020

Long distance adiabatic wireless energy transfer via multiple coils coupling

Recently, the wireless energy transfer model can be described as the Schrodinger equation [Annals of Physics, 2011, 326(3): 626-633; Annals of Physics, 2012, 327(9): 2245-2250]. Therefore, wireless energy transfer can be designed by coherent quantum control techniques, which can achieve efficient and robust energy transfer from transmitter to receiver device. In this paper, we propose a novel design of wireless energy transfer which obtains the longer distance, efficient and robust schematic of power transfer, via multiple states triangle crossing pattern. After our calculations, we demonstrate that our design can provide much longer transfer distance with relatively smaller decreasing in the transfer efficiency.

physics.app-ph↗

arXiv2020

Population transfer via a finite temperature state

We study quantum population transfer via a common intermediate state initially in thermal equilibrium with a finite temperature $T$, exhibiting a multi-level Stimulated Raman adiabatic passage structure. We consider two situations for the common intermediate state, namely a discrete two-level spin and a bosonic continuum. In both cases we show that the finite temperature strongly affects the efficiency of the population transfer. We also show in the discrete case that strong coupling with the intermediate state, or a longer duration of the controlled pulse would suppress the effect of finite temperature. In the continuous case, we adapt the thermofield-based chain-mapping matrix product states algorithm to study the time evolution of the system plus the continuum under time-dependent controlled pulses, which shows a great potential to be used to solve open quantum system problems in quantum optics.

quant-ph↗

arXiv2020

A highly sensitive piezoresistive sensor based on MXene and polyvinyl butyral with a wide detection limit and low power consumption

As a new class of two-dimensional transition-metal carbide and carbonitride, MXene have been widely used in the energy storage, sensor, catalysis, electromagnetic interference shielding and other field. It is a challenge to simultaneously realize a sensor of extremely high sensitivity, wide detection limits, low power consumption and good mechanical stability. In this work, taking advantage of high conductivity of MXene and porous structure of polyvinyl butyral, a highly sensitive piezoresistive sensor was fabricated. The fabricated MXene/PVB-based sensor exhibits highly sensitive reliably with a factor of ~11.9 kPa^-1, ~1.15 kPa^-1 and ~0.20 kPa^-1 in the ranges of 31.2 Pa-312 Pa, 312 Pa- 62.4 kPa and 62.4 kPa-1248.4 kPa, respectively. The sensor has a wide detection range (~31.2 Pa to ~2.205 MPa), low detection limit (6.8 Pa), low detection voltage (0.1 mV), low power consumption (~3.6 * 10^-10 W), fast response time ( ~110 ms), as well as good mechanical stability (over 10,000 maximum-pressure cycles). Moreover, it is demonstrated that the sensor can detect subtle bending and release activities of human, including arterial pulses and voice signal, which is potentially suitable as a wide detection range, highly sensitive and low power consumption piezoresistive sensor. This work provides a new avenue to expand the application of MXene-based flexible pressure sensor in the field of wide sensing range and ultra-low power consumption.

physics.app-ph↗

arXiv2020

Sn4+ Precursor Enables 12.4% Efficient Kesterite Solar Cell from DMSO Solution with Open Circuit Voltage Deficit Below 0.30 V

The limiting factor preventing kesterite (CZTSSe) thin film solar cell performance further improvement is the large open-circuit voltage deficit (Voc,def) issue, which is 0.345V for the current world record device with an efficiency of 12.6%. In this work, SnCl4 and SnCl2_2H2O are respectively used as tin precursor to investigate the Voc,def issue of dimethyl sulfoxide (DMSO) solution processed CZTSSe solar cells. Different complexations of tin compounds with thiourea and DMSO lead to different reaction pathways from solution to absorber material and thus dramatic difference in photovoltaic performance. The coordination of Sn2+ with Tu leads to the formation of SnS and ZnS and Cu2S in the precursor film, which converted to selenides first and then fused to CZTSSe, resulting in poor film quality and device performance. The highest efficiency obtained from this film is 8.84% with a Voc,def of 0.391V. The coordination of Sn4+ with DMSO facilitates direct formation ofkesterite CZTS phase in the precursor film which directed converted to CZTSSe during selenization, resulting in compositional uniform absorber and high device performance. A device with active area efficiency 12.2% and a Voc,def of 0.344 V was achieved from Sn4+ solution processed absorber. Furthermore, CZTSSe/CdS heterojunction heat treatment (JHT) significantly improved Sn4+ device performance but had slightly negative effect on Sn2+ device. A champion CZTSSe solar cell with a total area efficiency of 12.4% (active are efficiency 13.6%) and low Voc,def of 0.297 V was achieved from Sn4+ solution. Our results demonstrate the preformed uniform kesterite phase enabled by Sn4+ precursor is the key in achieving highly efficient kesterite absorber material. The lowest Voc-def and high efficiency achieved here shines new light on the future of kesterite solar cell.

physics.app-ph↗