arXiv subjects

Fang Liu

Publications and source records attributed to Fang Liu.

At least 235 records · Page 13Linked to original sources

arXiv2023

Dynamic brain spectrum acquired by a real-time ultra-spectral imaging chip with reconfigurable metasurfaces

Spectral imaging paves way for various fields and particular in biomedical research. However, spectral imaging mainly depending on spatial or temporal scanning, cannot achieve high temporal, spatial and spectral resolution simultaneously. In this study, we demonstrated a silicon real-time ultra-spectral imaging chip based on reconfigurable metasurfaces, comprising of 155,216 (356$\times$436) image-adaptive micro-spectrometers with ultra-high center-wavelength accuracy of 0.04 nm and spectral resolution of 0.8 nm. It is employed for imaging brain hemodynamics, and the dynamic spectral absorption properties of deoxyhemoglobin and oxyhemoglobin in a rat barrel cortex were obtained, which enlighten the spectroscopy in vivo studies and other real-time applications.

physics.optics↗

arXiv2023

Metasurface-Based Free-Space Multi-port Beam Splitter with Arbitrary Power Ratio

A beam splitter (BS) is one of the most critical building blocks in optical systems. Despite various attempts of flat-type BSs to miniaturize the conventional cube BS reported, it remains a challenge to realize an ultrathin optical BS with multi-port output, non-uniform splitting ratio and steerable outgoing directions. Herein, we have demonstrated a free-space optical multi-port beam splitter (MPBS) based on a polarization-independent all-dielectric metasurface. By applying an optimized phase-pattern paradigm via a gradient-descent-based iterative algorithm to amorphous silicon (a-Si) metasurfaces, we have prepared a variety of MPBS samples with arbitrarily predetermined output port number (2~7), power ratio and spatial distribution of output beams. The experimental results reveal that the fabricated MPBSs could achieve high total splitting efficiency (TSE, above 74.7%) and beam-splitting fidelity (similarity, above 78.4%) within the bandwidth of 100 nm (1500~1600 nm). We envision that such MPBS could provide fabulous flexibility for optical integrated system and diverse applications.

physics.optics↗

arXiv2023

Disclosure Risk from Homogeneity Attack in Differentially Private Frequency Distribution

Differential privacy (DP) provides a robust model to achieve privacy guarantees for released information. We examine the protection potency of sanitized multi-dimensional frequency distributions via DP randomization mechanisms against homogeneity attack (HA). HA allows adversaries to obtain the exact values on sensitive attributes for their targets without having to identify them from the released data. We propose measures for disclosure risk from HA and derive closed-form relationships between the privacy loss parameters in DP and the disclosure risk from HA. The availability of the closed-form relationships assists understanding the abstract concepts of DP and privacy loss parameters by putting them in the context of a concrete privacy attack and offers a perspective for choosing privacy loss parameters when employing DP mechanisms in information sanitization and release in practice. We apply the closed-form mathematical relationships in real-life datasets to demonstrate the assessment of disclosure risk due to HA on differentially private sanitized frequency distributions at various privacy loss parameters.

cs.CR↗

arXiv2023

Syntax and Domain Aware Model for Unsupervised Program Translation

There is growing interest in software migration as the development of software and society. Manually migrating projects between languages is error-prone and expensive. In recent years, researchers have begun to explore automatic program translation using supervised deep learning techniques by learning from large-scale parallel code corpus. However, parallel resources are scarce in the programming language domain, and it is costly to collect bilingual data manually. To address this issue, several unsupervised programming translation systems are proposed. However, these systems still rely on huge monolingual source code to train, which is very expensive. Besides, these models cannot perform well for translating the languages that are not seen during the pre-training procedure. In this paper, we propose SDA-Trans, a syntax and domain-aware model for program translation, which leverages the syntax structure and domain knowledge to enhance the cross-lingual transfer ability. SDA-Trans adopts unsupervised training on a smaller-scale corpus, including Python and Java monolingual programs. The experimental results on function translation tasks between Python, Java, and C++ show that SDA-Trans outperforms many large-scale pre-trained models, especially for unseen language translation.

cs.SE↗

arXiv2023

Exotic single-photon and enhanced deep-level emissions in hBN strain superlattice

The peculiar defect-related photon emission processes in 2D hexagonal boron nitride (hBN) have become a topic of intense research due to their potential applications in quantum information and sensing technologies. Recent efforts have focused on activating and modulating the defect energy levels in hBN by methods that can be integrated on a chip, and understanding the underlying physical mechanism. Here, we report on exotic single photon and enhanced deep-level emissions in 2D hBN strain superlattice, which is fabricated by transferring multilayer hBN onto hexagonal close-packed silica spheres on silica substrate. We realize effective activation of the single photon emissions (SPEs) in the multilayer hBN at the positions that are in contact with the apex of the SiO2 spheres. At these points, the local tensile strain induced blue-shift of the SPE is found to be up to 12 nm. Furthermore, high spatial resolution cathodoluminescence measurments show remarkable strain-enhanced deep-level (DL) emissions in the multilayer hBN with the emission intensity distribution following the periodic hexagonal pattern of the strain superlattice. The maximum DL emission enhancement is up to 350% with a energy redshift of 6 nm. Our results provide a simple on-chip compatible method for activating and tuning the defect-related photon emissions in multilayer hBN, demonstrating the potential of hBN strain superlattice as a building block for future on-chip quantum nanophotonic devices.

cond-mat.mtrl-sci↗

arXiv2023

Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Records

Electronic health records (EHR) often contain sensitive medical information about individual patients, posing significant limitations to sharing or releasing EHR data for downstream learning and inferential tasks. We use normalizing flows (NF), a family of deep generative models, to estimate the probability density of a dataset with differential privacy (DP) guarantees, from which privacy-preserving synthetic data are generated. We apply the technique to an EHR dataset containing patients with pulmonary hypertension. We assess the learning and inferential utility of the synthetic data by comparing the accuracy in the prediction of the hypertension status and variational posterior distribution of the parameters of a physics-based model. In addition, we use a simulated dataset from a nonlinear model to compare the results from variational inference (VI) based on privacy-preserving synthetic data, and privacy-preserving VI obtained from directly privatizing NFs for VI with DP guarantees given the original non-private dataset. The results suggest that synthetic data generated through differentially private density estimation with NF can yield good utility at a reasonable privacy cost. We also show that VI obtained from differentially private NF based on the free energy bound loss may produce variational approximations with significantly altered correlation structure, and loss formulations based on alternative dissimilarity metrics between two distributions might provide improved results.

stat.ML↗

arXiv2023

Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search

The construction of machine learning models involves many bi-level multi-objective optimization problems (BL-MOPs), where upper level (UL) candidate solutions must be evaluated via training weights of a model in the lower level (LL). Due to the Pareto optimality of sub-problems and the complex dependency across UL solutions and LL weights, an UL solution is feasible if and only if the LL weight is Pareto optimal. It is computationally expensive to determine which LL Pareto weight in the LL Pareto weight set is the most appropriate for each UL solution. This paper proposes a bi-level multi-objective learning framework (BLMOL), coupling the above decision-making process with the optimization process of the UL-MOP by introducing LL preference $r$. Specifically, the UL variable and $r$ are simultaneously searched to minimize multiple UL objectives by evolutionary multi-objective algorithms. The LL weight with respect to $r$ is trained to minimize multiple LL objectives via gradient-based preference multi-objective algorithms. In addition, the preference surrogate model is constructed to replace the expensive evaluation process of the UL-MOP. We consider a novel case study on multi-task graph neural topology search. It aims to find a set of Pareto topologies and their Pareto weights, representing different trade-offs across tasks at UL and LL, respectively. The found graph neural network is employed to solve multiple tasks simultaneously, including graph classification, node classification, and link prediction. Experimental results demonstrate that BLMOL can outperform some state-of-the-art algorithms and generate well-representative UL solutions and LL weights.

cs.NE↗

arXiv2023

Deep-learning-based on-chip rapid spectral imaging with high spatial resolution

Spectral imaging extends the concept of traditional color cameras to capture images across multiple spectral channels and has broad application prospects. Conventional spectral cameras based on scanning methods suffer from low acquisition speed and large volume. On-chip computational spectral imaging based on metasurface filters provides a promising scheme for portable applications, but endures long computation time for point-by-point iterative spectral reconstruction and mosaic effect in the reconstructed spectral images. In this study, we demonstrated on-chip rapid spectral imaging eliminating the mosaic effect in the spectral image by deep-learning-based spectral data cube reconstruction. We experimentally achieved four orders of magnitude speed improvement than iterative spectral reconstruction and high fidelity of spectral reconstruction over 99% for a standard color board. In particular, we demonstrated video-rate spectral imaging for moving objects and outdoor driving scenes with good performance for recognizing metamerism, where the concolorous sky and white cars can be distinguished via their spectra, showing great potential for autonomous driving and other practical applications in the field of intelligent perception.

eess.IV↗

arXiv2022

Angle-insensitive spectral imaging based on topology-optimized plasmonic metasurfaces

On-chip spectral imaging based on engineered spectral modulation and computational spectral reconstruction provides a promising scheme for portable spectral cameras. However, the angle dependence of modulation units results in the angle sensitivity of spectral imaging, which limits its practical applications. Here, we proposed a design for angle-robust spectral recovery based on a group of topology-optimized plasmonic metasurface units under a 30° field-of-view, and demonstrate angle-insensitive on-chip spectral imaging in the wavelength range of 450 to 750 nm for average polarization. Furthermore, we experimentally verified the angle-insensitive spectral filtering effects of the fabricated metasurface units, and demonstrated angle-robust spectral reconstruction with a fidelity of 98%. Our approach expands the application scale of spectral imaging, and provides a guidance for designing other angle-robust devices.

physics.optics↗

arXiv2022

High-dimensional quantum key distribution using energy-time entanglement over 242 km partially deployed fiber

Entanglement-based quantum key distribution (QKD) is an essential ingredient in quantum communication, owing to the property of source-independent security and the potential on constructing large-scale quantum communication networks. However, implementation of entanglement-based QKD over long-distance optical fiber links is still challenging, especially over deployed fibers. In this work, we report an experimental QKD using energy-time entangled photon pairs that transmit over optical fibers of 242 km (including a section of 19 km deployed fibers). High-quality entanglement distribution is verified by Franson-type interference with raw fringe visibilities of 94.1$\pm$1.9% and %92.4$\pm$5.4% in two non-orthogonal bases. The QKD is realized through the protocol of dispersive-optics QKD. A high-dimensional encoding is applied to utilize coincidence counts more efficiently. Using reliable, high-accuracy time synchronization technology, the system operates continuously for more than 7 days, even without active polarization or phase calibration. We ultimately generate secure keys with secure key rates of 0.22 bps and 0.06 bps in asymptotic and finite-size regime,respectively. This system is compatible with existing telecommunication infrastructures, showing great potential on realizing large-scale quantum communication networks in future.

quant-ph↗

arXiv2022

High-harmonic generation from artificially stacked 2D crystals

We report a coherent layer-by-layer high-order harmonic generation (HHG) build-up in artificially stacked transition metal dichalcogenides (TMDC) crystals in their various stacking configurations. In the experiments, millimeter-sized single crystalline monolayers are synthesized using the gold foil-exfoliation method, followed by artificially stacking on a transparent substrate. High-order harmonics up to the 19th order are generated by the interaction with an ultrafast mid-infrared (MIR) driving laser. We find that the generation is sensitive to the number of layers and their relative orientation. For AAAA stacking configuration, both odd- and even-orders exhibit a quadratic increase in intensity as a function of the number of layers, which is a signature of constructive interference of high-harmonic emission from successive layers. Particularly, we observe some deviations from this scaling at photon energies above the bandgap, which is explained by self-absorption effects. For AB and ABAB stacking, even-order harmonics remain below the detection level, consistent with the presence of inversion symmetry. Our study confirms the capability of producing non-perturbative high-order harmonics from stacked layered materials subjected to intense MIR fields without damaging samples. It has implications for optimizing solid-state HHG sources at the nano-scale and developing high-harmonics as an ultrafast probe of artificially stacked layered materials. Because the HHG process is a strong-field driven process, it has the potential to probe high-momentum and energy states in the bandstructure combined with atomic-scale sensitivity in real space, making it an attractive probe of novel material structures such as the Moiré pattern.

physics.optics↗

arXiv2022

Floquet engineering of strongly-driven excitons in monolayer tungsten disulfide

Interactions of quantum materials with strong-laser fields can induce exotic nonequilibrium electronic states. Monolayer transition-metal dichalcogenides, a new class of direct-gap semiconductors with prominent quantum confinement, offer exceptional opportunities toward Floquet engineering of quasiparticle electron-hole states, or excitons. Strong-field driving has a potential to achieve enhanced control of electronic band structure, thus a possibility to open a new realm of exciton light-matter interactions. However, experimental implementation of strongly-driven excitons has so far remained out of reach. Here, we use mid-infrared laser pulses below the optical bandgap to excite monolayer tungsten disulfide up to a field strength of 0.3 V/nm, and demonstrate strong-field light dressing of excitons in the excess of a hundred millielectronvolt. Our high-sensitivity transient absorption spectroscopy further reveals formation of a virtual absorption feature below the 1s-exciton resonance, which is assigned to a light-dressed sideband from the dark 2p-exciton state. Quantum-mechanical simulations substantiate the experimental results and enable us to retrieve real-space movies of the exciton dynamics. This study advances our understanding of the exciton dynamics in the strong-field regime, and showcases the possibility of harnessing ultrafast, strong-field phenomena in device applications of two-dimensional materials.

cond-mat.mtrl-sci↗

arXiv2022

Some Examples of Privacy-preserving Publication and Sharing of COVID-19 Pandemic Data

A considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and interpretation of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns. In this study, we use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. We investigate the inferential utility of privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the approaches in real-life data. All the approaches employed in the study are straightforward to apply. Our study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.

cs.CR↗

arXiv2022

Programmable Unitary Operations for Orbital Angular Momentum Encoded States

We have proposed and demonstrated a scalable and efficient scheme for programmable unitary operations in orbital angular momentum (OAM) domain. Based on matrix decomposition into diagonal and Fourier factors, arbitrary matrix operators can be implemented only by diagonal matrices alternately acting on orbital angular momentum domain and azimuthal angle domain, which are linked by Fourier transform. With numerical simulations, unitary matrices with dimensionality of 3*3 are designed and discussed for OAM domain. Meanwhile, the parallelism of our proposed scheme is also presented with two 3*3 matrices. Furthermore, as an alternative to verify our proposal, proof of principle experiments have been performed on path domain with the same matrix decomposition method, in which an average fidelity of 0.97 is evaluated through 80 experimental results with dimensionality of 3*3.

quant-ph↗

arXiv2022

dual unet:a novel siamese network for change detection with cascade differential fusion

Change detection (CD) of remote sensing images is to detect the change region by analyzing the difference between two bitemporal images. It is extensively used in land resource planning, natural hazards monitoring and other fields. In our study, we propose a novel Siamese neural network for change detection task, namely Dual-UNet. In contrast to previous individually encoded the bitemporal images, we design an encoder differential-attention module to focus on the spatial difference relationships of pixels. In order to improve the generalization of networks, it computes the attention weights between any pixels between bitemporal images and uses them to engender more discriminating features. In order to improve the feature fusion and avoid gradient vanishing, multi-scale weighted variance map fusion strategy is proposed in the decoding stage. Experiments demonstrate that the proposed approach consistently outperforms the most advanced methods on popular seasonal change detection datasets.

cs.CV↗

arXiv2022

Controllable Entanglement Distribution Network Based on Silicon Quantum Photonics

The entanglement distribution network connects remote users through sharing entanglement resources, which is essential for realizing quantum internet. We proposed a controllable entanglement distribution network (c-EDN) based on a silicon quantum photonic chip. The entanglement resources were generated by a quantum light source array based on spontaneous four-wave mixing (SFWM) in silicon waveguides and distributed to different users through time-reversed Hong-Ou-Mandel interferences in on-chip Mach-Zehnder interferometers with thermal phase shifters. A chip sample was designed and fabricated, supporting a c-EDN with 3 subnets and 24 users. The network topology of entanglement distributions could be reconfigured in three network states by controlling the quantum interferences through the phase shifters, which was demonstrated experimentally. Furthermore, a reconfigurable entanglement-based QKD network was realized as an application of the c-EDN. The reconfigurable network topology makes the c-EDN suitable for future quantum networks requiring complicated network control and management. Moreover, it is also shows that silicon quantum photonic chips have great potential for large-scale c-EDN, thanks to their capacities on generating and manipulating plenty of entanglement resources.

quant-ph↗

arXiv2022

A photon counting reconstructive spectrometer combining metasurfaces and superconducting nanowire single-photon detectors

Faint light spectroscopy has many important applications such as fluorescence spectroscopy, lidar and astronomical observations. However, long measurement time limit its application on real-time measurement. In this work, a photon counting reconstructive spectrometer combining metasurfaces and superconducting nanowire single photon detectors (SNSPDs) was proposed. A prototype device was fabricated on a silicon on isolator (SOI) substrate, and its performance was characterized. Experiment results show that this device support spectral reconstruction of mono-color lights with a resolution of 2 nm in the wavelength region of 1500 nm ~ 1600 nm. The detection efficiency of this device is 1.4% ~ 3.2% in this wavelength region. The measurement time required by this photon counting reconstructive spectrometer was also investigated experimentally, showing its potential to be applied in the scenarios requiring real-time measurement.

physics.optics↗

arXiv2022

Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon

Spatial-temporal dynamics of aboveground biomass (AGB) and forest area affect the carbon cycle, climate, and biodiversity in the Brazilian Amazon. Here we investigate inter-annual changes of AGB and forest area by analyzing satellite-based annual AGB and forest area datasets. We found the gross forest area loss was larger in 2019 than in 2015, possibly due to recent loosening of forest protection policies. However, net AGB loss was three times smaller in 2019 than in 2015. During 2010-2019, the Brazilian Amazon had a cumulative gross loss of 4.45 Pg C against a gross gain of 3.78 Pg C, resulting in net AGB loss of 0.67 Pg C. Forest degradation (73%) contributed three times more to the gross AGB loss than deforestation (27%), given that the areal extent of degradation exceeds deforestation. This indicates that forest degradation has become the largest process driving carbon loss and should become a higher policy priority.

physics.bio-ph↗