arXiv subjects

Fang Liu

Publications and source records attributed to Fang Liu.

At least 217 records · Page 12Linked to original sources

arXiv2023

DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning

Differential privacy (DP) is the state-of-the-art framework for guaranteeing privacy for individuals when releasing aggregated statistics or building statistical/machine learning models from data. We develop the open-source R package DPpack that provides a large toolkit of differentially private analysis. The current version of DPpack implements three popular mechanisms for ensuring DP: Laplace, Gaussian, and exponential. Beyond that, DPpack provides a large toolkit of easily accessible privacy-preserving descriptive statistics functions. These include mean, variance, covariance, and quantiles, as well as histograms and contingency tables. Finally, DPpack provides user-friendly implementation of privacy-preserving versions of logistic regression, SVM, and linear regression, as well as differentially private hyperparameter tuning for each of these models. This extensive collection of implemented differentially private statistics and models permits hassle-free utilization of differential privacy principles in commonly performed statistical analysis. We plan to continue developing DPpack and make it more comprehensive by including more differentially private machine learning techniques, statistical modeling and inference in the future.

stat.ML↗

arXiv2023

Sentiment Analysis and Effect of COVID-19 Pandemic using College SubReddit Data

Background: The COVID-19 pandemic has affected our society and human well-being in various ways. In this study, we investigate how the pandemic has influenced people's emotions and psychological states compared to a pre-pandemic period using real-world data from social media. Method: We collected Reddit social media data from 2019 (pre-pandemic) and 2020 (pandemic) from the subreddits communities associated with eight universities. We applied the pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) to learn text embedding from the Reddit messages, and leveraged the relational information among posted messages to train a graph attention network (GAT) for sentiment classification. Finally, we applied model stacking to combine the prediction probabilities from RoBERTa and GAT to yield the final classification on sentiment. With the model-predicted sentiment labels on the collected data, we used a generalized linear mixed-effects model to estimate the effects of pandemic and in-person teaching during the pandemic on sentiment. Results: The results suggest that the odds of negative sentiments in 2020 (pandemic) were 25.7% higher than the odds in 2019 (pre-pandemic) with a $p$-value $<0.001$; and the odds of negative sentiments associated in-person learning were 48.3% higher than with remote learning in 2020 with a $p$-value of 0.029. Conclusions: Our study results are consistent with the findings in the literature on the negative impacts of the pandemic on people's emotions and psychological states. Our study contributes to the growing real-world evidence on the various negative impacts of the pandemic on our society; it also provides a good example of using both ML techniques and statistical modeling and inference to make better use of real-world data.

cs.CL↗

arXiv2023

Has Sentiment Returned to the Pre-pandemic Level? A Sentiment Analysis Using U.S. College Subreddit Data from 2019 to 2022

As impact of COVID-19 pandemic winds down, both individuals and society gradually return to pre-pandemic activities. This study aims to explore how people's emotions have changed from the pre-pandemic during the pandemic to post-emergency period and whether it has returned to pre-pandemic level. We collected Reddit data in 2019 (pre-pandemic), 2020 (peak pandemic), 2021, and 2022 (late stages of pandemic, transitioning period to post-emergency period) from subreddits in 128 universities/colleges in the U.S., and a set of school-level characteristics. We predicted two sets of sentiments from a pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) and graph attention network (GAT) that leverages both rich semantic and relational information among posted messages and then applied a logistic stacking method to obtain the final sentiment classification. After obtaining sentiment label for each message, we used a generalized linear mixed-effects model to estimate temporal trend in sentiment from 2019 to 2022 and how school-level factors may affect sentiment. Compared to the year 2019, the odds of negative sentiment in years 2020, 2021, and 2022 are 24%, 4.3%, and 10.3% higher, respectively, which are all statistically significant(adjusted $p$<0.05). Our study findings suggest a partial recovery in the sentiment composition in the post-pandemic-emergency era. The results align with common expectations and provide a detailed quantification of how sentiments have evolved from 2019 to 2022.

cs.CL↗

arXiv2023

Super-compact universal quantum logic gates with inversedesigned elements

Integrated quantum photonic circuit is a promising platform for the realization of quantum information processing in the future. To achieve the largescale quantum photonic circuits, the applied quantum logic gates should be as small as possible for the high-density integration on chips. Here, we report the implementation of super-compact universal quantum logic gates on silicon chips by the method of inverse design. In particular, the fabricated controlled-NOT gate and Hadamard gate are both nearly a vacuum wavelength, being the smallest optical quantum gates reported up to now. We further design the quantum circuit by cascading these fundamental gates to perform arbitrary quantum processing, where the corresponding size is about several orders smaller than that of previous quantum photonic circuits. Our study paves the way for the realization of largescale quantum photonic chips with integrated sources, and can possess important applications in the field of quantum information processes.

physics.optics↗

arXiv2023

ZC3: Zero-Shot Cross-Language Code Clone Detection

Developers introduce code clones to improve programming productivity. Many existing studies have achieved impressive performance in monolingual code clone detection. However, during software development, more and more developers write semantically equivalent programs with different languages to support different platforms and help developers translate projects from one language to another. Considering that collecting cross-language parallel data, especially for low-resource languages, is expensive and time-consuming, how designing an effective cross-language model that does not rely on any parallel data is a significant problem. In this paper, we propose a novel method named ZC3 for Zero-shot Cross-language Code Clone detection. ZC3 designs the contrastive snippet prediction to form an isomorphic representation space among different programming languages. Based on this, ZC3 exploits domain-aware learning and cycle consistency learning to further constrain the model to generate representations that are aligned among different languages meanwhile are diacritical for different types of clones. To evaluate our approach, we conduct extensive experiments on four representative cross-language clone detection datasets. Experimental results show that ZC3 outperforms the state-of-the-art baselines by 67.12%, 51.39%, 14.85%, and 53.01% on the MAP score, respectively. We further investigate the representational distribution of different languages and discuss the effectiveness of our method.

cs.SE↗

arXiv2023

Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction

This study introduces a novel approach for image reconstruction based on a diffusion model conditioned on the native data domain. Our method is applied to multi-coil MRI and quantitative MRI reconstruction, leveraging the domain-conditioned diffusion model within the frequency and parameter domains. The prior MRI physics are used as embeddings in the diffusion model, enforcing data consistency to guide the training and sampling process, characterizing MRI k-space encoding in MRI reconstruction, and leveraging MR signal modeling for qMRI reconstruction. Furthermore, a gradient descent optimization is incorporated into the diffusion steps, enhancing feature learning and improving denoising. The proposed method demonstrates a significant promise, particularly for reconstructing images at high acceleration factors. Notably, it maintains great reconstruction accuracy and efficiency for static and quantitative MRI reconstruction across diverse anatomical structures. Beyond its immediate applications, this method provides potential generalization capability, making it adaptable to inverse problems across various domains.

cs.LG↗

arXiv2023

Referring Image Segmentation Using Text Supervision

Existing Referring Image Segmentation (RIS) methods typically require expensive pixel-level or box-level annotations for supervision. In this paper, we observe that the referring texts used in RIS already provide sufficient information to localize the target object. Hence, we propose a novel weakly-supervised RIS framework to formulate the target localization problem as a classification process to differentiate between positive and negative text expressions. While the referring text expressions for an image are used as positive expressions, the referring text expressions from other images can be used as negative expressions for this image. Our framework has three main novelties. First, we propose a bilateral prompt method to facilitate the classification process, by harmonizing the domain discrepancy between visual and linguistic features. Second, we propose a calibration method to reduce noisy background information and improve the correctness of the response maps for target object localization. Third, we propose a positive response map selection strategy to generate high-quality pseudo-labels from the enhanced response maps, for training a segmentation network for RIS inference. For evaluation, we propose a new metric to measure localization accuracy. Experiments on four benchmarks show that our framework achieves promising performances to existing fully-supervised RIS methods while outperforming state-of-the-art weakly-supervised methods adapted from related areas. Code is available at https://github.com/fawnliu/TRIS.

cs.CV↗

arXiv2023

Multi-model fusion for Aerial Vision and Dialog Navigation based on human attention aids

Drones have been widely used in many areas of our daily lives. It relieves people of the burden of holding a controller all the time and makes drone control easier to use for people with disabilities or occupied hands. However, the control of aerial robots is more complicated compared to normal robots due to factors such as uncontrollable height. Therefore, it is crucial to develop an intelligent UAV that has the ability to talk to humans and follow natural language commands. In this report, we present an aerial navigation task for the 2023 ICCV Conversation History. Based on the AVDN dataset containing more than 3k recorded navigation trajectories and asynchronous human-robot conversations, we propose an effective method of fusion training of Human Attention Aided Transformer model (HAA-Transformer) and Human Attention Aided LSTM (HAA-LSTM) model, which achieves the prediction of the navigation routing points and human attention. The method not only achieves high SR and SPL metrics, but also shows a 7% improvement in GP metrics compared to the baseline model.

cs.CV↗

arXiv2023

A Survey on Natural Language Processing for Programming

Natural language processing for programming aims to use NLP techniques to assist programming. It is increasingly prevalent for its effectiveness in improving productivity. Distinct from natural language, a programming language is highly structured and functional. Constructing a structure-based representation and a functionality-oriented algorithm is at the heart of program understanding and generation. In this paper, we conduct a systematic review covering tasks, datasets, evaluation methods, techniques, and models from the perspective of the structure-based and functionality-oriented property, aiming to understand the role of the two properties in each component. Based on the analysis, we illustrate unexplored areas and suggest potential directions for future work.

cs.CL↗

arXiv2023

Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement

This paper proposes a novel self-supervised learning method, RELAX-MORE, for quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll a model-based qMRI reconstruction into a deep learning framework, enabling the generation of highly accurate and robust MR parameter maps at imaging acceleration. Unlike conventional deep learning methods requiring a large amount of training data, RELAX-MORE is a subject-specific method that can be trained on single-subject data through self-supervised learning, making it accessible and practically applicable to many qMRI studies. Using the quantitative $T_1$ mapping as an example at different brain, knee and phantom experiments, the proposed method demonstrates excellent performance in reconstructing MR parameters, correcting imaging artifacts, removing noises, and recovering image features at imperfect imaging conditions. Compared with other state-of-the-art conventional and deep learning methods, RELAX-MORE significantly improves efficiency, accuracy, robustness, and generalizability for rapid MR parameter mapping. This work demonstrates the feasibility of a new self-supervised learning method for rapid MR parameter mapping, with great potential to enhance the clinical translation of qMRI.

physics.bio-ph↗

arXiv2023

LINFA: a Python library for variational inference with normalizing flow and annealing

Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions. We developed LINFA (Library for Inference with Normalizing Flow and Annealing), a Python library for variational inference to accommodate computationally expensive models and difficult-to-sample distributions with dependent parameters. We discuss the theoretical background, capabilities, and performance of LINFA in various benchmarks. LINFA is publicly available on GitHub at https://github.com/desResLab/LINFA.

cs.LG↗

arXiv2023

Phase Field Characterization of Rock Fractures in Brazilian Splitting Test Specimens Containing Voids and Inclusions

The Brazilian splitting test is a widely used testing procedure for characterizing the tensile strength of natural rock or rock-like material due to the fact. However, the results of Brazilian tests on specimens with naturally existing voids and inclusions are strongly influenced by size effects and boundary conditions, while numerical modeling can assist in explaining and understanding the mechanisms. On the other hand, the potential of utilizing Brazilian test to characterize inhomogeneous deformation of rock samples with voids and inclusions of dissimilar materials still awaits to be explored. In the present study, fracture mechanisms in Brazilian discs with circular voids and filled inclusions are investigated by using the phase field model (PFM). The PFM is implemented within the framework of finite element method to study the influence of diameter, eccentricity, and quantity of the voids and inclusions on the fracture patterns and stress-strain curves. The phase field simulations can reproduce previous experimental phenomena and furthermore it deepens the understanding of the influence of inclusion and voids on the fracture pattern, overall strength and deformation behavior of inhomogeneous rock. The findings in the study highlight the potential of characterizing inhomogeneous rock through combining Brazilian tests and numerical modeling.

physics.geo-ph↗

arXiv2023

Achiral dielectric metasurfaces for spectral and polarization control of valley specific light emission from monolayer MoS2

Excitons in two-dimensional transition metal dichalcogenides have a valley degree of freedom that can be optically accessed and manipulated for quantum information processing. Here, we integrate MoS2 with achiral silicon disk array metasurfaces to enhance and control valley-specific absorption and emission. Through the coupling to the metasurface Mie modes, the intensity and lifetime of the emission of neutral excitons, trions and defect bound excitons can be enhanced, while the spectral shape can be modified. Additionally, we demonstrate the symmetric enhancement of the degree-of-polarization (DOP) of neutral exciton and trions via valley-resolved PL measurements, and find that the DOP can be as high as 24% for exciton emission and 34% for trion emission at 100K. These results can be understood by analyzing the near-field impact of metasurface resonators on both the chiral absorption of MoS2 emitters as well as the enhanced emission from the Purcell effect. Combining Si-compatible photonic design with large-scale (mm-scale) 2D materials integration, our work makes an important step towards on-chip valleytronic applications approaching room-temperature operation.

physics.optics↗

arXiv2023

Privacy-preserving Inference of Group Mean Difference in Zero-inflated Right Skewed Data with Partitioning and Censoring

We examine privacy-preserving inferences of group mean differences in zero-inflated right-skewed (zirs) data. Zero inflation and right skewness are typical characteristics of ads clicks and purchases data collected from e-commerce and social media platforms, where we also want to preserve user privacy to ensure that individual data is protected. In this work, we develop likelihood-based and model-free approaches to analyzing zirs data with formal privacy guarantees. We first apply partitioning and censoring (PAC) to ``regularize'' zirs data to get the PAC data. We expect inferences based on PAC to have better inferential properties and more robust privacy considerations compared to analyzing the raw data directly. We conduct theoretical analysis to establish the MSE consistency of the privacy-preserving estimators from the proposed approaches based on the PAC data and examine the rate of convergence in the number of partitions and privacy loss parameters. The theoretical results also suggest that it is the sampling error of PAC data rather than the sanitization error that is the limiting factor in the convergence rate. We conduct extensive simulation studies to compare the inferential utility of the proposed approach for different types of zirs data, sample size and partition size combinations, censoring scenarios, mean differences, privacy budgets, and privacy loss composition schemes. We also apply the methods to obtain privacy-preserving inference for the group mean difference in a real digital ads click-through data set. Based on the theoretical and empirical results, we make recommendations regarding the usage of these methods in practice.

stat.ME↗

arXiv2023

Delving into Semantic Scale Imbalance

Model bias triggered by long-tailed data has been widely studied. However, measure based on the number of samples cannot explicate three phenomena simultaneously: (1) Given enough data, the classification performance gain is marginal with additional samples. (2) Classification performance decays precipitously as the number of training samples decreases when there is insufficient data. (3) Model trained on sample-balanced datasets still has different biases for different classes. In this work, we define and quantify the semantic scale of classes, which is used to measure the feature diversity of classes. It is exciting to find experimentally that there is a marginal effect of semantic scale, which perfectly describes the first two phenomena. Further, the quantitative measurement of semantic scale imbalance is proposed, which can accurately reflect model bias on multiple datasets, even on sample-balanced data, revealing a novel perspective for the study of class imbalance. Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations. Comprehensive experiments show that dynamic semantic-scale-balanced learning consistently enables the model to perform superiorly on large-scale long-tailed and non-long-tailed natural and medical datasets, which is a good starting point for mitigating the prevalent but unnoticed model bias.

cs.CV↗

arXiv2023

Solution-phase single-particle spectroscopy for probing multi-polaronic dynamics in quantum emitters at femtosecond resolution

The development of many optical quantum technologies depends on the availability of solid-state single quantum emitters with near-perfect optical coherence. However, a standing issue that limits systematic improvement is the significant sample heterogeneity and lack of mechanistic understanding of microscopic energy flow at the single emitter level and ultrafast timescales. Here we develop solution-phase single-particle pump-probe spectroscopy with photon correlation detection that captures sample-averaged dynamics in single molecules and/or defect states with unprecedented clarity at femtosecond resolution. We apply this technique to single quantum emitters in two-dimensional hexagonal boron nitride, which suffers from significant heterogeneity and low quantum efficiency. From millisecond to nanosecond timescales, the translation diffusion, metastable-state-related bunching shoulders, rotational dynamics, and antibunching features are disentangled by their distinct photon-correlation timescales, which collectively quantify the normalized two-photon emission quantum yield. Leveraging its femtosecond resolution, spectral selectivity and ultralow noise (two orders of magnitude improvement over solid-state methods), we visualize electron-phonon coupling in the time domain at the single defect level, and discover the acceleration of polaronic formation driven by multi-electron excitation. Corroborated with results from a theoretical polaron model, we show how this translates to sample-averaged photon fidelity characterization of cascaded emission efficiency and optical decoherence time. Our work provides a framework for ultrafast spectroscopy in single emitters, molecules, or defects prone to photoluminescence intermittency and heterogeneity, opening new avenues of extreme-scale characterization and synthetic improvements for quantum information applications.

cond-mat.mes-hall↗

arXiv2023

Giant room-temperature nonlinearities from a monolayer Janus topological semiconductor

Nonlinear optical materials possess wide applications, ranging from terahertz and mid-infrared detection to energy harvesting. Recently, the correlations between nonlinear optical responses and topological properties, such as Berry curvature and the quantum metric tensor, have stimulated great interest. Here, we report giant room-temperature nonlinearities in an emergent non-centrosymmetric two-dimensional topological material, the Janus transition metal dichalcogenides in the 1T' phase, which are synthesized by an advanced atomic-layer substitution method. High harmonic generation, terahertz emission spectroscopy, and second harmonic generation measurements consistently reveal orders-of-the-magnitude enhancement in terahertz-frequency nonlinearities of 1T' MoSSe (e.g., > 50 times higher than 2H MoS$_2$ for 18th order harmonic generation; > 20 times higher than 2H MoS$_2$ for terahertz emission). It is elucidated that such colossal nonlinear optical responses come from topological band mixing and strong inversion symmetry breaking due to the Janus structure. Our work defines general protocols for designing materials with large nonlinearities and preludes the applications of topological materials in optoelectronics down to the monolayer limit. This two-dimensional form of topological materials also constitute a unique platform for examining origin of the anomalous high-harmonic generation, with potential applications as building blocks for scalable attosecond sources.

cond-mat.mes-hall↗

arXiv2023

Anti-Stokes Photoluminescence in Monolayer WSe$_2$ Activated by Plasmonic Cavities through Resonant Excitation of Dark Excitons

Anti-Stokes photoluminescence (PL) is light emission at a higher photon energy than the excitation, with applications in optical cooling, bioimaging, lasing, and quantum optics. Here, we show how plasmonic nano-cavities activate anti-Stokes PL in WSe$_2$ monolayers through resonant excitation of a dark exciton. The tightly confined plasmonic fields excite the out-of-plane transition dipole of the dark exciton, leading to light emission from the bright exciton at higher energy. Through statistical measurements on hundreds of plasmonic cavities, we show that coupling to the dark exciton is key to achieving a near hundred-fold enhancement of the upconverted PL intensity. This is further corroborated by experiments in which the laser excitation wavelength is tuned across the dark exciton. Finally, we show that an asymmetric nanoparticle shape and precise geometry are key for consistent activation of the dark exciton and efficient PL upconversion. Our work introduces a new excitation channel for anti-Stokes PL in WSe$_2$ and paves the way for large-area substrates providing optical cooling, anti-Stokes lasing, and radiative engineering of excitons.

cond-mat.mes-hall↗