arXiv subjects

Yan Pan

Publications and source records attributed to Yan Pan.

At least 55 records · Page 3Linked to original sources

arXiv2022

Sub-Mbps key-rate continuous-variable quantum key distribution with local-local-oscillator over 100 km fiber

We experimentally demonstrated a sub-Mbps key rate Gaussian-modulated coherent-state continuous-variable quantum key distribution (CV-QKD) over 100 km transmission distance. To efficiently control the excess noise, the quantum signal and the pilot tone are co-transmitted in fiber channel based on wide-band frequency and polarization multiplexing methods. Furthermore, a high-accuracy data-assisted time domain equalization algorithm is carefully designed to compensate the phase noise and polarization variation in low signal-to-noise ratio. The asymptotic secure key rate (SKR) of the demonstrated CV-QKD is experimentally evaluated to be 10.36 Mbps, 2.59 Mbps, and 0.69 Mbps over transmission distance of 50 km, 75 km, and 100 km, respectively. The experimental demonstrated CV-QKD system significantly improves transmission distance and SKR compared to the state-of-art GMCS CV-QKD experimental results, and shows the potential for long-distance and high-speed secure quantum key distribution.

quant-ph↗

arXiv2022

Few-Shot Nested Named Entity Recognition

While Named Entity Recognition (NER) is a widely studied task, making inferences of entities with only a few labeled data has been challenging, especially for entities with nested structures. Unlike flat entities, entities and their nested entities are more likely to have similar semantic feature representations, drastically increasing difficulties in classifying different entity categories in the few-shot setting. Although prior work has briefly discussed nested structures in the context of few-shot learning, to our best knowledge, this paper is the first one specifically dedicated to studying the few-shot nested NER task. Leveraging contextual dependency to distinguish nested entities, we propose a Biaffine-based Contrastive Learning (BCL) framework. We first design a Biaffine span representation module for learning the contextual span dependency representation for each entity span rather than only learning its semantic representation. We then merge these two representations by the residual connection to distinguish nested entities. Finally, we build a contrastive learning framework to adjust the representation distribution for larger margin boundaries and more generalized domain transfer learning ability. We conducted experimental studies on three English, German, and Russian nested NER datasets. The results show that the BCL outperformed three baseline models on the 1-shot and 5-shot tasks in terms of F1 score.

cs.CL↗

arXiv2022

Convergence and Complexity of an Adaptive Planewave Method for Eigenvalue Computations

In this paper, we study the adaptive planewave discretization for a cluster of eigenvalues of second-order elliptic partial differential equations. We first design an a posteriori error estimator and prove both the upper and lower bounds. Based on the a posteriori error estimator, we propose an adaptive planewave method. We then prove that the adaptive planewave approximations have the linear convergence rate and quasi-optimal complexity.

math.NA↗

arXiv2022

Optimizing Evaluation Metrics for Multi-Task Learning via the Alternating Direction Method of Multipliers

Multi-task learning (MTL) aims to improve the generalization performance of multiple tasks by exploiting the shared factors among them. Various metrics (e.g., F-score, Area Under the ROC Curve) are used to evaluate the performances of MTL methods. Most existing MTL methods try to minimize either the misclassified errors for classification or the mean squared errors for regression. In this paper, we propose a method to directly optimize the evaluation metrics for a large family of MTL problems. The formulation of MTL that directly optimizes evaluation metrics is the combination of two parts: (1) a regularizer defined on the weight matrix over all tasks, in order to capture the relatedness of these tasks; (2) a sum of multiple structured hinge losses, each corresponding to a surrogate of some evaluation metric on one task. This formulation is challenging in optimization because both of its parts are non-smooth. To tackle this issue, we propose a novel optimization procedure based on the alternating direction scheme of multipliers, where we decompose the whole optimization problem into a sub-problem corresponding to the regularizer and another sub-problem corresponding to the structured hinge losses. For a large family of MTL problems, the first sub-problem has closed-form solutions. To solve the second sub-problem, we propose an efficient primal-dual algorithm via coordinate ascent. Extensive evaluation results demonstrate that, in a large family of MTL problems, the proposed MTL method of directly optimization evaluation metrics has superior performance gains against the corresponding baseline methods.

cs.LG↗

arXiv2022

Experimental Demonstration of High-Rate Discrete-Modulated Continuous-Variable Quantum Key Distribution System

A high-rate continuous-variable quantum key distribution (CV-QKD) system based on high-order discrete modulation is experimentally investigated. With the help of the novel system scheme, effective digital signal processing algorithms and advanced analytical security proof method, the transmission results of 5 km, 10 km, 25 km, and 50 km are achieved for the 1 GBaud optimized quantum signals. Correspondingly, the asymptotic secret key rate (SKR) is 288.421 Mbps, 159.395 Mbps, 50.004 Mbps and 7.579 Mbps for discrete Gaussian (DG) 64QAM, and 326.708 Mbps, 179.348 Mbps, 50.623 Mbps and 9.212 Mbps for DG 256QAM. Under the same parameters, the achieved SKRs of DG 256QAM is almost same to ideal Gaussian modulation. In this case, the demonstrated high-rate discrete modulated CV-QKD system has the application potential for high speed security communication under tens of kilometers.

quant-ph↗

arXiv2022

Secure two-way fiber-optic time transfer against sub-ns asymmetric delay attack

Two-way fiber-optic time transfer is a promising precise time synchronization technique with sub-nanosecond accuracy. However, asymmetric delay attack is a serious threat which cannot be prevent by any encryption method. In this paper, a dynamic model based scheme is proposed to defense the sub-nanosecond asymmetric delay attack. A threshold is set according to the estimated time difference by a two-state clock model where the fixed frequency difference is excluded from the time difference to detect the asymmetric delay attack which is smaller than the time difference induced by the fixed frequency difference. Theoretical simulation and experimental demonstration are implemented to prove the feasibility of the scheme. A two-way fiber-optic time transfer system with time stability with 24.5ps, 3.98ps, and 2.95ps at 1s, 10s, and 100s averaging time is shown under sub-ns asymmetric time delay attack experimentally. The proposed method provides a promising secure sub-ns precise time synchronization technique against asymmetric delay attack.

quant-ph↗

arXiv2022

Can depth-adaptive BERT perform better on binary classification tasks

In light of the success of transferring language models into NLP tasks, we ask whether the full BERT model is always the best and does it exist a simple but effective method to find the winning ticket in state-of-the-art deep neural networks without complex calculations. We construct a series of BERT-based models with different size and compare their predictions on 8 binary classification tasks. The results show there truly exist smaller sub-networks performing better than the full model. Then we present a further study and propose a simple method to shrink BERT appropriately before fine-tuning. Some extended experiments indicate that our method could save time and storage overhead extraordinarily with little even no accuracy loss.

cs.CL↗

arXiv2022

ViT2Hash: Unsupervised Information-Preserving Hashing

Unsupervised image hashing, which maps images into binary codes without supervision, is a compressor with a high compression rate. Hence, how to preserving meaningful information of the original data is a critical problem. Inspired by the large-scale vision pre-training model, known as ViT, which has shown significant progress for learning visual representations, in this paper, we propose a simple information-preserving compressor to finetune the ViT model for the target unsupervised hashing task. Specifically, from pixels to continuous features, we first propose a feature-preserving module, using the corrupted image as input to reconstruct the original feature from the pre-trained ViT model and the complete image, so that the feature extractor can focus on preserving the meaningful information of original data. Secondly, from continuous features to hash codes, we propose a hashing-preserving module, which aims to keep the semantic information from the pre-trained ViT model by using the proposed Kullback-Leibler divergence loss. Besides, the quantization loss and the similarity loss are added to minimize the quantization error. Our method is very simple and achieves a significantly higher degree of MAP on three benchmark image datasets.

cs.CV↗

arXiv2021

Sub-Gbps key rate four-state continuous-variable quantum key distribution within metropolitan area

Continuous-variable quantum key distribution (CVQKD) has potential advantages of high secret key rate, which is very suitable for high-speed metropolitan network application. However, the reported highest secret key rates of the CVQKD systems up to now are limited in a few Mbps. Here, we address the fundamental experimental problems and demonstrate a single-carrier four-state CVQKD with sub-Gbps key rate within metropolitan area. In the demonstrated four-state CVQKD using local local oscillator, an ultra-low level of excess noise is obtained and a high efficient post-processing setup is designed for practically extracting the final secure keys. Thus, the achieved secure key rates are 190.54 Mbps and 137.76 Mbps and 52.48 Mbps using linear channel assuming security analysis method and 233.87 Mbps, 133.6 Mbps and 21.53 Mbps using semidefinite programming security analysis method over transmission distances of 5 km, 10 km and 25 km, respectively. This record-breaking result increases the previous secret key rate record by an order of magnitude, which is sufficient to achieve the one-time pad cryptographic task. Our work shows the road for future high-rate and large-scale CVQKD deployment in secure broadband metropolitan and access networks.

quant-ph↗

arXiv2021

Phase-reference intensity attack on continuous-variable quantum key distribution with a real local oscillator

In practical continuous-variable quantum key distribution system using local local oscillator (LLO CV-QKD), the phase noise related to coherent detection and phase-reference pulse intensity that can be locally calibrated at the receiver side is considered to be trusted noise to improve the performance. However, if the intensity of the phase-reference pulse is not monitored precisely in real-time, a security loophole will be opened for the eavesdropper to launch attacks. To this end, we propose two practical phase-reference pulse intensity attack strategies, where Eve can reduce the trusted phase noise by manipulating the intensity of the phase-reference pulse, thereby hiding her attack on the signal pulse if the total excess noise remains unchanged. The performance of the LLO CV-QKD system under these attacks has been analyzed. We show that precisely monitoring the intensity of the phase-reference pulse in real-time is an essential countermeasure to prevent the proposed attacks. Moreover, an intensity-monitoring scheme for the phase-reference pulse is proposed to strengthen the security of the practical LLO CV-QKD system and make the trusted phase noise model more robust.

quant-ph↗

arXiv2021

How to Build Robust FAQ Chatbot with Controllable Question Generator?

Many unanswerable adversarial questions fool the question-answer (QA) system with some plausible answers. Building a robust, frequently asked questions (FAQ) chatbot needs a large amount of diverse adversarial examples. Recent question generation methods are ineffective at generating many high-quality and diverse adversarial question-answer pairs from unstructured text. We propose the diversity controllable semantically valid adversarial attacker (DCSA), a high-quality, diverse, controllable method to generate standard and adversarial samples with a semantic graph. The fluent and semantically generated QA pairs fool our passage retrieval model successfully. After that, we conduct a study on the robustness and generalization of the QA model with generated QA pairs among different domains. We find that the generated data set improves the generalizability of the QA model to the new target domain and the robustness of the QA model to detect unanswerable adversarial questions.

cs.CL↗

arXiv2021

3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction

Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm. However, reconstructing a 3D caricature from a 2D caricature image remains a challenging task, mostly due to the lack of data. We propose to fill this gap by introducing 3DCaricShop, the first large-scale 3D caricature dataset that contains 2000 high-quality diversified 3D caricatures manually crafted by professional artists. 3DCaricShop also provides rich annotations including a paired 2D caricature image, camera parameters and 3D facial landmarks. To demonstrate the advantage of 3DCaricShop, we present a novel baseline approach for single-view 3D caricature reconstruction. To ensure a faithful reconstruction with plausible face deformations, we propose to connect the good ends of the detailrich implicit functions and the parametric mesh representations. In particular, we first register a template mesh to the output of the implicit generator and iteratively project the registration result onto a pre-trained PCA space to resolve artifacts and self-intersections. To deal with the large deformation during non-rigid registration, we propose a novel view-collaborative graph convolution network (VCGCN) to extract key points from the implicit mesh for accurate alignment. Our method is able to generate highfidelity 3D caricature in a pre-defined mesh topology that is animation-ready. Extensive experiments have been conducted on 3DCaricShop to verify the significance of the database and the effectiveness of the proposed method.

cs.CV↗

arXiv2019

Distributed Low Precision Training Without Mixed Precision

Low precision training is one of the most popular strategies for deploying the deep model on limited hardware resources. Fixed point implementation of DCNs has the potential to alleviate complexities and facilitate potential deployment on embedded hardware. However, most low precision training solution is based on a mixed precision strategy. In this paper, we have presented an ablation study on different low precision training strategy and propose a solution for IEEE FP-16 format throughout the training process. We tested the ResNet50 on 128 GPU cluster on ImageNet-full dataset. We have viewed that it is not essential to use FP32 format to train the deep models. We have viewed that communication cost reduction, model compression, and large-scale distributed training are three coupled problems.

cs.CV↗

arXiv2019

Feature Pyramid Hashing

In recent years, deep-networks-based hashing has become a leading approach for large-scale image retrieval. Most deep hashing approaches use the high layer to extract the powerful semantic representations. However, these methods have limited ability for fine-grained image retrieval because the semantic features extracted from the high layer are difficult in capturing the subtle differences. To this end, we propose a novel two-pyramid hashing architecture to learn both the semantic information and the subtle appearance details for fine-grained image search. Inspired by the feature pyramids of convolutional neural network, a vertical pyramid is proposed to capture the high-layer features and a horizontal pyramid combines multiple low-layer features with structural information to capture the subtle differences. To fuse the low-level features, a novel combination strategy, called consensus fusion, is proposed to capture all subtle information from several low-layers for finer retrieval. Extensive evaluation on two fine-grained datasets CUB-200-2011 and Stanford Dogs demonstrate that the proposed method achieves significant performance compared with the state-of-art baselines.

cs.CV↗

arXiv2018

Regularizing Deep Hashing Networks Using GAN Generated Fake Images

Recently, deep-networks-based hashing (deep hashing) has become a leading approach for large-scale image retrieval. It aims to learn a compact bitwise representation for images via deep networks, so that similar images are mapped to nearby hash codes. Since a deep network model usually has a large number of parameters, it may probably be too complicated for the training data we have, leading to model over-fitting. To address this issue, in this paper, we propose a simple two-stage pipeline to learn deep hashing models, by regularizing the deep hashing networks using fake images. The first stage is to generate fake images from the original training set without extra data, via a generative adversarial network (GAN). In the second stage, we propose a deep architec- ture to learn hash functions, in which we use a maximum-entropy based loss to incorporate the newly created fake images by the GAN. We show that this loss acts as a strong regularizer of the deep architecture, by penalizing low-entropy output hash codes. This loss can also be interpreted as a model ensemble by simultaneously training many network models with massive weight sharing but over different training sets. Empirical evaluation results on several benchmark datasets show that the proposed method has superior performance gains over state-of-the-art hashing methods.

cs.CV↗

arXiv2018

Improving Deep Binary Embedding Networks by Order-aware Reweighting of Triplets

In this paper, we focus on triplet-based deep binary embedding networks for image retrieval task. The triplet loss has been shown to be most effective for the ranking problem. However, most of the previous works treat the triplets equally or select the hard triplets based on the loss. Such strategies do not consider the order relations, which is important for retrieval task. To this end, we propose an order-aware reweighting method to effectively train the triplet-based deep networks, which up-weights the important triplets and down-weights the uninformative triplets. First, we present the order-aware weighting factors to indicate the importance of the triplets, which depend on the rank order of binary codes. Then, we reshape the triplet loss to the squared triplet loss such that the loss function will put more weights on the important triplets. Extensive evaluations on four benchmark datasets show that the proposed method achieves significant performance compared with the state-of-the-art baselines.

cs.CV↗

arXiv2017

Personalized and Occupational-aware Age Progression by Generative Adversarial Networks

Face age progression, which aims to predict the future looks, is important for various applications and has been received considerable attentions. Existing methods and datasets are limited in exploring the effects of occupations which may influence the personal appearances. In this paper, we firstly introduce an occupational face aging dataset for studying the influences of occupations on the appearances. It includes five occupations, which enables the development of new algorithms for age progression and facilitate future researches. Second, we propose a new occupational-aware adversarial face aging network, which learns human aging process under different occupations. Two factors are taken into consideration in our aging process: personality-preserving and visually plausible texture change for different occupations. We propose personalized network with personalized loss in deep autoencoder network for keeping personalized facial characteristics, and occupational-aware adversarial network with occupational-aware adversarial loss for obtaining more realistic texture changes. Experimental results well demonstrate the advantages of the proposed method by comparing with other state-of-the-arts age progression methods.

cs.CV↗

arXiv2017

HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval

As the rapid growth of multi-modal data, hashing methods for cross-modal retrieval have received considerable attention. Deep-networks-based cross-modal hashing methods are appealing as they can integrate feature learning and hash coding into end-to-end trainable frameworks. However, it is still challenging to find content similarities between different modalities of data due to the heterogeneity gap. To further address this problem, we propose an adversarial hashing network with attention mechanism to enhance the measurement of content similarities by selectively focusing on informative parts of multi-modal data. The proposed new adversarial network, HashGAN, consists of three building blocks: 1) the feature learning module to obtain feature representations, 2) the generative attention module to generate an attention mask, which is used to obtain the attended (foreground) and the unattended (background) feature representations, 3) the discriminative hash coding module to learn hash functions that preserve the similarities between different modalities. In our framework, the generative module and the discriminative module are trained in an adversarial way: the generator is learned to make the discriminator cannot preserve the similarities of multi-modal data w.r.t. the background feature representations, while the discriminator aims to preserve the similarities of multi-modal data w.r.t. both the foreground and the background feature representations. Extensive evaluations on several benchmark datasets demonstrate that the proposed HashGAN brings substantial improvements over other state-of-the-art cross-modal hashing methods.

cs.CV↗