arXiv subjects

Dan Li

Publications and source records attributed to Dan Li.

At least 163 records · Page 9Linked to original sources

arXiv2022

On the initiation of fiber fuse damage in high-power ytterbium-doped fiber lasers

Fiber fuse effect can occur spontaneously and propagate along optical fibers to cause wide-spread damage; it threatens all applications involving optical fibers. This paper presents two results. First, it establishes that the initiation of fiber fuse (IFF) in silica fibers is caused by defect-induced absorption. Critical temperatures and critical optical powers for IFF are simulated for the first time using a 3D solid-state heat transfer model with heat source generated by defect-induced absorption. In this method, formation energies of the defects can be uniquely determined, which offers critical information on the chemical reasons for fiber fuse. Second, this paper offers a method to evaluate operating temperatures of fiber lasers. General analytical solutions of the operating temperatures along gain fibers are deduced. Results of 976-nm laser-diode-(LD)-pumped and 1018-nm tandem-pumped ytterbium-doped fiber (YDF) amplifiers using 10/130-μm YDFs are calculated. Potential limits caused by fiber fuse are discussed.

physics.optics↗

arXiv2022

Adversarial Momentum-Contrastive Pre-Training

Recently proposed adversarial self-supervised learning methods usually require big batches and long training epochs to extract robust features, which will bring heavy computational overhead on platforms with limited resources. In order to help the network learn more powerful feature representations in smaller batches and fewer epochs, this paper proposes a novel adversarial momentum contrastive learning method, which introduces two memory banks corresponding to clean samples and adversarial samples, respectively. These memory banks can be dynamically incorporated into the training process to track invariant features among historical mini-batches. Compared with the previous adversarial pre-training model, our method achieves superior performance with smaller batch size and less training epochs. In addition, the model outperforms some state-of-the-art supervised defensive methods on multiple benchmark datasets after being fine-tuned on downstream classification tasks.

cs.CV↗

arXiv2022

Controlled Alternate Quantum Walk based Block Hash Function

The hash function is an important branch of cryptology. Controlled quantum walk based hash function is a kind of novel hash function, which is safe, flexible, high-efficient, and compatible. All existing controlled quantum walk based hash functions are controlled by one bit message in each step. To process message in batch amounts, in this paper, controlled alternate quantum walk based block hash function is presented by using the time-position-dependent controlled quantum walks on complete graphs with self-loops. The presented hash function accelerate the hash processing dramatically, so it is more high-efficient.

quant-ph↗

arXiv2022

The Astrometric Performance Test of 80-cm Telescope at Yaoan Station and Precise CCD Positions of Apophis

The 80-cm azimuthal telescope is newly mounted at Yaoan Station, Purple Mountain Observatory in 2018. The astrometric performance of the telescope is tested in the following three aspects. (a) The geometric distortion of its CCD attached. It is stable in both a single epoch and multi epochs. Eight distortion solutions are derived over about one year. The maximum values range from 0.75 to 0.79 pixel and the median values range from 0.14 to 0.16 pixel. (b) The limit magnitude of stars. About 20.5 magnitude (Gaia-G) stars can be detected with Johnson-V filter exposured in 300 seconds. The astrometric error of about 20.5 magnitude stars is estimated at 0.14 arcsec using the fitted sigmoidal function. (c) The astrometric accuracy and the precision of stacked fast-moving faint object. 24 stacked frames of the potentially hazardous asteroid (PHA) (99942) Apophis are derived on April 14 and 15, 2021 (fainter than 18 mag) based on the ephemeris shifts. During data reduction, the newest Gaia EDR3 Catalog and Jet Propulsion Laboratory Horizons ephemeris are referenced as theoretical positions of stars and Apophis, respectively. Our results show that the mean (O-C)s (observed minus computed) of Apophis are -0.018 and 0.020 arcsec in right ascention and declination, and the dispersions are estimated at 0.094 and 0.085 arcsec, respectively, which show the consistency of the stacked results by Astrometrica.

astro-ph.IM↗

arXiv2022

Improve Deep Image Inpainting by Emphasizing the Complexity of Missing Regions

Deep image inpainting research mainly focuses on constructing various neural network architectures or imposing novel optimization objectives. However, on the one hand, building a state-of-the-art deep inpainting model is an extremely complex task, and on the other hand, the resulting performance gains are sometimes very limited. We believe that besides the frameworks of inpainting models, lightweight traditional image processing techniques, which are often overlooked, can actually be helpful to these deep models. In this paper, we enhance the deep image inpainting models with the help of classical image complexity metrics. A knowledge-assisted index composed of missingness complexity and forward loss is presented to guide the batch selection in the training procedure. This index helps find samples that are more conducive to optimization in each iteration and ultimately boost the overall inpainting performance. The proposed approach is simple and can be plugged into many deep inpainting models by changing only a few lines of code. We experimentally demonstrate the improvements for several recently developed image inpainting models on various datasets.

cs.CV↗

arXiv2022

Eigenvalues of signed graphs

Signed graphs have their edges labeled either as positive or negative. $ρ(M)$ denote the $M$-spectral radius of $Σ$, where $M=M(Σ)$ is a real symmetric graph matrix of $Σ$. Obviously, $ρ(M)=\mbox{max}\{λ_1(M),-λ_n(M)\}$. Let $A(Σ)$ be the adjacency matrix of $Σ$ and $(K_n,H^-)$ be a signed complete graph whose negative edges induce a subgraph $H$. In this paper, we first focus on a central problem in spectral extremal graph theory as follows: Which signed graph with maximum $ρ(A(Σ))$ among $(K_n,T^-)$ where $T$ is a spanning tree? To answer the problem, we characterize the extremal signed graph with maximum $λ_1(A(Σ))$ and minimum $λ_n(A(Σ))$ among $(K_n,T^-)$, respectively. Another interesting graph matrix of a signed graph is distance matrix, i.e. $D(Σ)$ which was defined by Hameed, Shijin, Soorya, Germina and Zaslavsky [8]. Note that $A(Σ)=D(Σ)$ when $Σ\in (K_n,T^-)$. In this paper, we give upper bounds on the least distance eigenvalue of a signed graph $Σ$ with diameter at least 2. This result implies a result proved by Lin [11] was originally conjectured by Aouchiche and Hansen [1].

math.CO↗

arXiv2021

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of biomarkers. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.

q-bio.PE↗

arXiv2021

BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing

Graph neural networks (GNNs) have extended the success of deep neural networks (DNNs) to non-Euclidean graph data, achieving ground-breaking performance on various tasks such as node classification and graph property prediction. Nonetheless, existing systems are inefficient to train large graphs with billions of nodes and edges with GPUs. The main bottlenecks are the process of preparing data for GPUs - subgraph sampling and feature retrieving. This paper proposes BGL, a distributed GNN training system designed to address the bottlenecks with a few key ideas. First, we propose a dynamic cache engine to minimize feature retrieving traffic. By a co-design of caching policy and the order of sampling, we find a sweet spot of low overhead and high cache hit ratio. Second, we improve the graph partition algorithm to reduce cross-partition communication during subgraph sampling. Finally, careful resource isolation reduces contention between different data preprocessing stages. Extensive experiments on various GNN models and large graph datasets show that BGL significantly outperforms existing GNN training systems by 20.68x on average.

cs.LG↗

arXiv2021

Soft magnetic microrobot doped with porous silica for stability-enhanced multimodal locomotion in nonideal environment

As an emerging field of robotics, magnetic-field-controlled soft microrobot has broad application prospects for its flexibility, locomotion diversity as well as remote controllability. Magnetic soft microrobots can perform multimodal locomotion under the control of a magnetic field, which may have potential applications in precision medicine. However, previous researches mainly focus on new locomotion in a relatively ideal environment, lacking exploration on the ability of magnetic microrobot locomotion to resist external disturbances and proceed in a nonideal environment. Here, a porous silica-doped soft magnetic microrobot is constructed for enhanced stability of multimodal locomotion in the nonideal biological environment. Porous silica spheres are doped into NdFeB-silicone elastomer base, improving adhesion properties as well as refining the comprehensive mechanical properties of the microrobot. Multimodal locomotions are achieved, and the influence of porous silica doping on the stability of each locomotion in nonideal environment is explored in depth. Motions in nonideal circumstances such as climbing, loading, current rushing, wind blowing, and obstacle hindering are conducted successfully with porous silica doping. Such a stability-enhanced multimodal locomotion system can be used in biocatalysis as well as thrombus removal, and its prospect for precision medicine is highlighted by in vivo demonstration of multimodal locomotion with nonideal disturbance.

cond-mat.mtrl-sci↗

arXiv2021

Blockchain Based Decentralized Replay Attack Detection for Large Scale Power Systems

Large scale power systems are comprised of regional utilities with assets that stream sensor readings in real time. In order to detect cyberattacks, the globally acquired, real time sensor data needs to be analyzed in a centralized fashion. However, owing to operational constraints, such a centralized sharing mechanism turns out to be a major obstacle. In this paper, we propose a blockchain based decentralized framework for detecting coordinated replay attacks with full privacy of sensor data. We develop a Bayesian inference mechanism employing locally reported attack probabilities that is tailor made for a blockchain framework. We compare our framework to a traditional decentralized algorithm based on the broadcast gossip framework both theoretically as well as empirically. With the help of experiments on a private Ethereum blockchain, we show that our approach achieves good detection quality and significantly outperforms gossip driven approaches in terms of accuracy, timeliness and scalability.

cs.DC↗

arXiv2021

Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poems

In this work we propose a new task: artistic visualization of classical Chinese poems, where the goal is to generatepaintings of a certain artistic style for classical Chinese poems. For this purpose, we construct a new dataset called Paint4Poem. Thefirst part of Paint4Poem consists of 301 high-quality poem-painting pairs collected manually from an influential modern Chinese artistFeng Zikai. As its small scale poses challenges for effectively training poem-to-painting generation models, we introduce the secondpart of Paint4Poem, which consists of 3,648 caption-painting pairs collected manually from Feng Zikai's paintings and 89,204 poem-painting pairs collected automatically from the web. We expect the former to help learning the artist painting style as it containshis most paintings, and the latter to help learning the semantic relevance between poems and paintings. Further, we analyze Paint4Poem regarding poem diversity, painting style, and the semantic relevance between poems and paintings. We create abenchmark for Paint4Poem: we train two representative text-to-image generation models: AttnGAN and MirrorGAN, and evaluate theirperformance regarding painting pictorial quality, painting stylistic relevance, and semantic relevance between poems and paintings.The results indicate that the models are able to generate paintings that have good pictorial quality and mimic Feng Zikai's style, but thereflection of poem semantics is limited. The dataset also poses many interesting research directions on this task, including transferlearning, few-shot learning, text-to-image generation for low-resource data etc. The dataset is publicly available.(https://github.com/paint4poem/paint4poem)

cs.CV↗

arXiv2021

CRISLoc: Reconstructable CSI Fingerprintingfor Indoor Smartphone Localization

Channel state information (CSI) based fingerprinting for WIFI indoor localization has attracted lots of attention very recently.The frequency diverse and temporally stable CSI better represents the location dependent channel characteristics than the coarsereceived signal strength (RSS). However, the acquisition of CSI requires the cooperation of access points (APs) and involves only dataframes, which imposes restrictions on real-world deployment. In this paper, we present CRISLoc, the first CSI fingerprinting basedlocalization prototype system using ubiquitous smartphones. CRISLoc operates in a completely passive mode, overhearing thepackets on-the-fly for his own CSI acquisition. The smartphone CSI is sanitized via calibrating the distortion enforced by WiFi amplifiercircuits. CRISLoc tackles the challenge of altered APs with a joint clustering and outlier detection method to find them. A novel transferlearning approach is proposed to reconstruct the high-dimensional CSI fingerprint database on the basis of the outdated fingerprintsand a few fresh measurements, and an enhanced KNN approach is proposed to pinpoint the location of a smartphone. Our studyreveals important properties about the stability and sensitivity of smartphone CSI that has not been reported previously. Experimentalresults show that CRISLoc can achieve a mean error of around 0.29m in a6m times 8mresearch laboratory. The mean error increases by 5.4 cm and 8.6 cm upon the movement of one and two APs, which validates the robustness of CRISLoc against environment changes.

cs.NI↗

arXiv2021

Reward-Based 1-bit Compressed Federated Distillation on Blockchain

The recent advent of various forms of Federated Knowledge Distillation (FD) paves the way for a new generation of robust and communication-efficient Federated Learning (FL), where mere soft-labels are aggregated, rather than whole gradients of Deep Neural Networks (DNN) as done in previous FL schemes. This security-per-design approach in combination with increasingly performant Internet of Things (IoT) and mobile devices opens up a new realm of possibilities to utilize private data from industries as well as from individuals as input for artificial intelligence model training. Yet in previous FL systems, lack of trust due to the imbalance of power between workers and a central authority, the assumption of altruistic worker participation and the inability to correctly measure and compare contributions of workers hinder this technology from scaling beyond small groups of already entrusted entities towards mass adoption. This work aims to mitigate the aforementioned issues by introducing a novel decentralized federated learning framework where heavily compressed 1-bit soft-labels, resembling 1-hot label predictions, are aggregated on a smart contract. In a context where workers' contributions are now easily comparable, we modify the Peer Truth Serum for Crowdsourcing mechanism (PTSC) for FD to reward honest participation based on peer consistency in an incentive compatible fashion. Due to heavy reductions of both computational complexity and storage, our framework is a fully on-blockchain FL system that is feasible on simple smart contracts and therefore blockchain agnostic. We experimentally test our new framework and validate its theoretical properties.

cs.LG↗

arXiv2021

Multi-spectral programmable absorbers

We designed and demonstrated a multi-spectral programmable perfect absorber that exploits two different phase-change materials. This programmability is possible by resonantly coupling two phase change materials, a Ge2Sb2Te5 layer to vanadium dioxide nanoparticles (VO2 NPs). The perfect absorption is attributed to the coalescence of gap plasmon modes excited between the NPs and waveguide cavity-like modes excited between the film and the NPs. The absorptance peak (>90%) can be tuned to four different infrared (IR) wavelengths from 1906 to 2960 nm by heating the structure to different temperatures. The perfect absorber is reconfigurable, lithography-free, large-scale, polarization-insensitive omnidirectional. Our strategy opens a new path for programmable infrared photonics.

physics.optics↗

arXiv2021

Analyzing Open-Source Serverless Platforms: Characteristics and Performance

Serverless computing is increasingly popular because of its lower cost and easier deployment. Several cloud service providers (CSPs) offer serverless computing on their public clouds, but it may bring the vendor lock-in risk. To avoid this limitation, many open-source serverless platforms come out to allow developers to freely deploy and manage functions on self-hosted clouds. However, building effective functions requires much expertise and thorough comprehension of platform frameworks and features that affect performance. It is a challenge for a service developer to differentiate and select the appropriate serverless platform for different demands and scenarios. Thus, we elaborate the frameworks and event processing models of four popular open-source serverless platforms and identify their salient idiosyncrasies. We analyze the root causes of performance differences between different service exporting and auto-scaling modes on those platforms. Further, we provide several insights for future work, such as auto-scaling and metric collection.

cs.DC↗

arXiv2021

High Performance and Scalable NAT System on Commodity Platforms

Quick network address translation (NAT) is proposed to improve the network performance of the NAT system on the commodity server by three ways. First, the quick NAT search algorithm is designed to use the Hash search instead of the sequential search to reduce latency when looking up the NAT rule table. Second, to leverage the power of the multi-core central processing unit (CPU) and the multi-queue network interface card, Quick NAT enables multiple CPU cores to process in parallel. The localized connection tracking table and the compare-and-swap based lock-free NAT Hash tables are designed to eliminate the lock overhead. Third, Quick NAT uses the polling and zero-copy delivery to reduce the cost of interrupt and packet copies. The evaluation results show that Quick NAT obtains high scalability and line-rate throughput on the commodity server.

cs.NI↗

arXiv2021

Quick NAT: High performance NAT system on commodity platforms

NAT gateway is an important network system in today's IPv4 network when translating a private IPv4 address to a public address. However, traditional NAT system based on Linux Netfilter cannot achieve high network throughput to meet modern requirements such as data centers. To address this challenge, we improve the network performance of NAT system by three ways. First, we leverage DPDK to enable polling and zero-copy delivery, so as to reduce the cost of interrupt and packet copies. Second, we enable multiple CPU cores to process in parallel and use lock-free hash table to minimize the contention between CPU cores. Third, we use hash search instead of sequential search when looking up the NAT rule table. Evaluation shows that our Quick NAT system significantly improves the performance of NAT on commodity platforms.

cs.NI↗

arXiv2021

Data-driven Predictive Control for a Class of Uncertain Control-Affine Systems

This paper studies a data-driven predictive control for a class of control-affine systems which is subject to uncertainty. With the accessibility to finite sample measurements of the uncertain variables, we aim to find controls which are feasible and provide superior performance guarantees with high probability. This results into the formulation of a stochastic optimization problem (P), which is intractable due to the unknown distribution of the uncertainty variables. By developing a distributionally robust optimization framework, we present an equivalent and yet tractable reformulation of (P). Further, we propose an efficient algorithm that provides online suboptimal data-driven solutions and guarantees performance with high probability. To illustrate the effectiveness of the proposed approach, we consider a highway speed-limit control problem. We then develop a set of data-driven speed controls that allow us to prevent traffic congestion with high probability. Finally, we employ the resulting control method on a traffic simulator to illustrate the effectiveness of this approach numerically.

math.OC↗