arXiv subjects

Jianfeng Li

Publications and source records attributed to Jianfeng Li.

At least 37 records · Page 2Linked to original sources

arXiv2023

Demystifying DeFi MEV Activities in Flashbots Bundle

Decentralized Finance, mushrooming in permissionless blockchains, has attracted a recent surge in popularity. Due to the transparency of permissionless blockchains, opportunistic traders can compete to earn revenue by extracting Miner Extractable Value (MEV), which undermines both the consensus security and efficiency of blockchain systems. The Flashbots bundle mechanism further aggravates the MEV competition because it empowers opportunistic traders with the capability of designing more sophisticated MEV extraction. In this paper, we conduct the first systematic study on DeFi MEV activities in Flashbots bundle by developing ActLifter, a novel automated tool for accurately identifying DeFi actions in transactions of each bundle, and ActCluster, a new approach that leverages iterative clustering to facilitate us to discover known/unknown DeFi MEV activities. Extensive experimental results show that ActLifter can achieve nearly 100% precision and recall in DeFi action identification, significantly outperforming state-of-the-art techniques. Moreover, with the help of ActCluster, we obtain many new observations and discover 17 new kinds of DeFi MEV activities, which occur in 53.12% of bundles but have not been reported in existing studies

cs.CR↗

arXiv2023

Quantum interference between non-identical single particles

Quantum interference between identical single particles reveals the intrinsic quantum statistic nature of particles, which could not be interpreted through classical physics. Here, we demonstrate quantum interference between non-identical bosons using a generalized beam splitter based on a quantum memory. The Hong-Ou-Mandel type interference between single photons and single magnons with high visibility is demonstrated, and the crossover from the bosonic to fermionic quantum statistics is observed by tuning the beam splitter to be non-Hermitian. Moreover, multi-particle interference that simulates the behavior of three fermions by three input photons is realized. Our work extends the understanding of the quantum interference effects and demonstrates a versatile experimental platform for studying and engineering quantum statistics of particles.

quant-ph↗

arXiv2023

Demonstration of the quantum principle of least action with single photons

The principle of least action is arguably the most fundamental principle in physics as it can be used to derive the equations of motion in various branches of physics. However, this principle has not been experimentally demonstrated at the quantum level because the propagators for Feymann's path integrals have never been observed. The propagator is a fundamental concept and contains various significant properties of a quantum system in path integral formulation, so its experimental observation is itself essential in quantum mechanics. Here we theoretically propose and experimentally observe single photons' propagators based on the method of directly measuring quantum wave-functions. Furthermore, we obtain the classical trajectories of the single photons in free space and in a harmonic trap based on the extremum of the observed propagators, thereby experimentally demonstrating the quantum principle of least action. Our work paves the way for experimentally exploring fundamental problems of quantum theory in the formulation of path integrals.

quant-ph↗

arXiv2023

Implantable Photonic Neural Probes with 3D-Printed Microfluidics and Applications to Uncaging

Advances in chip-scale photonic-electronic integration are enabling a new generation of foundry-manufacturable implantable silicon neural probes incorporating nanophotonic waveguides and microelectrodes for optogenetic stimulation and electrophysiological recording in neuroscience research. Further extending neural probe functionalities with integrated microfluidics is a direct approach to achieve neurochemical injection and sampling capabilities. In this work, we use two-photon polymerization 3D printing to integrate microfluidic channels onto photonic neural probes, which include silicon nitride nanophotonic waveguides and grating emitters. The customizability of 3D printing enables a unique geometry of microfluidics that conforms to the shape of each neural probe, enabling integration of microfluidics with a variety of existing neural probes while avoiding the complexities of monolithic microfluidics integration. We demonstrate the photonic and fluidic functionalities of the neural probes via fluorescein injection in agarose gel and photoloysis of caged fluorescein in solution and in flxed brain tissue.

physics.optics↗

arXiv2023

An End-to-End Network for Upright Adjustment of Panoramic Images

Nowadays, panoramic images can be easily obtained by panoramic cameras. However, when the panoramic camera orientation is tilted, a non-upright panoramic image will be captured. Existing upright adjustment models focus on how to estimate more accurate camera orientation, and attribute image reconstruction to offline or post-processing tasks. To this end, we propose an online end-to-end network for upright adjustment. Our network is designed to reconstruct the image while finding the angle. Our network consists of three modules: orientation estimation, LUT online generation, and upright reconstruction. Direction estimation estimates the tilt angle of the panoramic image. Then, a converter block with upsampling function is designed to generate angle to LUT. This module can output corresponding online LUT for different input angles. Finally, a lightweight generative adversarial network (GAN) aims to generate upright images from shallow features. The experimental results show that in terms of angles, we have improved the accuracy of small angle errors. In terms of image reconstruction, In image reconstruction, we have achieved the first real-time online upright reconstruction of panoramic images using deep learning networks.

cs.CV↗

arXiv2022

Image Segmentation Semantic Communication over Internet of Vehicles

In this paper, the problem of semantic-based efficient image transmission is studied over the Internet of Vehicles (IoV). In the considered model, a vehicle shares massive amount of visual data perceived by its visual sensors to assist other vehicles in making driving decisions. However, it is hard to maintain a high reliable visual data transmission due to the limited spectrum resources. To tackle this problem, a semantic communication approach is introduced to reduce the transmission data amount while ensuring the semantic-level accuracy. Particularly, an image segmentation semantic communication (ISSC) system is proposed, which can extract the semantic features from the perceived images and transmit the features to the receiving vehicle that reconstructs the image segmentations. The ISSC system consists of an encoder and a decoder at the transmitter and the receiver, respectively. To accurately extract the image semantic features, the ISSC system encoder employs a Swin Transformer based multi-scale semantic feature extractor. Then, to resist the wireless noise and reconstruct the image segmentation, a semantic feature decoder and a reconstructor are designed at the receiver. Simulation results show that the proposed ISSC system can reconstruct the image segmentation accurately with a high compression ratio, and can achieve robust transmission performance against channel noise, especially at the low signal-to-noise ratio (SNR). In terms of mean Intersection over Union (mIoU), the ISSC system can achieve an increase by 75%, compared to the baselines using traditional coding method

cs.NI↗

arXiv2022

Impact of Third Order Dispersion on Dissipative Soliton Resonance

Dissipative soliton resonance (DSR) is a promising way for high-energy pulse generation typically having a symmetrical square pulse profile. While this method is well known, the impact of third order dispersion (TOD) on DSR is yet to be fully addressed in the literature. In this article, the impact of TOD on DSR is numerically investigated under the frame of the complex cubic-quintic Ginzburg-Landau equation (CQGLE). Our numerical investigations indicate that DSR can stably exist under TOD with nearly the same pulse amplitude, but with a (significantly) different pulse duration. Depending on the value of chromatic dispersion, the pulse duration can be notably longer or shorter due to the presence of TOD. The TOD effect also alters the dependence of pulse duration on the nonlinear gain. Another impact of TOD on DSR is that the DSR exists with an asymmetric pulse profile, leading to steepening of one edge of the DSR pulse, while flattening of the other. Our results indicate that TOD has a critical role for realizing DSR in mode-locked lasers and it should be taken into consideration during design and development of DSR-based lasers.

physics.optics↗

arXiv2021

ezcox: An R/CRAN Package for Cox Model Batch Processing and Visualization

Cox analysis is a common clinical data analysis technique to link valuable variables to clinical outcomes including dead and relapse. In the omics era, Cox model batch processing is a basic strategy for screening clinically relevant variables, biomarker discovery and gene signature identification. However, all such analyses have been implemented with homebrew code in research community, thus lack of transparency and reproducibility. Here, we present ezcox, the first R/CRAN package for Cox model batch processing and visualization. ezcox is an open source R package under GPL-3 license and it is free available at https://github.com/ShixiangWang/ezcox and https://cran.r-project.org/package=ezcox.

q-bio.QM↗

arXiv2021

Synchronization and phase shaping of single photons with high-efficiency quantum memory

Time synchronization and phase shaping of single photons both play fundamental roles in quantum information applications that rely on multi-photon quantum interference. Phase shaping typically requires separate modulators with extra insertion losses. Here, we develop a all-optical built-in phase modulator for single photons using a quantum memory. The fast phase modulation of a single photon in both step and linear manner are verified by observing the efficient quantum-memory-assisted Hong-Ou-Mandel interference between two single photons, where the anti-coalescence effect of bosonic photon pairs is demonstrated. The developed phase modulator may push forward the practical quantum information applications.

quant-ph↗

arXiv2021

Unsupervised Joint Learning of Depth, Optical Flow, Ego-motion from Video

Estimating geometric elements such as depth, camera motion, and optical flow from images is an important part of the robot's visual perception. We use a joint self-supervised method to estimate the three geometric elements. Depth network, optical flow network and camera motion network are independent of each other but are jointly optimized during training phase. Compared with independent training, joint training can make full use of the geometric relationship between geometric elements and provide dynamic and static information of the scene. In this paper, we improve the joint self-supervision method from three aspects: network structure, dynamic object segmentation, and geometric constraints. In terms of network structure, we apply the attention mechanism to the camera motion network, which helps to take advantage of the similarity of camera movement between frames. And according to attention mechanism in Transformer, we propose a plug-and-play convolutional attention module. In terms of dynamic object, according to the different influences of dynamic objects in the optical flow self-supervised framework and the depth-pose self-supervised framework, we propose a threshold algorithm to detect dynamic regions, and mask that in the loss function respectively. In terms of geometric constraints, we use traditional methods to estimate the fundamental matrix from the corresponding points to constrain the camera motion network. We demonstrate the effectiveness of our method on the KITTI dataset. Compared with other joint self-supervised methods, our method achieves state-of-the-art performance in the estimation of pose and optical flow, and the depth estimation has also achieved competitive results. Code will be available https://github.com/jianfenglihg/Unsupervised_geometry.

cs.CV↗

arXiv2020

Single upper limb pose estimation method based on improved stacked hourglass network

At present, most high-accuracy single-person pose estimation methods have high computational complexity and insufficient real-time performance due to the complex structure of the network model. However, a single-person pose estimation method with high real-time performance also needs to improve its accuracy due to the simple structure of the network model. It is currently difficult to achieve both high accuracy and real-time performance in single-person pose estimation. For use in human-machine cooperative operations, this paper proposes a single-person upper limb pose estimation method based on an end-to-end approach for accurate and real-time limb pose estimation. Using the stacked hourglass network model, a single-person upper limb skeleton key point detection model was designed.Deconvolution was employed to replace the up-sampling operation of the hourglass module in the original model, solving the problem of rough feature maps. Integral regression was used to calculate the position coordinates of key points of the skeleton, reducing quantization errors and calculations. Experiments showed that the developed single-person upper limb skeleton key point detection model achieves high accuracy and that the pose estimation method based on the end-to-end approach provides high accuracy and real-time performance.

cs.CV↗

arXiv2020

Occlusion Aware Unsupervised Learning of Optical Flow From Video

In this paper, we proposed an unsupervised learning method for estimating the optical flow between video frames, especially to solve the occlusion problem. Occlusion is caused by the movement of an object or the movement of the camera, defined as when certain pixels are visible in one video frame but not in adjacent frames. Due to the lack of pixel correspondence between frames in the occluded area, incorrect photometric loss calculation can mislead the optical flow training process. In the video sequence, we found that the occlusion in the forward ($t\rightarrow t+1$) and backward ($t\rightarrow t-1$) frame pairs are usually complementary. That is, pixels that are occluded in subsequent frames are often not occluded in the previous frame and vice versa. Therefore, by using this complementarity, a new weighted loss is proposed to solve the occlusion problem. In addition, we calculate gradients in multiple directions to provide richer supervision information. Our method achieves competitive optical flow accuracy compared to the baseline and some supervised methods on KITTI 2012 and 2015 benchmarks. This source code has been released at https://github.com/jianfenglihg/UnOpticalFlow.git.

cs.CV↗

arXiv2020

Portable probe design for photoacoustic imaging in vivo

A low-cost adjustable illumination scheme for hand-held photoacoustic imaging probe is presented, manufactured and tested in this paper. Compared with traditional photoacoustic probe design, it has the following advantages: (1) Different excitation modes can be selected as needed. By tuning control parameters, it can achieve bright-field, dark-field, and hybrid field light illumination schemes. (2) The spot-adjustable unit (SAU) specifically designed for beam expansion, together with a water tank for transmitting ultrasonic waves, enable the device to break through the constraints of the transfer medium and is more widely used. The beam-expansion experiment is conducted to verify the function of SAU. After that, we built a PAT system based on our newly designed apparatus. Phantom and in vivo experimental results show different performance in different illumination schemes

eess.IV↗

arXiv2019

Einstein-Podolsky-Rosen Energy-Time Entanglement of Narrowband Biphotons

We report the direct characterization of energy-time entanglement of narrowband biphotons produced from spontaneous four-wave mixing in cold atoms. The Stokes and anti-Stokes two-photon temporal correlation is measured by single-photon counters with nano second temporal resolution, and their joint spectrum is determined by using a narrow linewidth optical cavity. The energy-time entanglement is verified by the joint frequency-time uncertainty product of 0.063 +/- 0.0044, which does not only violate the separability criterion but also satisfies the continuous variable Einstein-Podolsky-Rosen steering inequality.

quant-ph↗

arXiv2019

$δ$-Quench measurement of quantum wavefunction

Measurement of quantum state wavefunction not only acts as a fundamental part in quantum physics but also plays an important role in developing practical quantum technologies. Conventional quantum state tomography has been widely used to estimate quantum wavefunctions, which, however, requires huge measurement resources exponentially growing with the dimension of state. The recent weak-value-based quantum measurement circumvents this resources issue but relies on an extra pointer space. Here, we propose and demonstrate a simple and direct measurement strategy based on $δ$-quench probe: By quenching its complex probability amplitude one by one ($δ$-quench) in the given bases, we can directly obtain the quantum wavefunction by projecting the quenched state onto a post-selection state. As compared to the conventional approaches, it needs only one projection basis. We confirm its power by experimentally measuring photonic complex temporal wavefunctions. This new method is versatile and can find applications in quantum information science and engineering.

quant-ph↗

arXiv2018

Vehicle Traffic Driven Camera Placement for Better Metropolis Security Surveillance

Security surveillance is one of the most important issues in smart cities, especially in an era of terrorism. Deploying a number of (video) cameras is a common surveillance approach. Given the never-ending power offered by vehicles to metropolises, exploiting vehicle traffic to design camera placement strategies could potentially facilitate security surveillance. This article constitutes the first effort toward building the linkage between vehicle traffic and security surveillance, which is a critical problem for smart cities. We expect our study could influence the decision making of surveillance camera placement, and foster more research of principled ways of security surveillance beneficial to our physical-world life. Code has been made publicly available.

cs.CY↗

arXiv2018

Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis

Recent years have witnessed the unprecedented rising of time series from almost all kindes of academic and industrial fields. Various types of deep neural network models have been introduced to time series analysis, but the important frequency information is yet lack of effective modeling. In light of this, in this paper we propose a wavelet-based neural network structure called multilevel Wavelet Decomposition Network (mWDN) for building frequency-aware deep learning models for time series analysis. mWDN preserves the advantage of multilevel discrete wavelet decomposition in frequency learning while enables the fine-tuning of all parameters under a deep neural network framework. Based on mWDN, we further propose two deep learning models called Residual Classification Flow (RCF) and multi-frequecy Long Short-Term Memory (mLSTM) for time series classification and forecasting, respectively. The two models take all or partial mWDN decomposed sub-series in different frequencies as input, and resort to the back propagation algorithm to learn all the parameters globally, which enables seamless embedding of wavelet-based frequency analysis into deep learning frameworks. Extensive experiments on 40 UCR datasets and a real-world user volume dataset demonstrate the excellent performance of our time series models based on mWDN. In particular, we propose an importance analysis method to mWDN based models, which successfully identifies those time-series elements and mWDN layers that are crucially important to time series analysis. This indeed indicates the interpretability advantage of mWDN, and can be viewed as an indepth exploration to interpretable deep learning.

cs.LG↗

arXiv2017

High efficiency photonic storage of single photons in cold atoms

An efficient quantum storage is highly desired for quantum information processing. As indicated by certain applications, a universal quantum storage is required to have a storage efficiency above 50% to beat the no-cloning limit. Although significant progress has been achieved in improving various quantum storage, the best storage efficiency of single photons is still below this criteria. By integrating a highly controllable single photon source with an optimized quantum storage, here we demonstrate an optical storage of single photons with storage efficiency of 65% in a cold atomic ensemble based on electromagnetically induced transparency. Meanwhile, the nonclassical characteristics of our storage are verified through the well-maintained nonclassical and single photon nature of the retrieved single photons.

quant-ph↗