arXiv subjects

Jie Fang

Publications and source records attributed to Jie Fang.

27 records · Page 2Linked to original sources

arXiv2022

A Map-matching Algorithm with Extraction of Multi-group Information for Low-frequency Data

The growing use of probe vehicles generates a huge number of GNSS data. Limited by the satellite positioning technology, further improving the accuracy of map-matching is challenging work, especially for low-frequency trajectories. When matching a trajectory, the ego vehicle's spatial-temporal information of the present trip is the most useful with the least amount of data. In addition, there are a large amount of other data, e.g., other vehicles' state and past prediction results, but it is hard to extract useful information for matching maps and inferring paths. Most map-matching studies only used the ego vehicle's data and ignored other vehicles' data. Based on it, this paper designs a new map-matching method to make full use of "Big data". We first sort all data into four groups according to their spatial and temporal distance from the present matching probe which allows us to sort for their usefulness. Then we design three different methods to extract valuable information (scores) from them: a score for speed and bearing, a score for historical usage, and a score for traffic state using the spectral graph Markov neutral network. Finally, we use a modified top-K shortest-path method to search the candidate paths within an ellipse region and then use the fused score to infer the path (projected location). We test the proposed method against baseline algorithms using a real-world dataset in China. The results show that all scoring methods can enhance map-matching accuracy. Furthermore, our method outperforms the others, especially when GNSS probing frequency is less than 0.01 Hz.

eess.SY↗

arXiv2022

Coherent excitation energy transfer in model photosynthetic reaction center: Effects of non-Markovian quantum environment

Excitation energy transfer (EET) and electron transfer (ET) are crucially involved in photosynthetic processes. In reality, the photosynthetic reaction center constitutes an open quantum system of EET and ET, which manifests an interplay of pigments, solar light and phonon baths. So far theoretical studies have been mainly based on master equation approaches in the Markovian condition. The non-Markovian environmental effect, which may play a crucial role, has not been sufficiently considered. In this work, we propose a mixed dynamic approach to investigate this open system. The influence of phonon bath is treated via the exact dissipaton equation of motion (DEOM) while that of photon bath is via the Lindblad master equation. Specifically, we explore the effect of non-Markovian quantum phonon bath on the coherent transfer dynamics and its manipulation on the current-voltage behavior. Distinguished from the results of completely Markovian Lindblad equation and those adopting classical environment description, the mixed DEOM-Lindblad simulations exhibit transfer coherence up to a few hundreds femtoseconds and the related environmental manipulation effect on current. These non-Markovian quantum coherent effects be extended tomore complex and realistic systems and be helpful to the design of organic photovoltaic devices.

quant-ph↗

arXiv2022

Correlated driving-and-dissipation equation for non-Condon spectroscopy with the Herzberg-Teller vibronic coupling

Correlated driving-and-dissipation equation (CODDE) is an optimized complete second-order quantum dissipation approach, which is originally concerned with the reduced system dynamics only. However, one can actually extract the hybridized bath dynamics from CODDE with the aid of dissipaton-equation-of-motion theory, a statistical quasi-particle quantum dissipation formalism. Treated as an one{dissipaton theory, CODDE is successfully extended to deal with the Herzberg-Teller vibronic couplings in dipole-field interactions. Demonstrations will be carried out on the non-Condon spectroscopies of a model dimer system.

physics.chem-ph↗

arXiv2020

Alpha Discovery Neural Network based on Prior Knowledge

Genetic programming (GP) is the state-of-the-art in financial automated feature construction task. It employs reverse polish expression to represent features and then conducts the evolution process. However, with the development of deep learning, more powerful feature extraction tools are available. This paper proposes Alpha Discovery Neural Network (ADNN), a tailored neural network structure which can automatically construct diversified financial technical indicators based on prior knowledge. We mainly made three contributions. First, we use domain knowledge in quantitative trading to design the sampling rules and object function. Second, pre-training and model pruning has been used to replace genetic programming, because it can conduct more efficient evolution process. Third, the feature extractors in ADNN can be replaced by different feature extractors and produce different functions. The experiment results show that ADNN can construct more informative and diversified features than GP, which can effectively enriches the current factor pool. The fully-connected network and recurrent network are better at extracting information from the financial time series than the convolution neural network. In real practice, features constructed by ADNN can always improve multi-factor strategies' revenue, sharpe ratio, and max draw-down, compared with the investment strategies without these factors.

q-fin.ST↗

arXiv2020

Prior knowledge distillation based on financial time series

One of the major characteristics of financial time series is that they contain a large amount of non-stationary noise, which is challenging for deep neural networks. People normally use various features to address this problem. However, the performance of these features depends on the choice of hyper-parameters. In this paper, we propose to use neural networks to represent these indicators and train a large network constructed of smaller networks as feature layers to fine-tune the prior knowledge represented by the indicators. During back propagation, prior knowledge is transferred from human logic to machine logic via gradient descent. Prior knowledge is the deep belief of neural network and teaches the network to not be affected by non-stationary noise. Moreover, co-distillation is applied to distill the structure into a much smaller size to reduce redundant features and the risk of overfitting. In addition, the decisions of the smaller networks in terms of gradient descent are more robust and cautious than those of large networks. In numerical experiments, we find that our algorithm is faster and more accurate than traditional methods on real financial datasets. We also conduct experiments to verify and comprehend the method.

cs.LG↗

arXiv2020

Neural Network-based Automatic Factor Construction

Instead of conducting manual factor construction based on traditional and behavioural finance analysis, academic researchers and quantitative investment managers have leveraged Genetic Programming (GP) as an automatic feature construction tool in recent years, which builds reverse polish mathematical expressions from trading data into new factors. However, with the development of deep learning, more powerful feature extraction tools are available. This paper proposes Neural Network-based Automatic Factor Construction (NNAFC), a tailored neural network framework that can automatically construct diversified financial factors based on financial domain knowledge and a variety of neural network structures. The experiment results show that NNAFC can construct more informative and diversified factors than GP, to effectively enrich the current factor pool. For the current market, both fully connected and recurrent neural network structures are better at extracting information from financial time series than convolution neural network structures. Moreover, new factors constructed by NNAFC can always improve the return, Sharpe ratio, and the max draw-down of a multi-factor quantitative investment strategy due to their introducing more information and diversification to the existing factor pool.

q-fin.ST↗

arXiv2020

Directivity modulation of exciton emission using single dielectric nanospheres

Coupling emitters with nanoresonators is an effective strategy to control light emission at the subwavelength scale with high efficiency. Low-loss dielectric nanoantennas hold particular promise for this purpose, owing to their strong Mie resonances. Herein, we explore a highly miniaturized platform for the control of emission based on individual subwavelength Si nanospheres (SiNSs) to modulate the directional excitation and exciton emission of two-dimensional transition metal dichalcogenides (2D TMDs). A modified Mie theory for dipole-sphere hybrid systems is derived to instruct the optimal design for desirable modulation performance. Controllable forward-to-backward intensity ratios are experimentally validated in 532 nm laser excitation and 635 nm exciton emission from a monolayer WS2. Versatile light emission control along all device orientations is achieved for different emitters and excitation wavelengths, benefiting from the facile size control and isotropic shape of SiNSs. Simultaneous modulation of excitation and emission via a single SiNS at visible wavelengths significantly improves the efficiency and directivity of TMD exciton emission and leads to the potential of multifunctional integrated photonics. Overall, our work opens promising opportunities for nanophotonics and polaritonic systems, enabling efficient manipulation, enhancement and reconfigurability of light-matter interactions.

physics.optics↗

arXiv2020

Automatic Financial Feature Construction

In automatic financial feature construction task, the state-of-the-art technic leverages reverse polish expression to represent the features, then use genetic programming (GP) to conduct its evolution process. In this paper, we propose a new framework based on neural network, alpha discovery neural network (ADNN). In this work, we made several contributions. Firstly, in this task, we make full use of neural network overwhelming advantage in feature extraction to construct highly informative features. Secondly, we use domain knowledge to design the object function, batch size, and sampling rules. Thirdly, we use pre-training to replace the GP evolution process. According to neural network universal approximation theorem, pre-training can conduct a more effective and explainable evolution process. Experiment shows that ADNN can remarkably produce more diversified and higher informative features than GP. Besides, ADNN can serve as a data augmentation algorithm. It further improves the the performance of financial features constructed by GP.

cs.LG↗

arXiv2019

Suppressing Material Loss for Functional Nanophotonics Using Bandgap Engineering

All-dielectric nanoantennas have recently opened exciting opportunities for functional nanophotonics, owing to their strong optical resonances along with low material loss in the near-infrared range. Pushing these concepts to the visible range is hindered by a larger absorption coefficient of Si and other high-index semiconductors, thus encouraging the search for alternative dielectrics for nanophotonics. In this paper, we employ bandgap engineering to synthesize hydrogenated amorphous Si nanoparticles (a-Si:H NPs) offering ideal features for functional nanophotonics. We observe significant material loss suppression in a-Si:H NPs in the visible range caused by hydrogenation-induced bandgap renormalization, producing resonant modes in single a-Si:H NPs with Q factors up to ~100, in the visible and near-IR range for the first time. In order to demonstrate light-matter interaction enhancement, we realize highly tunable all-dielectric nanoantennas coupling them to photochromic spiropyran (SP) molecules. We show ~70% reversible all-optical tuning of light scattering at the high-Q resonant mode, along with minor tunability when out of resonance. This remarkable all-optical tuning effect is achieved under a low incident light intensity ~3.8 W/cm2 for UV light and ~1.1*10^2 W/cm2 for green light.

physics.optics↗