arXiv subjects

Shan Zhong

Publications and source records attributed to Shan Zhong.

27 records · Page 2Linked to original sources

arXiv2023

Cubature Kalman filter Based on generalized minimum error entropy with fiducial point

In real applications, non-Gaussian distributions are frequently caused by outliers and impulsive disturbances, and these will impair the performance of the classical cubature Kalman filter (CKF) algorithm. In this letter, a modified generalized minimum error entropy criterion with fiducial point (GMEEFP) is studied to ensure that the error comes together to around zero, and a new CKF algorithm based on the GMEEFP criterion, called GMEEFP-CKF algorithm, is developed. To demonstrate the practicality of the GMEEFP-CKF algorithm, several simulations are performed, and it is demonstrated that the proposed GMEEFP-CKF algorithm outperforms the existing CKF algorithms with impulse noise.

cs.IT↗

arXiv2023

Seasonality Based Reranking of E-commerce Autocomplete Using Natural Language Queries

Query autocomplete (QAC) also known as typeahead, suggests list of complete queries as user types prefix in the search box. It is one of the key features of modern search engines specially in e-commerce. One of the goals of typeahead is to suggest relevant queries to users which are seasonally important. In this paper we propose a neural network based natural language processing (NLP) algorithm to incorporate seasonality as a signal and present end to end evaluation of the QAC ranking model. Incorporating seasonality into autocomplete ranking model can improve autocomplete relevance and business metric.

cs.IR↗

arXiv2023

Minimum Error Entropy Rauch-Tung-Striebel Smoother

Outliers and impulsive disturbances often cause heavy-tailed distributions in practical applications, and these will degrade the performance of Gaussian approximation smoothing algorithms. To improve the robustness of the Rauch-Tung-Striebel (RTS) smother against complicated non-Gaussian noises, a new RTS-smoother integrated with the minimum error entropy (MEE) criterion (MEE-RTS) is proposed for linear systems, which is also extended to the state estimation of nonlinear systems by utilizing the Taylor series linearization approach. The mean error behavior, the mean square error behavior, as well as the computational complexity of the MEE-RTS smoother are analyzed. According to simulation results, the proposed smoothers perform better than several robust solutions in terms of steady-state error.

eess.SP↗

arXiv2022

Practical Deep Reinforcement Learning Approach for Stock Trading

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns.

cs.LG↗

arXiv2021

Orbital-Selective High-Temperature Cooper Pairing Developed in the Two-Dimensional Limit

The orbital multiplicity in multiband superconductors yields orbital differentiation in normal-state properties, and can lead to orbital-selective spin-fluctuation Cooper pairing. This phenomenon has become increasingly pivotal in clarifying the pairing 'enigma' particularly for multiband high-temperature superconductors. In one-unit-cell (1-UC) FeSe/SrTiO3, the thinnest and highest-Tc member of iron-based superconductors, the standard electron-hole Fermi pocket nesting scenario is apparently not applicable since the Gamma-centered hole pockets are absent, so the actual pairing mechanism is the subject of intense debate. Here, by measuring high-resolution Bogoliubov quasiparticle interference, we report observations of highly anisotropic magnetic Cooper pairing in 1-UC FeSe. From a theoretical point of view, it is important to incorporate effects of electronic correlations within a spin-fluctuation pairing calculation, where the dxy orbital becomes coherence-suppressed. The resulting pairing gap is compatible with the experimental findings, which suggests that high-Tc Cooper pairing with orbital selectivity applies to 1-UC FeSe. Our findings imply the general existence of orbital selectivity in iron-based superconductors and the universal importance of electron correlations in high-Tc superconductors.

cond-mat.supr-con↗

arXiv2021

Equally Spaced Quantum States in van der Waals Epitaxy-Grown Nanoislands

Pursuing the confinement of linearly dispersive relativistic fermions is of interest in both fundamental physics and potential applications. Here, we report strong STM evidence for the equally spaced, strikingly sharp, and densely distributed quantum well states (QWSs) near Fermi energy in Pb(111) nanoislands, van-der-Waals epitaxially grown on graphitized 6H-SiC(0001). The observations can be explained as the quantized energies of confined linearly dispersive [111] electrons, which essentially 'simulate' the out-of-plane relativistic quasiparticles. The equally spaced QWSs with an origin of confined relativistic electrons are supported by phenomenological simulations and Fabry-Perot fittings based on the relativistic fermions. First-principles calculations further reveal that the spin-orbit coupling strengthens the relativistic nature of electrons near Fermi energy. Our finding uncovers the unique equally spaced quantum states in electronic systems beyond Landau levels, and may inspire future studies on confined relativistic quasiparticles in flourishing topological materials and applications in structurally simpler quantum cascade laser.

cond-mat.mes-hall↗

arXiv2021

S&P 500 Stock Price Prediction Using Technical, Fundamental and Text Data

We summarized both common and novel predictive models used for stock price prediction and combined them with technical indices, fundamental characteristics and text-based sentiment data to predict S&P stock prices. A 66.18% accuracy in S&P 500 index directional prediction and 62.09% accuracy in individual stock directional prediction was achieved by combining different machine learning models such as Random Forest and LSTM together into state-of-the-art ensemble models. The data we use contains weekly historical prices, finance reports, and text information from news items associated with 518 different common stocks issued by current and former S&P 500 large-cap companies, from January 1, 2000 to December 31, 2019. Our study's innovation includes utilizing deep language models to categorize and infer financial news item sentiment; fusing different models containing different combinations of variables and stocks to jointly make predictions; and overcoming the insufficient data problem for machine learning models in time series by using data across different stocks.

stat.ML↗

arXiv2019

FPGA-Controlled Versatile Microwave Source for Cold Atom Experiments

We present a microwave source that is controlled by a commercially available field programmable gate array (FPGA). Using an FPGA allows for precise control of the time dependent microwave-dressing applied to a sample of trapped cold atoms. We test our microwave source by exciting Rabi oscillations in a Na spinor Bose-Einstein Condensate. We include, as supplements, the complete source code, parts lists, pin connection diagrams, and schematics to make it easy for any group to build and use this device.

physics.ins-det↗

arXiv2017

Joint Energy-Bandwidth Allocation for Multi-User Channels with Cooperating Hybrid Energy Nodes

In this paper, we consider the energy-bandwidth allocation for a network of multiple users, where the transmitters each powered by both an energy harvester and conventional grid, access the network orthogonally on the assigned frequency band. We assume that the energy harvesting state and channel gain of each transmitter can be predicted for $K$ time slots a priori. The different transmitters can cooperate by donating energy to each other. The tradeoff among the weighted sum throughput, the use of grid energy, and the amount of energy cooperation is studied through an optimization objective which is a linear combination of these quantities. This leads to an optimization problem with O($N^2K$) constraints, where $N$ is the total number of transmitter-receiver pairs, and the optimization is over seven sets of variables that denote energy and bandwidth allocation, grid energy utilization, and energy cooperation. To solve the problem efficiently, an iterative algorithm is proposed using the Proximal Jacobian ADMM. The optimization sub-problems corresponding to Proximal Jacobian ADMM steps are solved in closed form. We show that this algorithm converges to the optimal solution with an overall complexity of O($N^2K^2$). Numerical results show that the proposed algorithms can make efficient use of the harvested energy, grid energy, energy cooperation, and the available bandwidth.

cs.IT↗