Zhe Li

Zhe Li
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Zhe Li
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Mathematics - Numerical Analysis (4)
 
Physics - Materials Science (2)
 
Mathematics - Optimization and Control (2)
 
Statistics - Machine Learning (2)
 
Computer Science - Computer Vision and Pattern Recognition (2)
 
Physics - Atomic Physics (1)
 
Mathematics - Commutative Algebra (1)
 
Physics - Instrumentation and Detectors (1)
 
Computer Science - Artificial Intelligence (1)
 
Quantum Physics (1)
 
Computer Science - Learning (1)
 
Computer Science - Distributed; Parallel; and Cluster Computing (1)

Publications Authored By Zhe Li

In this work, we perform a series of phonon counting measurement with different methods in a 3-mode optomechanical system, and we compare the difference of the entanglement after measurement. In this article we focus on the two cases: imperfect measurement and on-off measurement. We find that whatever measurement you take, the entanglement will increase. Read More

Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecedented progress, achieving the accuracy close to, or even better than human-level perception in various tasks. There is a timely need to map the latest software DCNNs to application-specific hardware, in order to achieve orders of magnitude improvement in performance, energy efficiency and compactness. Stochastic Computing (SC), as a low-cost alternative to the conventional binary computing paradigm, has the potential to enable massively parallel and highly scalable hardware implementation of DCNNs. Read More

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. Read More

With recent advancing of Internet of Things (IoTs), it becomes very attractive to implement the deep convolutional neural networks (DCNNs) onto embedded/portable systems. Presently, executing the software-based DCNNs requires high-performance server clusters in practice, restricting their widespread deployment on the mobile devices. To overcome this issue, considerable research efforts have been conducted in the context of developing highly-parallel and specific DCNN hardware, utilizing GPGPUs, FPGAs, and ASICs. Read More

Recently, {\it stochastic momentum} methods have been widely adopted in training deep neural networks. However, their convergence analysis is still underexplored at the moment, in particular for non-convex optimization. This paper fills the gap between practice and theory by developing a basic convergence analysis of two stochastic momentum methods, namely stochastic heavy-ball method and the stochastic variant of Nesterov's accelerated gradient method. Read More

Dropout has been witnessed with great success in training deep neural networks by independently zeroing out the outputs of neurons at random. It has also received a surge of interest for shallow learning, e.g. Read More

Taking into account the phase fraction during transition for the first-order magnetocaloric materials, an improved isothermal entropy change determination has been put forward based on the Clausius-Clapeyron (CC) equation. It was found that the isothermal entropy change value evaluated by our method is in excellent agreement with those determined from the Maxwell-relation (MR) for Ni-Mn-Sn Heusler alloys, which usually presents a weak field-induced phase transforming behavior. In comparison with MR, this method could give rise to a favorable result derived from few thermomagnetic measurements. Read More

In this paper we study the algebraic structure of error formulas for ideal interpolation. We introduce the so-called "normal" error formulas and prove that the lexicographic order reduced Gr\"obner basis admits such a formula for all ideal interpolation. This formula is a generalization of the "good" error formula proposed by Carl de Boor. Read More

Scintillation detector has lower energy resolution for Gamma-ray as compared to semiconductor detector, better spectra analysis method is essential to traditional method. A model for describing the response function of scintillation detector over the range of incident Gamma-ray energies between 0.5 and 1. Read More

In this paper, we study pinning control problem of coupled dynamical systems with stochastically switching couplings and stochastically selected controller-node set. Here, the coupling matrices and the controller-node sets change with time, induced by a continuous-time Markovian chain. By constructing Lyapunov functions, we establish tractable sufficient conditions for exponentially stability of the coupled system. Read More

In this paper, we focus on two classes of D-invariant polynomial subspaces. The first is a classical type, while the second is a new class. With matrix computation, we prove that every ideal projector with each D-invariant subspace belonging to either the first class or the second is the pointwise limit of Lagrange projectors. Read More

The quotient bases for zero-dimensional ideals are often of interest in the investigation of multivariate polynomial interpolation, algebraic coding theory, and computational molecular biology, etc. In this paper, we discuss the properties of zero-dimensional ideals with unique monomial quotient bases, and verify that the vanishing ideals of Cartesian sets have unique monomial quotient bases. Furthermore, we reveal the relation between Cartesian sets and the point sets with unique associated monomial quotient bases. Read More

In this paper, we focus on a special class of ideal projectors. With the aid of algebraic geometry, we prove that for this special class of ideal projectors, there exist "good" error formulas as defined by C. de Boor. Read More

In this paper, we verify Carl de Boor's conjecture on ideal projectors for real ideal projectors of type partial derivative by proving that there exists a positive $\eta\in \mathbb{R}$ such that a real ideal projector of type partial derivative $P$ is the pointwise limit of a sequence of Lagrange projectors which are perturbed from $P$ up to $\eta$ in magnitude. Furthermore, we present an algorithm for computing the value of such $\eta$ when the range of the Lagrange projectors is spanned by the Gr\"{o}bner \'{e}scalier of their kernels w.r. Read More