arXiv subjects

Ziyan Luo

Publications and source records attributed to Ziyan Luo.

51 records · Page 3Linked to original sources

arXiv2018

Ultrathin All-in-one Spin Hall Magnetic Sensor with Built-in AC Excitation Enabled by Spin Current

Magnetoresistance (MR) sensors provide cost-effective solutions for diverse industrial and consumer applications, including emerging fields such as internet-of-things (IoT), artificial intelligence and smart living. Commercially available MR sensors such as anisotropic magnetoresistance (AMR) sensor, giant magnetoresistance (GMR) sensor and tunnel magnetoresistance (TMR) sensors typically require an appropriate magnetic bias for both output linearization and noise suppression, resulting in increased structural complexity and manufacturing cost. Here, we demonstrate an all-in-one spin Hall magnetoresistance (SMR) sensor with built-in AC excitation and rectification detection, which effectively eliminates the requirements of any linearization and domain stabilization mechanisms separate from the active sensing layer. This was made possible by the coexistence of SMR and spin-orbit torque (SOT) in ultrathin NiFe/Pt bilayers. Despite the simplest possible structure, the fabricated Wheatstone bridge sensor exhibits essentially zero DC offset, negligible hysteresis, and a detectivity of around 1nT/sqrt(Hz) at 1Hz. In addition, it also shows an angle dependence to external field similar to those of GMR and TMR, though it does have any reference layer (unlike GMR and TMR). The superior performances of SMR sensors are evidently demonstrated in the proof-of-concept experiments on rotation angle measurement, and vibration and finger motion detection.

physics.ins-det↗

arXiv2018

Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method

The octagonal shrinkage and clustering algorithm for regression (OSCAR), equipped with the $\ell_1$-norm and a pair-wise $\ell_{\infty}$-norm regularizer, is a useful tool for feature selection and grouping in high-dimensional data analysis. The computational challenge posed by OSCAR, for high dimensional and/or large sample size data, has not yet been well resolved due to the non-smoothness and inseparability of the regularizer involved. In this paper, we successfully resolve this numerical challenge by proposing a sparse semismooth Newton-based augmented Lagrangian method to solve the more general SLOPE (the sorted L-one penalized estimation) model. By appropriately exploiting the inherent sparse and low-rank property of the generalized Jacobian of the semismooth Newton system in the augmented Lagrangian subproblem, we show how the computational complexity can be substantially reduced. Our algorithm presents a notable advantage in the high-dimensional statistical regression settings. Numerical experiments are conducted on real data sets, and the results demonstrate that our algorithm is far superior, in both speed and robustness, than the existing state-of-the-art algorithms based on first-order iterative schemes, including the widely used accelerated proximal gradient (APG) method and the alternating direction method of multipliers (ADMM).

math.OC↗

arXiv2017

Macro-spin Modeling and Experimental Study of Spin-orbit Torque Biased Magnetic Sensors

We reported a systematic study of spin-orbit torque biased magnetic sensors based on NiFe/Pt bilayers through both macro-spin modeling and experiments. The simulation results show that it is possible to achieve a linear sensor with a dynamic range of 0.1 - 10 Oe, power consumption of 1uW - 1 mW, and sensitivity of 0.1-0.5 Ohm/Oe. These characteristics can be controlled by varying the sensor dimension and current density in the Pt layer. The latter is in the range of 1 x 10^5 - 10^7 A/cm^2. Experimental results of fabricated sensors with selected sizes agree well with the simulation results. For a Wheatstone bridge sensor comprising of four sensing elements, a sensitivity up to 0.548 Ohm/Oe, linearity error below 6%, and detectivity of about 2.8 nT/Sqrt(Hz) were obtained. The simple structure and ultrathin thickness greatly facilitate the integration of these sensors for on-chip applications. As a proof-of-concept experiment, we demonstrate its application in detection of current flowing in an on-chip Cu wire.

cond-mat.mes-hall↗

arXiv2017

Selective multiple domain wall injection using spin-orbit torque

We demonstrate from both simulation and experiment a simple scheme for selective injection of multiple domain walls in a magnetic nanowire. The structure consists of a side-contact misaligned Hall bar made of ferromagnet/heavy metal bilayers. The combination of current-induced spin-orbit torque and an external magnetic field allows for the formation of localized domains with specific magnetization direction and length, thereby creating domain walls in predetermined locations. With the side contacts at two sides misaligned for a distance that is comparable to the contact width, it is possible to create densely packed domains by simply applying current between different pairs of side contacts. Simulation results show that the proposed scheme is scalable to a large number of domains with its dimension limited only by the domain wall width.

cond-mat.mtrl-sci↗

arXiv2017

Static and Dynamic Magnetic Properties of FeMn/Pt Multilayers

Recently we have demonstrated the presence of spin-orbit toque in FeMn/Pt multilayers which, in combination with the anisotropy field, is able to rotate its magnetization consecutively from 0o to 360o without any external field. Here, we report on an investigation of static and dynamic magnetic properties of FeMn/Pt multilayers using combined techniques of magnetometry, ferromagnetic resonance, inverse spin Hall effect and spin Hall magnetoresistance measurements. The FeMn/Pt multilayer was found to exhibit ferromagnetic properties, and its temperature dependence of saturation magnetization can be fitted well using a phenomenological model by including a finite distribution in Curie temperature due to subtle thickness variations across the multilayer samples. The non-uniformity in static magnetic properties is also manifested in the ferromagnetic resonance spectra, which typically exhibit a broad resonance peak. A damping parameter of around 0.106 is derived from the frequency dependence of ferromagnetic resonance linewidth, which is comparable to the reported values for other types of Pt-based multilayers. Clear inverse spin Hall signals and spin Hall magnetoresistance have been observed in all samples below the Curie temperature, which corroborate the strong spin-orbit torque effect observed previously.

cond-mat.mtrl-sci↗

arXiv2016

Z-tensors and complementarity problems

Tensors are multidimensional analogs of matrices. In this paper, based on degree-theoretic ideas, we study homogeneous nonlinear complementarity problems induced by tensors. By specializing this to $Z$-tensors (which are tensors with non-positive off-diagonal entries), we describe various equivalent conditions for a $Z$-tensor to have the global solvability property. We show by an example that the global solvability need not imply unique solvability and provide a sufficient and easily checkable condition for unique solvability.

math.OC↗

arXiv2016

Computing The Analytic Connectivity of A Uniform Hypergraph

The analytic connectivity, proposed as a substitute of the algebraic connectivity in the setting of hypergraphs, is an important quantity in spectral hypergraph theory. The definition of the analytic connectivity for a uniform hypergraph involves a series of optimization problems (POPs) associated with the Laplacian tensor of the hypergraph with nonnegativity constraints and a sphere constraint, which poses difficulties in computation. To reduce the involved computation, properties on the algebraic connectivity are further exploited, and several important structured uniform hypergraphs are shown to attain their analytic connectivities at vertices of the minimum degrees, hence admit a relatively less computation by solving a small number of POPs. To efficiently solve each involved POP, we propose a feasible trust region algorithm ({\tt FTR}) by exploiting their special structures. The global convergence of {\tt FTR} to the second-order necessary conditions points is established, and numerical results for both small and large size examples with comparison to other existing algorithms for POPs are reported to demonstrate the efficiency of our proposed algorithm.

math.CO↗

arXiv2015

Completely Positive Tensors and Multi-Hypergraphs

Completely positive graphs have been employed to associate with completely positive matrices for characterizing the intrinsic zero patterns. As tensors have been widely recognized as a higher-order extension of matrices, the multi-hypergraph, regarded as a generalization of graphs, is then introduced to associate with tensors for the study of complete positivity. To describe the dependence of the corresponding zero pattern for a special type of completely positive tensors--the $\{0,1\}$ completely positive tensors, the completely positive multi-hypergraph is defined. By characterizing properties of the associated multi-hypergraph, we provide necessary and sufficient conditions for any $(0,1)$ associated tensor to be $\{0,1\}$ completely positive. Furthermore, a necessary and sufficient condition for a uniform multi-hypergraph to be completely positive multi-hypergraph is proposed as well.

math.CO↗

arXiv2015

Characterization Tensors of Balanced Incomplete Block Designs

Balanced incomplete block designs (BIBDs) have wide applications in engineering, business and sciences. In this paper, for each (v, k, λ)-BIBD, we construct a strongly symmetric k-th order v-dimensional tensor. We call such a strongly symmetric tensor the characterization tensor of that BIBD, and the absolute value tensor of the characterization tensor the signless characterization tensor of that BIBD. We study some spectral properties of such characterization tensors and signless characterization tensors. In this way, we provide a new tool to study BIBDs.

math.CO↗

arXiv2015

P-Tensors, P$_0$-Tensors, and Tensor Complementarity Problem

The concepts of P- and P$_0$-matrices are generalized to P- and P$_0$-tensors of even and odd orders via homogeneous formulae. Analog to the matrix case, our P-tensor definition encompasses many important classes of tensors such as the positive definite tensors, the nonsingular M-tensors, the nonsingular H-tensors with positive diagonal entries, the strictly diagonally dominant tensors with positive diagonal entries, etc. As even-order symmetric PSD tensors are exactly even-order symmetric P$_0$-tensors, our definition of P$_0$-tensors, to some extent, can be regarded as an extension of PSD tensors for the odd-order case. Along with the basic properties of P- and P$_0$-tensors, the relationship among P$_0$-tensors and other extensions of PSD tensors are then discussed for comparison. Many structured tensors are also shown to be P- and P$_0$-tensors. As a theoretical application, the P-tensor complementarity problem is discussed and shown to possess a nonempty and compact solution set.

math.SP↗

arXiv2015

Doubly Nonnegative Tensors, Completely Positive Tensors and Applications

The concept of double nonnegativity of matrices is generalized to doubly nonnegative tensors by means of the nonnegativity of all entries and $H$-eigenvalues. This generalization is defined for tensors of any order (even or odd), while it reduces to the class of nonnegative positive semidefinite tensors in the even order case. We show that many nonnegative structured tensors, which are positive semidefinite in the even order case, are indeed doubly nonnegative as well in the odd order case. As an important subclass of doubly nonnegative tensors, the completely positive tensors are further studied. By using dominance properties for completely positive tensors, we can easily exclude some doubly nonnegative tensors, such as the signless Laplacian tensor of a nonempty $m$-uniform hypergraph with $m\geq 3$, from the class of completely positive tensors. Properties of the doubly nonnegative tensor cone and the completely positive tensor cone are established. Their relation and difference are discussed. These show us a different phenomenon comparing to the matrix case. By employing the proposed properties, more subclasses of these two types of tensors are identified. Particularly, all positive Cauchy tensors with any order are shown to be completely positive. This gives an easily constructible subclass of completely positive tensors, which is significant for the study of completely positive tensor decomposition. A preprocessed Fan-Zhou algorithm is proposed which can efficiently verify the complete positivity of nonnegative symmetric tensors. We also give the solution analysis of tensor complementarity problems with the strongly doubly nonnegative tensor structure.

math.SP↗

arXiv2015

The Sparsest Solutions to $Z$-Tensor Complementarity Problems

Finding the sparsest solutions to a tensor complementarity problem is generally NP-hard due to the nonconvexity and noncontinuity of the involved $\ell_0$ norm. In this paper, a special type of tensor complementarity problems with $Z$-tensors has been considered. Under some mild conditions, we show that to pursuit the sparsest solutions is equivalent to solving polynomial programming with a linear objective function. The involved conditions guarantee the desired exact relaxation and also allow to achieve a global optimal solution to the relaxed nonconvex polynomial programming problem. Particularly, in comparison to existing exact relaxation conditions, such as RIP-type ones, our proposed conditions are easy to verify.

math.SP↗

arXiv2014

Sparse and Low-Rank Covariance Matrices Estimation

This paper aims at achieving a simultaneously sparse and low-rank estimator from the semidefinite population covariance matrices. We first benefit from a convex optimization which develops $l_1$-norm penalty to encourage the sparsity and nuclear norm to favor the low-rank property. For the proposed estimator, we then prove that with large probability, the Frobenious norm of the estimation rate can be of order $O(\sqrt{s(\log{r})/n})$ under a mild case, where $s$ and $r$ denote the number of sparse entries and the rank of the population covariance respectively, $n$ notes the sample capacity. Finally an efficient alternating direction method of multipliers with global convergence is proposed to tackle this problem, and meantime merits of the approach are also illustrated by practicing numerical simulations.

math.ST↗

arXiv2013

New RIC Bounds via l_q-minimization with 0<q<=1 in Compressed Sensing

The restricted isometry constants (RICs) play an important role in exact recovery theory of sparse signals via l_q(0 =4/3 to guarantee the exact recovery of k sparse signals through the l_1 minimization. This paper aims to establish new RICs bounds via l_q(0 1, (ii)several sufficient conditions can be derived, such as for any 0 =2, for any 1/2 =1, (iii) the bound on δ_k is given as well for any 0 =2) is even or δ_k<0.3203 when k(>=3) is odd.

cs.IT↗

arXiv2011

The Dominant Eigenvalue of an Essentially Nonnegative Tensor

It is well known that the dominant eigenvalue of a real essentially nonnegative matrix is a convex function of its diagonal entries. This convexity is of practical importance in population biology, graph theory, demography, analytic hierarchy process and so on. In this paper, the concept of essentially nonnegativity is extended from matrices to higher order tensors, and the convexity and log convexity of dominant eigenvalues for such a class of tensors are established. Particularly, for any nonnegative tensor, the spectral radius turns out to be the dominant eigenvalue and hence possesses these convexities. Finally, an algorithm is given to calculate the dominant eigenvalue, and numerical results are reported to show the effectiveness of the proposed algorithm.

math.NA↗